diff --git a/master/.buildinfo b/master/.buildinfo
index 458c93dd1..2ab302ff0 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: 1765f9fb54b06ed20817275083d0b26b
+config: 44ecf819919d54495a9d115c9a756342
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree
index 93d5f9946..6faf3f534 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 fdc1c61a5..b69e207af 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 b21759b4f..f49afe438 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 1104d7f4d..513f1b833 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 e8a6af59b..927ec9acf 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 e50de9a19..d36c80754 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 e97535b29..9dbaf7511 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 5c7fb59b0..9d91a4146 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 e52cad49f..7b6c01d61 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 d422e090d..4b7ab93a1 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 2b90d0ad8..b41370e95 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 342a628ce..156a28595 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 fe5251694..b94bcd53f 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/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree
index d118cebaa..b24fc8317 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 424432460..b06a2b01e 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 91c1d3096..f9fd39051 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 eb71b5ae0..25f831459 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 0798f67a1..6041f725a 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 7ea680f1f..a11216833 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 3db389d85..32d73bfa5 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 70f69ff7c..ca099eaff 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 8d557366d..b2d3007a8 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 71b9f0ee0..146e7a093 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 b6f8788fd..aece9c41b 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 a0b665047..2d723214d 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 ee855fc5b..dc42b2031 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 a85095784..7c6d2b966 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 cde15f966..c2fbdea79 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 e7b3c7287..487859ad6 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 8f67c3baf..d6244cec5 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 da661e78f..187b79290 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 e82ef29d7..7581e44e3 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 be32bddd4..03c345e77 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 4e06d89f1..b08f492e7 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 9d27c0c8d..668622a30 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 38239274f..900dc4dc4 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 746d4e4a9..cb186d34c 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 2b54887bd..76dd51e1c 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 616800c85..7cff3119e 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 5506b1927..08d42f0b1 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 b0e958f95..a81693fa3 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 23d3296cb..0c1d91943 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 7c1688c96..352941800 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 379380c6b..dbceb0a41 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 23bbeeea0..11fc8952d 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 29b06e5de..ad1475cf5 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 db0ed1b0f..2507b66d5 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 800b84f87..f74e761d5 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 d07add36e..c4662a7d5 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 3af1b5d35..3c15dfe13 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 e7e37b66f..3a1792947 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 93d06d82b..0e2c27f08 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 9cf249042..811ad8870 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 d2ed63959..54cb263b0 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 b2b04c9ce..af8f7b0b5 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 53b3e68c7..79e0f4f74 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 7e6068f40..f35757b60 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 bed48e4a1..869d1ee95 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 5809c2fd2..9c2ee33c9 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/fasttext.doctree b/master/.doctrees/cleanlab/models/fasttext.doctree
index f65b895d9..98b2833d7 100644
Binary files a/master/.doctrees/cleanlab/models/fasttext.doctree and b/master/.doctrees/cleanlab/models/fasttext.doctree differ
diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree
index 05899cab3..c3583465d 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 4da14c163..209d86675 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 baf8f4b51..59ee569e6 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 b0ccfba54..dc5e97f5e 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 10b2857ad..3499f3ef6 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 3f1ceeb80..df5eaea7a 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 34785960b..a32fcb1c6 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 f08f3167f..9c904d1e5 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 2c3566591..2ce596918 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 67206445b..2dabc732f 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 b84b669d0..b3de610b3 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 661ae3718..a4c3cf789 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 4a594f114..1f3e70295 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 765ecbec1..c86ef9e9d 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 ef2d2f862..bf0b281a9 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 e3303bf52..66b04d10f 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 62120f5cc..77d2dfbc1 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 dc5d27b44..657ca53a9 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 6d2c85576..0fac2b57d 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 9bb040a3f..7be7e94f9 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 8efca2605..c15c4f123 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 06c65cfa0..4bec0e213 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 99c8ff64c..70d7b364b 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 de82676b1..b24936208 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 60aea2abe..2c77571f6 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 a3a0e8862..dc4a39bac 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 177d4b2e1..7871636e6 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 835b9297f..0c56c3881 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": "2024-07-01T15:01:38.704463Z",
- "iopub.status.busy": "2024-07-01T15:01:38.704282Z",
- "iopub.status.idle": "2024-07-01T15:01:39.968773Z",
- "shell.execute_reply": "2024-07-01T15:01:39.968140Z"
+ "iopub.execute_input": "2024-07-02T12:00:24.117516Z",
+ "iopub.status.busy": "2024-07-02T12:00:24.117048Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.333194Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.332647Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:01:39.971457Z",
- "iopub.status.busy": "2024-07-01T15:01:39.971069Z",
- "iopub.status.idle": "2024-07-01T15:01:39.990015Z",
- "shell.execute_reply": "2024-07-01T15:01:39.989387Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.335570Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.335300Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.352966Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.352544Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:39.992806Z",
- "iopub.status.busy": "2024-07-01T15:01:39.992402Z",
- "iopub.status.idle": "2024-07-01T15:01:40.303536Z",
- "shell.execute_reply": "2024-07-01T15:01:40.302965Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.355177Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.354929Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.498882Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.498315Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.336204Z",
- "iopub.status.busy": "2024-07-01T15:01:40.335666Z",
- "iopub.status.idle": "2024-07-01T15:01:40.340138Z",
- "shell.execute_reply": "2024-07-01T15:01:40.339623Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.528732Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.528329Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.532259Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.531790Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.342354Z",
- "iopub.status.busy": "2024-07-01T15:01:40.342145Z",
- "iopub.status.idle": "2024-07-01T15:01:40.351148Z",
- "shell.execute_reply": "2024-07-01T15:01:40.350569Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.534236Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.534064Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.542721Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.542178Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.353562Z",
- "iopub.status.busy": "2024-07-01T15:01:40.353231Z",
- "iopub.status.idle": "2024-07-01T15:01:40.356046Z",
- "shell.execute_reply": "2024-07-01T15:01:40.355491Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.544841Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.544667Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.547142Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.546723Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.358053Z",
- "iopub.status.busy": "2024-07-01T15:01:40.357874Z",
- "iopub.status.idle": "2024-07-01T15:01:40.885000Z",
- "shell.execute_reply": "2024-07-01T15:01:40.884377Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.549121Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.548952Z",
+ "iopub.status.idle": "2024-07-02T12:00:26.069775Z",
+ "shell.execute_reply": "2024-07-02T12:00:26.069166Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.887806Z",
- "iopub.status.busy": "2024-07-01T15:01:40.887346Z",
- "iopub.status.idle": "2024-07-01T15:01:42.858439Z",
- "shell.execute_reply": "2024-07-01T15:01:42.857751Z"
+ "iopub.execute_input": "2024-07-02T12:00:26.072294Z",
+ "iopub.status.busy": "2024-07-02T12:00:26.072111Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.964122Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.963476Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.861505Z",
- "iopub.status.busy": "2024-07-01T15:01:42.860685Z",
- "iopub.status.idle": "2024-07-01T15:01:42.872129Z",
- "shell.execute_reply": "2024-07-01T15:01:42.871534Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.966793Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.966128Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.975803Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.975266Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.874722Z",
- "iopub.status.busy": "2024-07-01T15:01:42.874312Z",
- "iopub.status.idle": "2024-07-01T15:01:42.879185Z",
- "shell.execute_reply": "2024-07-01T15:01:42.878651Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.977956Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.977648Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.981829Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.981303Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.881719Z",
- "iopub.status.busy": "2024-07-01T15:01:42.881293Z",
- "iopub.status.idle": "2024-07-01T15:01:42.890936Z",
- "shell.execute_reply": "2024-07-01T15:01:42.890441Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.984025Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.983701Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.990825Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.990380Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.893152Z",
- "iopub.status.busy": "2024-07-01T15:01:42.892940Z",
- "iopub.status.idle": "2024-07-01T15:01:43.010191Z",
- "shell.execute_reply": "2024-07-01T15:01:43.009566Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.992803Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.992505Z",
+ "iopub.status.idle": "2024-07-02T12:00:28.104238Z",
+ "shell.execute_reply": "2024-07-02T12:00:28.103750Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:43.012877Z",
- "iopub.status.busy": "2024-07-01T15:01:43.012678Z",
- "iopub.status.idle": "2024-07-01T15:01:43.015881Z",
- "shell.execute_reply": "2024-07-01T15:01:43.015414Z"
+ "iopub.execute_input": "2024-07-02T12:00:28.106465Z",
+ "iopub.status.busy": "2024-07-02T12:00:28.106127Z",
+ "iopub.status.idle": "2024-07-02T12:00:28.108811Z",
+ "shell.execute_reply": "2024-07-02T12:00:28.108400Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:43.017749Z",
- "iopub.status.busy": "2024-07-01T15:01:43.017574Z",
- "iopub.status.idle": "2024-07-01T15:01:45.116344Z",
- "shell.execute_reply": "2024-07-01T15:01:45.115698Z"
+ "iopub.execute_input": "2024-07-02T12:00:28.110759Z",
+ "iopub.status.busy": "2024-07-02T12:00:28.110457Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.104044Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.103432Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:45.119290Z",
- "iopub.status.busy": "2024-07-01T15:01:45.118731Z",
- "iopub.status.idle": "2024-07-01T15:01:45.130593Z",
- "shell.execute_reply": "2024-07-01T15:01:45.130118Z"
+ "iopub.execute_input": "2024-07-02T12:00:30.106906Z",
+ "iopub.status.busy": "2024-07-02T12:00:30.106328Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.117548Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.117099Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:45.132594Z",
- "iopub.status.busy": "2024-07-01T15:01:45.132413Z",
- "iopub.status.idle": "2024-07-01T15:01:45.200709Z",
- "shell.execute_reply": "2024-07-01T15:01:45.200202Z"
+ "iopub.execute_input": "2024-07-02T12:00:30.119573Z",
+ "iopub.status.busy": "2024-07-02T12:00:30.119249Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.150922Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.150454Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index e5a2ac8fa..d42308ae9 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": "2024-07-01T15:01:48.389395Z",
- "iopub.status.busy": "2024-07-01T15:01:48.389202Z",
- "iopub.status.idle": "2024-07-01T15:01:51.596566Z",
- "shell.execute_reply": "2024-07-01T15:01:51.595964Z"
+ "iopub.execute_input": "2024-07-02T12:00:34.059784Z",
+ "iopub.status.busy": "2024-07-02T12:00:34.059279Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.809187Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.808623Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:01:51.599757Z",
- "iopub.status.busy": "2024-07-01T15:01:51.599136Z",
- "iopub.status.idle": "2024-07-01T15:01:51.603065Z",
- "shell.execute_reply": "2024-07-01T15:01:51.602415Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.811854Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.811437Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.814737Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.814309Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.605582Z",
- "iopub.status.busy": "2024-07-01T15:01:51.605171Z",
- "iopub.status.idle": "2024-07-01T15:01:51.608781Z",
- "shell.execute_reply": "2024-07-01T15:01:51.608196Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.816857Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.816534Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.819520Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.819089Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.611405Z",
- "iopub.status.busy": "2024-07-01T15:01:51.610984Z",
- "iopub.status.idle": "2024-07-01T15:01:51.666636Z",
- "shell.execute_reply": "2024-07-01T15:01:51.666058Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.821601Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.821264Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.862716Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.862142Z"
}
},
"outputs": [
@@ -312,10 +312,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.668846Z",
- "iopub.status.busy": "2024-07-01T15:01:51.668483Z",
- "iopub.status.idle": "2024-07-01T15:01:51.672233Z",
- "shell.execute_reply": "2024-07-01T15:01:51.671774Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.864907Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.864568Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.868079Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.867616Z"
}
},
"outputs": [],
@@ -330,10 +330,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.674498Z",
- "iopub.status.busy": "2024-07-01T15:01:51.674053Z",
- "iopub.status.idle": "2024-07-01T15:01:51.677796Z",
- "shell.execute_reply": "2024-07-01T15:01:51.677326Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.870408Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.870073Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.873573Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.873016Z"
}
},
"outputs": [
@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}\n"
+ "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}\n"
]
}
],
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.679875Z",
- "iopub.status.busy": "2024-07-01T15:01:51.679530Z",
- "iopub.status.idle": "2024-07-01T15:01:51.682840Z",
- "shell.execute_reply": "2024-07-01T15:01:51.682369Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.875763Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.875423Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.878670Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.878216Z"
}
},
"outputs": [
@@ -409,10 +409,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.684949Z",
- "iopub.status.busy": "2024-07-01T15:01:51.684614Z",
- "iopub.status.idle": "2024-07-01T15:01:51.687925Z",
- "shell.execute_reply": "2024-07-01T15:01:51.687477Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.880795Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.880374Z",
+ "iopub.status.idle": "2024-07-02T12:00:36.883787Z",
+ "shell.execute_reply": "2024-07-02T12:00:36.883314Z"
}
},
"outputs": [],
@@ -453,17 +453,17 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:51.690015Z",
- "iopub.status.busy": "2024-07-01T15:01:51.689695Z",
- "iopub.status.idle": "2024-07-01T15:01:58.269951Z",
- "shell.execute_reply": "2024-07-01T15:01:58.269375Z"
+ "iopub.execute_input": "2024-07-02T12:00:36.885847Z",
+ "iopub.status.busy": "2024-07-02T12:00:36.885533Z",
+ "iopub.status.idle": "2024-07-02T12:00:41.284528Z",
+ "shell.execute_reply": "2024-07-02T12:00:41.283984Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "62351de8abb94a038c8769c2df5c458f",
+ "model_id": "e89a8a43528e42c38eca656e48b7da7e",
"version_major": 2,
"version_minor": 0
},
@@ -477,7 +477,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "340785de497a4c63ab7144c513dbc840",
+ "model_id": "ca42a9ff17da48fab63132c9d67266dd",
"version_major": 2,
"version_minor": 0
},
@@ -491,7 +491,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "690a2c7ac41a426d9ea764ad3d62a191",
+ "model_id": "283fc6563d5645c9a2d53edd642983d4",
"version_major": 2,
"version_minor": 0
},
@@ -505,7 +505,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "91c85984c43b4bb6ac43cf0e512599f1",
+ "model_id": "e90e40189b0e460d90a444df7fe6d1a9",
"version_major": 2,
"version_minor": 0
},
@@ -519,7 +519,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a6a71af506bb4925baad0f1c7f46552e",
+ "model_id": "018045ff81b24bf7b8b7b92eeb3e59db",
"version_major": 2,
"version_minor": 0
},
@@ -533,7 +533,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "fce6374730574858a51fc6bb15b16ff0",
+ "model_id": "eb1f3f2a9964471a8a7688badac98c84",
"version_major": 2,
"version_minor": 0
},
@@ -547,7 +547,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e3101229ea6044f0bf8820ecad5523c3",
+ "model_id": "edb46e1892c744119cd3f4a130dfb3e3",
"version_major": 2,
"version_minor": 0
},
@@ -601,10 +601,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:58.272720Z",
- "iopub.status.busy": "2024-07-01T15:01:58.272512Z",
- "iopub.status.idle": "2024-07-01T15:01:58.275361Z",
- "shell.execute_reply": "2024-07-01T15:01:58.274859Z"
+ "iopub.execute_input": "2024-07-02T12:00:41.287341Z",
+ "iopub.status.busy": "2024-07-02T12:00:41.286878Z",
+ "iopub.status.idle": "2024-07-02T12:00:41.289761Z",
+ "shell.execute_reply": "2024-07-02T12:00:41.289214Z"
}
},
"outputs": [],
@@ -626,10 +626,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:58.277576Z",
- "iopub.status.busy": "2024-07-01T15:01:58.277231Z",
- "iopub.status.idle": "2024-07-01T15:01:58.279889Z",
- "shell.execute_reply": "2024-07-01T15:01:58.279457Z"
+ "iopub.execute_input": "2024-07-02T12:00:41.291735Z",
+ "iopub.status.busy": "2024-07-02T12:00:41.291455Z",
+ "iopub.status.idle": "2024-07-02T12:00:41.294547Z",
+ "shell.execute_reply": "2024-07-02T12:00:41.294136Z"
}
},
"outputs": [],
@@ -644,10 +644,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:58.281927Z",
- "iopub.status.busy": "2024-07-01T15:01:58.281583Z",
- "iopub.status.idle": "2024-07-01T15:02:01.122494Z",
- "shell.execute_reply": "2024-07-01T15:02:01.121708Z"
+ "iopub.execute_input": "2024-07-02T12:00:41.296484Z",
+ "iopub.status.busy": "2024-07-02T12:00:41.296165Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.031023Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.030422Z"
},
"scrolled": true
},
@@ -670,10 +670,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.125497Z",
- "iopub.status.busy": "2024-07-01T15:02:01.124853Z",
- "iopub.status.idle": "2024-07-01T15:02:01.132982Z",
- "shell.execute_reply": "2024-07-01T15:02:01.132483Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.034106Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.033286Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.041018Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.040563Z"
}
},
"outputs": [
@@ -774,10 +774,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.135273Z",
- "iopub.status.busy": "2024-07-01T15:02:01.134876Z",
- "iopub.status.idle": "2024-07-01T15:02:01.138920Z",
- "shell.execute_reply": "2024-07-01T15:02:01.138424Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.043124Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.042704Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.046699Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.046142Z"
}
},
"outputs": [],
@@ -791,10 +791,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.140950Z",
- "iopub.status.busy": "2024-07-01T15:02:01.140631Z",
- "iopub.status.idle": "2024-07-01T15:02:01.143747Z",
- "shell.execute_reply": "2024-07-01T15:02:01.143216Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.048913Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.048523Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.051593Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.051082Z"
}
},
"outputs": [
@@ -829,10 +829,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.145826Z",
- "iopub.status.busy": "2024-07-01T15:02:01.145406Z",
- "iopub.status.idle": "2024-07-01T15:02:01.148540Z",
- "shell.execute_reply": "2024-07-01T15:02:01.148071Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.053726Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.053345Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.056186Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.055762Z"
}
},
"outputs": [],
@@ -852,10 +852,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.150516Z",
- "iopub.status.busy": "2024-07-01T15:02:01.150212Z",
- "iopub.status.idle": "2024-07-01T15:02:01.157258Z",
- "shell.execute_reply": "2024-07-01T15:02:01.156695Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.058086Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.057784Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.064436Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.063922Z"
}
},
"outputs": [
@@ -980,10 +980,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.159339Z",
- "iopub.status.busy": "2024-07-01T15:02:01.159040Z",
- "iopub.status.idle": "2024-07-01T15:02:01.408184Z",
- "shell.execute_reply": "2024-07-01T15:02:01.407652Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.066501Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.066197Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.289398Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.288882Z"
},
"scrolled": true
},
@@ -1022,10 +1022,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.410863Z",
- "iopub.status.busy": "2024-07-01T15:02:01.410467Z",
- "iopub.status.idle": "2024-07-01T15:02:01.588794Z",
- "shell.execute_reply": "2024-07-01T15:02:01.588210Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.292040Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.291643Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.466523Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.466004Z"
},
"scrolled": true
},
@@ -1058,10 +1058,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:01.592672Z",
- "iopub.status.busy": "2024-07-01T15:02:01.591557Z",
- "iopub.status.idle": "2024-07-01T15:02:01.597151Z",
- "shell.execute_reply": "2024-07-01T15:02:01.596544Z"
+ "iopub.execute_input": "2024-07-02T12:00:44.469939Z",
+ "iopub.status.busy": "2024-07-02T12:00:44.468998Z",
+ "iopub.status.idle": "2024-07-02T12:00:44.473947Z",
+ "shell.execute_reply": "2024-07-02T12:00:44.473442Z"
},
"nbsphinx": "hidden"
},
@@ -1105,7 +1105,31 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "02e3e418c19240f486b6744bed9dc1a5": {
+ "018045ff81b24bf7b8b7b92eeb3e59db": {
+ "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_365ffce5f68c44638088fdbae83f6f7b",
+ "IPY_MODEL_c08801bd218a4933bad779d4baa0b544",
+ "IPY_MODEL_0f51dc70dda342b1b6b17e56b63d5f14"
+ ],
+ "layout": "IPY_MODEL_e14305181a4f4a1aba92bf0159aa7bf3",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "0298beb970f94688915a8e32a774126c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1158,7 +1182,51 @@
"width": null
}
},
- "04d9fd81f1a64f5d864276ca3169c4ae": {
+ "036fc6d42c084bba8e6ff1d651c36d55": {
+ "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_b03695b8bbe44d13a5612d5120ea2a28",
+ "max": 665.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_25890da51b1a4e519cdfb13a8c6a9b74",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 665.0
+ }
+ },
+ "038607d420fe468e857b145df291678c": {
+ "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
+ }
+ },
+ "03cd490c005341539cee9b6bd0c64509": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -1174,7 +1242,48 @@
"description_width": ""
}
},
- "06823c4e7990422188c080cab19edf7b": {
+ "060119cb42d74e19bb4e92690f697a5e": {
+ "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_0298beb970f94688915a8e32a774126c",
+ "placeholder": "",
+ "style": "IPY_MODEL_eb0935dc83294cc7a1acad3dd963f608",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 54.2M/54.2M [00:00<00:00, 200MB/s]"
+ }
+ },
+ "0607d3fa923e46caa07bbdf1220db2b6": {
+ "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
+ }
+ },
+ "078724370bc24c649597fb04791e1a0e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1227,30 +1336,7 @@
"width": null
}
},
- "079f3f1e033f4bbca8478c880a927afc": {
- "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_98055291c2ee452694ecc19e51ecaead",
- "placeholder": "",
- "style": "IPY_MODEL_58ef1824a5c949b8a654f8a0277b7b97",
- "tabbable": null,
- "tooltip": null,
- "value": "vocab.txt: 100%"
- }
- },
- "09f87f9e986947dda9b2e8c0bc235a7b": {
+ "0bade9b66cc6401491c957e37747b252": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1303,23 +1389,53 @@
"width": null
}
},
- "0b359b2ad56c46998822be17f96e77fb": {
+ "0f51dc70dda342b1b6b17e56b63d5f14": {
"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_581a302d09fd45e49c7f87d39dd3c921",
+ "placeholder": "",
+ "style": "IPY_MODEL_0607d3fa923e46caa07bbdf1220db2b6",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 466k/466k [00:00<00:00, 15.8MB/s]"
}
},
- "0cbf3b1097fc425e989d58c0735c519d": {
+ "1372d9e4b7724cdead58971eebb0f969": {
+ "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_285337fc5df34cbfa6c9ed52458ebf3d",
+ "placeholder": "",
+ "style": "IPY_MODEL_80145043305a4ffabe28cb2a16f379de",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 665/665 [00:00<00:00, 118kB/s]"
+ }
+ },
+ "1820812db0184a3ba2bd25871cc78e2f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1372,7 +1488,7 @@
"width": null
}
},
- "13325f0cbdac42e292d5ec55dd02d83e": {
+ "1d21e4a5af7c4ae0aadfd84ea1555716": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1425,7 +1541,25 @@
"width": null
}
},
- "14034de7bb7744e19694d7e8a08ca368": {
+ "20b3e8ae925a497db651bb3e420ccedd": {
+ "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
+ }
+ },
+ "25890da51b1a4e519cdfb13a8c6a9b74": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -1441,7 +1575,31 @@
"description_width": ""
}
},
- "1c6fb4c64d13487194c96f1bdc086e42": {
+ "283fc6563d5645c9a2d53edd642983d4": {
+ "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_d66a39fee74f43488e2d84a8afff525a",
+ "IPY_MODEL_036fc6d42c084bba8e6ff1d651c36d55",
+ "IPY_MODEL_1372d9e4b7724cdead58971eebb0f969"
+ ],
+ "layout": "IPY_MODEL_330425c288064ecea245f9a589f86dce",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "285337fc5df34cbfa6c9ed52458ebf3d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1494,30 +1652,7 @@
"width": null
}
},
- "1f3ed30be4ca4e5b97f396f778f155f5": {
- "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_9d38a50f62054962a5ac76122754b6df",
- "placeholder": "",
- "style": "IPY_MODEL_3aafc982b22a428aa27c5cd1250efa05",
- "tabbable": null,
- "tooltip": null,
- "value": " 54.2M/54.2M [00:01<00:00, 28.1MB/s]"
- }
- },
- "20de08960d534ff89e4d9748aae1b593": {
+ "290e38f7423c457a9127d9cd3394ab4e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -1533,53 +1668,60 @@
"description_width": ""
}
},
- "2d114fd4a23f44a9b28eb2eefa18ce30": {
- "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_f893ddf9db054e05b5719e8272e508e6",
- "placeholder": "",
- "style": "IPY_MODEL_8c1d39b74d17432a831b7e63d65fc7c0",
- "tabbable": null,
- "tooltip": null,
- "value": ".gitattributes: 100%"
- }
- },
- "3123835078454cf9b22abb8a471265a0": {
- "model_module": "@jupyter-widgets/controls",
+ "2adec3ea4ab147fca4247ce82a707f41": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "LayoutModel",
"state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "LayoutModel",
"_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_c7cd56b89e03457e8bfa240f778aeac4",
- "placeholder": "",
- "style": "IPY_MODEL_5e5e6673b88a48b6a1e0b40d0eb77931",
- "tabbable": null,
- "tooltip": null,
- "value": " 48.0/48.0 [00:00<00:00, 7.41kB/s]"
+ "_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
}
},
- "31d200dead6d4d7a9d475999bb9cd002": {
+ "330425c288064ecea245f9a589f86dce": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1632,75 +1774,101 @@
"width": null
}
},
- "33222d3a75f94b0badb9b80898204763": {
+ "33adffdfde344f24ab11355d2abf0744": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLStyleModel",
"_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_fe702e6a2dbf409c9c44ca8eb27dffcc",
- "max": 391.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_20de08960d534ff89e4d9748aae1b593",
- "tabbable": null,
- "tooltip": null,
- "value": 391.0
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "340785de497a4c63ab7144c513dbc840": {
+ "365ffce5f68c44638088fdbae83f6f7b": {
"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_f599421afd5149bfa8a6ee654777c7c1",
- "IPY_MODEL_48ac4d127e6141eb8fa1f6bf4d15e4c2",
- "IPY_MODEL_ae97d0f75f4740cbb7ce3ea7ed2d44aa"
- ],
- "layout": "IPY_MODEL_0cbf3b1097fc425e989d58c0735c519d",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_757f11a9bf36405e89b2715bc5278a25",
+ "placeholder": "",
+ "style": "IPY_MODEL_e7529ab3626947b3ac4bc76edafa711a",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "tokenizer.json: 100%"
}
},
- "35dadc5a2f124430a20eb48108c03ab1": {
- "model_module": "@jupyter-widgets/controls",
+ "3c023990927b4f04a4351901432e7d8f": {
+ "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
}
},
- "3aafc982b22a428aa27c5cd1250efa05": {
+ "465f0d2ce3444dfd9bd85fd2529dc52c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1718,7 +1886,7 @@
"text_color": null
}
},
- "3e413d091d0d4b6482bc9ed11d5d236c": {
+ "465f57a032274e4dadbee2eb87856ef1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1771,7 +1939,7 @@
"width": null
}
},
- "438118108888448cb339b8dc2230aa72": {
+ "4ed3c0ca07f04a87b53a6c5d68d36cf6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1824,7 +1992,7 @@
"width": null
}
},
- "48a7c543fbdc47eb91f1afe3d775a0f8": {
+ "52a83385affa4985b26bec259ee14740": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1839,94 +2007,31 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1c6fb4c64d13487194c96f1bdc086e42",
+ "layout": "IPY_MODEL_1d21e4a5af7c4ae0aadfd84ea1555716",
"placeholder": "",
- "style": "IPY_MODEL_f03c166056b94c999588193b57b1ab4f",
+ "style": "IPY_MODEL_038607d420fe468e857b145df291678c",
"tabbable": null,
"tooltip": null,
- "value": " 391/391 [00:00<00:00, 62.0kB/s]"
+ "value": "README.md: 100%"
}
},
- "48ac4d127e6141eb8fa1f6bf4d15e4c2": {
+ "5376601de0794106a7b8777224bcafd4": {
"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",
- "_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_91b179d4d8f345959726ca7ba76ac7a9",
- "max": 2211.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_04d9fd81f1a64f5d864276ca3169c4ae",
- "tabbable": null,
- "tooltip": null,
- "value": 2211.0
- }
- },
- "57162eef07c144edbbf29b08ca208753": {
- "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": "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": ""
}
},
- "58ef1824a5c949b8a654f8a0277b7b97": {
+ "57bc4c06dbfc46c89ae707951f55ed3a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1944,7 +2049,7 @@
"text_color": null
}
},
- "5b140acae5da41218fdb0bba821dd741": {
+ "581a302d09fd45e49c7f87d39dd3c921": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1997,49 +2102,7 @@
"width": null
}
},
- "5e5e6673b88a48b6a1e0b40d0eb77931": {
- "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
- }
- },
- "62351de8abb94a038c8769c2df5c458f": {
- "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_2d114fd4a23f44a9b28eb2eefa18ce30",
- "IPY_MODEL_33222d3a75f94b0badb9b80898204763",
- "IPY_MODEL_48a7c543fbdc47eb91f1afe3d775a0f8"
- ],
- "layout": "IPY_MODEL_02e3e418c19240f486b6744bed9dc1a5",
- "tabbable": null,
- "tooltip": null
- }
- },
- "624e82a0b0ec439696797f4a87dcb861": {
+ "5d425fc517de40599859741dfdf6bb2e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2092,7 +2155,7 @@
"width": null
}
},
- "673865438c8e4b0296bbe6ca3c060cb9": {
+ "5ec8a2f5f2034c158f6d2c361a60ea25": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2145,7 +2208,116 @@
"width": null
}
},
- "68e32e4abb734a84a60e14567ee5ab16": {
+ "5efd9793a22541dd9dd8f09fcfbec967": {
+ "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
+ }
+ },
+ "60b3d9bfd8e147ef947e8e81fd9fb70e": {
+ "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": ""
+ }
+ },
+ "654d58802fd94f6ead893ddbb0e3d131": {
+ "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_0bade9b66cc6401491c957e37747b252",
+ "max": 48.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_290e38f7423c457a9127d9cd3394ab4e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 48.0
+ }
+ },
+ "66ee22f8cf5b4a78ab33aee929a5fbd0": {
+ "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_fad0806770b14f9086fd1b3b755413fb",
+ "placeholder": "",
