diff --git a/master/.buildinfo b/master/.buildinfo
index 78490854b..130d6ba05 100644
--- a/master/.buildinfo
+++ b/master/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: e4fe37ef90e9fd174f1301a92f0b3ef8
+config: 6c1b6ba46e7b82b74029d5189564c63b
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree
index 196515a48..3787fc9c4 100644
Binary files a/master/.doctrees/cleanlab/benchmarking/index.doctree and b/master/.doctrees/cleanlab/benchmarking/index.doctree differ
diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree
index f19c8a72c..ed7d71081 100644
Binary files a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree and b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree differ
diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree
index a4ce7506b..c92fe8408 100644
Binary files a/master/.doctrees/cleanlab/classification.doctree and b/master/.doctrees/cleanlab/classification.doctree differ
diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree
index 72717fbaa..c3e898c86 100644
Binary files a/master/.doctrees/cleanlab/count.doctree and b/master/.doctrees/cleanlab/count.doctree differ
diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree
index 20b3c1388..86f86f761 100644
Binary files a/master/.doctrees/cleanlab/data_valuation.doctree and b/master/.doctrees/cleanlab/data_valuation.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree
index 225f0f5ac..18d77c2f9 100644
Binary files a/master/.doctrees/cleanlab/datalab/datalab.doctree and b/master/.doctrees/cleanlab/datalab/datalab.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree
index 6ef5ff215..59f0cd390 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree and b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree
index fb64fbc18..2e4967927 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree
index 40a6a5ed9..21ed5b2b5 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree and b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree
index 4baf2660d..39b5e1c48 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/index.doctree and b/master/.doctrees/cleanlab/datalab/guide/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree
index e410f80ee..bc551b6d9 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree and b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/table.doctree b/master/.doctrees/cleanlab/datalab/guide/table.doctree
index 13f4509bc..196394de9 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/table.doctree and b/master/.doctrees/cleanlab/datalab/guide/table.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree
index 14a8fe49d..575a5fa2a 100644
Binary files a/master/.doctrees/cleanlab/datalab/index.doctree and b/master/.doctrees/cleanlab/datalab/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree b/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree
index dbcf4f476..d1047312b 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree and b/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree b/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree
index b01ab5af4..9f68746b5 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree
index 4f59cfbc5..d54999be1 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/data.doctree and b/master/.doctrees/cleanlab/datalab/internal/data.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree
index 6b3affe1f..625aa8a6f 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree and b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/factory.doctree b/master/.doctrees/cleanlab/datalab/internal/factory.doctree
index 7c264cb71..e38897667 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/factory.doctree and b/master/.doctrees/cleanlab/datalab/internal/factory.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree
index 85f14883e..e6c2d457a 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree
index f2b21c1c6..a58ccd363 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree
index 189f05d84..72c409c2c 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree
index c266b5bbf..2129f3be7 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree
index 2b8621628..a60a9643e 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree
index ecb0cad53..876995248 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree
index b2ad4c1a1..bbac177f3 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree
index 2acd7837b..4b3e332e4 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree
index b3653eb11..803371b43 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree
index 4c75814a2..5e1717e32 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree
index ed00be1d6..04e4f5607 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree
index 206a6c393..a365698a6 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree
index 7490a0b32..092bf199b 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree
index 51f32664f..9a9a216c6 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree
index 49afbf845..d74d3d7f3 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree
index 79881bf45..8c745bcc3 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree
index 976445b62..a6ff95b19 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree b/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree
index ae8b4e4e5..858ba07dc 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree and b/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree
index b25a8eb82..3fab22896 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/report.doctree and b/master/.doctrees/cleanlab/datalab/internal/report.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/task.doctree b/master/.doctrees/cleanlab/datalab/internal/task.doctree
index c8ca253c3..4291e697f 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/task.doctree and b/master/.doctrees/cleanlab/datalab/internal/task.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree
index 15d7f288a..765e6dc88 100644
Binary files a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree and b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree differ
diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree
index 4726a8132..e50f79695 100644
Binary files a/master/.doctrees/cleanlab/dataset.doctree and b/master/.doctrees/cleanlab/dataset.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree
index 967a175ec..e18905766 100644
Binary files a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree and b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree
index 3a05c6815..f61266729 100644
Binary files a/master/.doctrees/cleanlab/experimental/coteaching.doctree and b/master/.doctrees/cleanlab/experimental/coteaching.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree
index 246374e6a..54f40e282 100644
Binary files a/master/.doctrees/cleanlab/experimental/index.doctree and b/master/.doctrees/cleanlab/experimental/index.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree
index 919346cdc..6f2be0e04 100644
Binary files a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree and b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree
index f5cb48121..06d6e3549 100644
Binary files a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree and b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree
index dd6c07427..2f528220d 100644
Binary files a/master/.doctrees/cleanlab/experimental/span_classification.doctree and b/master/.doctrees/cleanlab/experimental/span_classification.doctree differ
diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree
index daed46e96..15d730a57 100644
Binary files a/master/.doctrees/cleanlab/filter.doctree and b/master/.doctrees/cleanlab/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree
index c3c934dc1..7791925f8 100644
Binary files a/master/.doctrees/cleanlab/internal/index.doctree and b/master/.doctrees/cleanlab/internal/index.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree
index 4b9954ef5..f6fdb4ecf 100644
Binary files a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree and b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/latent_algebra.doctree b/master/.doctrees/cleanlab/internal/latent_algebra.doctree
index 65d9b3bc5..e0840fca3 100644
Binary files a/master/.doctrees/cleanlab/internal/latent_algebra.doctree and b/master/.doctrees/cleanlab/internal/latent_algebra.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree
index 6bf37b863..3db4d59f3 100644
Binary files a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree and b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree
index 908cdb45c..342086dd7 100644
Binary files a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree and b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree
index 4c24ef791..448ec3015 100644
Binary files a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree and b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/neighbor/index.doctree b/master/.doctrees/cleanlab/internal/neighbor/index.doctree
index 289c64876..3c22507a6 100644
Binary files a/master/.doctrees/cleanlab/internal/neighbor/index.doctree and b/master/.doctrees/cleanlab/internal/neighbor/index.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree
index 9d1e63929..a9dc9f30f 100644
Binary files a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree and b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree
index 228bb2663..c7a519e77 100644
Binary files a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree and b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree
index cd9afc739..0ef7889da 100644
Binary files a/master/.doctrees/cleanlab/internal/neighbor/search.doctree and b/master/.doctrees/cleanlab/internal/neighbor/search.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree
index ede760258..6ea814e66 100644
Binary files a/master/.doctrees/cleanlab/internal/outlier.doctree and b/master/.doctrees/cleanlab/internal/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree
index ac2a05f28..a449a7820 100644
Binary files a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree and b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree
index 13bb07ffa..d7db46d31 100644
Binary files a/master/.doctrees/cleanlab/internal/util.doctree and b/master/.doctrees/cleanlab/internal/util.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree
index edd570810..3dec2946a 100644
Binary files a/master/.doctrees/cleanlab/internal/validation.doctree and b/master/.doctrees/cleanlab/internal/validation.doctree differ
diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree
index 8006f9a32..1b80f4a60 100644
Binary files a/master/.doctrees/cleanlab/models/index.doctree and b/master/.doctrees/cleanlab/models/index.doctree differ
diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree
index 0cd44e6c7..39957fbec 100644
Binary files a/master/.doctrees/cleanlab/models/keras.doctree and b/master/.doctrees/cleanlab/models/keras.doctree differ
diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree
index 45f38563c..70b4faea9 100644
Binary files a/master/.doctrees/cleanlab/multiannotator.doctree and b/master/.doctrees/cleanlab/multiannotator.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree
index b2460e7ab..ee1cd6aa5 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree and b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree
index 661a4dcff..dd2c4c966 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree and b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree
index efedda8c1..17a00313a 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/index.doctree and b/master/.doctrees/cleanlab/multilabel_classification/index.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree
index 39e0baf42..f415b5e85 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree and b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree
index 62e1793cf..74389f8ee 100644
Binary files a/master/.doctrees/cleanlab/object_detection/filter.doctree and b/master/.doctrees/cleanlab/object_detection/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree
index 01e7193a6..b7a8e2b0b 100644
Binary files a/master/.doctrees/cleanlab/object_detection/index.doctree and b/master/.doctrees/cleanlab/object_detection/index.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree
index 605ec1147..91629228c 100644
Binary files a/master/.doctrees/cleanlab/object_detection/rank.doctree and b/master/.doctrees/cleanlab/object_detection/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree
index 43c99411d..bcec63f15 100644
Binary files a/master/.doctrees/cleanlab/object_detection/summary.doctree and b/master/.doctrees/cleanlab/object_detection/summary.doctree differ
diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree
index 6cccbb7f0..2d2f7ea83 100644
Binary files a/master/.doctrees/cleanlab/outlier.doctree and b/master/.doctrees/cleanlab/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree
index 71d708d6f..50a7b4c11 100644
Binary files a/master/.doctrees/cleanlab/rank.doctree and b/master/.doctrees/cleanlab/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree
index e2c96c80e..04972d6d0 100644
Binary files a/master/.doctrees/cleanlab/regression/index.doctree and b/master/.doctrees/cleanlab/regression/index.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree
index 58716cb25..15354ab29 100644
Binary files a/master/.doctrees/cleanlab/regression/learn.doctree and b/master/.doctrees/cleanlab/regression/learn.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree
index fec9e2606..0f297ab86 100644
Binary files a/master/.doctrees/cleanlab/regression/rank.doctree and b/master/.doctrees/cleanlab/regression/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree
index b135e2a71..8c4d286fa 100644
Binary files a/master/.doctrees/cleanlab/segmentation/filter.doctree and b/master/.doctrees/cleanlab/segmentation/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree
index 99a92b49f..7fc045089 100644
Binary files a/master/.doctrees/cleanlab/segmentation/index.doctree and b/master/.doctrees/cleanlab/segmentation/index.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree
index d3c6c4978..1d8062f6d 100644
Binary files a/master/.doctrees/cleanlab/segmentation/rank.doctree and b/master/.doctrees/cleanlab/segmentation/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree
index 1a3c99467..4c3a712f2 100644
Binary files a/master/.doctrees/cleanlab/segmentation/summary.doctree and b/master/.doctrees/cleanlab/segmentation/summary.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree
index d98422268..fee804d08 100644
Binary files a/master/.doctrees/cleanlab/token_classification/filter.doctree and b/master/.doctrees/cleanlab/token_classification/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree
index 316a0afd0..b4388ecf7 100644
Binary files a/master/.doctrees/cleanlab/token_classification/index.doctree and b/master/.doctrees/cleanlab/token_classification/index.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree
index 0ee622aa9..4f0395bb0 100644
Binary files a/master/.doctrees/cleanlab/token_classification/rank.doctree and b/master/.doctrees/cleanlab/token_classification/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree
index 95d850528..e15c33d9e 100644
Binary files a/master/.doctrees/cleanlab/token_classification/summary.doctree and b/master/.doctrees/cleanlab/token_classification/summary.doctree differ
diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index d76e1b57e..6b7ac1d7e 100644
Binary files a/master/.doctrees/environment.pickle and b/master/.doctrees/environment.pickle differ
diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
index c2cb0735f..fd393aed1 100644
Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ
diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index b42ae9e76..aa91f574d 100644
Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
index 5e85de9ac..9d1517aad 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:32:59.587012Z",
- "iopub.status.busy": "2024-09-05T19:32:59.586834Z",
- "iopub.status.idle": "2024-09-05T19:33:00.869499Z",
- "shell.execute_reply": "2024-09-05T19:33:00.868940Z"
+ "iopub.execute_input": "2024-09-06T19:32:51.069638Z",
+ "iopub.status.busy": "2024-09-06T19:32:51.069457Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.310694Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.310136Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:00.872309Z",
- "iopub.status.busy": "2024-09-05T19:33:00.871752Z",
- "iopub.status.idle": "2024-09-05T19:33:00.890028Z",
- "shell.execute_reply": "2024-09-05T19:33:00.889577Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.313494Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.312922Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.331174Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.330732Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:00.892312Z",
- "iopub.status.busy": "2024-09-05T19:33:00.892050Z",
- "iopub.status.idle": "2024-09-05T19:33:01.003828Z",
- "shell.execute_reply": "2024-09-05T19:33:01.003258Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.333414Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.333012Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.616135Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.615552Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.036111Z",
- "iopub.status.busy": "2024-09-05T19:33:01.035678Z",
- "iopub.status.idle": "2024-09-05T19:33:01.039351Z",
- "shell.execute_reply": "2024-09-05T19:33:01.038901Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.647632Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.647448Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.650810Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.650339Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.041401Z",
- "iopub.status.busy": "2024-09-05T19:33:01.041222Z",
- "iopub.status.idle": "2024-09-05T19:33:01.049517Z",
- "shell.execute_reply": "2024-09-05T19:33:01.049062Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.652810Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.652474Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.660488Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.660065Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.051546Z",
- "iopub.status.busy": "2024-09-05T19:33:01.051368Z",
- "iopub.status.idle": "2024-09-05T19:33:01.054060Z",
- "shell.execute_reply": "2024-09-05T19:33:01.053588Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.662789Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.662453Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.664910Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.664468Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.055990Z",
- "iopub.status.busy": "2024-09-05T19:33:01.055803Z",
- "iopub.status.idle": "2024-09-05T19:33:01.581009Z",
- "shell.execute_reply": "2024-09-05T19:33:01.580442Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.667005Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.666677Z",
+ "iopub.status.idle": "2024-09-06T19:32:53.186834Z",
+ "shell.execute_reply": "2024-09-06T19:32:53.186291Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.583425Z",
- "iopub.status.busy": "2024-09-05T19:33:01.583193Z",
- "iopub.status.idle": "2024-09-05T19:33:03.549090Z",
- "shell.execute_reply": "2024-09-05T19:33:03.548469Z"
+ "iopub.execute_input": "2024-09-06T19:32:53.189445Z",
+ "iopub.status.busy": "2024-09-06T19:32:53.189066Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.090605Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.089933Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.551875Z",
- "iopub.status.busy": "2024-09-05T19:33:03.551107Z",
- "iopub.status.idle": "2024-09-05T19:33:03.561703Z",
- "shell.execute_reply": "2024-09-05T19:33:03.561236Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.093443Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.092787Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.103390Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.102831Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.563830Z",
- "iopub.status.busy": "2024-09-05T19:33:03.563524Z",
- "iopub.status.idle": "2024-09-05T19:33:03.567711Z",
- "shell.execute_reply": "2024-09-05T19:33:03.567284Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.105571Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.105237Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.109432Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.108857Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.569933Z",
- "iopub.status.busy": "2024-09-05T19:33:03.569510Z",
- "iopub.status.idle": "2024-09-05T19:33:03.578212Z",
- "shell.execute_reply": "2024-09-05T19:33:03.577763Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.111438Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.111142Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.120139Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.119708Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.580544Z",
- "iopub.status.busy": "2024-09-05T19:33:03.579929Z",
- "iopub.status.idle": "2024-09-05T19:33:03.693855Z",
- "shell.execute_reply": "2024-09-05T19:33:03.693311Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.122107Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.121935Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.235206Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.234622Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.696060Z",
- "iopub.status.busy": "2024-09-05T19:33:03.695714Z",
- "iopub.status.idle": "2024-09-05T19:33:03.698630Z",
- "shell.execute_reply": "2024-09-05T19:33:03.698077Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.237464Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.237015Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.240074Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.239512Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.700629Z",
- "iopub.status.busy": "2024-09-05T19:33:03.700452Z",
- "iopub.status.idle": "2024-09-05T19:33:05.867604Z",
- "shell.execute_reply": "2024-09-05T19:33:05.866784Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.242072Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.241898Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.303999Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.303194Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:05.870677Z",
- "iopub.status.busy": "2024-09-05T19:33:05.870043Z",
- "iopub.status.idle": "2024-09-05T19:33:05.882124Z",
- "shell.execute_reply": "2024-09-05T19:33:05.881668Z"
+ "iopub.execute_input": "2024-09-06T19:32:57.307062Z",
+ "iopub.status.busy": "2024-09-06T19:32:57.306412Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.318236Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.317761Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:05.884250Z",
- "iopub.status.busy": "2024-09-05T19:33:05.884062Z",
- "iopub.status.idle": "2024-09-05T19:33:05.935772Z",
- "shell.execute_reply": "2024-09-05T19:33:05.935279Z"
+ "iopub.execute_input": "2024-09-06T19:32:57.320219Z",
+ "iopub.status.busy": "2024-09-06T19:32:57.320039Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.425487Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.424961Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index ac1d1b45f..7c3947e74 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:09.191218Z",
- "iopub.status.busy": "2024-09-05T19:33:09.191040Z",
- "iopub.status.idle": "2024-09-05T19:33:12.593196Z",
- "shell.execute_reply": "2024-09-05T19:33:12.592630Z"
+ "iopub.execute_input": "2024-09-06T19:33:00.675758Z",
+ "iopub.status.busy": "2024-09-06T19:33:00.675584Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.510616Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.510057Z"
},
"nbsphinx": "hidden"
},
@@ -135,7 +135,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.595901Z",
- "iopub.status.busy": "2024-09-05T19:33:12.595431Z",
- "iopub.status.idle": "2024-09-05T19:33:12.598938Z",
- "shell.execute_reply": "2024-09-05T19:33:12.598353Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.513184Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.512761Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.516199Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.515742Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.600924Z",
- "iopub.status.busy": "2024-09-05T19:33:12.600746Z",
- "iopub.status.idle": "2024-09-05T19:33:12.603877Z",
- "shell.execute_reply": "2024-09-05T19:33:12.603428Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.518261Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.517871Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.520905Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.520432Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.605996Z",
- "iopub.status.busy": "2024-09-05T19:33:12.605665Z",
- "iopub.status.idle": "2024-09-05T19:33:12.653121Z",
- "shell.execute_reply": "2024-09-05T19:33:12.652645Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.522785Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.522608Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.678565Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.678029Z"
}
},
"outputs": [
@@ -312,10 +312,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.655283Z",
- "iopub.status.busy": "2024-09-05T19:33:12.654920Z",
- "iopub.status.idle": "2024-09-05T19:33:12.658505Z",
- "shell.execute_reply": "2024-09-05T19:33:12.658052Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.680851Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.680423Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.684124Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.683591Z"
}
},
"outputs": [],
@@ -330,10 +330,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.660524Z",
- "iopub.status.busy": "2024-09-05T19:33:12.660198Z",
- "iopub.status.idle": "2024-09-05T19:33:12.663440Z",
- "shell.execute_reply": "2024-09-05T19:33:12.662933Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.686150Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.685759Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.689186Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.688640Z"
}
},
"outputs": [
@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'beneficiary_not_allowed', 'change_pin', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'getting_spare_card', 'lost_or_stolen_phone', 'card_about_to_expire', 'visa_or_mastercard', 'supported_cards_and_currencies'}\n"
+ "Classes: {'cancel_transfer', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'card_payment_fee_charged'}\n"
]
}
],
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.665438Z",
- "iopub.status.busy": "2024-09-05T19:33:12.665102Z",
- "iopub.status.idle": "2024-09-05T19:33:12.668366Z",
- "shell.execute_reply": "2024-09-05T19:33:12.667863Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.691223Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.690802Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.693946Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.693394Z"
}
},
"outputs": [
@@ -409,10 +409,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.670364Z",
- "iopub.status.busy": "2024-09-05T19:33:12.670023Z",
- "iopub.status.idle": "2024-09-05T19:33:12.673337Z",
- "shell.execute_reply": "2024-09-05T19:33:12.672869Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.695918Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.695618Z",
+ "iopub.status.idle": "2024-09-06T19:33:03.698740Z",
+ "shell.execute_reply": "2024-09-06T19:33:03.698281Z"
}
},
"outputs": [],
@@ -453,17 +453,17 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:12.675382Z",
- "iopub.status.busy": "2024-09-05T19:33:12.675048Z",
- "iopub.status.idle": "2024-09-05T19:33:17.078869Z",
- "shell.execute_reply": "2024-09-05T19:33:17.078217Z"
+ "iopub.execute_input": "2024-09-06T19:33:03.700642Z",
+ "iopub.status.busy": "2024-09-06T19:33:03.700468Z",
+ "iopub.status.idle": "2024-09-06T19:33:08.790650Z",
+ "shell.execute_reply": "2024-09-06T19:33:08.789991Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4d2c4ad5eacb4126bc698f579d4d28ae",
+ "model_id": "501ba738bb5947ccaad0e2cd1f842b14",
"version_major": 2,
"version_minor": 0
},
@@ -477,7 +477,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "393fbdb1c7654e43a3ed6f454efc540f",
+ "model_id": "31304fdb61a94d1eb88890ad65421b88",
"version_major": 2,
"version_minor": 0
},
@@ -491,7 +491,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "35db4effb4c04204b7551eff74a28dc8",
+ "model_id": "5a73d7a796fe45fca51bb3d3b1eb08df",
"version_major": 2,
"version_minor": 0
},
@@ -505,7 +505,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3ca50b4b144e4abdaff387f54e7c1616",
+ "model_id": "b4b2323ffd9349f1ad2d4d50a0288dc5",
"version_major": 2,
"version_minor": 0
},
@@ -519,7 +519,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5fe0357646744b24b9dd07d88736919e",
+ "model_id": "620076f191a74b5c914c7a2b17db4f55",
"version_major": 2,
"version_minor": 0
},
@@ -533,7 +533,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b45457b682634601809a638d2d51228a",
+ "model_id": "e6b938e7ce354e6ebb9c5105fe3bde01",
"version_major": 2,
"version_minor": 0
},
@@ -547,7 +547,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4478bdb14b2d46f09659ec72b6514d4b",
+ "model_id": "122704b7d1124989a50bdf83f04c3039",
"version_major": 2,
"version_minor": 0
},
@@ -601,10 +601,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:17.081636Z",
- "iopub.status.busy": "2024-09-05T19:33:17.081277Z",
- "iopub.status.idle": "2024-09-05T19:33:17.084159Z",
- "shell.execute_reply": "2024-09-05T19:33:17.083592Z"
+ "iopub.execute_input": "2024-09-06T19:33:08.793264Z",
+ "iopub.status.busy": "2024-09-06T19:33:08.793080Z",
+ "iopub.status.idle": "2024-09-06T19:33:08.795949Z",
+ "shell.execute_reply": "2024-09-06T19:33:08.795369Z"
}
},
"outputs": [],
@@ -626,10 +626,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:17.086157Z",
- "iopub.status.busy": "2024-09-05T19:33:17.085840Z",
- "iopub.status.idle": "2024-09-05T19:33:17.088592Z",
- "shell.execute_reply": "2024-09-05T19:33:17.088024Z"
+ "iopub.execute_input": "2024-09-06T19:33:08.797847Z",
+ "iopub.status.busy": "2024-09-06T19:33:08.797676Z",
+ "iopub.status.idle": "2024-09-06T19:33:08.800380Z",
+ "shell.execute_reply": "2024-09-06T19:33:08.799925Z"
}
},
"outputs": [],
@@ -644,10 +644,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:17.090704Z",
- "iopub.status.busy": "2024-09-05T19:33:17.090390Z",
- "iopub.status.idle": "2024-09-05T19:33:19.877083Z",
- "shell.execute_reply": "2024-09-05T19:33:19.876407Z"
+ "iopub.execute_input": "2024-09-06T19:33:08.802410Z",
+ "iopub.status.busy": "2024-09-06T19:33:08.802073Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.565675Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.564900Z"
},
"scrolled": true
},
@@ -670,10 +670,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.880334Z",
- "iopub.status.busy": "2024-09-05T19:33:19.879472Z",
- "iopub.status.idle": "2024-09-05T19:33:19.887396Z",
- "shell.execute_reply": "2024-09-05T19:33:19.886853Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.569102Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.568193Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.576067Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.575576Z"
}
},
"outputs": [
@@ -774,10 +774,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.889684Z",
- "iopub.status.busy": "2024-09-05T19:33:19.889333Z",
- "iopub.status.idle": "2024-09-05T19:33:19.893307Z",
- "shell.execute_reply": "2024-09-05T19:33:19.892849Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.578560Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.578144Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.582288Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.581717Z"
}
},
"outputs": [],
@@ -791,10 +791,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.895450Z",
- "iopub.status.busy": "2024-09-05T19:33:19.895002Z",
- "iopub.status.idle": "2024-09-05T19:33:19.898458Z",
- "shell.execute_reply": "2024-09-05T19:33:19.897977Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.584328Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.583988Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.587376Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.586902Z"
}
},
"outputs": [
@@ -829,10 +829,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.900655Z",
- "iopub.status.busy": "2024-09-05T19:33:19.900233Z",
- "iopub.status.idle": "2024-09-05T19:33:19.903348Z",
- "shell.execute_reply": "2024-09-05T19:33:19.902888Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.589547Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.589216Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.592104Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.591660Z"
}
},
"outputs": [],
@@ -852,10 +852,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.905406Z",
- "iopub.status.busy": "2024-09-05T19:33:19.904988Z",
- "iopub.status.idle": "2024-09-05T19:33:19.912000Z",
- "shell.execute_reply": "2024-09-05T19:33:19.911417Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.594213Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.593882Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.600605Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.600152Z"
}
},
"outputs": [
@@ -980,10 +980,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:19.914152Z",
- "iopub.status.busy": "2024-09-05T19:33:19.913747Z",
- "iopub.status.idle": "2024-09-05T19:33:20.177061Z",
- "shell.execute_reply": "2024-09-05T19:33:20.176509Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.602671Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.602343Z",
+ "iopub.status.idle": "2024-09-06T19:33:11.828596Z",
+ "shell.execute_reply": "2024-09-06T19:33:11.828033Z"
},
"scrolled": true
},
@@ -1022,10 +1022,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:20.179797Z",
- "iopub.status.busy": "2024-09-05T19:33:20.179252Z",
- "iopub.status.idle": "2024-09-05T19:33:20.355280Z",
- "shell.execute_reply": "2024-09-05T19:33:20.354741Z"
+ "iopub.execute_input": "2024-09-06T19:33:11.831240Z",
+ "iopub.status.busy": "2024-09-06T19:33:11.830841Z",
+ "iopub.status.idle": "2024-09-06T19:33:12.009186Z",
+ "shell.execute_reply": "2024-09-06T19:33:12.008615Z"
},
"scrolled": true
},
@@ -1073,10 +1073,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:20.358066Z",
- "iopub.status.busy": "2024-09-05T19:33:20.357656Z",
- "iopub.status.idle": "2024-09-05T19:33:20.361564Z",
- "shell.execute_reply": "2024-09-05T19:33:20.361057Z"
+ "iopub.execute_input": "2024-09-06T19:33:12.011827Z",
+ "iopub.status.busy": "2024-09-06T19:33:12.011435Z",
+ "iopub.status.idle": "2024-09-06T19:33:12.015256Z",
+ "shell.execute_reply": "2024-09-06T19:33:12.014755Z"
},
"nbsphinx": "hidden"
},
@@ -1120,30 +1120,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "09d35fb89ede417e83cf05378f4c1d39": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_6be25bacec524313800d853e93044293",
- "placeholder": "",
- "style": "IPY_MODEL_e9a515918bfe4e808d9c5f1b3a7d1141",
- "tabbable": null,
- "tooltip": null,
- "value": " 466k/466k [00:00<00:00, 14.7MB/s]"
- }
- },
- "0adfc109e92a46c9914db99c6c754136": {
+ "0459048c4f99420aa195e714b5d9a0fd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1196,82 +1173,33 @@
"width": null
}
},
- "0f1399976ca44acfa9a462ec0e9dc906": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "10fad256ee4b4ffbab65a5972fa499b5": {
+ "0828f51c6fc84a0da65b7ec09b69d580": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "12f629c93c5b4b2aaae0ba809890c2b8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "14de92ce42e5486488fc27bed6a386f3": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f5590a64e9214dd29b53569cfc71bf3b",
- "placeholder": "",
- "style": "IPY_MODEL_fe60b61ef74d44e29e5cec5f3fb9019e",
+ "layout": "IPY_MODEL_60ed3679f364477387473b90089b8273",
+ "max": 391.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_9c60afb1733442418ab367430bbb0d68",
"tabbable": null,
"tooltip": null,
- "value": "tokenizer.json: 100%"
+ "value": 391.0
}
},
- "1ac06872854b4fa5ba01e8f6b0bb583e": {
+ "08df668bdad845f9a6fca994bd1ecd5d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1286,49 +1214,68 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f2412463d1954925990846db01c4b70b",
+ "layout": "IPY_MODEL_66ad826330ec48dcbbb58f5f351a1112",
"placeholder": "",
- "style": "IPY_MODEL_10fad256ee4b4ffbab65a5972fa499b5",
+ "style": "IPY_MODEL_8adb666a2a6e4976a7051e3074b29507",
"tabbable": null,
"tooltip": null,
- "value": "tokenizer_config.json: 100%"
- }
- },
- "1d322b5da4c349b7ad65b5f30c24e940": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "value": "config.json: 100%"
}
},
- "1fe3817e6f6445c6b8fb7ef23736d705": {
- "model_module": "@jupyter-widgets/controls",
+ "094ab3c4cb4649dbb35cf60d64c57d3d": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "204183d3290441edb23978e5eeb45947": {
+ "10da9ba1022e498b8f5f3f98c7898223": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1381,7 +1328,49 @@
"width": null
}
},
- "220e17f41c8144cd9e5d27641e122289": {
+ "122704b7d1124989a50bdf83f04c3039": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_f6577867a1c041f490b073388151f1be",
+ "IPY_MODEL_3e00fec335784d3d8f67aef8d5205c3a",
+ "IPY_MODEL_9692da0a0ad949f39b93867a9112ab58"
+ ],
+ "layout": "IPY_MODEL_c876ccec48d84a04bb874ff6b48f8030",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "154d34bbb18c4e6bb7471d21431c8407": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "18fbcd7e9e6f47f89dbd1bf8da0aac40": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1434,7 +1423,7 @@
"width": null
}
},
- "27908f5b762941f6961358e4efa65dce": {
+ "1b4847f4ce0741c391f613e88b131aaa": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1487,7 +1476,67 @@
"width": null
}
},
- "2ecb4625af794883a0a5e08d6df7fda3": {
+ "27cccac5127445d09232f53a08657063": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "290f1d04116240fbaa62e5ec4b1a24a2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "2b4d16d0c52d49b4b207e9e5d8450870": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_0459048c4f99420aa195e714b5d9a0fd",
+ "max": 466062.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_49e660a0942a4523b89e94f5b3f10d5e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 466062.0
+ }
+ },
+ "2fa841989e734ca59ab3392a1c472375": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1540,23 +1589,72 @@
"width": null
}
},
- "2f191c7cfb9648d4bbff86c6252e1451": {
+ "31304fdb61a94d1eb88890ad65421b88": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_87bd24bf12c243a6b9cca97e02ea6f70",
+ "IPY_MODEL_ea5aad74db984381a9502d15f7877dc9",
+ "IPY_MODEL_b917898a95b64706aca98aba5a2b9969"
+ ],
+ "layout": "IPY_MODEL_748657e3cb9543698e92e614f2b8352c",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "3248bde1a32e421da1664cd2d4d3419e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_b8283fed28004000b84e2f53babadac1",
+ "placeholder": "",
+ "style": "IPY_MODEL_8d94e369761f41d087d990935dbe60c2",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "tokenizer.json: 100%"
+ }
+ },
+ "34ddf006f73a4753b211a5999ec0d671": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "329bd1ed69d04a719ef8be4db50a967c": {
+ "390080de929440ada6a97f2e0d2dc60f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1574,31 +1672,86 @@
"text_color": null
}
},
- "35db4effb4c04204b7551eff74a28dc8": {
+ "3e00fec335784d3d8f67aef8d5205c3a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_62a36fc72b1e4c45badc0f845a1935ce",
- "IPY_MODEL_e085586fcfed418eb8ebd9abb08a0974",
- "IPY_MODEL_8a0123876e904069832ad5927f07fc8e"
- ],
- "layout": "IPY_MODEL_27908f5b762941f6961358e4efa65dce",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_908e91876b774d93a02042ae9035283d",
+ "max": 231508.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_290f1d04116240fbaa62e5ec4b1a24a2",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": 231508.0
}
},
- "36f85cf1e9014355bc49f072d21fed52": {
+ "4071e96fc1124ad3bff4e7fe0f035295": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "470a184131ad4f9789eb904333469e81": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1616,7 +1769,39 @@
"text_color": null
}
},
- "37e0588b05bd4e82bbc0b13d6108f76e": {
+ "49e660a0942a4523b89e94f5b3f10d5e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "4bb99f8cedeb4182a94727c634341364": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "4cb28f8d4c2846bba02492de8371ef86": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1669,7 +1854,30 @@
"width": null
}
},
- "393fbdb1c7654e43a3ed6f454efc540f": {
+ "4dd3d95d46d846b98d4c8e1fca170cc1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_f7f7d73c85274703989c1a758316f306",
+ "placeholder": "",
+ "style": "IPY_MODEL_96ad5243a28540c1bbd13701050cd8c8",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 466k/466k [00:00<00:00, 44.9MB/s]"
+ }
+ },
+ "501ba738bb5947ccaad0e2cd1f842b14": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1684,16 +1892,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_720a780490394b6da77f76b31c32de99",
- "IPY_MODEL_cfcb99d1ea7046c6a9645e1361b08004",
- "IPY_MODEL_aae1bd61a7504a71b7b6aa2dfc3774d7"
+ "IPY_MODEL_5b8638296ff64c2abc68c70b1b8b7469",
+ "IPY_MODEL_0828f51c6fc84a0da65b7ec09b69d580",
+ "IPY_MODEL_dc79b458bbac454fbab119272509e252"
],
- "layout": "IPY_MODEL_bd3ca5b6f2154c3b89d837f999404304",
+ "layout": "IPY_MODEL_094ab3c4cb4649dbb35cf60d64c57d3d",
"tabbable": null,
"tooltip": null
}
},
- "3ca50b4b144e4abdaff387f54e7c1616": {
+ "5a73d7a796fe45fca51bb3d3b1eb08df": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1708,42 +1916,55 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_5cbd210d855842eb8f85d760de6897d8",
- "IPY_MODEL_647b3e44874d49b795446d0ad6d17df0",
- "IPY_MODEL_887c402846f642a2aacff7d020b047a0"
+ "IPY_MODEL_08df668bdad845f9a6fca994bd1ecd5d",
+ "IPY_MODEL_88b14d6b576d4c358576ada8914fc9ae",
+ "IPY_MODEL_efcccd1f66e4459cb1a7709eadb26866"
],
- "layout": "IPY_MODEL_97c2ddf30c1c4ff68739f5d5d4b76ccd",
+ "layout": "IPY_MODEL_18fbcd7e9e6f47f89dbd1bf8da0aac40",
"tabbable": null,
"tooltip": null
}
},
- "4433be0a8c424206af914515e961ee9c": {
+ "5b8638296ff64c2abc68c70b1b8b7469": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_9fad80c13d274d60a7a1ded9d3f95df9",
- "max": 466062.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_c6bc382ab4dd4d2fa54736c9f11cce15",
+ "layout": "IPY_MODEL_dd603ce908534d83a9e5812536cbaecf",
+ "placeholder": "",
+ "style": "IPY_MODEL_87f402b0764744998c823bf8713ee0ae",
"tabbable": null,
"tooltip": null,
- "value": 466062.0
+ "value": ".gitattributes: 100%"
+ }
+ },
+ "5bed445f56a545b89b799f73d2462bd9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "446f6672d1b74b828dbd588c8352d80e": {
+ "5f624de673e9405cb01619e550cd02b5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1796,31 +2017,7 @@
"width": null
}
},
- "4478bdb14b2d46f09659ec72b6514d4b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_7205dfdca188495e9dd703c5206fd644",
- "IPY_MODEL_7d92e68a368d47d9af6828212846a908",
- "IPY_MODEL_be833e8f5cff45b292b19ca38209e65c"
- ],
- "layout": "IPY_MODEL_733e3045825f4ca9be1696c7e613bf9d",
- "tabbable": null,
- "tooltip": null
- }
- },
- "460c4f37a9224f5188fb27a460758886": {
+ "60ed3679f364477387473b90089b8273": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1873,7 +2070,7 @@
"width": null
}
},
- "4d2c4ad5eacb4126bc698f579d4d28ae": {
+ "620076f191a74b5c914c7a2b17db4f55": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1888,16 +2085,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_6b909b0223e946268cc3ca7e7448bc41",
- "IPY_MODEL_6f78382944884d9e8744a34ecab2ec5f",
- "IPY_MODEL_7a94ccf6df194426bb37dba27b924cc5"
+ "IPY_MODEL_3248bde1a32e421da1664cd2d4d3419e",
+ "IPY_MODEL_2b4d16d0c52d49b4b207e9e5d8450870",
+ "IPY_MODEL_4dd3d95d46d846b98d4c8e1fca170cc1"
],
- "layout": "IPY_MODEL_6d84336e0bc54a529ae98aca184badd5",
+ "layout": "IPY_MODEL_4071e96fc1124ad3bff4e7fe0f035295",
"tabbable": null,
"tooltip": null
}
},
- "50b7c0409c64492eae519c364d30137d": {
+ "626cdb8c3d374c168811bd920a5a68f8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1950,25 +2147,23 @@
"width": null
}
},
- "52500c0b271b4bdc90670cc54f2fd9ec": {
+ "62cc1ededdbc4ebaa9a9455fc402d06e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "bar_color": null,
+ "description_width": ""
}
},
- "5cbd210d855842eb8f85d760de6897d8": {
+ "65182ed5c6764915b44ed26f3452c6e8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1983,62 +2178,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_e57f3e7d66d6414d98f5e60ac001480f",
+ "layout": "IPY_MODEL_93a4c1dfcfc44589926437f1ffdd3a85",
"placeholder": "",
- "style": "IPY_MODEL_81c54b14269546eab24143ae3f21efd5",
+ "style": "IPY_MODEL_c4c2ce7cae784b2093ba53e3609cc2c9",
"tabbable": null,
"tooltip": null,
"value": "pytorch_model.bin: 100%"
}
},
- "5fe0357646744b24b9dd07d88736919e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_14de92ce42e5486488fc27bed6a386f3",
- "IPY_MODEL_4433be0a8c424206af914515e961ee9c",
- "IPY_MODEL_09d35fb89ede417e83cf05378f4c1d39"
- ],
- "layout": "IPY_MODEL_ce93583585d6448d8a89bb80c5739bfc",
- "tabbable": null,
- "tooltip": null
- }
- },
- "62a36fc72b1e4c45badc0f845a1935ce": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_c20471b83bb54067b606f37c337e9199",
- "placeholder": "",
- "style": "IPY_MODEL_8386099c046b4e3ba5bddc0ce8fb1942",
- "tabbable": null,
- "tooltip": null,
- "value": "config.json: 100%"
- }
- },
- "62e233345d5d4506a596f58deb0fc0ab": {
+ "66ad826330ec48dcbbb58f5f351a1112": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2091,7 +2239,25 @@
"width": null
}
},
- "6396d98831cd4b97aa468c71944171c7": {
+ "673761d4701d4ee5aaabc0e22e4ec6cb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "6b23b979b2f7419a924a8685e13b11a7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2144,109 +2310,25 @@
"width": null
}
},
- "647b3e44874d49b795446d0ad6d17df0": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_6396d98831cd4b97aa468c71944171c7",
- "max": 54245363.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_7ed0850b473743459ae5102842a1fe06",
- "tabbable": null,
- "tooltip": null,
- "value": 54245363.0
- }
- },
- "6b909b0223e946268cc3ca7e7448bc41": {
+ "73e88da4acc94040992fd88d0a0d19ed": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_2ecb4625af794883a0a5e08d6df7fda3",
- "placeholder": "",
- "style": "IPY_MODEL_d0734afcec2e464abe0ac379e9f7a1e1",
- "tabbable": null,
- "tooltip": null,
- "value": ".gitattributes: 100%"
- }
- },
- "6be25bacec524313800d853e93044293": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "6d84336e0bc54a529ae98aca184badd5": {
+ "748657e3cb9543698e92e614f2b8352c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2299,79 +2381,49 @@
"width": null
}
},
- "6f78382944884d9e8744a34ecab2ec5f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_204183d3290441edb23978e5eeb45947",
- "max": 391.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2f191c7cfb9648d4bbff86c6252e1451",
- "tabbable": null,
- "tooltip": null,
- "value": 391.0
- }
- },
- "7205dfdca188495e9dd703c5206fd644": {
+ "766a43c7b32e464cb876b478a34ad457": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_37e0588b05bd4e82bbc0b13d6108f76e",
- "placeholder": "",
- "style": "IPY_MODEL_faae78ced480410f86e4cf305f9f022b",
- "tabbable": null,
- "tooltip": null,
- "value": "vocab.txt: 100%"
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "720a780490394b6da77f76b31c32de99": {
+ "84060f74615349bd9e7f70b839d37c3e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_460c4f37a9224f5188fb27a460758886",
- "placeholder": "",
- "style": "IPY_MODEL_52500c0b271b4bdc90670cc54f2fd9ec",
+ "layout": "IPY_MODEL_6b23b979b2f7419a924a8685e13b11a7",
+ "max": 54245363.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_766a43c7b32e464cb876b478a34ad457",
"tabbable": null,
"tooltip": null,
- "value": "README.md: 100%"
+ "value": 54245363.0
}
},
- "733e3045825f4ca9be1696c7e613bf9d": {
+ "846cc1a5fbd9438da4609b439141f308": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2424,25 +2476,7 @@
"width": null
}
},
- "7a45d65746874a788d93b8dc4a69efff": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "7a94ccf6df194426bb37dba27b924cc5": {
+ "87bd24bf12c243a6b9cca97e02ea6f70": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2457,15 +2491,33 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_82d46b97203b473d86967baca44f2f6d",
+ "layout": "IPY_MODEL_fa4036e56d43472283e556f828cf84fd",
"placeholder": "",
- "style": "IPY_MODEL_7a45d65746874a788d93b8dc4a69efff",
+ "style": "IPY_MODEL_470a184131ad4f9789eb904333469e81",
"tabbable": null,
"tooltip": null,
- "value": " 391/391 [00:00<00:00, 65.6kB/s]"
+ "value": "README.md: 100%"
+ }
+ },
+ "87f402b0764744998c823bf8713ee0ae": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "7d92e68a368d47d9af6828212846a908": {
+ "88b14d6b576d4c358576ada8914fc9ae": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2481,51 +2533,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_220e17f41c8144cd9e5d27641e122289",
- "max": 231508.0,
+ "layout": "IPY_MODEL_c634f1241a9e40659957b6a8dd57b66b",
+ "max": 665.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_0f1399976ca44acfa9a462ec0e9dc906",
+ "style": "IPY_MODEL_4bb99f8cedeb4182a94727c634341364",
"tabbable": null,
"tooltip": null,
- "value": 231508.0
- }
- },
- "7ed0850b473743459ae5102842a1fe06": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "81c54b14269546eab24143ae3f21efd5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "value": 665.0
}
},
- "82d46b97203b473d86967baca44f2f6d": {
+ "89c1af10a3bf46378b2d5ad1570f4844": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2578,7 +2596,7 @@
"width": null
}
},
- "8386099c046b4e3ba5bddc0ce8fb1942": {
+ "8adb666a2a6e4976a7051e3074b29507": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2596,53 +2614,25 @@
"text_color": null
}
},
- "887c402846f642a2aacff7d020b047a0": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_50b7c0409c64492eae519c364d30137d",
- "placeholder": "",
- "style": "IPY_MODEL_329bd1ed69d04a719ef8be4db50a967c",
- "tabbable": null,
- "tooltip": null,
- "value": " 54.2M/54.2M [00:00<00:00, 190MB/s]"
- }
- },
- "8a0123876e904069832ad5927f07fc8e": {
+ "8d94e369761f41d087d990935dbe60c2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_e569ddc2204f482ea62bedcc240f7f9c",
- "placeholder": "",
- "style": "IPY_MODEL_f226342980e94bc5b313958fa8521f8d",
- "tabbable": null,
- "tooltip": null,
- "value": " 665/665 [00:00<00:00, 112kB/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "97c2ddf30c1c4ff68739f5d5d4b76ccd": {
+ "908e91876b774d93a02042ae9035283d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2695,7 +2685,7 @@
"width": null
}
},
- "9fad80c13d274d60a7a1ded9d3f95df9": {
+ "93a4c1dfcfc44589926437f1ffdd3a85": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2748,7 +2738,7 @@