+ "style": "IPY_MODEL_814ed56db234446a92fe939efe5a477b",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 48.0/48.0 [00:00<00:00, 8.21kB/s]"
+ }
+ },
+ "6f739705eccc46afb2020460a828b56b": {
+ "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_5d425fc517de40599859741dfdf6bb2e",
+ "max": 391.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_7a181aaf8967410dae1ce2d0e0d9856a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 391.0
+ }
+ },
+ "757f11a9bf36405e89b2715bc5278a25": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2198,31 +2370,7 @@
"width": null
}
},
- "690a2c7ac41a426d9ea764ad3d62a191": {
- "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_a8d4e5776925443ebb839809dfaca832",
- "IPY_MODEL_e20b0fbd46bf4b59bc4da8f83218e600",
- "IPY_MODEL_aa7b347bc9c145b09318d5248c15af77"
- ],
- "layout": "IPY_MODEL_5b140acae5da41218fdb0bba821dd741",
- "tabbable": null,
- "tooltip": null
- }
- },
- "69cbdc81324248878408dbfed717680f": {
+ "796c15ee275d4dff935fe8e20583896d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2275,30 +2423,77 @@
"width": null
}
},
- "704c7b4ef65041a89cd5d3fc1f81e95f": {
+ "7a181aaf8967410dae1ce2d0e0d9856a": {
+ "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": ""
+ }
+ },
+ "80145043305a4ffabe28cb2a16f379de": {
+ "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
+ }
+ },
+ "814ed56db234446a92fe939efe5a477b": {
"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_ca4b9735d66b414697862773b6ae1163",
- "placeholder": "",
- "style": "IPY_MODEL_b49778737bd8479f946ac4060638f1d9",
- "tabbable": null,
- "tooltip": null,
- "value": " 466k/466k [00:00<00:00, 15.0MB/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "853f7130b22d49bfaf39b5d7bf7af4ce": {
+ "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
}
},
- "72404aea60be4c0aaa808f8f70065420": {
+ "8e1a3b5aa6b14adc8ebdc1566696e505": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2351,46 +2546,7 @@
"width": null
}
},
- "73bb6421a1694f5abed56acfff8df5c8": {
- "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_c9974b364cf9469292e4c0a172a720dd",
- "placeholder": "",
- "style": "IPY_MODEL_9facb5b56860415d974458a4f8a98e99",
- "tabbable": null,
- "tooltip": null,
- "value": "tokenizer_config.json: 100%"
- }
- },
- "7430ccc73950450389eb7ded2db99afc": {
- "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": ""
- }
- },
- "7443c554c2da4d4790de7a725d038932": {
+ "9814c1a1993c40a683e790580ecf178a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2408,30 +2564,7 @@
"text_color": null
}
},
- "7f9ca0d6a1fb49489d44ed52e9e5b6aa": {
- "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_72404aea60be4c0aaa808f8f70065420",
- "placeholder": "",
- "style": "IPY_MODEL_b8348d0d4075416fa30cf5f27f5fd6fd",
- "tabbable": null,
- "tooltip": null,
- "value": " 232k/232k [00:00<00:00, 32.8MB/s]"
- }
- },
- "7ffce27927fb488788c78c75defb22e0": {
+ "982a96cc3c0b4c00938e722c374cd707": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2484,7 +2617,7 @@
"width": null
}
},
- "8658abefb5a143a0ae1d78b86bb5e6a7": {
+ "98b6d5dd0c4546948933a4d8faa4bbc9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2537,7 +2670,7 @@
"width": null
}
},
- "8c1d39b74d17432a831b7e63d65fc7c0": {
+ "9e5f0df62415449eb138994f79e6d9e0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2555,33 +2688,7 @@
"text_color": null
}
},
- "8e023743450746ba98fb9cff5b8ab3ca": {
- "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_13325f0cbdac42e292d5ec55dd02d83e",
- "max": 48.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_f650bcae88e3498c9713ed7fad85c313",
- "tabbable": null,
- "tooltip": null,
- "value": 48.0
- }
- },
- "91b179d4d8f345959726ca7ba76ac7a9": {
+ "a6d4217338164b029ad977bd11fb8d9e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2634,31 +2741,7 @@
"width": null
}
},
- "91c85984c43b4bb6ac43cf0e512599f1": {
- "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_b7d8f867996c41a0b6bc2bffd9f7965d",
- "IPY_MODEL_9a7533d991e54982b696f43606dbe2b5",
- "IPY_MODEL_1f3ed30be4ca4e5b97f396f778f155f5"
- ],
- "layout": "IPY_MODEL_e5831f2ec42f4838ab828634c0bd28fd",
- "tabbable": null,
- "tooltip": null
- }
- },
- "98055291c2ee452694ecc19e51ecaead": {
+ "b03695b8bbe44d13a5612d5120ea2a28": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2711,7 +2794,7 @@
"width": null
}
},
- "9a7533d991e54982b696f43606dbe2b5": {
+ "b83ba78e112b427d90a55129eecf514c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2727,17 +2810,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_8658abefb5a143a0ae1d78b86bb5e6a7",
- "max": 54245363.0,
+ "layout": "IPY_MODEL_078724370bc24c649597fb04791e1a0e",
+ "max": 2211.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_fa52b986ca5d4e199fbbe62d54662389",
+ "style": "IPY_MODEL_5376601de0794106a7b8777224bcafd4",
"tabbable": null,
"tooltip": null,
- "value": 54245363.0
+ "value": 2211.0
}
},
- "9ac47759986e4465a5e745617e40364c": {
+ "c08801bd218a4933bad779d4baa0b544": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2753,88 +2836,56 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_fbfcfc4949a741f88d46ad1b0042769e",
- "max": 231508.0,
+ "layout": "IPY_MODEL_da42c63dc73b49f8b62f507344120b1a",
+ "max": 466062.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_14034de7bb7744e19694d7e8a08ca368",
+ "style": "IPY_MODEL_60b3d9bfd8e147ef947e8e81fd9fb70e",
"tabbable": null,
"tooltip": null,
- "value": 231508.0
+ "value": 466062.0
}
},
- "9d38a50f62054962a5ac76122754b6df": {
- "model_module": "@jupyter-widgets/base",
+ "c187a19bc3e941088eef67c273bf61ed": {
+ "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": ""
}
},
- "9facb5b56860415d974458a4f8a98e99": {
+ "c9ff9f5312794e5381359492c09edaaf": {
"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_5ec8a2f5f2034c158f6d2c361a60ea25",
+ "placeholder": "",
+ "style": "IPY_MODEL_853f7130b22d49bfaf39b5d7bf7af4ce",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "vocab.txt: 100%"
}
},
- "a6a71af506bb4925baad0f1c7f46552e": {
+ "ca42a9ff17da48fab63132c9d67266dd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -2849,16 +2900,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_b729c768ffe84c23ab3835b3f3f8051b",
- "IPY_MODEL_bdbdcb9609f647b6ac21adad814ab8dd",
- "IPY_MODEL_704c7b4ef65041a89cd5d3fc1f81e95f"
+ "IPY_MODEL_52a83385affa4985b26bec259ee14740",
+ "IPY_MODEL_b83ba78e112b427d90a55129eecf514c",
+ "IPY_MODEL_f7e3eba4cb63469d997967f982f4a1df"
],
- "layout": "IPY_MODEL_57162eef07c144edbbf29b08ca208753",
+ "layout": "IPY_MODEL_8e1a3b5aa6b14adc8ebdc1566696e505",
"tabbable": null,
"tooltip": null
}
},
- "a8d4e5776925443ebb839809dfaca832": {
+ "cc4d2d6857724ea0a2f3ec85a6f395ea": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2873,15 +2924,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_68e32e4abb734a84a60e14567ee5ab16",
+ "layout": "IPY_MODEL_4ed3c0ca07f04a87b53a6c5d68d36cf6",
"placeholder": "",
- "style": "IPY_MODEL_7443c554c2da4d4790de7a725d038932",
+ "style": "IPY_MODEL_5efd9793a22541dd9dd8f09fcfbec967",
"tabbable": null,
"tooltip": null,
- "value": "config.json: 100%"
+ "value": " 232k/232k [00:00<00:00, 31.3MB/s]"
}
},
- "aa7b347bc9c145b09318d5248c15af77": {
+ "d015bcf5ce69476ab54fd8e945eaa689": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2896,33 +2947,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_06823c4e7990422188c080cab19edf7b",
+ "layout": "IPY_MODEL_465f57a032274e4dadbee2eb87856ef1",
"placeholder": "",
- "style": "IPY_MODEL_fc9418d15a01426f91adaea86b81b6eb",
+ "style": "IPY_MODEL_465f0d2ce3444dfd9bd85fd2529dc52c",
"tabbable": null,
"tooltip": null,
- "value": " 665/665 [00:00<00:00, 98.3kB/s]"
- }
- },
- "ab8d06a6112542ae917846ddae4c46ac": {
- "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": " 391/391 [00:00<00:00, 66.4kB/s]"
}
},
- "ae97d0f75f4740cbb7ce3ea7ed2d44aa": {
+ "d25c524b9c4444829470ac057e0fae42": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2935,35 +2968,17 @@
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
"_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_31d200dead6d4d7a9d475999bb9cd002",
- "placeholder": "",
- "style": "IPY_MODEL_35dadc5a2f124430a20eb48108c03ab1",
- "tabbable": null,
- "tooltip": null,
- "value": " 2.21k/2.21k [00:00<00:00, 407kB/s]"
- }
- },
- "b49778737bd8479f946ac4060638f1d9": {
- "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
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_1820812db0184a3ba2bd25871cc78e2f",
+ "placeholder": "",
+ "style": "IPY_MODEL_57bc4c06dbfc46c89ae707951f55ed3a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "tokenizer_config.json: 100%"
}
},
- "b729c768ffe84c23ab3835b3f3f8051b": {
+ "d66a39fee74f43488e2d84a8afff525a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2978,15 +2993,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_673865438c8e4b0296bbe6ca3c060cb9",
+ "layout": "IPY_MODEL_e7e2566da91d4dde83d80ed19a91e263",
"placeholder": "",
- "style": "IPY_MODEL_ab8d06a6112542ae917846ddae4c46ac",
+ "style": "IPY_MODEL_20b3e8ae925a497db651bb3e420ccedd",
"tabbable": null,
"tooltip": null,
- "value": "tokenizer.json: 100%"
+ "value": "config.json: 100%"
}
},
- "b7d8f867996c41a0b6bc2bffd9f7965d": {
+ "d6ea8d2f7af14ef69353a0ae60cf677e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3001,33 +3016,68 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_438118108888448cb339b8dc2230aa72",
+ "layout": "IPY_MODEL_3c023990927b4f04a4351901432e7d8f",
"placeholder": "",
- "style": "IPY_MODEL_edc251780b214e988fe3e98369e79de3",
+ "style": "IPY_MODEL_9814c1a1993c40a683e790580ecf178a",
"tabbable": null,
"tooltip": null,
"value": "pytorch_model.bin: 100%"
}
},
- "b8348d0d4075416fa30cf5f27f5fd6fd": {
- "model_module": "@jupyter-widgets/controls",
+ "da42c63dc73b49f8b62f507344120b1a": {
+ "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
}
},
- "bdbdcb9609f647b6ac21adad814ab8dd": {
+ "dbc2947dd29d4e92a20d79a1f5606f98": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -3043,35 +3093,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_09f87f9e986947dda9b2e8c0bc235a7b",
- "max": 466062.0,
+ "layout": "IPY_MODEL_de5b529ba5b7466f857e19bdfdcddd30",
+ "max": 231508.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_7430ccc73950450389eb7ded2db99afc",
+ "style": "IPY_MODEL_c187a19bc3e941088eef67c273bf61ed",
"tabbable": null,
"tooltip": null,
- "value": 466062.0
- }
- },
- "c2a16ddd367b4238aa5afd5a22f28d8c": {
- "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": 231508.0
}
},
- "c7cd56b89e03457e8bfa240f778aeac4": {
+ "de5b529ba5b7466f857e19bdfdcddd30": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3124,7 +3156,7 @@
"width": null
}
},
- "c9974b364cf9469292e4c0a172a720dd": {
+ "e14305181a4f4a1aba92bf0159aa7bf3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3177,7 +3209,25 @@
"width": null
}
},
- "ca4b9735d66b414697862773b6ae1163": {
+ "e7529ab3626947b3ac4bc76edafa711a": {
+ "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
+ }
+ },
+ "e7e2566da91d4dde83d80ed19a91e263": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3230,33 +3280,31 @@
"width": null
}
},
- "e20b0fbd46bf4b59bc4da8f83218e600": {
+ "e89a8a43528e42c38eca656e48b7da7e": {
"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_3e413d091d0d4b6482bc9ed11d5d236c",
- "max": 665.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_0b359b2ad56c46998822be17f96e77fb",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ebe70157b15a43cebe4c33d29744783b",
+ "IPY_MODEL_6f739705eccc46afb2020460a828b56b",
+ "IPY_MODEL_d015bcf5ce69476ab54fd8e945eaa689"
+ ],
+ "layout": "IPY_MODEL_e9a141532bb34d7697db7a780d7a2002",
"tabbable": null,
- "tooltip": null,
- "value": 665.0
+ "tooltip": null
}
},
- "e3101229ea6044f0bf8820ecad5523c3": {
+ "e90e40189b0e460d90a444df7fe6d1a9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -3271,16 +3319,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_079f3f1e033f4bbca8478c880a927afc",
- "IPY_MODEL_9ac47759986e4465a5e745617e40364c",
- "IPY_MODEL_7f9ca0d6a1fb49489d44ed52e9e5b6aa"
+ "IPY_MODEL_d6ea8d2f7af14ef69353a0ae60cf677e",
+ "IPY_MODEL_eccbc90dc8114497bb7438d438edc7f2",
+ "IPY_MODEL_060119cb42d74e19bb4e92690f697a5e"
],
- "layout": "IPY_MODEL_624e82a0b0ec439696797f4a87dcb861",
+ "layout": "IPY_MODEL_a6d4217338164b029ad977bd11fb8d9e",
"tabbable": null,
"tooltip": null
}
},
- "e5831f2ec42f4838ab828634c0bd28fd": {
+ "e9a141532bb34d7697db7a780d7a2002": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3333,7 +3381,7 @@
"width": null
}
},
- "edc251780b214e988fe3e98369e79de3": {
+ "eb0935dc83294cc7a1acad3dd963f608": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -3351,25 +3399,31 @@
"text_color": null
}
},
- "f03c166056b94c999588193b57b1ab4f": {
+ "eb1f3f2a9964471a8a7688badac98c84": {
"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_d25c524b9c4444829470ac057e0fae42",
+ "IPY_MODEL_654d58802fd94f6ead893ddbb0e3d131",
+ "IPY_MODEL_66ee22f8cf5b4a78ab33aee929a5fbd0"
+ ],
+ "layout": "IPY_MODEL_98b6d5dd0c4546948933a4d8faa4bbc9",
+ "tabbable": null,
+ "tooltip": null
}
},
- "f599421afd5149bfa8a6ee654777c7c1": {
+ "ebe70157b15a43cebe4c33d29744783b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3384,100 +3438,65 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_7ffce27927fb488788c78c75defb22e0",
+ "layout": "IPY_MODEL_796c15ee275d4dff935fe8e20583896d",
"placeholder": "",
- "style": "IPY_MODEL_c2a16ddd367b4238aa5afd5a22f28d8c",
+ "style": "IPY_MODEL_33adffdfde344f24ab11355d2abf0744",
"tabbable": null,
"tooltip": null,
- "value": "README.md: 100%"
+ "value": ".gitattributes: 100%"
}
},
- "f650bcae88e3498c9713ed7fad85c313": {
+ "eccbc90dc8114497bb7438d438edc7f2": {
"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",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "f893ddf9db054e05b5719e8272e508e6": {
- "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": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_2adec3ea4ab147fca4247ce82a707f41",
+ "max": 54245363.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_03cd490c005341539cee9b6bd0c64509",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 54245363.0
}
},
- "fa52b986ca5d4e199fbbe62d54662389": {
+ "edb46e1892c744119cd3f4a130dfb3e3": {
"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_c9ff9f5312794e5381359492c09edaaf",
+ "IPY_MODEL_dbc2947dd29d4e92a20d79a1f5606f98",
+ "IPY_MODEL_cc4d2d6857724ea0a2f3ec85a6f395ea"
+ ],
+ "layout": "IPY_MODEL_f3decebc5af44971b456d5da642e43b2",
+ "tabbable": null,
+ "tooltip": null
}
},
- "fbfcfc4949a741f88d46ad1b0042769e": {
+ "f3decebc5af44971b456d5da642e43b2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3530,49 +3549,30 @@
"width": null
}
},
- "fc9418d15a01426f91adaea86b81b6eb": {
- "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
- }
- },
- "fce6374730574858a51fc6bb15b16ff0": {
+ "f7e3eba4cb63469d997967f982f4a1df": {
"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_73bb6421a1694f5abed56acfff8df5c8",
- "IPY_MODEL_8e023743450746ba98fb9cff5b8ab3ca",
- "IPY_MODEL_3123835078454cf9b22abb8a471265a0"
- ],
- "layout": "IPY_MODEL_69cbdc81324248878408dbfed717680f",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_982a96cc3c0b4c00938e722c374cd707",
+ "placeholder": "",
+ "style": "IPY_MODEL_9e5f0df62415449eb138994f79e6d9e0",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 2.21k/2.21k [00:00<00:00, 389kB/s]"
}
},
- "fe702e6a2dbf409c9c44ca8eb27dffcc": {
+ "fad0806770b14f9086fd1b3b755413fb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 2f2d2a40d..9db139a3f 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": "2024-07-01T15:02:05.352362Z",
- "iopub.status.busy": "2024-07-01T15:02:05.351840Z",
- "iopub.status.idle": "2024-07-01T15:02:11.364535Z",
- "shell.execute_reply": "2024-07-01T15:02:11.364016Z"
+ "iopub.execute_input": "2024-07-02T12:00:48.153712Z",
+ "iopub.status.busy": "2024-07-02T12:00:48.153535Z",
+ "iopub.status.idle": "2024-07-02T12:00:53.266339Z",
+ "shell.execute_reply": "2024-07-02T12:00:53.265786Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:11.367286Z",
- "iopub.status.busy": "2024-07-01T15:02:11.366756Z",
- "iopub.status.idle": "2024-07-01T15:02:11.369937Z",
- "shell.execute_reply": "2024-07-01T15:02:11.369499Z"
+ "iopub.execute_input": "2024-07-02T12:00:53.268847Z",
+ "iopub.status.busy": "2024-07-02T12:00:53.268512Z",
+ "iopub.status.idle": "2024-07-02T12:00:53.271688Z",
+ "shell.execute_reply": "2024-07-02T12:00:53.271237Z"
},
"id": "LaEiwXUiVHCS"
},
@@ -157,10 +157,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:11.372033Z",
- "iopub.status.busy": "2024-07-01T15:02:11.371712Z",
- "iopub.status.idle": "2024-07-01T15:02:11.376772Z",
- "shell.execute_reply": "2024-07-01T15:02:11.376263Z"
+ "iopub.execute_input": "2024-07-02T12:00:53.273790Z",
+ "iopub.status.busy": "2024-07-02T12:00:53.273468Z",
+ "iopub.status.idle": "2024-07-02T12:00:53.277843Z",
+ "shell.execute_reply": "2024-07-02T12:00:53.277413Z"
},
"nbsphinx": "hidden"
},
@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:11.378959Z",
- "iopub.status.busy": "2024-07-01T15:02:11.378766Z",
- "iopub.status.idle": "2024-07-01T15:02:12.901153Z",
- "shell.execute_reply": "2024-07-01T15:02:12.900530Z"
+ "iopub.execute_input": "2024-07-02T12:00:53.279840Z",
+ "iopub.status.busy": "2024-07-02T12:00:53.279499Z",
+ "iopub.status.idle": "2024-07-02T12:00:54.884749Z",
+ "shell.execute_reply": "2024-07-02T12:00:54.884125Z"
},
"id": "GRDPEg7-VOQe",
"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:12.904092Z",
- "iopub.status.busy": "2024-07-01T15:02:12.903651Z",
- "iopub.status.idle": "2024-07-01T15:02:12.914311Z",
- "shell.execute_reply": "2024-07-01T15:02:12.913807Z"
+ "iopub.execute_input": "2024-07-02T12:00:54.887464Z",
+ "iopub.status.busy": "2024-07-02T12:00:54.887081Z",
+ "iopub.status.idle": "2024-07-02T12:00:54.897463Z",
+ "shell.execute_reply": "2024-07-02T12:00:54.897041Z"
},
"id": "FDA5sGZwUSur",
"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:12.916523Z",
- "iopub.status.busy": "2024-07-01T15:02:12.916188Z",
- "iopub.status.idle": "2024-07-01T15:02:12.921874Z",
- "shell.execute_reply": "2024-07-01T15:02:12.921422Z"
+ "iopub.execute_input": "2024-07-02T12:00:54.899593Z",
+ "iopub.status.busy": "2024-07-02T12:00:54.899256Z",
+ "iopub.status.idle": "2024-07-02T12:00:54.904661Z",
+ "shell.execute_reply": "2024-07-02T12:00:54.904214Z"
},
"nbsphinx": "hidden"
},
@@ -380,10 +380,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:12.923867Z",
- "iopub.status.busy": "2024-07-01T15:02:12.923684Z",
- "iopub.status.idle": "2024-07-01T15:02:13.374643Z",
- "shell.execute_reply": "2024-07-01T15:02:13.374029Z"
+ "iopub.execute_input": "2024-07-02T12:00:54.906699Z",
+ "iopub.status.busy": "2024-07-02T12:00:54.906445Z",
+ "iopub.status.idle": "2024-07-02T12:00:55.370547Z",
+ "shell.execute_reply": "2024-07-02T12:00:55.370054Z"
},
"id": "dLBvUZLlII5w",
"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:13.376868Z",
- "iopub.status.busy": "2024-07-01T15:02:13.376659Z",
- "iopub.status.idle": "2024-07-01T15:02:14.191014Z",
- "shell.execute_reply": "2024-07-01T15:02:14.190519Z"
+ "iopub.execute_input": "2024-07-02T12:00:55.372729Z",
+ "iopub.status.busy": "2024-07-02T12:00:55.372455Z",
+ "iopub.status.idle": "2024-07-02T12:00:56.373788Z",
+ "shell.execute_reply": "2024-07-02T12:00:56.373190Z"
},
"id": "vL9lkiKsHvKr"
},
@@ -474,10 +474,10 @@
"height": 143
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:14.193499Z",
- "iopub.status.busy": "2024-07-01T15:02:14.193141Z",
- "iopub.status.idle": "2024-07-01T15:02:14.211506Z",
- "shell.execute_reply": "2024-07-01T15:02:14.210918Z"
+ "iopub.execute_input": "2024-07-02T12:00:56.376073Z",
+ "iopub.status.busy": "2024-07-02T12:00:56.375890Z",
+ "iopub.status.idle": "2024-07-02T12:00:56.393884Z",
+ "shell.execute_reply": "2024-07-02T12:00:56.393321Z"
},
"id": "obQYDKdLiUU6",
"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:14.213644Z",
- "iopub.status.busy": "2024-07-01T15:02:14.213459Z",
- "iopub.status.idle": "2024-07-01T15:02:14.216713Z",
- "shell.execute_reply": "2024-07-01T15:02:14.216187Z"
+ "iopub.execute_input": "2024-07-02T12:00:56.396057Z",
+ "iopub.status.busy": "2024-07-02T12:00:56.395720Z",
+ "iopub.status.idle": "2024-07-02T12:00:56.398930Z",
+ "shell.execute_reply": "2024-07-02T12:00:56.398478Z"
},
"id": "I8JqhOZgi94g"
},
@@ -582,10 +582,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:14.218727Z",
- "iopub.status.busy": "2024-07-01T15:02:14.218427Z",
- "iopub.status.idle": "2024-07-01T15:02:28.819416Z",
- "shell.execute_reply": "2024-07-01T15:02:28.818802Z"
+ "iopub.execute_input": "2024-07-02T12:00:56.400749Z",
+ "iopub.status.busy": "2024-07-02T12:00:56.400581Z",
+ "iopub.status.idle": "2024-07-02T12:01:10.956584Z",
+ "shell.execute_reply": "2024-07-02T12:01:10.955969Z"
},
"id": "2FSQ2GR9R_YA"
},
@@ -617,10 +617,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:28.822324Z",
- "iopub.status.busy": "2024-07-01T15:02:28.821907Z",
- "iopub.status.idle": "2024-07-01T15:02:28.825931Z",
- "shell.execute_reply": "2024-07-01T15:02:28.825366Z"
+ "iopub.execute_input": "2024-07-02T12:01:10.959440Z",
+ "iopub.status.busy": "2024-07-02T12:01:10.959028Z",
+ "iopub.status.idle": "2024-07-02T12:01:10.962902Z",
+ "shell.execute_reply": "2024-07-02T12:01:10.962374Z"
},
"id": "kAkY31IVXyr8",
"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -680,10 +680,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:28.827995Z",
- "iopub.status.busy": "2024-07-01T15:02:28.827681Z",
- "iopub.status.idle": "2024-07-01T15:02:29.521211Z",
- "shell.execute_reply": "2024-07-01T15:02:29.520635Z"
+ "iopub.execute_input": "2024-07-02T12:01:10.964878Z",
+ "iopub.status.busy": "2024-07-02T12:01:10.964705Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.664747Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.664181Z"
},
"id": "i_drkY9YOcw4"
},
@@ -717,10 +717,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.524947Z",
- "iopub.status.busy": "2024-07-01T15:02:29.524000Z",
- "iopub.status.idle": "2024-07-01T15:02:29.530833Z",
- "shell.execute_reply": "2024-07-01T15:02:29.530342Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.667592Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.667207Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.671960Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.671464Z"
},
"id": "_b-AQeoXOc7q",
"outputId": "15ae534a-f517-4906-b177-ca91931a8954"
@@ -767,10 +767,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.534459Z",
- "iopub.status.busy": "2024-07-01T15:02:29.533509Z",
- "iopub.status.idle": "2024-07-01T15:02:29.635169Z",
- "shell.execute_reply": "2024-07-01T15:02:29.634583Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.674352Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.673986Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.769978Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.769317Z"
}
},
"outputs": [
@@ -807,10 +807,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.637589Z",
- "iopub.status.busy": "2024-07-01T15:02:29.637281Z",
- "iopub.status.idle": "2024-07-01T15:02:29.650412Z",
- "shell.execute_reply": "2024-07-01T15:02:29.649911Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.772290Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.771936Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.785262Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.784787Z"
},
"scrolled": true
},
@@ -870,10 +870,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.652492Z",
- "iopub.status.busy": "2024-07-01T15:02:29.652304Z",
- "iopub.status.idle": "2024-07-01T15:02:29.660528Z",
- "shell.execute_reply": "2024-07-01T15:02:29.660066Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.787484Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.787145Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.795270Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.794713Z"
}
},
"outputs": [
@@ -977,10 +977,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.662724Z",
- "iopub.status.busy": "2024-07-01T15:02:29.662297Z",
- "iopub.status.idle": "2024-07-01T15:02:29.666626Z",
- "shell.execute_reply": "2024-07-01T15:02:29.666160Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.797390Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.797080Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.801551Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.800973Z"
}
},
"outputs": [
@@ -1018,10 +1018,10 @@
"height": 237
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.668442Z",
- "iopub.status.busy": "2024-07-01T15:02:29.668268Z",
- "iopub.status.idle": "2024-07-01T15:02:29.673924Z",
- "shell.execute_reply": "2024-07-01T15:02:29.673445Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.803467Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.803275Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.809289Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.808826Z"
},
"id": "FQwRHgbclpsO",
"outputId": "fee5c335-c00e-4fcc-f22b-718705e93182"
@@ -1148,10 +1148,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.675826Z",
- "iopub.status.busy": "2024-07-01T15:02:29.675650Z",
- "iopub.status.idle": "2024-07-01T15:02:29.788802Z",
- "shell.execute_reply": "2024-07-01T15:02:29.788252Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.811355Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.811010Z",
+ "iopub.status.idle": "2024-07-02T12:01:11.924674Z",
+ "shell.execute_reply": "2024-07-02T12:01:11.924087Z"
},
"id": "ff1NFVlDoysO",
"outputId": "8141a036-44c1-4349-c338-880432513e37"
@@ -1205,10 +1205,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.791024Z",
- "iopub.status.busy": "2024-07-01T15:02:29.790694Z",
- "iopub.status.idle": "2024-07-01T15:02:29.899192Z",
- "shell.execute_reply": "2024-07-01T15:02:29.898612Z"
+ "iopub.execute_input": "2024-07-02T12:01:11.927078Z",
+ "iopub.status.busy": "2024-07-02T12:01:11.926676Z",
+ "iopub.status.idle": "2024-07-02T12:01:12.029810Z",
+ "shell.execute_reply": "2024-07-02T12:01:12.029255Z"
},
"id": "GZgovGkdiaiP",
"outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7"
@@ -1253,10 +1253,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:29.901288Z",
- "iopub.status.busy": "2024-07-01T15:02:29.901099Z",
- "iopub.status.idle": "2024-07-01T15:02:30.006069Z",
- "shell.execute_reply": "2024-07-01T15:02:30.005505Z"
+ "iopub.execute_input": "2024-07-02T12:01:12.031968Z",
+ "iopub.status.busy": "2024-07-02T12:01:12.031613Z",
+ "iopub.status.idle": "2024-07-02T12:01:12.132022Z",
+ "shell.execute_reply": "2024-07-02T12:01:12.131402Z"
},
"id": "lfa2eHbMwG8R",
"outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c"
@@ -1297,10 +1297,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:30.008275Z",
- "iopub.status.busy": "2024-07-01T15:02:30.007928Z",
- "iopub.status.idle": "2024-07-01T15:02:30.113221Z",
- "shell.execute_reply": "2024-07-01T15:02:30.112655Z"
+ "iopub.execute_input": "2024-07-02T12:01:12.134167Z",
+ "iopub.status.busy": "2024-07-02T12:01:12.133985Z",
+ "iopub.status.idle": "2024-07-02T12:01:12.235981Z",
+ "shell.execute_reply": "2024-07-02T12:01:12.235470Z"
}
},
"outputs": [
@@ -1348,10 +1348,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:30.115374Z",
- "iopub.status.busy": "2024-07-01T15:02:30.115188Z",
- "iopub.status.idle": "2024-07-01T15:02:30.118487Z",
- "shell.execute_reply": "2024-07-01T15:02:30.117941Z"
+ "iopub.execute_input": "2024-07-02T12:01:12.237957Z",
+ "iopub.status.busy": "2024-07-02T12:01:12.237775Z",
+ "iopub.status.idle": "2024-07-02T12:01:12.241026Z",
+ "shell.execute_reply": "2024-07-02T12:01:12.240474Z"
},
"nbsphinx": "hidden"
},
@@ -1392,73 +1392,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "00fc7e046c8f4c308d5d222a635bb52c": {
- "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": ""
- }
- },
- "0a99e571562d43a7aa5ecd2abda2aa2d": {
- "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_4748994f76ed4bc388496a39e510d54d",
- "max": 2041.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_fcffd70673164f8aadd97b82906cc76e",
- "tabbable": null,
- "tooltip": null,
- "value": 2041.0
- }
- },
- "11d84ef51dce4f7aaa879bf5e2b72f38": {
- "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_b2e03ad67f4e4dd08bf9d4b1f4fb712d",
- "IPY_MODEL_b6a2cd8ff5374f089fad080858e3fd96",
- "IPY_MODEL_17cad868ac7f43f3a8289ad5c1dbdd25"
- ],
- "layout": "IPY_MODEL_4284b301638341a3b41f91a5b5daab48",
- "tabbable": null,
- "tooltip": null
- }
- },
- "169802f2389c4c4e8717f6593ecaccaf": {
+ "0b4a72514d5e48d9a5de944d58a5949b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1511,7 +1445,7 @@
"width": null
}
},
- "17cad868ac7f43f3a8289ad5c1dbdd25": {
+ "1879dcf933dc47ef80752de84b1be173": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1526,97 +1460,147 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_169802f2389c4c4e8717f6593ecaccaf",
+ "layout": "IPY_MODEL_1e578c121b074fe689ec2d0de3438add",
"placeholder": "",
- "style": "IPY_MODEL_692bb6d688714e569e4dfa9584bc0ad0",
+ "style": "IPY_MODEL_65678011a834483f81123fce6a847a0f",
"tabbable": null,
"tooltip": null,
- "value": " 3.20k/3.20k [00:00<00:00, 778kB/s]"
+ "value": "classifier.ckpt: 100%"
}
},
- "1a72151665fc4b27b30d0d9a8c74e578": {
- "model_module": "@jupyter-widgets/controls",
+ "1df8c0f2d29d45779e226e08c41aefd8": {
+ "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
}
},
- "208e173931a84902991001290bbcd8e8": {
- "model_module": "@jupyter-widgets/controls",
+ "1e578c121b074fe689ec2d0de3438add": {
+ "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": ""
- }
- },
- "299a08f2fa054f7bbe1daaa618adeb92": {
- "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_9583f3f76f304e7abc2f1a387e1d3df1",
- "IPY_MODEL_e0eec76f9c4b41dab7f14303a673e770",
- "IPY_MODEL_f2bf833e4ea64394b10f33d310c0ee23"
- ],
- "layout": "IPY_MODEL_2d9a8d5f92fe4b3aaec557c0461f8829",
- "tabbable": null,
- "tooltip": 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
}
},
- "2d6758bb73d44586a7740d66e26f26cf": {
+ "2073d888478c4148aa6af8f01d1a55c0": {
"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_7f01730dbf1446ca92cdfd900a358c1a",
- "IPY_MODEL_f07baddc31e847c595d90dd42ca379fb",
- "IPY_MODEL_3b887681781b4f25abe1998059b0ae53"
- ],
- "layout": "IPY_MODEL_fc74c0118a094bc3a970ec592d969b3d",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_f9d1becbce8d4a0e92d1291261488c36",
+ "max": 15856877.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_7fb74483ba564e44bf8528608dd0c00d",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": 15856877.0
}
},
- "2d9a8d5f92fe4b3aaec557c0461f8829": {
+ "249f3be21f17476cb286f35a4beae4ea": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1669,7 +1653,74 @@
"width": null
}
},
- "32de4a18c7f147a8be35c654b53457aa": {
+ "280097d7d8744e47bd5924ed20469bd6": {
+ "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_f1379d86855941e4a6388b556616e327",
+ "max": 2041.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_40a775a4588f452f8bf5d7fbc03bc9ba",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 2041.0
+ }
+ },
+ "28c2d54c60d54e18885566cb5f99ba0a": {
+ "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
+ }
+ },
+ "38e4ea17dfd74406801cb8ef4fa01bfb": {
+ "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_d3b969344b34427e9067d2e51d96dd59",
+ "placeholder": "",
+ "style": "IPY_MODEL_9ad076b10aa949c583d59ebf787d18d9",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 2.04k/2.04k [00:00<00:00, 501kB/s]"
+ }
+ },
+ "39378c4365b84e2ba8a40f5da8c49a6b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1722,7 +1773,25 @@
"width": null
}
},
- "391af44b9e68466688f4ac70187a5ea4": {
+ "3b3e7e9c2d3a481a8b66e18c87d96abe": {
+ "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
+ }
+ },
+ "3e0efe779a52444991f5fa51e7b53a87": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1737,15 +1806,57 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_8034cbf813a3405d8b1b6885128dbb55",
+ "layout": "IPY_MODEL_5740512f403c4e52a3ce092bc9b6022f",
"placeholder": "",
- "style": "IPY_MODEL_70432221e66e4c1b925252a9aabda6f1",
+ "style": "IPY_MODEL_9fbff358e60544609b6d2a561cf6f85c",
"tabbable": null,
"tooltip": null,
- "value": " 129k/129k [00:00<00:00, 10.6MB/s]"
+ "value": "embedding_model.ckpt: 100%"
+ }
+ },
+ "40a775a4588f452f8bf5d7fbc03bc9ba": {
+ "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": ""
}
},
- "3b887681781b4f25abe1998059b0ae53": {
+ "4209fa1ed28448c4a745350a1042e490": {
+ "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_0b4a72514d5e48d9a5de944d58a5949b",
+ "max": 128619.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_54ae4e63d9c84d078262621e2d8e350f",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 128619.0
+ }
+ },
+ "4d17c45bac644d16890f974d9bf14252": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1760,15 +1871,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f34dc10446834ea5b31b833757faa688",
+ "layout": "IPY_MODEL_1df8c0f2d29d45779e226e08c41aefd8",
"placeholder": "",
- "style": "IPY_MODEL_ca1c4425f72b4b4a83a222e1429dbfed",
+ "style": "IPY_MODEL_7a9eaff0d94f402eb6726bbbe282d200",
"tabbable": null,
"tooltip": null,
- "value": " 16.9M/16.9M [00:00<00:00, 94.6MB/s]"
+ "value": " 16.9M/16.9M [00:00<00:00, 121MB/s]"
}
},
- "4284b301638341a3b41f91a5b5daab48": {
+ "513013579fcb4448832ca2cf5f6e0fc2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1821,7 +1932,23 @@
"width": null
}
},
- "4748994f76ed4bc388496a39e510d54d": {
+ "54ae4e63d9c84d078262621e2d8e350f": {
+ "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": ""
+ }
+ },
+ "5740512f403c4e52a3ce092bc9b6022f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1874,7 +2001,7 @@
"width": null
}
},
- "4c63aa921de543f9a38a79a37dbf15a6": {
+ "5fc9fb12e7584385b6dbcc71ea004933": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1927,7 +2054,43 @@
"width": null
}
},
- "4fe53b9dff9148ffbaee866c5033bba3": {
+ "65678011a834483f81123fce6a847a0f": {
+ "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
+ }
+ },
+ "65c231ce763c440980afbfa1c4eaab86": {
+ "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
+ }
+ },
+ "68b7a074f4a5491984f03ffaed29102c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1942,15 +2105,31 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_d8487dd8ed3e42a38371219c3c729283",
+ "layout": "IPY_MODEL_6fe26799ef5342b7ae1161bc8ef4ec8a",
"placeholder": "",
- "style": "IPY_MODEL_c3d1ad19115840c0bd02e22d3f39fbd9",
+ "style": "IPY_MODEL_ba779947520844b1b4e9e788604b7ed8",
"tabbable": null,
"tooltip": null,
- "value": "hyperparams.yaml: 100%"
+ "value": "label_encoder.txt: 100%"
+ }
+ },
+ "6b1e51bd7dfd4e7e9b96650627dd4347": {
+ "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": ""
}
},
- "59b0ce30e35142928e041a6b8d2dc230": {
+ "6fe26799ef5342b7ae1161bc8ef4ec8a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2003,23 +2182,7 @@
"width": null
}
},
- "5a64304d36024180b26f4f79387324dc": {
- "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": ""
- }
- },
- "67737bdcaa0b4c8aad9b8db3c9fbeb51": {
+ "73fd95beddfa4d59833b8b729dc2ef1b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2034,15 +2197,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_868c56c897d2493bb075baa99c580d21",
+ "layout": "IPY_MODEL_513013579fcb4448832ca2cf5f6e0fc2",
"placeholder": "",
- "style": "IPY_MODEL_6cd92f1c94e145be925eb36473830cbb",
+ "style": "IPY_MODEL_edbc3e1e1e514aeea602d52281c3dfa3",
"tabbable": null,
"tooltip": null,
- "value": "label_encoder.txt: 100%"
+ "value": "hyperparams.yaml: 100%"
}
},
- "6884e940a8554f6589d336edaee67f33": {
+ "743444b6670b4fa99cc0396e4b32f5e4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2057,33 +2220,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f64888652c004082a1b036270e2702e0",
+ "layout": "IPY_MODEL_b95daff336854c7aa1ca5dbdf574af6e",
"placeholder": "",
- "style": "IPY_MODEL_78b6d5bc6077499094dfc9bd718f4db7",
+ "style": "IPY_MODEL_65c231ce763c440980afbfa1c4eaab86",
"tabbable": null,
"tooltip": null,
- "value": " 2.04k/2.04k [00:00<00:00, 481kB/s]"
- }
- },
- "692bb6d688714e569e4dfa9584bc0ad0": {
- "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": " 129k/129k [00:00<00:00, 10.6MB/s]"
}
},
- "6cd92f1c94e145be925eb36473830cbb": {
+ "7a9eaff0d94f402eb6726bbbe282d200": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2101,25 +2246,31 @@
"text_color": null
}
},
- "70432221e66e4c1b925252a9aabda6f1": {
+ "7ac998d738294e6c82fb7330a80ef819": {
"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_1879dcf933dc47ef80752de84b1be173",
+ "IPY_MODEL_2073d888478c4148aa6af8f01d1a55c0",
+ "IPY_MODEL_b182da74164e4c6da9b9218000f3b471"
+ ],
+ "layout": "IPY_MODEL_7b71cae594754cd4b51bdf0cc3937dc8",
+ "tabbable": null,
+ "tooltip": null
}
},
- "7687a1cfa1264a529fcedffaa6a4e467": {
+ "7b71cae594754cd4b51bdf0cc3937dc8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2172,7 +2323,7 @@
"width": null
}
},
- "78b6d5bc6077499094dfc9bd718f4db7": {
+ "7e28797dcb31400985bcdae5b8c5fb36": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2190,136 +2341,23 @@
"text_color": null
}
},
- "7f01730dbf1446ca92cdfd900a358c1a": {
+ "7fb74483ba564e44bf8528608dd0c00d": {
"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_8458463007ef42c0a82ddb9da20103ba",
- "placeholder": "",
- "style": "IPY_MODEL_1a72151665fc4b27b30d0d9a8c74e578",
- "tabbable": null,
- "tooltip": null,
- "value": "embedding_model.ckpt: 100%"
- }
- },
- "8034cbf813a3405d8b1b6885128dbb55": {
- "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
- }
- },
- "8458463007ef42c0a82ddb9da20103ba": {
- "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": "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": ""
}
},
- "868c56c897d2493bb075baa99c580d21": {
+ "96e34a60ae404938aef646ab844636ca": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2372,54 +2410,49 @@
"width": null
}
},
- "9583f3f76f304e7abc2f1a387e1d3df1": {
+ "9946d2c65d6146e5a82c395bc8875caf": {
"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_4c63aa921de543f9a38a79a37dbf15a6",
- "placeholder": "",
- "style": "IPY_MODEL_ab62c5cec180440ebb1975bcb91646f5",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_68b7a074f4a5491984f03ffaed29102c",
+ "IPY_MODEL_4209fa1ed28448c4a745350a1042e490",
+ "IPY_MODEL_743444b6670b4fa99cc0396e4b32f5e4"
+ ],
+ "layout": "IPY_MODEL_f70f441fdd5147c685302ccf8dbb2370",
"tabbable": null,
- "tooltip": null,
- "value": "classifier.ckpt: 100%"
+ "tooltip": null
}
},
- "999d991d513f43d38a94a4e22e5eae99": {
+ "9ad076b10aa949c583d59ebf787d18d9": {
"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_67737bdcaa0b4c8aad9b8db3c9fbeb51",
- "IPY_MODEL_cf3af27e08194934863770ad9095424f",
- "IPY_MODEL_391af44b9e68466688f4ac70187a5ea4"
- ],
- "layout": "IPY_MODEL_aef29a94b0394011a06ba738f227041b",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "ab62c5cec180440ebb1975bcb91646f5": {
+ "9fbff358e60544609b6d2a561cf6f85c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2437,7 +2470,7 @@
"text_color": null
}
},
- "aef29a94b0394011a06ba738f227041b": {
+ "a93eeb28cf894cdba2553c527a3bb1a7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2490,74 +2523,7 @@
"width": null
}
},
- "b2e03ad67f4e4dd08bf9d4b1f4fb712d": {
- "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_32de4a18c7f147a8be35c654b53457aa",
- "placeholder": "",
- "style": "IPY_MODEL_b5cb5977357e4fab995dcae8e2643177",
- "tabbable": null,
- "tooltip": null,
- "value": "mean_var_norm_emb.ckpt: 100%"
- }
- },
- "b5cb5977357e4fab995dcae8e2643177": {
- "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
- }
- },
- "b6a2cd8ff5374f089fad080858e3fd96": {
- "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_d0a6b2ff14e14058a38164d872d62aca",
- "max": 3201.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_208e173931a84902991001290bbcd8e8",
- "tabbable": null,
- "tooltip": null,
- "value": 3201.0
- }
- },
- "c1fc1902ebdc421e8d0afcb0d1027f63": {
+ "af604b66416942c3beafdb0f24f5fb27": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2610,67 +2576,30 @@
"width": null
}
},
- "c3d1ad19115840c0bd02e22d3f39fbd9": {
- "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
- }
- },
- "ca1c4425f72b4b4a83a222e1429dbfed": {
- "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
- }
- },
- "cf3a1744d9c64353a34451f3e0dc96ef": {
+ "b182da74164e4c6da9b9218000f3b471": {
"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_4fe53b9dff9148ffbaee866c5033bba3",
- "IPY_MODEL_0a99e571562d43a7aa5ecd2abda2aa2d",
- "IPY_MODEL_6884e940a8554f6589d336edaee67f33"
- ],
- "layout": "IPY_MODEL_dfeb5c97c0174fe3b02f601346925b4a",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_39378c4365b84e2ba8a40f5da8c49a6b",
+ "placeholder": "",
+ "style": "IPY_MODEL_7e28797dcb31400985bcdae5b8c5fb36",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 15.9M/15.9M [00:00<00:00, 133MB/s]"
}
},
- "cf3af27e08194934863770ad9095424f": {
+ "b4c75c905856457c980a958861d26460": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2686,17 +2615,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_59b0ce30e35142928e041a6b8d2dc230",
- "max": 128619.0,
+ "layout": "IPY_MODEL_a93eeb28cf894cdba2553c527a3bb1a7",
+ "max": 16887676.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_dff35f7ef39b489d8db92f12e36f0799",
+ "style": "IPY_MODEL_6b1e51bd7dfd4e7e9b96650627dd4347",
"tabbable": null,
"tooltip": null,
- "value": 128619.0
+ "value": 16887676.0
}
},
- "d0a6b2ff14e14058a38164d872d62aca": {
+ "b7930e24a3604c3783c6342017146161": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2749,7 +2678,7 @@
"width": null
}
},
- "d8487dd8ed3e42a38371219c3c729283": {
+ "b95daff336854c7aa1ca5dbdf574af6e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2802,7 +2731,25 @@
"width": null
}
},
- "dfeb5c97c0174fe3b02f601346925b4a": {
+ "ba779947520844b1b4e9e788604b7ed8": {
+ "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
+ }
+ },
+ "c5e86d52ed71402dbadb1a831d3a99dd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2855,7 +2802,31 @@
"width": null
}
},
- "dff35f7ef39b489d8db92f12e36f0799": {
+ "cb4111006dc844c69976ab2a2fd47bf5": {
+ "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_3e0efe779a52444991f5fa51e7b53a87",
+ "IPY_MODEL_b4c75c905856457c980a958861d26460",
+ "IPY_MODEL_4d17c45bac644d16890f974d9bf14252"
+ ],
+ "layout": "IPY_MODEL_af604b66416942c3beafdb0f24f5fb27",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "cbf0488c8e70416aabb7cb8cffe59c43": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -2871,7 +2842,7 @@
"description_width": ""
}
},
- "e0eec76f9c4b41dab7f14303a673e770": {
+ "cf0f8cce4150428582199659b7ecc31f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2887,17 +2858,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_e6590d45e9154268897f1c416b514bc9",
- "max": 15856877.0,
+ "layout": "IPY_MODEL_5fc9fb12e7584385b6dbcc71ea004933",
+ "max": 3201.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_00fc7e046c8f4c308d5d222a635bb52c",
+ "style": "IPY_MODEL_cbf0488c8e70416aabb7cb8cffe59c43",
"tabbable": null,
"tooltip": null,
- "value": 15856877.0
+ "value": 3201.0
}
},
- "e6590d45e9154268897f1c416b514bc9": {
+ "d3b969344b34427e9067d2e51d96dd59": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2950,74 +2921,48 @@
"width": null
}
},
- "ea4a743f2c1443eab20e9c8135c3321d": {
- "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
- }
- },
- "f07baddc31e847c595d90dd42ca379fb": {
+ "d5743decd00649e19fbee18925104825": {
"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_7687a1cfa1264a529fcedffaa6a4e467",
- "max": 16887676.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5a64304d36024180b26f4f79387324dc",
+ "layout": "IPY_MODEL_c5e86d52ed71402dbadb1a831d3a99dd",
+ "placeholder": "",
+ "style": "IPY_MODEL_3b3e7e9c2d3a481a8b66e18c87d96abe",
"tabbable": null,
"tooltip": null,
- "value": 16887676.0
+ "value": "mean_var_norm_emb.ckpt: 100%"
}
},
- "f2bf833e4ea64394b10f33d310c0ee23": {
+ "edbc3e1e1e514aeea602d52281c3dfa3": {
"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_c1fc1902ebdc421e8d0afcb0d1027f63",
- "placeholder": "",
- "style": "IPY_MODEL_ea4a743f2c1443eab20e9c8135c3321d",
- "tabbable": null,
- "tooltip": null,
- "value": " 15.9M/15.9M [00:00<00:00, 93.5MB/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "f34dc10446834ea5b31b833757faa688": {
+ "f1379d86855941e4a6388b556616e327": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3070,7 +3015,30 @@
"width": null
}
},
- "f64888652c004082a1b036270e2702e0": {
+ "f5b3c5a8ab94472a8c12d10627c4a3b2": {
+ "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_249f3be21f17476cb286f35a4beae4ea",
+ "placeholder": "",
+ "style": "IPY_MODEL_28c2d54c60d54e18885566cb5f99ba0a",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 3.20k/3.20k [00:00<00:00, 820kB/s]"
+ }
+ },
+ "f70f441fdd5147c685302ccf8dbb2370": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3123,7 +3091,7 @@
"width": null
}
},
- "fc74c0118a094bc3a970ec592d969b3d": {
+ "f9d1becbce8d4a0e92d1291261488c36": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3176,20 +3144,52 @@
"width": null
}
},
- "fcffd70673164f8aadd97b82906cc76e": {
+ "fbe4b54842f84d388c29da52e5a714cb": {
"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": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_73fd95beddfa4d59833b8b729dc2ef1b",
+ "IPY_MODEL_280097d7d8744e47bd5924ed20469bd6",
+ "IPY_MODEL_38e4ea17dfd74406801cb8ef4fa01bfb"
+ ],
+ "layout": "IPY_MODEL_96e34a60ae404938aef646ab844636ca",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "fc94797f46734484ae1adb8c6aac5095": {
+ "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": "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_d5743decd00649e19fbee18925104825",
+ "IPY_MODEL_cf0f8cce4150428582199659b7ecc31f",
+ "IPY_MODEL_f5b3c5a8ab94472a8c12d10627c4a3b2"
+ ],
+ "layout": "IPY_MODEL_b7930e24a3604c3783c6342017146161",
+ "tabbable": null,
+ "tooltip": null
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 1e7141136..58bbdaa8a 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": "2024-07-01T15:02:34.100510Z",
- "iopub.status.busy": "2024-07-01T15:02:34.100309Z",
- "iopub.status.idle": "2024-07-01T15:02:35.344393Z",
- "shell.execute_reply": "2024-07-01T15:02:35.343853Z"
+ "iopub.execute_input": "2024-07-02T12:01:15.541042Z",
+ "iopub.status.busy": "2024-07-02T12:01:15.540869Z",
+ "iopub.status.idle": "2024-07-02T12:01:16.706079Z",
+ "shell.execute_reply": "2024-07-02T12:01:16.705546Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:35.347246Z",
- "iopub.status.busy": "2024-07-01T15:02:35.346746Z",
- "iopub.status.idle": "2024-07-01T15:02:35.349837Z",
- "shell.execute_reply": "2024-07-01T15:02:35.349391Z"
+ "iopub.execute_input": "2024-07-02T12:01:16.708528Z",
+ "iopub.status.busy": "2024-07-02T12:01:16.708127Z",
+ "iopub.status.idle": "2024-07-02T12:01:16.711112Z",
+ "shell.execute_reply": "2024-07-02T12:01:16.710676Z"
}
},
"outputs": [],
@@ -252,10 +252,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:35.352173Z",
- "iopub.status.busy": "2024-07-01T15:02:35.351845Z",
- "iopub.status.idle": "2024-07-01T15:02:35.361048Z",
- "shell.execute_reply": "2024-07-01T15:02:35.360394Z"
+ "iopub.execute_input": "2024-07-02T12:01:16.713182Z",
+ "iopub.status.busy": "2024-07-02T12:01:16.712867Z",
+ "iopub.status.idle": "2024-07-02T12:01:16.721179Z",
+ "shell.execute_reply": "2024-07-02T12:01:16.720739Z"
},
"nbsphinx": "hidden"
},
@@ -353,10 +353,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:35.363704Z",
- "iopub.status.busy": "2024-07-01T15:02:35.363238Z",
- "iopub.status.idle": "2024-07-01T15:02:35.368807Z",
- "shell.execute_reply": "2024-07-01T15:02:35.368166Z"
+ "iopub.execute_input": "2024-07-02T12:01:16.723125Z",
+ "iopub.status.busy": "2024-07-02T12:01:16.722823Z",
+ "iopub.status.idle": "2024-07-02T12:01:16.727946Z",
+ "shell.execute_reply": "2024-07-02T12:01:16.727497Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:35.371407Z",
- "iopub.status.busy": "2024-07-01T15:02:35.370950Z",
- "iopub.status.idle": "2024-07-01T15:02:35.579618Z",
- "shell.execute_reply": "2024-07-01T15:02:35.578902Z"
+ "iopub.execute_input": "2024-07-02T12:01:16.730061Z",
+ "iopub.status.busy": "2024-07-02T12:01:16.729738Z",
+ "iopub.status.idle": "2024-07-02T12:01:16.910261Z",
+ "shell.execute_reply": "2024-07-02T12:01:16.909774Z"
},
"nbsphinx": "hidden"
},
@@ -517,10 +517,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:35.582660Z",
- "iopub.status.busy": "2024-07-01T15:02:35.582270Z",
- "iopub.status.idle": "2024-07-01T15:02:35.982874Z",
- "shell.execute_reply": "2024-07-01T15:02:35.982240Z"
+ "iopub.execute_input": "2024-07-02T12:01:16.912657Z",
+ "iopub.status.busy": "2024-07-02T12:01:16.912383Z",
+ "iopub.status.idle": "2024-07-02T12:01:17.280864Z",
+ "shell.execute_reply": "2024-07-02T12:01:17.280305Z"
}
},
"outputs": [
@@ -569,10 +569,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:35.985209Z",
- "iopub.status.busy": "2024-07-01T15:02:35.984882Z",
- "iopub.status.idle": "2024-07-01T15:02:36.009092Z",
- "shell.execute_reply": "2024-07-01T15:02:36.008586Z"
+ "iopub.execute_input": "2024-07-02T12:01:17.283183Z",
+ "iopub.status.busy": "2024-07-02T12:01:17.282742Z",
+ "iopub.status.idle": "2024-07-02T12:01:17.305912Z",
+ "shell.execute_reply": "2024-07-02T12:01:17.305342Z"
}
},
"outputs": [],
@@ -608,10 +608,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:36.011743Z",
- "iopub.status.busy": "2024-07-01T15:02:36.011310Z",
- "iopub.status.idle": "2024-07-01T15:02:36.023407Z",
- "shell.execute_reply": "2024-07-01T15:02:36.022817Z"
+ "iopub.execute_input": "2024-07-02T12:01:17.308190Z",
+ "iopub.status.busy": "2024-07-02T12:01:17.307876Z",
+ "iopub.status.idle": "2024-07-02T12:01:17.318887Z",
+ "shell.execute_reply": "2024-07-02T12:01:17.318342Z"
}
},
"outputs": [],
@@ -642,10 +642,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:36.025951Z",
- "iopub.status.busy": "2024-07-01T15:02:36.025605Z",
- "iopub.status.idle": "2024-07-01T15:02:38.178872Z",
- "shell.execute_reply": "2024-07-01T15:02:38.178173Z"
+ "iopub.execute_input": "2024-07-02T12:01:17.321139Z",
+ "iopub.status.busy": "2024-07-02T12:01:17.320805Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.303196Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.302567Z"
}
},
"outputs": [
@@ -714,10 +714,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.181613Z",
- "iopub.status.busy": "2024-07-01T15:02:38.181161Z",
- "iopub.status.idle": "2024-07-01T15:02:38.204407Z",
- "shell.execute_reply": "2024-07-01T15:02:38.203790Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.305724Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.305235Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.326596Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.326111Z"
}
},
"outputs": [
@@ -830,10 +830,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.207031Z",
- "iopub.status.busy": "2024-07-01T15:02:38.206574Z",
- "iopub.status.idle": "2024-07-01T15:02:38.225655Z",
- "shell.execute_reply": "2024-07-01T15:02:38.225020Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.328751Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.328411Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.346909Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.346408Z"
}
},
"outputs": [
@@ -937,10 +937,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.228105Z",
- "iopub.status.busy": "2024-07-01T15:02:38.227775Z",
- "iopub.status.idle": "2024-07-01T15:02:38.243503Z",
- "shell.execute_reply": "2024-07-01T15:02:38.242861Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.349172Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.348833Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.364109Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.363523Z"
}
},
"outputs": [
@@ -1075,17 +1075,17 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.245863Z",
- "iopub.status.busy": "2024-07-01T15:02:38.245474Z",
- "iopub.status.idle": "2024-07-01T15:02:38.267061Z",
- "shell.execute_reply": "2024-07-01T15:02:38.266454Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.366447Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.366041Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.385525Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.384972Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d16dede2bb2e40b282d000f989523e41",
+ "model_id": "59b4478dd8e7455d94d80c6cac5956e7",
"version_major": 2,
"version_minor": 0
},
@@ -1121,10 +1121,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.269410Z",
- "iopub.status.busy": "2024-07-01T15:02:38.269038Z",
- "iopub.status.idle": "2024-07-01T15:02:38.286735Z",
- "shell.execute_reply": "2024-07-01T15:02:38.286110Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.387568Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.387355Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.403995Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.403416Z"
}
},
"outputs": [
@@ -1247,10 +1247,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.289163Z",
- "iopub.status.busy": "2024-07-01T15:02:38.288765Z",
- "iopub.status.idle": "2024-07-01T15:02:38.295067Z",
- "shell.execute_reply": "2024-07-01T15:02:38.294503Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.406166Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.405840Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.411828Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.411266Z"
}
},
"outputs": [],
@@ -1307,10 +1307,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:38.297383Z",
- "iopub.status.busy": "2024-07-01T15:02:38.296989Z",
- "iopub.status.idle": "2024-07-01T15:02:38.317743Z",
- "shell.execute_reply": "2024-07-01T15:02:38.317117Z"
+ "iopub.execute_input": "2024-07-02T12:01:19.414062Z",
+ "iopub.status.busy": "2024-07-02T12:01:19.413631Z",
+ "iopub.status.idle": "2024-07-02T12:01:19.432239Z",
+ "shell.execute_reply": "2024-07-02T12:01:19.431665Z"
}
},
"outputs": [
@@ -1447,7 +1447,23 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0dc84865474c4bf7a312e538bc8f4a74": {
+ "160374201c2049b98c39d1da42e6f09d": {
+ "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": ""
+ }
+ },
+ "23609831ef654449b59fb8c4f8a2bb30": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1500,7 +1516,53 @@
"width": null
}
},
- "39e0d0ff92854bb5b45f8340a9c5c5eb": {
+ "2c61a80b080b4e158a20edb5c4a1ac84": {
+ "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_23609831ef654449b59fb8c4f8a2bb30",
+ "placeholder": "",
+ "style": "IPY_MODEL_ada4493def764ffa859a5d6ba4d315fb",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Saving the dataset (1/1 shards): 100%"
+ }
+ },
+ "430e528b6e30444ea44c9f7dacbfcc30": {
+ "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_8addd7af612b43d395a8dfcfeb6287ef",
+ "placeholder": "",
+ "style": "IPY_MODEL_bd9b705b24884f74a14e8bfdd7ee8634",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 132/132 [00:00<00:00, 13162.98 examples/s]"
+ }
+ },
+ "4d30844fcfff423583118cba2ebebe1b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -1516,17 +1578,41 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_0dc84865474c4bf7a312e538bc8f4a74",
+ "layout": "IPY_MODEL_92d343740ab348028d512cbabde596de",
"max": 132.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_bd9368c5e9b842ca818c69f779cd5276",
+ "style": "IPY_MODEL_160374201c2049b98c39d1da42e6f09d",
"tabbable": null,
"tooltip": null,
"value": 132.0
}
},
- "746118eed9d445c9a37e681cbacd9674": {
+ "59b4478dd8e7455d94d80c6cac5956e7": {
+ "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_2c61a80b080b4e158a20edb5c4a1ac84",
+ "IPY_MODEL_4d30844fcfff423583118cba2ebebe1b",
+ "IPY_MODEL_430e528b6e30444ea44c9f7dacbfcc30"
+ ],
+ "layout": "IPY_MODEL_5e818fd01e87406a87c87fc7bc810095",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "5e818fd01e87406a87c87fc7bc810095": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1579,7 +1665,7 @@
"width": null
}
},
- "890c2c0c04564f5da3221229c05800df": {
+ "8addd7af612b43d395a8dfcfeb6287ef": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1632,30 +1718,7 @@
"width": null
}
},
- "979503323663435eae635a194817476f": {
- "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_746118eed9d445c9a37e681cbacd9674",
- "placeholder": "",
- "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5",
- "tabbable": null,
- "tooltip": null,
- "value": "Saving the dataset (1/1 shards): 100%"
- }
- },
- "af633ab6f6924af0b3f4ac3691d76422": {
+ "92d343740ab348028d512cbabde596de": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1708,7 +1771,7 @@
"width": null
}
},
- "bcb7b5d047f845978925e2ef6da3385e": {
+ "ada4493def764ffa859a5d6ba4d315fb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1726,70 +1789,7 @@
"text_color": null
}
},
- "bd9368c5e9b842ca818c69f779cd5276": {
- "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": ""
- }
- },
- "cac3b162735445c0915d9ecfed155f4c": {
- "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_af633ab6f6924af0b3f4ac3691d76422",
- "placeholder": "",
- "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e",
- "tabbable": null,
- "tooltip": null,
- "value": " 132/132 [00:00<00:00, 11804.36 examples/s]"
- }
- },
- "d16dede2bb2e40b282d000f989523e41": {
- "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_979503323663435eae635a194817476f",
- "IPY_MODEL_39e0d0ff92854bb5b45f8340a9c5c5eb",
- "IPY_MODEL_cac3b162735445c0915d9ecfed155f4c"
- ],
- "layout": "IPY_MODEL_890c2c0c04564f5da3221229c05800df",
- "tabbable": null,
- "tooltip": null
- }
- },
- "d57ca1f4799d45229ae2f7c720c262f5": {
+ "bd9b705b24884f74a14e8bfdd7ee8634": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index e8c4bda9d..61c4891f1 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": "2024-07-01T15:02:41.409044Z",
- "iopub.status.busy": "2024-07-01T15:02:41.408875Z",
- "iopub.status.idle": "2024-07-01T15:02:42.611044Z",
- "shell.execute_reply": "2024-07-01T15:02:42.610498Z"
+ "iopub.execute_input": "2024-07-02T12:01:22.152510Z",
+ "iopub.status.busy": "2024-07-02T12:01:22.152333Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.345486Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.344925Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:42.613616Z",
- "iopub.status.busy": "2024-07-01T15:02:42.613300Z",
- "iopub.status.idle": "2024-07-01T15:02:42.616526Z",
- "shell.execute_reply": "2024-07-01T15:02:42.616068Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.348223Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.347674Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.350818Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.350357Z"
}
},
"outputs": [],
@@ -250,10 +250,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:42.618748Z",
- "iopub.status.busy": "2024-07-01T15:02:42.618428Z",
- "iopub.status.idle": "2024-07-01T15:02:42.627446Z",
- "shell.execute_reply": "2024-07-01T15:02:42.626999Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.352826Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.352642Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.361928Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.361407Z"
},
"nbsphinx": "hidden"
},
@@ -356,10 +356,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:42.629537Z",
- "iopub.status.busy": "2024-07-01T15:02:42.629203Z",
- "iopub.status.idle": "2024-07-01T15:02:42.633941Z",
- "shell.execute_reply": "2024-07-01T15:02:42.633516Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.363999Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.363568Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.368394Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.367822Z"
}
},
"outputs": [],
@@ -448,10 +448,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:42.636177Z",
- "iopub.status.busy": "2024-07-01T15:02:42.635851Z",
- "iopub.status.idle": "2024-07-01T15:02:42.823356Z",
- "shell.execute_reply": "2024-07-01T15:02:42.822807Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.370691Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.370280Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.560449Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.559925Z"
},
"nbsphinx": "hidden"
},
@@ -520,10 +520,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:42.826055Z",
- "iopub.status.busy": "2024-07-01T15:02:42.825690Z",
- "iopub.status.idle": "2024-07-01T15:02:43.206067Z",
- "shell.execute_reply": "2024-07-01T15:02:43.205474Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.563109Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.562666Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.933479Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.932844Z"
}
},
"outputs": [
@@ -559,10 +559,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:43.208532Z",
- "iopub.status.busy": "2024-07-01T15:02:43.208145Z",
- "iopub.status.idle": "2024-07-01T15:02:43.211102Z",
- "shell.execute_reply": "2024-07-01T15:02:43.210626Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.935860Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.935411Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.938217Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.937776Z"
}
},
"outputs": [],
@@ -602,10 +602,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:43.213282Z",
- "iopub.status.busy": "2024-07-01T15:02:43.212936Z",
- "iopub.status.idle": "2024-07-01T15:02:43.248404Z",
- "shell.execute_reply": "2024-07-01T15:02:43.247768Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.940195Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.940017Z",
+ "iopub.status.idle": "2024-07-02T12:01:23.974114Z",
+ "shell.execute_reply": "2024-07-02T12:01:23.973647Z"
}
},
"outputs": [],
@@ -638,10 +638,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:43.251350Z",
- "iopub.status.busy": "2024-07-01T15:02:43.250964Z",
- "iopub.status.idle": "2024-07-01T15:02:45.296650Z",
- "shell.execute_reply": "2024-07-01T15:02:45.296009Z"
+ "iopub.execute_input": "2024-07-02T12:01:23.976287Z",
+ "iopub.status.busy": "2024-07-02T12:01:23.976112Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.051828Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.051244Z"
}
},
"outputs": [
@@ -685,10 +685,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.298975Z",
- "iopub.status.busy": "2024-07-01T15:02:45.298607Z",
- "iopub.status.idle": "2024-07-01T15:02:45.317301Z",
- "shell.execute_reply": "2024-07-01T15:02:45.316762Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.054329Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.053806Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.073654Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.073152Z"
}
},
"outputs": [
@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.319610Z",
- "iopub.status.busy": "2024-07-01T15:02:45.319291Z",
- "iopub.status.idle": "2024-07-01T15:02:45.325606Z",
- "shell.execute_reply": "2024-07-01T15:02:45.325097Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.075978Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.075603Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.082158Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.081661Z"
}
},
"outputs": [
@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.327815Z",
- "iopub.status.busy": "2024-07-01T15:02:45.327438Z",
- "iopub.status.idle": "2024-07-01T15:02:45.333038Z",
- "shell.execute_reply": "2024-07-01T15:02:45.332566Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.084369Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.084032Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.090027Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.089524Z"
}
},
"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.335093Z",
- "iopub.status.busy": "2024-07-01T15:02:45.334787Z",
- "iopub.status.idle": "2024-07-01T15:02:45.345460Z",
- "shell.execute_reply": "2024-07-01T15:02:45.344912Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.092307Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.091888Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.102686Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.102114Z"
}
},
"outputs": [
@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.347438Z",
- "iopub.status.busy": "2024-07-01T15:02:45.347138Z",
- "iopub.status.idle": "2024-07-01T15:02:45.356126Z",
- "shell.execute_reply": "2024-07-01T15:02:45.355581Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.104843Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.104499Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.113923Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.113353Z"
}
},
"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.358103Z",
- "iopub.status.busy": "2024-07-01T15:02:45.357792Z",
- "iopub.status.idle": "2024-07-01T15:02:45.364571Z",
- "shell.execute_reply": "2024-07-01T15:02:45.364114Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.116196Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.115857Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.122959Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.122462Z"
},
"scrolled": true
},
@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.366559Z",
- "iopub.status.busy": "2024-07-01T15:02:45.366255Z",
- "iopub.status.idle": "2024-07-01T15:02:45.375353Z",
- "shell.execute_reply": "2024-07-01T15:02:45.374817Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.125128Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.124796Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.134864Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.134300Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:45.377315Z",
- "iopub.status.busy": "2024-07-01T15:02:45.377010Z",
- "iopub.status.idle": "2024-07-01T15:02:45.392963Z",
- "shell.execute_reply": "2024-07-01T15:02:45.392390Z"
+ "iopub.execute_input": "2024-07-02T12:01:26.137332Z",
+ "iopub.status.busy": "2024-07-02T12:01:26.136913Z",
+ "iopub.status.idle": "2024-07-02T12:01:26.152852Z",
+ "shell.execute_reply": "2024-07-02T12:01:26.152376Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 03d847503..3baceeb0b 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": "2024-07-01T15:02:48.074971Z",
- "iopub.status.busy": "2024-07-01T15:02:48.074723Z",
- "iopub.status.idle": "2024-07-01T15:02:51.342353Z",
- "shell.execute_reply": "2024-07-01T15:02:51.341605Z"
+ "iopub.execute_input": "2024-07-02T12:01:28.896200Z",
+ "iopub.status.busy": "2024-07-02T12:01:28.896023Z",
+ "iopub.status.idle": "2024-07-02T12:01:31.827318Z",
+ "shell.execute_reply": "2024-07-02T12:01:31.826688Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:51.345614Z",
- "iopub.status.busy": "2024-07-01T15:02:51.345060Z",
- "iopub.status.idle": "2024-07-01T15:02:51.349168Z",
- "shell.execute_reply": "2024-07-01T15:02:51.348682Z"
+ "iopub.execute_input": "2024-07-02T12:01:31.829957Z",
+ "iopub.status.busy": "2024-07-02T12:01:31.829648Z",
+ "iopub.status.idle": "2024-07-02T12:01:31.833462Z",
+ "shell.execute_reply": "2024-07-02T12:01:31.833002Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:02:51.351438Z",
- "iopub.status.busy": "2024-07-01T15:02:51.351054Z",
- "iopub.status.idle": "2024-07-01T15:03:02.526470Z",
- "shell.execute_reply": "2024-07-01T15:03:02.525870Z"
+ "iopub.execute_input": "2024-07-02T12:01:31.835341Z",
+ "iopub.status.busy": "2024-07-02T12:01:31.835170Z",
+ "iopub.status.idle": "2024-07-02T12:01:42.989836Z",
+ "shell.execute_reply": "2024-07-02T12:01:42.989362Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f",
+ "model_id": "d4c59b0bfa86424a8c95a71f890f5454",
"version_major": 2,
"version_minor": 0
},
@@ -176,7 +176,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a9efee99388e4bd987cba82e4c249be5",
+ "model_id": "2ffbe85316974d029eab626642378580",
"version_major": 2,
"version_minor": 0
},
@@ -190,7 +190,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b13b21c3b7544706aacfbba4f3504a8b",
+ "model_id": "1a9f98ff0f0446e7b89c4fe4fffc3418",
"version_major": 2,
"version_minor": 0
},
@@ -204,7 +204,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "dcfb76cdced842fd810c0329fa0f1c7f",
+ "model_id": "39838b65ab134d2a9a445437586fec98",
"version_major": 2,
"version_minor": 0
},
@@ -218,7 +218,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0febc72cf36d4d939a7991cbb880240e",
+ "model_id": "4d801b30b791427d9103f41505cf1a3e",
"version_major": 2,
"version_minor": 0
},
@@ -232,7 +232,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6296fc9f1a3947edb989ab3a35afbefe",
+ "model_id": "0d1f1b12cc3545b0b78b6f64afe61ba8",
"version_major": 2,
"version_minor": 0
},
@@ -246,7 +246,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "bc98754b340343f594559442ba450aa4",
+ "model_id": "495daf880acd479da7fa63fedf1e1368",
"version_major": 2,
"version_minor": 0
},
@@ -260,7 +260,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d60f32b2907d4a288385a30c717ef39d",
+ "model_id": "96b3b9a948504544be06e5692d10926d",
"version_major": 2,
"version_minor": 0
},
@@ -302,10 +302,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:02.528967Z",
- "iopub.status.busy": "2024-07-01T15:03:02.528621Z",
- "iopub.status.idle": "2024-07-01T15:03:02.532647Z",
- "shell.execute_reply": "2024-07-01T15:03:02.532080Z"
+ "iopub.execute_input": "2024-07-02T12:01:42.992144Z",
+ "iopub.status.busy": "2024-07-02T12:01:42.991695Z",
+ "iopub.status.idle": "2024-07-02T12:01:42.995507Z",
+ "shell.execute_reply": "2024-07-02T12:01:42.995062Z"
}
},
"outputs": [
@@ -330,17 +330,17 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:02.534910Z",
- "iopub.status.busy": "2024-07-01T15:03:02.534585Z",
- "iopub.status.idle": "2024-07-01T15:03:13.866603Z",
- "shell.execute_reply": "2024-07-01T15:03:13.865937Z"
+ "iopub.execute_input": "2024-07-02T12:01:42.997511Z",
+ "iopub.status.busy": "2024-07-02T12:01:42.997189Z",
+ "iopub.status.idle": "2024-07-02T12:01:54.313084Z",
+ "shell.execute_reply": "2024-07-02T12:01:54.312563Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "70b6c17f51c948158afefdd56830a23f",
+ "model_id": "5191d0744a454151b8fae157e5a21ef4",
"version_major": 2,
"version_minor": 0
},
@@ -378,10 +378,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:13.869049Z",
- "iopub.status.busy": "2024-07-01T15:03:13.868821Z",
- "iopub.status.idle": "2024-07-01T15:03:31.582919Z",
- "shell.execute_reply": "2024-07-01T15:03:31.582298Z"
+ "iopub.execute_input": "2024-07-02T12:01:54.315561Z",
+ "iopub.status.busy": "2024-07-02T12:01:54.315315Z",
+ "iopub.status.idle": "2024-07-02T12:02:13.013990Z",
+ "shell.execute_reply": "2024-07-02T12:02:13.013360Z"
}
},
"outputs": [],
@@ -414,10 +414,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:31.585953Z",
- "iopub.status.busy": "2024-07-01T15:03:31.585389Z",
- "iopub.status.idle": "2024-07-01T15:03:31.591279Z",
- "shell.execute_reply": "2024-07-01T15:03:31.590830Z"
+ "iopub.execute_input": "2024-07-02T12:02:13.016850Z",
+ "iopub.status.busy": "2024-07-02T12:02:13.016410Z",
+ "iopub.status.idle": "2024-07-02T12:02:13.021208Z",
+ "shell.execute_reply": "2024-07-02T12:02:13.020777Z"
}
},
"outputs": [],
@@ -455,10 +455,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:31.593306Z",
- "iopub.status.busy": "2024-07-01T15:03:31.592981Z",
- "iopub.status.idle": "2024-07-01T15:03:31.596855Z",
- "shell.execute_reply": "2024-07-01T15:03:31.596450Z"
+ "iopub.execute_input": "2024-07-02T12:02:13.023194Z",
+ "iopub.status.busy": "2024-07-02T12:02:13.022869Z",
+ "iopub.status.idle": "2024-07-02T12:02:13.027182Z",
+ "shell.execute_reply": "2024-07-02T12:02:13.026649Z"
},
"nbsphinx": "hidden"
},
@@ -595,10 +595,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:31.598838Z",
- "iopub.status.busy": "2024-07-01T15:03:31.598577Z",
- "iopub.status.idle": "2024-07-01T15:03:31.607398Z",
- "shell.execute_reply": "2024-07-01T15:03:31.606925Z"
+ "iopub.execute_input": "2024-07-02T12:02:13.029208Z",
+ "iopub.status.busy": "2024-07-02T12:02:13.028904Z",
+ "iopub.status.idle": "2024-07-02T12:02:13.037801Z",
+ "shell.execute_reply": "2024-07-02T12:02:13.037284Z"
},
"nbsphinx": "hidden"
},
@@ -723,10 +723,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:31.609325Z",
- "iopub.status.busy": "2024-07-01T15:03:31.609007Z",
- "iopub.status.idle": "2024-07-01T15:03:31.635278Z",
- "shell.execute_reply": "2024-07-01T15:03:31.634840Z"