"width": null
}
},
- "aae1bd61a7504a71b7b6aa2dfc3774d7": {
+ "9692da0a0ad949f39b93867a9112ab58": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2763,68 +2753,56 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_0adfc109e92a46c9914db99c6c754136",
+ "layout": "IPY_MODEL_d4493c8ca6d74012b8cda7556ffcfcbb",
"placeholder": "",
- "style": "IPY_MODEL_1d322b5da4c349b7ad65b5f30c24e940",
+ "style": "IPY_MODEL_154d34bbb18c4e6bb7471d21431c8407",
"tabbable": null,
"tooltip": null,
- "value": " 2.21k/2.21k [00:00<00:00, 403kB/s]"
+ "value": " 232k/232k [00:00<00:00, 3.64MB/s]"
}
},
- "ac1a3a5a7e344e189e814f4df1968771": {
- "model_module": "@jupyter-widgets/base",
+ "96ad5243a28540c1bbd13701050cd8c8": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "98a1dc889ed543ddb76c46e918f80a38": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_2fa841989e734ca59ab3392a1c472375",
+ "placeholder": "",
+ "style": "IPY_MODEL_73e88da4acc94040992fd88d0a0d19ed",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "tokenizer_config.json: 100%"
}
},
- "acdce822381f476590c651c7909c20cf": {
+ "99072ca746ea485cbb170400eb0e5a45": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2877,7 +2855,23 @@
"width": null
}
},
- "b0e5e57d6d8b488e86de00432d971a0c": {
+ "9c60afb1733442418ab367430bbb0d68": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "aa7d45a4b72346af98fb57bd52e6b237": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2930,7 +2924,43 @@
"width": null
}
},
- "b45457b682634601809a638d2d51228a": {
+ "ae26881588fc4f9695b5cbd0549eb30a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "af41f929d6ed42e8b6fa4fd762ea4ebe": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "b4b2323ffd9349f1ad2d4d50a0288dc5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -2945,32 +2975,39 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_1ac06872854b4fa5ba01e8f6b0bb583e",
- "IPY_MODEL_e898d89a2dee43af80061aebeb4e0678",
- "IPY_MODEL_ea77af57f6204512a6205d0aaaf3e3bd"
+ "IPY_MODEL_65182ed5c6764915b44ed26f3452c6e8",
+ "IPY_MODEL_84060f74615349bd9e7f70b839d37c3e",
+ "IPY_MODEL_b76df53d2353433cb2fea0cde0c2d1dd"
],
- "layout": "IPY_MODEL_62e233345d5d4506a596f58deb0fc0ab",
+ "layout": "IPY_MODEL_626cdb8c3d374c168811bd920a5a68f8",
"tabbable": null,
"tooltip": null
}
},
- "bc8cd9b955174427802da599715e80bb": {
+ "b76df53d2353433cb2fea0cde0c2d1dd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_4cb28f8d4c2846bba02492de8371ef86",
+ "placeholder": "",
+ "style": "IPY_MODEL_673761d4701d4ee5aaabc0e22e4ec6cb",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 54.2M/54.2M [00:00<00:00, 206MB/s]"
}
},
- "bd3ca5b6f2154c3b89d837f999404304": {
+ "b8283fed28004000b84e2f53babadac1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3023,7 +3060,30 @@
"width": null
}
},
- "be833e8f5cff45b292b19ca38209e65c": {
+ "b917898a95b64706aca98aba5a2b9969": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_846cc1a5fbd9438da4609b439141f308",
+ "placeholder": "",
+ "style": "IPY_MODEL_af41f929d6ed42e8b6fa4fd762ea4ebe",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 2.21k/2.21k [00:00<00:00, 314kB/s]"
+ }
+ },
+ "bc14afac06eb4c26bf5c7c100334328e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3038,15 +3098,33 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b0e5e57d6d8b488e86de00432d971a0c",
+ "layout": "IPY_MODEL_10da9ba1022e498b8f5f3f98c7898223",
"placeholder": "",
- "style": "IPY_MODEL_36f85cf1e9014355bc49f072d21fed52",
+ "style": "IPY_MODEL_34ddf006f73a4753b211a5999ec0d671",
"tabbable": null,
"tooltip": null,
- "value": " 232k/232k [00:00<00:00, 34.8MB/s]"
+ "value": " 48.0/48.0 [00:00<00:00, 8.65kB/s]"
+ }
+ },
+ "c4c2ce7cae784b2093ba53e3609cc2c9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "c20471b83bb54067b606f37c337e9199": {
+ "c634f1241a9e40659957b6a8dd57b66b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3099,23 +3177,7 @@
"width": null
}
},
- "c6bc382ab4dd4d2fa54736c9f11cce15": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "ce93583585d6448d8a89bb80c5739bfc": {
+ "c876ccec48d84a04bb874ff6b48f8030": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3168,77 +3230,7 @@
"width": null
}
},
- "cfcb99d1ea7046c6a9645e1361b08004": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_446f6672d1b74b828dbd588c8352d80e",
- "max": 2211.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_ef8447b2458e4ddf8e0e2eaf3e1bb8da",
- "tabbable": null,
- "tooltip": null,
- "value": 2211.0
- }
- },
- "d0734afcec2e464abe0ac379e9f7a1e1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "e085586fcfed418eb8ebd9abb08a0974": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_fae238875bf74b01ac59cf3dbf815caf",
- "max": 665.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_1fe3817e6f6445c6b8fb7ef23736d705",
- "tabbable": null,
- "tooltip": null,
- "value": 665.0
- }
- },
- "e569ddc2204f482ea62bedcc240f7f9c": {
+ "d4493c8ca6d74012b8cda7556ffcfcbb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3291,7 +3283,30 @@
"width": null
}
},
- "e57f3e7d66d6414d98f5e60ac001480f": {
+ "dc79b458bbac454fbab119272509e252": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_99072ca746ea485cbb170400eb0e5a45",
+ "placeholder": "",
+ "style": "IPY_MODEL_ae26881588fc4f9695b5cbd0549eb30a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 391/391 [00:00<00:00, 56.2kB/s]"
+ }
+ },
+ "dd603ce908534d83a9e5812536cbaecf": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3344,51 +3359,57 @@
"width": null
}
},
- "e898d89a2dee43af80061aebeb4e0678": {
+ "e6b938e7ce354e6ebb9c5105fe3bde01": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_ac1a3a5a7e344e189e814f4df1968771",
- "max": 48.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_bc8cd9b955174427802da599715e80bb",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_98a1dc889ed543ddb76c46e918f80a38",
+ "IPY_MODEL_fbbd836ae0ab4f63bed278c2565cd3f1",
+ "IPY_MODEL_bc14afac06eb4c26bf5c7c100334328e"
+ ],
+ "layout": "IPY_MODEL_aa7d45a4b72346af98fb57bd52e6b237",
"tabbable": null,
- "tooltip": null,
- "value": 48.0
+ "tooltip": null
}
},
- "e9a515918bfe4e808d9c5f1b3a7d1141": {
+ "ea5aad74db984381a9502d15f7877dc9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_5f624de673e9405cb01619e550cd02b5",
+ "max": 2211.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_62cc1ededdbc4ebaa9a9455fc402d06e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 2211.0
}
},
- "ea77af57f6204512a6205d0aaaf3e3bd": {
+ "efcccd1f66e4459cb1a7709eadb26866": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3403,49 +3424,38 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_acdce822381f476590c651c7909c20cf",
+ "layout": "IPY_MODEL_f94845de837b4883b0f99fb9f3b7ead2",
"placeholder": "",
- "style": "IPY_MODEL_12f629c93c5b4b2aaae0ba809890c2b8",
+ "style": "IPY_MODEL_27cccac5127445d09232f53a08657063",
"tabbable": null,
"tooltip": null,
- "value": " 48.0/48.0 [00:00<00:00, 8.23kB/s]"
- }
- },
- "ef8447b2458e4ddf8e0e2eaf3e1bb8da": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "value": " 665/665 [00:00<00:00, 124kB/s]"
}
},
- "f226342980e94bc5b313958fa8521f8d": {
+ "f6577867a1c041f490b073388151f1be": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_89c1af10a3bf46378b2d5ad1570f4844",
+ "placeholder": "",
+ "style": "IPY_MODEL_390080de929440ada6a97f2e0d2dc60f",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "vocab.txt: 100%"
}
},
- "f2412463d1954925990846db01c4b70b": {
+ "f7f7d73c85274703989c1a758316f306": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3498,7 +3508,7 @@
"width": null
}
},
- "f5590a64e9214dd29b53569cfc71bf3b": {
+ "f94845de837b4883b0f99fb9f3b7ead2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3551,25 +3561,7 @@
"width": null
}
},
- "faae78ced480410f86e4cf305f9f022b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "fae238875bf74b01ac59cf3dbf815caf": {
+ "fa4036e56d43472283e556f828cf84fd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3622,22 +3614,30 @@
"width": null
}
},
- "fe60b61ef74d44e29e5cec5f3fb9019e": {
+ "fbbd836ae0ab4f63bed278c2565cd3f1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_1b4847f4ce0741c391f613e88b131aaa",
+ "max": 48.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_5bed445f56a545b89b799f73d2462bd9",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 48.0
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index b45848f49..29bf50217 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:23.985917Z",
- "iopub.status.busy": "2024-09-05T19:33:23.985402Z",
- "iopub.status.idle": "2024-09-05T19:33:29.367561Z",
- "shell.execute_reply": "2024-09-05T19:33:29.367003Z"
+ "iopub.execute_input": "2024-09-06T19:33:15.412497Z",
+ "iopub.status.busy": "2024-09-06T19:33:15.412315Z",
+ "iopub.status.idle": "2024-09-06T19:33:20.744505Z",
+ "shell.execute_reply": "2024-09-06T19:33:20.743930Z"
},
"nbsphinx": "hidden"
},
@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:29.370261Z",
- "iopub.status.busy": "2024-09-05T19:33:29.369732Z",
- "iopub.status.idle": "2024-09-05T19:33:29.373038Z",
- "shell.execute_reply": "2024-09-05T19:33:29.372567Z"
+ "iopub.execute_input": "2024-09-06T19:33:20.747320Z",
+ "iopub.status.busy": "2024-09-06T19:33:20.746730Z",
+ "iopub.status.idle": "2024-09-06T19:33:20.750172Z",
+ "shell.execute_reply": "2024-09-06T19:33:20.749624Z"
},
"id": "LaEiwXUiVHCS"
},
@@ -157,10 +157,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:29.375058Z",
- "iopub.status.busy": "2024-09-05T19:33:29.374708Z",
- "iopub.status.idle": "2024-09-05T19:33:29.379445Z",
- "shell.execute_reply": "2024-09-05T19:33:29.379024Z"
+ "iopub.execute_input": "2024-09-06T19:33:20.752397Z",
+ "iopub.status.busy": "2024-09-06T19:33:20.751947Z",
+ "iopub.status.idle": "2024-09-06T19:33:20.756917Z",
+ "shell.execute_reply": "2024-09-06T19:33:20.756445Z"
},
"nbsphinx": "hidden"
},
@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:29.381661Z",
- "iopub.status.busy": "2024-09-05T19:33:29.381318Z",
- "iopub.status.idle": "2024-09-05T19:33:31.154738Z",
- "shell.execute_reply": "2024-09-05T19:33:31.154062Z"
+ "iopub.execute_input": "2024-09-06T19:33:20.758862Z",
+ "iopub.status.busy": "2024-09-06T19:33:20.758684Z",
+ "iopub.status.idle": "2024-09-06T19:33:22.662809Z",
+ "shell.execute_reply": "2024-09-06T19:33:22.662142Z"
},
"id": "GRDPEg7-VOQe",
"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:31.157544Z",
- "iopub.status.busy": "2024-09-05T19:33:31.157120Z",
- "iopub.status.idle": "2024-09-05T19:33:31.168372Z",
- "shell.execute_reply": "2024-09-05T19:33:31.167905Z"
+ "iopub.execute_input": "2024-09-06T19:33:22.665411Z",
+ "iopub.status.busy": "2024-09-06T19:33:22.665209Z",
+ "iopub.status.idle": "2024-09-06T19:33:22.675958Z",
+ "shell.execute_reply": "2024-09-06T19:33:22.675514Z"
},
"id": "FDA5sGZwUSur",
"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:31.170634Z",
- "iopub.status.busy": "2024-09-05T19:33:31.170294Z",
- "iopub.status.idle": "2024-09-05T19:33:31.177336Z",
- "shell.execute_reply": "2024-09-05T19:33:31.176887Z"
+ "iopub.execute_input": "2024-09-06T19:33:22.677986Z",
+ "iopub.status.busy": "2024-09-06T19:33:22.677801Z",
+ "iopub.status.idle": "2024-09-06T19:33:22.684956Z",
+ "shell.execute_reply": "2024-09-06T19:33:22.684474Z"
},
"nbsphinx": "hidden"
},
@@ -380,10 +380,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:31.179446Z",
- "iopub.status.busy": "2024-09-05T19:33:31.179118Z",
- "iopub.status.idle": "2024-09-05T19:33:31.679312Z",
- "shell.execute_reply": "2024-09-05T19:33:31.678789Z"
+ "iopub.execute_input": "2024-09-06T19:33:22.686790Z",
+ "iopub.status.busy": "2024-09-06T19:33:22.686606Z",
+ "iopub.status.idle": "2024-09-06T19:33:23.132191Z",
+ "shell.execute_reply": "2024-09-06T19:33:23.131660Z"
},
"id": "dLBvUZLlII5w",
"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:31.681541Z",
- "iopub.status.busy": "2024-09-05T19:33:31.681189Z",
- "iopub.status.idle": "2024-09-05T19:33:32.306849Z",
- "shell.execute_reply": "2024-09-05T19:33:32.306228Z"
+ "iopub.execute_input": "2024-09-06T19:33:23.134446Z",
+ "iopub.status.busy": "2024-09-06T19:33:23.134077Z",
+ "iopub.status.idle": "2024-09-06T19:33:24.169658Z",
+ "shell.execute_reply": "2024-09-06T19:33:24.169048Z"
},
"id": "vL9lkiKsHvKr"
},
@@ -474,10 +474,10 @@
"height": 143
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:32.309368Z",
- "iopub.status.busy": "2024-09-05T19:33:32.309050Z",
- "iopub.status.idle": "2024-09-05T19:33:32.327458Z",
- "shell.execute_reply": "2024-09-05T19:33:32.326924Z"
+ "iopub.execute_input": "2024-09-06T19:33:24.172059Z",
+ "iopub.status.busy": "2024-09-06T19:33:24.171874Z",
+ "iopub.status.idle": "2024-09-06T19:33:24.191001Z",
+ "shell.execute_reply": "2024-09-06T19:33:24.190537Z"
},
"id": "obQYDKdLiUU6",
"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:32.329740Z",
- "iopub.status.busy": "2024-09-05T19:33:32.329293Z",
- "iopub.status.idle": "2024-09-05T19:33:32.332721Z",
- "shell.execute_reply": "2024-09-05T19:33:32.332184Z"
+ "iopub.execute_input": "2024-09-06T19:33:24.193080Z",
+ "iopub.status.busy": "2024-09-06T19:33:24.192898Z",
+ "iopub.status.idle": "2024-09-06T19:33:24.196091Z",
+ "shell.execute_reply": "2024-09-06T19:33:24.195633Z"
},
"id": "I8JqhOZgi94g"
},
@@ -582,10 +582,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:32.334695Z",
- "iopub.status.busy": "2024-09-05T19:33:32.334412Z",
- "iopub.status.idle": "2024-09-05T19:33:46.947908Z",
- "shell.execute_reply": "2024-09-05T19:33:46.947343Z"
+ "iopub.execute_input": "2024-09-06T19:33:24.198156Z",
+ "iopub.status.busy": "2024-09-06T19:33:24.197822Z",
+ "iopub.status.idle": "2024-09-06T19:33:38.175563Z",
+ "shell.execute_reply": "2024-09-06T19:33:38.174995Z"
},
"id": "2FSQ2GR9R_YA"
},
@@ -617,10 +617,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:46.950681Z",
- "iopub.status.busy": "2024-09-05T19:33:46.950277Z",
- "iopub.status.idle": "2024-09-05T19:33:46.954313Z",
- "shell.execute_reply": "2024-09-05T19:33:46.953812Z"
+ "iopub.execute_input": "2024-09-06T19:33:38.178313Z",
+ "iopub.status.busy": "2024-09-06T19:33:38.177918Z",
+ "iopub.status.idle": "2024-09-06T19:33:38.181776Z",
+ "shell.execute_reply": "2024-09-06T19:33:38.181209Z"
},
"id": "kAkY31IVXyr8",
"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -680,10 +680,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:46.956474Z",
- "iopub.status.busy": "2024-09-05T19:33:46.956154Z",
- "iopub.status.idle": "2024-09-05T19:33:47.649876Z",
- "shell.execute_reply": "2024-09-05T19:33:47.649282Z"
+ "iopub.execute_input": "2024-09-06T19:33:38.183755Z",
+ "iopub.status.busy": "2024-09-06T19:33:38.183579Z",
+ "iopub.status.idle": "2024-09-06T19:33:38.879592Z",
+ "shell.execute_reply": "2024-09-06T19:33:38.878973Z"
},
"id": "i_drkY9YOcw4"
},
@@ -717,10 +717,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.652693Z",
- "iopub.status.busy": "2024-09-05T19:33:47.652256Z",
- "iopub.status.idle": "2024-09-05T19:33:47.657423Z",
- "shell.execute_reply": "2024-09-05T19:33:47.656894Z"
+ "iopub.execute_input": "2024-09-06T19:33:38.882730Z",
+ "iopub.status.busy": "2024-09-06T19:33:38.882295Z",
+ "iopub.status.idle": "2024-09-06T19:33:38.887349Z",
+ "shell.execute_reply": "2024-09-06T19:33:38.886834Z"
},
"id": "_b-AQeoXOc7q",
"outputId": "15ae534a-f517-4906-b177-ca91931a8954"
@@ -767,10 +767,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.659937Z",
- "iopub.status.busy": "2024-09-05T19:33:47.659554Z",
- "iopub.status.idle": "2024-09-05T19:33:47.788740Z",
- "shell.execute_reply": "2024-09-05T19:33:47.788068Z"
+ "iopub.execute_input": "2024-09-06T19:33:38.889963Z",
+ "iopub.status.busy": "2024-09-06T19:33:38.889560Z",
+ "iopub.status.idle": "2024-09-06T19:33:38.996371Z",
+ "shell.execute_reply": "2024-09-06T19:33:38.995754Z"
}
},
"outputs": [
@@ -807,10 +807,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.791246Z",
- "iopub.status.busy": "2024-09-05T19:33:47.791038Z",
- "iopub.status.idle": "2024-09-05T19:33:47.803816Z",
- "shell.execute_reply": "2024-09-05T19:33:47.803324Z"
+ "iopub.execute_input": "2024-09-06T19:33:38.998935Z",
+ "iopub.status.busy": "2024-09-06T19:33:38.998516Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.011487Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.010948Z"
},
"scrolled": true
},
@@ -870,10 +870,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.805893Z",
- "iopub.status.busy": "2024-09-05T19:33:47.805707Z",
- "iopub.status.idle": "2024-09-05T19:33:47.813801Z",
- "shell.execute_reply": "2024-09-05T19:33:47.813238Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.013725Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.013368Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.021505Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.020914Z"
}
},
"outputs": [
@@ -977,10 +977,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.815901Z",
- "iopub.status.busy": "2024-09-05T19:33:47.815566Z",
- "iopub.status.idle": "2024-09-05T19:33:47.819837Z",
- "shell.execute_reply": "2024-09-05T19:33:47.819362Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.023705Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.023358Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.027633Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.027085Z"
}
},
"outputs": [
@@ -1018,10 +1018,10 @@
"height": 237
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.821906Z",
- "iopub.status.busy": "2024-09-05T19:33:47.821589Z",
- "iopub.status.idle": "2024-09-05T19:33:47.827106Z",
- "shell.execute_reply": "2024-09-05T19:33:47.826568Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.029757Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.029380Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.035357Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.034867Z"
},
"id": "FQwRHgbclpsO",
"outputId": "fee5c335-c00e-4fcc-f22b-718705e93182"
@@ -1148,10 +1148,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.829139Z",
- "iopub.status.busy": "2024-09-05T19:33:47.828942Z",
- "iopub.status.idle": "2024-09-05T19:33:47.943778Z",
- "shell.execute_reply": "2024-09-05T19:33:47.943280Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.037583Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.037235Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.148961Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.148428Z"
},
"id": "ff1NFVlDoysO",
"outputId": "8141a036-44c1-4349-c338-880432513e37"
@@ -1205,10 +1205,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:47.946131Z",
- "iopub.status.busy": "2024-09-05T19:33:47.945662Z",
- "iopub.status.idle": "2024-09-05T19:33:48.054912Z",
- "shell.execute_reply": "2024-09-05T19:33:48.054339Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.151080Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.150801Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.254384Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.253890Z"
},
"id": "GZgovGkdiaiP",
"outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7"
@@ -1253,10 +1253,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:48.057270Z",
- "iopub.status.busy": "2024-09-05T19:33:48.056812Z",
- "iopub.status.idle": "2024-09-05T19:33:48.160591Z",
- "shell.execute_reply": "2024-09-05T19:33:48.160001Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.256524Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.256169Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.357567Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.356999Z"
},
"id": "lfa2eHbMwG8R",
"outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c"
@@ -1297,10 +1297,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:48.162787Z",
- "iopub.status.busy": "2024-09-05T19:33:48.162458Z",
- "iopub.status.idle": "2024-09-05T19:33:48.266360Z",
- "shell.execute_reply": "2024-09-05T19:33:48.265774Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.359754Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.359388Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.459179Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.458626Z"
}
},
"outputs": [
@@ -1348,10 +1348,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:48.268741Z",
- "iopub.status.busy": "2024-09-05T19:33:48.268263Z",
- "iopub.status.idle": "2024-09-05T19:33:48.271596Z",
- "shell.execute_reply": "2024-09-05T19:33:48.271058Z"
+ "iopub.execute_input": "2024-09-06T19:33:39.461397Z",
+ "iopub.status.busy": "2024-09-06T19:33:39.461068Z",
+ "iopub.status.idle": "2024-09-06T19:33:39.464273Z",
+ "shell.execute_reply": "2024-09-06T19:33:39.463742Z"
},
"nbsphinx": "hidden"
},
@@ -1392,7 +1392,66 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0ac3ab7ed1fa4817ad8d2ff6240058c2": {
+ "08327f8f533f49bb8518d3413af11e27": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_d563179a4e534dd4b15465aa4a240b93",
+ "placeholder": "",
+ "style": "IPY_MODEL_c0dd17ee1b414e0dbba6940c708a7553",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 16.9M/16.9M [00:00<00:00, 169MB/s]"
+ }
+ },
+ "0b18f93966c84db6bd40967285652faf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "0dc2781328864c55a124d3ba0119a934": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "0e3467cf59954459ab486aee2ba9c3a5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1445,7 +1504,7 @@
"width": null
}
},
- "0c9e0eca2bab4151953b7527eb340910": {
+ "10945e8601a446f2bb59fa1211f86f5b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1498,23 +1557,51 @@
"width": null
}
},
- "154e64bae12a4f2d8e65671c37840119": {
+ "132a82bf8c9844f59e250a9598747c76": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "16dc435309f84ebfb937af4723b9a019": {
+ "15a248e8576b4e1cace7306d79423606": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_9f5000cf7d6a4079b14d5f3c666d8d9a",
+ "max": 128619.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_c12a918e266b40caa2ad3eb5ba27297c",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 128619.0
+ }
+ },
+ "214135184cdb4c61851d09d4776b8681": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1532,7 +1619,7 @@
"text_color": null
}
},
- "1d83841509b44ed18468f978a2d28aaa": {
+ "2741fbda14764533a6d7865887e84821": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1585,79 +1672,133 @@
"width": null
}
},
- "1f3a028856264fee8e351bf1dd8383b1": {
- "model_module": "@jupyter-widgets/controls",
+ "2a2e0134b1234019be47ad459a2c7e6e": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "216b05e4aa5c4e1a8f3ee60f4f5a9dfe": {
+ "2a4a612a6d2846bca788bddf1043cc09": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_30eda8d6d1724ca0a98cb1391ef57cf4",
+ "IPY_MODEL_31da699ce27d4748a3b0908daaeff226",
+ "IPY_MODEL_c979e50a880a49aeaf6e580f1f3ff7e8"
+ ],
+ "layout": "IPY_MODEL_e41b0c6637564566b921cc1987c4bf9a",
+ "tabbable": null,
+ "tooltip": null
}
},
- "243a883e57e44f36a54b859f5da85f4e": {
+ "30eda8d6d1724ca0a98cb1391ef57cf4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_de6e8b7c78564371956a0428b0c94a2e",
- "IPY_MODEL_c967786b2b46488789662efb4800e58e",
- "IPY_MODEL_51b10ab6596944e4ae19611522f678ac"
- ],
- "layout": "IPY_MODEL_bffab5547626456a909cb47e0bbb3bbc",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_dd83345f7f5c46a38920bb555c0f6b9a",
+ "placeholder": "",
+ "style": "IPY_MODEL_0dc2781328864c55a124d3ba0119a934",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "classifier.ckpt: 100%"
}
},
- "399de27fc5fb401bb57ec59469e9385b": {
+ "318a448eedbf4c68b4b67978734a1ae9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_0e3467cf59954459ab486aee2ba9c3a5",
+ "max": 2041.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_6c036dfeb50042c8984a6a6692fc0f9b",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 2041.0
}
},
- "3f1b31d22e0d49009bbdccee244e7881": {
+ "31da699ce27d4748a3b0908daaeff226": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -1673,17 +1814,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f89d5ee8629d40ec8a14637207ab372f",
- "max": 16887676.0,
+ "layout": "IPY_MODEL_c920632573cc4fe0976140070f9677ec",
+ "max": 15856877.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_1f3a028856264fee8e351bf1dd8383b1",
+ "style": "IPY_MODEL_b97eebf068ca4e9c85cbadbb4cc103c4",
"tabbable": null,
"tooltip": null,
- "value": 16887676.0
+ "value": 15856877.0
}
},
- "443736061547483b9010861c93503201": {
+ "46e0a3db3d4b44aea458c9d15b1b45b8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1736,7 +1877,7 @@
"width": null
}
},
- "51b10ab6596944e4ae19611522f678ac": {
+ "4c2a6bff30b84735af3cf83ab6de7ddf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1751,15 +1892,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_adfc4c39485b47baa8219565f0ac4d12",
+ "layout": "IPY_MODEL_46e0a3db3d4b44aea458c9d15b1b45b8",
"placeholder": "",
- "style": "IPY_MODEL_ec833e24c78e4e30811c165aa3d20da9",
+ "style": "IPY_MODEL_b420c63e4f174bba8f48175457a23dfe",
"tabbable": null,
"tooltip": null,
- "value": " 129k/129k [00:00<00:00, 10.1MB/s]"
+ "value": " 3.20k/3.20k [00:00<00:00, 804kB/s]"
}
},
- "5a797a83468b42199c5a1b04fad5c52f": {
+ "4c6822f78a694818a65c111aed9cd2c2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1812,7 +1953,120 @@
"width": null
}
},
- "5c917033034f40ff8b3f18838ec90fba": {
+ "53dafb78e671406e89f3754d23b34684": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ef0ba41ae31d4fca866f944d42378821",
+ "IPY_MODEL_318a448eedbf4c68b4b67978734a1ae9",
+ "IPY_MODEL_6fc0bc69349045329fcb28a46a2fe14b"
+ ],
+ "layout": "IPY_MODEL_2a2e0134b1234019be47ad459a2c7e6e",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "5aded6b96abc4bfebf8a38a3dcad2d6f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_7ba99fd178de4f25867b6e37949a6d85",
+ "max": 3201.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_f321f0e3281b4b478dffcfb404472020",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 3201.0
+ }
+ },
+ "5ee4ab99d955426daf559df8bf71c44f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_b4b3cc9f4e4c408998a1c0ebec86d5bc",
+ "IPY_MODEL_5aded6b96abc4bfebf8a38a3dcad2d6f",
+ "IPY_MODEL_4c2a6bff30b84735af3cf83ab6de7ddf"
+ ],
+ "layout": "IPY_MODEL_6eec35a271e4402b8f28109615706306",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "6a89c26dda78427781977305cd34e44b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_f948f7fae2bd4b23b89c1a9f86de6cdc",
+ "placeholder": "",
+ "style": "IPY_MODEL_0b18f93966c84db6bd40967285652faf",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "embedding_model.ckpt: 100%"
+ }
+ },
+ "6c036dfeb50042c8984a6a6692fc0f9b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "6eec35a271e4402b8f28109615706306": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1865,7 +2119,7 @@
"width": null
}
},
- "66e19c92c51b496db44b15a5b507b710": {
+ "6fc0bc69349045329fcb28a46a2fe14b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1880,41 +2134,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ad1fc452c5504c439e590428a469b666",
+ "layout": "IPY_MODEL_2741fbda14764533a6d7865887e84821",
"placeholder": "",
- "style": "IPY_MODEL_d566fdd37641491ab41bf206cde557e5",
- "tabbable": null,
- "tooltip": null,
- "value": "hyperparams.yaml: 100%"
- }
- },
- "68ba7f342f464cc3947873e65c2d3569": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_443736061547483b9010861c93503201",
- "max": 15856877.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_c7c5aa776a8d43ab92ef711ff33f3cfb",
+ "style": "IPY_MODEL_132a82bf8c9844f59e250a9598747c76",
"tabbable": null,
"tooltip": null,
- "value": 15856877.0
+ "value": " 2.04k/2.04k [00:00<00:00, 482kB/s]"
}
},
- "6c722d72dc7646d9a5104acca236e442": {
+ "700bb6482b2c4111b4ed9390c8470861": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1967,7 +2195,7 @@
"width": null
}
},
- "762087561b9d4154b5c585ba2d8e2f20": {
+ "701065566fa2417290be013d76599838": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2020,31 +2248,7 @@
"width": null
}
},
- "8ee8d7e516804bc8a1d41bb8519177d1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_fab2732c47074338930c0c8ddd90a324",
- "IPY_MODEL_68ba7f342f464cc3947873e65c2d3569",
- "IPY_MODEL_d0e1290fa89542e2811205d7ff27d396"
- ],
- "layout": "IPY_MODEL_c4fe1edeaea44a6cb2e00460ef8510ea",
- "tabbable": null,
- "tooltip": null
- }
- },
- "90fd78ffdd834ac9b5ffac06beb8ed03": {
+ "71c1b39c84cf47b5a477e26da271fef9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2097,7 +2301,7 @@
"width": null
}
},
- "928f774bbc7544fba8840c9d28c2974b": {
+ "798cb5dd7eb545d5a4188e416dce6f88": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2115,106 +2319,7 @@
"text_color": null
}
},
- "951b9232041b40c1a813db11789edb21": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_5a797a83468b42199c5a1b04fad5c52f",
- "placeholder": "",
- "style": "IPY_MODEL_928f774bbc7544fba8840c9d28c2974b",
- "tabbable": null,
- "tooltip": null,
- "value": " 16.9M/16.9M [00:00<00:00, 185MB/s]"
- }
- },
- "a847230825a14abeae004419586a93d9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_6c722d72dc7646d9a5104acca236e442",
- "placeholder": "",
- "style": "IPY_MODEL_af2cbf510ad04f7fa3b5df8cc9366792",
- "tabbable": null,
- "tooltip": null,
- "value": " 3.20k/3.20k [00:00<00:00, 797kB/s]"
- }
- },
- "ad1fc452c5504c439e590428a469b666": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
- }
- },
- "adfc4c39485b47baa8219565f0ac4d12": {
+ "7ba99fd178de4f25867b6e37949a6d85": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2267,7 +2372,7 @@
"width": null
}
},
- "af2cbf510ad04f7fa3b5df8cc9366792": {
+ "7ca38434b4e44b5b86b173c7855b4ff3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2285,30 +2390,7 @@
"text_color": null
}
},
- "af9f54aad3474eac9227e6e4fb35da2d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_5c917033034f40ff8b3f18838ec90fba",
- "placeholder": "",
- "style": "IPY_MODEL_e6d62d02ded84fd4990ce798c0b364a3",
- "tabbable": null,
- "tooltip": null,
- "value": "mean_var_norm_emb.ckpt: 100%"
- }
- },
- "b083f3c18acd4001b212b89121e40255": {
+ "819ab4db9b9249cfb05199eec4ffe2ae": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2361,7 +2443,7 @@
"width": null
}
},
- "b22d55955fea4a6e81b4dad1f078f506": {
+ "8b0da1e1f92449e49814a4792b3a0a18": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2414,7 +2496,33 @@
"width": null
}
},
- "b420e431ecef46f9ab11cd0de264f596": {
+ "9a1d64682df846808b68e96652571971": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_8b0da1e1f92449e49814a4792b3a0a18",
+ "max": 16887676.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_fa5d63111e8040c8a46a0ef606a7b541",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 16887676.0
+ }
+ },
+ "9f5000cf7d6a4079b14d5f3c666d8d9a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2467,7 +2575,71 @@
"width": null
}
},
- "b7642133d8c34d4d912bd84e9ead2af8": {
+ "a79c4f7046b44587aab8c79e98299302": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_701065566fa2417290be013d76599838",
+ "placeholder": "",
+ "style": "IPY_MODEL_798cb5dd7eb545d5a4188e416dce6f88",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "label_encoder.txt: 100%"
+ }
+ },
+ "b420c63e4f174bba8f48175457a23dfe": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "b4b3cc9f4e4c408998a1c0ebec86d5bc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_71c1b39c84cf47b5a477e26da271fef9",
+ "placeholder": "",
+ "style": "IPY_MODEL_7ca38434b4e44b5b86b173c7855b4ff3",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "mean_var_norm_emb.ckpt: 100%"
+ }
+ },
+ "b8e950254c7c4bfc904715885aa32fa1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2520,7 +2692,23 @@
"width": null
}
},
- "ba22209daa4c40a88430a8226b0f346a": {
+ "b97eebf068ca4e9c85cbadbb4cc103c4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "c0dd17ee1b414e0dbba6940c708a7553": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2538,31 +2726,23 @@
"text_color": null
}
},
- "bf8df34d46b34a05ab2a83672aaed5e1": {
+ "c12a918e266b40caa2ad3eb5ba27297c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_e8a711cadf704f94a9e4ea158fab4c92",
- "IPY_MODEL_3f1b31d22e0d49009bbdccee244e7881",
- "IPY_MODEL_951b9232041b40c1a813db11789edb21"
- ],
- "layout": "IPY_MODEL_b22d55955fea4a6e81b4dad1f078f506",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "bffab5547626456a909cb47e0bbb3bbc": {
+ "c920632573cc4fe0976140070f9677ec": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2615,51 +2795,30 @@
"width": null
}
},
- "c237ab4dd5ec46be8804717537efe6cd": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "c47b9c77e0794583b74618d47ea5718a": {
+ "c979e50a880a49aeaf6e580f1f3ff7e8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_0c9e0eca2bab4151953b7527eb340910",
- "max": 3201.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_216b05e4aa5c4e1a8f3ee60f4f5a9dfe",
+ "layout": "IPY_MODEL_4c6822f78a694818a65c111aed9cd2c2",
+ "placeholder": "",
+ "style": "IPY_MODEL_e3c6b0d1ab4745c796d69b14cb880bf5",
"tabbable": null,
"tooltip": null,
- "value": 3201.0
+ "value": " 15.9M/15.9M [00:00<00:00, 212MB/s]"
}
},
- "c4fe1edeaea44a6cb2e00460ef8510ea": {
+ "d563179a4e534dd4b15465aa4a240b93": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2712,49 +2871,7 @@
"width": null
}
},
- "c7c5aa776a8d43ab92ef711ff33f3cfb": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "c967786b2b46488789662efb4800e58e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_0ac3ab7ed1fa4817ad8d2ff6240058c2",
- "max": 128619.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_399de27fc5fb401bb57ec59469e9385b",
- "tabbable": null,
- "tooltip": null,
- "value": 128619.0
- }
- },
- "cd18ca9fbef94a8db43306835e09a332": {
+ "dd83345f7f5c46a38920bb555c0f6b9a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2807,31 +2924,7 @@
"width": null
}
},
- "cdb25710e5ba4e95b957897c0a8ff15e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_66e19c92c51b496db44b15a5b507b710",
- "IPY_MODEL_ee907f9a6e9c491ca136fbfda0e5e0a1",
- "IPY_MODEL_f1525468540540bc9a15df1068e8130e"
- ],
- "layout": "IPY_MODEL_de761d347704476fa0a3e6b9e28080f8",
- "tabbable": null,
- "tooltip": null
- }
- },
- "d0e1290fa89542e2811205d7ff27d396": {
+ "de328081cf5143bda2f18a1f732277b0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2846,15 +2939,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b083f3c18acd4001b212b89121e40255",
+ "layout": "IPY_MODEL_10945e8601a446f2bb59fa1211f86f5b",
"placeholder": "",
- "style": "IPY_MODEL_ff7877413d814a208f7f702e4e75a961",
+ "style": "IPY_MODEL_214135184cdb4c61851d09d4776b8681",
"tabbable": null,
"tooltip": null,
- "value": " 15.9M/15.9M [00:00<00:00, 284MB/s]"
+ "value": " 129k/129k [00:00<00:00, 24.4MB/s]"
}
},
- "d566fdd37641491ab41bf206cde557e5": {
+ "e3c6b0d1ab4745c796d69b14cb880bf5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2872,54 +2965,7 @@
"text_color": null
}
},
- "db3e5da7fa9a41caa7a4be04f016b2c6": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_af9f54aad3474eac9227e6e4fb35da2d",
- "IPY_MODEL_c47b9c77e0794583b74618d47ea5718a",
- "IPY_MODEL_a847230825a14abeae004419586a93d9"
- ],
- "layout": "IPY_MODEL_1d83841509b44ed18468f978a2d28aaa",
- "tabbable": null,
- "tooltip": null
- }
- },
- "de6e8b7c78564371956a0428b0c94a2e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_b420e431ecef46f9ab11cd0de264f596",
- "placeholder": "",
- "style": "IPY_MODEL_fc55ef5dc3374f9893e79657e1ff3472",
- "tabbable": null,
- "tooltip": null,
- "value": "label_encoder.txt: 100%"
- }
- },
- "de761d347704476fa0a3e6b9e28080f8": {
+ "e41b0c6637564566b921cc1987c4bf9a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2972,25 +3018,31 @@
"width": null
}
},
- "e6d62d02ded84fd4990ce798c0b364a3": {
+ "e4e9c1fa715d49009ec1097cf561d5f6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_6a89c26dda78427781977305cd34e44b",
+ "IPY_MODEL_9a1d64682df846808b68e96652571971",
+ "IPY_MODEL_08327f8f533f49bb8518d3413af11e27"
+ ],
+ "layout": "IPY_MODEL_819ab4db9b9249cfb05199eec4ffe2ae",
+ "tabbable": null,
+ "tooltip": null
}
},
- "e8a711cadf704f94a9e4ea158fab4c92": {
+ "ef0ba41ae31d4fca866f944d42378821": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3005,82 +3057,55 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b7642133d8c34d4d912bd84e9ead2af8",
+ "layout": "IPY_MODEL_700bb6482b2c4111b4ed9390c8470861",
"placeholder": "",
- "style": "IPY_MODEL_ba22209daa4c40a88430a8226b0f346a",
+ "style": "IPY_MODEL_f9b3a44be9f34f6e93de758ee46b92ec",
"tabbable": null,
"tooltip": null,
- "value": "embedding_model.ckpt: 100%"
+ "value": "hyperparams.yaml: 100%"
}
},
- "ec833e24c78e4e30811c165aa3d20da9": {
+ "f321f0e3281b4b478dffcfb404472020": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "ee907f9a6e9c491ca136fbfda0e5e0a1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_cd18ca9fbef94a8db43306835e09a332",
- "max": 2041.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_154e64bae12a4f2d8e65671c37840119",
- "tabbable": null,
- "tooltip": null,
- "value": 2041.0
+ "bar_color": null,
+ "description_width": ""
}
},
- "f1525468540540bc9a15df1068e8130e": {
+ "f814d21bb5204a479acad09d629678fa": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_90fd78ffdd834ac9b5ffac06beb8ed03",
- "placeholder": "",
- "style": "IPY_MODEL_16dc435309f84ebfb937af4723b9a019",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_a79c4f7046b44587aab8c79e98299302",
+ "IPY_MODEL_15a248e8576b4e1cace7306d79423606",
+ "IPY_MODEL_de328081cf5143bda2f18a1f732277b0"
+ ],
+ "layout": "IPY_MODEL_b8e950254c7c4bfc904715885aa32fa1",
"tabbable": null,
- "tooltip": null,
- "value": " 2.04k/2.04k [00:00<00:00, 479kB/s]"
+ "tooltip": null
}
},
- "f89d5ee8629d40ec8a14637207ab372f": {
+ "f948f7fae2bd4b23b89c1a9f86de6cdc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3133,30 +3158,7 @@
"width": null
}
},
- "fab2732c47074338930c0c8ddd90a324": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_762087561b9d4154b5c585ba2d8e2f20",
- "placeholder": "",
- "style": "IPY_MODEL_c237ab4dd5ec46be8804717537efe6cd",
- "tabbable": null,
- "tooltip": null,
- "value": "classifier.ckpt: 100%"
- }
- },
- "fc55ef5dc3374f9893e79657e1ff3472": {
+ "f9b3a44be9f34f6e93de758ee46b92ec": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -3174,22 +3176,20 @@
"text_color": null
}
},
- "ff7877413d814a208f7f702e4e75a961": {
+ "fa5d63111e8040c8a46a0ef606a7b541": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "bar_color": null,
+ "description_width": ""
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 0c578e325..1028deca4 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:51.639223Z",
- "iopub.status.busy": "2024-09-05T19:33:51.639036Z",
- "iopub.status.idle": "2024-09-05T19:33:52.898899Z",
- "shell.execute_reply": "2024-09-05T19:33:52.898318Z"
+ "iopub.execute_input": "2024-09-06T19:33:42.774016Z",
+ "iopub.status.busy": "2024-09-06T19:33:42.773836Z",
+ "iopub.status.idle": "2024-09-06T19:33:43.987649Z",
+ "shell.execute_reply": "2024-09-06T19:33:43.987087Z"
},
"nbsphinx": "hidden"
},
@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -118,10 +118,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:52.901515Z",
- "iopub.status.busy": "2024-09-05T19:33:52.901051Z",
- "iopub.status.idle": "2024-09-05T19:33:52.904212Z",
- "shell.execute_reply": "2024-09-05T19:33:52.903723Z"
+ "iopub.execute_input": "2024-09-06T19:33:43.990323Z",
+ "iopub.status.busy": "2024-09-06T19:33:43.989863Z",
+ "iopub.status.idle": "2024-09-06T19:33:43.992901Z",
+ "shell.execute_reply": "2024-09-06T19:33:43.992377Z"
}
},
"outputs": [],
@@ -252,10 +252,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:52.906199Z",
- "iopub.status.busy": "2024-09-05T19:33:52.906021Z",
- "iopub.status.idle": "2024-09-05T19:33:52.914901Z",
- "shell.execute_reply": "2024-09-05T19:33:52.914437Z"
+ "iopub.execute_input": "2024-09-06T19:33:43.995557Z",
+ "iopub.status.busy": "2024-09-06T19:33:43.995114Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.005313Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.004712Z"
},
"nbsphinx": "hidden"
},
@@ -353,10 +353,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:52.916877Z",
- "iopub.status.busy": "2024-09-05T19:33:52.916698Z",
- "iopub.status.idle": "2024-09-05T19:33:52.921682Z",
- "shell.execute_reply": "2024-09-05T19:33:52.921248Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.007445Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.007143Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.012113Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.011528Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:52.923768Z",
- "iopub.status.busy": "2024-09-05T19:33:52.923431Z",
- "iopub.status.idle": "2024-09-05T19:33:53.109668Z",
- "shell.execute_reply": "2024-09-05T19:33:53.109095Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.014273Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.013973Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.198978Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.198432Z"
},
"nbsphinx": "hidden"
},
@@ -517,10 +517,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:53.112201Z",
- "iopub.status.busy": "2024-09-05T19:33:53.111801Z",
- "iopub.status.idle": "2024-09-05T19:33:53.486912Z",
- "shell.execute_reply": "2024-09-05T19:33:53.486234Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.201674Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.201189Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.572697Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.572078Z"
}
},
"outputs": [
@@ -569,10 +569,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:53.489255Z",
- "iopub.status.busy": "2024-09-05T19:33:53.488885Z",
- "iopub.status.idle": "2024-09-05T19:33:53.512031Z",
- "shell.execute_reply": "2024-09-05T19:33:53.511558Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.574957Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.574618Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.598417Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.597853Z"
}
},
"outputs": [],
@@ -608,10 +608,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:53.514134Z",
- "iopub.status.busy": "2024-09-05T19:33:53.513789Z",
- "iopub.status.idle": "2024-09-05T19:33:53.524954Z",
- "shell.execute_reply": "2024-09-05T19:33:53.524500Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.600565Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.600242Z",
+ "iopub.status.idle": "2024-09-06T19:33:44.611542Z",
+ "shell.execute_reply": "2024-09-06T19:33:44.611124Z"
}
},
"outputs": [],
@@ -642,10 +642,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:53.527001Z",
- "iopub.status.busy": "2024-09-05T19:33:53.526670Z",
- "iopub.status.idle": "2024-09-05T19:33:55.662115Z",
- "shell.execute_reply": "2024-09-05T19:33:55.661472Z"
+ "iopub.execute_input": "2024-09-06T19:33:44.613571Z",
+ "iopub.status.busy": "2024-09-06T19:33:44.613277Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.668917Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.668293Z"
}
},
"outputs": [
@@ -714,10 +714,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.664771Z",
- "iopub.status.busy": "2024-09-05T19:33:55.664277Z",
- "iopub.status.idle": "2024-09-05T19:33:55.685806Z",
- "shell.execute_reply": "2024-09-05T19:33:55.685335Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.671429Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.670969Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.692283Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.691706Z"
}
},
"outputs": [
@@ -830,10 +830,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.688099Z",
- "iopub.status.busy": "2024-09-05T19:33:55.687744Z",
- "iopub.status.idle": "2024-09-05T19:33:55.705725Z",
- "shell.execute_reply": "2024-09-05T19:33:55.705227Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.694643Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.694120Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.711969Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.711526Z"
}
},
"outputs": [
@@ -937,10 +937,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.707922Z",
- "iopub.status.busy": "2024-09-05T19:33:55.707593Z",
- "iopub.status.idle": "2024-09-05T19:33:55.722195Z",
- "shell.execute_reply": "2024-09-05T19:33:55.721729Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.713865Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.713694Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.728067Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.727609Z"
}
},
"outputs": [
@@ -1075,17 +1075,17 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.724401Z",
- "iopub.status.busy": "2024-09-05T19:33:55.724049Z",
- "iopub.status.idle": "2024-09-05T19:33:55.745888Z",
- "shell.execute_reply": "2024-09-05T19:33:55.745312Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.729970Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.729797Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.748313Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.747746Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "176d2317677745c2a258fde8d681c83c",
+ "model_id": "e8a80b5b1ace4f0e9399969281df7d06",
"version_major": 2,
"version_minor": 0
},
@@ -1121,10 +1121,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.748017Z",
- "iopub.status.busy": "2024-09-05T19:33:55.747658Z",
- "iopub.status.idle": "2024-09-05T19:33:55.762168Z",
- "shell.execute_reply": "2024-09-05T19:33:55.761701Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.750540Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.750202Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.765277Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.764810Z"
}
},
"outputs": [
@@ -1247,10 +1247,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.764365Z",
- "iopub.status.busy": "2024-09-05T19:33:55.764037Z",
- "iopub.status.idle": "2024-09-05T19:33:55.769869Z",
- "shell.execute_reply": "2024-09-05T19:33:55.769425Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.767376Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.767048Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.772946Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.772447Z"
}
},
"outputs": [],
@@ -1307,10 +1307,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:55.772010Z",
- "iopub.status.busy": "2024-09-05T19:33:55.771653Z",
- "iopub.status.idle": "2024-09-05T19:33:55.789767Z",
- "shell.execute_reply": "2024-09-05T19:33:55.789194Z"
+ "iopub.execute_input": "2024-09-06T19:33:46.774871Z",
+ "iopub.status.busy": "2024-09-06T19:33:46.774606Z",
+ "iopub.status.idle": "2024-09-06T19:33:46.792743Z",
+ "shell.execute_reply": "2024-09-06T19:33:46.792272Z"
}
},
"outputs": [
@@ -1447,31 +1447,51 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "176d2317677745c2a258fde8d681c83c": {
+ "1b84860876254aa29989a5bb614dca8d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "4440624d361b4a39a470c6b36d42b8d3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_fd73fc24b59540c2b09b86a70497ca80",
- "IPY_MODEL_f20430f0a23a450ba82d47e0db435359",
- "IPY_MODEL_e19f463e637d445eb4deccde754ebbb6"
- ],
- "layout": "IPY_MODEL_f66fdb10d529486cb82c871870bd5ab3",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_ec547a92716a409bb8eb86bc364258c9",
+ "max": 132.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_ba52f0b8569f404584f54443a28a0baf",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": 132.0
}
},
- "1cc2bf6b970748e6861611eaa6a3bc8d": {
+ "55ed31e23a4a443bbb5e734bb143d697": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1524,41 +1544,53 @@
"width": null
}
},
- "492d0243100b4918aa5d5b886805738f": {
+ "890c901184904e0883bb0bded31d86de": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_55ed31e23a4a443bbb5e734bb143d697",
+ "placeholder": "",
+ "style": "IPY_MODEL_fd6266d59b69432181af01e5eb3c389d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Saving the dataset (1/1 shards): 100%"
}
},
- "95eab14b7dbf4f61b9ccc05ac74d8bcf": {
+ "8e898dc9dd204b2f8ba985adc383a396": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_eae4cf97733c4548920387dc447b9d98",
+ "placeholder": "",
+ "style": "IPY_MODEL_1b84860876254aa29989a5bb614dca8d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 132/132 [00:00<00:00, 13525.06 examples/s]"
}
},
- "ba3943ede9804d09b17cca85b5dc5c1a": {
+ "94329361a5b3479e804741fa80c47e78": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1611,25 +1643,47 @@
"width": null
}
},
- "c11e8e6c36d9470698b5e4f30ab57c43": {
+ "ba52f0b8569f404584f54443a28a0baf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "bar_color": null,
+ "description_width": ""
}
},
- "d874f116cf3c4dbbb9ebe04233c9f4c4": {
+ "e8a80b5b1ace4f0e9399969281df7d06": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_890c901184904e0883bb0bded31d86de",
+ "IPY_MODEL_4440624d361b4a39a470c6b36d42b8d3",
+ "IPY_MODEL_8e898dc9dd204b2f8ba985adc383a396"
+ ],
+ "layout": "IPY_MODEL_94329361a5b3479e804741fa80c47e78",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "eae4cf97733c4548920387dc447b9d98": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1682,56 +1736,7 @@
"width": null
}
},
- "e19f463e637d445eb4deccde754ebbb6": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_ba3943ede9804d09b17cca85b5dc5c1a",
- "placeholder": "",
- "style": "IPY_MODEL_492d0243100b4918aa5d5b886805738f",
- "tabbable": null,
- "tooltip": null,
- "value": " 132/132 [00:00<00:00, 10900.52 examples/s]"
- }
- },
- "f20430f0a23a450ba82d47e0db435359": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_d874f116cf3c4dbbb9ebe04233c9f4c4",
- "max": 132.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_95eab14b7dbf4f61b9ccc05ac74d8bcf",
- "tabbable": null,
- "tooltip": null,
- "value": 132.0
- }
- },
- "f66fdb10d529486cb82c871870bd5ab3": {
+ "ec547a92716a409bb8eb86bc364258c9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1784,27 +1789,22 @@
"width": null
}
},
- "fd73fc24b59540c2b09b86a70497ca80": {
+ "fd6266d59b69432181af01e5eb3c389d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_1cc2bf6b970748e6861611eaa6a3bc8d",
- "placeholder": "",
- "style": "IPY_MODEL_c11e8e6c36d9470698b5e4f30ab57c43",
- "tabbable": null,
- "tooltip": null,
- "value": "Saving the dataset (1/1 shards): 100%"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index 0682692ed..a75ce5d83 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:58.621443Z",
- "iopub.status.busy": "2024-09-05T19:33:58.621264Z",
- "iopub.status.idle": "2024-09-05T19:33:59.859772Z",
- "shell.execute_reply": "2024-09-05T19:33:59.859206Z"
+ "iopub.execute_input": "2024-09-06T19:33:49.692668Z",
+ "iopub.status.busy": "2024-09-06T19:33:49.692505Z",
+ "iopub.status.idle": "2024-09-06T19:33:50.890931Z",
+ "shell.execute_reply": "2024-09-06T19:33:50.890368Z"
},
"nbsphinx": "hidden"
},
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:59.862442Z",
- "iopub.status.busy": "2024-09-05T19:33:59.861951Z",
- "iopub.status.idle": "2024-09-05T19:33:59.865116Z",
- "shell.execute_reply": "2024-09-05T19:33:59.864648Z"
+ "iopub.execute_input": "2024-09-06T19:33:50.893481Z",
+ "iopub.status.busy": "2024-09-06T19:33:50.892976Z",
+ "iopub.status.idle": "2024-09-06T19:33:50.895994Z",
+ "shell.execute_reply": "2024-09-06T19:33:50.895546Z"
}
},