+ "iopub.execute_input": "2024-07-02T12:02:13.039783Z",
+ "iopub.status.busy": "2024-07-02T12:02:13.039463Z",
+ "iopub.status.idle": "2024-07-02T12:02:13.066102Z",
+ "shell.execute_reply": "2024-07-02T12:02:13.065500Z"
}
},
"outputs": [],
@@ -763,10 +763,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:03:31.637322Z",
- "iopub.status.busy": "2024-07-01T15:03:31.636996Z",
- "iopub.status.idle": "2024-07-01T15:04:03.652341Z",
- "shell.execute_reply": "2024-07-01T15:04:03.651742Z"
+ "iopub.execute_input": "2024-07-02T12:02:13.068543Z",
+ "iopub.status.busy": "2024-07-02T12:02:13.068350Z",
+ "iopub.status.idle": "2024-07-02T12:02:45.178356Z",
+ "shell.execute_reply": "2024-07-02T12:02:45.177789Z"
}
},
"outputs": [
@@ -782,21 +782,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b69aa5fb137444eb962d31f239578d65",
+ "model_id": "ec86bd0afa46422aa85bf2778e427f2a",
"version_major": 2,
"version_minor": 0
},
@@ -817,7 +817,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6ca247bf72f54f03aabdd5d72546025f",
+ "model_id": "a0b406e9eaf143599fd4e302b57381b4",
"version_major": 2,
"version_minor": 0
},
@@ -840,21 +840,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d6465626e3264fa58f44ddccd18cfef2",
+ "model_id": "bfd46491d1764708be24b2103e5e6cb5",
"version_major": 2,
"version_minor": 0
},
@@ -875,7 +875,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3fa46dee97a14f9594eb60312b03e045",
+ "model_id": "1696a28972cf4c1c95e3e3bf755c8d21",
"version_major": 2,
"version_minor": 0
},
@@ -898,21 +898,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "76cd9d157bf74d6e93db6f5727c6f900",
+ "model_id": "32f22fc4e23745929d001d9647682786",
"version_major": 2,
"version_minor": 0
},
@@ -933,7 +933,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9717f3b4aaae491d9cb2e07d49a003a5",
+ "model_id": "846e19cb26a94bdba7b363dce398b69c",
"version_major": 2,
"version_minor": 0
},
@@ -1012,10 +1012,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:04:03.654962Z",
- "iopub.status.busy": "2024-07-01T15:04:03.654720Z",
- "iopub.status.idle": "2024-07-01T15:04:03.668632Z",
- "shell.execute_reply": "2024-07-01T15:04:03.668209Z"
+ "iopub.execute_input": "2024-07-02T12:02:45.181036Z",
+ "iopub.status.busy": "2024-07-02T12:02:45.180584Z",
+ "iopub.status.idle": "2024-07-02T12:02:45.194402Z",
+ "shell.execute_reply": "2024-07-02T12:02:45.193957Z"
}
},
"outputs": [],
@@ -1040,10 +1040,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:04:03.670732Z",
- "iopub.status.busy": "2024-07-01T15:04:03.670344Z",
- "iopub.status.idle": "2024-07-01T15:04:04.150524Z",
- "shell.execute_reply": "2024-07-01T15:04:04.149791Z"
+ "iopub.execute_input": "2024-07-02T12:02:45.196378Z",
+ "iopub.status.busy": "2024-07-02T12:02:45.196060Z",
+ "iopub.status.idle": "2024-07-02T12:02:45.659461Z",
+ "shell.execute_reply": "2024-07-02T12:02:45.658926Z"
}
},
"outputs": [],
@@ -1063,10 +1063,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:04:04.153028Z",
- "iopub.status.busy": "2024-07-01T15:04:04.152825Z",
- "iopub.status.idle": "2024-07-01T15:05:40.110641Z",
- "shell.execute_reply": "2024-07-01T15:05:40.110011Z"
+ "iopub.execute_input": "2024-07-02T12:02:45.661921Z",
+ "iopub.status.busy": "2024-07-02T12:02:45.661522Z",
+ "iopub.status.idle": "2024-07-02T12:04:21.084670Z",
+ "shell.execute_reply": "2024-07-02T12:04:21.084011Z"
}
},
"outputs": [
@@ -1105,7 +1105,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8b242b3757014ca08c0be26603c856e5",
+ "model_id": "683ea97790a64507b71e617e6bb1960f",
"version_major": 2,
"version_minor": 0
},
@@ -1144,10 +1144,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:40.113143Z",
- "iopub.status.busy": "2024-07-01T15:05:40.112512Z",
- "iopub.status.idle": "2024-07-01T15:05:40.560298Z",
- "shell.execute_reply": "2024-07-01T15:05:40.559714Z"
+ "iopub.execute_input": "2024-07-02T12:04:21.087384Z",
+ "iopub.status.busy": "2024-07-02T12:04:21.086898Z",
+ "iopub.status.idle": "2024-07-02T12:04:21.530187Z",
+ "shell.execute_reply": "2024-07-02T12:04:21.529650Z"
}
},
"outputs": [
@@ -1293,10 +1293,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:40.563315Z",
- "iopub.status.busy": "2024-07-01T15:05:40.562801Z",
- "iopub.status.idle": "2024-07-01T15:05:40.624738Z",
- "shell.execute_reply": "2024-07-01T15:05:40.624116Z"
+ "iopub.execute_input": "2024-07-02T12:04:21.532970Z",
+ "iopub.status.busy": "2024-07-02T12:04:21.532489Z",
+ "iopub.status.idle": "2024-07-02T12:04:21.594306Z",
+ "shell.execute_reply": "2024-07-02T12:04:21.593726Z"
}
},
"outputs": [
@@ -1400,10 +1400,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:40.627071Z",
- "iopub.status.busy": "2024-07-01T15:05:40.626639Z",
- "iopub.status.idle": "2024-07-01T15:05:40.635299Z",
- "shell.execute_reply": "2024-07-01T15:05:40.634756Z"
+ "iopub.execute_input": "2024-07-02T12:04:21.597613Z",
+ "iopub.status.busy": "2024-07-02T12:04:21.597278Z",
+ "iopub.status.idle": "2024-07-02T12:04:21.605873Z",
+ "shell.execute_reply": "2024-07-02T12:04:21.605434Z"
}
},
"outputs": [
@@ -1533,10 +1533,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:40.637389Z",
- "iopub.status.busy": "2024-07-01T15:05:40.636989Z",
- "iopub.status.idle": "2024-07-01T15:05:40.641711Z",
- "shell.execute_reply": "2024-07-01T15:05:40.641175Z"
+ "iopub.execute_input": "2024-07-02T12:04:21.607881Z",
+ "iopub.status.busy": "2024-07-02T12:04:21.607595Z",
+ "iopub.status.idle": "2024-07-02T12:04:21.612387Z",
+ "shell.execute_reply": "2024-07-02T12:04:21.611934Z"
},
"nbsphinx": "hidden"
},
@@ -1582,10 +1582,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:40.643683Z",
- "iopub.status.busy": "2024-07-01T15:05:40.643498Z",
- "iopub.status.idle": "2024-07-01T15:05:41.152016Z",
- "shell.execute_reply": "2024-07-01T15:05:41.151428Z"
+ "iopub.execute_input": "2024-07-02T12:04:21.614443Z",
+ "iopub.status.busy": "2024-07-02T12:04:21.614030Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.120240Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.119680Z"
}
},
"outputs": [
@@ -1620,10 +1620,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.154341Z",
- "iopub.status.busy": "2024-07-01T15:05:41.154029Z",
- "iopub.status.idle": "2024-07-01T15:05:41.162706Z",
- "shell.execute_reply": "2024-07-01T15:05:41.162252Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.122526Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.122160Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.130544Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.130091Z"
}
},
"outputs": [
@@ -1790,10 +1790,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.164766Z",
- "iopub.status.busy": "2024-07-01T15:05:41.164446Z",
- "iopub.status.idle": "2024-07-01T15:05:41.171486Z",
- "shell.execute_reply": "2024-07-01T15:05:41.171059Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.132648Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.132322Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.139582Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.139132Z"
},
"nbsphinx": "hidden"
},
@@ -1869,10 +1869,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.173399Z",
- "iopub.status.busy": "2024-07-01T15:05:41.173075Z",
- "iopub.status.idle": "2024-07-01T15:05:41.934946Z",
- "shell.execute_reply": "2024-07-01T15:05:41.934291Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.141499Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.141182Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.871798Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.871228Z"
}
},
"outputs": [
@@ -1909,10 +1909,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.937509Z",
- "iopub.status.busy": "2024-07-01T15:05:41.937076Z",
- "iopub.status.idle": "2024-07-01T15:05:41.952809Z",
- "shell.execute_reply": "2024-07-01T15:05:41.952240Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.874107Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.873751Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.889160Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.888693Z"
}
},
"outputs": [
@@ -2069,10 +2069,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.954986Z",
- "iopub.status.busy": "2024-07-01T15:05:41.954646Z",
- "iopub.status.idle": "2024-07-01T15:05:41.960097Z",
- "shell.execute_reply": "2024-07-01T15:05:41.959674Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.891280Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.890945Z",
+ "iopub.status.idle": "2024-07-02T12:04:22.896314Z",
+ "shell.execute_reply": "2024-07-02T12:04:22.895869Z"
},
"nbsphinx": "hidden"
},
@@ -2117,10 +2117,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:41.961941Z",
- "iopub.status.busy": "2024-07-01T15:05:41.961770Z",
- "iopub.status.idle": "2024-07-01T15:05:42.348365Z",
- "shell.execute_reply": "2024-07-01T15:05:42.347794Z"
+ "iopub.execute_input": "2024-07-02T12:04:22.898366Z",
+ "iopub.status.busy": "2024-07-02T12:04:22.898042Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.354782Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.354256Z"
}
},
"outputs": [
@@ -2202,10 +2202,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.350754Z",
- "iopub.status.busy": "2024-07-01T15:05:42.350573Z",
- "iopub.status.idle": "2024-07-01T15:05:42.359462Z",
- "shell.execute_reply": "2024-07-01T15:05:42.358866Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.357430Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.357055Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.366373Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.365890Z"
}
},
"outputs": [
@@ -2333,10 +2333,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.361756Z",
- "iopub.status.busy": "2024-07-01T15:05:42.361579Z",
- "iopub.status.idle": "2024-07-01T15:05:42.366530Z",
- "shell.execute_reply": "2024-07-01T15:05:42.365855Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.368851Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.368495Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.374119Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.373635Z"
},
"nbsphinx": "hidden"
},
@@ -2373,10 +2373,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.368583Z",
- "iopub.status.busy": "2024-07-01T15:05:42.368393Z",
- "iopub.status.idle": "2024-07-01T15:05:42.547146Z",
- "shell.execute_reply": "2024-07-01T15:05:42.546558Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.376452Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.376105Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.576168Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.575585Z"
}
},
"outputs": [
@@ -2418,10 +2418,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.549711Z",
- "iopub.status.busy": "2024-07-01T15:05:42.549286Z",
- "iopub.status.idle": "2024-07-01T15:05:42.557834Z",
- "shell.execute_reply": "2024-07-01T15:05:42.557185Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.578422Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.578237Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.586182Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.585742Z"
}
},
"outputs": [
@@ -2507,10 +2507,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.559948Z",
- "iopub.status.busy": "2024-07-01T15:05:42.559775Z",
- "iopub.status.idle": "2024-07-01T15:05:42.735572Z",
- "shell.execute_reply": "2024-07-01T15:05:42.734991Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.588296Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.587874Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.783615Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.783030Z"
}
},
"outputs": [
@@ -2550,10 +2550,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:42.737707Z",
- "iopub.status.busy": "2024-07-01T15:05:42.737529Z",
- "iopub.status.idle": "2024-07-01T15:05:42.742129Z",
- "shell.execute_reply": "2024-07-01T15:05:42.741571Z"
+ "iopub.execute_input": "2024-07-02T12:04:23.785886Z",
+ "iopub.status.busy": "2024-07-02T12:04:23.785554Z",
+ "iopub.status.idle": "2024-07-02T12:04:23.789936Z",
+ "shell.execute_reply": "2024-07-02T12:04:23.789389Z"
},
"nbsphinx": "hidden"
},
@@ -2590,107 +2590,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "01c1fefd1d1549a1ac3d1168746bc81e": {
- "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
- }
- },
- "0423a125c3324adc9f2433bebe77773b": {
- "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
- }
- },
- "070af112be824f3a9dfc9d11f6c99bdb": {
- "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_268de63ed1994c07842b4b562dfac3f6",
- "placeholder": "",
- "style": "IPY_MODEL_2a1a89abfab4432f93e32ce870b637ab",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "08550632bef14f9a894de4b50f4b0628": {
- "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_d78ce21ee1fc44e8b680bda13d37d943",
- "placeholder": "",
- "style": "IPY_MODEL_abecf5a0521e45f78a788d98cc335e5c",
- "tabbable": null,
- "tooltip": null,
- "value": " 4.42M/4.42M [00:00<00:00, 77.0MB/s]"
- }
- },
- "0abf4ae675284a88873e14026b73ab9e": {
- "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
- }
- },
- "0cfffd1a2e16481ba0fb26b13818b9d3": {
+ "007a3563b0514e35b0a7409f1a0e8668": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2743,49 +2643,7 @@
"width": null
}
},
- "0febc72cf36d4d939a7991cbb880240e": {
- "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_b374b826cf5a4a668332dc70f8c2fe4a",
- "IPY_MODEL_213628c3350547b8801807bf6755bac5",
- "IPY_MODEL_08550632bef14f9a894de4b50f4b0628"
- ],
- "layout": "IPY_MODEL_c87df7230ab74cf3b4ce36554b4f64b5",
- "tabbable": null,
- "tooltip": null
- }
- },
- "109113d68eb84c2eace6f4deda6b040b": {
- "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
- }
- },
- "11703dfe59744973b7ee5db1b50a7304": {
+ "0205ebdad1d64de8a1bd7d1c741d5fcb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2838,48 +2696,60 @@
"width": null
}
},
- "126a880302694496ab5834d71ecffa75": {
- "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_6bb8ca43d39f43d0933376cc22b61a88",
- "placeholder": "",
- "style": "IPY_MODEL_ef22307e229c427e9f2c45041e108373",
- "tabbable": null,
- "tooltip": null,
- "value": "Generating test split: 100%"
- }
- },
- "160c29ae9eb04803b9e5fc42229024de": {
- "model_module": "@jupyter-widgets/controls",
+ "052177a0e94b458eb71c811c3229f857": {
+ "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
}
},
- "182fe610a03c4f0c8820d62efd2942a9": {
+ "0826f288ec1f413988e02f4ef8849c28": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2932,7 +2802,7 @@
"width": null
}
},
- "197981c8f9c84c00abea85184c72fcd5": {
+ "088b60ea8401405ea27804efbb34b231": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -2948,17 +2818,40 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ac933a814c124345b0730704f11cfbc8",
+ "layout": "IPY_MODEL_40e0d768badf43758c95df2555d1a977",
"max": 40.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_2a940901f95f4ae58045f253ec9f48b7",
+ "style": "IPY_MODEL_29258ac24b084216b09e806906959044",
"tabbable": null,
"tooltip": null,
"value": 40.0
}
},
- "19d83e638f7a404ba0eb4fdd71302435": {
+ "0a39ed2c512b4f33865071d90e585e06": {
+ "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_ac0dd2e3b9574dfba4e088b07dc9917a",
+ "placeholder": "",
+ "style": "IPY_MODEL_7d2cf7fe1d884127b2ea048061916752",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 5.15k/5.15k [00:00<00:00, 778kB/s]"
+ }
+ },
+ "0b2e2b62fbba4e8e919185c698964e99": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3011,41 +2904,7 @@
"width": null
}
},
- "19dfb010e30849f38c848ea70dda5645": {
- "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
- }
- },
- "1a2b07452f87444f8ac98839eeafdd7b": {
- "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": ""
- }
- },
- "1ab8a64cb49340769b8aa2cc1774732d": {
+ "0cae058fc562457bbb502b466cfdfcab": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3098,49 +2957,57 @@
"width": null
}
},
- "1ba2c9f296904da78195fe3e45321059": {
+ "0d1f1b12cc3545b0b78b6f64afe61ba8": {
"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_833e5dbaba7546f582547cdf7571c108",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_21904a4bfca34e3295a0b17083f1cdb9",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_b1d5272979684bff96c71500a455d400",
+ "IPY_MODEL_6b095fb924ef41358579ec879bf0f9fe",
+ "IPY_MODEL_0a39ed2c512b4f33865071d90e585e06"
+ ],
+ "layout": "IPY_MODEL_72961bb987d24298bfdb11eb59546963",
"tabbable": null,
- "tooltip": null,
- "value": 40.0
+ "tooltip": null
}
},
- "1c8e1b8efcbf4cd6ad8ccd71c8886907": {
+ "0e97775e042e4049bb15d621385de0b0": {
"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_d84b7a5330814c309bc2e3a29fd936ef",
+ "max": 26421880.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_f4bfad9cd0da4d31a8d5c783407a73c8",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 26421880.0
}
},
- "213628c3350547b8801807bf6755bac5": {
+ "0ec179f1c53a4547a89ad81af9b56fa0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -3156,33 +3023,35 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_c71cb776520442f0baba7a63fdbf2324",
- "max": 4422102.0,
+ "layout": "IPY_MODEL_d90986d0f1bb4e8ebf632742ca0c49a3",
+ "max": 8845.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_a1990d1ffd9240a7a4bf2438eaf81754",
+ "style": "IPY_MODEL_7004a3b5592a40ee8328f00432adfc79",
"tabbable": null,
"tooltip": null,
- "value": 4422102.0
+ "value": 8845.0
}
},
- "21904a4bfca34e3295a0b17083f1cdb9": {
+ "0f8071081d82450c9dd7fd9a927b6b1f": {
"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
}
},
- "2529da9ccb564a7d98cf91d4e2f454fc": {
+ "0f83d37dd1394bc7a4f3ffc18b97b21c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3235,147 +3104,96 @@
"width": null
}
},
- "255e06fe46cb4c0eaa40336d22beaf70": {
- "model_module": "@jupyter-widgets/base",
+ "1262e1266fb7438b9a905b26b4129cf0": {
+ "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/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_eaa35cd4c5ae462181de3ad1ab98c2d1",
+ "placeholder": "",
+ "style": "IPY_MODEL_9bc90fd3c0264b26b5bf1c99f4b9caad",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Downloading data: 100%"
}
},
- "25aea1e677d84e4da5e2f1a580b847bb": {
+ "159251baf5e8425b8c9f8d7acb9abc55": {
"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
}
},
- "266601a55125440a88a8a16e00cebd66": {
+ "1696a28972cf4c1c95e3e3bf755c8d21": {
"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_bd7060172fb747a6ae92a503a3922356",
+ "IPY_MODEL_5779a50d69944438b953e80eb37bbcac",
+ "IPY_MODEL_359bb8b698c94cf8b32d650da4752723"
+ ],
+ "layout": "IPY_MODEL_be6b57b7d12a497fbc96e8e89b08f15a",
+ "tabbable": null,
+ "tooltip": null
}
},
- "268de63ed1994c07842b4b562dfac3f6": {
- "model_module": "@jupyter-widgets/base",
+ "1a9f98ff0f0446e7b89c4fe4fffc3418": {
+ "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/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_1262e1266fb7438b9a905b26b4129cf0",
+ "IPY_MODEL_0e97775e042e4049bb15d621385de0b0",
+ "IPY_MODEL_56000f81200d41e0bfa6f6b08d883916"
+ ],
+ "layout": "IPY_MODEL_75221b9dde234f55bcd73f8bba5f3fa5",
+ "tabbable": null,
+ "tooltip": null
}
},
- "295b6321cc6f4b1bbfa02a28f8581b1d": {
+ "1e5f76275f894a96a2d651d0a386fdf9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3428,7 +3246,30 @@
"width": null
}
},
- "299c0f0403e1447fb83af15e00e8f19f": {
+ "1ef3f8ae36734efb86b54939cb9711d4": {
+ "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_7b58054c0d6a418194fc7e1f039c639b",
+ "placeholder": "",
+ "style": "IPY_MODEL_864d773ce416475ba6a1e506b36063dc",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 4/4 [00:00<00:00, 1198.12it/s]"
+ }
+ },
+ "1f5d840cd56a4162b5ad826e1f2902c2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3481,25 +3322,23 @@
"width": null
}
},
- "2a1a89abfab4432f93e32ce870b637ab": {
+ "1fb6203f0a3f47309b3fefc7cf4a8522": {
"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": ""
}
},
- "2a940901f95f4ae58045f253ec9f48b7": {
+ "1fdf0f2ba1e440ccaf02e74fc4b28520": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -3515,60 +3354,146 @@
"description_width": ""
}
},
- "2cb677caec644d3ab2a8b6a70208559e": {
- "model_module": "@jupyter-widgets/base",
+ "21a5ba5f675d47938f0e977783a004ba": {
+ "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/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_2d5192bd86654b04b8a872e4841a73ee",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_1fb6203f0a3f47309b3fefc7cf4a8522",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 60000.0
+ }
+ },
+ "21a93c7e2dfc4569b325ed80637cf469": {
+ "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_ff08f8bfa09d42838688c6f725adb306",
+ "placeholder": "",
+ "style": "IPY_MODEL_aea843e930ac410596a0f4cb4f6520e0",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 65.61it/s]"
+ }
+ },
+ "240d9d285e4c42e9a24006d9d2988868": {
+ "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_1e5f76275f894a96a2d651d0a386fdf9",
+ "placeholder": "",
+ "style": "IPY_MODEL_416fa493cc9c4ff6ad13e8b6e6aedbaa",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Generating train split: 100%"
+ }
+ },
+ "25f2773590254b58b3ac4b1c0a886c35": {
+ "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_f1c238f4a14549229bdf80d577253ccf",
+ "placeholder": "",
+ "style": "IPY_MODEL_f0b50dd1b20c48b6911a694433d48e05",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 4.83k/4.83k [00:00<00:00, 623kB/s]"
+ }
+ },
+ "27b9b136f3bb4b1a80402df468b5c136": {
+ "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
+ }
+ },
+ "2865f56223344611aa17c9ec66a2d090": {
+ "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_f9978a29787547e3bc5e59bde742651c",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_cd6bbb8ca872405fa17b2571965191b3",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 60000.0
}
},
- "2d09848b13f94c7a9483a0063af7658b": {
+ "29258ac24b084216b09e806906959044": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -3584,7 +3509,7 @@
"description_width": ""
}
},
- "2f77ed4c555c4365a30f1c760cb59e74": {
+ "2c4aaf8a7a84451d93bb9c185069cfe6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -3602,23 +3527,190 @@
"text_color": null
}
},
- "348018e48d954c87b48ff2a6e7470304": {
+ "2d5192bd86654b04b8a872e4841a73ee": {
+ "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
+ }
+ },
+ "2ffbe85316974d029eab626642378580": {
"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_62e4f2d9c4534ebbab778993d057a978",
+ "IPY_MODEL_0ec179f1c53a4547a89ad81af9b56fa0",
+ "IPY_MODEL_5c88bd64b2f44f8ab2aa0198c89462d3"
+ ],
+ "layout": "IPY_MODEL_44a3e2129c4243e4862df806c2d8c5af",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "306584df97934511bd6de92ca49b025b": {
+ "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": "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
+ }
+ },
+ "30c3b868ba4b46ea9bcdb05e1c6d5613": {
+ "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
}
},
- "367462867871411f81ad39c2100a77d8": {
+ "3256f32acb0e4cf5b5de459cdd30f479": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -3633,15 +3725,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_93b948080aae40babe81f47f38dc60fa",
+ "layout": "IPY_MODEL_5e25f06738614802b41458873966a7a2",
"placeholder": "",
- "style": "IPY_MODEL_3f77fa09c365457f893db8dde97d6a1a",
+ "style": "IPY_MODEL_3b900abb40be48198b5e1814d396c692",
"tabbable": null,
"tooltip": null,
- "value": " 60000/60000 [00:07<00:00, 8559.65 examples/s]"
+ "value": " 10000/10000 [00:01<00:00, 8596.66 examples/s]"
}
},
- "3773b260a0514063a4cb27c7bcfcdb72": {
+ "32b658a174274a4997618547ef7ef447": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3694,53 +3786,110 @@
"width": null
}
},
- "3abd2a96be1649ed9f2a7594cfdf3bbd": {
+ "32f22fc4e23745929d001d9647682786": {
"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_f15ed2b240814bb897b777075c726c92",
- "placeholder": "",
- "style": "IPY_MODEL_956d09bfdd86417caf76bb337c098a81",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7684491703b342af8b42512fb30334e4",
+ "IPY_MODEL_fe1973b9b1fa4957b9894f465a0fe87c",
+ "IPY_MODEL_c9693739925b43cd83cb4e68fc01ecc9"
+ ],
+ "layout": "IPY_MODEL_dd1415dc221544d78c38cecb125e95de",
"tabbable": null,
- "tooltip": null,
- "value": "Downloading data: 100%"
+ "tooltip": null
}
},
- "3ddd27d196364f1db80581facbadf18f": {
+ "33c9e2d67e4e498a9badfc73dd036c12": {
"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_79d53959ea3648699bfb173209305675",
- "placeholder": "",
- "style": "IPY_MODEL_160c29ae9eb04803b9e5fc42229024de",
+ "layout": "IPY_MODEL_a2e082221e6d4efe981d8286fcaa40bd",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_422391611be34f58bd16d29a7a790f7f",
"tabbable": null,
"tooltip": null,
- "value": "100%"
+ "value": 40.0
+ }
+ },
+ "33e89871cb224a0bb17051bdb6a4736f": {
+ "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
}
},
- "3ec84e0ec4d94afd8f15a91eb757624e": {
+ "3598bd0162e44744bf0e88509c1fcc05": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3793,93 +3942,30 @@
"width": null
}
},
- "3edb1a47c3314c0abfc78f23d62ebe68": {
+ "359bb8b698c94cf8b32d650da4752723": {
"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_0cfffd1a2e16481ba0fb26b13818b9d3",
- "max": 60000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_70ca2521a7cf454c8f41cc8665bd8572",
+ "layout": "IPY_MODEL_d44de894f1eb4db0ad9f986867905216",
+ "placeholder": "",
+ "style": "IPY_MODEL_91eaa66c2eb641e689fc8028aee35c80",
"tabbable": null,
"tooltip": null,
- "value": 60000.0
- }
- },
- "3f77fa09c365457f893db8dde97d6a1a": {
- "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
- }
- },
- "3fa46dee97a14f9594eb60312b03e045": {
- "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_a50bac222b154e1e8f1af99c2d2f7cb5",
- "IPY_MODEL_f306b412103a4a999f13f32ecb0841fe",
- "IPY_MODEL_838e5992cb0f492aa79d9e4411b13437"
- ],
- "layout": "IPY_MODEL_1ab8a64cb49340769b8aa2cc1774732d",
- "tabbable": null,
- "tooltip": null
- }
- },
- "424a8de7240d4c088b2b2dbadf1113ab": {
- "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": " 40/40 [00:00<00:00, 64.56it/s]"
}
},
- "4367f895c508411fb72fbf123bfd2662": {
+ "372ab13ae5c8452ead0d7884763b3fc8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -3932,43 +4018,30 @@
"width": null
}
},
- "445bd193a0b443b68d88db83f68b1135": {
- "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
- }
- },
- "4751ad8ff2014ae4aefb61464aa0b996": {
+ "3746c22618f54648a82eac82edf13ec6": {
"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_0826f288ec1f413988e02f4ef8849c28",
+ "placeholder": "",
+ "style": "IPY_MODEL_7fd76734cd4b4cec9bba7cdd76aaea68",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 29.5k/29.5k [00:00<00:00, 4.49MB/s]"
}
},
- "4a97191b1a1643199e46244e6611352a": {
+ "3851a48a716b467ba9b981bd35b1822c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4021,7 +4094,31 @@
"width": null
}
},
- "4c9f6ca0676a4e6bbace6bdaf151e92c": {
+ "39838b65ab134d2a9a445437586fec98": {
+ "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_bf04eda28d94482ebdbf589d87951c61",
+ "IPY_MODEL_92fe75b0bd1341e9878165f8c906fc19",
+ "IPY_MODEL_3746c22618f54648a82eac82edf13ec6"
+ ],
+ "layout": "IPY_MODEL_5d3e10744606438787cdfd6315052b40",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "3b900abb40be48198b5e1814d396c692": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -4039,7 +4136,7 @@
"text_color": null
}
},
- "50042e9a2b5145a780c7c3d799e26aad": {
+ "3ed339ad73774a5cae0f763c178cbf4b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4092,7 +4189,25 @@
"width": null
}
},
- "5594cd23fb504b0dbdef0ecfe202453c": {
+ "409b5a2567834d99aba6129faa130451": {
+ "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
+ }
+ },
+ "40e0d768badf43758c95df2555d1a977": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4145,33 +4260,7 @@
"width": null
}
},
- "5695fa5956b04b45a058d58f6cb27b12": {
- "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_87900fda6f274f88ab3a99c82a5a9940",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_1a2b07452f87444f8ac98839eeafdd7b",
- "tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "57fa64edffdb4d37a90dbea656009ab1": {
+ "416fa493cc9c4ff6ad13e8b6e6aedbaa": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -4189,23 +4278,7 @@
"text_color": null
}
},
- "58ba69b1748d4a2bb5d07f10349efa22": {
- "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": ""
- }
- },
- "5b07f8803c514642801639b40c1aa34b": {
+ "422391611be34f58bd16d29a7a790f7f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -4221,7 +4294,7 @@
"description_width": ""
}
},
- "5c0a9c7370bb48c09729cb766ab1781a": {
+ "44a3e2129c4243e4862df806c2d8c5af": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4274,79 +4347,7 @@
"width": null
}
},
- "5d069f3fc0fd47028eaaf3380b16e427": {
- "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_97c91dea2ffc40f1830438b9a24225ce",
- "placeholder": "",
- "style": "IPY_MODEL_635f8889b38545dca62037898e9e98c2",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 71.33it/s]"
- }
- },
- "5d1a794999184d7c86308a072ca269b6": {
- "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_e5fa84089fac429dba7a0f14fcadf7b8",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2d09848b13f94c7a9483a0063af7658b",
- "tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "5f29c5efd23943e1a08518f859506345": {
- "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_a24112b64ebe4079b08768fea3888272",
- "placeholder": "",
- "style": "IPY_MODEL_19dfb010e30849f38c848ea70dda5645",
- "tabbable": null,
- "tooltip": null,
- "value": " 29.5k/29.5k [00:00<00:00, 4.28MB/s]"
- }
- },
- "61b5f9d9099a4161a69f1c58aaa1f54e": {
+ "46552aea691e492084a7278f7a059830": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -4364,30 +4365,23 @@
"text_color": null
}
},
- "61c7ca03d1b84ecdbe6bfb8765d33dd4": {
+ "488f0ba8d1c24d58adaefa23ebad9e9f": {
"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_version": "2.0.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_8c7ec74595b64ca194e8d3ba1c2a16fe",
- "placeholder": "",
- "style": "IPY_MODEL_d627f7cbc1534c10a92cc7fd0f874ca6",
- "tabbable": null,
- "tooltip": null,
- "value": " 60000/60000 [00:36<00:00, 1693.23it/s]"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "624903d3a14d4864906b939e0507936f": {
+ "4957f5fe7164427d8b712200dde5c3ab": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -4403,17 +4397,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_3773b260a0514063a4cb27c7bcfcdb72",
- "max": 60000.0,
+ "layout": "IPY_MODEL_ddb1bbe42c9e442091cc9c4122b5de26",
+ "max": 4422102.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_1c8e1b8efcbf4cd6ad8ccd71c8886907",
+ "style": "IPY_MODEL_e85e1e91a24f4199a9a4b3e9abe8696f",
"tabbable": null,
"tooltip": null,
- "value": 60000.0
+ "value": 4422102.0
}
},
- "6296fc9f1a3947edb989ab3a35afbefe": {
+ "495daf880acd479da7fa63fedf1e1368": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -4428,73 +4422,74 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_f247cc3e079b4ad2aa902a3150c5d9ec",
- "IPY_MODEL_783884a15ffc4bceb98545ac1564a7f5",
- "IPY_MODEL_fc671361cddb4f3e9903ef6aae4d3083"
+ "IPY_MODEL_240d9d285e4c42e9a24006d9d2988868",
+ "IPY_MODEL_21a5ba5f675d47938f0e977783a004ba",
+ "IPY_MODEL_513c96503e024d098d8203aa3b604a38"
],
- "layout": "IPY_MODEL_66aef97a11f842d89fcec4deed40e16d",
+ "layout": "IPY_MODEL_306584df97934511bd6de92ca49b025b",
"tabbable": null,
"tooltip": null
}
},
- "62c6658ea89d458aa1198b576f9eee56": {
+ "4a57dceefdc148afb5c7afa8adec5114": {
"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
}
},
- "635f8889b38545dca62037898e9e98c2": {
+ "4aadbbb3e859454a93556ff943f76e5b": {
"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": ""
}
},
- "64e2e67bdc244016895a62ad062fa077": {
+ "4d801b30b791427d9103f41505cf1a3e": {
"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_953f69d720d74dc0824fc0ac9a4ffa3b",
- "placeholder": "",
- "style": "IPY_MODEL_a38689b69ffd44938ebabe7dc9710b39",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_8dbd4d3399124f6d8275c1d0fdfe9983",
+ "IPY_MODEL_4957f5fe7164427d8b712200dde5c3ab",
+ "IPY_MODEL_cab386a86c594ee2885f6d1679103b3b"
+ ],
+ "layout": "IPY_MODEL_625866dbaec44035a15f3927c4b770e5",
"tabbable": null,
- "tooltip": null,
- "value": "100%"
+ "tooltip": null
}
},
- "66aef97a11f842d89fcec4deed40e16d": {
+ "4dda8d782c00439b8a9eefdfe211c961": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4547,60 +4542,25 @@
"width": null
}
},
- "6930211e1e8a42d88c3294247388155e": {
- "model_module": "@jupyter-widgets/base",
+ "4ec1fcd52f8c4d51bb3475d2f3c24732": {
+ "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
}
},
- "69835f01f4d24840900be4eafa6cd376": {
+ "4ff41db514e041798fc3d0bf13325104": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4653,7 +4613,54 @@
"width": null
}
},
- "6bb8ca43d39f43d0933376cc22b61a88": {
+ "513c96503e024d098d8203aa3b604a38": {
+ "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_9009c4b403d146b493913cc05ca55a44",
+ "placeholder": "",
+ "style": "IPY_MODEL_ac82938d47c543a89ca5def5e546d7da",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 60000/60000 [00:07<00:00, 8644.15 examples/s]"
+ }
+ },
+ "5191d0744a454151b8fae157e5a21ef4": {
+ "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_fffb62594db04599b3628dceafda46f1",
+ "IPY_MODEL_2865f56223344611aa17c9ec66a2d090",
+ "IPY_MODEL_d4be07fa12674628ae93c0119edbf6e1"
+ ],
+ "layout": "IPY_MODEL_052177a0e94b458eb71c811c3229f857",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "51bb6dd9acdc4544b4d17f4f020b1764": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4706,7 +4713,7 @@
"width": null
}
},
- "6c1c35f1bae449bb91822d760764fd52": {
+ "55923d8f76544ee5b5e53cb28dcbbcc5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4721,15 +4728,103 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_8205e7ba3ebf40938d66cce708243de9",
+ "layout": "IPY_MODEL_0f83d37dd1394bc7a4f3ffc18b97b21c",
"placeholder": "",
- "style": "IPY_MODEL_bc15b56e48ef468eb9e5db2eba40dd06",
+ "style": "IPY_MODEL_8be082c69bad41aa815fa99c34e5a9ea",
"tabbable": null,
"tooltip": null,
- "value": "Map (num_proc=4): 100%"
+ "value": " 40/40 [00:00<00:00, 65.15it/s]"
+ }
+ },
+ "56000f81200d41e0bfa6f6b08d883916": {
+ "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_9eace039e9d343e1a0113042a3582776",
+ "placeholder": "",
+ "style": "IPY_MODEL_409b5a2567834d99aba6129faa130451",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 26.4M/26.4M [00:00<00:00, 120MB/s]"
+ }
+ },
+ "5779a50d69944438b953e80eb37bbcac": {
+ "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_1f5d840cd56a4162b5ad826e1f2902c2",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_488f0ba8d1c24d58adaefa23ebad9e9f",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
+ }
+ },
+ "5c88bd64b2f44f8ab2aa0198c89462d3": {
+ "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_ed5bd36e898a43dc9c5cb8e283abbead",
+ "placeholder": "",
+ "style": "IPY_MODEL_890e03a51fce4dd185e5a42dc5da23ed",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 8.85k/8.85k [00:00<00:00, 1.48MB/s]"
+ }
+ },
+ "5c9cad60b37f4313a6899ca1a71bbee0": {
+ "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": ""
}
},
- "6c4353aa625f407c8370f78a1dc830c8": {
+ "5d3e10744606438787cdfd6315052b40": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4782,31 +4877,7 @@
"width": null
}
},
- "6ca247bf72f54f03aabdd5d72546025f": {
- "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_f035123f0a8341818f2529edc8258078",
- "IPY_MODEL_5d1a794999184d7c86308a072ca269b6",
- "IPY_MODEL_a774929f38e2462193ebeff227330d56"
- ],
- "layout": "IPY_MODEL_d460961168ad48bf8a3a86fe06603769",
- "tabbable": null,
- "tooltip": null
- }
- },
- "6cb79bd433b44c4c8cd62d84839582bc": {
+ "5e25f06738614802b41458873966a7a2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4854,38 +4925,12 @@
"overflow": null,
"padding": null,
"right": null,
- "top": null,
- "visibility": null,
- "width": null
- }
- },
- "6d4b2efa08f448b0a99ccda6ee415ef7": {
- "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_6cb79bd433b44c4c8cd62d84839582bc",
- "max": 4833.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_58ba69b1748d4a2bb5d07f10349efa22",
- "tabbable": null,
- "tooltip": null,
- "value": 4833.0