"outputs": [],
@@ -250,10 +250,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:59.867239Z",
- "iopub.status.busy": "2024-09-05T19:33:59.866893Z",
- "iopub.status.idle": "2024-09-05T19:33:59.876133Z",
- "shell.execute_reply": "2024-09-05T19:33:59.875647Z"
+ "iopub.execute_input": "2024-09-06T19:33:50.898095Z",
+ "iopub.status.busy": "2024-09-06T19:33:50.897919Z",
+ "iopub.status.idle": "2024-09-06T19:33:50.907050Z",
+ "shell.execute_reply": "2024-09-06T19:33:50.906577Z"
},
"nbsphinx": "hidden"
},
@@ -356,10 +356,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:59.878037Z",
- "iopub.status.busy": "2024-09-05T19:33:59.877859Z",
- "iopub.status.idle": "2024-09-05T19:33:59.882519Z",
- "shell.execute_reply": "2024-09-05T19:33:59.882088Z"
+ "iopub.execute_input": "2024-09-06T19:33:50.908860Z",
+ "iopub.status.busy": "2024-09-06T19:33:50.908672Z",
+ "iopub.status.idle": "2024-09-06T19:33:50.913284Z",
+ "shell.execute_reply": "2024-09-06T19:33:50.912693Z"
}
},
"outputs": [],
@@ -448,10 +448,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:59.884662Z",
- "iopub.status.busy": "2024-09-05T19:33:59.884315Z",
- "iopub.status.idle": "2024-09-05T19:34:00.070637Z",
- "shell.execute_reply": "2024-09-05T19:34:00.070087Z"
+ "iopub.execute_input": "2024-09-06T19:33:50.915417Z",
+ "iopub.status.busy": "2024-09-06T19:33:50.915238Z",
+ "iopub.status.idle": "2024-09-06T19:33:51.099306Z",
+ "shell.execute_reply": "2024-09-06T19:33:51.098789Z"
},
"nbsphinx": "hidden"
},
@@ -520,10 +520,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:00.073480Z",
- "iopub.status.busy": "2024-09-05T19:34:00.072974Z",
- "iopub.status.idle": "2024-09-05T19:34:00.450121Z",
- "shell.execute_reply": "2024-09-05T19:34:00.449499Z"
+ "iopub.execute_input": "2024-09-06T19:33:51.101790Z",
+ "iopub.status.busy": "2024-09-06T19:33:51.101450Z",
+ "iopub.status.idle": "2024-09-06T19:33:51.473593Z",
+ "shell.execute_reply": "2024-09-06T19:33:51.473003Z"
}
},
"outputs": [
@@ -559,10 +559,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:00.452506Z",
- "iopub.status.busy": "2024-09-05T19:34:00.452151Z",
- "iopub.status.idle": "2024-09-05T19:34:00.455103Z",
- "shell.execute_reply": "2024-09-05T19:34:00.454519Z"
+ "iopub.execute_input": "2024-09-06T19:33:51.475866Z",
+ "iopub.status.busy": "2024-09-06T19:33:51.475414Z",
+ "iopub.status.idle": "2024-09-06T19:33:51.478399Z",
+ "shell.execute_reply": "2024-09-06T19:33:51.477816Z"
}
},
"outputs": [],
@@ -602,10 +602,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:00.457413Z",
- "iopub.status.busy": "2024-09-05T19:34:00.456957Z",
- "iopub.status.idle": "2024-09-05T19:34:00.491429Z",
- "shell.execute_reply": "2024-09-05T19:34:00.490831Z"
+ "iopub.execute_input": "2024-09-06T19:33:51.480745Z",
+ "iopub.status.busy": "2024-09-06T19:33:51.480341Z",
+ "iopub.status.idle": "2024-09-06T19:33:51.514306Z",
+ "shell.execute_reply": "2024-09-06T19:33:51.513859Z"
}
},
"outputs": [],
@@ -638,10 +638,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:00.493928Z",
- "iopub.status.busy": "2024-09-05T19:34:00.493522Z",
- "iopub.status.idle": "2024-09-05T19:34:02.646706Z",
- "shell.execute_reply": "2024-09-05T19:34:02.646060Z"
+ "iopub.execute_input": "2024-09-06T19:33:51.516441Z",
+ "iopub.status.busy": "2024-09-06T19:33:51.516020Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.590850Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.590263Z"
}
},
"outputs": [
@@ -685,10 +685,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.649221Z",
- "iopub.status.busy": "2024-09-05T19:34:02.648683Z",
- "iopub.status.idle": "2024-09-05T19:34:02.667394Z",
- "shell.execute_reply": "2024-09-05T19:34:02.666809Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.593403Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.592894Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.611543Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.610984Z"
}
},
"outputs": [
@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.669753Z",
- "iopub.status.busy": "2024-09-05T19:34:02.669304Z",
- "iopub.status.idle": "2024-09-05T19:34:02.676014Z",
- "shell.execute_reply": "2024-09-05T19:34:02.675457Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.613666Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.613354Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.619845Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.619296Z"
}
},
"outputs": [
@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.678030Z",
- "iopub.status.busy": "2024-09-05T19:34:02.677711Z",
- "iopub.status.idle": "2024-09-05T19:34:02.683562Z",
- "shell.execute_reply": "2024-09-05T19:34:02.683103Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.621866Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.621559Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.628504Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.627959Z"
}
},
"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.685598Z",
- "iopub.status.busy": "2024-09-05T19:34:02.685423Z",
- "iopub.status.idle": "2024-09-05T19:34:02.695863Z",
- "shell.execute_reply": "2024-09-05T19:34:02.695434Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.630721Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.630404Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.640976Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.640522Z"
}
},
"outputs": [
@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.697910Z",
- "iopub.status.busy": "2024-09-05T19:34:02.697731Z",
- "iopub.status.idle": "2024-09-05T19:34:02.706917Z",
- "shell.execute_reply": "2024-09-05T19:34:02.706369Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.643037Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.642719Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.651678Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.651115Z"
}
},
"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.709149Z",
- "iopub.status.busy": "2024-09-05T19:34:02.708833Z",
- "iopub.status.idle": "2024-09-05T19:34:02.715398Z",
- "shell.execute_reply": "2024-09-05T19:34:02.714928Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.653852Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.653447Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.660374Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.659816Z"
},
"scrolled": true
},
@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.717285Z",
- "iopub.status.busy": "2024-09-05T19:34:02.717111Z",
- "iopub.status.idle": "2024-09-05T19:34:02.726582Z",
- "shell.execute_reply": "2024-09-05T19:34:02.726126Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.662428Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.662108Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.671181Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.670717Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:02.728531Z",
- "iopub.status.busy": "2024-09-05T19:34:02.728359Z",
- "iopub.status.idle": "2024-09-05T19:34:02.744474Z",
- "shell.execute_reply": "2024-09-05T19:34:02.744039Z"
+ "iopub.execute_input": "2024-09-06T19:33:53.673080Z",
+ "iopub.status.busy": "2024-09-06T19:33:53.672905Z",
+ "iopub.status.idle": "2024-09-06T19:33:53.689334Z",
+ "shell.execute_reply": "2024-09-06T19:33:53.688736Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index f298fc6b0..d0e19982d 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:05.525223Z",
- "iopub.status.busy": "2024-09-05T19:34:05.525049Z",
- "iopub.status.idle": "2024-09-05T19:34:08.556569Z",
- "shell.execute_reply": "2024-09-05T19:34:08.555871Z"
+ "iopub.execute_input": "2024-09-06T19:33:56.342254Z",
+ "iopub.status.busy": "2024-09-06T19:33:56.341754Z",
+ "iopub.status.idle": "2024-09-06T19:33:59.356819Z",
+ "shell.execute_reply": "2024-09-06T19:33:59.356183Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:08.559158Z",
- "iopub.status.busy": "2024-09-05T19:34:08.558850Z",
- "iopub.status.idle": "2024-09-05T19:34:08.562674Z",
- "shell.execute_reply": "2024-09-05T19:34:08.562129Z"
+ "iopub.execute_input": "2024-09-06T19:33:59.359528Z",
+ "iopub.status.busy": "2024-09-06T19:33:59.359236Z",
+ "iopub.status.idle": "2024-09-06T19:33:59.363077Z",
+ "shell.execute_reply": "2024-09-06T19:33:59.362504Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:08.564917Z",
- "iopub.status.busy": "2024-09-05T19:34:08.564578Z",
- "iopub.status.idle": "2024-09-05T19:34:10.909350Z",
- "shell.execute_reply": "2024-09-05T19:34:10.908728Z"
+ "iopub.execute_input": "2024-09-06T19:33:59.365205Z",
+ "iopub.status.busy": "2024-09-06T19:33:59.364886Z",
+ "iopub.status.idle": "2024-09-06T19:34:04.314293Z",
+ "shell.execute_reply": "2024-09-06T19:34:04.313807Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8089a6f0e258484bb6eb86bd123e100b",
+ "model_id": "7bcf07287e5846bcade12829a0129e5a",
"version_major": 2,
"version_minor": 0
},
@@ -176,7 +176,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "613725cc273047398f27ac74f75f9fd9",
+ "model_id": "3273abc0b1474d17ad8e620a0b9cd685",
"version_major": 2,
"version_minor": 0
},
@@ -190,7 +190,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "222bd1aa4e7242f68a04db434eb3f8b5",
+ "model_id": "468f054b84de4a46abae17b5d6030a66",
"version_major": 2,
"version_minor": 0
},
@@ -204,7 +204,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e1f7f496e0bd4db1ac83de92100a6631",
+ "model_id": "85a6da0e361d4bb78dac486525795dad",
"version_major": 2,
"version_minor": 0
},
@@ -218,7 +218,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4ed7e0e009174ad3afb25e53f562b45b",
+ "model_id": "16923bdba0af47908931030b52eaedca",
"version_major": 2,
"version_minor": 0
},
@@ -260,10 +260,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:10.911450Z",
- "iopub.status.busy": "2024-09-05T19:34:10.911254Z",
- "iopub.status.idle": "2024-09-05T19:34:10.915393Z",
- "shell.execute_reply": "2024-09-05T19:34:10.914911Z"
+ "iopub.execute_input": "2024-09-06T19:34:04.316479Z",
+ "iopub.status.busy": "2024-09-06T19:34:04.316130Z",
+ "iopub.status.idle": "2024-09-06T19:34:04.319984Z",
+ "shell.execute_reply": "2024-09-06T19:34:04.319538Z"
}
},
"outputs": [
@@ -288,17 +288,17 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:10.917527Z",
- "iopub.status.busy": "2024-09-05T19:34:10.917215Z",
- "iopub.status.idle": "2024-09-05T19:34:22.576299Z",
- "shell.execute_reply": "2024-09-05T19:34:22.575694Z"
+ "iopub.execute_input": "2024-09-06T19:34:04.321997Z",
+ "iopub.status.busy": "2024-09-06T19:34:04.321665Z",
+ "iopub.status.idle": "2024-09-06T19:34:15.824023Z",
+ "shell.execute_reply": "2024-09-06T19:34:15.823467Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "60d20ce4cd714b879c7671b282199048",
+ "model_id": "bfcb4b6339d14370bc404a61e757edfd",
"version_major": 2,
"version_minor": 0
},
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:22.579010Z",
- "iopub.status.busy": "2024-09-05T19:34:22.578618Z",
- "iopub.status.idle": "2024-09-05T19:34:40.816690Z",
- "shell.execute_reply": "2024-09-05T19:34:40.816141Z"
+ "iopub.execute_input": "2024-09-06T19:34:15.826734Z",
+ "iopub.status.busy": "2024-09-06T19:34:15.826342Z",
+ "iopub.status.idle": "2024-09-06T19:34:34.591212Z",
+ "shell.execute_reply": "2024-09-06T19:34:34.590672Z"
}
},
"outputs": [],
@@ -372,10 +372,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:40.819551Z",
- "iopub.status.busy": "2024-09-05T19:34:40.819116Z",
- "iopub.status.idle": "2024-09-05T19:34:40.824154Z",
- "shell.execute_reply": "2024-09-05T19:34:40.823637Z"
+ "iopub.execute_input": "2024-09-06T19:34:34.593912Z",
+ "iopub.status.busy": "2024-09-06T19:34:34.593533Z",
+ "iopub.status.idle": "2024-09-06T19:34:34.599439Z",
+ "shell.execute_reply": "2024-09-06T19:34:34.598956Z"
}
},
"outputs": [],
@@ -413,10 +413,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:40.826196Z",
- "iopub.status.busy": "2024-09-05T19:34:40.825878Z",
- "iopub.status.idle": "2024-09-05T19:34:40.830037Z",
- "shell.execute_reply": "2024-09-05T19:34:40.829503Z"
+ "iopub.execute_input": "2024-09-06T19:34:34.601473Z",
+ "iopub.status.busy": "2024-09-06T19:34:34.601136Z",
+ "iopub.status.idle": "2024-09-06T19:34:34.604946Z",
+ "shell.execute_reply": "2024-09-06T19:34:34.604479Z"
},
"nbsphinx": "hidden"
},
@@ -553,10 +553,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:40.832239Z",
- "iopub.status.busy": "2024-09-05T19:34:40.831883Z",
- "iopub.status.idle": "2024-09-05T19:34:40.841147Z",
- "shell.execute_reply": "2024-09-05T19:34:40.840689Z"
+ "iopub.execute_input": "2024-09-06T19:34:34.607009Z",
+ "iopub.status.busy": "2024-09-06T19:34:34.606678Z",
+ "iopub.status.idle": "2024-09-06T19:34:34.615441Z",
+ "shell.execute_reply": "2024-09-06T19:34:34.614962Z"
},
"nbsphinx": "hidden"
},
@@ -681,10 +681,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:40.843173Z",
- "iopub.status.busy": "2024-09-05T19:34:40.842829Z",
- "iopub.status.idle": "2024-09-05T19:34:40.869384Z",
- "shell.execute_reply": "2024-09-05T19:34:40.868884Z"
+ "iopub.execute_input": "2024-09-06T19:34:34.617637Z",
+ "iopub.status.busy": "2024-09-06T19:34:34.617189Z",
+ "iopub.status.idle": "2024-09-06T19:34:34.644027Z",
+ "shell.execute_reply": "2024-09-06T19:34:34.643475Z"
}
},
"outputs": [],
@@ -721,10 +721,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:34:40.871791Z",
- "iopub.status.busy": "2024-09-05T19:34:40.871443Z",
- "iopub.status.idle": "2024-09-05T19:35:14.975247Z",
- "shell.execute_reply": "2024-09-05T19:35:14.974585Z"
+ "iopub.execute_input": "2024-09-06T19:34:34.646190Z",
+ "iopub.status.busy": "2024-09-06T19:34:34.645869Z",
+ "iopub.status.idle": "2024-09-06T19:35:07.856682Z",
+ "shell.execute_reply": "2024-09-06T19:35:07.856077Z"
}
},
"outputs": [
@@ -740,21 +740,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.999\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.923\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.733\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.597\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c31bcb9951714ea7b4d0d8e9793f409b",
+ "model_id": "60b6605a27b343f3a046b38e2ee92eb3",
"version_major": 2,
"version_minor": 0
},
@@ -775,7 +775,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6c99a5fc3f1d42269032bffe9d6a65ff",
+ "model_id": "328179309f4646028e9f8909eefb6c74",
"version_major": 2,
"version_minor": 0
},
@@ -798,21 +798,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.060\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.922\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.772\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.912\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ec9e629fd2904b8fbf99b7c637113a54",
+ "model_id": "958c94ac86804e8fbd31685a6f87d389",
"version_major": 2,
"version_minor": 0
},
@@ -833,7 +833,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "80768ed8be224e0ea7b3cc4f25feb960",
+ "model_id": "593399f7ed16479cabf5d6887e2046b5",
"version_major": 2,
"version_minor": 0
},
@@ -856,21 +856,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.210\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.879\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.693\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.556\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "506f569cc4834fd5b04e9abd3570d2f0",
+ "model_id": "0a3d18201bb14d5c9e73af43adbe2cd8",
"version_major": 2,
"version_minor": 0
},
@@ -891,7 +891,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f2c0fcf1952846d5b613a055c1f1e0bc",
+ "model_id": "cef86182d7ef449481f59dfea70aa34a",
"version_major": 2,
"version_minor": 0
},
@@ -970,10 +970,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:35:14.977942Z",
- "iopub.status.busy": "2024-09-05T19:35:14.977466Z",
- "iopub.status.idle": "2024-09-05T19:35:14.995105Z",
- "shell.execute_reply": "2024-09-05T19:35:14.994526Z"
+ "iopub.execute_input": "2024-09-06T19:35:07.859270Z",
+ "iopub.status.busy": "2024-09-06T19:35:07.859022Z",
+ "iopub.status.idle": "2024-09-06T19:35:07.875302Z",
+ "shell.execute_reply": "2024-09-06T19:35:07.874880Z"
}
},
"outputs": [],
@@ -998,10 +998,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:35:14.997437Z",
- "iopub.status.busy": "2024-09-05T19:35:14.997093Z",
- "iopub.status.idle": "2024-09-05T19:35:15.472222Z",
- "shell.execute_reply": "2024-09-05T19:35:15.471571Z"
+ "iopub.execute_input": "2024-09-06T19:35:07.877195Z",
+ "iopub.status.busy": "2024-09-06T19:35:07.877017Z",
+ "iopub.status.idle": "2024-09-06T19:35:08.338418Z",
+ "shell.execute_reply": "2024-09-06T19:35:08.337844Z"
}
},
"outputs": [],
@@ -1021,10 +1021,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:35:15.474780Z",
- "iopub.status.busy": "2024-09-05T19:35:15.474581Z",
- "iopub.status.idle": "2024-09-05T19:37:07.170464Z",
- "shell.execute_reply": "2024-09-05T19:37:07.169763Z"
+ "iopub.execute_input": "2024-09-06T19:35:08.340738Z",
+ "iopub.status.busy": "2024-09-06T19:35:08.340554Z",
+ "iopub.status.idle": "2024-09-06T19:36:59.451053Z",
+ "shell.execute_reply": "2024-09-06T19:36:59.450444Z"
}
},
"outputs": [
@@ -1063,7 +1063,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2d18ac7cb0b941438d58c73413469ec9",
+ "model_id": "b8c0903ec57a4db09eef7c66d76ad798",
"version_major": 2,
"version_minor": 0
},
@@ -1109,10 +1109,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:07.173215Z",
- "iopub.status.busy": "2024-09-05T19:37:07.172522Z",
- "iopub.status.idle": "2024-09-05T19:37:07.637129Z",
- "shell.execute_reply": "2024-09-05T19:37:07.636559Z"
+ "iopub.execute_input": "2024-09-06T19:36:59.453745Z",
+ "iopub.status.busy": "2024-09-06T19:36:59.453098Z",
+ "iopub.status.idle": "2024-09-06T19:36:59.910431Z",
+ "shell.execute_reply": "2024-09-06T19:36:59.909867Z"
}
},
"outputs": [
@@ -1258,10 +1258,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:07.639604Z",
- "iopub.status.busy": "2024-09-05T19:37:07.639182Z",
- "iopub.status.idle": "2024-09-05T19:37:07.701336Z",
- "shell.execute_reply": "2024-09-05T19:37:07.700772Z"
+ "iopub.execute_input": "2024-09-06T19:36:59.913045Z",
+ "iopub.status.busy": "2024-09-06T19:36:59.912484Z",
+ "iopub.status.idle": "2024-09-06T19:36:59.974160Z",
+ "shell.execute_reply": "2024-09-06T19:36:59.973682Z"
}
},
"outputs": [
@@ -1365,10 +1365,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:07.703899Z",
- "iopub.status.busy": "2024-09-05T19:37:07.703260Z",
- "iopub.status.idle": "2024-09-05T19:37:07.712222Z",
- "shell.execute_reply": "2024-09-05T19:37:07.711736Z"
+ "iopub.execute_input": "2024-09-06T19:36:59.976368Z",
+ "iopub.status.busy": "2024-09-06T19:36:59.976019Z",
+ "iopub.status.idle": "2024-09-06T19:36:59.984805Z",
+ "shell.execute_reply": "2024-09-06T19:36:59.984360Z"
}
},
"outputs": [
@@ -1498,10 +1498,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:07.714512Z",
- "iopub.status.busy": "2024-09-05T19:37:07.714075Z",
- "iopub.status.idle": "2024-09-05T19:37:07.719026Z",
- "shell.execute_reply": "2024-09-05T19:37:07.718573Z"
+ "iopub.execute_input": "2024-09-06T19:36:59.986926Z",
+ "iopub.status.busy": "2024-09-06T19:36:59.986597Z",
+ "iopub.status.idle": "2024-09-06T19:36:59.991039Z",
+ "shell.execute_reply": "2024-09-06T19:36:59.990559Z"
},
"nbsphinx": "hidden"
},
@@ -1547,10 +1547,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:07.721051Z",
- "iopub.status.busy": "2024-09-05T19:37:07.720871Z",
- "iopub.status.idle": "2024-09-05T19:37:08.234485Z",
- "shell.execute_reply": "2024-09-05T19:37:08.233873Z"
+ "iopub.execute_input": "2024-09-06T19:36:59.992932Z",
+ "iopub.status.busy": "2024-09-06T19:36:59.992715Z",
+ "iopub.status.idle": "2024-09-06T19:37:00.505081Z",
+ "shell.execute_reply": "2024-09-06T19:37:00.504451Z"
}
},
"outputs": [
@@ -1585,10 +1585,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.236757Z",
- "iopub.status.busy": "2024-09-05T19:37:08.236519Z",
- "iopub.status.idle": "2024-09-05T19:37:08.245226Z",
- "shell.execute_reply": "2024-09-05T19:37:08.244765Z"
+ "iopub.execute_input": "2024-09-06T19:37:00.507663Z",
+ "iopub.status.busy": "2024-09-06T19:37:00.507293Z",
+ "iopub.status.idle": "2024-09-06T19:37:00.516488Z",
+ "shell.execute_reply": "2024-09-06T19:37:00.515888Z"
}
},
"outputs": [
@@ -1755,10 +1755,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.247312Z",
- "iopub.status.busy": "2024-09-05T19:37:08.247037Z",
- "iopub.status.idle": "2024-09-05T19:37:08.254096Z",
- "shell.execute_reply": "2024-09-05T19:37:08.253625Z"
+ "iopub.execute_input": "2024-09-06T19:37:00.518970Z",
+ "iopub.status.busy": "2024-09-06T19:37:00.518520Z",
+ "iopub.status.idle": "2024-09-06T19:37:00.525985Z",
+ "shell.execute_reply": "2024-09-06T19:37:00.525525Z"
},
"nbsphinx": "hidden"
},
@@ -1834,10 +1834,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.256220Z",
- "iopub.status.busy": "2024-09-05T19:37:08.255757Z",
- "iopub.status.idle": "2024-09-05T19:37:08.724478Z",
- "shell.execute_reply": "2024-09-05T19:37:08.723840Z"
+ "iopub.execute_input": "2024-09-06T19:37:00.528061Z",
+ "iopub.status.busy": "2024-09-06T19:37:00.527749Z",
+ "iopub.status.idle": "2024-09-06T19:37:00.996315Z",
+ "shell.execute_reply": "2024-09-06T19:37:00.995664Z"
}
},
"outputs": [
@@ -1874,10 +1874,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.726943Z",
- "iopub.status.busy": "2024-09-05T19:37:08.726570Z",
- "iopub.status.idle": "2024-09-05T19:37:08.742787Z",
- "shell.execute_reply": "2024-09-05T19:37:08.742174Z"
+ "iopub.execute_input": "2024-09-06T19:37:00.998663Z",
+ "iopub.status.busy": "2024-09-06T19:37:00.998226Z",
+ "iopub.status.idle": "2024-09-06T19:37:01.014613Z",
+ "shell.execute_reply": "2024-09-06T19:37:01.014119Z"
}
},
"outputs": [
@@ -2034,10 +2034,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.744895Z",
- "iopub.status.busy": "2024-09-05T19:37:08.744707Z",
- "iopub.status.idle": "2024-09-05T19:37:08.750513Z",
- "shell.execute_reply": "2024-09-05T19:37:08.750018Z"
+ "iopub.execute_input": "2024-09-06T19:37:01.016951Z",
+ "iopub.status.busy": "2024-09-06T19:37:01.016496Z",
+ "iopub.status.idle": "2024-09-06T19:37:01.022189Z",
+ "shell.execute_reply": "2024-09-06T19:37:01.021616Z"
},
"nbsphinx": "hidden"
},
@@ -2082,10 +2082,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:08.752659Z",
- "iopub.status.busy": "2024-09-05T19:37:08.752244Z",
- "iopub.status.idle": "2024-09-05T19:37:09.459291Z",
- "shell.execute_reply": "2024-09-05T19:37:09.458714Z"
+ "iopub.execute_input": "2024-09-06T19:37:01.024335Z",
+ "iopub.status.busy": "2024-09-06T19:37:01.024003Z",
+ "iopub.status.idle": "2024-09-06T19:37:01.818216Z",
+ "shell.execute_reply": "2024-09-06T19:37:01.817601Z"
}
},
"outputs": [
@@ -2167,10 +2167,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.465380Z",
- "iopub.status.busy": "2024-09-05T19:37:09.464389Z",
- "iopub.status.idle": "2024-09-05T19:37:09.476151Z",
- "shell.execute_reply": "2024-09-05T19:37:09.475607Z"
+ "iopub.execute_input": "2024-09-06T19:37:01.821086Z",
+ "iopub.status.busy": "2024-09-06T19:37:01.820573Z",
+ "iopub.status.idle": "2024-09-06T19:37:01.831141Z",
+ "shell.execute_reply": "2024-09-06T19:37:01.830605Z"
}
},
"outputs": [
@@ -2195,47 +2195,47 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
"
\n",
" \n",
" 34848 | \n",
- " 0.203922 | \n",
" True | \n",
+ " 0.203922 | \n",
"
\n",
" \n",
" 50270 | \n",
- " 0.204588 | \n",
" True | \n",
+ " 0.204588 | \n",
"
\n",
" \n",
" 3936 | \n",
- " 0.213098 | \n",
" True | \n",
+ " 0.213098 | \n",
"
\n",
" \n",
" 733 | \n",
- " 0.217686 | \n",
" True | \n",
+ " 0.217686 | \n",
"
\n",
" \n",
" 8094 | \n",
- " 0.230118 | \n",
" True | \n",
+ " 0.230118 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " dark_score is_dark_issue\n",
- "34848 0.203922 True\n",
- "50270 0.204588 True\n",
- "3936 0.213098 True\n",
- "733 0.217686 True\n",
- "8094 0.230118 True"
+ " is_dark_issue dark_score\n",
+ "34848 True 0.203922\n",
+ "50270 True 0.204588\n",
+ "3936 True 0.213098\n",
+ "733 True 0.217686\n",
+ "8094 True 0.230118"
]
},
"execution_count": 26,
@@ -2298,10 +2298,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.479850Z",
- "iopub.status.busy": "2024-09-05T19:37:09.478927Z",
- "iopub.status.idle": "2024-09-05T19:37:09.484511Z",
- "shell.execute_reply": "2024-09-05T19:37:09.484078Z"
+ "iopub.execute_input": "2024-09-06T19:37:01.833972Z",
+ "iopub.status.busy": "2024-09-06T19:37:01.833571Z",
+ "iopub.status.idle": "2024-09-06T19:37:01.839439Z",
+ "shell.execute_reply": "2024-09-06T19:37:01.838936Z"
},
"nbsphinx": "hidden"
},
@@ -2338,10 +2338,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.487390Z",
- "iopub.status.busy": "2024-09-05T19:37:09.486635Z",
- "iopub.status.idle": "2024-09-05T19:37:09.658646Z",
- "shell.execute_reply": "2024-09-05T19:37:09.658024Z"
+ "iopub.execute_input": "2024-09-06T19:37:01.841836Z",
+ "iopub.status.busy": "2024-09-06T19:37:01.841454Z",
+ "iopub.status.idle": "2024-09-06T19:37:02.045788Z",
+ "shell.execute_reply": "2024-09-06T19:37:02.045180Z"
}
},
"outputs": [
@@ -2383,10 +2383,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.660921Z",
- "iopub.status.busy": "2024-09-05T19:37:09.660563Z",
- "iopub.status.idle": "2024-09-05T19:37:09.668286Z",
- "shell.execute_reply": "2024-09-05T19:37:09.667818Z"
+ "iopub.execute_input": "2024-09-06T19:37:02.048026Z",
+ "iopub.status.busy": "2024-09-06T19:37:02.047682Z",
+ "iopub.status.idle": "2024-09-06T19:37:02.055980Z",
+ "shell.execute_reply": "2024-09-06T19:37:02.055509Z"
}
},
"outputs": [
@@ -2411,47 +2411,47 @@
" \n",
" \n",
" | \n",
- " is_low_information_issue | \n",
" low_information_score | \n",
+ " is_low_information_issue | \n",
"
\n",
" \n",
" \n",
" \n",
" 53050 | \n",
- " True | \n",
" 0.067975 | \n",
+ " True | \n",
"
\n",
" \n",
" 40875 | \n",
- " True | \n",
" 0.089929 | \n",
+ " True | \n",
"
\n",
" \n",
" 9594 | \n",
- " True | \n",
" 0.092601 | \n",
+ " True | \n",
"
\n",
" \n",
" 34825 | \n",
- " True | \n",
" 0.107744 | \n",
+ " True | \n",
"
\n",
" \n",
" 37530 | \n",
- " True | \n",
" 0.108516 | \n",
+ " True | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " is_low_information_issue low_information_score\n",
- "53050 True 0.067975\n",
- "40875 True 0.089929\n",
- "9594 True 0.092601\n",
- "34825 True 0.107744\n",
- "37530 True 0.108516"
+ " low_information_score is_low_information_issue\n",
+ "53050 0.067975 True\n",
+ "40875 0.089929 True\n",
+ "9594 0.092601 True\n",
+ "34825 0.107744 True\n",
+ "37530 0.108516 True"
]
},
"execution_count": 29,
@@ -2472,10 +2472,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.670466Z",
- "iopub.status.busy": "2024-09-05T19:37:09.670128Z",
- "iopub.status.idle": "2024-09-05T19:37:09.865056Z",
- "shell.execute_reply": "2024-09-05T19:37:09.864458Z"
+ "iopub.execute_input": "2024-09-06T19:37:02.058027Z",
+ "iopub.status.busy": "2024-09-06T19:37:02.057684Z",
+ "iopub.status.idle": "2024-09-06T19:37:02.256206Z",
+ "shell.execute_reply": "2024-09-06T19:37:02.255652Z"
}
},
"outputs": [
@@ -2515,10 +2515,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:09.867524Z",
- "iopub.status.busy": "2024-09-05T19:37:09.867164Z",
- "iopub.status.idle": "2024-09-05T19:37:09.871703Z",
- "shell.execute_reply": "2024-09-05T19:37:09.871128Z"
+ "iopub.execute_input": "2024-09-06T19:37:02.258543Z",
+ "iopub.status.busy": "2024-09-06T19:37:02.258213Z",
+ "iopub.status.idle": "2024-09-06T19:37:02.262761Z",
+ "shell.execute_reply": "2024-09-06T19:37:02.262194Z"
},
"nbsphinx": "hidden"
},
@@ -2555,7 +2555,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "00cc2bccf49e4abe8b47884216705c03": {
+ "024f58b175f14cdca925ad2ec59e5f75": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2608,7 +2608,43 @@
"width": null
}
},
- "0240b26e2e8347049e4a5df861bb7a0d": {
+ "0445112b7e674aa2a14ea027dc8bc2f8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "06c85507cf49495584b002e6aaa044e8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "06fc43e80ab4403ca27ca8d667aca1b3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2623,15 +2659,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1f3aa8b95cdd4842a621c7fe6c7a29fe",
+ "layout": "IPY_MODEL_4e22cd2b18d5465c8bfc301a968400ec",
"placeholder": "",
- "style": "IPY_MODEL_49f52b7cadd644dfbe381087cf21b374",
+ "style": "IPY_MODEL_0445112b7e674aa2a14ea027dc8bc2f8",
"tabbable": null,
"tooltip": null,
- "value": "100%"
+ "value": "Generating test split: 100%"
}
},
- "02bb02a3558642f7ae049fe605dabbea": {
+ "076942f2bb5542e9a9c126f736c4b427": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2684,7 +2720,7 @@
"width": null
}
},
- "0777e52962fe4e3c8c417ed705b0e27a": {
+ "07c128e462924e039bd30ffd94caeafe": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2702,60 +2738,49 @@
"text_color": null
}
},
- "07d8ecbe4aaf4a9ea4aa1e00be62b06b": {
- "model_module": "@jupyter-widgets/base",
+ "0a3d18201bb14d5c9e73af43adbe2cd8": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "HBoxModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_e6d9313a802d4513bc93abf9ffa9fc9b",
+ "IPY_MODEL_6d3f4ee5439044088f65368ef798b3b6",
+ "IPY_MODEL_351c45295ff8422c8718ee4bdefa510f"
+ ],
+ "layout": "IPY_MODEL_7c5730df719649e6ae1137849667983e",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "0a606b97ecff4d89be8f66714f909ba3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "0a7f73a4ae2a4e5ab8db8790ef824541": {
+ "0b9bdabf441e4113805b54ee83d92f75": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2770,15 +2795,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_42c78a87167c4c7d8ffa3cc9532ac783",
+ "layout": "IPY_MODEL_34e238c0c2c24406adb5d978aec7e807",
"placeholder": "",
- "style": "IPY_MODEL_2df53c624a43411fa4091264b5c130fb",
+ "style": "IPY_MODEL_511e7568ac814ea8846c93d586063e8e",
"tabbable": null,
"tooltip": null,
- "value": "Downloading data: 100%"
+ "value": " 60000/60000 [00:00<00:00, 285192.56 examples/s]"
}
},
- "0bf8b4b0c9ce4e819a7f21eaed682092": {
+ "0c2a412a844140bd80a53f2ac3fc325d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2831,7 +2856,7 @@
"width": null
}
},
- "0e62ee29a15e4b038ba6a7584ca16961": {
+ "0f5233a082d94dddbdb0503eb9250ed4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2847,17 +2872,40 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_0f93410bb3374b62a08a4c4560575830",
- "max": 60000.0,
+ "layout": "IPY_MODEL_024f58b175f14cdca925ad2ec59e5f75",
+ "max": 10000.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_32aa1b55f20340c4b80b5c1a4bcf07b9",
+ "style": "IPY_MODEL_c0366a87be804af7bcf6f5cd7f11bc3b",
"tabbable": null,
"tooltip": null,
- "value": 60000.0
+ "value": 10000.0
+ }
+ },
+ "0f970e1142174368bfc637a6cd8d6fd5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_881dec995ffa41c3b5cbe3a2f2955ed3",
+ "placeholder": "",
+ "style": "IPY_MODEL_7500e8476ee64140b7338f73ff7b6e53",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Downloading readme: 100%"
}
},
- "0f93410bb3374b62a08a4c4560575830": {
+ "10a3911ad0ae43f599855a0ad46d4195": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2910,7 +2958,7 @@
"width": null
}
},
- "10186f0c08c84916988b4064fb8fbbb6": {
+ "11645e47817743048239554d8f897a74": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2963,23 +3011,7 @@
"width": null
}
},
- "1403cd0920194d9f88d2e816c0e364a5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "195f6f44461b4637898d20c3f277f04c": {
+ "118d13a1737e460b986120e1cd8488c6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3032,78 +3064,49 @@
"width": null
}
},
- "1a190a961ec641a28b3c636af3b264dc": {
+ "11c48316135a461ea55c3d08dc541755": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_d54af3f6955d4b968858d238a0210190",
+ "max": 9015.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_f2fa408e34274722bd36f3791c967fb2",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 9015.0
}
},
- "1b516e69e31f4ff4a0ecb31c1298142e": {
- "model_module": "@jupyter-widgets/base",
+ "13063e602ee246eb9b552b3b781fa85e": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "1c26fb9f22394b9299592a1f50ca98d5": {
+ "1510cd85e1a74691abd66fcc8f87c34c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3156,7 +3159,7 @@
"width": null
}
},
- "1e80382ec0ec4737898dcc5f400fdb22": {
+ "156d3569cd004246a2f548957d78f2bc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3209,20 +3212,125 @@
"width": null
}
},
- "1f3aa8b95cdd4842a621c7fe6c7a29fe": {
- "model_module": "@jupyter-widgets/base",
+ "15c77a61cbac476e99fb0331858d1d8c": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "1658cd683cbd496c9ff193ba8d7c35ea": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_b8a497f4724b458c8448b91e3ce44d15",
+ "placeholder": "",
+ "style": "IPY_MODEL_3c640d11994b4372834d50a9621d95c5",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 60000/60000 [00:51<00:00, 1138.50it/s]"
+ }
+ },
+ "16923bdba0af47908931030b52eaedca": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_06fc43e80ab4403ca27ca8d667aca1b3",
+ "IPY_MODEL_0f5233a082d94dddbdb0503eb9250ed4",
+ "IPY_MODEL_8efceb1c08634d06817a3fa57d1a8f06"
+ ],
+ "layout": "IPY_MODEL_a9b1da96fea74c509d14483d998a7cf8",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "1e5e214067f448fe820c272b4d8b60b6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_929f19e8f15344c999957b2ce7569264",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_6c95b5f8fda84c369cae7cba5624ccfe",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
+ }
+ },
+ "210dbd38b6ce4f9e9f8b810ac64d03bf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "23d399d3b46a4f8d82116059129fd43f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
@@ -3262,30 +3370,7 @@
"width": null
}
},
- "1fa1bf487aa146f7b63aba1b37340dad": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_82aff8f27b4c41438fb70e6ac7827816",
- "placeholder": "",
- "style": "IPY_MODEL_3852b37239ad47a8bea5bf09447ccb1b",
- "tabbable": null,
- "tooltip": null,
- "value": "Generating test split: 100%"
- }
- },
- "209db2922df34007b2eea98c10204bb3": {
+ "25c0e4e85ebb41299102ad0b3e0880b4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3338,139 +3423,30 @@
"width": null
}
},
- "21aa7e557ff2445fb6f9a8d7299a17ff": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "222bd1aa4e7242f68a04db434eb3f8b5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_68f553c7ffe64681af4d9d9b609619d2",
- "IPY_MODEL_5ac7e0143087455db2057a05611b903a",
- "IPY_MODEL_b57eeb2e85db4e6c90b70606981a862d"
- ],
- "layout": "IPY_MODEL_2fe068acde9747ab80cba22c22af96d4",
- "tabbable": null,
- "tooltip": null
- }
- },
- "24f5d44a2aba41bab7b5b00ccd550704": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "27d51d7d7c394b8ca5b9c6dc1f2f28ef": {
+ "26b545a844a84c278f68d51645f7e371": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "2a0e717d35f142e2ba3bffe16092cee4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "2d18ac7cb0b941438d58c73413469ec9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_30efd8b2b2434f878122eddd7664ec22",
- "IPY_MODEL_0e62ee29a15e4b038ba6a7584ca16961",
- "IPY_MODEL_63dacf2552ad40888642091c1d2b28f2"
- ],
- "layout": "IPY_MODEL_58df9bc980ff41e19f86d334f4e6affd",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_ac970746b6684c3ba6bb43eee4014e2b",
+ "placeholder": "",
+ "style": "IPY_MODEL_c77f86be94734e2ba274bf4267c5a824",
"tabbable": null,
- "tooltip": null
- }
- },
- "2df53c624a43411fa4091264b5c130fb": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "tooltip": null,
+ "value": " 2/2 [00:00<00:00, 680.45it/s]"
}
},
- "2fe068acde9747ab80cba22c22af96d4": {
+ "294184439d7d474cbfc6043c1efa9d3d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3523,96 +3499,7 @@
"width": null
}
},
- "30efd8b2b2434f878122eddd7664ec22": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_02bb02a3558642f7ae049fe605dabbea",
- "placeholder": "",
- "style": "IPY_MODEL_83ed1ab9ea0a4965aa59eb8257d4f46b",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "32aa1b55f20340c4b80b5c1a4bcf07b9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "35a155d26b984654ab25b9e85ccccd6f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "3852b37239ad47a8bea5bf09447ccb1b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "3e22696dc8414bd180d0e07a4a0a62a7": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "40c882020753475486c7353c336a7276": {
+ "29ace475fdbe4624840332dd0e509ed6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3665,7 +3552,81 @@
"width": null
}
},
- "42c78a87167c4c7d8ffa3cc9532ac783": {
+ "2b755b9c572e43a396e62c74fba5329f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_11645e47817743048239554d8f897a74",
+ "max": 5175617.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_7c13df622dfc4f04853781143737296f",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 5175617.0
+ }
+ },
+ "3273abc0b1474d17ad8e620a0b9cd685": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_a53594c87ee944d2ab253fdeb3aeae5f",
+ "IPY_MODEL_e38940706f02447996928468d3f523eb",
+ "IPY_MODEL_e127093a41b442febda97da50e709395"
+ ],
+ "layout": "IPY_MODEL_d9bfaf958ae54bdaa41267876482d6af",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "328179309f4646028e9f8909eefb6c74": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_471bce1ec58f4d4da4ed7ed290449c5f",
+ "IPY_MODEL_a8fc249e1ca44d4084ed4e8e978d6058",
+ "IPY_MODEL_f03a0d23baa4409abb0c7271bd76ab8a"
+ ],
+ "layout": "IPY_MODEL_bb2fc960507949aea6439fcd3c77de5b",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "33d5aee8319348e485ec3980bc726f23": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3718,7 +3679,7 @@
"width": null
}
},
- "44a4010eb7a944249e17c4373fb6fae1": {
+ "34b3d7273cdd4ceca25a11fbb32359ce": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3771,71 +3732,7 @@
"width": null
}
},
- "490e0855ef974a42b2647cfedac70b97": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_00cc2bccf49e4abe8b47884216705c03",
- "placeholder": "",
- "style": "IPY_MODEL_7030838d622a49648a433355d555396d",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "496cb780de334794994864ce22ab43f9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_b330cae0665c496d9f38d6b6c39c32cf",
- "placeholder": "",
- "style": "IPY_MODEL_94dbf69483ae44dc86b8e989c13af0b1",
- "tabbable": null,
- "tooltip": null,
- "value": "Map (num_proc=4): 100%"
- }
- },
- "49f52b7cadd644dfbe381087cf21b374": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "4b14e0caa5af4f9688d2d2d294a95764": {
+ "34e238c0c2c24406adb5d978aec7e807": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3888,7 +3785,48 @@
"width": null
}
},
- "4ed7e0e009174ad3afb25e53f562b45b": {
+ "351c45295ff8422c8718ee4bdefa510f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_8e65bd8055fd45d8999b608481497477",
+ "placeholder": "",
+ "style": "IPY_MODEL_82b18a92ec124a52a6892b31409db80e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 64.40it/s]"
+ }
+ },
+ "3c640d11994b4372834d50a9621d95c5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "468f054b84de4a46abae17b5d6030a66": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -3903,16 +3841,85 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_1fa1bf487aa146f7b63aba1b37340dad",
- "IPY_MODEL_95d93d3f700940b592f8552c390c914b",
- "IPY_MODEL_79aa1e210ebd44dfa879c72b499b33e4"
+ "IPY_MODEL_49329574ba97425f8b3b12f07b8d53cf",
+ "IPY_MODEL_2b755b9c572e43a396e62c74fba5329f",
+ "IPY_MODEL_861644c40333498094e62f8ac990f5a3"
],
- "layout": "IPY_MODEL_cda1df54889f4e3bb275c8c2fd8ec82a",
+ "layout": "IPY_MODEL_0c2a412a844140bd80a53f2ac3fc325d",
"tabbable": null,
"tooltip": null
}
},
- "4fd6ca7a7dd446879a6f2f55860227d9": {
+ "471bce1ec58f4d4da4ed7ed290449c5f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_d9b3afcffed047abb3d7c9215eacb041",
+ "placeholder": "",
+ "style": "IPY_MODEL_d0dfbf919e5f422689492055a00be836",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
+ }
+ },
+ "49329574ba97425f8b3b12f07b8d53cf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_77caecf6cce84ac885a2c431ec321e76",
+ "placeholder": "",
+ "style": "IPY_MODEL_df804279770c4bbdbba569e996a72047",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Downloading data: 100%"
+ }
+ },
+ "4a900a7bd2894dc2905c92a999845c41": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_78ec8846971b49dc8ac35c9865ab7855",
+ "placeholder": "",
+ "style": "IPY_MODEL_a1e0fb35ccfd46ac9b640c1e3a97a83e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 58.88it/s]"
+ }
+ },
+ "4e22cd2b18d5465c8bfc301a968400ec": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3965,31 +3972,7 @@
"width": null
}
},
- "506f569cc4834fd5b04e9abd3570d2f0": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_dccf399c747d4d1d82bce77b5b675956",
- "IPY_MODEL_e8f51e9d01d447c3b27b88d0c146d11b",
- "IPY_MODEL_c701552093d1471bb9ace0f6dcd2c9bb"
- ],
- "layout": "IPY_MODEL_40c882020753475486c7353c336a7276",
- "tabbable": null,
- "tooltip": null
- }
- },
- "50c7134622e34fbb83b07d440594438c": {
+ "4e5be3b38c73499194dd5bfcb00e9476": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4042,48 +4025,7 @@
"width": null
}
},
- "51120e68bd384037a4668fe393f7be13": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_aff0b45e2c1f43a9bb8830f843804ede",
- "placeholder": "",
- "style": "IPY_MODEL_6933130e7fa747cca85775e2390e2883",
- "tabbable": null,
- "tooltip": null,
- "value": " 60000/60000 [00:00<00:00, 280070.61 examples/s]"
- }
- },
- "527d0aaee5894f2da400bf5690853965": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "53f7d5a3fe5045aea631347f5778ed5b": {
+ "50780f5c92b44525bf711232bc998378": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4136,7 +4078,25 @@
"width": null
}
},
- "5421281ef4324fc3960b976b5b1e7be1": {
+ "511e7568ac814ea8846c93d586063e8e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "5264eae69a4a44c5af270b7caaa7eeb4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4189,7 +4149,7 @@
"width": null
}
},
- "5460c3ab53244166b5b83bce1568fb9a": {
+ "58bd914194a245c2b1a963606103a9bd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4204,33 +4164,39 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_5affd8364ad84dce9ccb9e64e3595daa",
+ "layout": "IPY_MODEL_65a24ce68aaf46d6b97c06a7d9ffc735",
"placeholder": "",
- "style": "IPY_MODEL_f282b3d3edd34bd19404aa81c4bd2be9",
+ "style": "IPY_MODEL_ed24804748ab483289fec871eb4e7ebf",
"tabbable": null,
"tooltip": null,
- "value": " 30.9M/30.9M [00:00<00:00, 82.2MB/s]"
+ "value": " 40/40 [00:00<00:00, 65.43it/s]"
}
},
- "56ab7eb7d7514ac4a5ca99fbec95176c": {
+ "593399f7ed16479cabf5d6887e2046b5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_9db52ec22ade4fc2a207b214ed54d964",
+ "IPY_MODEL_7774247051444f258f5cb5a4624a6d83",
+ "IPY_MODEL_f082a4eab5444b019ea911ae0fb7a92d"
+ ],
+ "layout": "IPY_MODEL_8a312c718675404cb5eaf36ae41d943d",
+ "tabbable": null,
+ "tooltip": null
}
},
- "57c3bbf911934c1b954d08e7df293a56": {
+ "5bc3a1038e00432097a539e27f83e00f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4283,7 +4249,7 @@
"width": null
}
},
- "57c3c61cde5d4837a1643acc4efaa530": {
+ "5cf6c3b877784057a47d544871ab0987": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -4301,86 +4267,95 @@
"text_color": null
}
},
- "58df9bc980ff41e19f86d334f4e6affd": {
- "model_module": "@jupyter-widgets/base",
+ "5e5e4490daf942669f04f85596a7308d": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "HTMLModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_29ace475fdbe4624840332dd0e509ed6",
+ "placeholder": "",
+ "style": "IPY_MODEL_f784e45cbe9248ae9c16491028d6bf8e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
+ }
+ },
+ "606a0ff67cfd457c88c691c81de63a4c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "5ac7e0143087455db2057a05611b903a": {
+ "60b6605a27b343f3a046b38e2ee92eb3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_f9f45d26f74148d5aafa521c2e42894d",
+ "IPY_MODEL_f11e1a00f1c942a080552a095321e730",
+ "IPY_MODEL_b323c9ad204c424e81ad897a8a41faa8"
+ ],
+ "layout": "IPY_MODEL_f1c64d058ec34988a144f78b8ce7cbc8",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "612a34311c13432a923b885221f461b0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_bf7b754fe88b42c2a0ebefbef27aaa5d",
- "max": 5175617.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_3e22696dc8414bd180d0e07a4a0a62a7",
+ "layout": "IPY_MODEL_156d3569cd004246a2f548957d78f2bc",
+ "placeholder": "",
+ "style": "IPY_MODEL_0a606b97ecff4d89be8f66714f909ba3",
"tabbable": null,
"tooltip": null,
- "value": 5175617.0
+ "value": "Generating train split: 100%"
}
},
- "5affd8364ad84dce9ccb9e64e3595daa": {
+ "62625d830bc54505aa74a2a30ef3af9d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4433,7 +4408,25 @@
"width": null
}
},
- "5d7fd3a699f145eaa96b37d43b96276e": {
+ "63223699d1124f63b67f209182bf8e11": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "65a24ce68aaf46d6b97c06a7d9ffc735": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4486,7 +4479,7 @@
"width": null
}
},
- "5e3bb0ee765649eb8ed9db6a7ad199b2": {
+ "6739176497a24677bed9ce1d499ce111": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4539,7 +4532,30 @@
"width": null
}
},
- "5f93eedb157d4345bc780bd62a65204e": {
+ "67956635d769462da5b0f8ec7ca4575b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_34b3d7273cdd4ceca25a11fbb32359ce",
+ "placeholder": "",
+ "style": "IPY_MODEL_07c128e462924e039bd30ffd94caeafe",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Map (num_proc=4): 100%"
+ }
+ },
+ "69af7b2b232142ffa08b9e4439628311": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4592,78 +4608,39 @@
"width": null
}
},
- "60d20ce4cd714b879c7671b282199048": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_496cb780de334794994864ce22ab43f9",
- "IPY_MODEL_ae929a9d89e44834b81a2fd963b2c35f",
- "IPY_MODEL_d37c5155b2a140bc9d293b5579737109"
- ],
- "layout": "IPY_MODEL_1c26fb9f22394b9299592a1f50ca98d5",
- "tabbable": null,
- "tooltip": null
- }
- },
- "613725cc273047398f27ac74f75f9fd9": {
+ "6c5276f1cdeb4d6dafd955e313dfb495": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_0a7f73a4ae2a4e5ab8db8790ef824541",
- "IPY_MODEL_b9de52fff41e480faf1069d54e51bb01",
- "IPY_MODEL_5460c3ab53244166b5b83bce1568fb9a"
- ],
- "layout": "IPY_MODEL_5e3bb0ee765649eb8ed9db6a7ad199b2",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "63dacf2552ad40888642091c1d2b28f2": {
+ "6c95b5f8fda84c369cae7cba5624ccfe": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_5421281ef4324fc3960b976b5b1e7be1",
- "placeholder": "",
- "style": "IPY_MODEL_713797fcd2e443099904db1582f72e04",
- "tabbable": null,
- "tooltip": null,
- "value": " 60000/60000 [00:51<00:00, 1093.09it/s]"
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "6492748e048c4fe69d67f2f82d4e0597": {
+ "6d3f4ee5439044088f65368ef798b3b6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -4679,17 +4656,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1e80382ec0ec4737898dcc5f400fdb22",
+ "layout": "IPY_MODEL_10a3911ad0ae43f599855a0ad46d4195",
"max": 40.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_6e9d6a9e82ee4cadaf4d6aae8966e15c",
+ "style": "IPY_MODEL_ac4bae02a5884435aa084f8524ec36ab",
"tabbable": null,
"tooltip": null,
"value": 40.0
}
},
- "65f9a6bec4e948baa940c3046dc97c27": {
+ "6dd2d74eb1d04d61844ec3c03149c90b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -4705,7 +4682,33 @@
"description_width": ""
}
},
- "68f553c7ffe64681af4d9d9b609619d2": {
+ "6e84e471535f4aa89081faaaa485d6c3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_eec22da0f1c144399d3b96c5a790810e",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_747540cae7c946758cd31d80531063d5",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 60000.0
+ }
+ },
+ "6e8d203ea1f24d86a8504e8c8f549098": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4720,33 +4723,121 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_c970b98ad16d4e058311518f87d4705f",
+ "layout": "IPY_MODEL_6fdd97c1ed34454aa0a60e98a62224d2",
"placeholder": "",
- "style": "IPY_MODEL_d5f0f111e29b4da996f76d93a33612b5",
+ "style": "IPY_MODEL_9bec32080a844b95be00f98699eec0a5",
"tabbable": null,
"tooltip": null,
- "value": "Downloading data: 100%"
+ "value": " 9.02k/9.02k [00:00<00:00, 1.14MB/s]"
}
},
- "6933130e7fa747cca85775e2390e2883": {
- "model_module": "@jupyter-widgets/controls",
+ "6f0a00d7d264477684a40569e5e3fb89": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "6f4b003f65a3475b87ea4dfb49e22177": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "6954ba9a0a8544c8ab34caf9382b3919": {
+ "6fdd97c1ed34454aa0a60e98a62224d2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4799,46 +4890,67 @@
"width": null
}
},
- "69ec4f0b4df24afeb44979768207ccb2": {
+ "747540cae7c946758cd31d80531063d5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "7500e8476ee64140b7338f73ff7b6e53": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_961e00b1790446eab77bd4a467f184f2",
- "placeholder": "",
- "style": "IPY_MODEL_c35a2bc3029c426483bd38115de30f66",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 58.82it/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "6ac7f8c8c82c405ea6e0f563824be3e4": {
+ "7774247051444f258f5cb5a4624a6d83": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_23d399d3b46a4f8d82116059129fd43f",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_13063e602ee246eb9b552b3b781fa85e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
}
},
- "6bd96238c24c4e66a330bba2b167695c": {
+ "77caecf6cce84ac885a2c431ec321e76": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4891,54 +5003,7 @@
"width": null
}
},
- "6c99a5fc3f1d42269032bffe9d6a65ff": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_490e0855ef974a42b2647cfedac70b97",
- "IPY_MODEL_ad9ad38f87fc4507894591d2e971b931",
- "IPY_MODEL_943f54a9f4ed4ae88c0921a8b76bf464"
- ],
- "layout": "IPY_MODEL_72c90d5012e24845b7a5539b09d24a76",
- "tabbable": null,
- "tooltip": null
- }
- },
- "6ce5166958664174ac671372b3559fa4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_6cecdf39711e44c98e32290468b92b89",
- "placeholder": "",
- "style": "IPY_MODEL_aa92cf8d9af14ef481b63d80bff1ec7b",
- "tabbable": null,
- "tooltip": null,
- "value": " 9.02k/9.02k [00:00<00:00, 1.13MB/s]"
- }
- },
- "6cecdf39711e44c98e32290468b92b89": {
+ "78ec8846971b49dc8ac35c9865ab7855": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4991,30 +5056,49 @@
"width": null
}
},
- "6de53660f780414f83690503c02977c3": {
+ "7acec2eb1b9b4c74860f49bf17a12246": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "7bcf07287e5846bcade12829a0129e5a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_74cac39f1ec9456089f019cf6afaf7b6",
- "placeholder": "",
- "style": "IPY_MODEL_8e9c46198cc34d838d789c567dc8fb39",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_0f970e1142174368bfc637a6cd8d6fd5",
+ "IPY_MODEL_11c48316135a461ea55c3d08dc541755",
+ "IPY_MODEL_6e8d203ea1f24d86a8504e8c8f549098"
+ ],
+ "layout": "IPY_MODEL_6f4b003f65a3475b87ea4dfb49e22177",
"tabbable": null,
- "tooltip": null,
- "value": "Downloading readme: 100%"
+ "tooltip": null
}
},
- "6e9d6a9e82ee4cadaf4d6aae8966e15c": {
+ "7c13df622dfc4f04853781143737296f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -5030,7 +5114,7 @@
"description_width": ""
}
},
- "6fa9185a75e4476a9f7bb3766fa59734": {