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "6d5821fefb5648caac6867e9f7a4c67b": {
+ "6095c69a4f934899a783495c289c15a3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4938,63 +4983,30 @@
"width": null
}
},
- "6de62af38a2f43a9846f333eda1224f2": {
- "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": ""
- }
- },
- "70b6c17f51c948158afefdd56830a23f": {
+ "6102ff846ce741a4aa1c83f94cf213b4": {
"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_6c1c35f1bae449bb91822d760764fd52",
- "IPY_MODEL_766722799c914e01bd6ba03a4860a722",
- "IPY_MODEL_81491c5e50a34db7ab3810ebfafe7b12"
- ],
- "layout": "IPY_MODEL_2529da9ccb564a7d98cf91d4e2f454fc",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_0b2e2b62fbba4e8e919185c698964e99",
+ "placeholder": "",
+ "style": "IPY_MODEL_a2c46edab30a43d3ad1274496e23dc19",
"tabbable": null,
- "tooltip": null
- }
- },
- "70ca2521a7cf454c8f41cc8665bd8572": {
- "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": ""
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 68.42it/s]"
}
},
- "72cf872836144d31ac5f693e9157dde9": {
+ "625866dbaec44035a15f3927c4b770e5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5047,41 +5059,30 @@
"width": null
}
},
- "732da230ecd142e79d60e75c14f8749a": {
- "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": ""
- }
- },
- "7662e69c3e7b45a1bc4d55996a7060cc": {
+ "62e4f2d9c4534ebbab778993d057a978": {
"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_4dda8d782c00439b8a9eefdfe211c961",
+ "placeholder": "",
+ "style": "IPY_MODEL_d89ee00461d14abe96f3f0cdcfa3da61",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Downloading readme: 100%"
}
},
- "766722799c914e01bd6ba03a4860a722": {
+ "640d5d2692d64656a67cd09cee644495": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -5097,67 +5098,40 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_88f5a8e733a449bf9d33c407463abc0b",
- "max": 60000.0,
+ "layout": "IPY_MODEL_007a3563b0514e35b0a7409f1a0e8668",
+ "max": 4.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_ad6f123f6f11456097bebc2f41cfafd7",
+ "style": "IPY_MODEL_ded2f3b115fb46e48b5699012c011fa5",
"tabbable": null,
"tooltip": null,
- "value": 60000.0
- }
- },
- "76cd9d157bf74d6e93db6f5727c6f900": {
- "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_7f5b0df354d74d42b00745784e61cbb5",
- "IPY_MODEL_d844173c6e344ebcbb4c2988636ce3a8",
- "IPY_MODEL_f68300c0882c4adca9ac2341e102684c"
- ],
- "layout": "IPY_MODEL_ddea9d00c6b648c2ab8a73e6395f71ba",
- "tabbable": null,
- "tooltip": null
+ "value": 4.0
}
},
- "783884a15ffc4bceb98545ac1564a7f5": {
+ "645dbaa1cd59415da2cc2b69430972fa": {
"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_c5cfb97d21914845a48cbeea88f4d542",
- "max": 5148.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_cb7c5ad137844450a4dd8bb2cd5b55d5",
+ "layout": "IPY_MODEL_33e89871cb224a0bb17051bdb6a4736f",
+ "placeholder": "",
+ "style": "IPY_MODEL_159251baf5e8425b8c9f8d7acb9abc55",
"tabbable": null,
"tooltip": null,
- "value": 5148.0
+ "value": "100%"
}
},
- "786e4946ac9d4815956096e2294ce1a9": {
+ "647ec368e3354d458cfed043dad850ce": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5175,30 +5149,57 @@
"text_color": null
}
},
- "78c28ce7e495439eb9b5618da236dfff": {
+ "683ea97790a64507b71e617e6bb1960f": {
"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",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_e1aad335fcff4496842cc4f52b05fb6a",
+ "IPY_MODEL_be4ee2ccce154754b1f1e8d49134ee62",
+ "IPY_MODEL_edb3baaca40743c08801b6e9bff25752"
+ ],
+ "layout": "IPY_MODEL_51bb6dd9acdc4544b4d17f4f020b1764",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "6b095fb924ef41358579ec879bf0f9fe": {
+ "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_6930211e1e8a42d88c3294247388155e",
- "placeholder": "",
- "style": "IPY_MODEL_d5b89a2622784b2684e5d0e4a1d8e288",
+ "layout": "IPY_MODEL_3ed339ad73774a5cae0f763c178cbf4b",
+ "max": 5148.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_1fdf0f2ba1e440ccaf02e74fc4b28520",
"tabbable": null,
"tooltip": null,
- "value": "Downloading builder script: 100%"
+ "value": 5148.0
}
},
- "79d53959ea3648699bfb173209305675": {
+ "6ca7ade3edba440ba7226afd89340130": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5251,94 +5252,60 @@
"width": null
}
},
- "7a5e963277ae42569b466604816aee6f": {
- "model_module": "@jupyter-widgets/controls",
+ "6ccb235983834c00ac32be1422c16641": {
+ "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
- }
- },
- "7ec457de82a94296bda5ce8043891a1e": {
- "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_a1f03e62934145f48df36ed07ef54135",
- "placeholder": "",
- "style": "IPY_MODEL_57fa64edffdb4d37a90dbea656009ab1",
- "tabbable": null,
- "tooltip": null,
- "value": " 8.85k/8.85k [00:00<00:00, 1.45MB/s]"
- }
- },
- "7f5b0df354d74d42b00745784e61cbb5": {
- "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_b56fcd9aca4c49c0b688460c48f52150",
- "placeholder": "",
- "style": "IPY_MODEL_0423a125c3324adc9f2433bebe77773b",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "81491c5e50a34db7ab3810ebfafe7b12": {
- "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_c0717785491f4d29856b706b3233759a",
- "placeholder": "",
- "style": "IPY_MODEL_c9c1491278bb49a0a831ab5dea82a8c5",
- "tabbable": null,
- "tooltip": null,
- "value": " 60000/60000 [00:11<00:00, 7815.27 examples/s]"
+ "_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
}
},
- "8205e7ba3ebf40938d66cce708243de9": {
+ "6f3f698e19f14817b2b2fa6c67e55a47": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5391,33 +5358,23 @@
"width": null
}
},
- "8224804dbbd04b29bd71274520c5718d": {
+ "7004a3b5592a40ee8328f00432adfc79": {
"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_299c0f0403e1447fb83af15e00e8f19f",
- "max": 29515.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_8824a37abc7b47a7b37e0eb6c3e6cc11",
- "tabbable": null,
- "tooltip": null,
- "value": 29515.0
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "8239b72ad4ca43fb92f0bc981abfd95b": {
+ "72961bb987d24298bfdb11eb59546963": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5470,7 +5427,7 @@
"width": null
}
},
- "833e5dbaba7546f582547cdf7571c108": {
+ "736ef5bce23e47429bfcb196fb8b85b3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5523,7 +5480,7 @@
"width": null
}
},
- "838e5992cb0f492aa79d9e4411b13437": {
+ "743f0d74186d4b7093498a547ab9ac95": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5538,15 +5495,33 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_c07a9548b6234526a02e4a5ca84bc144",
+ "layout": "IPY_MODEL_7b5cc6d910b546ce964b6b2bbec05343",
"placeholder": "",
- "style": "IPY_MODEL_266601a55125440a88a8a16e00cebd66",
+ "style": "IPY_MODEL_b3495f2ab71b4f848590703a190ddebc",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 63.32it/s]"
+ "value": " 40/40 [00:00<00:00, 68.82it/s]"
+ }
+ },
+ "751475cab3c24730bab0fbab4d5284f2": {
+ "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
}
},
- "8767f058e5b649bfb0c2d444b49f3f9d": {
+ "75221b9dde234f55bcd73f8bba5f3fa5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5599,7 +5574,33 @@
"width": null
}
},
- "87900fda6f274f88ab3a99c82a5a9940": {
+ "759a332c02e64ce79a364cd518bc163e": {
+ "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_6095c69a4f934899a783495c289c15a3",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_5c9cad60b37f4313a6899ca1a71bbee0",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
+ }
+ },
+ "75af0524012d41858477feaee9949557": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5652,23 +5653,7 @@
"width": null
}
},
- "8824a37abc7b47a7b37e0eb6c3e6cc11": {
- "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": ""
- }
- },
- "88c76968e9da4cb6ae77b6883ae64280": {
+ "7684491703b342af8b42512fb30334e4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5683,15 +5668,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_69835f01f4d24840900be4eafa6cd376",
+ "layout": "IPY_MODEL_a61e85ca959b4dafbe01836e2add2005",
"placeholder": "",
- "style": "IPY_MODEL_4c9f6ca0676a4e6bbace6bdaf151e92c",
+ "style": "IPY_MODEL_4ec1fcd52f8c4d51bb3475d2f3c24732",
"tabbable": null,
"tooltip": null,
- "value": "Generating train split: 100%"
+ "value": "100%"
}
},
- "88f5a8e733a449bf9d33c407463abc0b": {
+ "7b58054c0d6a418194fc7e1f039c639b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5744,7 +5729,7 @@
"width": null
}
},
- "8a368087070540ac84cfd57d04c80102": {
+ "7b5cc6d910b546ce964b6b2bbec05343": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5797,54 +5782,7 @@
"width": null
}
},
- "8b242b3757014ca08c0be26603c856e5": {
- "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_070af112be824f3a9dfc9d11f6c99bdb",
- "IPY_MODEL_624903d3a14d4864906b939e0507936f",
- "IPY_MODEL_61c7ca03d1b84ecdbe6bfb8765d33dd4"
- ],
- "layout": "IPY_MODEL_b6006aab30524546a2357c0b60b3422c",
- "tabbable": null,
- "tooltip": null
- }
- },
- "8b62a322205f4dbd9b437035f7710286": {
- "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_bd4ca863b31b4a6087a17d6cfa52ba96",
- "placeholder": "",
- "style": "IPY_MODEL_8be92883d1f447b7b27692d27d470559",
- "tabbable": null,
- "tooltip": null,
- "value": "Computing checksums: 100%"
- }
- },
- "8be92883d1f447b7b27692d27d470559": {
+ "7d2cf7fe1d884127b2ea048061916752": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5862,7 +5800,7 @@
"text_color": null
}
},
- "8c7ec74595b64ca194e8d3ba1c2a16fe": {
+ "7df97d24399f4f8a980e937ed234ad3c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5915,7 +5853,43 @@
"width": null
}
},
- "8e4fdf510840497e9b8908d892a617e3": {
+ "7fd76734cd4b4cec9bba7cdd76aaea68": {
+ "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
+ }
+ },
+ "823bddadbc6644c283f25f9cc6a18fc8": {
+ "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
+ }
+ },
+ "831b82774fb644faa4c010b37968f99b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -5930,41 +5904,114 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ef43f60fb2ce4517935a3a3d655e6e4c",
+ "layout": "IPY_MODEL_32b658a174274a4997618547ef7ef447",
"placeholder": "",
- "style": "IPY_MODEL_445bd193a0b443b68d88db83f68b1135",
+ "style": "IPY_MODEL_0f8071081d82450c9dd7fd9a927b6b1f",
"tabbable": null,
"tooltip": null,
- "value": "Downloading readme: 100%"
+ "value": "100%"
}
},
- "8fd06c99c9a747d8b7932274f22d7a1f": {
+ "846e19cb26a94bdba7b363dce398b69c": {
"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_99c1a284b0e04c9a85ab4ee83de08fc0",
+ "IPY_MODEL_759a332c02e64ce79a364cd518bc163e",
+ "IPY_MODEL_743f0d74186d4b7093498a547ab9ac95"
+ ],
+ "layout": "IPY_MODEL_c5594aabc34c40e2bd69e47ea0624f4a",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "864d773ce416475ba6a1e506b36063dc": {
+ "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
+ }
+ },
+ "877c0b06e48d4616b74e70c7b9e6abff": {
+ "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": ""
+ }
+ },
+ "890e03a51fce4dd185e5a42dc5da23ed": {
+ "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
+ }
+ },
+ "89956ffd6bd842798b8752c8f8fbcef6": {
+ "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_8a368087070540ac84cfd57d04c80102",
- "max": 4.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_25aea1e677d84e4da5e2f1a580b847bb",
+ "layout": "IPY_MODEL_bd6988dd8c6745d9a63c01e21b21baa3",
+ "placeholder": "",
+ "style": "IPY_MODEL_647ec368e3354d458cfed043dad850ce",
"tabbable": null,
"tooltip": null,
- "value": 4.0
+ "value": "100%"
}
},
- "93b948080aae40babe81f47f38dc60fa": {
+ "8a3f12d334d645c8a42ca8a0292075a9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6017,7 +6064,48 @@
"width": null
}
},
- "953f69d720d74dc0824fc0ac9a4ffa3b": {
+ "8be082c69bad41aa815fa99c34e5a9ea": {
+ "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
+ }
+ },
+ "8dbd4d3399124f6d8275c1d0fdfe9983": {
+ "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_6ca7ade3edba440ba7226afd89340130",
+ "placeholder": "",
+ "style": "IPY_MODEL_bdd6eef2f72d4192953e456956c77bd9",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Downloading data: 100%"
+ }
+ },
+ "9009c4b403d146b493913cc05ca55a44": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6070,7 +6158,7 @@
"width": null
}
},
- "956d09bfdd86417caf76bb337c098a81": {
+ "91eaa66c2eb641e689fc8028aee35c80": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -6088,7 +6176,59 @@
"text_color": null
}
},
- "9717f3b4aaae491d9cb2e07d49a003a5": {
+ "92fe75b0bd1341e9878165f8c906fc19": {
+ "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_3851a48a716b467ba9b981bd35b1822c",
+ "max": 29515.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_4aadbbb3e859454a93556ff943f76e5b",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 29515.0
+ }
+ },
+ "94beed83d4f34e37885835c7ee53b3e7": {
+ "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_c9e6261cabb7413784e072a38690acc3",
+ "max": 4833.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_c9722775eec64862b3c787b4a9da67b5",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 4833.0
+ }
+ },
+ "96b3b9a948504544be06e5692d10926d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -6103,16 +6243,57 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_ab1b471e80f9406fabb0d7e0b174084c",
- "IPY_MODEL_197981c8f9c84c00abea85184c72fcd5",
- "IPY_MODEL_5d069f3fc0fd47028eaaf3380b16e427"
+ "IPY_MODEL_e451b86b3b73462bb7d10f31b67e7f35",
+ "IPY_MODEL_ff2c7db5dbda44b8b24ca18490cb3473",
+ "IPY_MODEL_3256f32acb0e4cf5b5de459cdd30f479"
],
- "layout": "IPY_MODEL_19d83e638f7a404ba0eb4fdd71302435",
+ "layout": "IPY_MODEL_ed769dcb47ca423fb840b23690485ebe",
"tabbable": null,
"tooltip": null
}
},
- "97c91dea2ffc40f1830438b9a24225ce": {
+ "99c1a284b0e04c9a85ab4ee83de08fc0": {
+ "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_da9f7efe26b243e7bed64d6bd9746699",
+ "placeholder": "",
+ "style": "IPY_MODEL_bff32efaaebb4a2789332d03ffc174f6",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
+ }
+ },
+ "9bc90fd3c0264b26b5bf1c99f4b9caad": {
+ "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
+ }
+ },
+ "9eace039e9d343e1a0113042a3582776": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6165,25 +6346,31 @@
"width": null
}
},
- "98e13251741f42169572ab3c91ade8e4": {
+ "a0b406e9eaf143599fd4e302b57381b4": {
"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_89956ffd6bd842798b8752c8f8fbcef6",
+ "IPY_MODEL_088b60ea8401405ea27804efbb34b231",
+ "IPY_MODEL_6102ff846ce741a4aa1c83f94cf213b4"
+ ],
+ "layout": "IPY_MODEL_7df97d24399f4f8a980e937ed234ad3c",
+ "tabbable": null,
+ "tooltip": null
}
},
- "9a36f96ef3224e05847983090297222a": {
+ "a201453cadfa4bccaf26c6f446b4e7ee": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6236,30 +6423,25 @@
"width": null
}
},
- "9f1afcead928423ab651a3cfb6bc7cc4": {
+ "a2c46edab30a43d3ad1274496e23dc19": {
"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_cfc5b02015154a29bae32de6ea0acd51",
- "placeholder": "",
- "style": "IPY_MODEL_7a5e963277ae42569b466604816aee6f",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 59.96it/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "9fda0321906e4bb7a73fc697ca19c8e3": {
+ "a2e082221e6d4efe981d8286fcaa40bd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6312,41 +6494,7 @@
"width": null
}
},
- "a145248aa276456f95640b1b4a24d4bf": {
- "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
- }
- },
- "a1990d1ffd9240a7a4bf2438eaf81754": {
- "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": ""
- }
- },
- "a1f03e62934145f48df36ed07ef54135": {
+ "a3af79b4c771458595c44a209a768d66": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6399,7 +6547,7 @@
"width": null
}
},
- "a24112b64ebe4079b08768fea3888272": {
+ "a61e85ca959b4dafbe01836e2add2005": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6452,66 +6600,23 @@
"width": null
}
},
- "a31c56a1f6f544c1846467d596dd7b15": {
- "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
- }
- },
- "a38689b69ffd44938ebabe7dc9710b39": {
+ "a955b675afa4453385243c0af21d7bb7": {
"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
- }
- },
- "a50bac222b154e1e8f1af99c2d2f7cb5": {
- "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_5c0a9c7370bb48c09729cb766ab1781a",
- "placeholder": "",
- "style": "IPY_MODEL_2f77ed4c555c4365a30f1c760cb59e74",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
+ "bar_color": null,
+ "description_width": ""
}
},
- "a6307f65c4f14a07ad202a2748b4132f": {
+ "ac0dd2e3b9574dfba4e088b07dc9917a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6564,7 +6669,43 @@
"width": null
}
},
- "a774929f38e2462193ebeff227330d56": {
+ "ac82938d47c543a89ca5def5e546d7da": {
+ "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
+ }
+ },
+ "aea843e930ac410596a0f4cb4f6520e0": {
+ "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
+ }
+ },
+ "b1d5272979684bff96c71500a455d400": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -6579,63 +6720,49 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_11703dfe59744973b7ee5db1b50a7304",
+ "layout": "IPY_MODEL_372ab13ae5c8452ead0d7884763b3fc8",
"placeholder": "",
- "style": "IPY_MODEL_786e4946ac9d4815956096e2294ce1a9",
+ "style": "IPY_MODEL_27b9b136f3bb4b1a80402df468b5c136",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 67.06it/s]"
+ "value": "Downloading data: 100%"
}
},
- "a7cf6a20863544c78418cc502f39b678": {
+ "b3495f2ab71b4f848590703a190ddebc": {
"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_8b62a322205f4dbd9b437035f7710286",
- "IPY_MODEL_8fd06c99c9a747d8b7932274f22d7a1f",
- "IPY_MODEL_db1fd46972524542bbb7ec0eeef4b2ba"
- ],
- "layout": "IPY_MODEL_182fe610a03c4f0c8820d62efd2942a9",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "a9efee99388e4bd987cba82e4c249be5": {
+ "b3f80c6a23394b5dbcb460b9a19b34ff": {
"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_8e4fdf510840497e9b8908d892a617e3",
- "IPY_MODEL_de7773b079344e19b9f86e5643e773ed",
- "IPY_MODEL_7ec457de82a94296bda5ce8043891a1e"
- ],
- "layout": "IPY_MODEL_a6307f65c4f14a07ad202a2748b4132f",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "aa609ade2290479691002af80a845d3d": {
+ "bd6988dd8c6745d9a63c01e21b21baa3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6688,7 +6815,7 @@
"width": null
}
},
- "ab1b471e80f9406fabb0d7e0b174084c": {
+ "bd7060172fb747a6ae92a503a3922356": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -6703,33 +6830,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_255e06fe46cb4c0eaa40336d22beaf70",
+ "layout": "IPY_MODEL_a201453cadfa4bccaf26c6f446b4e7ee",
"placeholder": "",
- "style": "IPY_MODEL_98e13251741f42169572ab3c91ade8e4",
+ "style": "IPY_MODEL_2c4aaf8a7a84451d93bb9c185069cfe6",
"tabbable": null,
"tooltip": null,
"value": "100%"
}
},
- "abecf5a0521e45f78a788d98cc335e5c": {
- "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
- }
- },
- "ac339d14f5334e5fbce36888ade893b6": {
+ "bdd1580a8e924b29bc29315b007f1f26": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6782,7 +6891,51 @@
"width": null
}
},
- "ac933a814c124345b0730704f11cfbc8": {
+ "bdd6eef2f72d4192953e456956c77bd9": {
+ "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
+ }
+ },
+ "be4ee2ccce154754b1f1e8d49134ee62": {
+ "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_75af0524012d41858477feaee9949557",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_877c0b06e48d4616b74e70c7b9e6abff",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 60000.0
+ }
+ },
+ "be6b57b7d12a497fbc96e8e89b08f15a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6835,49 +6988,30 @@
"width": null
}
},
- "ad6f123f6f11456097bebc2f41cfafd7": {
- "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": ""
- }
- },
- "aeda1e802b974623a7d922ff793e1002": {
+ "bf04eda28d94482ebdbf589d87951c61": {
"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_f4f96f825f2043db8c6d5179d06cd01c",
- "max": 10000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_348018e48d954c87b48ff2a6e7470304",
+ "layout": "IPY_MODEL_0205ebdad1d64de8a1bd7d1c741d5fcb",
+ "placeholder": "",
+ "style": "IPY_MODEL_f0fcd694fc07412a83769122e48dd5c1",
"tabbable": null,
"tooltip": null,
- "value": 10000.0
+ "value": "Downloading data: 100%"
}
},
- "b13b21c3b7544706aacfbba4f3504a8b": {
+ "bfd46491d1764708be24b2103e5e6cb5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -6892,39 +7026,52 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_3abd2a96be1649ed9f2a7594cfdf3bbd",
- "IPY_MODEL_d7462fe7497245929fb556be574c9b66",
- "IPY_MODEL_e7d291738cf84330bb7fbd4947e558a1"
+ "IPY_MODEL_645dbaa1cd59415da2cc2b69430972fa",
+ "IPY_MODEL_33c9e2d67e4e498a9badfc73dd036c12",
+ "IPY_MODEL_21a93c7e2dfc4569b325ed80637cf469"
],
- "layout": "IPY_MODEL_295b6321cc6f4b1bbfa02a28f8581b1d",
+ "layout": "IPY_MODEL_4ff41db514e041798fc3d0bf13325104",
"tabbable": null,
"tooltip": null
}
},
- "b374b826cf5a4a668332dc70f8c2fe4a": {
+ "bff32efaaebb4a2789332d03ffc174f6": {
"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_f679da4310d84ae0a1679faa5659f6c7",
- "placeholder": "",
- "style": "IPY_MODEL_0abf4ae675284a88873e14026b73ab9e",
- "tabbable": null,
- "tooltip": null,
- "value": "Downloading data: 100%"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "c34dc272c24c40c697da60df89a38f25": {
+ "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
}
},
- "b56fcd9aca4c49c0b688460c48f52150": {
+ "c4fcca6ce699447399e56dfc004afddb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -6977,7 +7124,7 @@
"width": null
}
},
- "b6006aab30524546a2357c0b60b3422c": {
+ "c5594aabc34c40e2bd69e47ea0624f4a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7030,31 +7177,46 @@
"width": null
}
},
- "b69aa5fb137444eb962d31f239578d65": {
+ "c9693739925b43cd83cb4e68fc01ecc9": {
"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_64e2e67bdc244016895a62ad062fa077",
- "IPY_MODEL_1ba2c9f296904da78195fe3e45321059",
- "IPY_MODEL_9f1afcead928423ab651a3cfb6bc7cc4"
- ],
- "layout": "IPY_MODEL_ba0143519d7f4309b1af7b6461fd8a90",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_bdd1580a8e924b29bc29315b007f1f26",
+ "placeholder": "",
+ "style": "IPY_MODEL_751475cab3c24730bab0fbab4d5284f2",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 40/40 [00:00<00:00, 68.93it/s]"
+ }
+ },
+ "c9722775eec64862b3c787b4a9da67b5": {
+ "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": ""
}
},
- "ba0143519d7f4309b1af7b6461fd8a90": {
+ "c9e6261cabb7413784e072a38690acc3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7107,7 +7269,7 @@
"width": null
}
},
- "ba30ea8e68274773937367758a4abf1b": {
+ "cab386a86c594ee2885f6d1679103b3b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -7122,134 +7284,83 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_72cf872836144d31ac5f693e9157dde9",
+ "layout": "IPY_MODEL_6f3f698e19f14817b2b2fa6c67e55a47",
"placeholder": "",
- "style": "IPY_MODEL_e7eedd97596a479c9a69c82d61e53877",
+ "style": "IPY_MODEL_c34dc272c24c40c697da60df89a38f25",
"tabbable": null,
"tooltip": null,
- "value": " 40/40 [00:00<00:00, 64.53it/s]"
+ "value": " 4.42M/4.42M [00:00<00:00, 53.3MB/s]"
}
},
- "bc15b56e48ef468eb9e5db2eba40dd06": {
+ "cd6bbb8ca872405fa17b2571965191b3": {
"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": ""
}
},
- "bc98754b340343f594559442ba450aa4": {
+ "cf4a395a6d57451092b05d86340035d6": {
"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_88c76968e9da4cb6ae77b6883ae64280",
- "IPY_MODEL_3edb1a47c3314c0abfc78f23d62ebe68",
- "IPY_MODEL_367462867871411f81ad39c2100a77d8"
- ],
- "layout": "IPY_MODEL_9a36f96ef3224e05847983090297222a",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "bd4ca863b31b4a6087a17d6cfa52ba96": {
- "model_module": "@jupyter-widgets/base",
+ "d15f06ef2c454254bee0f59957e49d4b": {
+ "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_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "bd4e5e775e0d4b5d90568b686f8fd56f": {
+ "d2775a5c6f9b4c439174c1b5f34446b5": {
"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_78c28ce7e495439eb9b5618da236dfff",
- "IPY_MODEL_6d4b2efa08f448b0a99ccda6ee415ef7",
- "IPY_MODEL_ee2470d66aed4014820acd898b666713"
- ],
- "layout": "IPY_MODEL_ccb6f4531ee843d4ad0080744e6078d2",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "c0717785491f4d29856b706b3233759a": {
+ "d44de894f1eb4db0ad9f986867905216": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7302,60 +7413,72 @@
"width": null
}
},
- "c07a9548b6234526a02e4a5ca84bc144": {
- "model_module": "@jupyter-widgets/base",
+ "d4be07fa12674628ae93c0119edbf6e1": {
+ "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_d82285fcd9b84504a28fb9c9b1ad268f",
+ "placeholder": "",
+ "style": "IPY_MODEL_edb2e6f6c0214dae8109a703bb56a3ba",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 60000/60000 [00:11<00:00, 5257.56 examples/s]"
+ }
+ },
+ "d4c59b0bfa86424a8c95a71f890f5454": {
+ "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_f2b0f79e655c4494923cc58f73494551",
+ "IPY_MODEL_94beed83d4f34e37885835c7ee53b3e7",
+ "IPY_MODEL_25f2773590254b58b3ac4b1c0a886c35"
+ ],
+ "layout": "IPY_MODEL_d75e706d71cc4c7d8ec28bc9b0e5e02a",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "d72826ff906b4e3e81e8eb6291d1cc9a": {
+ "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
}
},
- "c5cfb97d21914845a48cbeea88f4d542": {
+ "d75e706d71cc4c7d8ec28bc9b0e5e02a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7408,7 +7531,7 @@
"width": null
}
},
- "c71cb776520442f0baba7a63fdbf2324": {
+ "d82285fcd9b84504a28fb9c9b1ad268f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7461,30 +7584,7 @@
"width": null
}
},
- "c8467f56948a44689852152948675255": {
- "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_5594cd23fb504b0dbdef0ecfe202453c",
- "placeholder": "",
- "style": "IPY_MODEL_109113d68eb84c2eace6f4deda6b040b",
- "tabbable": null,
- "tooltip": null,
- "value": " 10000/10000 [00:01<00:00, 8360.91 examples/s]"
- }
- },
- "c87df7230ab74cf3b4ce36554b4f64b5": {
+ "d84b7a5330814c309bc2e3a29fd936ef": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7537,7 +7637,7 @@
"width": null
}
},
- "c9c1491278bb49a0a831ab5dea82a8c5": {
+ "d89ee00461d14abe96f3f0cdcfa3da61": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -7555,23 +7655,7 @@
"text_color": null
}
},
- "cb7c5ad137844450a4dd8bb2cd5b55d5": {
- "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": ""
- }
- },
- "ccb6f4531ee843d4ad0080744e6078d2": {
+ "d90986d0f1bb4e8ebf632742ca0c49a3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7624,7 +7708,30 @@
"width": null
}
},
- "cf6f623149dc4d96afc8c831b7926d0a": {
+ "d9f443785177406fb3840783c441fddc": {
+ "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_e186a328526e44f2abaeb0fbc2e6a273",
+ "placeholder": "",
+ "style": "IPY_MODEL_d2775a5c6f9b4c439174c1b5f34446b5",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "Computing checksums: 100%"
+ }
+ },
+ "da9f7efe26b243e7bed64d6bd9746699": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7677,7 +7784,7 @@
"width": null
}
},
- "cfc5b02015154a29bae32de6ea0acd51": {
+ "dd1415dc221544d78c38cecb125e95de": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7730,7 +7837,7 @@
"width": null
}
},
- "d460961168ad48bf8a3a86fe06603769": {
+ "ddb1bbe42c9e442091cc9c4122b5de26": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7783,117 +7890,23 @@
"width": null
}
},
- "d5b89a2622784b2684e5d0e4a1d8e288": {
- "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
- }
- },
- "d60f32b2907d4a288385a30c717ef39d": {
- "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_126a880302694496ab5834d71ecffa75",
- "IPY_MODEL_aeda1e802b974623a7d922ff793e1002",
- "IPY_MODEL_c8467f56948a44689852152948675255"
- ],
- "layout": "IPY_MODEL_50042e9a2b5145a780c7c3d799e26aad",
- "tabbable": null,
- "tooltip": null
- }
- },
- "d627f7cbc1534c10a92cc7fd0f874ca6": {
- "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
- }
- },
- "d6465626e3264fa58f44ddccd18cfef2": {
- "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_3ddd27d196364f1db80581facbadf18f",
- "IPY_MODEL_5695fa5956b04b45a058d58f6cb27b12",
- "IPY_MODEL_ba30ea8e68274773937367758a4abf1b"
- ],
- "layout": "IPY_MODEL_6c4353aa625f407c8370f78a1dc830c8",
- "tabbable": null,
- "tooltip": null
- }
- },
- "d7462fe7497245929fb556be574c9b66": {
+ "ded2f3b115fb46e48b5699012c011fa5": {
"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_version": "2.0.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_allow_html": false,
- "layout": "IPY_MODEL_3ec84e0ec4d94afd8f15a91eb757624e",
- "max": 26421880.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_6de62af38a2f43a9846f333eda1224f2",
- "tabbable": null,
- "tooltip": null,
- "value": 26421880.0
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "d78ce21ee1fc44e8b680bda13d37d943": {
+ "e186a328526e44f2abaeb0fbc2e6a273": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -7946,33 +7959,30 @@
"width": null
}
},
- "d844173c6e344ebcbb4c2988636ce3a8": {
+ "e1aad335fcff4496842cc4f52b05fb6a": {
"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_aa609ade2290479691002af80a845d3d",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_732da230ecd142e79d60e75c14f8749a",
+ "layout": "IPY_MODEL_a3af79b4c771458595c44a209a768d66",
+ "placeholder": "",
+ "style": "IPY_MODEL_823bddadbc6644c283f25f9cc6a18fc8",
"tabbable": null,
"tooltip": null,
- "value": 40.0
+ "value": "100%"
}
},
- "db1fd46972524542bbb7ec0eeef4b2ba": {
+ "e451b86b3b73462bb7d10f31b67e7f35": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -7987,15 +7997,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_2cb677caec644d3ab2a8b6a70208559e",
+ "layout": "IPY_MODEL_8a3f12d334d645c8a42ca8a0292075a9",
"placeholder": "",
- "style": "IPY_MODEL_7662e69c3e7b45a1bc4d55996a7060cc",
+ "style": "IPY_MODEL_4a57dceefdc148afb5c7afa8adec5114",
"tabbable": null,
"tooltip": null,
- "value": " 4/4 [00:00<00:00, 1226.49it/s]"
+ "value": "Generating test split: 100%"
}
},
- "dcea0f344da44aa4bd657710ee8edf49": {
+ "e57ec2490fdc4028b01834557f09baa2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8048,31 +8058,49 @@
"width": null
}
},
- "dcfb76cdced842fd810c0329fa0f1c7f": {
+ "e6e9051f4c2a49228c32a25f39a57f4c": {
"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_ff86e317738e43de99250a83cfb7ad7f",
- "IPY_MODEL_8224804dbbd04b29bd71274520c5718d",
- "IPY_MODEL_5f29c5efd23943e1a08518f859506345"
- ],
- "layout": "IPY_MODEL_4a97191b1a1643199e46244e6611352a",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_e57ec2490fdc4028b01834557f09baa2",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_d15f06ef2c454254bee0f59957e49d4b",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": 40.0
+ }
+ },
+ "e85e1e91a24f4199a9a4b3e9abe8696f": {
+ "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": ""
}
},
- "ddea9d00c6b648c2ab8a73e6395f71ba": {
+ "eaa35cd4c5ae462181de3ad1ab98c2d1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8125,33 +8153,31 @@
"width": null
}
},
- "de7773b079344e19b9f86e5643e773ed": {
+ "ec86bd0afa46422aa85bf2778e427f2a": {
"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_9fda0321906e4bb7a73fc697ca19c8e3",
- "max": 8845.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5b07f8803c514642801639b40c1aa34b",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_831b82774fb644faa4c010b37968f99b",
+ "IPY_MODEL_e6e9051f4c2a49228c32a25f39a57f4c",
+ "IPY_MODEL_55923d8f76544ee5b5e53cb28dcbbcc5"
+ ],
+ "layout": "IPY_MODEL_0cae058fc562457bbb502b466cfdfcab",
"tabbable": null,
- "tooltip": null,
- "value": 8845.0
+ "tooltip": null
}
},
- "df8581440a0d42198080a4cd32ee9a4a": {
+ "ed5bd36e898a43dc9c5cb8e283abbead": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8204,7 +8230,7 @@
"width": null
}
},
- "e5fa84089fac429dba7a0f14fcadf7b8": {
+ "ed769dcb47ca423fb840b23690485ebe": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8257,7 +8283,25 @@
"width": null
}
},
- "e7d291738cf84330bb7fbd4947e558a1": {
+ "edb2e6f6c0214dae8109a703bb56a3ba": {
+ "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
+ }
+ },
+ "edb3baaca40743c08801b6e9bff25752": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -8272,56 +8316,57 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_df8581440a0d42198080a4cd32ee9a4a",
+ "layout": "IPY_MODEL_3598bd0162e44744bf0e88509c1fcc05",
"placeholder": "",
- "style": "IPY_MODEL_4751ad8ff2014ae4aefb61464aa0b996",
+ "style": "IPY_MODEL_cf4a395a6d57451092b05d86340035d6",
"tabbable": null,
"tooltip": null,
- "value": " 26.4M/26.4M [00:00<00:00, 105MB/s]"
+ "value": " 60000/60000 [00:36<00:00, 1626.06it/s]"
}
},
- "e7eedd97596a479c9a69c82d61e53877": {
+ "ee5d45a366aa46a4a7c3de67f844b7ba": {
"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_d9f443785177406fb3840783c441fddc",
+ "IPY_MODEL_640d5d2692d64656a67cd09cee644495",
+ "IPY_MODEL_1ef3f8ae36734efb86b54939cb9711d4"
+ ],
+ "layout": "IPY_MODEL_c4fcca6ce699447399e56dfc004afddb",
+ "tabbable": null,
+ "tooltip": null
}
},
- "ee2470d66aed4014820acd898b666713": {
+ "f0b50dd1b20c48b6911a694433d48e05": {
"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_dcea0f344da44aa4bd657710ee8edf49",
- "placeholder": "",
- "style": "IPY_MODEL_61b5f9d9099a4161a69f1c58aaa1f54e",
- "tabbable": null,
- "tooltip": null,
- "value": " 4.83k/4.83k [00:00<00:00, 527kB/s]"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "ef22307e229c427e9f2c45041e108373": {
+ "f0fcd694fc07412a83769122e48dd5c1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -8339,7 +8384,7 @@
"text_color": null
}
},
- "ef43f60fb2ce4517935a3a3d655e6e4c": {
+ "f1c238f4a14549229bdf80d577253ccf": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8392,7 +8437,7 @@
"width": null
}
},
- "f035123f0a8341818f2529edc8258078": {
+ "f2b0f79e655c4494923cc58f73494551": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -8407,15 +8452,31 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_6d5821fefb5648caac6867e9f7a4c67b",
+ "layout": "IPY_MODEL_f8aafa4a992b42cf95a5ace2356676d6",
"placeholder": "",
- "style": "IPY_MODEL_a145248aa276456f95640b1b4a24d4bf",
+ "style": "IPY_MODEL_d72826ff906b4e3e81e8eb6291d1cc9a",
"tabbable": null,
"tooltip": null,
- "value": "100%"
+ "value": "Downloading builder script: 100%"
+ }
+ },
+ "f4bfad9cd0da4d31a8d5c783407a73c8": {
+ "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": ""
}
},
- "f15ed2b240814bb897b777075c726c92": {
+ "f8aafa4a992b42cf95a5ace2356676d6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8468,56 +8529,7 @@
"width": null
}
},
- "f247cc3e079b4ad2aa902a3150c5d9ec": {
- "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_8239b72ad4ca43fb92f0bc981abfd95b",
- "placeholder": "",
- "style": "IPY_MODEL_fed701653a71451196013b62e55b30ef",
- "tabbable": null,
- "tooltip": null,
- "value": "Downloading data: 100%"
- }
- },
- "f306b412103a4a999f13f32ecb0841fe": {
- "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_4367f895c508411fb72fbf123bfd2662",
- "max": 40.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_62c6658ea89d458aa1198b576f9eee56",
- "tabbable": null,
- "tooltip": null,
- "value": 40.0
- }
- },
- "f4f96f825f2043db8c6d5179d06cd01c": {
+ "f9978a29787547e3bc5e59bde742651c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8570,7 +8582,33 @@
"width": null
}
},
- "f679da4310d84ae0a1679faa5659f6c7": {
+ "fe1973b9b1fa4957b9894f465a0fe87c": {
+ "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_736ef5bce23e47429bfcb196fb8b85b3",
+ "max": 40.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_b3f80c6a23394b5dbcb460b9a19b34ff",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 40.0
+ }
+ },
+ "ff08f8bfa09d42838688c6f725adb306": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -8623,71 +8661,33 @@
"width": null
}
},
- "f68300c0882c4adca9ac2341e102684c": {
- "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_ac339d14f5334e5fbce36888ade893b6",
- "placeholder": "",
- "style": "IPY_MODEL_424a8de7240d4c088b2b2dbadf1113ab",
- "tabbable": null,
- "tooltip": null,
- "value": " 40/40 [00:00<00:00, 67.31it/s]"
- }
- },
- "fc671361cddb4f3e9903ef6aae4d3083": {
+ "ff2c7db5dbda44b8b24ca18490cb3473": {
"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_cf6f623149dc4d96afc8c831b7926d0a",
- "placeholder": "",
- "style": "IPY_MODEL_a31c56a1f6f544c1846467d596dd7b15",
+ "layout": "IPY_MODEL_6ccb235983834c00ac32be1422c16641",
+ "max": 10000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_a955b675afa4453385243c0af21d7bb7",