+ "7c5730df719649e6ae1137849667983e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5083,43 +5167,7 @@
"width": null
}
},
- "7030838d622a49648a433355d555396d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "713797fcd2e443099904db1582f72e04": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "72c90d5012e24845b7a5539b09d24a76": {
+ "7d5c3f6e3cdc47378b7a095dc828c708": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5172,7 +5220,30 @@
"width": null
}
},
- "73da71f202fa4d19a5b13c8abd8c3da4": {
+ "7e624075712f49aeb75f702d9f7850d8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_5264eae69a4a44c5af270b7caaa7eeb4",
+ "placeholder": "",
+ "style": "IPY_MODEL_dac3e32ab9a846e79186067c2b27a96c",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Computing checksums: 100%"
+ }
+ },
+ "82b18a92ec124a52a6892b31409db80e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5190,7 +5261,7 @@
"text_color": null
}
},
- "74cac39f1ec9456089f019cf6afaf7b6": {
+ "85965cbbe5ef40678b86b3d3f8e4fc95": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5243,7 +5314,7 @@
"width": null
}
},
- "76b7a3bbd445436cbe99a45727980913": {
+ "85a6da0e361d4bb78dac486525795dad": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -5258,16 +5329,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_bdfc00756c4b4ea291b1b12d3c453adb",
- "IPY_MODEL_beb7c925e927474da59521fcae36f1b5",
- "IPY_MODEL_bbe8e85b50614b6895dfc5261cefbd3f"
+ "IPY_MODEL_612a34311c13432a923b885221f461b0",
+ "IPY_MODEL_c20bdfdc755d478c8d2c59d296af1748",
+ "IPY_MODEL_0b9bdabf441e4113805b54ee83d92f75"
],
- "layout": "IPY_MODEL_07d8ecbe4aaf4a9ea4aa1e00be62b06b",
+ "layout": "IPY_MODEL_5bc3a1038e00432097a539e27f83e00f",
"tabbable": null,
"tooltip": null
}
},
- "79aa1e210ebd44dfa879c72b499b33e4": {
+ "861644c40333498094e62f8ac990f5a3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5282,41 +5353,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_6bd96238c24c4e66a330bba2b167695c",
+ "layout": "IPY_MODEL_a77a4e7f5f8140bf8475f4d847910210",
"placeholder": "",
- "style": "IPY_MODEL_0777e52962fe4e3c8c417ed705b0e27a",
- "tabbable": null,
- "tooltip": null,
- "value": " 10000/10000 [00:00<00:00, 247365.46 examples/s]"
- }
- },
- "79d0cd0446fb44d18bdfb6375cea394c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_7bffdee10c6243ff866a02214b5a648a",
- "max": 9015.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_65f9a6bec4e948baa940c3046dc97c27",
+ "style": "IPY_MODEL_5cf6c3b877784057a47d544871ab0987",
"tabbable": null,
"tooltip": null,
- "value": 9015.0
+ "value": " 5.18M/5.18M [00:00<00:00, 25.9MB/s]"
}
},
- "7bffdee10c6243ff866a02214b5a648a": {
+ "881dec995ffa41c3b5cbe3a2f2955ed3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5369,94 +5414,25 @@
"width": null
}
},
- "803d8c89e37c4695af839e22e27d45ad": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_bf2c115cc0d44b6fa22a95924ce0e2c1",
- "placeholder": "",
- "style": "IPY_MODEL_be908d64fea14e7a8dacc12ac0c6dd97",
- "tabbable": null,
- "tooltip": null,
- "value": "Generating train split: 100%"
- }
- },
- "80768ed8be224e0ea7b3cc4f25feb960": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_c501ce55b0824b50bbbd81608041645c",
- "IPY_MODEL_97dadb01cdf243fcb11e8a41b9d2c694",
- "IPY_MODEL_a9318ad2f8454532a38c05af6ab2b318"
- ],
- "layout": "IPY_MODEL_4b14e0caa5af4f9688d2d2d294a95764",
- "tabbable": null,
- "tooltip": null
- }
- },
- "8089a6f0e258484bb6eb86bd123e100b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_6de53660f780414f83690503c02977c3",
- "IPY_MODEL_79d0cd0446fb44d18bdfb6375cea394c",
- "IPY_MODEL_6ce5166958664174ac671372b3559fa4"
- ],
- "layout": "IPY_MODEL_e8da8c5e504b47e9b752f785493250ce",
- "tabbable": null,
- "tooltip": null
- }
- },
- "81b854f5964b47979c3b45402b07d21d": {
+ "884d6ce901e24a3797e35af5711b0f35": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "82aff8f27b4c41438fb70e6ac7827816": {
+ "8a312c718675404cb5eaf36ae41d943d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5509,41 +5485,7 @@
"width": null
}
},
- "83ed1ab9ea0a4965aa59eb8257d4f46b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "8a031d58353a4a56b2ee095968c9120f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "8c1eafd77d904a36aa9f617ae2a33e13": {
+ "8cf5382c91f64f789a1af9c7918f14bb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5558,51 +5500,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_50c7134622e34fbb83b07d440594438c",
+ "layout": "IPY_MODEL_d5d9319b3ee0495d88f51937931cd00c",
"placeholder": "",
- "style": "IPY_MODEL_527d0aaee5894f2da400bf5690853965",
+ "style": "IPY_MODEL_af8db4b8467844b8be9927dab8c5e3d9",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 61.56it/s]"
- }
- },
- "8e9c46198cc34d838d789c567dc8fb39": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "8ef171290e7f471a827e41fd51067134": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "value": "100%"
}
},
- "926a96fc307846809864b9a1f01356cf": {
+ "8e65bd8055fd45d8999b608481497477": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5655,7 +5561,7 @@
"width": null
}
},
- "943f54a9f4ed4ae88c0921a8b76bf464": {
+ "8efceb1c08634d06817a3fa57d1a8f06": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5670,59 +5576,68 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1b516e69e31f4ff4a0ecb31c1298142e",
+ "layout": "IPY_MODEL_85965cbbe5ef40678b86b3d3f8e4fc95",
"placeholder": "",
- "style": "IPY_MODEL_8ef171290e7f471a827e41fd51067134",
+ "style": "IPY_MODEL_d86fc4609ac5440a807e454ed938d58e",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 61.32it/s]"
+ "value": " 10000/10000 [00:00<00:00, 249921.29 examples/s]"
}
},
- "94dbf69483ae44dc86b8e989c13af0b1": {
- "model_module": "@jupyter-widgets/controls",
+ "92880a6894cd410ba97664fbbbbe340e": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "95d93d3f700940b592f8552c390c914b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_f9fd3a8802ff4c8799b62137434f622c",
- "max": 10000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_6ac7f8c8c82c405ea6e0f563824be3e4",
- "tabbable": null,
- "tooltip": null,
- "value": 10000.0
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "961e00b1790446eab77bd4a467f184f2": {
+ "929f19e8f15344c999957b2ce7569264": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5775,51 +5690,31 @@
"width": null
}
},
- "97dadb01cdf243fcb11e8a41b9d2c694": {
+ "958c94ac86804e8fbd31685a6f87d389": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_209db2922df34007b2eea98c10204bb3",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_24f5d44a2aba41bab7b5b00ccd550704",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ac11acf9e07f48d1af94fcdf26f8c615",
+ "IPY_MODEL_a671667e5adf4b9798a98eda0ac57dc8",
+ "IPY_MODEL_4a900a7bd2894dc2905c92a999845c41"
+ ],
+ "layout": "IPY_MODEL_4e5be3b38c73499194dd5bfcb00e9476",
"tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "9ee9ae8ff5f64abd9dba00576d9248cd": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "tooltip": null
}
},
- "a0a0e34b0dda43afb7891d6f5af3db95": {
+ "97578dccf99646909cc139834ab78ea9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5872,7 +5767,25 @@
"width": null
}
},
- "a3cce43cc858462a882adcb42f8671a7": {
+ "9bec32080a844b95be00f98699eec0a5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "9db52ec22ade4fc2a207b214ed54d964": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5887,15 +5800,51 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_44a4010eb7a944249e17c4373fb6fae1",
+ "layout": "IPY_MODEL_62625d830bc54505aa74a2a30ef3af9d",
"placeholder": "",
- "style": "IPY_MODEL_c5c385c1288d47d3b192dde525ec8305",
+ "style": "IPY_MODEL_afce32644b8041548c6a8fefb3255e26",
"tabbable": null,
"tooltip": null,
"value": "100%"
}
},
- "a9318ad2f8454532a38c05af6ab2b318": {
+ "9fcb012408c9489cb4882ad5d8d37ecf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "a1e0fb35ccfd46ac9b640c1e3a97a83e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "a53594c87ee944d2ab253fdeb3aeae5f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5910,33 +5859,41 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b544909353d742da90404a2a70352fdf",
+ "layout": "IPY_MODEL_50780f5c92b44525bf711232bc998378",
"placeholder": "",
- "style": "IPY_MODEL_b6f2ef3dc1a54297b205d90f139423c5",
+ "style": "IPY_MODEL_eefff7211ed94c5b90094ff9520c50b5",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 57.51it/s]"
+ "value": "Downloading data: 100%"
}
},
- "aa92cf8d9af14ef481b63d80bff1ec7b": {
+ "a671667e5adf4b9798a98eda0ac57dc8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_1510cd85e1a74691abd66fcc8f87c34c",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_15c77a61cbac476e99fb0331858d1d8c",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
}
},
- "abc24829115b46edb7572dc55403960a": {
+ "a77a4e7f5f8140bf8475f4d847910210": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5989,33 +5946,7 @@
"width": null
}
},
- "ad9ad38f87fc4507894591d2e971b931": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_0bf8b4b0c9ce4e819a7f21eaed682092",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_eda9c8fc9ec84b47b274aead1d312c12",
- "tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "ae860c8ab42f4e1a96dddec1fd6b7a29": {
+ "a88f012a925b438fbc901a161c09cf50": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6068,7 +5999,7 @@
"width": null
}
},
- "ae929a9d89e44834b81a2fd963b2c35f": {
+ "a8fc249e1ca44d4084ed4e8e978d6058": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -6084,17 +6015,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b0135714d63c4ad599890917e7581170",
- "max": 60000.0,
+ "layout": "IPY_MODEL_076942f2bb5542e9a9c126f736c4b427",
+ "max": 40.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_2a0e717d35f142e2ba3bffe16092cee4",
+ "style": "IPY_MODEL_6c5276f1cdeb4d6dafd955e313dfb495",
"tabbable": null,
"tooltip": null,
- "value": 60000.0
+ "value": 40.0
}
},
- "afb259c49ba34750a8a8814d7dc507b6": {
+ "a9b1da96fea74c509d14483d998a7cf8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6147,7 +6078,7 @@
"width": null
}
},
- "aff0b45e2c1f43a9bb8830f843804ede": {
+ "a9e701e6d5bf4ec2a9c900edea6104e5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6200,7 +6131,7 @@
"width": null
}
},
- "b0135714d63c4ad599890917e7581170": {
+ "aba8cf45b54448a59ae5e30586981cc2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6253,7 +6184,7 @@
"width": null
}
},
- "b1d1946f6c3a4ee8bc68f10cfe33d799": {
+ "ac11acf9e07f48d1af94fcdf26f8c615": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -6268,15 +6199,31 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_10186f0c08c84916988b4064fb8fbbb6",
+ "layout": "IPY_MODEL_69af7b2b232142ffa08b9e4439628311",
"placeholder": "",
- "style": "IPY_MODEL_73da71f202fa4d19a5b13c8abd8c3da4",
+ "style": "IPY_MODEL_9fcb012408c9489cb4882ad5d8d37ecf",
"tabbable": null,
"tooltip": null,
"value": "100%"
}
},
- "b2edc3bafbc4407f8f9a8b986aa82b90": {
+ "ac4bae02a5884435aa084f8524ec36ab": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "ac970746b6684c3ba6bb43eee4014e2b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6329,7 +6276,43 @@
"width": null
}
},
- "b330cae0665c496d9f38d6b6c39c32cf": {
+ "af8db4b8467844b8be9927dab8c5e3d9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "afce32644b8041548c6a8fefb3255e26": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "b149b2726a33413c8e2fde403bed8e98": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6382,7 +6365,46 @@
"width": null
}
},
- "b544909353d742da90404a2a70352fdf": {
+ "b1625d60d8254709b2fbc8015a483069": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "b323c9ad204c424e81ad897a8a41faa8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_294184439d7d474cbfc6043c1efa9d3d",
+ "placeholder": "",
+ "style": "IPY_MODEL_63223699d1124f63b67f209182bf8e11",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 62.13it/s]"
+ }
+ },
+ "b8a497f4724b458c8448b91e3ce44d15": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6435,97 +6457,31 @@
"width": null
}
},
- "b57eeb2e85db4e6c90b70606981a862d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_926a96fc307846809864b9a1f01356cf",
- "placeholder": "",
- "style": "IPY_MODEL_21aa7e557ff2445fb6f9a8d7299a17ff",
- "tabbable": null,
- "tooltip": null,
- "value": " 5.18M/5.18M [00:00<00:00, 59.9MB/s]"
- }
- },
- "b6f2ef3dc1a54297b205d90f139423c5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "b754e4894e834fc19df74d003d4ab747": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_a0a0e34b0dda43afb7891d6f5af3db95",
- "placeholder": "",
- "style": "IPY_MODEL_c5ae7198f4aa46179022a46d7c59764e",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 58.46it/s]"
- }
- },
- "b9de52fff41e480faf1069d54e51bb01": {
+ "b8c0903ec57a4db09eef7c66d76ad798": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_ae860c8ab42f4e1a96dddec1fd6b7a29",
- "max": 30931277.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_1403cd0920194d9f88d2e816c0e364a5",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_5e5e4490daf942669f04f85596a7308d",
+ "IPY_MODEL_c1960242d8a3445da3b5cfd12aa829f9",
+ "IPY_MODEL_1658cd683cbd496c9ff193ba8d7c35ea"
+ ],
+ "layout": "IPY_MODEL_6739176497a24677bed9ce1d499ce111",
"tabbable": null,
- "tooltip": null,
- "value": 30931277.0
+ "tooltip": null
}
},
- "ba89e00a862d448e8dc72d837a38fe5a": {
+ "bb2fc960507949aea6439fcd3c77de5b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6578,48 +6534,7 @@
"width": null
}
},
- "badfbcc018f64af28731edbb3cf0c438": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "bbe8e85b50614b6895dfc5261cefbd3f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_6954ba9a0a8544c8ab34caf9382b3919",
- "placeholder": "",
- "style": "IPY_MODEL_9ee9ae8ff5f64abd9dba00576d9248cd",
- "tabbable": null,
- "tooltip": null,
- "value": " 2/2 [00:00<00:00, 597.27it/s]"
- }
- },
- "bd82670a06d84e1fa6cb4197d311667d": {
+ "bcb6b4fdcebe40208119e7b000c67176": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6672,48 +6587,73 @@
"width": null
}
},
- "bdfc00756c4b4ea291b1b12d3c453adb": {
+ "bfcb4b6339d14370bc404a61e757edfd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_53f7d5a3fe5045aea631347f5778ed5b",
- "placeholder": "",
- "style": "IPY_MODEL_57c3c61cde5d4837a1643acc4efaa530",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_67956635d769462da5b0f8ec7ca4575b",
+ "IPY_MODEL_6e84e471535f4aa89081faaaa485d6c3",
+ "IPY_MODEL_f0cb3f6ef1cd478f8be08c7d0285e829"
+ ],
+ "layout": "IPY_MODEL_cc31bc3295e7426890cf527d78a13416",
"tabbable": null,
- "tooltip": null,
- "value": "Computing checksums: 100%"
+ "tooltip": null
}
},
- "be908d64fea14e7a8dacc12ac0c6dd97": {
+ "c0366a87be804af7bcf6f5cd7f11bc3b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "c1960242d8a3445da3b5cfd12aa829f9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_92880a6894cd410ba97664fbbbbe340e",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_b1625d60d8254709b2fbc8015a483069",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 60000.0
}
},
- "beb7c925e927474da59521fcae36f1b5": {
+ "c20bdfdc755d478c8d2c59d296af1748": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -6729,17 +6669,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_d0cf2f4503034449b080c3b3081f634c",
- "max": 2.0,
+ "layout": "IPY_MODEL_6f0a00d7d264477684a40569e5e3fb89",
+ "max": 60000.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_27d51d7d7c394b8ca5b9c6dc1f2f28ef",
+ "style": "IPY_MODEL_210dbd38b6ce4f9e9f8b810ac64d03bf",
"tabbable": null,
"tooltip": null,
- "value": 2.0
+ "value": 60000.0
}
},
- "bf2c115cc0d44b6fa22a95924ce0e2c1": {
+ "c65df96729f9462e8df514c9d2bab3e8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6792,7 +6732,25 @@
"width": null
}
},
- "bf7b754fe88b42c2a0ebefbef27aaa5d": {
+ "c77f86be94734e2ba274bf4267c5a824": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "cc31bc3295e7426890cf527d78a13416": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6845,33 +6803,23 @@
"width": null
}
},
- "c0f63161d8b64e1d88fb06474e6eb468": {
+ "cdf6adba96a64fc5a04910695f01468c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_afb259c49ba34750a8a8814d7dc507b6",
- "max": 60000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_8a031d58353a4a56b2ee095968c9120f",
- "tabbable": null,
- "tooltip": null,
- "value": 60000.0
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "c31bcb9951714ea7b4d0d8e9793f409b": {
+ "cef86182d7ef449481f59dfea70aa34a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -6886,75 +6834,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_b1d1946f6c3a4ee8bc68f10cfe33d799",
- "IPY_MODEL_6492748e048c4fe69d67f2f82d4e0597",
- "IPY_MODEL_b754e4894e834fc19df74d003d4ab747"
+ "IPY_MODEL_8cf5382c91f64f789a1af9c7918f14bb",
+ "IPY_MODEL_1e5e214067f448fe820c272b4d8b60b6",
+ "IPY_MODEL_58bd914194a245c2b1a963606103a9bd"
],
- "layout": "IPY_MODEL_df6d40c4585b4191920844c6eef8bc16",
+ "layout": "IPY_MODEL_25c0e4e85ebb41299102ad0b3e0880b4",
"tabbable": null,
"tooltip": null
}
},
- "c35a2bc3029c426483bd38115de30f66": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "c501ce55b0824b50bbbd81608041645c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_57c3bbf911934c1b954d08e7df293a56",
- "placeholder": "",
- "style": "IPY_MODEL_e777bd11a1bc40dbb3b01b6f0a31135b",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "c5ae7198f4aa46179022a46d7c59764e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "c5c385c1288d47d3b192dde525ec8305": {
+ "d0dfbf919e5f422689492055a00be836": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -6972,46 +6861,60 @@
"text_color": null
}
},
- "c701552093d1471bb9ace0f6dcd2c9bb": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_195f6f44461b4637898d20c3f277f04c",
- "placeholder": "",
- "style": "IPY_MODEL_1a190a961ec641a28b3c636af3b264dc",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 62.73it/s]"
- }
- },
- "c87c43a894a24676985679a70e0b0d69": {
- "model_module": "@jupyter-widgets/controls",
+ "d54af3f6955d4b968858d238a0210190": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "c970b98ad16d4e058311518f87d4705f": {
+ "d5d9319b3ee0495d88f51937931cd00c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7064,7 +6967,25 @@
"width": null
}
},
- "cb746766609e4be886215339a1a76de1": {
+ "d86fc4609ac5440a807e454ed938d58e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "d936e9a2111644719473853bc9465d85": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -7080,17 +7001,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_5d7fd3a699f145eaa96b37d43b96276e",
- "max": 40.0,
+ "layout": "IPY_MODEL_aba8cf45b54448a59ae5e30586981cc2",
+ "max": 2.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_35a155d26b984654ab25b9e85ccccd6f",
+ "style": "IPY_MODEL_e06e8dd00ef946db9d9676c674e9f1ff",
"tabbable": null,
"tooltip": null,
- "value": 40.0
+ "value": 2.0
}
},
- "cda1df54889f4e3bb275c8c2fd8ec82a": {
+ "d9b3afcffed047abb3d7c9215eacb041": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7143,7 +7064,7 @@
"width": null
}
},
- "d0cf2f4503034449b080c3b3081f634c": {
+ "d9bfaf958ae54bdaa41267876482d6af": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7196,7 +7117,77 @@
"width": null
}
},
- "d37c5155b2a140bc9d293b5579737109": {
+ "da5cdaff84244e95b85f4f6729933e89": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "dac3e32ab9a846e79186067c2b27a96c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "df804279770c4bbdbba569e996a72047": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "e06e8dd00ef946db9d9676c674e9f1ff": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "e127093a41b442febda97da50e709395": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -7211,15 +7202,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_6fa9185a75e4476a9f7bb3766fa59734",
+ "layout": "IPY_MODEL_7d5c3f6e3cdc47378b7a095dc828c708",
"placeholder": "",
- "style": "IPY_MODEL_56ab7eb7d7514ac4a5ca99fbec95176c",
+ "style": "IPY_MODEL_7acec2eb1b9b4c74860f49bf17a12246",
"tabbable": null,
"tooltip": null,
- "value": " 60000/60000 [00:11<00:00, 6991.32 examples/s]"
+ "value": " 30.9M/30.9M [00:00<00:00, 85.2MB/s]"
}
},
- "d4701b3f33d74bc4be0e3b87b63572da": {
+ "e38940706f02447996928468d3f523eb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -7235,17 +7226,40 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_4fd6ca7a7dd446879a6f2f55860227d9",
- "max": 40.0,
+ "layout": "IPY_MODEL_97578dccf99646909cc139834ab78ea9",
+ "max": 30931277.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_c87c43a894a24676985679a70e0b0d69",
+ "style": "IPY_MODEL_cdf6adba96a64fc5a04910695f01468c",
"tabbable": null,
"tooltip": null,
- "value": 40.0
+ "value": 30931277.0
+ }
+ },
+ "e6d9313a802d4513bc93abf9ffa9fc9b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_bcb6b4fdcebe40208119e7b000c67176",
+ "placeholder": "",
+ "style": "IPY_MODEL_606a0ff67cfd457c88c691c81de63a4c",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
}
},
- "d5f0f111e29b4da996f76d93a33612b5": {
+ "ed24804748ab483289fec871eb4e7ebf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -7263,30 +7277,31 @@
"text_color": null
}
},
- "dccf399c747d4d1d82bce77b5b675956": {
+ "ee5568e238c045b59cf17074e12437c9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_bd82670a06d84e1fa6cb4197d311667d",
- "placeholder": "",
- "style": "IPY_MODEL_badfbcc018f64af28731edbb3cf0c438",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7e624075712f49aeb75f702d9f7850d8",
+ "IPY_MODEL_d936e9a2111644719473853bc9465d85",
+ "IPY_MODEL_26b545a844a84c278f68d51645f7e371"
+ ],
+ "layout": "IPY_MODEL_33d5aee8319348e485ec3980bc726f23",
"tabbable": null,
- "tooltip": null,
- "value": "100%"
+ "tooltip": null
}
},
- "df6d40c4585b4191920844c6eef8bc16": {
+ "eec22da0f1c144399d3b96c5a790810e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7339,49 +7354,120 @@
"width": null
}
},
- "e1f7f496e0bd4db1ac83de92100a6631": {
+ "eefff7211ed94c5b90094ff9520c50b5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "f03a0d23baa4409abb0c7271bd76ab8a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_803d8c89e37c4695af839e22e27d45ad",
- "IPY_MODEL_c0f63161d8b64e1d88fb06474e6eb468",
- "IPY_MODEL_51120e68bd384037a4668fe393f7be13"
- ],
- "layout": "IPY_MODEL_ba89e00a862d448e8dc72d837a38fe5a",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_c65df96729f9462e8df514c9d2bab3e8",
+ "placeholder": "",
+ "style": "IPY_MODEL_06c85507cf49495584b002e6aaa044e8",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 61.46it/s]"
}
},
- "e777bd11a1bc40dbb3b01b6f0a31135b": {
+ "f082a4eab5444b019ea911ae0fb7a92d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_a88f012a925b438fbc901a161c09cf50",
+ "placeholder": "",
+ "style": "IPY_MODEL_f7b0cd88615641199d9599093664c3f3",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 63.47it/s]"
+ }
+ },
+ "f0cb3f6ef1cd478f8be08c7d0285e829": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_118d13a1737e460b986120e1cd8488c6",
+ "placeholder": "",
+ "style": "IPY_MODEL_884d6ce901e24a3797e35af5711b0f35",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 60000/60000 [00:11<00:00, 5023.35 examples/s]"
+ }
+ },
+ "f11e1a00f1c942a080552a095321e730": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_b149b2726a33413c8e2fde403bed8e98",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_6dd2d74eb1d04d61844ec3c03149c90b",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
}
},
- "e8da8c5e504b47e9b752f785493250ce": {
+ "f1c64d058ec34988a144f78b8ce7cbc8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7434,73 +7520,41 @@
"width": null
}
},
- "e8f51e9d01d447c3b27b88d0c146d11b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_abc24829115b46edb7572dc55403960a",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_81b854f5964b47979c3b45402b07d21d",
- "tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "ec9e629fd2904b8fbf99b7c637113a54": {
+ "f2fa408e34274722bd36f3791c967fb2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_0240b26e2e8347049e4a5df861bb7a0d",
- "IPY_MODEL_d4701b3f33d74bc4be0e3b87b63572da",
- "IPY_MODEL_8c1eafd77d904a36aa9f617ae2a33e13"
- ],
- "layout": "IPY_MODEL_5f93eedb157d4345bc780bd62a65204e",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "eda9c8fc9ec84b47b274aead1d312c12": {
+ "f784e45cbe9248ae9c16491028d6bf8e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "f282b3d3edd34bd19404aa81c4bd2be9": {
+ "f7b0cd88615641199d9599093664c3f3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -7518,81 +7572,27 @@
"text_color": null
}
},
- "f2c0fcf1952846d5b613a055c1f1e0bc": {
+ "f9f45d26f74148d5aafa521c2e42894d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_a3cce43cc858462a882adcb42f8671a7",
- "IPY_MODEL_cb746766609e4be886215339a1a76de1",
- "IPY_MODEL_69ec4f0b4df24afeb44979768207ccb2"
- ],
- "layout": "IPY_MODEL_b2edc3bafbc4407f8f9a8b986aa82b90",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_a9e701e6d5bf4ec2a9c900edea6104e5",
+ "placeholder": "",
+ "style": "IPY_MODEL_da5cdaff84244e95b85f4f6729933e89",
"tabbable": null,
- "tooltip": null
- }
- },
- "f9fd3a8802ff4c8799b62137434f622c": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "tooltip": null,
+ "value": "100%"
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index b3092f1d2..55a26f513 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
@@ -73,10 +73,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:13.469873Z",
- "iopub.status.busy": "2024-09-05T19:37:13.469691Z",
- "iopub.status.idle": "2024-09-05T19:37:14.643451Z",
- "shell.execute_reply": "2024-09-05T19:37:14.642938Z"
+ "iopub.execute_input": "2024-09-06T19:37:06.951842Z",
+ "iopub.status.busy": "2024-09-06T19:37:06.951670Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.104160Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.103605Z"
},
"nbsphinx": "hidden"
},
@@ -86,7 +86,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -111,10 +111,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.646200Z",
- "iopub.status.busy": "2024-09-05T19:37:14.645631Z",
- "iopub.status.idle": "2024-09-05T19:37:14.663967Z",
- "shell.execute_reply": "2024-09-05T19:37:14.663492Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.106594Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.106312Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.124373Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.123937Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.666331Z",
- "iopub.status.busy": "2024-09-05T19:37:14.665911Z",
- "iopub.status.idle": "2024-09-05T19:37:14.706816Z",
- "shell.execute_reply": "2024-09-05T19:37:14.706224Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.126574Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.126159Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.148467Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.148011Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.709267Z",
- "iopub.status.busy": "2024-09-05T19:37:14.708881Z",
- "iopub.status.idle": "2024-09-05T19:37:14.712569Z",
- "shell.execute_reply": "2024-09-05T19:37:14.712080Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.150542Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.150195Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.153510Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.153043Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.714680Z",
- "iopub.status.busy": "2024-09-05T19:37:14.714329Z",
- "iopub.status.idle": "2024-09-05T19:37:14.721981Z",
- "shell.execute_reply": "2024-09-05T19:37:14.721544Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.155506Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.155162Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.163216Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.162658Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.724136Z",
- "iopub.status.busy": "2024-09-05T19:37:14.723769Z",
- "iopub.status.idle": "2024-09-05T19:37:14.726297Z",
- "shell.execute_reply": "2024-09-05T19:37:14.725816Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.165384Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.164978Z",
+ "iopub.status.idle": "2024-09-06T19:37:08.167532Z",
+ "shell.execute_reply": "2024-09-06T19:37:08.167093Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:14.728450Z",
- "iopub.status.busy": "2024-09-05T19:37:14.728116Z",
- "iopub.status.idle": "2024-09-05T19:37:17.842883Z",
- "shell.execute_reply": "2024-09-05T19:37:17.842351Z"
+ "iopub.execute_input": "2024-09-06T19:37:08.169550Z",
+ "iopub.status.busy": "2024-09-06T19:37:08.169205Z",
+ "iopub.status.idle": "2024-09-06T19:37:11.232996Z",
+ "shell.execute_reply": "2024-09-06T19:37:11.232340Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:17.845676Z",
- "iopub.status.busy": "2024-09-05T19:37:17.845310Z",
- "iopub.status.idle": "2024-09-05T19:37:17.854733Z",
- "shell.execute_reply": "2024-09-05T19:37:17.854165Z"
+ "iopub.execute_input": "2024-09-06T19:37:11.235550Z",
+ "iopub.status.busy": "2024-09-06T19:37:11.235362Z",
+ "iopub.status.idle": "2024-09-06T19:37:11.244291Z",
+ "shell.execute_reply": "2024-09-06T19:37:11.243862Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:17.857062Z",
- "iopub.status.busy": "2024-09-05T19:37:17.856748Z",
- "iopub.status.idle": "2024-09-05T19:37:19.875306Z",
- "shell.execute_reply": "2024-09-05T19:37:19.874590Z"
+ "iopub.execute_input": "2024-09-06T19:37:11.246379Z",
+ "iopub.status.busy": "2024-09-06T19:37:11.246205Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.219249Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.218645Z"
}
},
"outputs": [
@@ -476,10 +476,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.877872Z",
- "iopub.status.busy": "2024-09-05T19:37:19.877351Z",
- "iopub.status.idle": "2024-09-05T19:37:19.896585Z",
- "shell.execute_reply": "2024-09-05T19:37:19.895985Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.221677Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.221173Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.240218Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.239749Z"
},
"scrolled": true
},
@@ -609,10 +609,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.898896Z",
- "iopub.status.busy": "2024-09-05T19:37:19.898559Z",
- "iopub.status.idle": "2024-09-05T19:37:19.907000Z",
- "shell.execute_reply": "2024-09-05T19:37:19.906535Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.242381Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.242042Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.250225Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.249765Z"
}
},
"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.909010Z",
- "iopub.status.busy": "2024-09-05T19:37:19.908684Z",
- "iopub.status.idle": "2024-09-05T19:37:19.917678Z",
- "shell.execute_reply": "2024-09-05T19:37:19.917096Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.252315Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.251975Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.260671Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.260195Z"
}
},
"outputs": [
@@ -848,10 +848,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.919930Z",
- "iopub.status.busy": "2024-09-05T19:37:19.919439Z",
- "iopub.status.idle": "2024-09-05T19:37:19.927966Z",
- "shell.execute_reply": "2024-09-05T19:37:19.927364Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.262712Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.262373Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.270531Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.269960Z"
}
},
"outputs": [
@@ -965,10 +965,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.930006Z",
- "iopub.status.busy": "2024-09-05T19:37:19.929680Z",
- "iopub.status.idle": "2024-09-05T19:37:19.938743Z",
- "shell.execute_reply": "2024-09-05T19:37:19.938175Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.272557Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.272379Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.281035Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.280557Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.940940Z",
- "iopub.status.busy": "2024-09-05T19:37:19.940628Z",
- "iopub.status.idle": "2024-09-05T19:37:19.948170Z",
- "shell.execute_reply": "2024-09-05T19:37:19.947590Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.283068Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.282889Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.290486Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.290023Z"
}
},
"outputs": [
@@ -1197,10 +1197,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.950230Z",
- "iopub.status.busy": "2024-09-05T19:37:19.949916Z",
- "iopub.status.idle": "2024-09-05T19:37:19.957426Z",
- "shell.execute_reply": "2024-09-05T19:37:19.956989Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.292532Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.292191Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.299536Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.298963Z"
}
},
"outputs": [
@@ -1306,10 +1306,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:19.959710Z",
- "iopub.status.busy": "2024-09-05T19:37:19.959220Z",
- "iopub.status.idle": "2024-09-05T19:37:19.967448Z",
- "shell.execute_reply": "2024-09-05T19:37:19.967010Z"
+ "iopub.execute_input": "2024-09-06T19:37:13.301807Z",
+ "iopub.status.busy": "2024-09-06T19:37:13.301492Z",
+ "iopub.status.idle": "2024-09-06T19:37:13.309949Z",
+ "shell.execute_reply": "2024-09-06T19:37:13.309476Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 9e0aa3195..0357de56a 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:22.667514Z",
- "iopub.status.busy": "2024-09-05T19:37:22.667337Z",
- "iopub.status.idle": "2024-09-05T19:37:25.554239Z",
- "shell.execute_reply": "2024-09-05T19:37:25.553659Z"
+ "iopub.execute_input": "2024-09-06T19:37:16.238148Z",
+ "iopub.status.busy": "2024-09-06T19:37:16.237968Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.032647Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.031997Z"
},
"nbsphinx": "hidden"
},
@@ -96,7 +96,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.556973Z",
- "iopub.status.busy": "2024-09-05T19:37:25.556474Z",
- "iopub.status.idle": "2024-09-05T19:37:25.559630Z",
- "shell.execute_reply": "2024-09-05T19:37:25.559168Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.035274Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.034943Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.038478Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.037992Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.561758Z",
- "iopub.status.busy": "2024-09-05T19:37:25.561422Z",
- "iopub.status.idle": "2024-09-05T19:37:25.564391Z",
- "shell.execute_reply": "2024-09-05T19:37:25.563936Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.040624Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.040295Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.043522Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.043021Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.566426Z",
- "iopub.status.busy": "2024-09-05T19:37:25.566086Z",
- "iopub.status.idle": "2024-09-05T19:37:25.606289Z",
- "shell.execute_reply": "2024-09-05T19:37:25.605745Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.045678Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.045330Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.065598Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.065087Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.608523Z",
- "iopub.status.busy": "2024-09-05T19:37:25.608173Z",
- "iopub.status.idle": "2024-09-05T19:37:25.611849Z",
- "shell.execute_reply": "2024-09-05T19:37:25.611337Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.067819Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.067470Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.071077Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.070583Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'card_about_to_expire', 'change_pin', 'visa_or_mastercard', 'beneficiary_not_allowed', 'getting_spare_card', 'lost_or_stolen_phone', 'supported_cards_and_currencies'}\n"
+ "Classes: {'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'change_pin'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.613929Z",
- "iopub.status.busy": "2024-09-05T19:37:25.613661Z",
- "iopub.status.idle": "2024-09-05T19:37:25.616746Z",
- "shell.execute_reply": "2024-09-05T19:37:25.616205Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.073199Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.072859Z",
+ "iopub.status.idle": "2024-09-06T19:37:19.075873Z",
+ "shell.execute_reply": "2024-09-06T19:37:19.075346Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:25.618901Z",
- "iopub.status.busy": "2024-09-05T19:37:25.618568Z",
- "iopub.status.idle": "2024-09-05T19:37:29.268375Z",
- "shell.execute_reply": "2024-09-05T19:37:29.267707Z"
+ "iopub.execute_input": "2024-09-06T19:37:19.077966Z",
+ "iopub.status.busy": "2024-09-06T19:37:19.077636Z",
+ "iopub.status.idle": "2024-09-06T19:37:23.171760Z",
+ "shell.execute_reply": "2024-09-06T19:37:23.171196Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:29.271118Z",
- "iopub.status.busy": "2024-09-05T19:37:29.270673Z",
- "iopub.status.idle": "2024-09-05T19:37:30.169853Z",
- "shell.execute_reply": "2024-09-05T19:37:30.169263Z"
+ "iopub.execute_input": "2024-09-06T19:37:23.174471Z",
+ "iopub.status.busy": "2024-09-06T19:37:23.174274Z",
+ "iopub.status.idle": "2024-09-06T19:37:24.103567Z",
+ "shell.execute_reply": "2024-09-06T19:37:24.102969Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:30.172922Z",
- "iopub.status.busy": "2024-09-05T19:37:30.172351Z",
- "iopub.status.idle": "2024-09-05T19:37:30.175456Z",
- "shell.execute_reply": "2024-09-05T19:37:30.174947Z"
+ "iopub.execute_input": "2024-09-06T19:37:24.107438Z",
+ "iopub.status.busy": "2024-09-06T19:37:24.106451Z",
+ "iopub.status.idle": "2024-09-06T19:37:24.110626Z",
+ "shell.execute_reply": "2024-09-06T19:37:24.110110Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:30.177920Z",
- "iopub.status.busy": "2024-09-05T19:37:30.177550Z",
- "iopub.status.idle": "2024-09-05T19:37:32.209250Z",
- "shell.execute_reply": "2024-09-05T19:37:32.208579Z"
+ "iopub.execute_input": "2024-09-06T19:37:24.114244Z",
+ "iopub.status.busy": "2024-09-06T19:37:24.113304Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.122882Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.122195Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.212395Z",
- "iopub.status.busy": "2024-09-05T19:37:32.211737Z",
- "iopub.status.idle": "2024-09-05T19:37:32.235720Z",
- "shell.execute_reply": "2024-09-05T19:37:32.235203Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.126146Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.125493Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.149493Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.148954Z"
},
"scrolled": true
},
@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.238297Z",
- "iopub.status.busy": "2024-09-05T19:37:32.237890Z",
- "iopub.status.idle": "2024-09-05T19:37:32.247569Z",
- "shell.execute_reply": "2024-09-05T19:37:32.247122Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.152122Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.151750Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.163613Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.163031Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.249546Z",
- "iopub.status.busy": "2024-09-05T19:37:32.249251Z",
- "iopub.status.idle": "2024-09-05T19:37:32.253306Z",
- "shell.execute_reply": "2024-09-05T19:37:32.252844Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.165819Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.165507Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.169927Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.169445Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.255258Z",
- "iopub.status.busy": "2024-09-05T19:37:32.255073Z",
- "iopub.status.idle": "2024-09-05T19:37:32.261559Z",
- "shell.execute_reply": "2024-09-05T19:37:32.261077Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.171802Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.171622Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.178323Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.177759Z"
}
},
"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.263657Z",
- "iopub.status.busy": "2024-09-05T19:37:32.263325Z",
- "iopub.status.idle": "2024-09-05T19:37:32.269747Z",
- "shell.execute_reply": "2024-09-05T19:37:32.269278Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.180430Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.180102Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.186371Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.185807Z"
}
},
"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.271867Z",
- "iopub.status.busy": "2024-09-05T19:37:32.271500Z",
- "iopub.status.idle": "2024-09-05T19:37:32.277119Z",
- "shell.execute_reply": "2024-09-05T19:37:32.276609Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.188480Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.188150Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.194198Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.193624Z"
}
},
"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.279176Z",
- "iopub.status.busy": "2024-09-05T19:37:32.278837Z",
- "iopub.status.idle": "2024-09-05T19:37:32.287109Z",
- "shell.execute_reply": "2024-09-05T19:37:32.286569Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.196327Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.195981Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.204376Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.203913Z"
}
},
"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.289281Z",
- "iopub.status.busy": "2024-09-05T19:37:32.288965Z",
- "iopub.status.idle": "2024-09-05T19:37:32.294242Z",
- "shell.execute_reply": "2024-09-05T19:37:32.293707Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.206432Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.206091Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.211539Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.211070Z"
}
},
"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.296374Z",
- "iopub.status.busy": "2024-09-05T19:37:32.296057Z",
- "iopub.status.idle": "2024-09-05T19:37:32.301404Z",
- "shell.execute_reply": "2024-09-05T19:37:32.300861Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.213685Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.213350Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.218528Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.218074Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.303349Z",
- "iopub.status.busy": "2024-09-05T19:37:32.303171Z",
- "iopub.status.idle": "2024-09-05T19:37:32.306705Z",
- "shell.execute_reply": "2024-09-05T19:37:32.306262Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.220571Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.220232Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.223906Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.223327Z"
}
},
"outputs": [
@@ -1449,10 +1449,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:32.308852Z",
- "iopub.status.busy": "2024-09-05T19:37:32.308521Z",
- "iopub.status.idle": "2024-09-05T19:37:32.313482Z",
- "shell.execute_reply": "2024-09-05T19:37:32.313025Z"
+ "iopub.execute_input": "2024-09-06T19:37:26.226160Z",
+ "iopub.status.busy": "2024-09-06T19:37:26.225819Z",
+ "iopub.status.idle": "2024-09-06T19:37:26.231140Z",
+ "shell.execute_reply": "2024-09-06T19:37:26.230573Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index b458579ea..0c93ce2cb 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:35.608990Z",
- "iopub.status.busy": "2024-09-05T19:37:35.608804Z",
- "iopub.status.idle": "2024-09-05T19:37:36.049800Z",
- "shell.execute_reply": "2024-09-05T19:37:36.049289Z"
+ "iopub.execute_input": "2024-09-06T19:37:29.604724Z",
+ "iopub.status.busy": "2024-09-06T19:37:29.604545Z",
+ "iopub.status.idle": "2024-09-06T19:37:30.035194Z",
+ "shell.execute_reply": "2024-09-06T19:37:30.034674Z"
}
},
"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:36.052464Z",
- "iopub.status.busy": "2024-09-05T19:37:36.052028Z",
- "iopub.status.idle": "2024-09-05T19:37:36.185608Z",
- "shell.execute_reply": "2024-09-05T19:37:36.185012Z"
+ "iopub.execute_input": "2024-09-06T19:37:30.037845Z",
+ "iopub.status.busy": "2024-09-06T19:37:30.037406Z",
+ "iopub.status.idle": "2024-09-06T19:37:30.168185Z",
+ "shell.execute_reply": "2024-09-06T19:37:30.167636Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:36.188213Z",
- "iopub.status.busy": "2024-09-05T19:37:36.187785Z",
- "iopub.status.idle": "2024-09-05T19:37:36.211771Z",
- "shell.execute_reply": "2024-09-05T19:37:36.211203Z"
+ "iopub.execute_input": "2024-09-06T19:37:30.170587Z",
+ "iopub.status.busy": "2024-09-06T19:37:30.170087Z",
+ "iopub.status.idle": "2024-09-06T19:37:30.193350Z",
+ "shell.execute_reply": "2024-09-06T19:37:30.192776Z"
}
},
"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:36.214778Z",
- "iopub.status.busy": "2024-09-05T19:37:36.214297Z",
- "iopub.status.idle": "2024-09-05T19:37:39.119007Z",
- "shell.execute_reply": "2024-09-05T19:37:39.118344Z"
+ "iopub.execute_input": "2024-09-06T19:37:30.195997Z",
+ "iopub.status.busy": "2024-09-06T19:37:30.195790Z",
+ "iopub.status.idle": "2024-09-06T19:37:32.997740Z",
+ "shell.execute_reply": "2024-09-06T19:37:32.997128Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:39.121802Z",
- "iopub.status.busy": "2024-09-05T19:37:39.121239Z",
- "iopub.status.idle": "2024-09-05T19:37:48.039020Z",
- "shell.execute_reply": "2024-09-05T19:37:48.038375Z"
+ "iopub.execute_input": "2024-09-06T19:37:33.000426Z",
+ "iopub.status.busy": "2024-09-06T19:37:32.999838Z",
+ "iopub.status.idle": "2024-09-06T19:37:42.839981Z",
+ "shell.execute_reply": "2024-09-06T19:37:42.839475Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:48.041586Z",
- "iopub.status.busy": "2024-09-05T19:37:48.041203Z",
- "iopub.status.idle": "2024-09-05T19:37:48.208174Z",
- "shell.execute_reply": "2024-09-05T19:37:48.207546Z"
+ "iopub.execute_input": "2024-09-06T19:37:42.842458Z",
+ "iopub.status.busy": "2024-09-06T19:37:42.842052Z",
+ "iopub.status.idle": "2024-09-06T19:37:43.014469Z",
+ "shell.execute_reply": "2024-09-06T19:37:43.013871Z"
}
},
"outputs": [],
@@ -838,10 +838,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:48.210728Z",
- "iopub.status.busy": "2024-09-05T19:37:48.210421Z",
- "iopub.status.idle": "2024-09-05T19:37:49.609527Z",
- "shell.execute_reply": "2024-09-05T19:37:49.609004Z"
+ "iopub.execute_input": "2024-09-06T19:37:43.016817Z",
+ "iopub.status.busy": "2024-09-06T19:37:43.016641Z",
+ "iopub.status.idle": "2024-09-06T19:37:44.396004Z",
+ "shell.execute_reply": "2024-09-06T19:37:44.395431Z"
}
},
"outputs": [
@@ -1000,10 +1000,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:49.611837Z",
- "iopub.status.busy": "2024-09-05T19:37:49.611455Z",
- "iopub.status.idle": "2024-09-05T19:37:50.149593Z",
- "shell.execute_reply": "2024-09-05T19:37:50.149038Z"
+ "iopub.execute_input": "2024-09-06T19:37:44.398298Z",
+ "iopub.status.busy": "2024-09-06T19:37:44.397931Z",
+ "iopub.status.idle": "2024-09-06T19:37:44.810929Z",
+ "shell.execute_reply": "2024-09-06T19:37:44.810371Z"
}
},
"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.152292Z",
- "iopub.status.busy": "2024-09-05T19:37:50.151696Z",
- "iopub.status.idle": "2024-09-05T19:37:50.165608Z",
- "shell.execute_reply": "2024-09-05T19:37:50.165110Z"
+ "iopub.execute_input": "2024-09-06T19:37:44.813440Z",
+ "iopub.status.busy": "2024-09-06T19:37:44.812940Z",
+ "iopub.status.idle": "2024-09-06T19:37:44.826271Z",
+ "shell.execute_reply": "2024-09-06T19:37:44.825842Z"
}
},
"outputs": [],
@@ -1115,10 +1115,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.167994Z",
- "iopub.status.busy": "2024-09-05T19:37:50.167587Z",
- "iopub.status.idle": "2024-09-05T19:37:50.186934Z",
- "shell.execute_reply": "2024-09-05T19:37:50.186405Z"
+ "iopub.execute_input": "2024-09-06T19:37:44.828390Z",
+ "iopub.status.busy": "2024-09-06T19:37:44.828044Z",
+ "iopub.status.idle": "2024-09-06T19:37:44.847179Z",
+ "shell.execute_reply": "2024-09-06T19:37:44.846760Z"
}
},
"outputs": [],
@@ -1146,10 +1146,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.189317Z",
- "iopub.status.busy": "2024-09-05T19:37:50.189017Z",
- "iopub.status.idle": "2024-09-05T19:37:50.417357Z",
- "shell.execute_reply": "2024-09-05T19:37:50.416811Z"
+ "iopub.execute_input": "2024-09-06T19:37:44.849314Z",
+ "iopub.status.busy": "2024-09-06T19:37:44.848979Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.077019Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.076447Z"
}
},
"outputs": [],
@@ -1189,10 +1189,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.420095Z",
- "iopub.status.busy": "2024-09-05T19:37:50.419658Z",
- "iopub.status.idle": "2024-09-05T19:37:50.440418Z",
- "shell.execute_reply": "2024-09-05T19:37:50.439793Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.079688Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.079281Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.098946Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.098466Z"
}
},
"outputs": [
@@ -1390,10 +1390,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.442606Z",
- "iopub.status.busy": "2024-09-05T19:37:50.442414Z",
- "iopub.status.idle": "2024-09-05T19:37:50.615431Z",
- "shell.execute_reply": "2024-09-05T19:37:50.614827Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.101100Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.100762Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.277489Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.276850Z"
}
},
"outputs": [
@@ -1460,10 +1460,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.617758Z",
- "iopub.status.busy": "2024-09-05T19:37:50.617562Z",
- "iopub.status.idle": "2024-09-05T19:37:50.628108Z",
- "shell.execute_reply": "2024-09-05T19:37:50.627590Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.279928Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.279722Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.290798Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.290229Z"
}
},
"outputs": [
@@ -1729,10 +1729,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.630389Z",
- "iopub.status.busy": "2024-09-05T19:37:50.630031Z",
- "iopub.status.idle": "2024-09-05T19:37:50.640010Z",
- "shell.execute_reply": "2024-09-05T19:37:50.639473Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.292867Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.292672Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.302178Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.301745Z"
}
},
"outputs": [
@@ -1919,10 +1919,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.642181Z",
- "iopub.status.busy": "2024-09-05T19:37:50.641826Z",
- "iopub.status.idle": "2024-09-05T19:37:50.671287Z",