"tabbable": null,
"tooltip": null,
- "value": " 5.15k/5.15k [00:00<00:00, 866kB/s]"
- }
- },
- "fed701653a71451196013b62e55b30ef": {
- "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": 10000.0
}
},
- "ff86e317738e43de99250a83cfb7ad7f": {
+ "fffb62594db04599b3628dceafda46f1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -8702,12 +8702,12 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_8767f058e5b649bfb0c2d444b49f3f9d",
+ "layout": "IPY_MODEL_30c3b868ba4b46ea9bcdb05e1c6d5613",
"placeholder": "",
- "style": "IPY_MODEL_01c1fefd1d1549a1ac3d1168746bc81e",
+ "style": "IPY_MODEL_46552aea691e492084a7278f7a059830",
"tabbable": null,
"tooltip": null,
- "value": "Downloading data: 100%"
+ "value": "Map (num_proc=4): 100%"
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index 452755a26..32831e810 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": "2024-07-01T15:05:46.317874Z",
- "iopub.status.busy": "2024-07-01T15:05:46.317719Z",
- "iopub.status.idle": "2024-07-01T15:05:47.417876Z",
- "shell.execute_reply": "2024-07-01T15:05:47.417402Z"
+ "iopub.execute_input": "2024-07-02T12:04:27.356934Z",
+ "iopub.status.busy": "2024-07-02T12:04:27.356523Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.474290Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.473753Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:05:47.420276Z",
- "iopub.status.busy": "2024-07-01T15:05:47.420000Z",
- "iopub.status.idle": "2024-07-01T15:05:47.437670Z",
- "shell.execute_reply": "2024-07-01T15:05:47.437224Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.476781Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.476419Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.493512Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.493079Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:47.439750Z",
- "iopub.status.busy": "2024-07-01T15:05:47.439498Z",
- "iopub.status.idle": "2024-07-01T15:05:47.478024Z",
- "shell.execute_reply": "2024-07-01T15:05:47.477526Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.495747Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.495323Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.552204Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.551635Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:47.480262Z",
- "iopub.status.busy": "2024-07-01T15:05:47.479916Z",
- "iopub.status.idle": "2024-07-01T15:05:47.483182Z",
- "shell.execute_reply": "2024-07-01T15:05:47.482737Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.554311Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.553993Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.557548Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.557017Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:47.485314Z",
- "iopub.status.busy": "2024-07-01T15:05:47.484937Z",
- "iopub.status.idle": "2024-07-01T15:05:47.492797Z",
- "shell.execute_reply": "2024-07-01T15:05:47.492370Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.559563Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.559241Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.566506Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.566080Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:47.494826Z",
- "iopub.status.busy": "2024-07-01T15:05:47.494542Z",
- "iopub.status.idle": "2024-07-01T15:05:47.497119Z",
- "shell.execute_reply": "2024-07-01T15:05:47.496587Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.568485Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.568190Z",
+ "iopub.status.idle": "2024-07-02T12:04:28.570814Z",
+ "shell.execute_reply": "2024-07-02T12:04:28.570270Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:47.499036Z",
- "iopub.status.busy": "2024-07-01T15:05:47.498842Z",
- "iopub.status.idle": "2024-07-01T15:05:50.430868Z",
- "shell.execute_reply": "2024-07-01T15:05:50.430331Z"
+ "iopub.execute_input": "2024-07-02T12:04:28.572815Z",
+ "iopub.status.busy": "2024-07-02T12:04:28.572491Z",
+ "iopub.status.idle": "2024-07-02T12:04:31.525677Z",
+ "shell.execute_reply": "2024-07-02T12:04:31.525153Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:50.433520Z",
- "iopub.status.busy": "2024-07-01T15:05:50.433131Z",
- "iopub.status.idle": "2024-07-01T15:05:50.442780Z",
- "shell.execute_reply": "2024-07-01T15:05:50.442322Z"
+ "iopub.execute_input": "2024-07-02T12:04:31.528465Z",
+ "iopub.status.busy": "2024-07-02T12:04:31.528045Z",
+ "iopub.status.idle": "2024-07-02T12:04:31.537314Z",
+ "shell.execute_reply": "2024-07-02T12:04:31.536783Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:50.444757Z",
- "iopub.status.busy": "2024-07-01T15:05:50.444440Z",
- "iopub.status.idle": "2024-07-01T15:05:52.320323Z",
- "shell.execute_reply": "2024-07-01T15:05:52.319680Z"
+ "iopub.execute_input": "2024-07-02T12:04:31.539264Z",
+ "iopub.status.busy": "2024-07-02T12:04:31.539089Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.395993Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.395329Z"
}
},
"outputs": [
@@ -476,10 +476,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.322868Z",
- "iopub.status.busy": "2024-07-01T15:05:52.322271Z",
- "iopub.status.idle": "2024-07-01T15:05:52.341011Z",
- "shell.execute_reply": "2024-07-01T15:05:52.340483Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.398417Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.397878Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.416211Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.415751Z"
},
"scrolled": true
},
@@ -609,10 +609,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.343025Z",
- "iopub.status.busy": "2024-07-01T15:05:52.342731Z",
- "iopub.status.idle": "2024-07-01T15:05:52.350595Z",
- "shell.execute_reply": "2024-07-01T15:05:52.350103Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.418164Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.417840Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.425514Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.425080Z"
}
},
"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.352704Z",
- "iopub.status.busy": "2024-07-01T15:05:52.352276Z",
- "iopub.status.idle": "2024-07-01T15:05:52.361059Z",
- "shell.execute_reply": "2024-07-01T15:05:52.360522Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.427421Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.427245Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.435924Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.435472Z"
}
},
"outputs": [
@@ -848,10 +848,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.363255Z",
- "iopub.status.busy": "2024-07-01T15:05:52.362931Z",
- "iopub.status.idle": "2024-07-01T15:05:52.370565Z",
- "shell.execute_reply": "2024-07-01T15:05:52.370092Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.437900Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.437577Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.445125Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.444685Z"
}
},
"outputs": [
@@ -965,10 +965,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.372696Z",
- "iopub.status.busy": "2024-07-01T15:05:52.372359Z",
- "iopub.status.idle": "2024-07-01T15:05:52.380928Z",
- "shell.execute_reply": "2024-07-01T15:05:52.380440Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.447029Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.446852Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.455323Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.454897Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.382940Z",
- "iopub.status.busy": "2024-07-01T15:05:52.382568Z",
- "iopub.status.idle": "2024-07-01T15:05:52.389986Z",
- "shell.execute_reply": "2024-07-01T15:05:52.389445Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.457305Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.457003Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.464266Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.463800Z"
}
},
"outputs": [
@@ -1197,10 +1197,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.392057Z",
- "iopub.status.busy": "2024-07-01T15:05:52.391736Z",
- "iopub.status.idle": "2024-07-01T15:05:52.398743Z",
- "shell.execute_reply": "2024-07-01T15:05:52.398311Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.466390Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.465996Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.473134Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.472705Z"
}
},
"outputs": [
@@ -1300,10 +1300,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:52.400864Z",
- "iopub.status.busy": "2024-07-01T15:05:52.400548Z",
- "iopub.status.idle": "2024-07-01T15:05:52.408413Z",
- "shell.execute_reply": "2024-07-01T15:05:52.407979Z"
+ "iopub.execute_input": "2024-07-02T12:04:33.475300Z",
+ "iopub.status.busy": "2024-07-02T12:04:33.474982Z",
+ "iopub.status.idle": "2024-07-02T12:04:33.482977Z",
+ "shell.execute_reply": "2024-07-02T12:04:33.482536Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 94ec2b5de..8395c410d 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": "2024-07-01T15:05:55.109624Z",
- "iopub.status.busy": "2024-07-01T15:05:55.109456Z",
- "iopub.status.idle": "2024-07-01T15:05:57.756143Z",
- "shell.execute_reply": "2024-07-01T15:05:57.755510Z"
+ "iopub.execute_input": "2024-07-02T12:04:36.240740Z",
+ "iopub.status.busy": "2024-07-02T12:04:36.240404Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.828958Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.828416Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:05:57.758704Z",
- "iopub.status.busy": "2024-07-01T15:05:57.758362Z",
- "iopub.status.idle": "2024-07-01T15:05:57.761689Z",
- "shell.execute_reply": "2024-07-01T15:05:57.761157Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.831414Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.831139Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.834207Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.833787Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:57.763881Z",
- "iopub.status.busy": "2024-07-01T15:05:57.763378Z",
- "iopub.status.idle": "2024-07-01T15:05:57.766675Z",
- "shell.execute_reply": "2024-07-01T15:05:57.766123Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.836176Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.835855Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.838727Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.838306Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:57.768614Z",
- "iopub.status.busy": "2024-07-01T15:05:57.768315Z",
- "iopub.status.idle": "2024-07-01T15:05:57.808437Z",
- "shell.execute_reply": "2024-07-01T15:05:57.807887Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.840549Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.840377Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.923955Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.923459Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:57.810722Z",
- "iopub.status.busy": "2024-07-01T15:05:57.810309Z",
- "iopub.status.idle": "2024-07-01T15:05:57.814281Z",
- "shell.execute_reply": "2024-07-01T15:05:57.813706Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.926011Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.925614Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.929422Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.928857Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n"
+ "Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:57.816292Z",
- "iopub.status.busy": "2024-07-01T15:05:57.816001Z",
- "iopub.status.idle": "2024-07-01T15:05:57.819153Z",
- "shell.execute_reply": "2024-07-01T15:05:57.818607Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.931544Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.931095Z",
+ "iopub.status.idle": "2024-07-02T12:04:38.934251Z",
+ "shell.execute_reply": "2024-07-02T12:04:38.933726Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:05:57.821168Z",
- "iopub.status.busy": "2024-07-01T15:05:57.820783Z",
- "iopub.status.idle": "2024-07-01T15:06:01.454864Z",
- "shell.execute_reply": "2024-07-01T15:06:01.454218Z"
+ "iopub.execute_input": "2024-07-02T12:04:38.936534Z",
+ "iopub.status.busy": "2024-07-02T12:04:38.936327Z",
+ "iopub.status.idle": "2024-07-02T12:04:42.537806Z",
+ "shell.execute_reply": "2024-07-02T12:04:42.537162Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:01.457576Z",
- "iopub.status.busy": "2024-07-01T15:06:01.457191Z",
- "iopub.status.idle": "2024-07-01T15:06:02.359759Z",
- "shell.execute_reply": "2024-07-01T15:06:02.359194Z"
+ "iopub.execute_input": "2024-07-02T12:04:42.540458Z",
+ "iopub.status.busy": "2024-07-02T12:04:42.540268Z",
+ "iopub.status.idle": "2024-07-02T12:04:43.423626Z",
+ "shell.execute_reply": "2024-07-02T12:04:43.423064Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:02.362504Z",
- "iopub.status.busy": "2024-07-01T15:06:02.362099Z",
- "iopub.status.idle": "2024-07-01T15:06:02.365173Z",
- "shell.execute_reply": "2024-07-01T15:06:02.364692Z"
+ "iopub.execute_input": "2024-07-02T12:04:43.426912Z",
+ "iopub.status.busy": "2024-07-02T12:04:43.426508Z",
+ "iopub.status.idle": "2024-07-02T12:04:43.429416Z",
+ "shell.execute_reply": "2024-07-02T12:04:43.428926Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:02.368303Z",
- "iopub.status.busy": "2024-07-01T15:06:02.367393Z",
- "iopub.status.idle": "2024-07-01T15:06:04.354878Z",
- "shell.execute_reply": "2024-07-01T15:06:04.354255Z"
+ "iopub.execute_input": "2024-07-02T12:04:43.431781Z",
+ "iopub.status.busy": "2024-07-02T12:04:43.431407Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.304891Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.304275Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.359326Z",
- "iopub.status.busy": "2024-07-01T15:06:04.358175Z",
- "iopub.status.idle": "2024-07-01T15:06:04.383863Z",
- "shell.execute_reply": "2024-07-01T15:06:04.383356Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.309001Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.307874Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.333199Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.332708Z"
},
"scrolled": true
},
@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.387331Z",
- "iopub.status.busy": "2024-07-01T15:06:04.386438Z",
- "iopub.status.idle": "2024-07-01T15:06:04.396138Z",
- "shell.execute_reply": "2024-07-01T15:06:04.395755Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.336771Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.335844Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.346004Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.345452Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.398058Z",
- "iopub.status.busy": "2024-07-01T15:06:04.397776Z",
- "iopub.status.idle": "2024-07-01T15:06:04.401475Z",
- "shell.execute_reply": "2024-07-01T15:06:04.401092Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.348315Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.347931Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.352195Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.351669Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.403322Z",
- "iopub.status.busy": "2024-07-01T15:06:04.403036Z",
- "iopub.status.idle": "2024-07-01T15:06:04.408720Z",
- "shell.execute_reply": "2024-07-01T15:06:04.408332Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.354318Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.354009Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.360212Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.359737Z"
}
},
"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.410591Z",
- "iopub.status.busy": "2024-07-01T15:06:04.410423Z",
- "iopub.status.idle": "2024-07-01T15:06:04.416683Z",
- "shell.execute_reply": "2024-07-01T15:06:04.416154Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.362212Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.361899Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.368332Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.367912Z"
}
},
"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.418724Z",
- "iopub.status.busy": "2024-07-01T15:06:04.418385Z",
- "iopub.status.idle": "2024-07-01T15:06:04.424043Z",
- "shell.execute_reply": "2024-07-01T15:06:04.423521Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.370347Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.370035Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.375916Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.375352Z"
}
},
"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.426089Z",
- "iopub.status.busy": "2024-07-01T15:06:04.425788Z",
- "iopub.status.idle": "2024-07-01T15:06:04.434068Z",
- "shell.execute_reply": "2024-07-01T15:06:04.433526Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.377933Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.377533Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.386285Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.385744Z"
}
},
"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.435974Z",
- "iopub.status.busy": "2024-07-01T15:06:04.435800Z",
- "iopub.status.idle": "2024-07-01T15:06:04.441070Z",
- "shell.execute_reply": "2024-07-01T15:06:04.440586Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.388235Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.387909Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.393341Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.392791Z"
}
},
"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.443100Z",
- "iopub.status.busy": "2024-07-01T15:06:04.442719Z",
- "iopub.status.idle": "2024-07-01T15:06:04.447928Z",
- "shell.execute_reply": "2024-07-01T15:06:04.447468Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.395404Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.395057Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.400341Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.399863Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.450005Z",
- "iopub.status.busy": "2024-07-01T15:06:04.449608Z",
- "iopub.status.idle": "2024-07-01T15:06:04.453217Z",
- "shell.execute_reply": "2024-07-01T15:06:04.452674Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.402359Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.402038Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.405437Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.405020Z"
}
},
"outputs": [
@@ -1443,10 +1443,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:04.455383Z",
- "iopub.status.busy": "2024-07-01T15:06:04.455056Z",
- "iopub.status.idle": "2024-07-01T15:06:04.460142Z",
- "shell.execute_reply": "2024-07-01T15:06:04.459596Z"
+ "iopub.execute_input": "2024-07-02T12:04:45.407623Z",
+ "iopub.status.busy": "2024-07-02T12:04:45.407307Z",
+ "iopub.status.idle": "2024-07-02T12:04:45.412091Z",
+ "shell.execute_reply": "2024-07-02T12:04:45.411668Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index 71ffac131..62a8a980c 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": "2024-07-01T15:06:07.601006Z",
- "iopub.status.busy": "2024-07-01T15:06:07.600505Z",
- "iopub.status.idle": "2024-07-01T15:06:08.023065Z",
- "shell.execute_reply": "2024-07-01T15:06:08.022566Z"
+ "iopub.execute_input": "2024-07-02T12:04:48.475916Z",
+ "iopub.status.busy": "2024-07-02T12:04:48.475349Z",
+ "iopub.status.idle": "2024-07-02T12:04:48.903298Z",
+ "shell.execute_reply": "2024-07-02T12:04:48.902818Z"
}
},
"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:08.025689Z",
- "iopub.status.busy": "2024-07-01T15:06:08.025283Z",
- "iopub.status.idle": "2024-07-01T15:06:08.152849Z",
- "shell.execute_reply": "2024-07-01T15:06:08.152350Z"
+ "iopub.execute_input": "2024-07-02T12:04:48.905906Z",
+ "iopub.status.busy": "2024-07-02T12:04:48.905515Z",
+ "iopub.status.idle": "2024-07-02T12:04:49.030978Z",
+ "shell.execute_reply": "2024-07-02T12:04:49.030445Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:08.155131Z",
- "iopub.status.busy": "2024-07-01T15:06:08.154741Z",
- "iopub.status.idle": "2024-07-01T15:06:08.177601Z",
- "shell.execute_reply": "2024-07-01T15:06:08.177069Z"
+ "iopub.execute_input": "2024-07-02T12:04:49.033125Z",
+ "iopub.status.busy": "2024-07-02T12:04:49.032895Z",
+ "iopub.status.idle": "2024-07-02T12:04:49.055963Z",
+ "shell.execute_reply": "2024-07-02T12:04:49.055416Z"
}
},
"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:08.180286Z",
- "iopub.status.busy": "2024-07-01T15:06:08.179872Z",
- "iopub.status.idle": "2024-07-01T15:06:10.839277Z",
- "shell.execute_reply": "2024-07-01T15:06:10.838727Z"
+ "iopub.execute_input": "2024-07-02T12:04:49.058382Z",
+ "iopub.status.busy": "2024-07-02T12:04:49.057963Z",
+ "iopub.status.idle": "2024-07-02T12:04:51.680557Z",
+ "shell.execute_reply": "2024-07-02T12:04:51.680002Z"
}
},
"outputs": [
@@ -235,7 +235,7 @@
"Finding class_imbalance issues ...\n",
"Finding underperforming_group issues ...\n",
"\n",
- "Audit complete. 523 issues found in the dataset.\n"
+ "Audit complete. 524 issues found in the dataset.\n"
]
},
{
@@ -280,13 +280,13 @@
" \n",
" | 2 | \n",
" outlier | \n",
- " 0.356958 | \n",
- " 362 | \n",
+ " 0.356924 | \n",
+ " 363 | \n",
"
\n",
" \n",
" | 3 | \n",
" near_duplicate | \n",
- " 0.619565 | \n",
+ " 0.619581 | \n",
" 108 | \n",
"
\n",
" \n",
@@ -315,8 +315,8 @@
" issue_type score num_issues\n",
"0 null 1.000000 0\n",
"1 label 0.991400 52\n",
- "2 outlier 0.356958 362\n",
- "3 near_duplicate 0.619565 108\n",
+ "2 outlier 0.356924 363\n",
+ "3 near_duplicate 0.619581 108\n",
"4 non_iid 0.000000 1\n",
"5 class_imbalance 0.500000 0\n",
"6 underperforming_group 0.651929 0"
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:10.841884Z",
- "iopub.status.busy": "2024-07-01T15:06:10.841353Z",
- "iopub.status.idle": "2024-07-01T15:06:18.620342Z",
- "shell.execute_reply": "2024-07-01T15:06:18.619784Z"
+ "iopub.execute_input": "2024-07-02T12:04:51.683932Z",
+ "iopub.status.busy": "2024-07-02T12:04:51.683392Z",
+ "iopub.status.idle": "2024-07-02T12:04:59.515985Z",
+ "shell.execute_reply": "2024-07-02T12:04:59.515371Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:18.622535Z",
- "iopub.status.busy": "2024-07-01T15:06:18.622344Z",
- "iopub.status.idle": "2024-07-01T15:06:18.765943Z",
- "shell.execute_reply": "2024-07-01T15:06:18.765367Z"
+ "iopub.execute_input": "2024-07-02T12:04:59.518078Z",
+ "iopub.status.busy": "2024-07-02T12:04:59.517894Z",
+ "iopub.status.idle": "2024-07-02T12:04:59.659289Z",
+ "shell.execute_reply": "2024-07-02T12:04:59.658739Z"
}
},
"outputs": [],
@@ -838,10 +838,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:18.768372Z",
- "iopub.status.busy": "2024-07-01T15:06:18.768184Z",
- "iopub.status.idle": "2024-07-01T15:06:20.089155Z",
- "shell.execute_reply": "2024-07-01T15:06:20.088659Z"
+ "iopub.execute_input": "2024-07-02T12:04:59.661683Z",
+ "iopub.status.busy": "2024-07-02T12:04:59.661350Z",
+ "iopub.status.idle": "2024-07-02T12:05:00.957856Z",
+ "shell.execute_reply": "2024-07-02T12:05:00.957311Z"
}
},
"outputs": [
@@ -1000,10 +1000,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.091428Z",
- "iopub.status.busy": "2024-07-01T15:06:20.091067Z",
- "iopub.status.idle": "2024-07-01T15:06:20.540474Z",
- "shell.execute_reply": "2024-07-01T15:06:20.539777Z"
+ "iopub.execute_input": "2024-07-02T12:05:00.960128Z",
+ "iopub.status.busy": "2024-07-02T12:05:00.959785Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.381421Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.380807Z"
}
},
"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.543076Z",
- "iopub.status.busy": "2024-07-01T15:06:20.542728Z",
- "iopub.status.idle": "2024-07-01T15:06:20.551688Z",
- "shell.execute_reply": "2024-07-01T15:06:20.551243Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.383745Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.383267Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.392315Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.391863Z"
}
},
"outputs": [],
@@ -1115,10 +1115,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.553781Z",
- "iopub.status.busy": "2024-07-01T15:06:20.553342Z",
- "iopub.status.idle": "2024-07-01T15:06:20.571023Z",
- "shell.execute_reply": "2024-07-01T15:06:20.570476Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.394282Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.393956Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.411562Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.411139Z"
}
},
"outputs": [],
@@ -1146,10 +1146,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.573201Z",
- "iopub.status.busy": "2024-07-01T15:06:20.572911Z",
- "iopub.status.idle": "2024-07-01T15:06:20.803461Z",
- "shell.execute_reply": "2024-07-01T15:06:20.802856Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.413543Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.413221Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.630162Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.629562Z"
}
},
"outputs": [],
@@ -1189,10 +1189,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.806333Z",
- "iopub.status.busy": "2024-07-01T15:06:20.805945Z",
- "iopub.status.idle": "2024-07-01T15:06:20.825160Z",
- "shell.execute_reply": "2024-07-01T15:06:20.824612Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.632639Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.632236Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.650528Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.649988Z"
}
},
"outputs": [
@@ -1390,10 +1390,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.827381Z",
- "iopub.status.busy": "2024-07-01T15:06:20.826963Z",
- "iopub.status.idle": "2024-07-01T15:06:20.993329Z",
- "shell.execute_reply": "2024-07-01T15:06:20.992779Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.652709Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.652303Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.816760Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.816173Z"
}
},
"outputs": [
@@ -1460,10 +1460,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:20.995655Z",
- "iopub.status.busy": "2024-07-01T15:06:20.995315Z",
- "iopub.status.idle": "2024-07-01T15:06:21.005096Z",
- "shell.execute_reply": "2024-07-01T15:06:21.004627Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.818813Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.818633Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.828263Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.827827Z"
}
},
"outputs": [
@@ -1729,10 +1729,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.007134Z",
- "iopub.status.busy": "2024-07-01T15:06:21.006797Z",
- "iopub.status.idle": "2024-07-01T15:06:21.015972Z",
- "shell.execute_reply": "2024-07-01T15:06:21.015502Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.830285Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.830099Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.839416Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.838852Z"
}
},
"outputs": [
@@ -1919,10 +1919,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.017811Z",
- "iopub.status.busy": "2024-07-01T15:06:21.017637Z",
- "iopub.status.idle": "2024-07-01T15:06:21.046279Z",
- "shell.execute_reply": "2024-07-01T15:06:21.045826Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.841444Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.841118Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.878960Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.878541Z"
}
},
"outputs": [],
@@ -1956,10 +1956,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.048363Z",
- "iopub.status.busy": "2024-07-01T15:06:21.048043Z",
- "iopub.status.idle": "2024-07-01T15:06:21.050539Z",
- "shell.execute_reply": "2024-07-01T15:06:21.050117Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.881007Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.880679Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.883255Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.882829Z"
}
},
"outputs": [],
@@ -1981,10 +1981,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.052583Z",
- "iopub.status.busy": "2024-07-01T15:06:21.052269Z",
- "iopub.status.idle": "2024-07-01T15:06:21.070692Z",
- "shell.execute_reply": "2024-07-01T15:06:21.070162Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.885223Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.884900Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.903469Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.903010Z"
}
},
"outputs": [
@@ -2142,10 +2142,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.072780Z",
- "iopub.status.busy": "2024-07-01T15:06:21.072455Z",
- "iopub.status.idle": "2024-07-01T15:06:21.076730Z",
- "shell.execute_reply": "2024-07-01T15:06:21.076273Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.905390Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.905216Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.909303Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.908869Z"
}
},
"outputs": [],
@@ -2178,10 +2178,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.078798Z",
- "iopub.status.busy": "2024-07-01T15:06:21.078478Z",
- "iopub.status.idle": "2024-07-01T15:06:21.105961Z",
- "shell.execute_reply": "2024-07-01T15:06:21.105424Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.911113Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.910943Z",
+ "iopub.status.idle": "2024-07-02T12:05:01.938117Z",
+ "shell.execute_reply": "2024-07-02T12:05:01.937659Z"
}
},
"outputs": [
@@ -2327,10 +2327,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.108003Z",
- "iopub.status.busy": "2024-07-01T15:06:21.107678Z",
- "iopub.status.idle": "2024-07-01T15:06:21.447286Z",
- "shell.execute_reply": "2024-07-01T15:06:21.446728Z"
+ "iopub.execute_input": "2024-07-02T12:05:01.940161Z",
+ "iopub.status.busy": "2024-07-02T12:05:01.939837Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.252666Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.252098Z"
}
},
"outputs": [
@@ -2397,10 +2397,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.449527Z",
- "iopub.status.busy": "2024-07-01T15:06:21.449200Z",
- "iopub.status.idle": "2024-07-01T15:06:21.452276Z",
- "shell.execute_reply": "2024-07-01T15:06:21.451757Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.254862Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.254429Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.257607Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.257069Z"
}
},
"outputs": [
@@ -2451,10 +2451,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.454363Z",
- "iopub.status.busy": "2024-07-01T15:06:21.454023Z",
- "iopub.status.idle": "2024-07-01T15:06:21.466646Z",
- "shell.execute_reply": "2024-07-01T15:06:21.466203Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.259719Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.259383Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.272004Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.271534Z"
}
},
"outputs": [
@@ -2733,10 +2733,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.468583Z",
- "iopub.status.busy": "2024-07-01T15:06:21.468405Z",
- "iopub.status.idle": "2024-07-01T15:06:21.481924Z",
- "shell.execute_reply": "2024-07-01T15:06:21.481439Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.273862Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.273687Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.287267Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.286829Z"
}
},
"outputs": [
@@ -3003,10 +3003,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.484047Z",
- "iopub.status.busy": "2024-07-01T15:06:21.483628Z",
- "iopub.status.idle": "2024-07-01T15:06:21.493274Z",
- "shell.execute_reply": "2024-07-01T15:06:21.492857Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.289083Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.288916Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.298453Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.298027Z"
}
},
"outputs": [],
@@ -3031,10 +3031,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.495372Z",
- "iopub.status.busy": "2024-07-01T15:06:21.495048Z",
- "iopub.status.idle": "2024-07-01T15:06:21.504141Z",
- "shell.execute_reply": "2024-07-01T15:06:21.503595Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.300283Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.300116Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.309664Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.309126Z"
}
},
"outputs": [
@@ -3206,10 +3206,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.506165Z",
- "iopub.status.busy": "2024-07-01T15:06:21.505846Z",
- "iopub.status.idle": "2024-07-01T15:06:21.509379Z",
- "shell.execute_reply": "2024-07-01T15:06:21.508840Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.311452Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.311286Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.314989Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.314531Z"
}
},
"outputs": [],
@@ -3241,10 +3241,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.511428Z",
- "iopub.status.busy": "2024-07-01T15:06:21.511040Z",
- "iopub.status.idle": "2024-07-01T15:06:21.560989Z",
- "shell.execute_reply": "2024-07-01T15:06:21.560473Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.316924Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.316631Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.366687Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.366234Z"
}
},
"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": "2024-07-01T15:06:21.563215Z",
- "iopub.status.busy": "2024-07-01T15:06:21.562809Z",
- "iopub.status.idle": "2024-07-01T15:06:21.568340Z",
- "shell.execute_reply": "2024-07-01T15:06:21.567916Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.368916Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.368532Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.374010Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.373493Z"
}
},
"outputs": [],
@@ -3593,10 +3593,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.570390Z",
- "iopub.status.busy": "2024-07-01T15:06:21.570048Z",
- "iopub.status.idle": "2024-07-01T15:06:21.580186Z",
- "shell.execute_reply": "2024-07-01T15:06:21.579714Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.375995Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.375691Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.386423Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.385887Z"
}
},
"outputs": [
@@ -3632,10 +3632,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.582185Z",
- "iopub.status.busy": "2024-07-01T15:06:21.581851Z",
- "iopub.status.idle": "2024-07-01T15:06:21.792455Z",
- "shell.execute_reply": "2024-07-01T15:06:21.791912Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.388574Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.388272Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.563243Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.562691Z"
}
},
"outputs": [
@@ -3687,10 +3687,10 @@
"execution_count": 32,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.794636Z",
- "iopub.status.busy": "2024-07-01T15:06:21.794453Z",
- "iopub.status.idle": "2024-07-01T15:06:21.802233Z",
- "shell.execute_reply": "2024-07-01T15:06:21.801679Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.565412Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.565240Z",
+ "iopub.status.idle": "2024-07-02T12:05:02.572732Z",
+ "shell.execute_reply": "2024-07-02T12:05:02.572280Z"
},
"nbsphinx": "hidden"
},
@@ -3720,22 +3720,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Find Spurious Correlation between Vision Dataset features and class labels\n",
+ "## Identify Spurious Correlations in Image Datasets\n",
"\n",
- "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n",
+ "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n",
+ "These correlations could lead to unreliable model predictions and poor generalization.\n",
"\n",
- "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models."