- "shell.execute_reply": "2024-09-05T19:37:50.670793Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.304034Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.303861Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.329485Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.329066Z"
}
},
"outputs": [],
@@ -1956,10 +1956,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.673864Z",
- "iopub.status.busy": "2024-09-05T19:37:50.673387Z",
- "iopub.status.idle": "2024-09-05T19:37:50.676503Z",
- "shell.execute_reply": "2024-09-05T19:37:50.675922Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.331450Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.331118Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.333941Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.333348Z"
}
},
"outputs": [],
@@ -1981,10 +1981,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.678746Z",
- "iopub.status.busy": "2024-09-05T19:37:50.678397Z",
- "iopub.status.idle": "2024-09-05T19:37:50.698912Z",
- "shell.execute_reply": "2024-09-05T19:37:50.698309Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.336081Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.335742Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.354797Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.354315Z"
}
},
"outputs": [
@@ -2142,10 +2142,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.701349Z",
- "iopub.status.busy": "2024-09-05T19:37:50.700967Z",
- "iopub.status.idle": "2024-09-05T19:37:50.705309Z",
- "shell.execute_reply": "2024-09-05T19:37:50.704840Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.356897Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.356543Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.360935Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.360328Z"
}
},
"outputs": [],
@@ -2178,10 +2178,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.707600Z",
- "iopub.status.busy": "2024-09-05T19:37:50.707256Z",
- "iopub.status.idle": "2024-09-05T19:37:50.737701Z",
- "shell.execute_reply": "2024-09-05T19:37:50.737181Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.363152Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.362835Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.390311Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.389739Z"
}
},
"outputs": [
@@ -2327,10 +2327,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:50.740155Z",
- "iopub.status.busy": "2024-09-05T19:37:50.739645Z",
- "iopub.status.idle": "2024-09-05T19:37:51.096104Z",
- "shell.execute_reply": "2024-09-05T19:37:51.095515Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.392321Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.392005Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.759141Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.758581Z"
}
},
"outputs": [
@@ -2397,10 +2397,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.098341Z",
- "iopub.status.busy": "2024-09-05T19:37:51.098156Z",
- "iopub.status.idle": "2024-09-05T19:37:51.101609Z",
- "shell.execute_reply": "2024-09-05T19:37:51.101125Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.761452Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.761084Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.764398Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.763923Z"
}
},
"outputs": [
@@ -2451,10 +2451,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.103763Z",
- "iopub.status.busy": "2024-09-05T19:37:51.103431Z",
- "iopub.status.idle": "2024-09-05T19:37:51.117191Z",
- "shell.execute_reply": "2024-09-05T19:37:51.116674Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.766685Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.766351Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.779490Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.779045Z"
}
},
"outputs": [
@@ -2733,10 +2733,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.119477Z",
- "iopub.status.busy": "2024-09-05T19:37:51.119122Z",
- "iopub.status.idle": "2024-09-05T19:37:51.136499Z",
- "shell.execute_reply": "2024-09-05T19:37:51.135847Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.781428Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.781250Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.796041Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.795601Z"
}
},
"outputs": [
@@ -3003,10 +3003,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.138657Z",
- "iopub.status.busy": "2024-09-05T19:37:51.138457Z",
- "iopub.status.idle": "2024-09-05T19:37:51.148925Z",
- "shell.execute_reply": "2024-09-05T19:37:51.148440Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.798043Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.797870Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.807740Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.807165Z"
}
},
"outputs": [],
@@ -3031,10 +3031,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.151091Z",
- "iopub.status.busy": "2024-09-05T19:37:51.150913Z",
- "iopub.status.idle": "2024-09-05T19:37:51.161080Z",
- "shell.execute_reply": "2024-09-05T19:37:51.160404Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.809952Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.809629Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.818832Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.818256Z"
}
},
"outputs": [
@@ -3206,10 +3206,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.163142Z",
- "iopub.status.busy": "2024-09-05T19:37:51.162957Z",
- "iopub.status.idle": "2024-09-05T19:37:51.166908Z",
- "shell.execute_reply": "2024-09-05T19:37:51.166441Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.821154Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.820691Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.824900Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.824317Z"
}
},
"outputs": [],
@@ -3241,10 +3241,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.168894Z",
- "iopub.status.busy": "2024-09-05T19:37:51.168718Z",
- "iopub.status.idle": "2024-09-05T19:37:51.221568Z",
- "shell.execute_reply": "2024-09-05T19:37:51.220997Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.826963Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.826647Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.876648Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.876084Z"
}
},
"outputs": [
@@ -3252,230 +3252,230 @@
"data": {
"text/html": [
"\n",
- "\n",
+ "\n",
" \n",
" \n",
" | \n",
- " Age | \n",
- " Gender | \n",
- " Location | \n",
- " Annual_Spending | \n",
- " Number_of_Transactions | \n",
- " Last_Purchase_Date | \n",
- " | | \n",
- " is_null_issue | \n",
- " null_score | \n",
+ " Age | \n",
+ " Gender | \n",
+ " Location | \n",
+ " Annual_Spending | \n",
+ " Number_of_Transactions | \n",
+ " Last_Purchase_Date | \n",
+ " | | \n",
+ " is_null_issue | \n",
+ " null_score | \n",
"
\n",
" \n",
" \n",
" \n",
- " 8 | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
- " NaT | \n",
- " | \n",
- " True | \n",
- " 0.000000 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " nan | \n",
- " Female | \n",
- " Rural | \n",
- " 6421.160000 | \n",
- " 5.000000 | \n",
- " NaT | \n",
- " | \n",
- " False | \n",
- " 0.666667 | \n",
- "
\n",
- " \n",
- " 9 | \n",
- " nan | \n",
- " Male | \n",
- " Rural | \n",
- " 4655.820000 | \n",
- " 1.000000 | \n",
- " NaT | \n",
- " | \n",
- " False | \n",
- " 0.666667 | \n",
- "
\n",
- " \n",
- " 14 | \n",
- " nan | \n",
- " Male | \n",
- " Rural | \n",
- " 6790.460000 | \n",
- " 3.000000 | \n",
- " NaT | \n",
- " | \n",
- " False | \n",
- " 0.666667 | \n",
- "
\n",
- " \n",
- " 13 | \n",
- " nan | \n",
- " Male | \n",
- " Urban | \n",
- " 9167.470000 | \n",
- " 4.000000 | \n",
- " 2024-01-02 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 0.833333 | \n",
- "
\n",
- " \n",
- " 15 | \n",
- " nan | \n",
- " Other | \n",
- " Rural | \n",
- " 5327.960000 | \n",
- " 8.000000 | \n",
- " 2024-01-03 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 0.833333 | \n",
- "
\n",
- " \n",
- " 0 | \n",
- " 56.000000 | \n",
- " Other | \n",
- " Rural | \n",
- " 4099.620000 | \n",
- " 3.000000 | \n",
- " 2024-01-03 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " 46.000000 | \n",
- " Male | \n",
- " Suburban | \n",
- " 5436.550000 | \n",
- " 3.000000 | \n",
- " 2024-02-26 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " 32.000000 | \n",
- " Female | \n",
- " Rural | \n",
- " 4046.660000 | \n",
- " 3.000000 | \n",
- " 2024-03-23 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " 60.000000 | \n",
- " Female | \n",
- " Suburban | \n",
- " 3467.670000 | \n",
- " 6.000000 | \n",
- " 2024-03-01 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 5 | \n",
- " 25.000000 | \n",
- " Female | \n",
- " Suburban | \n",
- " 4757.370000 | \n",
- " 4.000000 | \n",
- " 2024-01-03 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 6 | \n",
- " 38.000000 | \n",
- " Female | \n",
- " Rural | \n",
- " 4199.530000 | \n",
- " 6.000000 | \n",
- " 2024-01-03 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 7 | \n",
- " 56.000000 | \n",
- " Male | \n",
- " Suburban | \n",
- " 4991.710000 | \n",
- " 6.000000 | \n",
- " 2024-04-03 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 10 | \n",
- " 40.000000 | \n",
- " Female | \n",
- " Rural | \n",
- " 5584.020000 | \n",
- " 7.000000 | \n",
- " 2024-03-29 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 11 | \n",
- " 28.000000 | \n",
- " Female | \n",
- " Urban | \n",
- " 3102.320000 | \n",
- " 2.000000 | \n",
- " 2024-04-07 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " 12 | \n",
- " 28.000000 | \n",
- " Male | \n",
- " Rural | \n",
- " 6637.990000 | \n",
- " 11.000000 | \n",
- " 2024-04-08 00:00:00 | \n",
- " | \n",
- " False | \n",
- " 1.000000 | \n",
+ " 8 | \n",
+ " nan | \n",
+ " nan | \n",
+ " nan | \n",
+ " nan | \n",
+ " nan | \n",
+ " NaT | \n",
+ " | \n",
+ " True | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " nan | \n",
+ " Female | \n",
+ " Rural | \n",
+ " 6421.160000 | \n",
+ " 5.000000 | \n",
+ " NaT | \n",
+ " | \n",
+ " False | \n",
+ " 0.666667 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " nan | \n",
+ " Male | \n",
+ " Rural | \n",
+ " 4655.820000 | \n",
+ " 1.000000 | \n",
+ " NaT | \n",
+ " | \n",
+ " False | \n",
+ " 0.666667 | \n",
+ "
\n",
+ " \n",
+ " 14 | \n",
+ " nan | \n",
+ " Male | \n",
+ " Rural | \n",
+ " 6790.460000 | \n",
+ " 3.000000 | \n",
+ " NaT | \n",
+ " | \n",
+ " False | \n",
+ " 0.666667 | \n",
+ "
\n",
+ " \n",
+ " 13 | \n",
+ " nan | \n",
+ " Male | \n",
+ " Urban | \n",
+ " 9167.470000 | \n",
+ " 4.000000 | \n",
+ " 2024-01-02 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 0.833333 | \n",
+ "
\n",
+ " \n",
+ " 15 | \n",
+ " nan | \n",
+ " Other | \n",
+ " Rural | \n",
+ " 5327.960000 | \n",
+ " 8.000000 | \n",
+ " 2024-01-03 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 0.833333 | \n",
+ "
\n",
+ " \n",
+ " 0 | \n",
+ " 56.000000 | \n",
+ " Other | \n",
+ " Rural | \n",
+ " 4099.620000 | \n",
+ " 3.000000 | \n",
+ " 2024-01-03 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 46.000000 | \n",
+ " Male | \n",
+ " Suburban | \n",
+ " 5436.550000 | \n",
+ " 3.000000 | \n",
+ " 2024-02-26 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 32.000000 | \n",
+ " Female | \n",
+ " Rural | \n",
+ " 4046.660000 | \n",
+ " 3.000000 | \n",
+ " 2024-03-23 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 60.000000 | \n",
+ " Female | \n",
+ " Suburban | \n",
+ " 3467.670000 | \n",
+ " 6.000000 | \n",
+ " 2024-03-01 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 25.000000 | \n",
+ " Female | \n",
+ " Suburban | \n",
+ " 4757.370000 | \n",
+ " 4.000000 | \n",
+ " 2024-01-03 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 38.000000 | \n",
+ " Female | \n",
+ " Rural | \n",
+ " 4199.530000 | \n",
+ " 6.000000 | \n",
+ " 2024-01-03 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 56.000000 | \n",
+ " Male | \n",
+ " Suburban | \n",
+ " 4991.710000 | \n",
+ " 6.000000 | \n",
+ " 2024-04-03 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 10 | \n",
+ " 40.000000 | \n",
+ " Female | \n",
+ " Rural | \n",
+ " 5584.020000 | \n",
+ " 7.000000 | \n",
+ " 2024-03-29 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 11 | \n",
+ " 28.000000 | \n",
+ " Female | \n",
+ " Urban | \n",
+ " 3102.320000 | \n",
+ " 2.000000 | \n",
+ " 2024-04-07 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 12 | \n",
+ " 28.000000 | \n",
+ " Male | \n",
+ " Rural | \n",
+ " 6637.990000 | \n",
+ " 11.000000 | \n",
+ " 2024-04-08 00:00:00 | \n",
+ " | \n",
+ " False | \n",
+ " 1.000000 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -3551,10 +3551,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.224233Z",
- "iopub.status.busy": "2024-09-05T19:37:51.223659Z",
- "iopub.status.idle": "2024-09-05T19:37:51.229976Z",
- "shell.execute_reply": "2024-09-05T19:37:51.229459Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.878907Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.878480Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.884204Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.883634Z"
}
},
"outputs": [],
@@ -3593,10 +3593,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.232017Z",
- "iopub.status.busy": "2024-09-05T19:37:51.231819Z",
- "iopub.status.idle": "2024-09-05T19:37:51.243359Z",
- "shell.execute_reply": "2024-09-05T19:37:51.242861Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.886291Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.885973Z",
+ "iopub.status.idle": "2024-09-06T19:37:45.897008Z",
+ "shell.execute_reply": "2024-09-06T19:37:45.896438Z"
}
},
"outputs": [
@@ -3632,10 +3632,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.245457Z",
- "iopub.status.busy": "2024-09-05T19:37:51.245279Z",
- "iopub.status.idle": "2024-09-05T19:37:51.475856Z",
- "shell.execute_reply": "2024-09-05T19:37:51.475287Z"
+ "iopub.execute_input": "2024-09-06T19:37:45.899243Z",
+ "iopub.status.busy": "2024-09-06T19:37:45.898904Z",
+ "iopub.status.idle": "2024-09-06T19:37:46.075809Z",
+ "shell.execute_reply": "2024-09-06T19:37:46.075226Z"
}
},
"outputs": [
@@ -3687,10 +3687,10 @@
"execution_count": 32,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.478345Z",
- "iopub.status.busy": "2024-09-05T19:37:51.477903Z",
- "iopub.status.idle": "2024-09-05T19:37:51.486021Z",
- "shell.execute_reply": "2024-09-05T19:37:51.485523Z"
+ "iopub.execute_input": "2024-09-06T19:37:46.078430Z",
+ "iopub.status.busy": "2024-09-06T19:37:46.077957Z",
+ "iopub.status.idle": "2024-09-06T19:37:46.085812Z",
+ "shell.execute_reply": "2024-09-06T19:37:46.085244Z"
},
"nbsphinx": "hidden"
},
@@ -3756,10 +3756,10 @@
"execution_count": 33,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.488092Z",
- "iopub.status.busy": "2024-09-05T19:37:51.487894Z",
- "iopub.status.idle": "2024-09-05T19:37:51.860873Z",
- "shell.execute_reply": "2024-09-05T19:37:51.860210Z"
+ "iopub.execute_input": "2024-09-06T19:37:46.087762Z",
+ "iopub.status.busy": "2024-09-06T19:37:46.087589Z",
+ "iopub.status.idle": "2024-09-06T19:37:46.522443Z",
+ "shell.execute_reply": "2024-09-06T19:37:46.521749Z"
}
},
"outputs": [
@@ -3767,25 +3767,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-09-05 19:37:51-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n",
- "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.109.153, 185.199.108.153, 185.199.111.153, ...\r\n",
- "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.109.153|:443... connected.\r\n",
- "HTTP request sent, awaiting response... 200 OK\r\n",
- "Length: 986707 (964K) [application/zip]\r\n",
- "Saving to: ‘CIFAR-10-subset.zip’\r\n",
- "\r\n",
- "\r",
- "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s "
+ "--2024-09-06 19:37:46-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n",
+ "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n",
+ "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n",
+ "HTTP request sent, awaiting response... "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "200 OK\r\n",
+ "Length: 986707 (964K) [application/zip]\r\n",
+ "Saving to: ‘CIFAR-10-subset.zip’\r\n",
+ "\r\n",
"\r",
- "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.03s \r\n",
+ "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r",
+ "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.005s \r\n",
"\r\n",
- "2024-09-05 19:37:51 (32.9 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n",
+ "2024-09-06 19:37:46 (176 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n",
"\r\n"
]
}
@@ -3801,10 +3801,10 @@
"execution_count": 34,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:51.863648Z",
- "iopub.status.busy": "2024-09-05T19:37:51.863430Z",
- "iopub.status.idle": "2024-09-05T19:37:53.857499Z",
- "shell.execute_reply": "2024-09-05T19:37:53.856936Z"
+ "iopub.execute_input": "2024-09-06T19:37:46.525178Z",
+ "iopub.status.busy": "2024-09-06T19:37:46.524748Z",
+ "iopub.status.idle": "2024-09-06T19:37:48.452276Z",
+ "shell.execute_reply": "2024-09-06T19:37:48.451758Z"
}
},
"outputs": [],
@@ -3850,10 +3850,10 @@
"execution_count": 35,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:53.860032Z",
- "iopub.status.busy": "2024-09-05T19:37:53.859675Z",
- "iopub.status.idle": "2024-09-05T19:37:54.515213Z",
- "shell.execute_reply": "2024-09-05T19:37:54.514565Z"
+ "iopub.execute_input": "2024-09-06T19:37:48.454913Z",
+ "iopub.status.busy": "2024-09-06T19:37:48.454468Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.092778Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.092169Z"
}
},
"outputs": [
@@ -3868,7 +3868,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6a8633562ced4f21b9e1b849b611603d",
+ "model_id": "a5793cf283c046f188f735beef4577a5",
"version_major": 2,
"version_minor": 0
},
@@ -4008,10 +4008,10 @@
"execution_count": 36,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:54.518192Z",
- "iopub.status.busy": "2024-09-05T19:37:54.517837Z",
- "iopub.status.idle": "2024-09-05T19:37:54.531474Z",
- "shell.execute_reply": "2024-09-05T19:37:54.530950Z"
+ "iopub.execute_input": "2024-09-06T19:37:49.095580Z",
+ "iopub.status.busy": "2024-09-06T19:37:49.095115Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.108940Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.108334Z"
}
},
"outputs": [
@@ -4130,35 +4130,35 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
- " 0.237196 | \n",
" True | \n",
+ " 0.237196 | \n",
"
\n",
" \n",
" 1 | \n",
- " 0.197229 | \n",
" True | \n",
+ " 0.197229 | \n",
"
\n",
" \n",
" 2 | \n",
- " 0.254188 | \n",
" True | \n",
+ " 0.254188 | \n",
"
\n",
" \n",
" 3 | \n",
- " 0.229170 | \n",
" True | \n",
+ " 0.229170 | \n",
"
\n",
" \n",
" 4 | \n",
- " 0.208907 | \n",
" True | \n",
+ " 0.208907 | \n",
"
\n",
" \n",
" ... | \n",
@@ -4167,28 +4167,28 @@
"
\n",
" \n",
" 195 | \n",
- " 0.793840 | \n",
" False | \n",
+ " 0.793840 | \n",
"
\n",
" \n",
" 196 | \n",
- " 1.000000 | \n",
" False | \n",
+ " 1.000000 | \n",
"
\n",
" \n",
" 197 | \n",
- " 0.971560 | \n",
" False | \n",
+ " 0.971560 | \n",
"
\n",
" \n",
" 198 | \n",
- " 0.862236 | \n",
" False | \n",
+ " 0.862236 | \n",
"
\n",
" \n",
" 199 | \n",
- " 0.973533 | \n",
" False | \n",
+ " 0.973533 | \n",
"
\n",
" \n",
"
\n",
@@ -4196,18 +4196,18 @@
""
],
"text/plain": [
- " dark_score is_dark_issue\n",
- "0 0.237196 True\n",
- "1 0.197229 True\n",
- "2 0.254188 True\n",
- "3 0.229170 True\n",
- "4 0.208907 True\n",
- ".. ... ...\n",
- "195 0.793840 False\n",
- "196 1.000000 False\n",
- "197 0.971560 False\n",
- "198 0.862236 False\n",
- "199 0.973533 False\n",
+ " is_dark_issue dark_score\n",
+ "0 True 0.237196\n",
+ "1 True 0.197229\n",
+ "2 True 0.254188\n",
+ "3 True 0.229170\n",
+ "4 True 0.208907\n",
+ ".. ... ...\n",
+ "195 False 0.793840\n",
+ "196 False 1.000000\n",
+ "197 False 0.971560\n",
+ "198 False 0.862236\n",
+ "199 False 0.973533\n",
"\n",
"[200 rows x 2 columns]"
]
@@ -4257,10 +4257,10 @@
"execution_count": 37,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:54.534111Z",
- "iopub.status.busy": "2024-09-05T19:37:54.533778Z",
- "iopub.status.idle": "2024-09-05T19:37:54.685884Z",
- "shell.execute_reply": "2024-09-05T19:37:54.685435Z"
+ "iopub.execute_input": "2024-09-06T19:37:49.112413Z",
+ "iopub.status.busy": "2024-09-06T19:37:49.112212Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.262201Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.261645Z"
}
},
"outputs": [
@@ -4325,10 +4325,10 @@
"execution_count": 38,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:54.688065Z",
- "iopub.status.busy": "2024-09-05T19:37:54.687765Z",
- "iopub.status.idle": "2024-09-05T19:37:55.189906Z",
- "shell.execute_reply": "2024-09-05T19:37:55.189260Z"
+ "iopub.execute_input": "2024-09-06T19:37:49.264493Z",
+ "iopub.status.busy": "2024-09-06T19:37:49.264138Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.776468Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.775810Z"
},
"nbsphinx": "hidden"
},
@@ -4344,7 +4344,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0491ab817f3e4abfae647f24171e651f",
+ "model_id": "e53b81d02870488ca1d70faf1534371f",
"version_major": 2,
"version_minor": 0
},
@@ -4473,35 +4473,35 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
- " 0.797509 | \n",
" False | \n",
+ " 0.797509 | \n",
"
\n",
" \n",
" 1 | \n",
- " 0.663760 | \n",
" False | \n",
+ " 0.663760 | \n",
"
\n",
" \n",
" 2 | \n",
- " 0.849826 | \n",
" False | \n",
+ " 0.849826 | \n",
"
\n",
" \n",
" 3 | \n",
- " 0.773951 | \n",
" False | \n",
+ " 0.773951 | \n",
"
\n",
" \n",
" 4 | \n",
- " 0.699518 | \n",
" False | \n",
+ " 0.699518 | \n",
"
\n",
" \n",
" ... | \n",
@@ -4510,28 +4510,28 @@
"
\n",
" \n",
" 195 | \n",
- " 0.793840 | \n",
" False | \n",
+ " 0.793840 | \n",
"
\n",
" \n",
" 196 | \n",
- " 1.000000 | \n",
" False | \n",
+ " 1.000000 | \n",
"
\n",
" \n",
" 197 | \n",
- " 0.971560 | \n",
" False | \n",
+ " 0.971560 | \n",
"
\n",
" \n",
" 198 | \n",
- " 0.862236 | \n",
" False | \n",
+ " 0.862236 | \n",
"
\n",
" \n",
" 199 | \n",
- " 0.973533 | \n",
" False | \n",
+ " 0.973533 | \n",
"
\n",
" \n",
"\n",
@@ -4539,18 +4539,18 @@
""
],
"text/plain": [
- " dark_score is_dark_issue\n",
- "0 0.797509 False\n",
- "1 0.663760 False\n",
- "2 0.849826 False\n",
- "3 0.773951 False\n",
- "4 0.699518 False\n",
- ".. ... ...\n",
- "195 0.793840 False\n",
- "196 1.000000 False\n",
- "197 0.971560 False\n",
- "198 0.862236 False\n",
- "199 0.973533 False\n",
+ " is_dark_issue dark_score\n",
+ "0 False 0.797509\n",
+ "1 False 0.663760\n",
+ "2 False 0.849826\n",
+ "3 False 0.773951\n",
+ "4 False 0.699518\n",
+ ".. ... ...\n",
+ "195 False 0.793840\n",
+ "196 False 1.000000\n",
+ "197 False 0.971560\n",
+ "198 False 0.862236\n",
+ "199 False 0.973533\n",
"\n",
"[200 rows x 2 columns]"
]
@@ -4598,10 +4598,10 @@
"execution_count": 39,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:55.192457Z",
- "iopub.status.busy": "2024-09-05T19:37:55.192089Z",
- "iopub.status.idle": "2024-09-05T19:37:55.344435Z",
- "shell.execute_reply": "2024-09-05T19:37:55.343755Z"
+ "iopub.execute_input": "2024-09-06T19:37:49.778901Z",
+ "iopub.status.busy": "2024-09-06T19:37:49.778528Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.924980Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.924477Z"
},
"nbsphinx": "hidden"
},
@@ -4653,31 +4653,83 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0491ab817f3e4abfae647f24171e651f": {
+ "021a50164b8c491ebb069bd57b11ce1a": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "2cb88e5e7d0f4849b336950480e87a06": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_7e84cf312cfa4d80a5107dcdb0a45949",
- "IPY_MODEL_7557f4205917445ca0c596993a114685",
- "IPY_MODEL_dc9edc5341cb452cb27aada834ac562d"
- ],
- "layout": "IPY_MODEL_a1d59c28e7064efc92b1e3caf26f9346",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_5349f02a0bb24786bba46192aa1d90ff",
+ "placeholder": "",
+ "style": "IPY_MODEL_b93c4b8b97f34f0b93a2d334e5065e1b",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "100%"
}
},
- "37eeb5a0817c455a8d0efe07a3d6bd44": {
+ "313b234230ce4ce4850b3fa6a5e1b1ee": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4730,7 +4782,7 @@
"width": null
}
},
- "37f6d18ef4a24f62a2d67a08d8dae98c": {
+ "41bdd318b6d1453a8daca74a0776e419": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4783,39 +4835,7 @@
"width": null
}
},
- "3f81fed11b61470bac6f5d0b3b537a4f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "4bbbb6bbcc9648459b5d261cb8ab6826": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "550c28ddb9cf43c3b62b1a0b54f7bd12": {
+ "5349f02a0bb24786bba46192aa1d90ff": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4868,49 +4888,56 @@
"width": null
}
},
- "62793e1d0aa84395bb5ab3f9ff86c9b5": {
+ "5664879b48124f5cac1e0a8c43742995": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_da820a1ccd2b42d4a8c12ea0328d1169",
+ "max": 200.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_da8ad4a548a8409389fab7ddc0e601bc",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 200.0
}
},
- "6a8633562ced4f21b9e1b849b611603d": {
+ "6529bc3e5e35424f967dab0385030a5c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_db046a62f4e34aa594499d33fa68145f",
- "IPY_MODEL_a6c8cc603a414e80a6ba376c29f15b21",
- "IPY_MODEL_df91c80300534ab59fe080ab28475f7a"
- ],
- "layout": "IPY_MODEL_37f6d18ef4a24f62a2d67a08d8dae98c",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_41bdd318b6d1453a8daca74a0776e419",
+ "placeholder": "",
+ "style": "IPY_MODEL_dfac24cbd04d4a6a9c6a2f3d7e34c87e",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 200/200 [00:00<00:00, 682.83it/s]"
}
},
- "7557f4205917445ca0c596993a114685": {
+ "733b0d114c6e48e6af9ced8acfb5bf3a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -4926,17 +4953,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_daa6b8f2481040acb873f24cfbfedc9e",
+ "layout": "IPY_MODEL_953f4c82aabd472c9e8dfebdf70939d8",
"max": 200.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_3f81fed11b61470bac6f5d0b3b537a4f",
+ "style": "IPY_MODEL_86445ca79c764836a406520c67b4b945",
"tabbable": null,
"tooltip": null,
"value": 200.0
}
},
- "7e84cf312cfa4d80a5107dcdb0a45949": {
+ "7b9c39c715b849dbb886ceaeb96e5c35": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4951,33 +4978,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_550c28ddb9cf43c3b62b1a0b54f7bd12",
+ "layout": "IPY_MODEL_7fb3eb018b9d446294207573ca64cda2",
"placeholder": "",
- "style": "IPY_MODEL_81f7fdaaa60c4cc5b1e29545f2666e55",
+ "style": "IPY_MODEL_ec3f09ac595d4dadbd0cf34793d57087",
"tabbable": null,
"tooltip": null,
"value": "100%"
}
},
- "81f7fdaaa60c4cc5b1e29545f2666e55": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "a1d59c28e7064efc92b1e3caf26f9346": {
+ "7fb3eb018b9d446294207573ca64cda2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5030,7 +5039,23 @@
"width": null
}
},
- "a4073ffc99c94224857f26d6f4931b59": {
+ "86445ca79c764836a406520c67b4b945": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "953f4c82aabd472c9e8dfebdf70939d8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5083,33 +5108,30 @@
"width": null
}
},
- "a6c8cc603a414e80a6ba376c29f15b21": {
+ "9ffc7a8014b64edfad1dd643172601d1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_a4073ffc99c94224857f26d6f4931b59",
- "max": 200.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_4bbbb6bbcc9648459b5d261cb8ab6826",
+ "layout": "IPY_MODEL_021a50164b8c491ebb069bd57b11ce1a",
+ "placeholder": "",
+ "style": "IPY_MODEL_da74a2af2dfa4378a23a6009ae2f264c",
"tabbable": null,
"tooltip": null,
- "value": 200.0
+ "value": " 200/200 [00:00<00:00, 785.38it/s]"
}
},
- "ad306c43da45461c99570f45d29010bd": {
+ "a185cb088b4a4b50933699f586275482": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5162,7 +5184,31 @@
"width": null
}
},
- "ae6c7383e12f421aaeac59c5f8586ff1": {
+ "a5793cf283c046f188f735beef4577a5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7b9c39c715b849dbb886ceaeb96e5c35",
+ "IPY_MODEL_5664879b48124f5cac1e0a8c43742995",
+ "IPY_MODEL_9ffc7a8014b64edfad1dd643172601d1"
+ ],
+ "layout": "IPY_MODEL_313b234230ce4ce4850b3fa6a5e1b1ee",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "b93c4b8b97f34f0b93a2d334e5065e1b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5180,7 +5226,7 @@
"text_color": null
}
},
- "b54c1a0ec3ea4129adde7e57857e1a0e": {
+ "da74a2af2dfa4378a23a6009ae2f264c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5198,7 +5244,7 @@
"text_color": null
}
},
- "c362069cd48048d7ac53b007e87189cb": {
+ "da820a1ccd2b42d4a8c12ea0328d1169": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5251,126 +5297,80 @@
"width": null
}
},
- "daa6b8f2481040acb873f24cfbfedc9e": {
- "model_module": "@jupyter-widgets/base",
+ "da8ad4a548a8409389fab7ddc0e601bc": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "db046a62f4e34aa594499d33fa68145f": {
+ "dfac24cbd04d4a6a9c6a2f3d7e34c87e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_c362069cd48048d7ac53b007e87189cb",
- "placeholder": "",
- "style": "IPY_MODEL_ae6c7383e12f421aaeac59c5f8586ff1",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "dc9edc5341cb452cb27aada834ac562d": {
+ "e53b81d02870488ca1d70faf1534371f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_ad306c43da45461c99570f45d29010bd",
- "placeholder": "",
- "style": "IPY_MODEL_62793e1d0aa84395bb5ab3f9ff86c9b5",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_2cb88e5e7d0f4849b336950480e87a06",
+ "IPY_MODEL_733b0d114c6e48e6af9ced8acfb5bf3a",
+ "IPY_MODEL_6529bc3e5e35424f967dab0385030a5c"
+ ],
+ "layout": "IPY_MODEL_a185cb088b4a4b50933699f586275482",
"tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 716.02it/s]"
+ "tooltip": null
}
},
- "df91c80300534ab59fe080ab28475f7a": {
+ "ec3f09ac595d4dadbd0cf34793d57087": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_37eeb5a0817c455a8d0efe07a3d6bd44",
- "placeholder": "",
- "style": "IPY_MODEL_b54c1a0ec3ea4129adde7e57857e1a0e",
- "tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 771.41it/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index a14eec2f4..e932968f7 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
@@ -70,10 +70,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:00.542736Z",
- "iopub.status.busy": "2024-09-05T19:38:00.542299Z",
- "iopub.status.idle": "2024-09-05T19:38:01.779270Z",
- "shell.execute_reply": "2024-09-05T19:38:01.778627Z"
+ "iopub.execute_input": "2024-09-06T19:37:53.970574Z",
+ "iopub.status.busy": "2024-09-06T19:37:53.970388Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.134808Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.134157Z"
},
"nbsphinx": "hidden"
},
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -110,10 +110,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.782074Z",
- "iopub.status.busy": "2024-09-05T19:38:01.781768Z",
- "iopub.status.idle": "2024-09-05T19:38:01.784644Z",
- "shell.execute_reply": "2024-09-05T19:38:01.784156Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.137505Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.137230Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.140659Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.140221Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.786827Z",
- "iopub.status.busy": "2024-09-05T19:38:01.786518Z",
- "iopub.status.idle": "2024-09-05T19:38:01.798674Z",
- "shell.execute_reply": "2024-09-05T19:38:01.798101Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.142857Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.142554Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.154394Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.153913Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.800785Z",
- "iopub.status.busy": "2024-09-05T19:38:01.800454Z",
- "iopub.status.idle": "2024-09-05T19:38:06.755445Z",
- "shell.execute_reply": "2024-09-05T19:38:06.754955Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.156367Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.156193Z",
+ "iopub.status.idle": "2024-09-06T19:38:03.213180Z",
+ "shell.execute_reply": "2024-09-06T19:38:03.212490Z"
},
"id": "dhTHOg8Pyv5G"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index c5223bdbd..cec52a458 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
"id": "2a4efdde",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:09.258191Z",
- "iopub.status.busy": "2024-09-05T19:38:09.258029Z",
- "iopub.status.idle": "2024-09-05T19:38:10.419368Z",
- "shell.execute_reply": "2024-09-05T19:38:10.418813Z"
+ "iopub.execute_input": "2024-09-06T19:38:05.442254Z",
+ "iopub.status.busy": "2024-09-06T19:38:05.441754Z",
+ "iopub.status.idle": "2024-09-06T19:38:06.608058Z",
+ "shell.execute_reply": "2024-09-06T19:38:06.607439Z"
},
"nbsphinx": "hidden"
},
@@ -137,10 +137,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:10.422097Z",
- "iopub.status.busy": "2024-09-05T19:38:10.421633Z",
- "iopub.status.idle": "2024-09-05T19:38:10.425143Z",
- "shell.execute_reply": "2024-09-05T19:38:10.424685Z"
+ "iopub.execute_input": "2024-09-06T19:38:06.610846Z",
+ "iopub.status.busy": "2024-09-06T19:38:06.610375Z",
+ "iopub.status.idle": "2024-09-06T19:38:06.613802Z",
+ "shell.execute_reply": "2024-09-06T19:38:06.613322Z"
}
},
"outputs": [],
@@ -176,10 +176,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:10.427337Z",
- "iopub.status.busy": "2024-09-05T19:38:10.427003Z",
- "iopub.status.idle": "2024-09-05T19:38:13.858204Z",
- "shell.execute_reply": "2024-09-05T19:38:13.857536Z"
+ "iopub.execute_input": "2024-09-06T19:38:06.615798Z",
+ "iopub.status.busy": "2024-09-06T19:38:06.615518Z",
+ "iopub.status.idle": "2024-09-06T19:38:09.981363Z",
+ "shell.execute_reply": "2024-09-06T19:38:09.980664Z"
}
},
"outputs": [],
@@ -202,10 +202,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.861501Z",
- "iopub.status.busy": "2024-09-05T19:38:13.860747Z",
- "iopub.status.idle": "2024-09-05T19:38:13.908095Z",
- "shell.execute_reply": "2024-09-05T19:38:13.907257Z"
+ "iopub.execute_input": "2024-09-06T19:38:09.984620Z",
+ "iopub.status.busy": "2024-09-06T19:38:09.983724Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.027299Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.026694Z"
}
},
"outputs": [],
@@ -228,10 +228,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.911086Z",
- "iopub.status.busy": "2024-09-05T19:38:13.910659Z",
- "iopub.status.idle": "2024-09-05T19:38:13.954360Z",
- "shell.execute_reply": "2024-09-05T19:38:13.953711Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.030074Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.029673Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.069413Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.068633Z"
}
},
"outputs": [],
@@ -253,10 +253,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.957448Z",
- "iopub.status.busy": "2024-09-05T19:38:13.956977Z",
- "iopub.status.idle": "2024-09-05T19:38:13.960154Z",
- "shell.execute_reply": "2024-09-05T19:38:13.959671Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.072131Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.071875Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.075127Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.074582Z"
}
},
"outputs": [],
@@ -278,10 +278,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.962224Z",
- "iopub.status.busy": "2024-09-05T19:38:13.961891Z",
- "iopub.status.idle": "2024-09-05T19:38:13.964636Z",
- "shell.execute_reply": "2024-09-05T19:38:13.964076Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.077352Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.077011Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.079576Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.079132Z"
}
},
"outputs": [],
@@ -363,10 +363,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.966749Z",
- "iopub.status.busy": "2024-09-05T19:38:13.966438Z",
- "iopub.status.idle": "2024-09-05T19:38:13.992059Z",
- "shell.execute_reply": "2024-09-05T19:38:13.991451Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.081910Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.081719Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.109741Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.109183Z"
}
},
"outputs": [
@@ -380,7 +380,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5e9f1ab6ba2a4e2299cdd03dc9abc834",
+ "model_id": "10e11ec38b13425280381ff5281c4450",
"version_major": 2,
"version_minor": 0
},
@@ -394,7 +394,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c8b20d5d9286458984753646a34d3bf1",
+ "model_id": "7e2d5adb59434e2081db18c696100263",
"version_major": 2,
"version_minor": 0
},
@@ -452,10 +452,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:13.998276Z",
- "iopub.status.busy": "2024-09-05T19:38:13.997819Z",
- "iopub.status.idle": "2024-09-05T19:38:14.004565Z",
- "shell.execute_reply": "2024-09-05T19:38:14.004112Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.115104Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.114762Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.121297Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.120726Z"
},
"nbsphinx": "hidden"
},
@@ -486,10 +486,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.006609Z",
- "iopub.status.busy": "2024-09-05T19:38:14.006302Z",
- "iopub.status.idle": "2024-09-05T19:38:14.009865Z",
- "shell.execute_reply": "2024-09-05T19:38:14.009315Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.123497Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.123043Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.126503Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.126056Z"
},
"nbsphinx": "hidden"
},
@@ -512,10 +512,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.011915Z",
- "iopub.status.busy": "2024-09-05T19:38:14.011613Z",
- "iopub.status.idle": "2024-09-05T19:38:14.017930Z",
- "shell.execute_reply": "2024-09-05T19:38:14.017490Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.128505Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.128204Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.134549Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.134003Z"
}
},
"outputs": [],
@@ -565,10 +565,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.019956Z",
- "iopub.status.busy": "2024-09-05T19:38:14.019601Z",
- "iopub.status.idle": "2024-09-05T19:38:14.064732Z",
- "shell.execute_reply": "2024-09-05T19:38:14.063970Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.136656Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.136338Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.179181Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.178556Z"
}
},
"outputs": [],
@@ -585,10 +585,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.067600Z",
- "iopub.status.busy": "2024-09-05T19:38:14.067120Z",
- "iopub.status.idle": "2024-09-05T19:38:14.111610Z",
- "shell.execute_reply": "2024-09-05T19:38:14.110852Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.181945Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.181555Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.218200Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.217453Z"
},
"nbsphinx": "hidden"
},
@@ -667,10 +667,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.114596Z",
- "iopub.status.busy": "2024-09-05T19:38:14.114229Z",
- "iopub.status.idle": "2024-09-05T19:38:14.248717Z",
- "shell.execute_reply": "2024-09-05T19:38:14.248086Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.220958Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.220569Z",
+ "iopub.status.idle": "2024-09-06T19:38:10.349381Z",
+ "shell.execute_reply": "2024-09-06T19:38:10.348725Z"
}
},
"outputs": [
@@ -737,10 +737,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:14.251515Z",
- "iopub.status.busy": "2024-09-05T19:38:14.250905Z",
- "iopub.status.idle": "2024-09-05T19:38:17.282534Z",
- "shell.execute_reply": "2024-09-05T19:38:17.281859Z"
+ "iopub.execute_input": "2024-09-06T19:38:10.352202Z",
+ "iopub.status.busy": "2024-09-06T19:38:10.351437Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.390257Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.389586Z"
}
},
"outputs": [
@@ -826,10 +826,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:17.284960Z",
- "iopub.status.busy": "2024-09-05T19:38:17.284770Z",
- "iopub.status.idle": "2024-09-05T19:38:17.345859Z",
- "shell.execute_reply": "2024-09-05T19:38:17.345257Z"
+ "iopub.execute_input": "2024-09-06T19:38:13.392707Z",
+ "iopub.status.busy": "2024-09-06T19:38:13.392511Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.450827Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.450261Z"
}
},
"outputs": [
@@ -1285,10 +1285,10 @@
"id": "af3052ac",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:17.348231Z",
- "iopub.status.busy": "2024-09-05T19:38:17.347702Z",
- "iopub.status.idle": "2024-09-05T19:38:17.389681Z",
- "shell.execute_reply": "2024-09-05T19:38:17.389132Z"
+ "iopub.execute_input": "2024-09-06T19:38:13.453108Z",
+ "iopub.status.busy": "2024-09-06T19:38:13.452688Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.493414Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.492941Z"
}
},
"outputs": [
@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "5ac521f4",
+ "id": "368f0547",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1327,7 +1327,7 @@
},
{
"cell_type": "markdown",
- "id": "eebcc205",
+ "id": "dc65d1a9",
"metadata": {},
"source": [
"The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n",
@@ -1338,7 +1338,7 @@
},
{
"cell_type": "markdown",
- "id": "7a6303e8",
+ "id": "e31bf904",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by Datalab?\n",
@@ -1349,13 +1349,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "cc272ead",
+ "id": "0365a86d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:17.392137Z",
- "iopub.status.busy": "2024-09-05T19:38:17.391761Z",
- "iopub.status.idle": "2024-09-05T19:38:17.399373Z",
- "shell.execute_reply": "2024-09-05T19:38:17.398899Z"
+ "iopub.execute_input": "2024-09-06T19:38:13.495546Z",
+ "iopub.status.busy": "2024-09-06T19:38:13.495269Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.502952Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.502358Z"
}
},
"outputs": [],
@@ -1457,7 +1457,7 @@
},
{
"cell_type": "markdown",
- "id": "02a9d389",
+ "id": "1c944acb",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1472,13 +1472,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "c59e687d",
+ "id": "c713e4cb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:17.401606Z",
- "iopub.status.busy": "2024-09-05T19:38:17.401261Z",
- "iopub.status.idle": "2024-09-05T19:38:17.420424Z",
- "shell.execute_reply": "2024-09-05T19:38:17.419931Z"
+ "iopub.execute_input": "2024-09-06T19:38:13.504946Z",
+ "iopub.status.busy": "2024-09-06T19:38:13.504608Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.523104Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.522534Z"
}
},
"outputs": [
@@ -1521,13 +1521,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "01304147",
+ "id": "59184bfc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:17.422733Z",
- "iopub.status.busy": "2024-09-05T19:38:17.422383Z",
- "iopub.status.idle": "2024-09-05T19:38:17.425820Z",
- "shell.execute_reply": "2024-09-05T19:38:17.425263Z"
+ "iopub.execute_input": "2024-09-06T19:38:13.525068Z",
+ "iopub.status.busy": "2024-09-06T19:38:13.524743Z",
+ "iopub.status.idle": "2024-09-06T19:38:13.528122Z",
+ "shell.execute_reply": "2024-09-06T19:38:13.527552Z"
}
},
"outputs": [
@@ -1622,7 +1622,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "1d1157c21ea1455bb4eba32221cfd80e": {
+ "0a20db80d8ee4c558ba192d544a0f48a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1675,7 +1675,7 @@
"width": null
}
},
- "2bb52b5e34e0423ea8ebfa5fa2197991": {
+ "0e1e83d9b67447b1a76b3a2c668a8439": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1728,7 +1728,49 @@
"width": null
}
},
- "2ca77e6e6a914a409e7751bbd43cd433": {
+ "0ea8c549fffe4418b122c5d1daacdcf9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "10e11ec38b13425280381ff5281c4450": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_98a30ff8d08f40f5a59fa3959a1bfd7a",
+ "IPY_MODEL_9e361a1c4f7d49d28575030ed31684b4",
+ "IPY_MODEL_5d93e4fbfc844d82994983ca2900ac04"
+ ],
+ "layout": "IPY_MODEL_4c9d550f7159424fb6452da47b5cb51f",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "1989c2b222ef4983ba1d80fd96d80f9d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1781,33 +1823,23 @@
"width": null
}
},
- "2e05d5f980a44775b5be3782d3eb29a8": {
+ "278fa17d981b49c5a5afac9215c11437": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_519341eb823746dc9a9b5c92d467d3d1",
- "max": 50.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_ae0adc12747d4f129ee5677a88c40300",
- "tabbable": null,
- "tooltip": null,
- "value": 50.0
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "519341eb823746dc9a9b5c92d467d3d1": {
+ "4c9d550f7159424fb6452da47b5cb51f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1860,7 +1892,30 @@
"width": null
}
},
- "58f64c5fbd2b49039759f382c08a00f5": {
+ "5d93e4fbfc844d82994983ca2900ac04": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_0a20db80d8ee4c558ba192d544a0f48a",
+ "placeholder": "",
+ "style": "IPY_MODEL_85eb048e0015452a98d2585ecd3acea6",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 10000/? [00:00<00:00, 908211.86it/s]"
+ }
+ },
+ "62148dc5598f487787910111c96b2850": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1913,25 +1968,7 @@
"width": null
}
},
- "5a2ddf974a63406796b88696f6a9e322": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "5d6f15bd3523470e8f8a7049768802e2": {
+ "663eab6313474ab4b43a56ac15375332": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1946,15 +1983,68 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_fbc3a6811fa1407b9fb04a0f39c1e560",
+ "layout": "IPY_MODEL_0e1e83d9b67447b1a76b3a2c668a8439",
"placeholder": "",
- "style": "IPY_MODEL_5a2ddf974a63406796b88696f6a9e322",
+ "style": "IPY_MODEL_c8b6ee68eda04b79b9d7e8ba44708601",
"tabbable": null,
"tooltip": null,
- "value": " 10000/? [00:00<00:00, 1587308.51it/s]"
+ "value": "number of examples processed for checking labels: "
}
},
- "5e9f1ab6ba2a4e2299cdd03dc9abc834": {
+ "71bf8e249c8e494a8293a1368b4cde75": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "7e2d5adb59434e2081db18c696100263": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1969,16 +2059,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_89998dc91f404fc1ae7b7f0af753e147",
- "IPY_MODEL_2e05d5f980a44775b5be3782d3eb29a8",
- "IPY_MODEL_af016b9649554c3aa9157c6db78c9c93"
+ "IPY_MODEL_663eab6313474ab4b43a56ac15375332",
+ "IPY_MODEL_cb19cecdebc048139ef9e5b0697091e8",
+ "IPY_MODEL_b09565a1c786456187dacb880907b06f"
],
- "layout": "IPY_MODEL_e518b7c1bace4a43bdf2824bb3ce3af4",
+ "layout": "IPY_MODEL_62148dc5598f487787910111c96b2850",
"tabbable": null,
"tooltip": null
}
},
- "7a097385a6104b0280db3ea1bd2b3b67": {
+ "85eb048e0015452a98d2585ecd3acea6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1996,30 +2086,7 @@
"text_color": null
}
},
- "80f0f3a7d63640f88804ff3d3f2dc1e3": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_58f64c5fbd2b49039759f382c08a00f5",
- "placeholder": "",
- "style": "IPY_MODEL_7a097385a6104b0280db3ea1bd2b3b67",
- "tabbable": null,
- "tooltip": null,
- "value": "number of examples processed for checking labels: "
- }
- },
- "85c3014ae8d449b685c081ae4fa6d4f6": {
+ "8a29c209506d4b1f809b0eee618845ff": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2037,7 +2104,60 @@
"text_color": null
}
},
- "89998dc91f404fc1ae7b7f0af753e147": {
+ "8a7564586c364ea6ab0b8036f15d75de": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "98a30ff8d08f40f5a59fa3959a1bfd7a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2052,65 +2172,41 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_2bb52b5e34e0423ea8ebfa5fa2197991",
+ "layout": "IPY_MODEL_cbbdcb4211b04decb44b6be6dae0e74f",
"placeholder": "",
- "style": "IPY_MODEL_85c3014ae8d449b685c081ae4fa6d4f6",
+ "style": "IPY_MODEL_0ea8c549fffe4418b122c5d1daacdcf9",
"tabbable": null,
"tooltip": null,
"value": "number of examples processed for estimating thresholds: "
}
},
- "96c604f720804752bdd7e004c2d5b976": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "a626f902bf2b447aa9efceb999cecea2": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "ae0adc12747d4f129ee5677a88c40300": {
+ "9e361a1c4f7d49d28575030ed31684b4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_8a7564586c364ea6ab0b8036f15d75de",
+ "max": 50.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_278fa17d981b49c5a5afac9215c11437",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 50.0
}
},
- "af016b9649554c3aa9157c6db78c9c93": {
+ "b09565a1c786456187dacb880907b06f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2125,39 +2221,33 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1d1157c21ea1455bb4eba32221cfd80e",
+ "layout": "IPY_MODEL_71bf8e249c8e494a8293a1368b4cde75",
"placeholder": "",
- "style": "IPY_MODEL_96c604f720804752bdd7e004c2d5b976",
+ "style": "IPY_MODEL_8a29c209506d4b1f809b0eee618845ff",
"tabbable": null,
"tooltip": null,
- "value": " 10000/? [00:00<00:00, 891911.71it/s]"
+ "value": " 10000/? [00:00<00:00, 1197722.38it/s]"
}
},
- "c8b20d5d9286458984753646a34d3bf1": {
+ "c8b6ee68eda04b79b9d7e8ba44708601": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_80f0f3a7d63640f88804ff3d3f2dc1e3",
- "IPY_MODEL_d492675a8a4b4225ab936898d59675f6",
- "IPY_MODEL_5d6f15bd3523470e8f8a7049768802e2"
- ],
- "layout": "IPY_MODEL_2ca77e6e6a914a409e7751bbd43cd433",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "d492675a8a4b4225ab936898d59675f6": {
+ "cb19cecdebc048139ef9e5b0697091e8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2173,17 +2263,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_de835bb464834a46b37dd59d97b91150",
+ "layout": "IPY_MODEL_1989c2b222ef4983ba1d80fd96d80f9d",
"max": 50.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_a626f902bf2b447aa9efceb999cecea2",
+ "style": "IPY_MODEL_cd96842c5f86404599e6a57c4439dccf",
"tabbable": null,
"tooltip": null,
"value": 50.0
}
},
- "de835bb464834a46b37dd59d97b91150": {
+ "cbbdcb4211b04decb44b6be6dae0e74f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2236,110 +2326,20 @@
"width": null
}
},
- "e518b7c1bace4a43bdf2824bb3ce3af4": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
- }
- },
- "fbc3a6811fa1407b9fb04a0f39c1e560": {
- "model_module": "@jupyter-widgets/base",
+ "cd96842c5f86404599e6a57c4439dccf": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
index 355de44f6..0126898fa 100644
--- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
@@ -60,10 +60,10 @@
"id": "2d638465",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:20.938341Z",
- "iopub.status.busy": "2024-09-05T19:38:20.938181Z",
- "iopub.status.idle": "2024-09-05T19:38:22.134719Z",
- "shell.execute_reply": "2024-09-05T19:38:22.134142Z"
+ "iopub.execute_input": "2024-09-06T19:38:17.966921Z",
+ "iopub.status.busy": "2024-09-06T19:38:17.966743Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.153643Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.153020Z"
},
"nbsphinx": "hidden"
},
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -99,10 +99,10 @@
"id": "b0bbf715-47c6-44ea-b15e-89800e62ee04",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.137472Z",
- "iopub.status.busy": "2024-09-05T19:38:22.137010Z",
- "iopub.status.idle": "2024-09-05T19:38:22.141005Z",
- "shell.execute_reply": "2024-09-05T19:38:22.140442Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.156468Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.155927Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.159820Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.159280Z"
}
},
"outputs": [],
@@ -140,10 +140,10 @@
"id": "c58f8015-d051-411c-9e03-5659cf3ad956",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.143101Z",
- "iopub.status.busy": "2024-09-05T19:38:22.142800Z",
- "iopub.status.idle": "2024-09-05T19:38:22.317437Z",
- "shell.execute_reply": "2024-09-05T19:38:22.316874Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.161985Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.161628Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.848074Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.847540Z"
}
},
"outputs": [
@@ -273,10 +273,10 @@
"id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.319775Z",
- "iopub.status.busy": "2024-09-05T19:38:22.319343Z",
- "iopub.status.idle": "2024-09-05T19:38:22.325394Z",
- "shell.execute_reply": "2024-09-05T19:38:22.324864Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.850305Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.849961Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.855710Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.855268Z"
}
},
"outputs": [],
@@ -312,10 +312,10 @@
"id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.327562Z",
- "iopub.status.busy": "2024-09-05T19:38:22.327255Z",
- "iopub.status.idle": "2024-09-05T19:38:22.334139Z",
- "shell.execute_reply": "2024-09-05T19:38:22.333587Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.857664Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.857483Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.864510Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.863928Z"
}
},
"outputs": [
@@ -418,10 +418,10 @@
"id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.336134Z",
- "iopub.status.busy": "2024-09-05T19:38:22.335809Z",
- "iopub.status.idle": "2024-09-05T19:38:22.340537Z",
- "shell.execute_reply": "2024-09-05T19:38:22.339975Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.866738Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.866419Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.871181Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.870718Z"
}
},
"outputs": [],
@@ -449,10 +449,10 @@
"id": "46275634-da56-4e58-9061-8108be2b585d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.342582Z",
- "iopub.status.busy": "2024-09-05T19:38:22.342279Z",
- "iopub.status.idle": "2024-09-05T19:38:22.348079Z",
- "shell.execute_reply": "2024-09-05T19:38:22.347521Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.873167Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.872989Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.879315Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.878873Z"
}
},
"outputs": [],
@@ -488,10 +488,10 @@
"id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.350256Z",
- "iopub.status.busy": "2024-09-05T19:38:22.349941Z",
- "iopub.status.idle": "2024-09-05T19:38:22.354060Z",
- "shell.execute_reply": "2024-09-05T19:38:22.353506Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.881299Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.881109Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.885448Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.884866Z"
}
},
"outputs": [],
@@ -506,10 +506,10 @@
"id": "7ac47c3d-9e87-45b7-9064-bfa45578872e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.356038Z",
- "iopub.status.busy": "2024-09-05T19:38:22.355647Z",
- "iopub.status.idle": "2024-09-05T19:38:22.423064Z",
- "shell.execute_reply": "2024-09-05T19:38:22.422472Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.887541Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.887226Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.952333Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.951659Z"
}
},
"outputs": [
@@ -609,10 +609,10 @@
"id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.426072Z",
- "iopub.status.busy": "2024-09-05T19:38:22.425579Z",
- "iopub.status.idle": "2024-09-05T19:38:22.436851Z",
- "shell.execute_reply": "2024-09-05T19:38:22.436355Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.955055Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.954571Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.965639Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.965092Z"
}
},
"outputs": [
@@ -724,10 +724,10 @@
"id": "b68e0418-86cf-431f-9107-2dd0a310ca42",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.439378Z",
- "iopub.status.busy": "2024-09-05T19:38:22.438783Z",
- "iopub.status.idle": "2024-09-05T19:38:22.460540Z",
- "shell.execute_reply": "2024-09-05T19:38:22.459904Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.968612Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.968081Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.989523Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.988990Z"
}
},
"outputs": [
@@ -931,10 +931,10 @@
"id": "0e9bd131-429f-48af-b4fc-ed8b907950b9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.463026Z",
- "iopub.status.busy": "2024-09-05T19:38:22.462648Z",
- "iopub.status.idle": "2024-09-05T19:38:22.466619Z",
- "shell.execute_reply": "2024-09-05T19:38:22.466139Z"
+ "iopub.execute_input": "2024-09-06T19:38:19.992484Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.991953Z",
+ "iopub.status.idle": "2024-09-06T19:38:19.996496Z",
+ "shell.execute_reply": "2024-09-06T19:38:19.995963Z"
}
},
"outputs": [
@@ -968,10 +968,10 @@
"id": "e72320ec-7792-4347-b2fb-630f2519127c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.468979Z",
- "iopub.status.busy": "2024-09-05T19:38:22.468612Z",
- "iopub.status.idle": "2024-09-05T19:38:22.472717Z",
- "shell.execute_reply": "2024-09-05T19:38:22.472228Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.000004Z",
+ "iopub.status.busy": "2024-09-06T19:38:19.999084Z",
+ "iopub.status.idle": "2024-09-06T19:38:20.005225Z",
+ "shell.execute_reply": "2024-09-06T19:38:20.004698Z"
}
},
"outputs": [
@@ -1005,10 +1005,10 @@
"id": "8520ba4a-3ad6-408a-b377-3f47c32d745a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.476188Z",
- "iopub.status.busy": "2024-09-05T19:38:22.475241Z",
- "iopub.status.idle": "2024-09-05T19:38:22.486948Z",
- "shell.execute_reply": "2024-09-05T19:38:22.486537Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.008748Z",
+ "iopub.status.busy": "2024-09-06T19:38:20.007824Z",
+ "iopub.status.idle": "2024-09-06T19:38:20.018446Z",
+ "shell.execute_reply": "2024-09-06T19:38:20.018010Z"
}
},
"outputs": [
@@ -1205,10 +1205,10 @@
"id": "3c002665-c48b-4f04-91f7-ad112a49efc7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.489195Z",
- "iopub.status.busy": "2024-09-05T19:38:22.488861Z",
- "iopub.status.idle": "2024-09-05T19:38:22.493151Z",
- "shell.execute_reply": "2024-09-05T19:38:22.492710Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.020571Z",
+ "iopub.status.busy": "2024-09-06T19:38:20.020204Z",
+ "iopub.status.idle": "2024-09-06T19:38:20.024666Z",
+ "shell.execute_reply": "2024-09-06T19:38:20.024096Z"
}
},
"outputs": [],
@@ -1234,10 +1234,10 @@
"id": "36319f39-f563-4f63-913f-821373180350",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.495336Z",
- "iopub.status.busy": "2024-09-05T19:38:22.495008Z",
- "iopub.status.idle": "2024-09-05T19:38:22.606998Z",
- "shell.execute_reply": "2024-09-05T19:38:22.606408Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.026677Z",
+ "iopub.status.busy": "2024-09-06T19:38:20.026505Z",
+ "iopub.status.idle": "2024-09-06T19:38:20.138981Z",
+ "shell.execute_reply": "2024-09-06T19:38:20.138473Z"
}
},
"outputs": [
@@ -1711,10 +1711,10 @@
"id": "044c0eb1-299a-4851-b1bf-268d5bce56c1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.609473Z",
- "iopub.status.busy": "2024-09-05T19:38:22.609016Z",
- "iopub.status.idle": "2024-09-05T19:38:22.617971Z",
- "shell.execute_reply": "2024-09-05T19:38:22.617376Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.141251Z",
+ "iopub.status.busy": "2024-09-06T19:38:20.140804Z",
+ "iopub.status.idle": "2024-09-06T19:38:20.147269Z",
+ "shell.execute_reply": "2024-09-06T19:38:20.146678Z"
}
},
"outputs": [],
@@ -1738,10 +1738,10 @@
"id": "c43df278-abfe-40e5-9d48-2df3efea9379",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:22.620315Z",
- "iopub.status.busy": "2024-09-05T19:38:22.620121Z",
- "iopub.status.idle": "2024-09-05T19:38:24.659647Z",
- "shell.execute_reply": "2024-09-05T19:38:24.659017Z"
+ "iopub.execute_input": "2024-09-06T19:38:20.149710Z",
+ "iopub.status.busy": "2024-09-06T19:38:20.149204Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.175679Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.175042Z"
}
},
"outputs": [
@@ -1953,10 +1953,10 @@
"id": "77c7f776-54b3-45b5-9207-715d6d2e90c0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:24.662641Z",
- "iopub.status.busy": "2024-09-05T19:38:24.662119Z",
- "iopub.status.idle": "2024-09-05T19:38:24.675048Z",
- "shell.execute_reply": "2024-09-05T19:38:24.674550Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.179907Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.178817Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.193599Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.193081Z"
}
},
"outputs": [
@@ -2073,10 +2073,10 @@
"id": "7e218d04-0729-4f42-b264-51c73601ebe6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:24.677529Z",
- "iopub.status.busy": "2024-09-05T19:38:24.677151Z",
- "iopub.status.idle": "2024-09-05T19:38:24.680012Z",
- "shell.execute_reply": "2024-09-05T19:38:24.679501Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.197201Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.196240Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.200280Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.199770Z"
}
},
"outputs": [],
@@ -2090,10 +2090,10 @@
"id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:24.682370Z",
- "iopub.status.busy": "2024-09-05T19:38:24.681990Z",
- "iopub.status.idle": "2024-09-05T19:38:24.686455Z",
- "shell.execute_reply": "2024-09-05T19:38:24.685959Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.203753Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.202840Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.208375Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.207870Z"
}
},
"outputs": [],
@@ -2117,10 +2117,10 @@
"id": "5ce2d89f-e832-448d-bfac-9941da15c895",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:24.688843Z",
- "iopub.status.busy": "2024-09-05T19:38:24.688467Z",
- "iopub.status.idle": "2024-09-05T19:38:24.725249Z",
- "shell.execute_reply": "2024-09-05T19:38:24.724758Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.211876Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.210955Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.243013Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.242528Z"
}
},
"outputs": [
@@ -2160,10 +2160,10 @@
"id": "9f437756-112e-4531-84fc-6ceadd0c9ef5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:24.727654Z",
- "iopub.status.busy": "2024-09-05T19:38:24.727295Z",
- "iopub.status.idle": "2024-09-05T19:38:25.269267Z",
- "shell.execute_reply": "2024-09-05T19:38:25.268697Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.246118Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.245468Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.754137Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.753573Z"
}
},
"outputs": [],
@@ -2194,10 +2194,10 @@
"id": "707625f6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.272182Z",
- "iopub.status.busy": "2024-09-05T19:38:25.271755Z",
- "iopub.status.idle": "2024-09-05T19:38:25.404404Z",
- "shell.execute_reply": "2024-09-05T19:38:25.403688Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.757125Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.756730Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.893326Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.892578Z"
}
},
"outputs": [
@@ -2408,10 +2408,10 @@
"id": "25afe46c-a521-483c-b168-728c76d970dc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.408052Z",
- "iopub.status.busy": "2024-09-05T19:38:25.407073Z",
- "iopub.status.idle": "2024-09-05T19:38:25.415814Z",
- "shell.execute_reply": "2024-09-05T19:38:25.415311Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.896382Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.896143Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.903618Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.903032Z"
}
},
"outputs": [
@@ -2441,10 +2441,10 @@
"id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.419323Z",
- "iopub.status.busy": "2024-09-05T19:38:25.418402Z",
- "iopub.status.idle": "2024-09-05T19:38:25.426295Z",
- "shell.execute_reply": "2024-09-05T19:38:25.425789Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.906322Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.906102Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.914842Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.914319Z"
}
},
"outputs": [
@@ -2477,10 +2477,10 @@
"id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.429748Z",
- "iopub.status.busy": "2024-09-05T19:38:25.428807Z",
- "iopub.status.idle": "2024-09-05T19:38:25.436120Z",
- "shell.execute_reply": "2024-09-05T19:38:25.435593Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.917418Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.917212Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.924586Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.924068Z"
}
},
"outputs": [
@@ -2513,10 +2513,10 @@
"id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.439616Z",
- "iopub.status.busy": "2024-09-05T19:38:25.438651Z",
- "iopub.status.idle": "2024-09-05T19:38:25.444907Z",
- "shell.execute_reply": "2024-09-05T19:38:25.444411Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.927978Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.927001Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.932989Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.932417Z"
}
},
"outputs": [
@@ -2542,10 +2542,10 @@
"id": "08080458-0cd7-447d-80e6-384cb8d31eaf",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.447298Z",
- "iopub.status.busy": "2024-09-05T19:38:25.447124Z",
- "iopub.status.idle": "2024-09-05T19:38:25.452420Z",
- "shell.execute_reply": "2024-09-05T19:38:25.451830Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.935455Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.935286Z",
+ "iopub.status.idle": "2024-09-06T19:38:22.940366Z",
+ "shell.execute_reply": "2024-09-06T19:38:22.939926Z"
}
},
"outputs": [],
@@ -2569,10 +2569,10 @@
"id": "009bb215-4d26-47da-a230-d0ccf4122629",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.454421Z",
- "iopub.status.busy": "2024-09-05T19:38:25.454250Z",
- "iopub.status.idle": "2024-09-05T19:38:25.536458Z",
- "shell.execute_reply": "2024-09-05T19:38:25.535873Z"
+ "iopub.execute_input": "2024-09-06T19:38:22.942577Z",
+ "iopub.status.busy": "2024-09-06T19:38:22.942242Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.018404Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.017754Z"
}
},
"outputs": [
@@ -3052,10 +3052,10 @@
"id": "dcaeda51-9b24-4c04-889d-7e63563594fc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.538788Z",
- "iopub.status.busy": "2024-09-05T19:38:25.538612Z",
- "iopub.status.idle": "2024-09-05T19:38:25.547223Z",
- "shell.execute_reply": "2024-09-05T19:38:25.546738Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.021060Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.020492Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.034062Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.033451Z"
}
},
"outputs": [
@@ -3111,10 +3111,10 @@
"id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.550442Z",
- "iopub.status.busy": "2024-09-05T19:38:25.549562Z",
- "iopub.status.idle": "2024-09-05T19:38:25.553319Z",
- "shell.execute_reply": "2024-09-05T19:38:25.552755Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.036553Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.036240Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.039008Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.038465Z"
}
},
"outputs": [],
@@ -3150,10 +3150,10 @@
"id": "941ab2a6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.555448Z",
- "iopub.status.busy": "2024-09-05T19:38:25.555270Z",
- "iopub.status.idle": "2024-09-05T19:38:25.565627Z",
- "shell.execute_reply": "2024-09-05T19:38:25.565195Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.041147Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.040695Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.050646Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.050044Z"
}
},
"outputs": [],
@@ -3261,10 +3261,10 @@
"id": "50666fb9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.567569Z",
- "iopub.status.busy": "2024-09-05T19:38:25.567400Z",
- "iopub.status.idle": "2024-09-05T19:38:25.573998Z",
- "shell.execute_reply": "2024-09-05T19:38:25.573414Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.053067Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.052637Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.059254Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.058781Z"
},
"nbsphinx": "hidden"
},
@@ -3346,10 +3346,10 @@
"id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.576080Z",
- "iopub.status.busy": "2024-09-05T19:38:25.575748Z",
- "iopub.status.idle": "2024-09-05T19:38:25.579065Z",
- "shell.execute_reply": "2024-09-05T19:38:25.578516Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.061114Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.060934Z",
+ "iopub.status.idle": "2024-09-06T19:38:23.064369Z",
+ "shell.execute_reply": "2024-09-06T19:38:23.063906Z"
}
},
"outputs": [],
@@ -3373,10 +3373,10 @@
"id": "ce1c0ada-88b1-4654-b43f-3c0b59002979",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:25.581087Z",
- "iopub.status.busy": "2024-09-05T19:38:25.580780Z",
- "iopub.status.idle": "2024-09-05T19:38:29.675098Z",
- "shell.execute_reply": "2024-09-05T19:38:29.674482Z"
+ "iopub.execute_input": "2024-09-06T19:38:23.066492Z",
+ "iopub.status.busy": "2024-09-06T19:38:23.066088Z",
+ "iopub.status.idle": "2024-09-06T19:38:27.075896Z",
+ "shell.execute_reply": "2024-09-06T19:38:27.075361Z"
}
},
"outputs": [
@@ -3419,10 +3419,10 @@
"id": "3f572acf-31c3-4874-9100-451796e35b06",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:29.678980Z",
- "iopub.status.busy": "2024-09-05T19:38:29.678139Z",
- "iopub.status.idle": "2024-09-05T19:38:29.682800Z",
- "shell.execute_reply": "2024-09-05T19:38:29.682367Z"
+ "iopub.execute_input": "2024-09-06T19:38:27.079119Z",
+ "iopub.status.busy": "2024-09-06T19:38:27.078209Z",
+ "iopub.status.idle": "2024-09-06T19:38:27.082469Z",
+ "shell.execute_reply": "2024-09-06T19:38:27.082025Z"
}
},
"outputs": [
@@ -3460,10 +3460,10 @@
"id": "6a025a88",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:29.685051Z",
- "iopub.status.busy": "2024-09-05T19:38:29.684864Z",
- "iopub.status.idle": "2024-09-05T19:38:29.687798Z",
- "shell.execute_reply": "2024-09-05T19:38:29.687349Z"
+ "iopub.execute_input": "2024-09-06T19:38:27.084613Z",
+ "iopub.status.busy": "2024-09-06T19:38:27.084277Z",
+ "iopub.status.idle": "2024-09-06T19:38:27.087400Z",
+ "shell.execute_reply": "2024-09-06T19:38:27.086984Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index ff39a0ce9..d4d06d3f8 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:33.024311Z",
- "iopub.status.busy": "2024-09-05T19:38:33.024154Z",
- "iopub.status.idle": "2024-09-05T19:38:34.264701Z",
- "shell.execute_reply": "2024-09-05T19:38:34.264082Z"
+ "iopub.execute_input": "2024-09-06T19:38:29.945055Z",
+ "iopub.status.busy": "2024-09-06T19:38:29.944859Z",
+ "iopub.status.idle": "2024-09-06T19:38:31.152677Z",
+ "shell.execute_reply": "2024-09-06T19:38:31.152154Z"
},
"nbsphinx": "hidden"
},
@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -95,10 +95,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:34.267586Z",
- "iopub.status.busy": "2024-09-05T19:38:34.267100Z",
- "iopub.status.idle": "2024-09-05T19:38:34.450638Z",
- "shell.execute_reply": "2024-09-05T19:38:34.449996Z"
+ "iopub.execute_input": "2024-09-06T19:38:31.155349Z",
+ "iopub.status.busy": "2024-09-06T19:38:31.154914Z",
+ "iopub.status.idle": "2024-09-06T19:38:31.333867Z",
+ "shell.execute_reply": "2024-09-06T19:38:31.333299Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:34.453452Z",
- "iopub.status.busy": "2024-09-05T19:38:34.453074Z",
- "iopub.status.idle": "2024-09-05T19:38:34.464955Z",
- "shell.execute_reply": "2024-09-05T19:38:34.464480Z"
+ "iopub.execute_input": "2024-09-06T19:38:31.336296Z",
+ "iopub.status.busy": "2024-09-06T19:38:31.336106Z",
+ "iopub.status.idle": "2024-09-06T19:38:31.347492Z",
+ "shell.execute_reply": "2024-09-06T19:38:31.347045Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:34.467204Z",
- "iopub.status.busy": "2024-09-05T19:38:34.466843Z",
- "iopub.status.idle": "2024-09-05T19:38:34.705791Z",
- "shell.execute_reply": "2024-09-05T19:38:34.705183Z"
+ "iopub.execute_input": "2024-09-06T19:38:31.349587Z",
+ "iopub.status.busy": "2024-09-06T19:38:31.349239Z",
+ "iopub.status.idle": "2024-09-06T19:38:31.559000Z",
+ "shell.execute_reply": "2024-09-06T19:38:31.558435Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:34.708181Z",
- "iopub.status.busy": "2024-09-05T19:38:34.707976Z",
- "iopub.status.idle": "2024-09-05T19:38:34.734780Z",
- "shell.execute_reply": "2024-09-05T19:38:34.734318Z"
+ "iopub.execute_input": "2024-09-06T19:38:31.561389Z",
+ "iopub.status.busy": "2024-09-06T19:38:31.561027Z",
+ "iopub.status.idle": "2024-09-06T19:38:31.587035Z",
+ "shell.execute_reply": "2024-09-06T19:38:31.586568Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:34.736860Z",
- "iopub.status.busy": "2024-09-05T19:38:34.736672Z",
- "iopub.status.idle": "2024-09-05T19:38:36.884795Z",
- "shell.execute_reply": "2024-09-05T19:38:36.884146Z"
+ "iopub.execute_input": "2024-09-06T19:38:31.589259Z",
+ "iopub.status.busy": "2024-09-06T19:38:31.588898Z",
+ "iopub.status.idle": "2024-09-06T19:38:33.659672Z",
+ "shell.execute_reply": "2024-09-06T19:38:33.658986Z"
}
},
"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:36.887499Z",
- "iopub.status.busy": "2024-09-05T19:38:36.886853Z",
- "iopub.status.idle": "2024-09-05T19:38:36.905206Z",
- "shell.execute_reply": "2024-09-05T19:38:36.904650Z"
+ "iopub.execute_input": "2024-09-06T19:38:33.662234Z",
+ "iopub.status.busy": "2024-09-06T19:38:33.661770Z",
+ "iopub.status.idle": "2024-09-06T19:38:33.679880Z",
+ "shell.execute_reply": "2024-09-06T19:38:33.679304Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:36.907433Z",
- "iopub.status.busy": "2024-09-05T19:38:36.907012Z",
- "iopub.status.idle": "2024-09-05T19:38:38.506025Z",
- "shell.execute_reply": "2024-09-05T19:38:38.505370Z"
+ "iopub.execute_input": "2024-09-06T19:38:33.682125Z",
+ "iopub.status.busy": "2024-09-06T19:38:33.681797Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.246559Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.245952Z"
},
"id": "AaHC5MRKjruT"
},
@@ -729,10 +729,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.508964Z",
- "iopub.status.busy": "2024-09-05T19:38:38.508264Z",
- "iopub.status.idle": "2024-09-05T19:38:38.522533Z",
- "shell.execute_reply": "2024-09-05T19:38:38.522045Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.249384Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.248692Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.262909Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.262437Z"
},
"id": "Wy27rvyhjruU"
},
@@ -781,10 +781,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.524864Z",
- "iopub.status.busy": "2024-09-05T19:38:38.524513Z",
- "iopub.status.idle": "2024-09-05T19:38:38.607517Z",
- "shell.execute_reply": "2024-09-05T19:38:38.606813Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.265091Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.264657Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.347361Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.346752Z"
},
"id": "Db8YHnyVjruU"
},
@@ -891,10 +891,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.610151Z",
- "iopub.status.busy": "2024-09-05T19:38:38.609680Z",
- "iopub.status.idle": "2024-09-05T19:38:38.823788Z",
- "shell.execute_reply": "2024-09-05T19:38:38.823190Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.349859Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.349553Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.568160Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.567596Z"
},
"id": "iJqAHuS2jruV"
},
@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.826041Z",
- "iopub.status.busy": "2024-09-05T19:38:38.825845Z",
- "iopub.status.idle": "2024-09-05T19:38:38.843439Z",
- "shell.execute_reply": "2024-09-05T19:38:38.842891Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.570518Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.570156Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.587030Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.586565Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.845804Z",
- "iopub.status.busy": "2024-09-05T19:38:38.845404Z",
- "iopub.status.idle": "2024-09-05T19:38:38.855289Z",
- "shell.execute_reply": "2024-09-05T19:38:38.854744Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.589095Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.588739Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.598220Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.597755Z"
},
"id": "0lonvOYvjruV"
},
@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.857352Z",
- "iopub.status.busy": "2024-09-05T19:38:38.857028Z",
- "iopub.status.idle": "2024-09-05T19:38:38.948792Z",
- "shell.execute_reply": "2024-09-05T19:38:38.948130Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.600262Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.599918Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.692538Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.691918Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:38.951301Z",
- "iopub.status.busy": "2024-09-05T19:38:38.950892Z",
- "iopub.status.idle": "2024-09-05T19:38:39.093684Z",
- "shell.execute_reply": "2024-09-05T19:38:39.093010Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.694934Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.694629Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.833017Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.832312Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1697,10 +1697,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.096217Z",
- "iopub.status.busy": "2024-09-05T19:38:39.095689Z",
- "iopub.status.idle": "2024-09-05T19:38:39.099737Z",
- "shell.execute_reply": "2024-09-05T19:38:39.099183Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.835595Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.835206Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.839051Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.838497Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1738,10 +1738,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.101881Z",
- "iopub.status.busy": "2024-09-05T19:38:39.101611Z",
- "iopub.status.idle": "2024-09-05T19:38:39.105429Z",
- "shell.execute_reply": "2024-09-05T19:38:39.104857Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.841055Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.840887Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.844523Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.843987Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1796,10 +1796,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.107547Z",
- "iopub.status.busy": "2024-09-05T19:38:39.107212Z",
- "iopub.status.idle": "2024-09-05T19:38:39.143655Z",
- "shell.execute_reply": "2024-09-05T19:38:39.143146Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.846624Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.846289Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.883516Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.883025Z"
},
"id": "ZpipUliyjruW"
},
@@ -1850,10 +1850,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.145813Z",
- "iopub.status.busy": "2024-09-05T19:38:39.145467Z",
- "iopub.status.idle": "2024-09-05T19:38:39.185783Z",
- "shell.execute_reply": "2024-09-05T19:38:39.185245Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.885707Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.885360Z",
+ "iopub.status.idle": "2024-09-06T19:38:35.926415Z",
+ "shell.execute_reply": "2024-09-06T19:38:35.925951Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1922,10 +1922,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.187839Z",
- "iopub.status.busy": "2024-09-05T19:38:39.187520Z",
- "iopub.status.idle": "2024-09-05T19:38:39.289564Z",
- "shell.execute_reply": "2024-09-05T19:38:39.288933Z"
+ "iopub.execute_input": "2024-09-06T19:38:35.928488Z",
+ "iopub.status.busy": "2024-09-06T19:38:35.928146Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.031351Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.030698Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1957,10 +1957,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.292121Z",
- "iopub.status.busy": "2024-09-05T19:38:39.291892Z",
- "iopub.status.idle": "2024-09-05T19:38:39.400843Z",
- "shell.execute_reply": "2024-09-05T19:38:39.400129Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.034301Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.033912Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.132017Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.131369Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.403530Z",
- "iopub.status.busy": "2024-09-05T19:38:39.403100Z",
- "iopub.status.idle": "2024-09-05T19:38:39.617987Z",
- "shell.execute_reply": "2024-09-05T19:38:39.617380Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.134718Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.134254Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.372737Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.372155Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.620312Z",
- "iopub.status.busy": "2024-09-05T19:38:39.619935Z",
- "iopub.status.idle": "2024-09-05T19:38:39.847199Z",
- "shell.execute_reply": "2024-09-05T19:38:39.846563Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.374987Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.374694Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.587886Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.587278Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.849646Z",
- "iopub.status.busy": "2024-09-05T19:38:39.849312Z",
- "iopub.status.idle": "2024-09-05T19:38:39.855523Z",
- "shell.execute_reply": "2024-09-05T19:38:39.855075Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.590343Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.589956Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.595878Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.595334Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:39.857741Z",
- "iopub.status.busy": "2024-09-05T19:38:39.857296Z",
- "iopub.status.idle": "2024-09-05T19:38:40.076699Z",
- "shell.execute_reply": "2024-09-05T19:38:40.076059Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.598057Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.597740Z",
+ "iopub.status.idle": "2024-09-06T19:38:36.811700Z",
+ "shell.execute_reply": "2024-09-06T19:38:36.811079Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:40.079111Z",
- "iopub.status.busy": "2024-09-05T19:38:40.078742Z",
- "iopub.status.idle": "2024-09-05T19:38:41.162000Z",
- "shell.execute_reply": "2024-09-05T19:38:41.161484Z"
+ "iopub.execute_input": "2024-09-06T19:38:36.813989Z",
+ "iopub.status.busy": "2024-09-06T19:38:36.813680Z",
+ "iopub.status.idle": "2024-09-06T19:38:37.873549Z",
+ "shell.execute_reply": "2024-09-06T19:38:37.872901Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 872e74175..0b05cce8c 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:44.675764Z",
- "iopub.status.busy": "2024-09-05T19:38:44.675335Z",
- "iopub.status.idle": "2024-09-05T19:38:45.838314Z",
- "shell.execute_reply": "2024-09-05T19:38:45.837761Z"
+ "iopub.execute_input": "2024-09-06T19:38:41.455901Z",
+ "iopub.status.busy": "2024-09-06T19:38:41.455732Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.611358Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.610733Z"
},
"nbsphinx": "hidden"
},
@@ -101,7 +101,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.840955Z",
- "iopub.status.busy": "2024-09-05T19:38:45.840492Z",
- "iopub.status.idle": "2024-09-05T19:38:45.843468Z",
- "shell.execute_reply": "2024-09-05T19:38:45.843021Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.614152Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.613703Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.617474Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.616914Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.845741Z",
- "iopub.status.busy": "2024-09-05T19:38:45.845402Z",
- "iopub.status.idle": "2024-09-05T19:38:45.853153Z",
- "shell.execute_reply": "2024-09-05T19:38:45.852719Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.619686Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.619396Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.627253Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.626804Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.855196Z",
- "iopub.status.busy": "2024-09-05T19:38:45.854862Z",
- "iopub.status.idle": "2024-09-05T19:38:45.900555Z",
- "shell.execute_reply": "2024-09-05T19:38:45.900040Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.629251Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.628912Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.675739Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.675250Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.902601Z",
- "iopub.status.busy": "2024-09-05T19:38:45.902427Z",
- "iopub.status.idle": "2024-09-05T19:38:45.919866Z",
- "shell.execute_reply": "2024-09-05T19:38:45.919444Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.677746Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.677566Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.695187Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.694600Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.922151Z",
- "iopub.status.busy": "2024-09-05T19:38:45.921714Z",
- "iopub.status.idle": "2024-09-05T19:38:45.925609Z",
- "shell.execute_reply": "2024-09-05T19:38:45.925082Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.697240Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.696927Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.700805Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.700357Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.927793Z",
- "iopub.status.busy": "2024-09-05T19:38:45.927382Z",
- "iopub.status.idle": "2024-09-05T19:38:45.943665Z",
- "shell.execute_reply": "2024-09-05T19:38:45.943100Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.703011Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.702619Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.719152Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.718696Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.945827Z",
- "iopub.status.busy": "2024-09-05T19:38:45.945552Z",
- "iopub.status.idle": "2024-09-05T19:38:45.971204Z",
- "shell.execute_reply": "2024-09-05T19:38:45.970648Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.721153Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.720797Z",
+ "iopub.status.idle": "2024-09-06T19:38:42.746197Z",
+ "shell.execute_reply": "2024-09-06T19:38:42.745739Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:45.973422Z",
- "iopub.status.busy": "2024-09-05T19:38:45.973114Z",
- "iopub.status.idle": "2024-09-05T19:38:47.957844Z",
- "shell.execute_reply": "2024-09-05T19:38:47.957281Z"
+ "iopub.execute_input": "2024-09-06T19:38:42.748111Z",
+ "iopub.status.busy": "2024-09-06T19:38:42.747776Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.708904Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.708307Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:47.960323Z",
- "iopub.status.busy": "2024-09-05T19:38:47.960036Z",
- "iopub.status.idle": "2024-09-05T19:38:47.966782Z",
- "shell.execute_reply": "2024-09-05T19:38:47.966212Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.711480Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.710993Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.717750Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.717182Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:47.969001Z",
- "iopub.status.busy": "2024-09-05T19:38:47.968670Z",
- "iopub.status.idle": "2024-09-05T19:38:47.982271Z",
- "shell.execute_reply": "2024-09-05T19:38:47.981722Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.719963Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.719631Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.732695Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.732259Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:47.984470Z",
- "iopub.status.busy": "2024-09-05T19:38:47.984002Z",
- "iopub.status.idle": "2024-09-05T19:38:47.990230Z",
- "shell.execute_reply": "2024-09-05T19:38:47.989775Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.734719Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.734386Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.740630Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.740080Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:47.992351Z",
- "iopub.status.busy": "2024-09-05T19:38:47.992012Z",
- "iopub.status.idle": "2024-09-05T19:38:47.994554Z",
- "shell.execute_reply": "2024-09-05T19:38:47.994115Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.742715Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.742407Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.745203Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.744635Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:47.996534Z",
- "iopub.status.busy": "2024-09-05T19:38:47.996204Z",
- "iopub.status.idle": "2024-09-05T19:38:47.999737Z",
- "shell.execute_reply": "2024-09-05T19:38:47.999187Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.747300Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.746906Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.750594Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.750021Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:48.001908Z",
- "iopub.status.busy": "2024-09-05T19:38:48.001582Z",
- "iopub.status.idle": "2024-09-05T19:38:48.003999Z",
- "shell.execute_reply": "2024-09-05T19:38:48.003506Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.752864Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.752447Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.755290Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.754743Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:48.006110Z",
- "iopub.status.busy": "2024-09-05T19:38:48.005783Z",
- "iopub.status.idle": "2024-09-05T19:38:48.009935Z",
- "shell.execute_reply": "2024-09-05T19:38:48.009399Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.757347Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.757015Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.761164Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.760669Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:48.012094Z",
- "iopub.status.busy": "2024-09-05T19:38:48.011735Z",
- "iopub.status.idle": "2024-09-05T19:38:48.039799Z",
- "shell.execute_reply": "2024-09-05T19:38:48.039323Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.763225Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.762830Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.791503Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.790922Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:48.042183Z",
- "iopub.status.busy": "2024-09-05T19:38:48.041817Z",
- "iopub.status.idle": "2024-09-05T19:38:48.046713Z",
- "shell.execute_reply": "2024-09-05T19:38:48.046238Z"
+ "iopub.execute_input": "2024-09-06T19:38:44.793778Z",
+ "iopub.status.busy": "2024-09-06T19:38:44.793374Z",
+ "iopub.status.idle": "2024-09-06T19:38:44.798051Z",
+ "shell.execute_reply": "2024-09-06T19:38:44.797497Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 1509def8d..7626ff8d8 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:51.101575Z",
- "iopub.status.busy": "2024-09-05T19:38:51.101202Z",
- "iopub.status.idle": "2024-09-05T19:38:52.356767Z",
- "shell.execute_reply": "2024-09-05T19:38:52.356160Z"
+ "iopub.execute_input": "2024-09-06T19:38:47.803342Z",
+ "iopub.status.busy": "2024-09-06T19:38:47.803172Z",
+ "iopub.status.idle": "2024-09-06T19:38:49.010459Z",
+ "shell.execute_reply": "2024-09-06T19:38:49.009894Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -105,10 +105,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:52.359268Z",
- "iopub.status.busy": "2024-09-05T19:38:52.358948Z",
- "iopub.status.idle": "2024-09-05T19:38:52.562776Z",
- "shell.execute_reply": "2024-09-05T19:38:52.562204Z"
+ "iopub.execute_input": "2024-09-06T19:38:49.013219Z",
+ "iopub.status.busy": "2024-09-06T19:38:49.012725Z",
+ "iopub.status.idle": "2024-09-06T19:38:49.210289Z",
+ "shell.execute_reply": "2024-09-06T19:38:49.209783Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:52.565447Z",
- "iopub.status.busy": "2024-09-05T19:38:52.565141Z",
- "iopub.status.idle": "2024-09-05T19:38:52.579312Z",
- "shell.execute_reply": "2024-09-05T19:38:52.578691Z"
+ "iopub.execute_input": "2024-09-06T19:38:49.212873Z",
+ "iopub.status.busy": "2024-09-06T19:38:49.212501Z",
+ "iopub.status.idle": "2024-09-06T19:38:49.226305Z",
+ "shell.execute_reply": "2024-09-06T19:38:49.225843Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:52.581436Z",
- "iopub.status.busy": "2024-09-05T19:38:52.581245Z",
- "iopub.status.idle": "2024-09-05T19:38:55.240942Z",
- "shell.execute_reply": "2024-09-05T19:38:55.240413Z"
+ "iopub.execute_input": "2024-09-06T19:38:49.228339Z",
+ "iopub.status.busy": "2024-09-06T19:38:49.227999Z",
+ "iopub.status.idle": "2024-09-06T19:38:51.870134Z",
+ "shell.execute_reply": "2024-09-06T19:38:51.869617Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:55.243248Z",
- "iopub.status.busy": "2024-09-05T19:38:55.242887Z",
- "iopub.status.idle": "2024-09-05T19:38:56.591285Z",
- "shell.execute_reply": "2024-09-05T19:38:56.590684Z"
+ "iopub.execute_input": "2024-09-06T19:38:51.872305Z",
+ "iopub.status.busy": "2024-09-06T19:38:51.872107Z",
+ "iopub.status.idle": "2024-09-06T19:38:53.221496Z",
+ "shell.execute_reply": "2024-09-06T19:38:53.220930Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:56.593787Z",
- "iopub.status.busy": "2024-09-05T19:38:56.593425Z",
- "iopub.status.idle": "2024-09-05T19:38:56.597557Z",
- "shell.execute_reply": "2024-09-05T19:38:56.597077Z"
+ "iopub.execute_input": "2024-09-06T19:38:53.223970Z",
+ "iopub.status.busy": "2024-09-06T19:38:53.223773Z",
+ "iopub.status.idle": "2024-09-06T19:38:53.227537Z",
+ "shell.execute_reply": "2024-09-06T19:38:53.226991Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:56.599600Z",
- "iopub.status.busy": "2024-09-05T19:38:56.599258Z",
- "iopub.status.idle": "2024-09-05T19:38:58.742931Z",
- "shell.execute_reply": "2024-09-05T19:38:58.742243Z"
+ "iopub.execute_input": "2024-09-06T19:38:53.229541Z",
+ "iopub.status.busy": "2024-09-06T19:38:53.229360Z",
+ "iopub.status.idle": "2024-09-06T19:38:55.301308Z",
+ "shell.execute_reply": "2024-09-06T19:38:55.300645Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:58.745759Z",
- "iopub.status.busy": "2024-09-05T19:38:58.745328Z",
- "iopub.status.idle": "2024-09-05T19:38:58.754011Z",
- "shell.execute_reply": "2024-09-05T19:38:58.753467Z"
+ "iopub.execute_input": "2024-09-06T19:38:55.303915Z",
+ "iopub.status.busy": "2024-09-06T19:38:55.303372Z",
+ "iopub.status.idle": "2024-09-06T19:38:55.311571Z",
+ "shell.execute_reply": "2024-09-06T19:38:55.311093Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:58.756118Z",
- "iopub.status.busy": "2024-09-05T19:38:58.755779Z",
- "iopub.status.idle": "2024-09-05T19:39:01.611717Z",
- "shell.execute_reply": "2024-09-05T19:39:01.611077Z"
+ "iopub.execute_input": "2024-09-06T19:38:55.313528Z",
+ "iopub.status.busy": "2024-09-06T19:38:55.313186Z",
+ "iopub.status.idle": "2024-09-06T19:38:58.079187Z",
+ "shell.execute_reply": "2024-09-06T19:38:58.078607Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:01.613917Z",
- "iopub.status.busy": "2024-09-05T19:39:01.613717Z",
- "iopub.status.idle": "2024-09-05T19:39:01.617617Z",
- "shell.execute_reply": "2024-09-05T19:39:01.617137Z"
+ "iopub.execute_input": "2024-09-06T19:38:58.081586Z",
+ "iopub.status.busy": "2024-09-06T19:38:58.081221Z",
+ "iopub.status.idle": "2024-09-06T19:38:58.084505Z",
+ "shell.execute_reply": "2024-09-06T19:38:58.083969Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:01.619758Z",
- "iopub.status.busy": "2024-09-05T19:39:01.619426Z",
- "iopub.status.idle": "2024-09-05T19:39:01.622958Z",
- "shell.execute_reply": "2024-09-05T19:39:01.622500Z"
+ "iopub.execute_input": "2024-09-06T19:38:58.086650Z",
+ "iopub.status.busy": "2024-09-06T19:38:58.086312Z",
+ "iopub.status.idle": "2024-09-06T19:38:58.089596Z",
+ "shell.execute_reply": "2024-09-06T19:38:58.089116Z"
}
},
"outputs": [],
@@ -769,10 +769,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:01.625218Z",
- "iopub.status.busy": "2024-09-05T19:39:01.624881Z",
- "iopub.status.idle": "2024-09-05T19:39:01.628607Z",
- "shell.execute_reply": "2024-09-05T19:39:01.628173Z"
+ "iopub.execute_input": "2024-09-06T19:38:58.091573Z",
+ "iopub.status.busy": "2024-09-06T19:38:58.091252Z",
+ "iopub.status.idle": "2024-09-06T19:38:58.095249Z",
+ "shell.execute_reply": "2024-09-06T19:38:58.094671Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index f25e1e63d..d7703f8af 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:04.529066Z",
- "iopub.status.busy": "2024-09-05T19:39:04.528893Z",
- "iopub.status.idle": "2024-09-05T19:39:05.764668Z",
- "shell.execute_reply": "2024-09-05T19:39:05.764097Z"
+ "iopub.execute_input": "2024-09-06T19:39:00.696602Z",
+ "iopub.status.busy": "2024-09-06T19:39:00.696186Z",
+ "iopub.status.idle": "2024-09-06T19:39:01.907009Z",
+ "shell.execute_reply": "2024-09-06T19:39:01.906453Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -109,10 +109,10 @@
"id": "c90449c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:05.767346Z",
- "iopub.status.busy": "2024-09-05T19:39:05.766811Z",
- "iopub.status.idle": "2024-09-05T19:39:06.974939Z",
- "shell.execute_reply": "2024-09-05T19:39:06.974219Z"
+ "iopub.execute_input": "2024-09-06T19:39:01.909568Z",
+ "iopub.status.busy": "2024-09-06T19:39:01.909050Z",
+ "iopub.status.idle": "2024-09-06T19:39:04.631163Z",
+ "shell.execute_reply": "2024-09-06T19:39:04.630426Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:06.977709Z",
- "iopub.status.busy": "2024-09-05T19:39:06.977293Z",
- "iopub.status.idle": "2024-09-05T19:39:06.980536Z",
- "shell.execute_reply": "2024-09-05T19:39:06.980078Z"
+ "iopub.execute_input": "2024-09-06T19:39:04.633881Z",
+ "iopub.status.busy": "2024-09-06T19:39:04.633499Z",
+ "iopub.status.idle": "2024-09-06T19:39:04.637616Z",
+ "shell.execute_reply": "2024-09-06T19:39:04.637024Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:06.982537Z",
- "iopub.status.busy": "2024-09-05T19:39:06.982351Z",
- "iopub.status.idle": "2024-09-05T19:39:06.989001Z",
- "shell.execute_reply": "2024-09-05T19:39:06.988551Z"
+ "iopub.execute_input": "2024-09-06T19:39:04.639736Z",
+ "iopub.status.busy": "2024-09-06T19:39:04.639557Z",
+ "iopub.status.idle": "2024-09-06T19:39:04.646473Z",
+ "shell.execute_reply": "2024-09-06T19:39:04.646014Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:06.990912Z",
- "iopub.status.busy": "2024-09-05T19:39:06.990730Z",
- "iopub.status.idle": "2024-09-05T19:39:07.491100Z",
- "shell.execute_reply": "2024-09-05T19:39:07.490495Z"
+ "iopub.execute_input": "2024-09-06T19:39:04.648396Z",
+ "iopub.status.busy": "2024-09-06T19:39:04.648219Z",
+ "iopub.status.idle": "2024-09-06T19:39:05.143459Z",
+ "shell.execute_reply": "2024-09-06T19:39:05.142840Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:07.494085Z",
- "iopub.status.busy": "2024-09-05T19:39:07.493615Z",
- "iopub.status.idle": "2024-09-05T19:39:07.499172Z",
- "shell.execute_reply": "2024-09-05T19:39:07.498620Z"
+ "iopub.execute_input": "2024-09-06T19:39:05.146327Z",
+ "iopub.status.busy": "2024-09-06T19:39:05.146000Z",
+ "iopub.status.idle": "2024-09-06T19:39:05.151442Z",
+ "shell.execute_reply": "2024-09-06T19:39:05.150979Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:07.501404Z",
- "iopub.status.busy": "2024-09-05T19:39:07.500976Z",
- "iopub.status.idle": "2024-09-05T19:39:07.504945Z",
- "shell.execute_reply": "2024-09-05T19:39:07.504506Z"
+ "iopub.execute_input": "2024-09-06T19:39:05.153485Z",
+ "iopub.status.busy": "2024-09-06T19:39:05.153173Z",
+ "iopub.status.idle": "2024-09-06T19:39:05.157137Z",
+ "shell.execute_reply": "2024-09-06T19:39:05.156658Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:07.507284Z",
- "iopub.status.busy": "2024-09-05T19:39:07.506712Z",
- "iopub.status.idle": "2024-09-05T19:39:08.384414Z",
- "shell.execute_reply": "2024-09-05T19:39:08.383806Z"
+ "iopub.execute_input": "2024-09-06T19:39:05.159200Z",
+ "iopub.status.busy": "2024-09-06T19:39:05.158859Z",
+ "iopub.status.idle": "2024-09-06T19:39:06.019168Z",
+ "shell.execute_reply": "2024-09-06T19:39:06.018545Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:08.386698Z",
- "iopub.status.busy": "2024-09-05T19:39:08.386502Z",
- "iopub.status.idle": "2024-09-05T19:39:08.587093Z",
- "shell.execute_reply": "2024-09-05T19:39:08.586454Z"
+ "iopub.execute_input": "2024-09-06T19:39:06.021668Z",
+ "iopub.status.busy": "2024-09-06T19:39:06.021221Z",
+ "iopub.status.idle": "2024-09-06T19:39:06.237090Z",
+ "shell.execute_reply": "2024-09-06T19:39:06.236553Z"
}
},
"outputs": [
@@ -627,14 +627,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered."