+ "\n",
+ "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n",
+ "\n",
+ "- Darkness\n",
+ "- Blurriness\n",
+ "- Aspect ratio anomalies\n",
+ "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n",
+ "\n",
+ "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 1. Load the dataset\n",
+ "### 1. Load the Dataset\n",
+ "\n",
+ "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n",
"\n",
- "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n",
+ "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n",
+ "- The correlation scores range from 0 to 1, where:\n",
+ " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n",
+ " - Scores close to 1 suggest little to no correlation between the property and class labels.\n",
+ "- By introducing this bias, we expect to see:\n",
+ " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n",
+ " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n",
"\n",
- "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height."
+ "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels."
]
},
{
@@ -3743,10 +3760,10 @@
"execution_count": 33,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:21.804166Z",
- "iopub.status.busy": "2024-07-01T15:06:21.803993Z",
- "iopub.status.idle": "2024-07-01T15:06:27.357122Z",
- "shell.execute_reply": "2024-07-01T15:06:27.356570Z"
+ "iopub.execute_input": "2024-07-02T12:05:02.574589Z",
+ "iopub.status.busy": "2024-07-02T12:05:02.574422Z",
+ "iopub.status.idle": "2024-07-02T12:05:08.693945Z",
+ "shell.execute_reply": "2024-07-02T12:05:08.693406Z"
}
},
"outputs": [
@@ -3770,7 +3787,39 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 917504/170498071 [00:00<00:20, 8269545.10it/s]"
+ " 1%| | 917504/170498071 [00:00<00:20, 8347158.96it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 6%|▌ | 9601024/170498071 [00:00<00:03, 52614403.72it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 11%|█ | 18481152/170498071 [00:00<00:02, 68746962.66it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 15%|█▍ | 25493504/170498071 [00:00<00:02, 68028252.66it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 19%|█▉ | 32571392/170498071 [00:00<00:02, 68946396.69it/s]"
]
},
{
@@ -3778,7 +3827,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▌ | 10387456/170498071 [00:00<00:02, 56842742.05it/s]"
+ " 23%|██▎ | 39845888/170498071 [00:00<00:01, 70065798.28it/s]"
]
},
{
@@ -3786,7 +3835,7 @@
"output_type": "stream",
"text": [
"\r",
- " 13%|█▎ | 21495808/170498071 [00:00<00:01, 80950295.23it/s]"
+ " 28%|██▊ | 46891008/170498071 [00:00<00:01, 68706053.96it/s]"
]
},
{
@@ -3794,7 +3843,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▉ | 32440320/170498071 [00:00<00:01, 91992107.79it/s]"
+ " 32%|███▏ | 54394880/170498071 [00:00<00:01, 70657768.03it/s]"
]
},
{
@@ -3802,7 +3851,7 @@
"output_type": "stream",
"text": [
"\r",
- " 26%|██▌ | 43483136/170498071 [00:00<00:01, 98507606.82it/s]"
+ " 36%|███▌ | 61505536/170498071 [00:00<00:01, 69454102.48it/s]"
]
},
{
@@ -3810,7 +3859,7 @@
"output_type": "stream",
"text": [
"\r",
- " 32%|███▏ | 54427648/170498071 [00:00<00:01, 102169111.94it/s]"
+ " 41%|████ | 69074944/170498071 [00:01<00:01, 71043124.94it/s]"
]
},
{
@@ -3818,7 +3867,7 @@
"output_type": "stream",
"text": [
"\r",
- " 38%|███▊ | 64716800/170498071 [00:00<00:01, 102261059.68it/s]"
+ " 45%|████▍ | 76218368/170498071 [00:01<00:01, 69909000.72it/s]"
]
},
{
@@ -3826,7 +3875,7 @@
"output_type": "stream",
"text": [
"\r",
- " 44%|████▍ | 75759616/170498071 [00:00<00:00, 104794429.63it/s]"
+ " 49%|████▉ | 83230720/170498071 [00:01<00:01, 69743647.72it/s]"
]
},
{
@@ -3834,7 +3883,7 @@
"output_type": "stream",
"text": [
"\r",
- " 51%|█████ | 86573056/170498071 [00:00<00:00, 105786259.21it/s]"
+ " 55%|█████▍ | 92930048/170498071 [00:01<00:00, 77765718.10it/s]"
]
},
{
@@ -3842,7 +3891,7 @@
"output_type": "stream",
"text": [
"\r",
- " 57%|█████▋ | 97779712/170498071 [00:01<00:00, 107658431.70it/s]"
+ " 59%|█████▉ | 100794368/170498071 [00:01<00:00, 77921748.40it/s]"
]
},
{
@@ -3850,7 +3899,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▎ | 108560384/170498071 [00:01<00:00, 107631887.95it/s]"
+ " 64%|██████▎ | 108625920/170498071 [00:01<00:00, 74716954.24it/s]"
]
},
{
@@ -3858,7 +3907,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|███████ | 119635968/170498071 [00:01<00:00, 108510039.53it/s]"
+ " 68%|██████▊ | 116162560/170498071 [00:01<00:00, 72297265.09it/s]"
]
},
{
@@ -3866,7 +3915,7 @@
"output_type": "stream",
"text": [
"\r",
- " 77%|███████▋ | 130514944/170498071 [00:01<00:00, 108504773.07it/s]"
+ " 72%|███████▏ | 123437056/170498071 [00:01<00:00, 71923065.09it/s]"
]
},
{
@@ -3874,7 +3923,7 @@
"output_type": "stream",
"text": [
"\r",
- " 83%|████████▎ | 141557760/170498071 [00:01<00:00, 109047203.13it/s]"
+ " 77%|███████▋ | 130678784/170498071 [00:01<00:00, 70005310.34it/s]"
]
},
{
@@ -3882,7 +3931,7 @@
"output_type": "stream",
"text": [
"\r",
- " 89%|████████▉ | 152567808/170498071 [00:01<00:00, 109231770.68it/s]"
+ " 81%|████████ | 138280960/170498071 [00:01<00:00, 71572260.33it/s]"
]
},
{
@@ -3890,7 +3939,7 @@
"output_type": "stream",
"text": [
"\r",
- " 96%|█████████▌| 163512320/170498071 [00:01<00:00, 109164928.11it/s]"
+ " 85%|████████▌ | 145489920/170498071 [00:02<00:00, 69364483.36it/s]"
]
},
{
@@ -3898,7 +3947,31 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]"
+ " 90%|████████▉ | 152961024/170498071 [00:02<00:00, 70793844.72it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 94%|█████████▍| 160071680/170498071 [00:02<00:00, 69055168.81it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 98%|█████████▊| 167608320/170498071 [00:02<00:00, 70630354.07it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]"
]
},
{
@@ -3964,7 +4037,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find vision-related issues"
+ "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find image-related issues"
]
},
{
@@ -3972,10 +4045,10 @@
"execution_count": 34,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:27.360097Z",
- "iopub.status.busy": "2024-07-01T15:06:27.359363Z",
- "iopub.status.idle": "2024-07-01T15:06:27.426972Z",
- "shell.execute_reply": "2024-07-01T15:06:27.426512Z"
+ "iopub.execute_input": "2024-07-02T12:05:08.696598Z",
+ "iopub.status.busy": "2024-07-02T12:05:08.696059Z",
+ "iopub.status.idle": "2024-07-02T12:05:08.763426Z",
+ "shell.execute_reply": "2024-07-02T12:05:08.762929Z"
}
},
"outputs": [],
@@ -3997,10 +4070,10 @@
"execution_count": 35,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:27.429234Z",
- "iopub.status.busy": "2024-07-01T15:06:27.429051Z",
- "iopub.status.idle": "2024-07-01T15:06:27.469917Z",
- "shell.execute_reply": "2024-07-01T15:06:27.469464Z"
+ "iopub.execute_input": "2024-07-02T12:05:08.765613Z",
+ "iopub.status.busy": "2024-07-02T12:05:08.765285Z",
+ "iopub.status.idle": "2024-07-02T12:05:08.805806Z",
+ "shell.execute_reply": "2024-07-02T12:05:08.805281Z"
}
},
"outputs": [],
@@ -4034,10 +4107,10 @@
"execution_count": 36,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:27.472017Z",
- "iopub.status.busy": "2024-07-01T15:06:27.471718Z",
- "iopub.status.idle": "2024-07-01T15:06:28.881795Z",
- "shell.execute_reply": "2024-07-01T15:06:28.881232Z"
+ "iopub.execute_input": "2024-07-02T12:05:08.807933Z",
+ "iopub.status.busy": "2024-07-02T12:05:08.807600Z",
+ "iopub.status.idle": "2024-07-02T12:05:10.199005Z",
+ "shell.execute_reply": "2024-07-02T12:05:10.198447Z"
}
},
"outputs": [
@@ -4111,10 +4184,10 @@
"execution_count": 37,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:28.884131Z",
- "iopub.status.busy": "2024-07-01T15:06:28.883805Z",
- "iopub.status.idle": "2024-07-01T15:06:29.648363Z",
- "shell.execute_reply": "2024-07-01T15:06:29.647874Z"
+ "iopub.execute_input": "2024-07-02T12:05:10.201199Z",
+ "iopub.status.busy": "2024-07-02T12:05:10.200858Z",
+ "iopub.status.idle": "2024-07-02T12:05:10.987916Z",
+ "shell.execute_reply": "2024-07-02T12:05:10.987295Z"
}
},
"outputs": [
@@ -4129,7 +4202,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "1fa7dd59335f4faa818bcf4e966c7c70",
+ "model_id": "ab730d681373436cbffc495350a9abe1",
"version_major": 2,
"version_minor": 0
},
@@ -4153,7 +4226,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "bc30a28b7d6044ff9e4159420fbd7a37",
+ "model_id": "e44decacc70f4d08b59475e297136aab",
"version_major": 2,
"version_minor": 0
},
@@ -4176,7 +4249,7 @@
{
"data": {
"text/markdown": [
- "### Vision-specific property scores in the original dataset"
+ "### Image-specific property scores in the original dataset"
],
"text/plain": [
""
@@ -4267,7 +4340,7 @@
{
"data": {
"text/markdown": [
- "### Vision-specific property scores in the transformed dataset"
+ "### Image-specific property scores in the transformed dataset"
],
"text/plain": [
""
@@ -4372,9 +4445,9 @@
"transformed_property_scores = get_property_scores(transformed_dataset)\n",
"\n",
"# Displaying the scores dataframe\n",
- "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n",
+ "display(Markdown(\"### Image-specific property scores in the original dataset\"))\n",
"display(standard_property_scores)\n",
- "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n",
+ "display(Markdown(\"### Image-specific property scores in the transformed dataset\"))\n",
"display(transformed_property_scores)\n",
"\n",
"# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n",
@@ -4403,7 +4476,30 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0e8630e122434af98c6e33cb01b60aa5": {
+ "06e95a0f1df9408095248eef0924c604": {
+ "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_5fccbfa0a7a94b55a6825fc52ecdeee3",
+ "placeholder": "",
+ "style": "IPY_MODEL_9d67c6a8b80b4718975da970d5ba6be1",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 200/200 [00:00<00:00, 725.51it/s]"
+ }
+ },
+ "1245fefd15c748ca9a6c437e90990634": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4456,7 +4552,7 @@
"width": null
}
},
- "11db28fa3e49460cb17bc5b1cbe02474": {
+ "22612fb7095f4323876a32fa6832ebee": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4509,31 +4605,23 @@
"width": null
}
},
- "1fa7dd59335f4faa818bcf4e966c7c70": {
+ "2ce33b586399430db7231ec582a8ad1c": {
"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_b80a9a6f3cc8460ba1eacd1eebdd1695",
- "IPY_MODEL_bd44e36d29ce400580eb9c39706e9764",
- "IPY_MODEL_6681d426ed2e406a80e7d1e331ba04a8"
- ],
- "layout": "IPY_MODEL_11db28fa3e49460cb17bc5b1cbe02474",
- "tabbable": null,
- "tooltip": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "277e9c9f4f2942e8ba4a67175d4da165": {
+ "302d670260304f5d973a1863227c2b38": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4586,7 +4674,69 @@
"width": null
}
},
- "2b75c32350ca4a21bcc6c23534605260": {
+ "34fad403248e49fb9d7ed5541db4875e": {
+ "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": ""
+ }
+ },
+ "37657cc47549425e81123fbc00061dcd": {
+ "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_57d53163a3e24cfb8adf32a3c2859334",
+ "placeholder": "",
+ "style": "IPY_MODEL_d6c64d036d3c464bba338c11b7d7e118",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
+ }
+ },
+ "3f75258f70194866856b4da554e4dbeb": {
+ "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_1245fefd15c748ca9a6c437e90990634",
+ "placeholder": "",
+ "style": "IPY_MODEL_4c9fcf59ee52451aad0a525849ecf86b",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "100%"
+ }
+ },
+ "440b53038a3d4c4c964a83e8b710361f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4639,7 +4789,7 @@
"width": null
}
},
- "5c30ade94053466c8c64d7b0abffbc55": {
+ "4c9fcf59ee52451aad0a525849ecf86b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -4657,7 +4807,7 @@
"text_color": null
}
},
- "5daa3a2aa6bb4c9f84ea2674afd64ead": {
+ "57d53163a3e24cfb8adf32a3c2859334": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4710,138 +4860,7 @@
"width": null
}
},
- "5fe8b6850a5246f4896ff6995970d3c4": {
- "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_277e9c9f4f2942e8ba4a67175d4da165",
- "max": 200.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_a1e2e6018ec643d38a6a7f43c30c3d0b",
- "tabbable": null,
- "tooltip": null,
- "value": 200.0
- }
- },
- "6681d426ed2e406a80e7d1e331ba04a8": {
- "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_981dd7e875cd448284efead3ec98c9ab",
- "placeholder": "",
- "style": "IPY_MODEL_5c30ade94053466c8c64d7b0abffbc55",
- "tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 784.17it/s]"
- }
- },
- "6cc234a88c8e4669af66844757fac7f7": {
- "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_2b75c32350ca4a21bcc6c23534605260",
- "placeholder": "",
- "style": "IPY_MODEL_7055ef739a1f4669a5783ae634c9e715",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "7055ef739a1f4669a5783ae634c9e715": {
- "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
- }
- },
- "7d1e93d6b2384e198a1aab9f26b5c1fa": {
- "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
- }
- },
- "7df90a685b4747759b5a5df590246cc1": {
- "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_0e8630e122434af98c6e33cb01b60aa5",
- "placeholder": "",
- "style": "IPY_MODEL_f812040db2d642d08a03d0f598afe058",
- "tabbable": null,
- "tooltip": null,
- "value": " 200/200 [00:00<00:00, 847.08it/s]"
- }
- },
- "981dd7e875cd448284efead3ec98c9ab": {
+ "5fccbfa0a7a94b55a6825fc52ecdeee3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4894,23 +4913,7 @@
"width": null
}
},
- "a1e2e6018ec643d38a6a7f43c30c3d0b": {
- "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": ""
- }
- },
- "b80a9a6f3cc8460ba1eacd1eebdd1695": {
+ "6bdd7248294f4094a2da7c7af2e67e50": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -4925,15 +4928,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ef85a24fdc2e4cf783b12b95dc654a54",
+ "layout": "IPY_MODEL_d6941ea7ad6a41efb80f48dde9923682",
"placeholder": "",
- "style": "IPY_MODEL_7d1e93d6b2384e198a1aab9f26b5c1fa",
+ "style": "IPY_MODEL_a55c5a0d7aca4c16a982994a5595ca08",
"tabbable": null,
"tooltip": null,
- "value": "100%"
+ "value": " 200/200 [00:00<00:00, 811.85it/s]"
}
},
- "ba235d6a06cb4c11a73df384a365fbe8": {
+ "797a5104afa24ca5b172ddc308a704ec": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -4986,7 +4989,43 @@
"width": null
}
},
- "bc30a28b7d6044ff9e4159420fbd7a37": {
+ "9d67c6a8b80b4718975da970d5ba6be1": {
+ "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
+ }
+ },
+ "a55c5a0d7aca4c16a982994a5595ca08": {
+ "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
+ }
+ },
+ "ab730d681373436cbffc495350a9abe1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -5001,16 +5040,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_6cc234a88c8e4669af66844757fac7f7",
- "IPY_MODEL_5fe8b6850a5246f4896ff6995970d3c4",
- "IPY_MODEL_7df90a685b4747759b5a5df590246cc1"
+ "IPY_MODEL_37657cc47549425e81123fbc00061dcd",
+ "IPY_MODEL_ccd3930d3b25423fb8d520dc87205752",
+ "IPY_MODEL_6bdd7248294f4094a2da7c7af2e67e50"
],
- "layout": "IPY_MODEL_ba235d6a06cb4c11a73df384a365fbe8",
+ "layout": "IPY_MODEL_440b53038a3d4c4c964a83e8b710361f",
"tabbable": null,
"tooltip": null
}
},
- "bd44e36d29ce400580eb9c39706e9764": {
+ "ccd3930d3b25423fb8d520dc87205752": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -5026,17 +5065,17 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_5daa3a2aa6bb4c9f84ea2674afd64ead",
+ "layout": "IPY_MODEL_302d670260304f5d973a1863227c2b38",
"max": 200.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_fc145740c00f4075a0ddec1338af6a17",
+ "style": "IPY_MODEL_2ce33b586399430db7231ec582a8ad1c",
"tabbable": null,
"tooltip": null,
"value": 200.0
}
},
- "ef85a24fdc2e4cf783b12b95dc654a54": {
+ "d6941ea7ad6a41efb80f48dde9923682": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -5089,7 +5128,7 @@
"width": null
}
},
- "f812040db2d642d08a03d0f598afe058": {
+ "d6c64d036d3c464bba338c11b7d7e118": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -5107,20 +5146,54 @@
"text_color": null
}
},
- "fc145740c00f4075a0ddec1338af6a17": {
+ "e44decacc70f4d08b59475e297136aab": {
"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_3f75258f70194866856b4da554e4dbeb",
+ "IPY_MODEL_e621caf6c19d4d638ba32cd7caed9a15",
+ "IPY_MODEL_06e95a0f1df9408095248eef0924c604"
+ ],
+ "layout": "IPY_MODEL_22612fb7095f4323876a32fa6832ebee",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "e621caf6c19d4d638ba32cd7caed9a15": {
+ "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_797a5104afa24ca5b172ddc308a704ec",
+ "max": 200.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_34fad403248e49fb9d7ed5541db4875e",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 200.0
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 2c0bb8a06..05afc2f2e 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": "2024-07-01T15:06:34.327040Z",
- "iopub.status.busy": "2024-07-01T15:06:34.326556Z",
- "iopub.status.idle": "2024-07-01T15:06:35.430619Z",
- "shell.execute_reply": "2024-07-01T15:06:35.430106Z"
+ "iopub.execute_input": "2024-07-02T12:05:14.883207Z",
+ "iopub.status.busy": "2024-07-02T12:05:14.882732Z",
+ "iopub.status.idle": "2024-07-02T12:05:15.976658Z",
+ "shell.execute_reply": "2024-07-02T12:05:15.976156Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:06:35.433295Z",
- "iopub.status.busy": "2024-07-01T15:06:35.432846Z",
- "iopub.status.idle": "2024-07-01T15:06:35.435562Z",
- "shell.execute_reply": "2024-07-01T15:06:35.435139Z"
+ "iopub.execute_input": "2024-07-02T12:05:15.979210Z",
+ "iopub.status.busy": "2024-07-02T12:05:15.978822Z",
+ "iopub.status.idle": "2024-07-02T12:05:15.981689Z",
+ "shell.execute_reply": "2024-07-02T12:05:15.981162Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:35.437647Z",
- "iopub.status.busy": "2024-07-01T15:06:35.437334Z",
- "iopub.status.idle": "2024-07-01T15:06:35.448830Z",
- "shell.execute_reply": "2024-07-01T15:06:35.448366Z"
+ "iopub.execute_input": "2024-07-02T12:05:15.983805Z",
+ "iopub.status.busy": "2024-07-02T12:05:15.983602Z",
+ "iopub.status.idle": "2024-07-02T12:05:15.994757Z",
+ "shell.execute_reply": "2024-07-02T12:05:15.994295Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:35.450965Z",
- "iopub.status.busy": "2024-07-01T15:06:35.450626Z",
- "iopub.status.idle": "2024-07-01T15:06:39.722894Z",
- "shell.execute_reply": "2024-07-01T15:06:39.722312Z"
+ "iopub.execute_input": "2024-07-02T12:05:15.996851Z",
+ "iopub.status.busy": "2024-07-02T12:05:15.996526Z",
+ "iopub.status.idle": "2024-07-02T12:05:19.883673Z",
+ "shell.execute_reply": "2024-07-02T12:05:19.883072Z"
},
"id": "dhTHOg8Pyv5G"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index 86b92fc2a..964629f99 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": "2024-07-01T15:06:41.824444Z",
- "iopub.status.busy": "2024-07-01T15:06:41.824251Z",
- "iopub.status.idle": "2024-07-01T15:06:42.924912Z",
- "shell.execute_reply": "2024-07-01T15:06:42.924299Z"
+ "iopub.execute_input": "2024-07-02T12:05:21.944164Z",
+ "iopub.status.busy": "2024-07-02T12:05:21.943684Z",
+ "iopub.status.idle": "2024-07-02T12:05:23.029911Z",
+ "shell.execute_reply": "2024-07-02T12:05:23.029367Z"
},
"nbsphinx": "hidden"
},
@@ -137,10 +137,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:42.927716Z",
- "iopub.status.busy": "2024-07-01T15:06:42.927441Z",
- "iopub.status.idle": "2024-07-01T15:06:42.930770Z",
- "shell.execute_reply": "2024-07-01T15:06:42.930234Z"
+ "iopub.execute_input": "2024-07-02T12:05:23.032775Z",
+ "iopub.status.busy": "2024-07-02T12:05:23.032157Z",
+ "iopub.status.idle": "2024-07-02T12:05:23.035645Z",
+ "shell.execute_reply": "2024-07-02T12:05:23.035092Z"
}
},
"outputs": [],
@@ -176,10 +176,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:42.932829Z",
- "iopub.status.busy": "2024-07-01T15:06:42.932448Z",
- "iopub.status.idle": "2024-07-01T15:06:46.102573Z",
- "shell.execute_reply": "2024-07-01T15:06:46.101934Z"
+ "iopub.execute_input": "2024-07-02T12:05:23.037598Z",
+ "iopub.status.busy": "2024-07-02T12:05:23.037330Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.140141Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.139387Z"
}
},
"outputs": [],
@@ -202,10 +202,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.105551Z",
- "iopub.status.busy": "2024-07-01T15:06:46.104970Z",
- "iopub.status.idle": "2024-07-01T15:06:46.139232Z",
- "shell.execute_reply": "2024-07-01T15:06:46.138546Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.143157Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.142519Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.174588Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.174022Z"
}
},
"outputs": [],
@@ -228,10 +228,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.141703Z",
- "iopub.status.busy": "2024-07-01T15:06:46.141464Z",
- "iopub.status.idle": "2024-07-01T15:06:46.166417Z",
- "shell.execute_reply": "2024-07-01T15:06:46.165807Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.177140Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.176847Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.205277Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.204606Z"
}
},
"outputs": [],
@@ -253,10 +253,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.168919Z",
- "iopub.status.busy": "2024-07-01T15:06:46.168681Z",
- "iopub.status.idle": "2024-07-01T15:06:46.171591Z",
- "shell.execute_reply": "2024-07-01T15:06:46.171158Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.208173Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.207802Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.210662Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.210230Z"
}
},
"outputs": [],
@@ -278,10 +278,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.173661Z",
- "iopub.status.busy": "2024-07-01T15:06:46.173228Z",
- "iopub.status.idle": "2024-07-01T15:06:46.175926Z",
- "shell.execute_reply": "2024-07-01T15:06:46.175394Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.212655Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.212352Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.214801Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.214383Z"
}
},
"outputs": [],
@@ -363,10 +363,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.178276Z",
- "iopub.status.busy": "2024-07-01T15:06:46.177828Z",
- "iopub.status.idle": "2024-07-01T15:06:46.201962Z",
- "shell.execute_reply": "2024-07-01T15:06:46.201387Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.216825Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.216567Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.239503Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.238989Z"
}
},
"outputs": [
@@ -380,7 +380,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4fd339b3d01f445392d6c990fdab5a89",
+ "model_id": "b3fbed235b41419c8dcc7c6dc31f69a4",
"version_major": 2,
"version_minor": 0
},
@@ -394,7 +394,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0e05780aa0694da2b04037e49f9ac6f9",
+ "model_id": "55f5d02e58414e189c4d35720f6593e4",
"version_major": 2,
"version_minor": 0
},
@@ -452,10 +452,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.208658Z",
- "iopub.status.busy": "2024-07-01T15:06:46.208178Z",
- "iopub.status.idle": "2024-07-01T15:06:46.214827Z",
- "shell.execute_reply": "2024-07-01T15:06:46.214300Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.245285Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.244763Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.251470Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.251055Z"
},
"nbsphinx": "hidden"
},
@@ -486,10 +486,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.216919Z",
- "iopub.status.busy": "2024-07-01T15:06:46.216531Z",
- "iopub.status.idle": "2024-07-01T15:06:46.220056Z",
- "shell.execute_reply": "2024-07-01T15:06:46.219618Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.253486Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.253192Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.256606Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.256082Z"
},
"nbsphinx": "hidden"
},
@@ -512,10 +512,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.221958Z",
- "iopub.status.busy": "2024-07-01T15:06:46.221652Z",
- "iopub.status.idle": "2024-07-01T15:06:46.228007Z",
- "shell.execute_reply": "2024-07-01T15:06:46.227489Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.258538Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.258279Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.264446Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.264008Z"
}
},
"outputs": [],
@@ -565,10 +565,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.229956Z",
- "iopub.status.busy": "2024-07-01T15:06:46.229562Z",
- "iopub.status.idle": "2024-07-01T15:06:46.262926Z",
- "shell.execute_reply": "2024-07-01T15:06:46.262323Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.266379Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.266007Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.301431Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.300735Z"
}
},
"outputs": [],
@@ -585,10 +585,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.265438Z",
- "iopub.status.busy": "2024-07-01T15:06:46.265079Z",
- "iopub.status.idle": "2024-07-01T15:06:46.292805Z",
- "shell.execute_reply": "2024-07-01T15:06:46.292125Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.304129Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.303754Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.336384Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.335710Z"
},
"nbsphinx": "hidden"
},
@@ -667,10 +667,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.295480Z",
- "iopub.status.busy": "2024-07-01T15:06:46.295249Z",
- "iopub.status.idle": "2024-07-01T15:06:46.413036Z",
- "shell.execute_reply": "2024-07-01T15:06:46.412421Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.339079Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.338735Z",
+ "iopub.status.idle": "2024-07-02T12:05:26.455537Z",
+ "shell.execute_reply": "2024-07-02T12:05:26.454922Z"
}
},
"outputs": [
@@ -737,10 +737,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:46.415958Z",
- "iopub.status.busy": "2024-07-01T15:06:46.415232Z",
- "iopub.status.idle": "2024-07-01T15:06:49.386585Z",
- "shell.execute_reply": "2024-07-01T15:06:49.386024Z"
+ "iopub.execute_input": "2024-07-02T12:05:26.458378Z",
+ "iopub.status.busy": "2024-07-02T12:05:26.457687Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.464168Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.463628Z"
}
},
"outputs": [
@@ -826,10 +826,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:49.389098Z",
- "iopub.status.busy": "2024-07-01T15:06:49.388711Z",
- "iopub.status.idle": "2024-07-01T15:06:49.445289Z",
- "shell.execute_reply": "2024-07-01T15:06:49.444750Z"
+ "iopub.execute_input": "2024-07-02T12:05:29.466470Z",
+ "iopub.status.busy": "2024-07-02T12:05:29.466106Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.522164Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.521722Z"
}
},
"outputs": [
@@ -1285,10 +1285,10 @@
"id": "af3052ac",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:49.447480Z",
- "iopub.status.busy": "2024-07-01T15:06:49.447130Z",
- "iopub.status.idle": "2024-07-01T15:06:49.487292Z",
- "shell.execute_reply": "2024-07-01T15:06:49.486835Z"
+ "iopub.execute_input": "2024-07-02T12:05:29.524149Z",
+ "iopub.status.busy": "2024-07-02T12:05:29.523825Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.563088Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.562637Z"
}
},
"outputs": [
@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "6cb95977",
+ "id": "c8a16553",
"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": "7616eae0",
+ "id": "fae60230",
"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": "c8e20eef",
+ "id": "9569bf2b",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by Datalab?\n",
@@ -1349,13 +1349,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "5c7c2dee",
+ "id": "570b1222",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:49.489589Z",
- "iopub.status.busy": "2024-07-01T15:06:49.489256Z",
- "iopub.status.idle": "2024-07-01T15:06:49.496732Z",
- "shell.execute_reply": "2024-07-01T15:06:49.496311Z"
+ "iopub.execute_input": "2024-07-02T12:05:29.565181Z",
+ "iopub.status.busy": "2024-07-02T12:05:29.564854Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.572447Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.571983Z"
}
},
"outputs": [],
@@ -1457,7 +1457,7 @@
},
{
"cell_type": "markdown",
- "id": "02fbab1c",
+ "id": "a87b6fe0",
"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": "80ce97c2",
+ "id": "26953078",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:49.498789Z",
- "iopub.status.busy": "2024-07-01T15:06:49.498386Z",
- "iopub.status.idle": "2024-07-01T15:06:49.516684Z",
- "shell.execute_reply": "2024-07-01T15:06:49.516122Z"
+ "iopub.execute_input": "2024-07-02T12:05:29.574436Z",
+ "iopub.status.busy": "2024-07-02T12:05:29.574108Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.592051Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.591598Z"
}
},
"outputs": [
@@ -1521,13 +1521,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "f382c568",
+ "id": "948c6a32",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:49.518671Z",
- "iopub.status.busy": "2024-07-01T15:06:49.518371Z",
- "iopub.status.idle": "2024-07-01T15:06:49.521416Z",
- "shell.execute_reply": "2024-07-01T15:06:49.520910Z"
+ "iopub.execute_input": "2024-07-02T12:05:29.594121Z",
+ "iopub.status.busy": "2024-07-02T12:05:29.593804Z",
+ "iopub.status.idle": "2024-07-02T12:05:29.596796Z",
+ "shell.execute_reply": "2024-07-02T12:05:29.596261Z"
}
},
"outputs": [
@@ -1622,7 +1622,25 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0111e2bffb134a42896020ea74d7e0c2": {
+ "1e8b9b429c6a4df5b632d8335fdb02e7": {
+ "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
+ }
+ },
+ "31b4169790de40918177589ab5b35e53": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1675,33 +1693,7 @@
"width": null
}
},
- "08c89a132fac493d9d7986ef465e2458": {
- "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_852d38b7e7464bfb919e2fb9d8164e6e",
- "max": 50.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_091b3aa8f4304d839459836b00997219",
- "tabbable": null,
- "tooltip": null,
- "value": 50.0
- }
- },
- "091b3aa8f4304d839459836b00997219": {
+ "3e0c64c5666d42f5a0006507f8bef3cf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -1717,84 +1709,23 @@
"description_width": ""
}
},
- "0e05780aa0694da2b04037e49f9ac6f9": {
+ "42ef207d69534acdbaf463021cfc93cf": {
"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",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_ac61742632624d8e99ae7cb5f710a566",
- "IPY_MODEL_faad99f698554ee5a74327e7ca036115",
- "IPY_MODEL_ddebd597ac7544839e8b864a9d0ee839"
- ],
- "layout": "IPY_MODEL_70ea901211b24a5e83c22f533f32e36f",
- "tabbable": null,
- "tooltip": null
- }
- },
- "1447fe2269d345b3ab6d777d2314a43f": {
- "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": "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": ""
}
},
- "1def5cc260bc404184c09a073ccd4bd2": {
+ "47199e38a1de47d2b40f863611c9c287": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1847,7 +1778,7 @@
"width": null
}
},
- "40e84f876df34c24a0156c5fdf89b318": {
+ "507bd342f43644e28c3e257c443121b3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1865,7 +1796,7 @@
"text_color": null
}
},
- "460a43df0d3b417d8996679fabe135e8": {
+ "546f976ecd3443c7ae6b00cfbd3063d7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1918,7 +1849,7 @@
"width": null
}
},
- "4fd339b3d01f445392d6c990fdab5a89": {
+ "55f5d02e58414e189c4d35720f6593e4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1933,16 +1864,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_be7586025bf846d68f14028779700b35",
- "IPY_MODEL_08c89a132fac493d9d7986ef465e2458",
- "IPY_MODEL_664279e521a34f979477f41a406cb5c1"
+ "IPY_MODEL_f01dfba04fcd45ceb75c23f67e8886c7",
+ "IPY_MODEL_617a9f7f01f040228329a5ec756d97f6",
+ "IPY_MODEL_ed7c570506e6416fb02ab5e72e3ceb03"
],
- "layout": "IPY_MODEL_1447fe2269d345b3ab6d777d2314a43f",
+ "layout": "IPY_MODEL_829e711fb4284e36a06bf2a1d8c1d975",
"tabbable": null,
"tooltip": null
}
},
- "60b07213fb4d4ba79fddd321add6003b": {
+ "60269b97e2604128baf4ce6cdc816ee4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1995,7 +1926,82 @@
"width": null
}
},
- "651b9c830e68446881c37217e971cf46": {
+ "6135cc292d2a4431bf055b0d0936e234": {
+ "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_60269b97e2604128baf4ce6cdc816ee4",
+ "max": 50.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_3e0c64c5666d42f5a0006507f8bef3cf",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 50.0
+ }
+ },
+ "617a9f7f01f040228329a5ec756d97f6": {
+ "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_ba0bb27ac92840eeb82c447bf3772478",
+ "max": 50.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_42ef207d69534acdbaf463021cfc93cf",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 50.0
+ }
+ },
+ "73aeda6759084147870440cb627c1d38": {
+ "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_b60d3a9c115940b998532778042a9156",
+ "placeholder": "",
+ "style": "IPY_MODEL_1e8b9b429c6a4df5b632d8335fdb02e7",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 10000/? [00:00<00:00, 1101879.42it/s]"
+ }
+ },
+ "829e711fb4284e36a06bf2a1d8c1d975": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2048,7 +2054,25 @@
"width": null
}
},
- "664279e521a34f979477f41a406cb5c1": {
+ "894752d3c3ad46abbf5f852be62b9157": {
+ "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
+ }
+ },
+ "94d1e49e872241989a3aaf081a4914f3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2063,33 +2087,39 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_60b07213fb4d4ba79fddd321add6003b",
+ "layout": "IPY_MODEL_546f976ecd3443c7ae6b00cfbd3063d7",
"placeholder": "",
- "style": "IPY_MODEL_40e84f876df34c24a0156c5fdf89b318",
+ "style": "IPY_MODEL_894752d3c3ad46abbf5f852be62b9157",
"tabbable": null,
"tooltip": null,
- "value": " 10000/? [00:00<00:00, 1061876.00it/s]"
+ "value": "number of examples processed for estimating thresholds: "
}
},
- "6887dcd8b8cd4f4d89261e3c68e959a9": {
+ "b3fbed235b41419c8dcc7c6dc31f69a4": {
"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_94d1e49e872241989a3aaf081a4914f3",
+ "IPY_MODEL_6135cc292d2a4431bf055b0d0936e234",
+ "IPY_MODEL_73aeda6759084147870440cb627c1d38"
+ ],
+ "layout": "IPY_MODEL_47199e38a1de47d2b40f863611c9c287",
+ "tabbable": null,
+ "tooltip": null
}
},
- "6957d92bb0cf4c47bfffb9903128dcf5": {
+ "b60c569797aa438d8c67c0007154831b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2107,7 +2137,7 @@
"text_color": null
}
},
- "70ea901211b24a5e83c22f533f32e36f": {
+ "b60d3a9c115940b998532778042a9156": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2160,23 +2190,60 @@
"width": null
}
},
- "762d2f4c294343aaac239f8510aee6d2": {
- "model_module": "@jupyter-widgets/controls",
+ "ba0bb27ac92840eeb82c447bf3772478": {