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
+ "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered.\n"
]
},
{
@@ -667,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:08.589453Z",
- "iopub.status.busy": "2024-09-05T19:39:08.589096Z",
- "iopub.status.idle": "2024-09-05T19:39:08.593402Z",
- "shell.execute_reply": "2024-09-05T19:39:08.592837Z"
+ "iopub.execute_input": "2024-09-06T19:39:06.239343Z",
+ "iopub.status.busy": "2024-09-06T19:39:06.238930Z",
+ "iopub.status.idle": "2024-09-06T19:39:06.243194Z",
+ "shell.execute_reply": "2024-09-06T19:39:06.242735Z"
}
},
"outputs": [
@@ -707,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:08.595644Z",
- "iopub.status.busy": "2024-09-05T19:39:08.595324Z",
- "iopub.status.idle": "2024-09-05T19:39:09.054015Z",
- "shell.execute_reply": "2024-09-05T19:39:09.053402Z"
+ "iopub.execute_input": "2024-09-06T19:39:06.245282Z",
+ "iopub.status.busy": "2024-09-06T19:39:06.244951Z",
+ "iopub.status.idle": "2024-09-06T19:39:06.697627Z",
+ "shell.execute_reply": "2024-09-06T19:39:06.697015Z"
}
},
"outputs": [
@@ -769,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:09.057259Z",
- "iopub.status.busy": "2024-09-05T19:39:09.056867Z",
- "iopub.status.idle": "2024-09-05T19:39:09.390945Z",
- "shell.execute_reply": "2024-09-05T19:39:09.390389Z"
+ "iopub.execute_input": "2024-09-06T19:39:06.700924Z",
+ "iopub.status.busy": "2024-09-06T19:39:06.700539Z",
+ "iopub.status.idle": "2024-09-06T19:39:07.035472Z",
+ "shell.execute_reply": "2024-09-06T19:39:07.034925Z"
}
},
"outputs": [
@@ -819,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:09.393790Z",
- "iopub.status.busy": "2024-09-05T19:39:09.393435Z",
- "iopub.status.idle": "2024-09-05T19:39:09.732820Z",
- "shell.execute_reply": "2024-09-05T19:39:09.732244Z"
+ "iopub.execute_input": "2024-09-06T19:39:07.038382Z",
+ "iopub.status.busy": "2024-09-06T19:39:07.038001Z",
+ "iopub.status.idle": "2024-09-06T19:39:07.401507Z",
+ "shell.execute_reply": "2024-09-06T19:39:07.400918Z"
}
},
"outputs": [
@@ -869,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:09.736269Z",
- "iopub.status.busy": "2024-09-05T19:39:09.735858Z",
- "iopub.status.idle": "2024-09-05T19:39:10.156424Z",
- "shell.execute_reply": "2024-09-05T19:39:10.155802Z"
+ "iopub.execute_input": "2024-09-06T19:39:07.404511Z",
+ "iopub.status.busy": "2024-09-06T19:39:07.404090Z",
+ "iopub.status.idle": "2024-09-06T19:39:07.846501Z",
+ "shell.execute_reply": "2024-09-06T19:39:07.845952Z"
}
},
"outputs": [
@@ -932,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:10.161076Z",
- "iopub.status.busy": "2024-09-05T19:39:10.160681Z",
- "iopub.status.idle": "2024-09-05T19:39:10.609859Z",
- "shell.execute_reply": "2024-09-05T19:39:10.609188Z"
+ "iopub.execute_input": "2024-09-06T19:39:07.851154Z",
+ "iopub.status.busy": "2024-09-06T19:39:07.850706Z",
+ "iopub.status.idle": "2024-09-06T19:39:08.296657Z",
+ "shell.execute_reply": "2024-09-06T19:39:08.296063Z"
}
},
"outputs": [
@@ -978,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:10.612839Z",
- "iopub.status.busy": "2024-09-05T19:39:10.612663Z",
- "iopub.status.idle": "2024-09-05T19:39:10.829289Z",
- "shell.execute_reply": "2024-09-05T19:39:10.828816Z"
+ "iopub.execute_input": "2024-09-06T19:39:08.300087Z",
+ "iopub.status.busy": "2024-09-06T19:39:08.299623Z",
+ "iopub.status.idle": "2024-09-06T19:39:08.513354Z",
+ "shell.execute_reply": "2024-09-06T19:39:08.512755Z"
}
},
"outputs": [
@@ -1024,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:10.831596Z",
- "iopub.status.busy": "2024-09-05T19:39:10.831265Z",
- "iopub.status.idle": "2024-09-05T19:39:11.014176Z",
- "shell.execute_reply": "2024-09-05T19:39:11.013572Z"
+ "iopub.execute_input": "2024-09-06T19:39:08.515572Z",
+ "iopub.status.busy": "2024-09-06T19:39:08.515168Z",
+ "iopub.status.idle": "2024-09-06T19:39:08.694654Z",
+ "shell.execute_reply": "2024-09-06T19:39:08.694085Z"
}
},
"outputs": [
@@ -1074,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:11.016423Z",
- "iopub.status.busy": "2024-09-05T19:39:11.016076Z",
- "iopub.status.idle": "2024-09-05T19:39:11.018873Z",
- "shell.execute_reply": "2024-09-05T19:39:11.018432Z"
+ "iopub.execute_input": "2024-09-06T19:39:08.697419Z",
+ "iopub.status.busy": "2024-09-06T19:39:08.697030Z",
+ "iopub.status.idle": "2024-09-06T19:39:08.699909Z",
+ "shell.execute_reply": "2024-09-06T19:39:08.699453Z"
}
},
"outputs": [],
@@ -1097,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:11.020936Z",
- "iopub.status.busy": "2024-09-05T19:39:11.020606Z",
- "iopub.status.idle": "2024-09-05T19:39:11.923610Z",
- "shell.execute_reply": "2024-09-05T19:39:11.922981Z"
+ "iopub.execute_input": "2024-09-06T19:39:08.701948Z",
+ "iopub.status.busy": "2024-09-06T19:39:08.701622Z",
+ "iopub.status.idle": "2024-09-06T19:39:09.635839Z",
+ "shell.execute_reply": "2024-09-06T19:39:09.635227Z"
}
},
"outputs": [
@@ -1179,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:11.925987Z",
- "iopub.status.busy": "2024-09-05T19:39:11.925773Z",
- "iopub.status.idle": "2024-09-05T19:39:12.101177Z",
- "shell.execute_reply": "2024-09-05T19:39:12.100675Z"
+ "iopub.execute_input": "2024-09-06T19:39:09.637949Z",
+ "iopub.status.busy": "2024-09-06T19:39:09.637773Z",
+ "iopub.status.idle": "2024-09-06T19:39:09.767317Z",
+ "shell.execute_reply": "2024-09-06T19:39:09.766833Z"
}
},
"outputs": [
@@ -1221,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:12.103362Z",
- "iopub.status.busy": "2024-09-05T19:39:12.103006Z",
- "iopub.status.idle": "2024-09-05T19:39:12.232243Z",
- "shell.execute_reply": "2024-09-05T19:39:12.231719Z"
+ "iopub.execute_input": "2024-09-06T19:39:09.769238Z",
+ "iopub.status.busy": "2024-09-06T19:39:09.769067Z",
+ "iopub.status.idle": "2024-09-06T19:39:09.969227Z",
+ "shell.execute_reply": "2024-09-06T19:39:09.968617Z"
}
},
"outputs": [],
@@ -1273,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:12.234678Z",
- "iopub.status.busy": "2024-09-05T19:39:12.234336Z",
- "iopub.status.idle": "2024-09-05T19:39:12.941539Z",
- "shell.execute_reply": "2024-09-05T19:39:12.940835Z"
+ "iopub.execute_input": "2024-09-06T19:39:09.971377Z",
+ "iopub.status.busy": "2024-09-06T19:39:09.971032Z",
+ "iopub.status.idle": "2024-09-06T19:39:10.691109Z",
+ "shell.execute_reply": "2024-09-06T19:39:10.690570Z"
}
},
"outputs": [
@@ -1358,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:12.943960Z",
- "iopub.status.busy": "2024-09-05T19:39:12.943615Z",
- "iopub.status.idle": "2024-09-05T19:39:12.947337Z",
- "shell.execute_reply": "2024-09-05T19:39:12.946788Z"
+ "iopub.execute_input": "2024-09-06T19:39:10.693528Z",
+ "iopub.status.busy": "2024-09-06T19:39:10.693149Z",
+ "iopub.status.idle": "2024-09-06T19:39:10.697005Z",
+ "shell.execute_reply": "2024-09-06T19:39:10.696512Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index f57af3305..ab02f6a16 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:15.405392Z",
- "iopub.status.busy": "2024-09-05T19:39:15.405034Z",
- "iopub.status.idle": "2024-09-05T19:39:18.302373Z",
- "shell.execute_reply": "2024-09-05T19:39:18.301795Z"
+ "iopub.execute_input": "2024-09-06T19:39:13.100046Z",
+ "iopub.status.busy": "2024-09-06T19:39:13.099622Z",
+ "iopub.status.idle": "2024-09-06T19:39:15.925691Z",
+ "shell.execute_reply": "2024-09-06T19:39:15.925058Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"id": "4396f544",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:18.305069Z",
- "iopub.status.busy": "2024-09-05T19:39:18.304571Z",
- "iopub.status.idle": "2024-09-05T19:39:18.636404Z",
- "shell.execute_reply": "2024-09-05T19:39:18.635775Z"
+ "iopub.execute_input": "2024-09-06T19:39:15.928762Z",
+ "iopub.status.busy": "2024-09-06T19:39:15.928196Z",
+ "iopub.status.idle": "2024-09-06T19:39:16.252610Z",
+ "shell.execute_reply": "2024-09-06T19:39:16.252054Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:18.639073Z",
- "iopub.status.busy": "2024-09-05T19:39:18.638746Z",
- "iopub.status.idle": "2024-09-05T19:39:18.643058Z",
- "shell.execute_reply": "2024-09-05T19:39:18.642503Z"
+ "iopub.execute_input": "2024-09-06T19:39:16.255233Z",
+ "iopub.status.busy": "2024-09-06T19:39:16.254751Z",
+ "iopub.status.idle": "2024-09-06T19:39:16.259089Z",
+ "shell.execute_reply": "2024-09-06T19:39:16.258660Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:18.645337Z",
- "iopub.status.busy": "2024-09-05T19:39:18.644989Z",
- "iopub.status.idle": "2024-09-05T19:39:23.136533Z",
- "shell.execute_reply": "2024-09-05T19:39:23.136022Z"
+ "iopub.execute_input": "2024-09-06T19:39:16.261376Z",
+ "iopub.status.busy": "2024-09-06T19:39:16.260945Z",
+ "iopub.status.idle": "2024-09-06T19:39:23.300858Z",
+ "shell.execute_reply": "2024-09-06T19:39:23.300244Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 917504/170498071 [00:00<00:20, 8230282.28it/s]"
+ " 0%| | 32768/170498071 [00:00<09:50, 288460.96it/s]"
]
},
{
@@ -260,7 +260,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▌ | 10518528/170498071 [00:00<00:02, 57525326.14it/s]"
+ " 0%| | 229376/170498071 [00:00<02:31, 1124759.70it/s]"
]
},
{
@@ -268,7 +268,7 @@
"output_type": "stream",
"text": [
"\r",
- " 12%|█▏ | 21200896/170498071 [00:00<00:01, 79443214.95it/s]"
+ " 1%| | 884736/170498071 [00:00<00:52, 3225591.40it/s]"
]
},
{
@@ -276,7 +276,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▊ | 31883264/170498071 [00:00<00:01, 90066862.77it/s]"
+ " 2%|▏ | 3571712/170498071 [00:00<00:14, 11574707.14it/s]"
]
},
{
@@ -284,7 +284,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▌ | 42795008/170498071 [00:00<00:01, 96847189.42it/s]"
+ " 6%|▌ | 9633792/170498071 [00:00<00:06, 25807611.79it/s]"
]
},
{
@@ -292,7 +292,7 @@
"output_type": "stream",
"text": [
"\r",
- " 31%|███▏ | 53608448/170498071 [00:00<00:01, 100648722.32it/s]"
+ " 9%|▉ | 15892480/170498071 [00:00<00:04, 35393042.76it/s]"
]
},
{
@@ -300,7 +300,7 @@
"output_type": "stream",
"text": [
"\r",
- " 38%|███▊ | 64421888/170498071 [00:00<00:01, 103069662.64it/s]"
+ " 13%|█▎ | 22052864/170498071 [00:00<00:03, 41375940.12it/s]"
]
},
{
@@ -308,7 +308,7 @@
"output_type": "stream",
"text": [
"\r",
- " 44%|████▍ | 75497472/170498071 [00:00<00:00, 105497848.58it/s]"
+ " 16%|█▋ | 27918336/170498071 [00:00<00:03, 46336247.02it/s]"
]
},
{
@@ -316,7 +316,7 @@
"output_type": "stream",
"text": [
"\r",
- " 51%|█████ | 86540288/170498071 [00:00<00:00, 106981473.95it/s]"
+ " 19%|█▉ | 32604160/170498071 [00:01<00:03, 45410241.06it/s]"
]
},
{
@@ -324,7 +324,7 @@
"output_type": "stream",
"text": [
"\r",
- " 57%|█████▋ | 97484800/170498071 [00:01<00:00, 107701030.20it/s]"
+ " 22%|██▏ | 37978112/170498071 [00:01<00:02, 46512554.13it/s]"
]
},
{
@@ -332,7 +332,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▎ | 108363776/170498071 [00:01<00:00, 107950290.42it/s]"
+ " 26%|██▌ | 44072960/170498071 [00:01<00:02, 50196826.35it/s]"
]
},
{
@@ -340,7 +340,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|██████▉ | 119177216/170498071 [00:01<00:00, 106306696.89it/s]"
+ " 29%|██▉ | 49217536/170498071 [00:01<00:02, 50515326.91it/s]"
]
},
{
@@ -348,7 +348,7 @@
"output_type": "stream",
"text": [
"\r",
- " 76%|███████▌ | 129826816/170498071 [00:01<00:00, 105708288.67it/s]"
+ " 32%|███▏ | 54296576/170498071 [00:01<00:02, 49331301.44it/s]"
]
},
{
@@ -356,7 +356,7 @@
"output_type": "stream",
"text": [
"\r",
- " 82%|████████▏ | 140410880/170498071 [00:01<00:00, 105156306.35it/s]"
+ " 35%|███▌ | 60129280/170498071 [00:01<00:02, 51745509.08it/s]"
]
},
{
@@ -364,7 +364,7 @@
"output_type": "stream",
"text": [
"\r",
- " 89%|████████▉ | 151355392/170498071 [00:01<00:00, 106351362.25it/s]"
+ " 38%|███▊ | 65339392/170498071 [00:01<00:02, 51498978.62it/s]"
]
},
{
@@ -372,7 +372,7 @@
"output_type": "stream",
"text": [
"\r",
- " 95%|█████████▌| 162004992/170498071 [00:01<00:00, 105346005.50it/s]"
+ " 41%|████▏ | 70516736/170498071 [00:01<00:01, 50172708.54it/s]"
]
},
{
@@ -380,7 +380,151 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 170498071/170498071 [00:01<00:00, 99832064.92it/s] "
+ " 45%|████▍ | 76251136/170498071 [00:01<00:01, 52173671.62it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 48%|████▊ | 81559552/170498071 [00:01<00:01, 52429909.15it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 51%|█████ | 86835200/170498071 [00:02<00:01, 50316420.17it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 54%|█████▍ | 92438528/170498071 [00:02<00:01, 51729464.30it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 57%|█████▋ | 97878016/170498071 [00:02<00:01, 52469802.74it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 61%|██████ | 103153664/170498071 [00:02<00:01, 51263628.20it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 64%|██████▎ | 108396544/170498071 [00:02<00:01, 51439851.19it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 67%|██████▋ | 114130944/170498071 [00:02<00:01, 53113973.23it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 70%|███████ | 119472128/170498071 [00:02<00:00, 51879482.02it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 73%|███████▎ | 124682240/170498071 [00:02<00:00, 50047274.18it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 77%|███████▋ | 130547712/170498071 [00:02<00:00, 52494107.90it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 80%|███████▉ | 135823360/170498071 [00:03<00:00, 52004524.51it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 83%|████████▎ | 141066240/170498071 [00:03<00:00, 50983301.18it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 86%|████████▌ | 146636800/170498071 [00:03<00:00, 52034590.57it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 89%|████████▉ | 151879680/170498071 [00:03<00:00, 52140968.39it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 92%|█████████▏| 157122560/170498071 [00:03<00:00, 50962142.96it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 95%|█████████▌| 162463744/170498071 [00:03<00:00, 51228143.58it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 99%|█████████▊| 168329216/170498071 [00:03<00:00, 53366850.10it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "100%|██████████| 170498071/170498071 [00:03<00:00, 46456493.64it/s]"
]
},
{
@@ -498,10 +642,10 @@
"id": "9b64e0aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:23.138923Z",
- "iopub.status.busy": "2024-09-05T19:39:23.138570Z",
- "iopub.status.idle": "2024-09-05T19:39:23.143265Z",
- "shell.execute_reply": "2024-09-05T19:39:23.142817Z"
+ "iopub.execute_input": "2024-09-06T19:39:23.303328Z",
+ "iopub.status.busy": "2024-09-06T19:39:23.302943Z",
+ "iopub.status.idle": "2024-09-06T19:39:23.307938Z",
+ "shell.execute_reply": "2024-09-06T19:39:23.307365Z"
},
"nbsphinx": "hidden"
},
@@ -552,10 +696,10 @@
"id": "a00aa3ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:23.145648Z",
- "iopub.status.busy": "2024-09-05T19:39:23.145075Z",
- "iopub.status.idle": "2024-09-05T19:39:23.692735Z",
- "shell.execute_reply": "2024-09-05T19:39:23.692010Z"
+ "iopub.execute_input": "2024-09-06T19:39:23.310122Z",
+ "iopub.status.busy": "2024-09-06T19:39:23.309822Z",
+ "iopub.status.idle": "2024-09-06T19:39:23.850296Z",
+ "shell.execute_reply": "2024-09-06T19:39:23.849793Z"
}
},
"outputs": [
@@ -588,10 +732,10 @@
"id": "41e5cb6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:23.695080Z",
- "iopub.status.busy": "2024-09-05T19:39:23.694701Z",
- "iopub.status.idle": "2024-09-05T19:39:24.214620Z",
- "shell.execute_reply": "2024-09-05T19:39:24.214041Z"
+ "iopub.execute_input": "2024-09-06T19:39:23.852466Z",
+ "iopub.status.busy": "2024-09-06T19:39:23.852115Z",
+ "iopub.status.idle": "2024-09-06T19:39:24.358610Z",
+ "shell.execute_reply": "2024-09-06T19:39:24.358030Z"
}
},
"outputs": [
@@ -629,10 +773,10 @@
"id": "1cf25354",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:24.216960Z",
- "iopub.status.busy": "2024-09-05T19:39:24.216573Z",
- "iopub.status.idle": "2024-09-05T19:39:24.220264Z",
- "shell.execute_reply": "2024-09-05T19:39:24.219778Z"
+ "iopub.execute_input": "2024-09-06T19:39:24.360839Z",
+ "iopub.status.busy": "2024-09-06T19:39:24.360464Z",
+ "iopub.status.idle": "2024-09-06T19:39:24.363781Z",
+ "shell.execute_reply": "2024-09-06T19:39:24.363295Z"
}
},
"outputs": [],
@@ -655,17 +799,17 @@
"id": "85a58d41",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:24.222316Z",
- "iopub.status.busy": "2024-09-05T19:39:24.221972Z",
- "iopub.status.idle": "2024-09-05T19:39:36.692173Z",
- "shell.execute_reply": "2024-09-05T19:39:36.691528Z"
+ "iopub.execute_input": "2024-09-06T19:39:24.365783Z",
+ "iopub.status.busy": "2024-09-06T19:39:24.365442Z",
+ "iopub.status.idle": "2024-09-06T19:39:36.716347Z",
+ "shell.execute_reply": "2024-09-06T19:39:36.715721Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7c531849220347c4bbd1314510f1888e",
+ "model_id": "3ceaa047f5ed4611b974d3fa414e2507",
"version_major": 2,
"version_minor": 0
},
@@ -724,10 +868,10 @@
"id": "feb0f519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:36.694834Z",
- "iopub.status.busy": "2024-09-05T19:39:36.694331Z",
- "iopub.status.idle": "2024-09-05T19:39:38.805431Z",
- "shell.execute_reply": "2024-09-05T19:39:38.804752Z"
+ "iopub.execute_input": "2024-09-06T19:39:36.718898Z",
+ "iopub.status.busy": "2024-09-06T19:39:36.718487Z",
+ "iopub.status.idle": "2024-09-06T19:39:38.825920Z",
+ "shell.execute_reply": "2024-09-06T19:39:38.825316Z"
}
},
"outputs": [
@@ -771,10 +915,10 @@
"id": "089d5860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:38.808473Z",
- "iopub.status.busy": "2024-09-05T19:39:38.807868Z",
- "iopub.status.idle": "2024-09-05T19:39:39.068245Z",
- "shell.execute_reply": "2024-09-05T19:39:39.067640Z"
+ "iopub.execute_input": "2024-09-06T19:39:38.828812Z",
+ "iopub.status.busy": "2024-09-06T19:39:38.828333Z",
+ "iopub.status.idle": "2024-09-06T19:39:39.084401Z",
+ "shell.execute_reply": "2024-09-06T19:39:39.083812Z"
}
},
"outputs": [
@@ -810,10 +954,10 @@
"id": "78b1951c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:39.070920Z",
- "iopub.status.busy": "2024-09-05T19:39:39.070467Z",
- "iopub.status.idle": "2024-09-05T19:39:39.749637Z",
- "shell.execute_reply": "2024-09-05T19:39:39.749023Z"
+ "iopub.execute_input": "2024-09-06T19:39:39.087122Z",
+ "iopub.status.busy": "2024-09-06T19:39:39.086611Z",
+ "iopub.status.idle": "2024-09-06T19:39:39.754107Z",
+ "shell.execute_reply": "2024-09-06T19:39:39.753534Z"
}
},
"outputs": [
@@ -863,10 +1007,10 @@
"id": "e9dff81b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:39.752728Z",
- "iopub.status.busy": "2024-09-05T19:39:39.752257Z",
- "iopub.status.idle": "2024-09-05T19:39:40.092896Z",
- "shell.execute_reply": "2024-09-05T19:39:40.092212Z"
+ "iopub.execute_input": "2024-09-06T19:39:39.756937Z",
+ "iopub.status.busy": "2024-09-06T19:39:39.756623Z",
+ "iopub.status.idle": "2024-09-06T19:39:40.092242Z",
+ "shell.execute_reply": "2024-09-06T19:39:40.091655Z"
}
},
"outputs": [
@@ -914,10 +1058,10 @@
"id": "616769f8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:40.095138Z",
- "iopub.status.busy": "2024-09-05T19:39:40.094925Z",
- "iopub.status.idle": "2024-09-05T19:39:40.341300Z",
- "shell.execute_reply": "2024-09-05T19:39:40.340684Z"
+ "iopub.execute_input": "2024-09-06T19:39:40.094221Z",
+ "iopub.status.busy": "2024-09-06T19:39:40.094058Z",
+ "iopub.status.idle": "2024-09-06T19:39:40.335215Z",
+ "shell.execute_reply": "2024-09-06T19:39:40.334660Z"
}
},
"outputs": [
@@ -973,10 +1117,10 @@
"id": "40fed4ef",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:40.344290Z",
- "iopub.status.busy": "2024-09-05T19:39:40.343761Z",
- "iopub.status.idle": "2024-09-05T19:39:40.431779Z",
- "shell.execute_reply": "2024-09-05T19:39:40.431283Z"
+ "iopub.execute_input": "2024-09-06T19:39:40.337846Z",
+ "iopub.status.busy": "2024-09-06T19:39:40.337645Z",
+ "iopub.status.idle": "2024-09-06T19:39:40.434888Z",
+ "shell.execute_reply": "2024-09-06T19:39:40.434380Z"
}
},
"outputs": [],
@@ -997,10 +1141,10 @@
"id": "89f9db72",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:40.434261Z",
- "iopub.status.busy": "2024-09-05T19:39:40.433923Z",
- "iopub.status.idle": "2024-09-05T19:39:50.771833Z",
- "shell.execute_reply": "2024-09-05T19:39:50.771095Z"
+ "iopub.execute_input": "2024-09-06T19:39:40.437135Z",
+ "iopub.status.busy": "2024-09-06T19:39:40.436969Z",
+ "iopub.status.idle": "2024-09-06T19:39:50.846992Z",
+ "shell.execute_reply": "2024-09-06T19:39:50.846365Z"
}
},
"outputs": [
@@ -1037,10 +1181,10 @@
"id": "874c885a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:50.774513Z",
- "iopub.status.busy": "2024-09-05T19:39:50.774240Z",
- "iopub.status.idle": "2024-09-05T19:39:53.048664Z",
- "shell.execute_reply": "2024-09-05T19:39:53.048057Z"
+ "iopub.execute_input": "2024-09-06T19:39:50.849274Z",
+ "iopub.status.busy": "2024-09-06T19:39:50.849079Z",
+ "iopub.status.idle": "2024-09-06T19:39:53.085840Z",
+ "shell.execute_reply": "2024-09-06T19:39:53.085209Z"
}
},
"outputs": [
@@ -1071,10 +1215,10 @@
"id": "e110fc4b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:53.051447Z",
- "iopub.status.busy": "2024-09-05T19:39:53.051056Z",
- "iopub.status.idle": "2024-09-05T19:39:53.260971Z",
- "shell.execute_reply": "2024-09-05T19:39:53.260424Z"
+ "iopub.execute_input": "2024-09-06T19:39:53.088386Z",
+ "iopub.status.busy": "2024-09-06T19:39:53.087986Z",
+ "iopub.status.idle": "2024-09-06T19:39:53.295938Z",
+ "shell.execute_reply": "2024-09-06T19:39:53.295309Z"
}
},
"outputs": [],
@@ -1088,10 +1232,10 @@
"id": "85b60cbf",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:53.263397Z",
- "iopub.status.busy": "2024-09-05T19:39:53.263043Z",
- "iopub.status.idle": "2024-09-05T19:39:53.266100Z",
- "shell.execute_reply": "2024-09-05T19:39:53.265661Z"
+ "iopub.execute_input": "2024-09-06T19:39:53.298578Z",
+ "iopub.status.busy": "2024-09-06T19:39:53.298149Z",
+ "iopub.status.idle": "2024-09-06T19:39:53.301396Z",
+ "shell.execute_reply": "2024-09-06T19:39:53.300847Z"
}
},
"outputs": [],
@@ -1129,10 +1273,10 @@
"id": "17f96fa6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:53.268739Z",
- "iopub.status.busy": "2024-09-05T19:39:53.267783Z",
- "iopub.status.idle": "2024-09-05T19:39:53.276580Z",
- "shell.execute_reply": "2024-09-05T19:39:53.276119Z"
+ "iopub.execute_input": "2024-09-06T19:39:53.303545Z",
+ "iopub.status.busy": "2024-09-06T19:39:53.303235Z",
+ "iopub.status.idle": "2024-09-06T19:39:53.311553Z",
+ "shell.execute_reply": "2024-09-06T19:39:53.311013Z"
},
"nbsphinx": "hidden"
},
@@ -1177,7 +1321,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "1349f610634f47e3a436b32454886eaf": {
+ "2a68a2d432424faba9fe0b5e6944b5e9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1230,25 +1374,47 @@
"width": null
}
},
- "17b81af4f234437a8808eadad363b86b": {
+ "3ceaa047f5ed4611b974d3fa414e2507": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_c213a022f9994559b2b3155f2f77656c",
+ "IPY_MODEL_d04af2b6417a48e88c2bb6ac7a1a352f",
+ "IPY_MODEL_d330cb5a3ec245d28c20140821dff479"
+ ],
+ "layout": "IPY_MODEL_8965ea1fe0204e49bbde2ee4ed6b5dbe",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "653de3cf6239488fa0adf55f2a1ae049": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "bar_color": null,
+ "description_width": ""
}
},
- "1faec7121f484abdb6d2297ca62c549d": {
+ "8965ea1fe0204e49bbde2ee4ed6b5dbe": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1301,7 +1467,7 @@
"width": null
}
},
- "26c77578263941ad9a13a20bef319656": {
+ "b06f361a24974d5a8b8c89476e47f817": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1354,54 +1520,7 @@
"width": null
}
},
- "39aa8065c5b8424498ce8391b1a41734": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_1faec7121f484abdb6d2297ca62c549d",
- "placeholder": "",
- "style": "IPY_MODEL_810d42e3457741f5879220bcee73da3b",
- "tabbable": null,
- "tooltip": null,
- "value": "model.safetensors: 100%"
- }
- },
- "7c531849220347c4bbd1314510f1888e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_39aa8065c5b8424498ce8391b1a41734",
- "IPY_MODEL_9ebe6590bdfb470397e8cdde6f7f6b02",
- "IPY_MODEL_b2a369aac2ff425a88c2e810df948da8"
- ],
- "layout": "IPY_MODEL_f8dbae4a023d4586b20790fd6be925eb",
- "tabbable": null,
- "tooltip": null
- }
- },
- "810d42e3457741f5879220bcee73da3b": {
+ "b895588a207f4f0ca89d7c4764c3d066": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1419,49 +1538,7 @@
"text_color": null
}
},
- "9cc64681cf4a45ae867cf79d8b667320": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "9ebe6590bdfb470397e8cdde6f7f6b02": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_26c77578263941ad9a13a20bef319656",
- "max": 102469840.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_9cc64681cf4a45ae867cf79d8b667320",
- "tabbable": null,
- "tooltip": null,
- "value": 102469840.0
- }
- },
- "b2a369aac2ff425a88c2e810df948da8": {
+ "c213a022f9994559b2b3155f2f77656c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1476,15 +1553,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1349f610634f47e3a436b32454886eaf",
+ "layout": "IPY_MODEL_ccda1205dcc748f99c76cf1800b182ef",
"placeholder": "",
- "style": "IPY_MODEL_17b81af4f234437a8808eadad363b86b",
+ "style": "IPY_MODEL_b895588a207f4f0ca89d7c4764c3d066",
"tabbable": null,
"tooltip": null,
- "value": " 102M/102M [00:00<00:00, 257MB/s]"
+ "value": "model.safetensors: 100%"
}
},
- "f8dbae4a023d4586b20790fd6be925eb": {
+ "ccda1205dcc748f99c76cf1800b182ef": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1536,6 +1613,73 @@
"visibility": null,
"width": null
}
+ },
+ "cd03cf3d325849b9a2597fce8db90de1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "d04af2b6417a48e88c2bb6ac7a1a352f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_2a68a2d432424faba9fe0b5e6944b5e9",
+ "max": 102469840.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_653de3cf6239488fa0adf55f2a1ae049",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 102469840.0
+ }
+ },
+ "d330cb5a3ec245d28c20140821dff479": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_b06f361a24974d5a8b8c89476e47f817",
+ "placeholder": "",
+ "style": "IPY_MODEL_cd03cf3d325849b9a2597fce8db90de1",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 102M/102M [00:00<00:00, 304MB/s]"
+ }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index f384516c7..4e72a9c31 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:57.424289Z",
- "iopub.status.busy": "2024-09-05T19:39:57.424123Z",
- "iopub.status.idle": "2024-09-05T19:39:58.655592Z",
- "shell.execute_reply": "2024-09-05T19:39:58.655038Z"
+ "iopub.execute_input": "2024-09-06T19:39:57.671183Z",
+ "iopub.status.busy": "2024-09-06T19:39:57.671012Z",
+ "iopub.status.idle": "2024-09-06T19:39:58.889426Z",
+ "shell.execute_reply": "2024-09-06T19:39:58.888863Z"
},
"nbsphinx": "hidden"
},
@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -142,10 +142,10 @@
"id": "4fb10b8f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:58.658099Z",
- "iopub.status.busy": "2024-09-05T19:39:58.657787Z",
- "iopub.status.idle": "2024-09-05T19:39:58.676619Z",
- "shell.execute_reply": "2024-09-05T19:39:58.676046Z"
+ "iopub.execute_input": "2024-09-06T19:39:58.892009Z",
+ "iopub.status.busy": "2024-09-06T19:39:58.891558Z",
+ "iopub.status.idle": "2024-09-06T19:39:58.909420Z",
+ "shell.execute_reply": "2024-09-06T19:39:58.908966Z"
}
},
"outputs": [],
@@ -164,10 +164,10 @@
"id": "284dc264",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:58.679179Z",
- "iopub.status.busy": "2024-09-05T19:39:58.678732Z",
- "iopub.status.idle": "2024-09-05T19:39:58.681876Z",
- "shell.execute_reply": "2024-09-05T19:39:58.681391Z"
+ "iopub.execute_input": "2024-09-06T19:39:58.911380Z",
+ "iopub.status.busy": "2024-09-06T19:39:58.911122Z",
+ "iopub.status.idle": "2024-09-06T19:39:58.914071Z",
+ "shell.execute_reply": "2024-09-06T19:39:58.913630Z"
},
"nbsphinx": "hidden"
},
@@ -198,10 +198,10 @@
"id": "0f7450db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:58.684044Z",
- "iopub.status.busy": "2024-09-05T19:39:58.683742Z",
- "iopub.status.idle": "2024-09-05T19:39:58.780574Z",
- "shell.execute_reply": "2024-09-05T19:39:58.779978Z"
+ "iopub.execute_input": "2024-09-06T19:39:58.916066Z",
+ "iopub.status.busy": "2024-09-06T19:39:58.915883Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.147435Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.146903Z"
}
},
"outputs": [
@@ -374,10 +374,10 @@
"id": "55513fed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:58.782788Z",
- "iopub.status.busy": "2024-09-05T19:39:58.782453Z",
- "iopub.status.idle": "2024-09-05T19:39:58.968491Z",
- "shell.execute_reply": "2024-09-05T19:39:58.967817Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.149566Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.149370Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.331007Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.330438Z"
},
"nbsphinx": "hidden"
},
@@ -417,10 +417,10 @@
"id": "df5a0f59",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:58.970900Z",
- "iopub.status.busy": "2024-09-05T19:39:58.970711Z",
- "iopub.status.idle": "2024-09-05T19:39:59.181624Z",
- "shell.execute_reply": "2024-09-05T19:39:59.181029Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.333486Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.333040Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.576590Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.575968Z"
}
},
"outputs": [
@@ -456,10 +456,10 @@
"id": "7af78a8a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:59.183736Z",
- "iopub.status.busy": "2024-09-05T19:39:59.183554Z",
- "iopub.status.idle": "2024-09-05T19:39:59.187818Z",
- "shell.execute_reply": "2024-09-05T19:39:59.187379Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.578938Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.578553Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.582923Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.582473Z"
}
},
"outputs": [],
@@ -477,10 +477,10 @@
"id": "9556c624",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:59.189662Z",
- "iopub.status.busy": "2024-09-05T19:39:59.189487Z",
- "iopub.status.idle": "2024-09-05T19:39:59.195334Z",
- "shell.execute_reply": "2024-09-05T19:39:59.194907Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.584759Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.584580Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.590790Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.590351Z"
}
},
"outputs": [],
@@ -527,10 +527,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:59.197263Z",
- "iopub.status.busy": "2024-09-05T19:39:59.197091Z",
- "iopub.status.idle": "2024-09-05T19:39:59.199733Z",
- "shell.execute_reply": "2024-09-05T19:39:59.199275Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.592686Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.592515Z",
+ "iopub.status.idle": "2024-09-06T19:39:59.595225Z",
+ "shell.execute_reply": "2024-09-06T19:39:59.594766Z"
}
},
"outputs": [],
@@ -545,10 +545,10 @@
"id": "7e1b7860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:39:59.201738Z",
- "iopub.status.busy": "2024-09-05T19:39:59.201336Z",
- "iopub.status.idle": "2024-09-05T19:40:08.299512Z",
- "shell.execute_reply": "2024-09-05T19:40:08.298965Z"
+ "iopub.execute_input": "2024-09-06T19:39:59.597032Z",
+ "iopub.status.busy": "2024-09-06T19:39:59.596865Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.597697Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.597120Z"
}
},
"outputs": [],
@@ -572,10 +572,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.302415Z",
- "iopub.status.busy": "2024-09-05T19:40:08.301779Z",
- "iopub.status.idle": "2024-09-05T19:40:08.309417Z",
- "shell.execute_reply": "2024-09-05T19:40:08.308954Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.600635Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.599991Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.607726Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.607259Z"
}
},
"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.311541Z",
- "iopub.status.busy": "2024-09-05T19:40:08.311201Z",
- "iopub.status.idle": "2024-09-05T19:40:08.314649Z",
- "shell.execute_reply": "2024-09-05T19:40:08.314198Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.609816Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.609470Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.613036Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.612542Z"
}
},
"outputs": [],
@@ -696,10 +696,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.316646Z",
- "iopub.status.busy": "2024-09-05T19:40:08.316310Z",
- "iopub.status.idle": "2024-09-05T19:40:08.319658Z",
- "shell.execute_reply": "2024-09-05T19:40:08.319198Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.615042Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.614643Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.618056Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.617486Z"
}
},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.321765Z",
- "iopub.status.busy": "2024-09-05T19:40:08.321376Z",
- "iopub.status.idle": "2024-09-05T19:40:08.324503Z",
- "shell.execute_reply": "2024-09-05T19:40:08.324037Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.620104Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.619791Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.622907Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.622416Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.326471Z",
- "iopub.status.busy": "2024-09-05T19:40:08.326155Z",
- "iopub.status.idle": "2024-09-05T19:40:08.333988Z",
- "shell.execute_reply": "2024-09-05T19:40:08.333552Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.624768Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.624594Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.632747Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.632288Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.336061Z",
- "iopub.status.busy": "2024-09-05T19:40:08.335706Z",
- "iopub.status.idle": "2024-09-05T19:40:08.338225Z",
- "shell.execute_reply": "2024-09-05T19:40:08.337767Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.634564Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.634392Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.637116Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.636642Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.340281Z",
- "iopub.status.busy": "2024-09-05T19:40:08.339935Z",
- "iopub.status.idle": "2024-09-05T19:40:08.465417Z",
- "shell.execute_reply": "2024-09-05T19:40:08.464849Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.639192Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.638877Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.766647Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.765685Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.467579Z",
- "iopub.status.busy": "2024-09-05T19:40:08.467388Z",
- "iopub.status.idle": "2024-09-05T19:40:08.576982Z",
- "shell.execute_reply": "2024-09-05T19:40:08.576322Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.769173Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.768972Z",
+ "iopub.status.idle": "2024-09-06T19:40:08.878186Z",
+ "shell.execute_reply": "2024-09-06T19:40:08.877593Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:08.579538Z",
- "iopub.status.busy": "2024-09-05T19:40:08.579142Z",
- "iopub.status.idle": "2024-09-05T19:40:09.091088Z",
- "shell.execute_reply": "2024-09-05T19:40:09.090540Z"
+ "iopub.execute_input": "2024-09-06T19:40:08.880641Z",
+ "iopub.status.busy": "2024-09-06T19:40:08.880289Z",
+ "iopub.status.idle": "2024-09-06T19:40:09.386974Z",
+ "shell.execute_reply": "2024-09-06T19:40:09.386324Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:09.093910Z",
- "iopub.status.busy": "2024-09-05T19:40:09.093465Z",
- "iopub.status.idle": "2024-09-05T19:40:09.190047Z",
- "shell.execute_reply": "2024-09-05T19:40:09.189451Z"
+ "iopub.execute_input": "2024-09-06T19:40:09.389675Z",
+ "iopub.status.busy": "2024-09-06T19:40:09.389308Z",
+ "iopub.status.idle": "2024-09-06T19:40:09.485553Z",
+ "shell.execute_reply": "2024-09-06T19:40:09.484996Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:09.192506Z",
- "iopub.status.busy": "2024-09-05T19:40:09.192090Z",
- "iopub.status.idle": "2024-09-05T19:40:09.200751Z",
- "shell.execute_reply": "2024-09-05T19:40:09.200261Z"
+ "iopub.execute_input": "2024-09-06T19:40:09.487964Z",
+ "iopub.status.busy": "2024-09-06T19:40:09.487496Z",
+ "iopub.status.idle": "2024-09-06T19:40:09.496128Z",
+ "shell.execute_reply": "2024-09-06T19:40:09.495570Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:09.202776Z",
- "iopub.status.busy": "2024-09-05T19:40:09.202460Z",
- "iopub.status.idle": "2024-09-05T19:40:09.205281Z",
- "shell.execute_reply": "2024-09-05T19:40:09.204801Z"
+ "iopub.execute_input": "2024-09-06T19:40:09.498303Z",
+ "iopub.status.busy": "2024-09-06T19:40:09.497989Z",
+ "iopub.status.idle": "2024-09-06T19:40:09.500756Z",
+ "shell.execute_reply": "2024-09-06T19:40:09.500274Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:09.207302Z",
- "iopub.status.busy": "2024-09-05T19:40:09.206969Z",
- "iopub.status.idle": "2024-09-05T19:40:14.941265Z",
- "shell.execute_reply": "2024-09-05T19:40:14.940657Z"
+ "iopub.execute_input": "2024-09-06T19:40:09.502626Z",
+ "iopub.status.busy": "2024-09-06T19:40:09.502453Z",
+ "iopub.status.idle": "2024-09-06T19:40:15.134668Z",
+ "shell.execute_reply": "2024-09-06T19:40:15.134055Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:14.943675Z",
- "iopub.status.busy": "2024-09-05T19:40:14.943446Z",
- "iopub.status.idle": "2024-09-05T19:40:14.952597Z",
- "shell.execute_reply": "2024-09-05T19:40:14.952017Z"
+ "iopub.execute_input": "2024-09-06T19:40:15.137003Z",
+ "iopub.status.busy": "2024-09-06T19:40:15.136794Z",
+ "iopub.status.idle": "2024-09-06T19:40:15.145626Z",
+ "shell.execute_reply": "2024-09-06T19:40:15.145149Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:14.954943Z",
- "iopub.status.busy": "2024-09-05T19:40:14.954611Z",
- "iopub.status.idle": "2024-09-05T19:40:15.024368Z",
- "shell.execute_reply": "2024-09-05T19:40:15.023700Z"
+ "iopub.execute_input": "2024-09-06T19:40:15.147739Z",
+ "iopub.status.busy": "2024-09-06T19:40:15.147560Z",
+ "iopub.status.idle": "2024-09-06T19:40:15.212105Z",
+ "shell.execute_reply": "2024-09-06T19:40:15.211592Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index f4a359b72..3d1ba85ed 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:18.199733Z",
- "iopub.status.busy": "2024-09-05T19:40:18.199551Z",
- "iopub.status.idle": "2024-09-05T19:40:19.726748Z",
- "shell.execute_reply": "2024-09-05T19:40:19.726012Z"
+ "iopub.execute_input": "2024-09-06T19:40:18.378801Z",
+ "iopub.status.busy": "2024-09-06T19:40:18.378438Z",
+ "iopub.status.idle": "2024-09-06T19:40:21.013953Z",
+ "shell.execute_reply": "2024-09-06T19:40:21.013191Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:40:19.729634Z",
- "iopub.status.busy": "2024-09-05T19:40:19.729307Z",
- "iopub.status.idle": "2024-09-05T19:41:12.917082Z",
- "shell.execute_reply": "2024-09-05T19:41:12.916305Z"
+ "iopub.execute_input": "2024-09-06T19:40:21.016497Z",
+ "iopub.status.busy": "2024-09-06T19:40:21.016297Z",
+ "iopub.status.idle": "2024-09-06T19:41:26.205588Z",
+ "shell.execute_reply": "2024-09-06T19:41:26.204905Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:12.919767Z",
- "iopub.status.busy": "2024-09-05T19:41:12.919563Z",
- "iopub.status.idle": "2024-09-05T19:41:14.110304Z",
- "shell.execute_reply": "2024-09-05T19:41:14.109686Z"
+ "iopub.execute_input": "2024-09-06T19:41:26.208261Z",
+ "iopub.status.busy": "2024-09-06T19:41:26.207954Z",
+ "iopub.status.idle": "2024-09-06T19:41:27.363762Z",
+ "shell.execute_reply": "2024-09-06T19:41:27.363213Z"
},
"nbsphinx": "hidden"
},
@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -137,10 +137,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:14.112987Z",
- "iopub.status.busy": "2024-09-05T19:41:14.112652Z",
- "iopub.status.idle": "2024-09-05T19:41:14.116314Z",
- "shell.execute_reply": "2024-09-05T19:41:14.115719Z"
+ "iopub.execute_input": "2024-09-06T19:41:27.366273Z",
+ "iopub.status.busy": "2024-09-06T19:41:27.365850Z",
+ "iopub.status.idle": "2024-09-06T19:41:27.369197Z",
+ "shell.execute_reply": "2024-09-06T19:41:27.368626Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:14.118606Z",
- "iopub.status.busy": "2024-09-05T19:41:14.118193Z",
- "iopub.status.idle": "2024-09-05T19:41:14.122291Z",
- "shell.execute_reply": "2024-09-05T19:41:14.121859Z"
+ "iopub.execute_input": "2024-09-06T19:41:27.371272Z",
+ "iopub.status.busy": "2024-09-06T19:41:27.370943Z",
+ "iopub.status.idle": "2024-09-06T19:41:27.374872Z",
+ "shell.execute_reply": "2024-09-06T19:41:27.374336Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:14.124633Z",
- "iopub.status.busy": "2024-09-05T19:41:14.124214Z",
- "iopub.status.idle": "2024-09-05T19:41:14.128275Z",
- "shell.execute_reply": "2024-09-05T19:41:14.127671Z"
+ "iopub.execute_input": "2024-09-06T19:41:27.377058Z",
+ "iopub.status.busy": "2024-09-06T19:41:27.376708Z",
+ "iopub.status.idle": "2024-09-06T19:41:27.380273Z",
+ "shell.execute_reply": "2024-09-06T19:41:27.379824Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:14.130477Z",
- "iopub.status.busy": "2024-09-05T19:41:14.130083Z",
- "iopub.status.idle": "2024-09-05T19:41:14.132962Z",
- "shell.execute_reply": "2024-09-05T19:41:14.132498Z"
+ "iopub.execute_input": "2024-09-06T19:41:27.382286Z",
+ "iopub.status.busy": "2024-09-06T19:41:27.381955Z",
+ "iopub.status.idle": "2024-09-06T19:41:27.384835Z",
+ "shell.execute_reply": "2024-09-06T19:41:27.384366Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:14.134947Z",
- "iopub.status.busy": "2024-09-05T19:41:14.134627Z",
- "iopub.status.idle": "2024-09-05T19:41:52.181715Z",
- "shell.execute_reply": "2024-09-05T19:41:52.181015Z"
+ "iopub.execute_input": "2024-09-06T19:41:27.386838Z",
+ "iopub.status.busy": "2024-09-06T19:41:27.386506Z",
+ "iopub.status.idle": "2024-09-06T19:42:04.890778Z",
+ "shell.execute_reply": "2024-09-06T19:42:04.890135Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a4b3e7cfcb62474f9a340c5c39023be9",
+ "model_id": "00ec60662f03441f8733d768775a0ed1",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "fb3d14222f3f42b487321867e4e431ee",
+ "model_id": "af401850ebaa408dae00a90bb34bc54a",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:52.184254Z",
- "iopub.status.busy": "2024-09-05T19:41:52.184042Z",
- "iopub.status.idle": "2024-09-05T19:41:52.858875Z",
- "shell.execute_reply": "2024-09-05T19:41:52.858345Z"
+ "iopub.execute_input": "2024-09-06T19:42:04.893407Z",
+ "iopub.status.busy": "2024-09-06T19:42:04.893064Z",
+ "iopub.status.idle": "2024-09-06T19:42:05.569760Z",
+ "shell.execute_reply": "2024-09-06T19:42:05.569193Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:52.861209Z",
- "iopub.status.busy": "2024-09-05T19:41:52.860921Z",
- "iopub.status.idle": "2024-09-05T19:41:55.873081Z",
- "shell.execute_reply": "2024-09-05T19:41:55.872487Z"
+ "iopub.execute_input": "2024-09-06T19:42:05.572221Z",
+ "iopub.status.busy": "2024-09-06T19:42:05.571699Z",
+ "iopub.status.idle": "2024-09-06T19:42:08.487750Z",
+ "shell.execute_reply": "2024-09-06T19:42:08.487151Z"
}
},
"outputs": [
@@ -519,17 +519,17 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:41:55.875319Z",
- "iopub.status.busy": "2024-09-05T19:41:55.874961Z",
- "iopub.status.idle": "2024-09-05T19:42:28.673127Z",
- "shell.execute_reply": "2024-09-05T19:42:28.672574Z"
+ "iopub.execute_input": "2024-09-06T19:42:08.490015Z",
+ "iopub.status.busy": "2024-09-06T19:42:08.489812Z",
+ "iopub.status.idle": "2024-09-06T19:42:42.122207Z",
+ "shell.execute_reply": "2024-09-06T19:42:42.121639Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ece025087d704900ab9e6ddd077e3061",
+ "model_id": "a8ef1d6ee6da4d52bd3aa4ef30d9915f",
"version_major": 2,
"version_minor": 0
},
@@ -769,10 +769,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:28.675297Z",
- "iopub.status.busy": "2024-09-05T19:42:28.674959Z",
- "iopub.status.idle": "2024-09-05T19:42:44.105535Z",
- "shell.execute_reply": "2024-09-05T19:42:44.104946Z"
+ "iopub.execute_input": "2024-09-06T19:42:42.124501Z",
+ "iopub.status.busy": "2024-09-06T19:42:42.124158Z",
+ "iopub.status.idle": "2024-09-06T19:42:57.234866Z",
+ "shell.execute_reply": "2024-09-06T19:42:57.234293Z"
}
},
"outputs": [],
@@ -786,10 +786,10 @@
"id": "716c74f3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:44.108179Z",
- "iopub.status.busy": "2024-09-05T19:42:44.107699Z",
- "iopub.status.idle": "2024-09-05T19:42:47.883916Z",
- "shell.execute_reply": "2024-09-05T19:42:47.883404Z"
+ "iopub.execute_input": "2024-09-06T19:42:57.237390Z",
+ "iopub.status.busy": "2024-09-06T19:42:57.237016Z",
+ "iopub.status.idle": "2024-09-06T19:43:00.971913Z",
+ "shell.execute_reply": "2024-09-06T19:43:00.971312Z"
}
},
"outputs": [
@@ -858,17 +858,17 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:47.886159Z",
- "iopub.status.busy": "2024-09-05T19:42:47.885812Z",
- "iopub.status.idle": "2024-09-05T19:42:49.385378Z",
- "shell.execute_reply": "2024-09-05T19:42:49.384804Z"
+ "iopub.execute_input": "2024-09-06T19:43:00.974009Z",
+ "iopub.status.busy": "2024-09-06T19:43:00.973827Z",
+ "iopub.status.idle": "2024-09-06T19:43:02.404764Z",
+ "shell.execute_reply": "2024-09-06T19:43:02.404239Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2ca8a72007b34f2fbd2f2ae6f2cb7931",
+ "model_id": "33547ea19ce34215b8f9bbd75c870924",
"version_major": 2,
"version_minor": 0
},
@@ -898,10 +898,10 @@
"id": "390780a1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:49.388228Z",
- "iopub.status.busy": "2024-09-05T19:42:49.387773Z",
- "iopub.status.idle": "2024-09-05T19:42:49.418203Z",
- "shell.execute_reply": "2024-09-05T19:42:49.417626Z"
+ "iopub.execute_input": "2024-09-06T19:43:02.407222Z",
+ "iopub.status.busy": "2024-09-06T19:43:02.406914Z",
+ "iopub.status.idle": "2024-09-06T19:43:02.435740Z",
+ "shell.execute_reply": "2024-09-06T19:43:02.435223Z"
}
},
"outputs": [],
@@ -915,10 +915,10 @@
"id": "933d6ef0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:49.420836Z",
- "iopub.status.busy": "2024-09-05T19:42:49.420472Z",
- "iopub.status.idle": "2024-09-05T19:42:55.430102Z",
- "shell.execute_reply": "2024-09-05T19:42:55.429512Z"
+ "iopub.execute_input": "2024-09-06T19:43:02.438408Z",
+ "iopub.status.busy": "2024-09-06T19:43:02.438030Z",
+ "iopub.status.idle": "2024-09-06T19:43:08.523002Z",
+ "shell.execute_reply": "2024-09-06T19:43:08.522439Z"
}
},
"outputs": [
@@ -991,10 +991,10 @@
"id": "86bac686",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:55.432343Z",
- "iopub.status.busy": "2024-09-05T19:42:55.431963Z",
- "iopub.status.idle": "2024-09-05T19:42:55.487961Z",
- "shell.execute_reply": "2024-09-05T19:42:55.487400Z"
+ "iopub.execute_input": "2024-09-06T19:43:08.525189Z",
+ "iopub.status.busy": "2024-09-06T19:43:08.524868Z",
+ "iopub.status.idle": "2024-09-06T19:43:08.580916Z",
+ "shell.execute_reply": "2024-09-06T19:43:08.580242Z"
},
"nbsphinx": "hidden"
},
@@ -1038,53 +1038,31 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0271e3b5aa734c59a2c1e77ab40c0fdf": {
+ "00ec60662f03441f8733d768775a0ed1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_bd1a028a18b94bbbb600a39d327b5d2d",
- "placeholder": "",
- "style": "IPY_MODEL_d102c214e0294328845f42c3a4fc31af",
- "tabbable": null,
- "tooltip": null,
- "value": " 30/30 [00:01<00:00, 20.22it/s]"
- }
- },
- "0543eae296d34562bf713d819bab46fa": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_b9ce6baf57594214a8a7669202c8db27",
- "placeholder": "",
- "style": "IPY_MODEL_8141ad12728f457e8d88d842133942f9",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_42d03e2415284486b25cf67ccd387444",
+ "IPY_MODEL_5050585031b24c079460a52a9a4fc488",
+ "IPY_MODEL_b1848abf52f742ed9f7657ba08af06f7"
+ ],
+ "layout": "IPY_MODEL_d73b0ac161c9411fb176d09cfe007d5d",
"tabbable": null,
- "tooltip": null,
- "value": "images processed using softmin: 100%"
+ "tooltip": null
}
},
- "154ab31a8edf479db912cbff400be313": {
+ "0555e6f1fc524e749446c0929d265eab": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1137,117 +1115,101 @@
"width": null
}
},
- "19a073f2103145ff8d3edb1fc13352fd": {
- "model_module": "@jupyter-widgets/controls",
+ "08c7a2f2c6804a7da25a3555d45832fe": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "LayoutModel",
"state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "LayoutModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_8e787c2b073e463881f6c5e2cc8dc67a",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_df29b384d8354feea3a86fb145690034",
- "tabbable": null,
- "tooltip": null,
- "value": 30.0
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "1c9c63d3b81344a393e31ec0b1420510": {
+ "0f26c903e03a409eb8eb23a06ad068a1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "2ca8a72007b34f2fbd2f2ae6f2cb7931": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_0543eae296d34562bf713d819bab46fa",
- "IPY_MODEL_b1f2065c0c4643e5aecd80658d1aaa37",
- "IPY_MODEL_0271e3b5aa734c59a2c1e77ab40c0fdf"
- ],
- "layout": "IPY_MODEL_acf13d87464e4f629212a388add13530",
- "tabbable": null,
- "tooltip": null
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "2ff9cc8a4b654553be47d8b435944b7f": {
+ "219cc478643a4ee5ac3bd50beeb53306": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_b18e4f016fcf408a98576f5ec0eaa44b",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_c13620cfc24b48c0ba51a5593d66841e",
+ "layout": "IPY_MODEL_7b66b5652e59476aab6385c55f338eaf",
+ "placeholder": "",
+ "style": "IPY_MODEL_c70c5f7b514a4120b47fe4694b8aa561",
"tabbable": null,
"tooltip": null,
- "value": 30.0
- }
- },
- "30327c6cf5554d71a80f362c0e3c517a": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "value": " 30/30 [00:01<00:00, 21.35it/s]"
}
},
- "373286fca444445097e38e012eec4165": {
+ "23f68f6bcc9f4247ac306e707ae76a3e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1300,7 +1262,23 @@
"width": null
}
},
- "44a2a05278754fb8b099f76a80aa6e48": {
+ "26c9d71cd2b144f5a62f2e547396cf9d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "31810f3656744673bb829bd7c19b4796": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1353,48 +1331,47 @@
"width": null
}
},
- "44c9f78d8aa147219badd49c88116bf5": {
+ "33547ea19ce34215b8f9bbd75c870924": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_6901169080b04ad499942dc391b9b336",
+ "IPY_MODEL_90fef083c08c4c3c927458dfb8b00fe9",
+ "IPY_MODEL_219cc478643a4ee5ac3bd50beeb53306"
+ ],
+ "layout": "IPY_MODEL_571062df41e24ec2a51ede636c1c40ae",
+ "tabbable": null,
+ "tooltip": null
}
},
- "45ed018ec81f4df08d861bcb58442dd3": {
+ "428890bcca0c4c398b4c85e7b197ef23": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_44a2a05278754fb8b099f76a80aa6e48",
- "placeholder": "",
- "style": "IPY_MODEL_c2b264c49d234675bf6ed2efbeefbaef",
- "tabbable": null,
- "tooltip": null,
- "value": "number of examples processed for checking labels: 100%"
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "6d920735297d48d5aa5fa76eb77c5faa": {
+ "42d03e2415284486b25cf67ccd387444": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1409,31 +1386,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ac3b63d4a32f44cdb880bf34eae8e38f",
+ "layout": "IPY_MODEL_08c7a2f2c6804a7da25a3555d45832fe",
"placeholder": "",
- "style": "IPY_MODEL_fe96e6f2b3d44ed9a068d86177dba9d9",
+ "style": "IPY_MODEL_5428bca92792410db3731a76852725a2",
"tabbable": null,
"tooltip": null,
- "value": " 30/30 [00:00<00:00, 817.50it/s]"
- }
- },
- "72b3805a9e3b46659f904bc081e85a45": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "value": "number of examples processed for estimating thresholds: 100%"
}
},
- "770a9991bd4f479da0fceb5900cbf417": {
+ "44a941086c164d5bb775c41c7d4ac57f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1486,48 +1447,7 @@
"width": null
}
},
- "780bdc9cdfc146f4b64fe526a99ff03b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_b89d909a19a7460f81b9b346038cdfe0",
- "placeholder": "",
- "style": "IPY_MODEL_b9e08204838a45a489517f7aac01fcdb",
- "tabbable": null,
- "tooltip": null,
- "value": " 4997683/4997683 [00:32<00:00, 154657.78it/s]"
- }
- },
- "8141ad12728f457e8d88d842133942f9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "8e787c2b073e463881f6c5e2cc8dc67a": {
+ "474187191bb2423bbeaab8075807fc8d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1580,60 +1500,51 @@
"width": null
}
},
- "915659784e954336952dbe532ca9c568": {
- "model_module": "@jupyter-widgets/base",
+ "5050585031b24c079460a52a9a4fc488": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "FloatProgressModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_fefad91592514c8b93cde6a9aa658432",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_26c9d71cd2b144f5a62f2e547396cf9d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 30.0
+ }
+ },
+ "5428bca92792410db3731a76852725a2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "9d3137e0b66a43d2bd580b2b48653afc": {
+ "56086d38b6e24dd381b3d2d8adfc7dee": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1648,15 +1559,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_770a9991bd4f479da0fceb5900cbf417",
+ "layout": "IPY_MODEL_8b4141a6045142c1b9ba131103d924f0",
"placeholder": "",
- "style": "IPY_MODEL_d654f626f86948388172098f7cc43d25",
+ "style": "IPY_MODEL_62e07c87b8f14d10ae3081dc89c264cb",
"tabbable": null,
"tooltip": null,
- "value": "number of examples processed for estimating thresholds: 100%"
+ "value": " 30/30 [00:25<00:00, 1.19it/s]"
}
},
- "a1e731438d6d45e5966433bcf063a059": {
+ "571062df41e24ec2a51ede636c1c40ae": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1709,54 +1620,69 @@
"width": null
}
},
- "a38a937b167f4ba482c62fb6f9795bb4": {
+ "5aa47fe7e6cf4464bcbe167e6d3ba68a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "5e211e7a482d4ffc95757eed7f7aa9cc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_c8fa8dfe739640758359553a0e57be14",
- "placeholder": "",
- "style": "IPY_MODEL_44c9f78d8aa147219badd49c88116bf5",
+ "layout": "IPY_MODEL_71cc03f01ffb487095fef61fe310cb72",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_428890bcca0c4c398b4c85e7b197ef23",
"tabbable": null,
"tooltip": null,
- "value": "100%"
+ "value": 30.0
}
},
- "a4b3e7cfcb62474f9a340c5c39023be9": {
+ "62e07c87b8f14d10ae3081dc89c264cb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_9d3137e0b66a43d2bd580b2b48653afc",
- "IPY_MODEL_19a073f2103145ff8d3edb1fc13352fd",
- "IPY_MODEL_6d920735297d48d5aa5fa76eb77c5faa"
- ],
- "layout": "IPY_MODEL_f612b67a99514134b4f6b0b836455efc",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "a8e5b9a64739480e863ba68bb4d8600f": {
+ "6901169080b04ad499942dc391b9b336": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1771,15 +1697,33 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_fd5942b1092f42aeaedd8058d4d911de",
+ "layout": "IPY_MODEL_fa2dd8d15728476eac598aeb95576e3b",
"placeholder": "",
- "style": "IPY_MODEL_30327c6cf5554d71a80f362c0e3c517a",
+ "style": "IPY_MODEL_a247c69930644302aed767d71b7ec676",
"tabbable": null,
"tooltip": null,
- "value": " 30/30 [00:25<00:00, 1.15it/s]"
+ "value": "images processed using softmin: 100%"
}
},
- "ac3b63d4a32f44cdb880bf34eae8e38f": {
+ "71684d8531234f3d9d16e15f5e2a1318": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "71cc03f01ffb487095fef61fe310cb72": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1832,7 +1776,7 @@
"width": null
}
},
- "acf13d87464e4f629212a388add13530": {
+ "71d9c9ff1e934321985ce73f6d70432d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1885,7 +1829,25 @@
"width": null
}
},
- "b18e4f016fcf408a98576f5ec0eaa44b": {
+ "72aa2b7d62f44bfba1fef33687cd2d9c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "733932bb0ae3401390e27945e01e9afa": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1938,33 +1900,7 @@
"width": null
}
},
- "b1f2065c0c4643e5aecd80658d1aaa37": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_373286fca444445097e38e012eec4165",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_1c9c63d3b81344a393e31ec0b1420510",
- "tabbable": null,
- "tooltip": null,
- "value": 30.0
- }
- },
- "b89d909a19a7460f81b9b346038cdfe0": {
+ "7b66b5652e59476aab6385c55f338eaf": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2017,7 +1953,7 @@
"width": null
}
},
- "b9ce6baf57594214a8a7669202c8db27": {
+ "8b4141a6045142c1b9ba131103d924f0": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2070,7 +2006,210 @@
"width": null
}
},
- "b9e08204838a45a489517f7aac01fcdb": {
+ "8c44f5cb10834552b9f054ccff28de8f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_0555e6f1fc524e749446c0929d265eab",
+ "max": 4997683.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_b4ff48b5ef42475cb8d931380feef05a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 4997683.0
+ }
+ },
+ "90fef083c08c4c3c927458dfb8b00fe9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_d8f70224ecee42f48ecf14d646040c54",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_e468d38bc9454ebf87117d355645f3f1",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 30.0
+ }
+ },
+ "a247c69930644302aed767d71b7ec676": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "a8ef1d6ee6da4d52bd3aa4ef30d9915f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_e7a051930ecf4f8da5a7114fa550bc7c",
+ "IPY_MODEL_8c44f5cb10834552b9f054ccff28de8f",
+ "IPY_MODEL_abb55722ee8a4e9383f54ba9776bfb21"
+ ],
+ "layout": "IPY_MODEL_44a941086c164d5bb775c41c7d4ac57f",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "abb55722ee8a4e9383f54ba9776bfb21": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_23f68f6bcc9f4247ac306e707ae76a3e",
+ "placeholder": "",
+ "style": "IPY_MODEL_0f26c903e03a409eb8eb23a06ad068a1",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 4997683/4997683 [00:33<00:00, 147431.29it/s]"
+ }
+ },
+ "ac71e20e794944a5ad10d81bd3802d6a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_733932bb0ae3401390e27945e01e9afa",
+ "placeholder": "",
+ "style": "IPY_MODEL_72aa2b7d62f44bfba1fef33687cd2d9c",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "number of examples processed for checking labels: 100%"
+ }
+ },
+ "af401850ebaa408dae00a90bb34bc54a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ac71e20e794944a5ad10d81bd3802d6a",
+ "IPY_MODEL_5e211e7a482d4ffc95757eed7f7aa9cc",
+ "IPY_MODEL_56086d38b6e24dd381b3d2d8adfc7dee"
+ ],
+ "layout": "IPY_MODEL_71d9c9ff1e934321985ce73f6d70432d",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "b1848abf52f742ed9f7657ba08af06f7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_474187191bb2423bbeaab8075807fc8d",
+ "placeholder": "",
+ "style": "IPY_MODEL_5aa47fe7e6cf4464bcbe167e6d3ba68a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 30/30 [00:00<00:00, 787.55it/s]"
+ }
+ },
+ "b4ff48b5ef42475cb8d931380feef05a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "c70c5f7b514a4120b47fe4694b8aa561": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2088,7 +2227,7 @@
"text_color": null
}
},
- "bd1a028a18b94bbbb600a39d327b5d2d": {
+ "d73b0ac161c9411fb176d09cfe007d5d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2141,41 +2280,7 @@
"width": null
}
},
- "c13620cfc24b48c0ba51a5593d66841e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "c2b264c49d234675bf6ed2efbeefbaef": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "c8fa8dfe739640758359553a0e57be14": {
+ "d8f70224ecee42f48ecf14d646040c54": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2228,43 +2333,7 @@
"width": null
}
},
- "d102c214e0294328845f42c3a4fc31af": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "d654f626f86948388172098f7cc43d25": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "df29b384d8354feea3a86fb145690034": {
+ "e468d38bc9454ebf87117d355645f3f1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -2280,57 +2349,30 @@
"description_width": ""
}
},
- "ece025087d704900ab9e6ddd077e3061": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_a38a937b167f4ba482c62fb6f9795bb4",
- "IPY_MODEL_f5051711724c4bc0acdc14f8b27478fe",
- "IPY_MODEL_780bdc9cdfc146f4b64fe526a99ff03b"
- ],
- "layout": "IPY_MODEL_154ab31a8edf479db912cbff400be313",
- "tabbable": null,
- "tooltip": null
- }
- },
- "f5051711724c4bc0acdc14f8b27478fe": {
+ "e7a051930ecf4f8da5a7114fa550bc7c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_a1e731438d6d45e5966433bcf063a059",
- "max": 4997683.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_72b3805a9e3b46659f904bc081e85a45",
+ "layout": "IPY_MODEL_31810f3656744673bb829bd7c19b4796",
+ "placeholder": "",
+ "style": "IPY_MODEL_71684d8531234f3d9d16e15f5e2a1318",
"tabbable": null,
"tooltip": null,
- "value": 4997683.0
+ "value": "100%"
}
},
- "f612b67a99514134b4f6b0b836455efc": {
+ "fa2dd8d15728476eac598aeb95576e3b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2383,31 +2425,7 @@
"width": null
}
},
- "fb3d14222f3f42b487321867e4e431ee": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_45ed018ec81f4df08d861bcb58442dd3",
- "IPY_MODEL_2ff9cc8a4b654553be47d8b435944b7f",
- "IPY_MODEL_a8e5b9a64739480e863ba68bb4d8600f"
- ],
- "layout": "IPY_MODEL_915659784e954336952dbe532ca9c568",
- "tabbable": null,
- "tooltip": null
- }
- },
- "fd5942b1092f42aeaedd8058d4d911de": {
+ "fefad91592514c8b93cde6a9aa658432": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2459,24 +2477,6 @@
"visibility": null,
"width": null
}
- },
- "fe96e6f2b3d44ed9a068d86177dba9d9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 49d76911a..c988c12c2 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:57.901570Z",
- "iopub.status.busy": "2024-09-05T19:42:57.901387Z",
- "iopub.status.idle": "2024-09-05T19:42:59.034492Z",
- "shell.execute_reply": "2024-09-05T19:42:59.033839Z"
+ "iopub.execute_input": "2024-09-06T19:43:11.117353Z",
+ "iopub.status.busy": "2024-09-06T19:43:11.117178Z",
+ "iopub.status.idle": "2024-09-06T19:43:13.210573Z",
+ "shell.execute_reply": "2024-09-06T19:43:13.209958Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-09-05 19:42:57-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-09-06 19:43:11-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,15 +94,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "169.150.236.98, 2400:52e0:1a00::1067:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "connected.\r\n",
+ "169.150.249.167, 2400:52e0:1a01::907:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|169.150.249.167|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -125,7 +118,7 @@
"\r",
"conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-09-05 19:42:58 (6.36 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-09-06 19:43:11 (7.82 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -145,9 +138,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-09-05 19:42:58-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.17.152, 3.5.30.212, 54.231.228.65, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.17.152|:443... connected.\r\n",
+ "--2024-09-06 19:43:11-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.201.17, 52.217.193.233, 52.217.81.84, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.201.17|:443... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "connected.\r\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"HTTP request sent, awaiting response... "
]
},
@@ -168,9 +174,33 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n",
+ "pred_probs.npz 0%[ ] 142.53K 668KB/s "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 8%[> ] 1.35M 3.16MB/s "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 50%[=========> ] 8.28M 12.9MB/s "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 100%[===================>] 16.26M 20.4MB/s in 0.8s \r\n",
"\r\n",
- "2024-09-05 19:42:58 (169 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-09-06 19:43:13 (20.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -187,10 +217,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:42:59.037334Z",
- "iopub.status.busy": "2024-09-05T19:42:59.036934Z",
- "iopub.status.idle": "2024-09-05T19:43:00.344176Z",
- "shell.execute_reply": "2024-09-05T19:43:00.343566Z"
+ "iopub.execute_input": "2024-09-06T19:43:13.213109Z",
+ "iopub.status.busy": "2024-09-06T19:43:13.212725Z",
+ "iopub.status.idle": "2024-09-06T19:43:14.513752Z",
+ "shell.execute_reply": "2024-09-06T19:43:14.513226Z"
},
"nbsphinx": "hidden"
},
@@ -201,7 +231,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -227,10 +257,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:00.346737Z",
- "iopub.status.busy": "2024-09-05T19:43:00.346278Z",
- "iopub.status.idle": "2024-09-05T19:43:00.349652Z",
- "shell.execute_reply": "2024-09-05T19:43:00.349193Z"
+ "iopub.execute_input": "2024-09-06T19:43:14.516436Z",
+ "iopub.status.busy": "2024-09-06T19:43:14.515941Z",
+ "iopub.status.idle": "2024-09-06T19:43:14.519305Z",
+ "shell.execute_reply": "2024-09-06T19:43:14.518871Z"
}
},
"outputs": [],
@@ -280,10 +310,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:00.351675Z",
- "iopub.status.busy": "2024-09-05T19:43:00.351340Z",
- "iopub.status.idle": "2024-09-05T19:43:00.354237Z",
- "shell.execute_reply": "2024-09-05T19:43:00.353818Z"
+ "iopub.execute_input": "2024-09-06T19:43:14.521508Z",
+ "iopub.status.busy": "2024-09-06T19:43:14.521171Z",
+ "iopub.status.idle": "2024-09-06T19:43:14.524052Z",
+ "shell.execute_reply": "2024-09-06T19:43:14.523615Z"
},
"nbsphinx": "hidden"
},
@@ -301,10 +331,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:00.356189Z",
- "iopub.status.busy": "2024-09-05T19:43:00.355844Z",
- "iopub.status.idle": "2024-09-05T19:43:09.562038Z",
- "shell.execute_reply": "2024-09-05T19:43:09.561398Z"
+ "iopub.execute_input": "2024-09-06T19:43:14.526149Z",
+ "iopub.status.busy": "2024-09-06T19:43:14.525818Z",
+ "iopub.status.idle": "2024-09-06T19:43:23.627822Z",
+ "shell.execute_reply": "2024-09-06T19:43:23.627249Z"
}
},
"outputs": [],
@@ -378,10 +408,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:09.564850Z",
- "iopub.status.busy": "2024-09-05T19:43:09.564291Z",
- "iopub.status.idle": "2024-09-05T19:43:09.570178Z",
- "shell.execute_reply": "2024-09-05T19:43:09.569596Z"
+ "iopub.execute_input": "2024-09-06T19:43:23.630427Z",
+ "iopub.status.busy": "2024-09-06T19:43:23.630129Z",
+ "iopub.status.idle": "2024-09-06T19:43:23.635623Z",
+ "shell.execute_reply": "2024-09-06T19:43:23.635160Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +451,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:09.572335Z",
- "iopub.status.busy": "2024-09-05T19:43:09.571988Z",
- "iopub.status.idle": "2024-09-05T19:43:09.932566Z",
- "shell.execute_reply": "2024-09-05T19:43:09.931972Z"
+ "iopub.execute_input": "2024-09-06T19:43:23.637682Z",
+ "iopub.status.busy": "2024-09-06T19:43:23.637404Z",
+ "iopub.status.idle": "2024-09-06T19:43:23.985761Z",
+ "shell.execute_reply": "2024-09-06T19:43:23.985192Z"
}
},
"outputs": [],
@@ -461,10 +491,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:09.935037Z",
- "iopub.status.busy": "2024-09-05T19:43:09.934669Z",
- "iopub.status.idle": "2024-09-05T19:43:09.939240Z",
- "shell.execute_reply": "2024-09-05T19:43:09.938758Z"
+ "iopub.execute_input": "2024-09-06T19:43:23.988095Z",
+ "iopub.status.busy": "2024-09-06T19:43:23.987906Z",
+ "iopub.status.idle": "2024-09-06T19:43:23.992118Z",
+ "shell.execute_reply": "2024-09-06T19:43:23.991556Z"
}
},
"outputs": [
@@ -536,10 +566,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:09.941448Z",
- "iopub.status.busy": "2024-09-05T19:43:09.941115Z",
- "iopub.status.idle": "2024-09-05T19:43:12.624115Z",
- "shell.execute_reply": "2024-09-05T19:43:12.623415Z"
+ "iopub.execute_input": "2024-09-06T19:43:23.994018Z",
+ "iopub.status.busy": "2024-09-06T19:43:23.993843Z",
+ "iopub.status.idle": "2024-09-06T19:43:26.637725Z",
+ "shell.execute_reply": "2024-09-06T19:43:26.636888Z"
}
},
"outputs": [],
@@ -561,10 +591,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:12.627139Z",
- "iopub.status.busy": "2024-09-05T19:43:12.626540Z",
- "iopub.status.idle": "2024-09-05T19:43:12.630609Z",
- "shell.execute_reply": "2024-09-05T19:43:12.630068Z"
+ "iopub.execute_input": "2024-09-06T19:43:26.641128Z",
+ "iopub.status.busy": "2024-09-06T19:43:26.640324Z",
+ "iopub.status.idle": "2024-09-06T19:43:26.644620Z",
+ "shell.execute_reply": "2024-09-06T19:43:26.644038Z"
}
},
"outputs": [
@@ -600,10 +630,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:12.632540Z",
- "iopub.status.busy": "2024-09-05T19:43:12.632364Z",
- "iopub.status.idle": "2024-09-05T19:43:12.637863Z",
- "shell.execute_reply": "2024-09-05T19:43:12.637338Z"
+ "iopub.execute_input": "2024-09-06T19:43:26.646963Z",
+ "iopub.status.busy": "2024-09-06T19:43:26.646497Z",
+ "iopub.status.idle": "2024-09-06T19:43:26.651999Z",
+ "shell.execute_reply": "2024-09-06T19:43:26.651546Z"
}
},
"outputs": [
@@ -781,10 +811,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:12.640008Z",
- "iopub.status.busy": "2024-09-05T19:43:12.639588Z",
- "iopub.status.idle": "2024-09-05T19:43:12.666041Z",
- "shell.execute_reply": "2024-09-05T19:43:12.665536Z"
+ "iopub.execute_input": "2024-09-06T19:43:26.654071Z",
+ "iopub.status.busy": "2024-09-06T19:43:26.653731Z",
+ "iopub.status.idle": "2024-09-06T19:43:26.680854Z",
+ "shell.execute_reply": "2024-09-06T19:43:26.680272Z"
}
},
"outputs": [
@@ -886,10 +916,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:12.668048Z",
- "iopub.status.busy": "2024-09-05T19:43:12.667853Z",
- "iopub.status.idle": "2024-09-05T19:43:12.672253Z",
- "shell.execute_reply": "2024-09-05T19:43:12.671653Z"
+ "iopub.execute_input": "2024-09-06T19:43:26.683063Z",
+ "iopub.status.busy": "2024-09-06T19:43:26.682748Z",
+ "iopub.status.idle": "2024-09-06T19:43:26.687165Z",
+ "shell.execute_reply": "2024-09-06T19:43:26.686677Z"
}
},
"outputs": [
@@ -963,10 +993,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:12.674498Z",
- "iopub.status.busy": "2024-09-05T19:43:12.674041Z",
- "iopub.status.idle": "2024-09-05T19:43:14.136811Z",
- "shell.execute_reply": "2024-09-05T19:43:14.136171Z"
+ "iopub.execute_input": "2024-09-06T19:43:26.689077Z",
+ "iopub.status.busy": "2024-09-06T19:43:26.688908Z",
+ "iopub.status.idle": "2024-09-06T19:43:28.095086Z",
+ "shell.execute_reply": "2024-09-06T19:43:28.094529Z"
}
},
"outputs": [
@@ -1138,10 +1168,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:43:14.139136Z",
- "iopub.status.busy": "2024-09-05T19:43:14.138650Z",
- "iopub.status.idle": "2024-09-05T19:43:14.142973Z",
- "shell.execute_reply": "2024-09-05T19:43:14.142382Z"
+ "iopub.execute_input": "2024-09-06T19:43:28.097561Z",
+ "iopub.status.busy": "2024-09-06T19:43:28.097109Z",
+ "iopub.status.idle": "2024-09-06T19:43:28.101190Z",
+ "shell.execute_reply": "2024-09-06T19:43:28.100749Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree
index 40ae7ff7f..41b7e3f4e 100644
Binary files a/master/.doctrees/tutorials/clean_learning/index.doctree and b/master/.doctrees/tutorials/clean_learning/index.doctree differ
diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree
index 0d6884ef2..dea8d934f 100644
Binary files a/master/.doctrees/tutorials/clean_learning/tabular.doctree and b/master/.doctrees/tutorials/clean_learning/tabular.doctree differ
diff --git a/master/.doctrees/tutorials/clean_learning/text.doctree b/master/.doctrees/tutorials/clean_learning/text.doctree
index 878a7550d..90395ba3d 100644
Binary files a/master/.doctrees/tutorials/clean_learning/text.doctree and b/master/.doctrees/tutorials/clean_learning/text.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree
index 91c6d7ef0..5cea7eca7 100644
Binary files a/master/.doctrees/tutorials/datalab/audio.doctree and b/master/.doctrees/tutorials/datalab/audio.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree
index 744e87f25..9474aa59a 100644
Binary files a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree and b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree
index d1a662cf0..6ee346b21 100644
Binary files a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree and b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree
index c3ae1a00e..272a5859e 100644
Binary files a/master/.doctrees/tutorials/datalab/image.doctree and b/master/.doctrees/tutorials/datalab/image.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree
index 61b9e41f8..2452b1622 100644
Binary files a/master/.doctrees/tutorials/datalab/index.doctree and b/master/.doctrees/tutorials/datalab/index.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree
index b35d79e61..a7ea38c93 100644
Binary files a/master/.doctrees/tutorials/datalab/tabular.doctree and b/master/.doctrees/tutorials/datalab/tabular.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree
index 39cbd042c..30340024b 100644
Binary files a/master/.doctrees/tutorials/datalab/text.doctree and b/master/.doctrees/tutorials/datalab/text.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/workflows.doctree b/master/.doctrees/tutorials/datalab/workflows.doctree
index 935410968..f6534518e 100644
Binary files a/master/.doctrees/tutorials/datalab/workflows.doctree and b/master/.doctrees/tutorials/datalab/workflows.doctree differ
diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree
index 3dd65c335..f4d9c9605 100644
Binary files a/master/.doctrees/tutorials/dataset_health.doctree and b/master/.doctrees/tutorials/dataset_health.doctree differ
diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree
index 7e9a36519..ab8195074 100644
Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ
diff --git a/master/.doctrees/tutorials/improving_ml_performance.doctree b/master/.doctrees/tutorials/improving_ml_performance.doctree
index 55ef04d65..abaf10fb6 100644
Binary files a/master/.doctrees/tutorials/improving_ml_performance.doctree and b/master/.doctrees/tutorials/improving_ml_performance.doctree differ
diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree
index 2c27e5296..124c2e420 100644
Binary files a/master/.doctrees/tutorials/indepth_overview.doctree and b/master/.doctrees/tutorials/indepth_overview.doctree differ
diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree
index 61e0c7d19..433c4f517 100644
Binary files a/master/.doctrees/tutorials/index.doctree and b/master/.doctrees/tutorials/index.doctree differ
diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree
index 0d88a84b5..8e296d6cf 100644
Binary files a/master/.doctrees/tutorials/multiannotator.doctree and b/master/.doctrees/tutorials/multiannotator.doctree differ
diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree
index 6c733652a..d09ff0b65 100644
Binary files a/master/.doctrees/tutorials/multilabel_classification.doctree and b/master/.doctrees/tutorials/multilabel_classification.doctree differ
diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree
index c55def560..94f991fc8 100644
Binary files a/master/.doctrees/tutorials/object_detection.doctree and b/master/.doctrees/tutorials/object_detection.doctree differ
diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree
index fabd27bc6..3d0d6d533 100644
Binary files a/master/.doctrees/tutorials/outliers.doctree and b/master/.doctrees/tutorials/outliers.doctree differ
diff --git a/master/.doctrees/tutorials/pred_probs_cross_val.doctree b/master/.doctrees/tutorials/pred_probs_cross_val.doctree
index 316ec0efd..cb12241c5 100644
Binary files a/master/.doctrees/tutorials/pred_probs_cross_val.doctree and b/master/.doctrees/tutorials/pred_probs_cross_val.doctree differ
diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree
index 2264cca5b..b0b64f5a5 100644
Binary files a/master/.doctrees/tutorials/regression.doctree and b/master/.doctrees/tutorials/regression.doctree differ
diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree
index b6d91c694..9d1d72787 100644
Binary files a/master/.doctrees/tutorials/segmentation.doctree and b/master/.doctrees/tutorials/segmentation.doctree differ
diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
index 13761a5b5..d1c6aa6d5 100644
Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ
diff --git a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
index c3eaabfa7..2e72a08fd 100644
--- a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
+++ b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
@@ -806,6 +806,14 @@ To customize optional parameters for specific image issue types, you can provide
For more information, view the cleanvision `docs `_.
+Spurious Correlations Issue Parameters
+--------------------------------------
+
+.. code-block:: python
+
+ spurious_correlations_kwargs = {
+ "threshold": 0.3, # Non-negative floating value between 0 and 1, lower value implies fewer image properties may have a low enough label uncorrelatedness score to be marked as issue and vice versa.
+ }
Cleanlab Studio (Easy Mode)
---------------------------
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 900359041..dfa986166 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index ea752b492..96aa2015a 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index 9280c802f..fb9f7a38a 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 89e2c0500..1f4d94f78 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index 18b6d59ef..e9f1461fe 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index c60b5df9e..5dcb0dd9f 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index 4a3a1d3e9..5fca24e96 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 334b6ea56..ca90c3c59 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb
index 09d436c41..d104da0e9 100644
--- a/master/_sources/tutorials/improving_ml_performance.ipynb
+++ b/master/_sources/tutorials/improving_ml_performance.ipynb
@@ -67,7 +67,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index 73e38aba1..ec9b0c142 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index 372648258..a0ba8e763 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 6976a084b..8b7654606 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index e993e4885..155a9b7d0 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 3c1170dec..40dabd38c 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index 960094e91..b36b8a466 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index c18da4750..eb6cafaa0 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index 4a41dbe71..d3bb49df4 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/datalab/guide/index.html b/master/cleanlab/datalab/guide/index.html
index cfc73541d..82ade157c 100644
--- a/master/cleanlab/datalab/guide/index.html
+++ b/master/cleanlab/datalab/guide/index.html
@@ -670,6 +670,7 @@ Data Valuation Issue Parameters
Image Issue Parameters
+Spurious Correlations Issue Parameters
Cleanlab Studio (Easy Mode)
diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html
index cee862e61..0911e92be 100644
--- a/master/cleanlab/datalab/guide/issue_type_description.html
+++ b/master/cleanlab/datalab/guide/issue_type_description.html
@@ -1493,6 +1493,14 @@ Image Issue Parametersdocs.
+
+Spurious Correlations Issue Parameters
+spurious_correlations_kwargs = {
+ "threshold": 0.3, # Non-negative floating value between 0 and 1, lower value implies fewer image properties may have a low enough label uncorrelatedness score to be marked as issue and vice versa.
+}
+
+
+
Cleanlab Studio (Easy Mode)
Cleanlab Studio is a fully automated platform that can detect the same data issues as this package, as well as many more types of issues, all without you having to do any Machine Learning (or even write any code). Beyond being 100x faster to use and producing more useful results, Cleanlab Studio also provides an intelligent data correction interface for you to quickly fix the issues detected in your dataset (a single data scientist can fix millions of data points thanks to AI suggestions).
@@ -1644,6 +1652,7 @@ Cleanlab Studio (Easy Mode)
Data Valuation Issue Parameters
Image Issue Parameters
+Spurious Correlations Issue Parameters
Cleanlab Studio (Easy Mode)
diff --git a/master/objects.inv b/master/objects.inv
index 1c7070311..b35e54d50 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/searchindex.js b/master/searchindex.js
index 9de1d3563..3b057ca24 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/adapter/imagelab", "cleanlab/datalab/internal/adapter/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/improving_ml_performance", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/improving_ml_performance.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "imagelab", "adapter", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "Improving ML Performance via Data Curation with Train vs Test Splits", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 97, 98, 99, 100, 101, 105, 110], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105, 110], "680894": 10, "41": [10, 97, 98, 100, 105, 108], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108, 110], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "scaling_factor": [31, 57], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "enough": [43, 59, 97, 99], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105, 106], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "strength": [57, 71, 97], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101, 110], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105, 110], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": 88, "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "beneficiary_not_allow": [89, 96], "change_pin": [89, 96], "card_payment_fee_charg": [89, 96], "apple_pay_or_google_pai": [89, 96], "getting_spare_card": [89, 96], "lost_or_stolen_phon": [89, 96], "card_about_to_expir": [89, 96], "visa_or_mastercard": [89, 96], "supported_cards_and_curr": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109, 110], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105, 110], "039122": 91, "044598": 91, "105": [91, 105], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "999": [93, 96], "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "733": 93, "493": 93, "060": 93, "330": [93, 100, 105], "505": 93, "772": 93, "476": [93, 100], "340": [93, 100], "210": 93, "328": [93, 105], "310": 93, "693": 93, "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "523": [97, 100], "991400": 97, "356958": 97, "362": 97, "619565": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101, 110], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "153": [97, 100, 105], "111": [97, 103, 108], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": [97, 110], "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105], "167": [98, 101, 105], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "965": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "605": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "597": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "363": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105, 110], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": [101, 110], "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105], "174": 101, "150": [101, 103, 105, 110], "169": [101, 110], "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "912": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": 104, "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": [105, 110], "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": [105, 110], "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": 105, "237": 105, "207": 105, "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "110": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "99832064": 106, "92it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a00": 110, "1067": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "inflat": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [18, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [21, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[99, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[101, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[101, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[101, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[101, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[101, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[101, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[101, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[101, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/adapter/imagelab", "cleanlab/datalab/internal/adapter/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/improving_ml_performance", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/improving_ml_performance.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "imagelab", "adapter", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "Improving ML Performance via Data Curation with Train vs Test Splits", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 98, 99, 100, 101, 105], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105], "680894": 10, "41": [10, 97, 98, 100, 105, 108], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "spurious_correlations_kwarg": 10, "enough": [10, 43, 59, 97, 99], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "scaling_factor": [31, 57], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "strength": [57, 71, 97], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 106, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": [88, 110], "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "change_pin": [89, 96], "visa_or_mastercard": [89, 96], "getting_spare_card": [89, 96], "supported_cards_and_curr": [89, 96], "lost_or_stolen_phon": [89, 96], "beneficiary_not_allow": [89, 96], "apple_pay_or_google_pai": [89, 96], "card_about_to_expir": [89, 96], "card_payment_fee_charg": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109, 110], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 97, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105, 110], "039122": 91, "044598": 91, "105": [91, 105], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105, 110], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105, 110], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "923": 93, "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "597": [93, 100], "493": 93, "060": 93, "922": 93, "330": [93, 100, 105], "505": 93, "912": [93, 101], "476": [93, 100], "340": [93, 100], "879": 93, "328": [93, 105], "310": 93, "556": 93, "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 97, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "733": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "999": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "523": [97, 100], "991400": 97, "356958": 97, "362": 97, "619565": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108, 110], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "111": [97, 103, 108], "153": [97, 100, 105], "110": [97, 105], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": [97, 110], "005": 97, "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105], "167": [98, 101, 105, 110], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "965": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "605": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108, 110], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "363": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101, 110], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105, 110], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": 101, "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105, 110], "174": 101, "150": [101, 103, 105, 110], "169": [101, 110], "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": 104, "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": [105, 110], "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": 105, "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": [105, 110], "237": 105, "207": 105, "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "46456493": 106, "64it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a01": 110, "907": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "inflat": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "4mb": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [18, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [21, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Spurious Correlations Issue Parameters": [[10, "spurious-correlations-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[99, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[101, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[101, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[101, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[101, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[101, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[101, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[101, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[101, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 5e85de9ac..9d1517aad 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:32:59.587012Z",
- "iopub.status.busy": "2024-09-05T19:32:59.586834Z",
- "iopub.status.idle": "2024-09-05T19:33:00.869499Z",
- "shell.execute_reply": "2024-09-05T19:33:00.868940Z"
+ "iopub.execute_input": "2024-09-06T19:32:51.069638Z",
+ "iopub.status.busy": "2024-09-06T19:32:51.069457Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.310694Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.310136Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:00.872309Z",
- "iopub.status.busy": "2024-09-05T19:33:00.871752Z",
- "iopub.status.idle": "2024-09-05T19:33:00.890028Z",
- "shell.execute_reply": "2024-09-05T19:33:00.889577Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.313494Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.312922Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.331174Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.330732Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:00.892312Z",
- "iopub.status.busy": "2024-09-05T19:33:00.892050Z",
- "iopub.status.idle": "2024-09-05T19:33:01.003828Z",
- "shell.execute_reply": "2024-09-05T19:33:01.003258Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.333414Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.333012Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.616135Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.615552Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.036111Z",
- "iopub.status.busy": "2024-09-05T19:33:01.035678Z",
- "iopub.status.idle": "2024-09-05T19:33:01.039351Z",
- "shell.execute_reply": "2024-09-05T19:33:01.038901Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.647632Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.647448Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.650810Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.650339Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.041401Z",
- "iopub.status.busy": "2024-09-05T19:33:01.041222Z",
- "iopub.status.idle": "2024-09-05T19:33:01.049517Z",
- "shell.execute_reply": "2024-09-05T19:33:01.049062Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.652810Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.652474Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.660488Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.660065Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.051546Z",
- "iopub.status.busy": "2024-09-05T19:33:01.051368Z",
- "iopub.status.idle": "2024-09-05T19:33:01.054060Z",
- "shell.execute_reply": "2024-09-05T19:33:01.053588Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.662789Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.662453Z",
+ "iopub.status.idle": "2024-09-06T19:32:52.664910Z",
+ "shell.execute_reply": "2024-09-06T19:32:52.664468Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.055990Z",
- "iopub.status.busy": "2024-09-05T19:33:01.055803Z",
- "iopub.status.idle": "2024-09-05T19:33:01.581009Z",
- "shell.execute_reply": "2024-09-05T19:33:01.580442Z"
+ "iopub.execute_input": "2024-09-06T19:32:52.667005Z",
+ "iopub.status.busy": "2024-09-06T19:32:52.666677Z",
+ "iopub.status.idle": "2024-09-06T19:32:53.186834Z",
+ "shell.execute_reply": "2024-09-06T19:32:53.186291Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:01.583425Z",
- "iopub.status.busy": "2024-09-05T19:33:01.583193Z",
- "iopub.status.idle": "2024-09-05T19:33:03.549090Z",
- "shell.execute_reply": "2024-09-05T19:33:03.548469Z"
+ "iopub.execute_input": "2024-09-06T19:32:53.189445Z",
+ "iopub.status.busy": "2024-09-06T19:32:53.189066Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.090605Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.089933Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.551875Z",
- "iopub.status.busy": "2024-09-05T19:33:03.551107Z",
- "iopub.status.idle": "2024-09-05T19:33:03.561703Z",
- "shell.execute_reply": "2024-09-05T19:33:03.561236Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.093443Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.092787Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.103390Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.102831Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.563830Z",
- "iopub.status.busy": "2024-09-05T19:33:03.563524Z",
- "iopub.status.idle": "2024-09-05T19:33:03.567711Z",
- "shell.execute_reply": "2024-09-05T19:33:03.567284Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.105571Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.105237Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.109432Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.108857Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.569933Z",
- "iopub.status.busy": "2024-09-05T19:33:03.569510Z",
- "iopub.status.idle": "2024-09-05T19:33:03.578212Z",
- "shell.execute_reply": "2024-09-05T19:33:03.577763Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.111438Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.111142Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.120139Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.119708Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.580544Z",
- "iopub.status.busy": "2024-09-05T19:33:03.579929Z",
- "iopub.status.idle": "2024-09-05T19:33:03.693855Z",
- "shell.execute_reply": "2024-09-05T19:33:03.693311Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.122107Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.121935Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.235206Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.234622Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.696060Z",
- "iopub.status.busy": "2024-09-05T19:33:03.695714Z",
- "iopub.status.idle": "2024-09-05T19:33:03.698630Z",
- "shell.execute_reply": "2024-09-05T19:33:03.698077Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.237464Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.237015Z",
+ "iopub.status.idle": "2024-09-06T19:32:55.240074Z",
+ "shell.execute_reply": "2024-09-06T19:32:55.239512Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:03.700629Z",
- "iopub.status.busy": "2024-09-05T19:33:03.700452Z",
- "iopub.status.idle": "2024-09-05T19:33:05.867604Z",
- "shell.execute_reply": "2024-09-05T19:33:05.866784Z"
+ "iopub.execute_input": "2024-09-06T19:32:55.242072Z",
+ "iopub.status.busy": "2024-09-06T19:32:55.241898Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.303999Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.303194Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:05.870677Z",
- "iopub.status.busy": "2024-09-05T19:33:05.870043Z",
- "iopub.status.idle": "2024-09-05T19:33:05.882124Z",
- "shell.execute_reply": "2024-09-05T19:33:05.881668Z"
+ "iopub.execute_input": "2024-09-06T19:32:57.307062Z",
+ "iopub.status.busy": "2024-09-06T19:32:57.306412Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.318236Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.317761Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:33:05.884250Z",
- "iopub.status.busy": "2024-09-05T19:33:05.884062Z",
- "iopub.status.idle": "2024-09-05T19:33:05.935772Z",
- "shell.execute_reply": "2024-09-05T19:33:05.935279Z"
+ "iopub.execute_input": "2024-09-06T19:32:57.320219Z",
+ "iopub.status.busy": "2024-09-06T19:32:57.320039Z",
+ "iopub.status.idle": "2024-09-06T19:32:57.425487Z",
+ "shell.execute_reply": "2024-09-06T19:32:57.424961Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index fceb6095b..9f22a5e70 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -821,7 +821,7 @@ 2. Load and format the text dataset
+
|
- Age |
- Gender |
- Location |
- Annual_Spending |
- Number_of_Transactions |
- Last_Purchase_Date |
- | |
- is_null_issue |
- null_score |
+ Age |
+ Gender |
+ Location |
+ Annual_Spending |
+ Number_of_Transactions |
+ Last_Purchase_Date |
+ | |
+ is_null_issue |
+ null_score |
- 8 |
- nan |
- nan |
- nan |
- nan |
- nan |
- NaT |
- |
- True |
- 0.000000 |
-
-
- 1 |
- nan |
- Female |
- Rural |
- 6421.160000 |
- 5.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 9 |
- nan |
- Male |
- Rural |
- 4655.820000 |
- 1.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 14 |
- nan |
- Male |
- Rural |
- 6790.460000 |
- 3.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 13 |
- nan |
- Male |
- Urban |
- 9167.470000 |
- 4.000000 |
- 2024-01-02 00:00:00 |
- |
- False |
- 0.833333 |
-
-
- 15 |
- nan |
- Other |
- Rural |
- 5327.960000 |
- 8.000000 |
- 2024-01-03 00:00:00 |
- |
- False |
- 0.833333 |
-
-
- 0 |
- 56.000000 |
- Other |
- Rural |
- 4099.620000 |
- 3.000000 |
- 2024-01-03 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 2 |
- 46.000000 |
- Male |
- Suburban |
- 5436.550000 |
- 3.000000 |
- 2024-02-26 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 3 |
- 32.000000 |
- Female |
- Rural |
- 4046.660000 |
- 3.000000 |
- 2024-03-23 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 4 |
- 60.000000 |
- Female |
- Suburban |
- 3467.670000 |
- 6.000000 |
- 2024-03-01 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 5 |
- 25.000000 |
- Female |
- Suburban |
- 4757.370000 |
- 4.000000 |
- 2024-01-03 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 6 |
- 38.000000 |
- Female |
- Rural |
- 4199.530000 |
- 6.000000 |
- 2024-01-03 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 7 |
- 56.000000 |
- Male |
- Suburban |
- 4991.710000 |
- 6.000000 |
- 2024-04-03 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 10 |
- 40.000000 |
- Female |
- Rural |
- 5584.020000 |
- 7.000000 |
- 2024-03-29 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 11 |
- 28.000000 |
- Female |
- Urban |
- 3102.320000 |
- 2.000000 |
- 2024-04-07 00:00:00 |
- |
- False |
- 1.000000 |
-
-
- 12 |
- 28.000000 |
- Male |
- Rural |
- 6637.990000 |
- 11.000000 |
- 2024-04-08 00:00:00 |
- |
- False |
- 1.000000 |
+ 8 |
+ nan |
+ nan |
+ nan |
+ nan |
+ nan |
+ NaT |
+ |
+ True |
+ 0.000000 |
+
+
+ 1 |
+ nan |
+ Female |
+ Rural |
+ 6421.160000 |
+ 5.000000 |
+ NaT |
+ |
+ False |
+ 0.666667 |
+
+
+ 9 |
+ nan |
+ Male |
+ Rural |
+ 4655.820000 |
+ 1.000000 |
+ NaT |
+ |
+ False |
+ 0.666667 |
+
+
+ 14 |
+ nan |
+ Male |
+ Rural |
+ 6790.460000 |
+ 3.000000 |
+ NaT |
+ |
+ False |
+ 0.666667 |
+
+
+ 13 |
+ nan |
+ Male |
+ Urban |
+ 9167.470000 |
+ 4.000000 |
+ 2024-01-02 00:00:00 |
+ |
+ False |
+ 0.833333 |
+
+
+ 15 |
+ nan |
+ Other |
+ Rural |
+ 5327.960000 |
+ 8.000000 |
+ 2024-01-03 00:00:00 |
+ |
+ False |
+ 0.833333 |
+
+
+ 0 |
+ 56.000000 |
+ Other |
+ Rural |
+ 4099.620000 |
+ 3.000000 |
+ 2024-01-03 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 2 |
+ 46.000000 |
+ Male |
+ Suburban |
+ 5436.550000 |
+ 3.000000 |
+ 2024-02-26 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 3 |
+ 32.000000 |
+ Female |
+ Rural |
+ 4046.660000 |
+ 3.000000 |
+ 2024-03-23 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 4 |
+ 60.000000 |
+ Female |
+ Suburban |
+ 3467.670000 |
+ 6.000000 |
+ 2024-03-01 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 5 |
+ 25.000000 |
+ Female |
+ Suburban |
+ 4757.370000 |
+ 4.000000 |
+ 2024-01-03 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 6 |
+ 38.000000 |
+ Female |
+ Rural |
+ 4199.530000 |
+ 6.000000 |
+ 2024-01-03 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 7 |
+ 56.000000 |
+ Male |
+ Suburban |
+ 4991.710000 |
+ 6.000000 |
+ 2024-04-03 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 10 |
+ 40.000000 |
+ Female |
+ Rural |
+ 5584.020000 |
+ 7.000000 |
+ 2024-03-29 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 11 |
+ 28.000000 |
+ Female |
+ Urban |
+ 3102.320000 |
+ 2.000000 |
+ 2024-04-07 00:00:00 |
+ |
+ False |
+ 1.000000 |
+
+
+ 12 |
+ 28.000000 |
+ Male |
+ Rural |
+ 6637.990000 |
+ 11.000000 |
+ 2024-04-08 00:00:00 |
+ |
+ False |
+ 1.000000 |
@@ -3507,16 +3507,16 @@ 1. Load the Dataset
\n",
@@ -4539,18 +4539,18 @@
""
],
"text/plain": [
- " dark_score is_dark_issue\n",
- "0 0.797509 False\n",
- "1 0.663760 False\n",
- "2 0.849826 False\n",
- "3 0.773951 False\n",
- "4 0.699518 False\n",
- ".. ... ...\n",
- "195 0.793840 False\n",
- "196 1.000000 False\n",
- "197 0.971560 False\n",
- "198 0.862236 False\n",
- "199 0.973533 False\n",
+ " is_dark_issue dark_score\n",
+ "0 False 0.797509\n",
+ "1 False 0.663760\n",
+ "2 False 0.849826\n",
+ "3 False 0.773951\n",
+ "4 False 0.699518\n",
+ ".. ... ...\n",
+ "195 False 0.793840\n",
+ "196 False 1.000000\n",
+ "197 False 0.971560\n",
+ "198 False 0.862236\n",
+ "199 False 0.973533\n",
"\n",
"[200 rows x 2 columns]"
]
@@ -4598,10 +4598,10 @@
"execution_count": 39,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:37:55.192457Z",
- "iopub.status.busy": "2024-09-05T19:37:55.192089Z",
- "iopub.status.idle": "2024-09-05T19:37:55.344435Z",
- "shell.execute_reply": "2024-09-05T19:37:55.343755Z"
+ "iopub.execute_input": "2024-09-06T19:37:49.778901Z",
+ "iopub.status.busy": "2024-09-06T19:37:49.778528Z",
+ "iopub.status.idle": "2024-09-06T19:37:49.924980Z",
+ "shell.execute_reply": "2024-09-06T19:37:49.924477Z"
},
"nbsphinx": "hidden"
},
@@ -4653,31 +4653,83 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0491ab817f3e4abfae647f24171e651f": {
+ "021a50164b8c491ebb069bd57b11ce1a": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "2cb88e5e7d0f4849b336950480e87a06": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_7e84cf312cfa4d80a5107dcdb0a45949",
- "IPY_MODEL_7557f4205917445ca0c596993a114685",
- "IPY_MODEL_dc9edc5341cb452cb27aada834ac562d"
- ],
- "layout": "IPY_MODEL_a1d59c28e7064efc92b1e3caf26f9346",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_5349f02a0bb24786bba46192aa1d90ff",
+ "placeholder": "",
+ "style": "IPY_MODEL_b93c4b8b97f34f0b93a2d334e5065e1b",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "100%"
}
},
- "37eeb5a0817c455a8d0efe07a3d6bd44": {
+ "313b234230ce4ce4850b3fa6a5e1b1ee": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4730,7 +4782,7 @@
"width": null
}
},
- "37f6d18ef4a24f62a2d67a08d8dae98c": {
+ "41bdd318b6d1453a8daca74a0776e419": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4783,39 +4835,7 @@
"width": null
}
},
- "3f81fed11b61470bac6f5d0b3b537a4f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "4bbbb6bbcc9648459b5d261cb8ab6826": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "550c28ddb9cf43c3b62b1a0b54f7bd12": {
+ "5349f02a0bb24786bba46192aa1d90ff": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4868,49 +4888,56 @@
"width": null
}
},
- "62793e1d0aa84395bb5ab3f9ff86c9b5": {
+ "5664879b48124f5cac1e0a8c43742995": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_da820a1ccd2b42d4a8c12ea0328d1169",
+ "max": 200.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_da8ad4a548a8409389fab7ddc0e601bc",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 200.0
}
},
- "6a8633562ced4f21b9e1b849b611603d": {
+ "6529bc3e5e35424f967dab0385030a5c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_db046a62f4e34aa594499d33fa68145f",
- "IPY_MODEL_a6c8cc603a414e80a6ba376c29f15b21",
- "IPY_MODEL_df91c80300534ab59fe080ab28475f7a"
- ],
- "layout": "IPY_MODEL_37f6d18ef4a24f62a2d67a08d8dae98c",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_41bdd318b6d1453a8daca74a0776e419",
+ "placeholder": "",
+ "style": "IPY_MODEL_dfac24cbd04d4a6a9c6a2f3d7e34c87e",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 200/200 [00:00<00:00, 682.83it/s]"
}
},
- "7557f4205917445ca0c596993a114685": {
+ "733b0d114c6e48e6af9ced8acfb5bf3a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -4926,17 +4953,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_daa6b8f2481040acb873f24cfbfedc9e",
+ "layout": "IPY_MODEL_953f4c82aabd472c9e8dfebdf70939d8",
"max": 200.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_3f81fed11b61470bac6f5d0b3b537a4f",
+ "style": "IPY_MODEL_86445ca79c764836a406520c67b4b945",
"tabbable": null,
"tooltip": null,
"value": 200.0
}
},
- "7e84cf312cfa4d80a5107dcdb0a45949": {
+ "7b9c39c715b849dbb886ceaeb96e5c35": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4951,33 +4978,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_550c28ddb9cf43c3b62b1a0b54f7bd12",
+ "layout": "IPY_MODEL_7fb3eb018b9d446294207573ca64cda2",
"placeholder": "",
- "style": "IPY_MODEL_81f7fdaaa60c4cc5b1e29545f2666e55",
+ "style": "IPY_MODEL_ec3f09ac595d4dadbd0cf34793d57087",
"tabbable": null,
"tooltip": null,
"value": "100%"
}
},
- "81f7fdaaa60c4cc5b1e29545f2666e55": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "HTMLStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "HTMLStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "background": null,
- "description_width": "",
- "font_size": null,
- "text_color": null
- }
- },
- "a1d59c28e7064efc92b1e3caf26f9346": {
+ "7fb3eb018b9d446294207573ca64cda2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5030,7 +5039,23 @@
"width": null
}
},
- "a4073ffc99c94224857f26d6f4931b59": {
+ "86445ca79c764836a406520c67b4b945": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "953f4c82aabd472c9e8dfebdf70939d8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5083,33 +5108,30 @@
"width": null
}
},
- "a6c8cc603a414e80a6ba376c29f15b21": {
+ "9ffc7a8014b64edfad1dd643172601d1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_a4073ffc99c94224857f26d6f4931b59",
- "max": 200.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_4bbbb6bbcc9648459b5d261cb8ab6826",
+ "layout": "IPY_MODEL_021a50164b8c491ebb069bd57b11ce1a",
+ "placeholder": "",
+ "style": "IPY_MODEL_da74a2af2dfa4378a23a6009ae2f264c",
"tabbable": null,
"tooltip": null,
- "value": 200.0
+ "value": " 200/200 [00:00<00:00, 785.38it/s]"
}
},
- "ad306c43da45461c99570f45d29010bd": {
+ "a185cb088b4a4b50933699f586275482": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5162,7 +5184,31 @@
"width": null
}
},
- "ae6c7383e12f421aaeac59c5f8586ff1": {
+ "a5793cf283c046f188f735beef4577a5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7b9c39c715b849dbb886ceaeb96e5c35",
+ "IPY_MODEL_5664879b48124f5cac1e0a8c43742995",
+ "IPY_MODEL_9ffc7a8014b64edfad1dd643172601d1"
+ ],
+ "layout": "IPY_MODEL_313b234230ce4ce4850b3fa6a5e1b1ee",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "b93c4b8b97f34f0b93a2d334e5065e1b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5180,7 +5226,7 @@
"text_color": null
}
},
- "b54c1a0ec3ea4129adde7e57857e1a0e": {
+ "da74a2af2dfa4378a23a6009ae2f264c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5198,7 +5244,7 @@
"text_color": null
}
},
- "c362069cd48048d7ac53b007e87189cb": {
+ "da820a1ccd2b42d4a8c12ea0328d1169": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5251,126 +5297,80 @@
"width": null
}
},
- "daa6b8f2481040acb873f24cfbfedc9e": {
- "model_module": "@jupyter-widgets/base",
+ "da8ad4a548a8409389fab7ddc0e601bc": {
+ "model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "LayoutModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
+ "_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "LayoutModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border_bottom": null,
- "border_left": null,
- "border_right": null,
- "border_top": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "db046a62f4e34aa594499d33fa68145f": {
+ "dfac24cbd04d4a6a9c6a2f3d7e34c87e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_c362069cd48048d7ac53b007e87189cb",
- "placeholder": "",
- "style": "IPY_MODEL_ae6c7383e12f421aaeac59c5f8586ff1",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "dc9edc5341cb452cb27aada834ac562d": {
+ "e53b81d02870488ca1d70faf1534371f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_ad306c43da45461c99570f45d29010bd",
- "placeholder": "",
- "style": "IPY_MODEL_62793e1d0aa84395bb5ab3f9ff86c9b5",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_2cb88e5e7d0f4849b336950480e87a06",
+ "IPY_MODEL_733b0d114c6e48e6af9ced8acfb5bf3a",
+ "IPY_MODEL_6529bc3e5e35424f967dab0385030a5c"
+ ],
+ "layout": "IPY_MODEL_a185cb088b4a4b50933699f586275482",
"tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 716.02it/s]"
+ "tooltip": null
}
},
- "df91c80300534ab59fe080ab28475f7a": {
+ "ec3f09ac595d4dadbd0cf34793d57087": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HTMLStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
+ "_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_37eeb5a0817c455a8d0efe07a3d6bd44",
- "placeholder": "",
- "style": "IPY_MODEL_b54c1a0ec3ea4129adde7e57857e1a0e",
- "tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 771.41it/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
}
},
diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb
index a14eec2f4..e932968f7 100644
--- a/master/tutorials/dataset_health.ipynb
+++ b/master/tutorials/dataset_health.ipynb
@@ -70,10 +70,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:00.542736Z",
- "iopub.status.busy": "2024-09-05T19:38:00.542299Z",
- "iopub.status.idle": "2024-09-05T19:38:01.779270Z",
- "shell.execute_reply": "2024-09-05T19:38:01.778627Z"
+ "iopub.execute_input": "2024-09-06T19:37:53.970574Z",
+ "iopub.status.busy": "2024-09-06T19:37:53.970388Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.134808Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.134157Z"
},
"nbsphinx": "hidden"
},
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bf41a3a84454bec7d8f3943f3af833aabd335529\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -110,10 +110,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.782074Z",
- "iopub.status.busy": "2024-09-05T19:38:01.781768Z",
- "iopub.status.idle": "2024-09-05T19:38:01.784644Z",
- "shell.execute_reply": "2024-09-05T19:38:01.784156Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.137505Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.137230Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.140659Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.140221Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.786827Z",
- "iopub.status.busy": "2024-09-05T19:38:01.786518Z",
- "iopub.status.idle": "2024-09-05T19:38:01.798674Z",
- "shell.execute_reply": "2024-09-05T19:38:01.798101Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.142857Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.142554Z",
+ "iopub.status.idle": "2024-09-06T19:37:55.154394Z",
+ "shell.execute_reply": "2024-09-06T19:37:55.153913Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-09-05T19:38:01.800785Z",
- "iopub.status.busy": "2024-09-05T19:38:01.800454Z",
- "iopub.status.idle": "2024-09-05T19:38:06.755445Z",
- "shell.execute_reply": "2024-09-05T19:38:06.754955Z"
+ "iopub.execute_input": "2024-09-06T19:37:55.156367Z",
+ "iopub.status.busy": "2024-09-06T19:37:55.156193Z",
+ "iopub.status.idle": "2024-09-06T19:38:03.213180Z",
+ "shell.execute_reply": "2024-09-06T19:38:03.212490Z"
},
"id": "dhTHOg8Pyv5G"
},
diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html
index 5033d68f9..5da9f6de6 100644
--- a/master/tutorials/faq.html
+++ b/master/tutorials/faq.html
@@ -835,13 +835,13 @@