+ "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
}
},
- "852d38b7e7464bfb919e2fb9d8164e6e": {
+ "d554d48cbc1b4a5caca9da8c04018917": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2229,30 +2296,7 @@
"width": null
}
},
- "ac61742632624d8e99ae7cb5f710a566": {
- "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_651b9c830e68446881c37217e971cf46",
- "placeholder": "",
- "style": "IPY_MODEL_6887dcd8b8cd4f4d89261e3c68e959a9",
- "tabbable": null,
- "tooltip": null,
- "value": "number of examples processed for checking labels: "
- }
- },
- "be7586025bf846d68f14028779700b35": {
+ "ed7c570506e6416fb02ab5e72e3ceb03": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2267,33 +2311,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_0111e2bffb134a42896020ea74d7e0c2",
+ "layout": "IPY_MODEL_31b4169790de40918177589ab5b35e53",
"placeholder": "",
- "style": "IPY_MODEL_dc2d26ad64ec4759a2171fea5ff47da6",
+ "style": "IPY_MODEL_b60c569797aa438d8c67c0007154831b",
"tabbable": null,
"tooltip": null,
- "value": "number of examples processed for estimating thresholds: "
- }
- },
- "dc2d26ad64ec4759a2171fea5ff47da6": {
- "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": " 10000/? [00:00<00:00, 1638080.06it/s]"
}
},
- "ddebd597ac7544839e8b864a9d0ee839": {
+ "f01dfba04fcd45ceb75c23f67e8886c7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -2308,38 +2334,12 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_1def5cc260bc404184c09a073ccd4bd2",
+ "layout": "IPY_MODEL_d554d48cbc1b4a5caca9da8c04018917",
"placeholder": "",
- "style": "IPY_MODEL_6957d92bb0cf4c47bfffb9903128dcf5",
- "tabbable": null,
- "tooltip": null,
- "value": " 10000/? [00:00<00:00, 1591222.73it/s]"
- }
- },
- "faad99f698554ee5a74327e7ca036115": {
- "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_460a43df0d3b417d8996679fabe135e8",
- "max": 50.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_762d2f4c294343aaac239f8510aee6d2",
+ "style": "IPY_MODEL_507bd342f43644e28c3e257c443121b3",
"tabbable": null,
"tooltip": null,
- "value": 50.0
+ "value": "number of examples processed for checking labels: "
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index 4c0344124..31db58268 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": "2024-07-01T15:06:52.670679Z",
- "iopub.status.busy": "2024-07-01T15:06:52.670319Z",
- "iopub.status.idle": "2024-07-01T15:06:53.828911Z",
- "shell.execute_reply": "2024-07-01T15:06:53.828417Z"
+ "iopub.execute_input": "2024-07-02T12:05:32.646814Z",
+ "iopub.status.busy": "2024-07-02T12:05:32.646634Z",
+ "iopub.status.idle": "2024-07-02T12:05:33.799016Z",
+ "shell.execute_reply": "2024-07-02T12:05:33.798421Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:06:53.831480Z",
- "iopub.status.busy": "2024-07-01T15:06:53.831210Z",
- "iopub.status.idle": "2024-07-01T15:06:54.013309Z",
- "shell.execute_reply": "2024-07-01T15:06:54.012774Z"
+ "iopub.execute_input": "2024-07-02T12:05:33.801518Z",
+ "iopub.status.busy": "2024-07-02T12:05:33.801117Z",
+ "iopub.status.idle": "2024-07-02T12:05:33.979293Z",
+ "shell.execute_reply": "2024-07-02T12:05:33.978808Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:54.015638Z",
- "iopub.status.busy": "2024-07-01T15:06:54.015446Z",
- "iopub.status.idle": "2024-07-01T15:06:54.026660Z",
- "shell.execute_reply": "2024-07-01T15:06:54.026227Z"
+ "iopub.execute_input": "2024-07-02T12:05:33.981747Z",
+ "iopub.status.busy": "2024-07-02T12:05:33.981411Z",
+ "iopub.status.idle": "2024-07-02T12:05:33.992581Z",
+ "shell.execute_reply": "2024-07-02T12:05:33.992150Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:54.028614Z",
- "iopub.status.busy": "2024-07-01T15:06:54.028276Z",
- "iopub.status.idle": "2024-07-01T15:06:54.258453Z",
- "shell.execute_reply": "2024-07-01T15:06:54.257871Z"
+ "iopub.execute_input": "2024-07-02T12:05:33.994624Z",
+ "iopub.status.busy": "2024-07-02T12:05:33.994295Z",
+ "iopub.status.idle": "2024-07-02T12:05:34.203292Z",
+ "shell.execute_reply": "2024-07-02T12:05:34.202749Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:54.260736Z",
- "iopub.status.busy": "2024-07-01T15:06:54.260424Z",
- "iopub.status.idle": "2024-07-01T15:06:54.286726Z",
- "shell.execute_reply": "2024-07-01T15:06:54.286174Z"
+ "iopub.execute_input": "2024-07-02T12:05:34.205578Z",
+ "iopub.status.busy": "2024-07-02T12:05:34.205242Z",
+ "iopub.status.idle": "2024-07-02T12:05:34.231392Z",
+ "shell.execute_reply": "2024-07-02T12:05:34.230966Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:54.288988Z",
- "iopub.status.busy": "2024-07-01T15:06:54.288686Z",
- "iopub.status.idle": "2024-07-01T15:06:56.272596Z",
- "shell.execute_reply": "2024-07-01T15:06:56.271981Z"
+ "iopub.execute_input": "2024-07-02T12:05:34.233560Z",
+ "iopub.status.busy": "2024-07-02T12:05:34.233135Z",
+ "iopub.status.idle": "2024-07-02T12:05:36.181908Z",
+ "shell.execute_reply": "2024-07-02T12:05:36.181255Z"
}
},
"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:56.275147Z",
- "iopub.status.busy": "2024-07-01T15:06:56.274650Z",
- "iopub.status.idle": "2024-07-01T15:06:56.292694Z",
- "shell.execute_reply": "2024-07-01T15:06:56.292260Z"
+ "iopub.execute_input": "2024-07-02T12:05:36.184389Z",
+ "iopub.status.busy": "2024-07-02T12:05:36.183843Z",
+ "iopub.status.idle": "2024-07-02T12:05:36.201856Z",
+ "shell.execute_reply": "2024-07-02T12:05:36.201294Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:56.294630Z",
- "iopub.status.busy": "2024-07-01T15:06:56.294450Z",
- "iopub.status.idle": "2024-07-01T15:06:57.712997Z",
- "shell.execute_reply": "2024-07-01T15:06:57.712387Z"
+ "iopub.execute_input": "2024-07-02T12:05:36.204241Z",
+ "iopub.status.busy": "2024-07-02T12:05:36.203963Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.598285Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.597675Z"
},
"id": "AaHC5MRKjruT"
},
@@ -729,10 +729,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:57.715656Z",
- "iopub.status.busy": "2024-07-01T15:06:57.715048Z",
- "iopub.status.idle": "2024-07-01T15:06:57.728518Z",
- "shell.execute_reply": "2024-07-01T15:06:57.728061Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.600758Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.600219Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.613480Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.612921Z"
},
"id": "Wy27rvyhjruU"
},
@@ -781,10 +781,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:57.730397Z",
- "iopub.status.busy": "2024-07-01T15:06:57.730225Z",
- "iopub.status.idle": "2024-07-01T15:06:57.801036Z",
- "shell.execute_reply": "2024-07-01T15:06:57.800476Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.615558Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.615275Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.682573Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.681981Z"
},
"id": "Db8YHnyVjruU"
},
@@ -891,10 +891,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:57.803338Z",
- "iopub.status.busy": "2024-07-01T15:06:57.802990Z",
- "iopub.status.idle": "2024-07-01T15:06:58.016553Z",
- "shell.execute_reply": "2024-07-01T15:06:58.016010Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.685019Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.684694Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.893897Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.893417Z"
},
"id": "iJqAHuS2jruV"
},
@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.018803Z",
- "iopub.status.busy": "2024-07-01T15:06:58.018381Z",
- "iopub.status.idle": "2024-07-01T15:06:58.034965Z",
- "shell.execute_reply": "2024-07-01T15:06:58.034432Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.896031Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.895697Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.912159Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.911619Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.037248Z",
- "iopub.status.busy": "2024-07-01T15:06:58.036814Z",
- "iopub.status.idle": "2024-07-01T15:06:58.046245Z",
- "shell.execute_reply": "2024-07-01T15:06:58.045792Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.914291Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.913990Z",
+ "iopub.status.idle": "2024-07-02T12:05:37.923838Z",
+ "shell.execute_reply": "2024-07-02T12:05:37.923277Z"
},
"id": "0lonvOYvjruV"
},
@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.048367Z",
- "iopub.status.busy": "2024-07-01T15:06:58.048051Z",
- "iopub.status.idle": "2024-07-01T15:06:58.130967Z",
- "shell.execute_reply": "2024-07-01T15:06:58.130377Z"
+ "iopub.execute_input": "2024-07-02T12:05:37.925873Z",
+ "iopub.status.busy": "2024-07-02T12:05:37.925449Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.005405Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.004805Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.133536Z",
- "iopub.status.busy": "2024-07-01T15:06:58.133071Z",
- "iopub.status.idle": "2024-07-01T15:06:58.249759Z",
- "shell.execute_reply": "2024-07-01T15:06:58.249159Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.007885Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.007370Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.126166Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.125636Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1697,10 +1697,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.252274Z",
- "iopub.status.busy": "2024-07-01T15:06:58.251852Z",
- "iopub.status.idle": "2024-07-01T15:06:58.255729Z",
- "shell.execute_reply": "2024-07-01T15:06:58.255184Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.128463Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.128096Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.132029Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.131380Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1738,10 +1738,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.257572Z",
- "iopub.status.busy": "2024-07-01T15:06:58.257399Z",
- "iopub.status.idle": "2024-07-01T15:06:58.261023Z",
- "shell.execute_reply": "2024-07-01T15:06:58.260496Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.134113Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.133792Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.137656Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.137186Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1796,10 +1796,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.263055Z",
- "iopub.status.busy": "2024-07-01T15:06:58.262734Z",
- "iopub.status.idle": "2024-07-01T15:06:58.298627Z",
- "shell.execute_reply": "2024-07-01T15:06:58.298174Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.139628Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.139306Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.175873Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.175335Z"
},
"id": "ZpipUliyjruW"
},
@@ -1850,10 +1850,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.300556Z",
- "iopub.status.busy": "2024-07-01T15:06:58.300384Z",
- "iopub.status.idle": "2024-07-01T15:06:58.341152Z",
- "shell.execute_reply": "2024-07-01T15:06:58.340674Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.177802Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.177621Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.222062Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.221459Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1922,10 +1922,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.343232Z",
- "iopub.status.busy": "2024-07-01T15:06:58.343056Z",
- "iopub.status.idle": "2024-07-01T15:06:58.437535Z",
- "shell.execute_reply": "2024-07-01T15:06:58.436855Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.225715Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.225497Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.315625Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.315082Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1957,10 +1957,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.440127Z",
- "iopub.status.busy": "2024-07-01T15:06:58.439842Z",
- "iopub.status.idle": "2024-07-01T15:06:58.527589Z",
- "shell.execute_reply": "2024-07-01T15:06:58.526960Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.318154Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.317969Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.405501Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.404891Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.529972Z",
- "iopub.status.busy": "2024-07-01T15:06:58.529737Z",
- "iopub.status.idle": "2024-07-01T15:06:58.741167Z",
- "shell.execute_reply": "2024-07-01T15:06:58.740717Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.407826Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.407489Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.614829Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.614370Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.743495Z",
- "iopub.status.busy": "2024-07-01T15:06:58.743153Z",
- "iopub.status.idle": "2024-07-01T15:06:58.920954Z",
- "shell.execute_reply": "2024-07-01T15:06:58.920411Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.617073Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.616735Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.796547Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.796035Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.923453Z",
- "iopub.status.busy": "2024-07-01T15:06:58.923009Z",
- "iopub.status.idle": "2024-07-01T15:06:58.928872Z",
- "shell.execute_reply": "2024-07-01T15:06:58.928426Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.798843Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.798472Z",
+ "iopub.status.idle": "2024-07-02T12:05:38.804480Z",
+ "shell.execute_reply": "2024-07-02T12:05:38.804052Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:58.930892Z",
- "iopub.status.busy": "2024-07-01T15:06:58.930502Z",
- "iopub.status.idle": "2024-07-01T15:06:59.148406Z",
- "shell.execute_reply": "2024-07-01T15:06:59.147826Z"
+ "iopub.execute_input": "2024-07-02T12:05:38.806348Z",
+ "iopub.status.busy": "2024-07-02T12:05:38.806175Z",
+ "iopub.status.idle": "2024-07-02T12:05:39.020330Z",
+ "shell.execute_reply": "2024-07-02T12:05:39.019866Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:06:59.150754Z",
- "iopub.status.busy": "2024-07-01T15:06:59.150391Z",
- "iopub.status.idle": "2024-07-01T15:07:00.213417Z",
- "shell.execute_reply": "2024-07-01T15:07:00.212813Z"
+ "iopub.execute_input": "2024-07-02T12:05:39.022452Z",
+ "iopub.status.busy": "2024-07-02T12:05:39.022256Z",
+ "iopub.status.idle": "2024-07-02T12:05:40.077777Z",
+ "shell.execute_reply": "2024-07-02T12:05:40.077247Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index b426f5b7a..dfb026440 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": "2024-07-01T15:07:03.695403Z",
- "iopub.status.busy": "2024-07-01T15:07:03.695236Z",
- "iopub.status.idle": "2024-07-01T15:07:04.786480Z",
- "shell.execute_reply": "2024-07-01T15:07:04.785971Z"
+ "iopub.execute_input": "2024-07-02T12:05:43.484936Z",
+ "iopub.status.busy": "2024-07-02T12:05:43.484760Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.574684Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.574061Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:04.789244Z",
- "iopub.status.busy": "2024-07-01T15:07:04.788665Z",
- "iopub.status.idle": "2024-07-01T15:07:04.791892Z",
- "shell.execute_reply": "2024-07-01T15:07:04.791444Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.577417Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.576983Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.579868Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.579405Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.793920Z",
- "iopub.status.busy": "2024-07-01T15:07:04.793593Z",
- "iopub.status.idle": "2024-07-01T15:07:04.801265Z",
- "shell.execute_reply": "2024-07-01T15:07:04.800810Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.581906Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.581588Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.588930Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.588511Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.803286Z",
- "iopub.status.busy": "2024-07-01T15:07:04.802900Z",
- "iopub.status.idle": "2024-07-01T15:07:04.850047Z",
- "shell.execute_reply": "2024-07-01T15:07:04.849570Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.591022Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.590587Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.643404Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.642882Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.852247Z",
- "iopub.status.busy": "2024-07-01T15:07:04.852061Z",
- "iopub.status.idle": "2024-07-01T15:07:04.869485Z",
- "shell.execute_reply": "2024-07-01T15:07:04.869018Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.645347Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.645170Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.661922Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.661404Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.871391Z",
- "iopub.status.busy": "2024-07-01T15:07:04.871213Z",
- "iopub.status.idle": "2024-07-01T15:07:04.875222Z",
- "shell.execute_reply": "2024-07-01T15:07:04.874787Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.663786Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.663593Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.667360Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.666837Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.877103Z",
- "iopub.status.busy": "2024-07-01T15:07:04.876935Z",
- "iopub.status.idle": "2024-07-01T15:07:04.890567Z",
- "shell.execute_reply": "2024-07-01T15:07:04.890109Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.669486Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.669101Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.685613Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.685195Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.892340Z",
- "iopub.status.busy": "2024-07-01T15:07:04.892165Z",
- "iopub.status.idle": "2024-07-01T15:07:04.917921Z",
- "shell.execute_reply": "2024-07-01T15:07:04.917510Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.687438Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.687261Z",
+ "iopub.status.idle": "2024-07-02T12:05:44.713068Z",
+ "shell.execute_reply": "2024-07-02T12:05:44.712511Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:04.919909Z",
- "iopub.status.busy": "2024-07-01T15:07:04.919740Z",
- "iopub.status.idle": "2024-07-01T15:07:06.770405Z",
- "shell.execute_reply": "2024-07-01T15:07:06.769771Z"
+ "iopub.execute_input": "2024-07-02T12:05:44.714998Z",
+ "iopub.status.busy": "2024-07-02T12:05:44.714828Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.561058Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.560413Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.773094Z",
- "iopub.status.busy": "2024-07-01T15:07:06.772569Z",
- "iopub.status.idle": "2024-07-01T15:07:06.779189Z",
- "shell.execute_reply": "2024-07-01T15:07:06.778660Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.563695Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.563390Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.570695Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.570276Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.781060Z",
- "iopub.status.busy": "2024-07-01T15:07:06.780797Z",
- "iopub.status.idle": "2024-07-01T15:07:06.793132Z",
- "shell.execute_reply": "2024-07-01T15:07:06.792613Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.572666Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.572452Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.585257Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.584820Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.795311Z",
- "iopub.status.busy": "2024-07-01T15:07:06.794896Z",
- "iopub.status.idle": "2024-07-01T15:07:06.801219Z",
- "shell.execute_reply": "2024-07-01T15:07:06.800801Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.587355Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.586953Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.593328Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.592850Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.803175Z",
- "iopub.status.busy": "2024-07-01T15:07:06.802994Z",
- "iopub.status.idle": "2024-07-01T15:07:06.805670Z",
- "shell.execute_reply": "2024-07-01T15:07:06.805234Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.595350Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.595021Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.597564Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.597149Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.807492Z",
- "iopub.status.busy": "2024-07-01T15:07:06.807328Z",
- "iopub.status.idle": "2024-07-01T15:07:06.810895Z",
- "shell.execute_reply": "2024-07-01T15:07:06.810453Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.599508Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.599184Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.602546Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.602058Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.812899Z",
- "iopub.status.busy": "2024-07-01T15:07:06.812510Z",
- "iopub.status.idle": "2024-07-01T15:07:06.815130Z",
- "shell.execute_reply": "2024-07-01T15:07:06.814702Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.604583Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.604261Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.606854Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.606416Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.817057Z",
- "iopub.status.busy": "2024-07-01T15:07:06.816734Z",
- "iopub.status.idle": "2024-07-01T15:07:06.820893Z",
- "shell.execute_reply": "2024-07-01T15:07:06.820448Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.608809Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.608533Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.612540Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.612106Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.822875Z",
- "iopub.status.busy": "2024-07-01T15:07:06.822704Z",
- "iopub.status.idle": "2024-07-01T15:07:06.851357Z",
- "shell.execute_reply": "2024-07-01T15:07:06.850916Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.614617Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.614295Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.642333Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.641923Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:06.853186Z",
- "iopub.status.busy": "2024-07-01T15:07:06.853017Z",
- "iopub.status.idle": "2024-07-01T15:07:06.857526Z",
- "shell.execute_reply": "2024-07-01T15:07:06.857095Z"
+ "iopub.execute_input": "2024-07-02T12:05:46.644398Z",
+ "iopub.status.busy": "2024-07-02T12:05:46.644076Z",
+ "iopub.status.idle": "2024-07-02T12:05:46.648349Z",
+ "shell.execute_reply": "2024-07-02T12:05:46.647909Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index f9fccade5..02d580b54 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": "2024-07-01T15:07:09.805934Z",
- "iopub.status.busy": "2024-07-01T15:07:09.805760Z",
- "iopub.status.idle": "2024-07-01T15:07:10.951874Z",
- "shell.execute_reply": "2024-07-01T15:07:10.951332Z"
+ "iopub.execute_input": "2024-07-02T12:05:49.390201Z",
+ "iopub.status.busy": "2024-07-02T12:05:49.390029Z",
+ "iopub.status.idle": "2024-07-02T12:05:50.506272Z",
+ "shell.execute_reply": "2024-07-02T12:05:50.505689Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:10.954229Z",
- "iopub.status.busy": "2024-07-01T15:07:10.953974Z",
- "iopub.status.idle": "2024-07-01T15:07:11.145898Z",
- "shell.execute_reply": "2024-07-01T15:07:11.145328Z"
+ "iopub.execute_input": "2024-07-02T12:05:50.508865Z",
+ "iopub.status.busy": "2024-07-02T12:05:50.508468Z",
+ "iopub.status.idle": "2024-07-02T12:05:50.696756Z",
+ "shell.execute_reply": "2024-07-02T12:05:50.696292Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:11.148952Z",
- "iopub.status.busy": "2024-07-01T15:07:11.148446Z",
- "iopub.status.idle": "2024-07-01T15:07:11.162195Z",
- "shell.execute_reply": "2024-07-01T15:07:11.161701Z"
+ "iopub.execute_input": "2024-07-02T12:05:50.698941Z",
+ "iopub.status.busy": "2024-07-02T12:05:50.698699Z",
+ "iopub.status.idle": "2024-07-02T12:05:50.711704Z",
+ "shell.execute_reply": "2024-07-02T12:05:50.711226Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:11.164347Z",
- "iopub.status.busy": "2024-07-01T15:07:11.163932Z",
- "iopub.status.idle": "2024-07-01T15:07:13.785011Z",
- "shell.execute_reply": "2024-07-01T15:07:13.784429Z"
+ "iopub.execute_input": "2024-07-02T12:05:50.713503Z",
+ "iopub.status.busy": "2024-07-02T12:05:50.713332Z",
+ "iopub.status.idle": "2024-07-02T12:05:53.318405Z",
+ "shell.execute_reply": "2024-07-02T12:05:53.317873Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:13.787263Z",
- "iopub.status.busy": "2024-07-01T15:07:13.787077Z",
- "iopub.status.idle": "2024-07-01T15:07:15.129078Z",
- "shell.execute_reply": "2024-07-01T15:07:15.128523Z"
+ "iopub.execute_input": "2024-07-02T12:05:53.320633Z",
+ "iopub.status.busy": "2024-07-02T12:05:53.320318Z",
+ "iopub.status.idle": "2024-07-02T12:05:54.676476Z",
+ "shell.execute_reply": "2024-07-02T12:05:54.675931Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:15.131446Z",
- "iopub.status.busy": "2024-07-01T15:07:15.131256Z",
- "iopub.status.idle": "2024-07-01T15:07:15.135063Z",
- "shell.execute_reply": "2024-07-01T15:07:15.134547Z"
+ "iopub.execute_input": "2024-07-02T12:05:54.678848Z",
+ "iopub.status.busy": "2024-07-02T12:05:54.678408Z",
+ "iopub.status.idle": "2024-07-02T12:05:54.682336Z",
+ "shell.execute_reply": "2024-07-02T12:05:54.681800Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:15.137004Z",
- "iopub.status.busy": "2024-07-01T15:07:15.136825Z",
- "iopub.status.idle": "2024-07-01T15:07:17.134343Z",
- "shell.execute_reply": "2024-07-01T15:07:17.133735Z"
+ "iopub.execute_input": "2024-07-02T12:05:54.684325Z",
+ "iopub.status.busy": "2024-07-02T12:05:54.683937Z",
+ "iopub.status.idle": "2024-07-02T12:05:56.558099Z",
+ "shell.execute_reply": "2024-07-02T12:05:56.557479Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:17.136852Z",
- "iopub.status.busy": "2024-07-01T15:07:17.136398Z",
- "iopub.status.idle": "2024-07-01T15:07:17.144283Z",
- "shell.execute_reply": "2024-07-01T15:07:17.143851Z"
+ "iopub.execute_input": "2024-07-02T12:05:56.560538Z",
+ "iopub.status.busy": "2024-07-02T12:05:56.560208Z",
+ "iopub.status.idle": "2024-07-02T12:05:56.567803Z",
+ "shell.execute_reply": "2024-07-02T12:05:56.567265Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:17.146377Z",
- "iopub.status.busy": "2024-07-01T15:07:17.146026Z",
- "iopub.status.idle": "2024-07-01T15:07:19.687541Z",
- "shell.execute_reply": "2024-07-01T15:07:19.686980Z"
+ "iopub.execute_input": "2024-07-02T12:05:56.569739Z",
+ "iopub.status.busy": "2024-07-02T12:05:56.569446Z",
+ "iopub.status.idle": "2024-07-02T12:05:59.160999Z",
+ "shell.execute_reply": "2024-07-02T12:05:59.160450Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:19.689886Z",
- "iopub.status.busy": "2024-07-01T15:07:19.689435Z",
- "iopub.status.idle": "2024-07-01T15:07:19.692913Z",
- "shell.execute_reply": "2024-07-01T15:07:19.692502Z"
+ "iopub.execute_input": "2024-07-02T12:05:59.163107Z",
+ "iopub.status.busy": "2024-07-02T12:05:59.162773Z",
+ "iopub.status.idle": "2024-07-02T12:05:59.166191Z",
+ "shell.execute_reply": "2024-07-02T12:05:59.165684Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:19.694737Z",
- "iopub.status.busy": "2024-07-01T15:07:19.694568Z",
- "iopub.status.idle": "2024-07-01T15:07:19.698060Z",
- "shell.execute_reply": "2024-07-01T15:07:19.697520Z"
+ "iopub.execute_input": "2024-07-02T12:05:59.168252Z",
+ "iopub.status.busy": "2024-07-02T12:05:59.167849Z",
+ "iopub.status.idle": "2024-07-02T12:05:59.171322Z",
+ "shell.execute_reply": "2024-07-02T12:05:59.170794Z"
}
},
"outputs": [],
@@ -752,10 +752,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:19.700087Z",
- "iopub.status.busy": "2024-07-01T15:07:19.699691Z",
- "iopub.status.idle": "2024-07-01T15:07:19.702785Z",
- "shell.execute_reply": "2024-07-01T15:07:19.702302Z"
+ "iopub.execute_input": "2024-07-02T12:05:59.173235Z",
+ "iopub.status.busy": "2024-07-02T12:05:59.172937Z",
+ "iopub.status.idle": "2024-07-02T12:05:59.176035Z",
+ "shell.execute_reply": "2024-07-02T12:05:59.175500Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 013f8cdd9..7ce8a7f2b 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": "2024-07-01T15:07:22.044165Z",
- "iopub.status.busy": "2024-07-01T15:07:22.043990Z",
- "iopub.status.idle": "2024-07-01T15:07:23.190619Z",
- "shell.execute_reply": "2024-07-01T15:07:23.190110Z"
+ "iopub.execute_input": "2024-07-02T12:06:01.378322Z",
+ "iopub.status.busy": "2024-07-02T12:06:01.377923Z",
+ "iopub.status.idle": "2024-07-02T12:06:02.503419Z",
+ "shell.execute_reply": "2024-07-02T12:06:02.502819Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:23.192992Z",
- "iopub.status.busy": "2024-07-01T15:07:23.192742Z",
- "iopub.status.idle": "2024-07-01T15:07:24.642885Z",
- "shell.execute_reply": "2024-07-01T15:07:24.642213Z"
+ "iopub.execute_input": "2024-07-02T12:06:02.505878Z",
+ "iopub.status.busy": "2024-07-02T12:06:02.505606Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.484637Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.483911Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:24.645453Z",
- "iopub.status.busy": "2024-07-01T15:07:24.645208Z",
- "iopub.status.idle": "2024-07-01T15:07:24.648320Z",
- "shell.execute_reply": "2024-07-01T15:07:24.647888Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.487478Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.486983Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.490372Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.489937Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:24.650220Z",
- "iopub.status.busy": "2024-07-01T15:07:24.650036Z",
- "iopub.status.idle": "2024-07-01T15:07:24.656028Z",
- "shell.execute_reply": "2024-07-01T15:07:24.655606Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.492668Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.492302Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.499701Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.499223Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:24.657894Z",
- "iopub.status.busy": "2024-07-01T15:07:24.657721Z",
- "iopub.status.idle": "2024-07-01T15:07:25.141231Z",
- "shell.execute_reply": "2024-07-01T15:07:25.140651Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.501657Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.501478Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.984496Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.983911Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:25.144290Z",
- "iopub.status.busy": "2024-07-01T15:07:25.143822Z",
- "iopub.status.idle": "2024-07-01T15:07:25.149165Z",
- "shell.execute_reply": "2024-07-01T15:07:25.148739Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.987155Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.986711Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.992050Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.991587Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:25.151192Z",
- "iopub.status.busy": "2024-07-01T15:07:25.150870Z",
- "iopub.status.idle": "2024-07-01T15:07:25.154548Z",
- "shell.execute_reply": "2024-07-01T15:07:25.154108Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.993958Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.993639Z",
+ "iopub.status.idle": "2024-07-02T12:06:03.997330Z",
+ "shell.execute_reply": "2024-07-02T12:06:03.996906Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:25.156514Z",
- "iopub.status.busy": "2024-07-01T15:07:25.156335Z",
- "iopub.status.idle": "2024-07-01T15:07:26.038062Z",
- "shell.execute_reply": "2024-07-01T15:07:26.037425Z"
+ "iopub.execute_input": "2024-07-02T12:06:03.999294Z",
+ "iopub.status.busy": "2024-07-02T12:06:03.998989Z",
+ "iopub.status.idle": "2024-07-02T12:06:04.886721Z",
+ "shell.execute_reply": "2024-07-02T12:06:04.886183Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:26.040299Z",
- "iopub.status.busy": "2024-07-01T15:07:26.040059Z",
- "iopub.status.idle": "2024-07-01T15:07:26.281733Z",
- "shell.execute_reply": "2024-07-01T15:07:26.281238Z"
+ "iopub.execute_input": "2024-07-02T12:06:04.889094Z",
+ "iopub.status.busy": "2024-07-02T12:06:04.888730Z",
+ "iopub.status.idle": "2024-07-02T12:06:05.104977Z",
+ "shell.execute_reply": "2024-07-02T12:06:05.104560Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:26.284005Z",
- "iopub.status.busy": "2024-07-01T15:07:26.283674Z",
- "iopub.status.idle": "2024-07-01T15:07:26.287739Z",
- "shell.execute_reply": "2024-07-01T15:07:26.287303Z"
+ "iopub.execute_input": "2024-07-02T12:06:05.107009Z",
+ "iopub.status.busy": "2024-07-02T12:06:05.106744Z",
+ "iopub.status.idle": "2024-07-02T12:06:05.111011Z",
+ "shell.execute_reply": "2024-07-02T12:06:05.110475Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:26.289717Z",
- "iopub.status.busy": "2024-07-01T15:07:26.289415Z",
- "iopub.status.idle": "2024-07-01T15:07:26.747330Z",
- "shell.execute_reply": "2024-07-01T15:07:26.746844Z"
+ "iopub.execute_input": "2024-07-02T12:06:05.112841Z",
+ "iopub.status.busy": "2024-07-02T12:06:05.112667Z",
+ "iopub.status.idle": "2024-07-02T12:06:05.549544Z",
+ "shell.execute_reply": "2024-07-02T12:06:05.548895Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:26.749504Z",
- "iopub.status.busy": "2024-07-01T15:07:26.749157Z",
- "iopub.status.idle": "2024-07-01T15:07:27.049969Z",
- "shell.execute_reply": "2024-07-01T15:07:27.049390Z"
+ "iopub.execute_input": "2024-07-02T12:06:05.552420Z",
+ "iopub.status.busy": "2024-07-02T12:06:05.552234Z",
+ "iopub.status.idle": "2024-07-02T12:06:05.880895Z",
+ "shell.execute_reply": "2024-07-02T12:06:05.880300Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:27.052016Z",
- "iopub.status.busy": "2024-07-01T15:07:27.051834Z",
- "iopub.status.idle": "2024-07-01T15:07:27.386953Z",
- "shell.execute_reply": "2024-07-01T15:07:27.386354Z"
+ "iopub.execute_input": "2024-07-02T12:06:05.883106Z",
+ "iopub.status.busy": "2024-07-02T12:06:05.882705Z",
+ "iopub.status.idle": "2024-07-02T12:06:06.240971Z",
+ "shell.execute_reply": "2024-07-02T12:06:06.240404Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:27.390094Z",
- "iopub.status.busy": "2024-07-01T15:07:27.389720Z",
- "iopub.status.idle": "2024-07-01T15:07:27.826810Z",
- "shell.execute_reply": "2024-07-01T15:07:27.826201Z"
+ "iopub.execute_input": "2024-07-02T12:06:06.243379Z",
+ "iopub.status.busy": "2024-07-02T12:06:06.243189Z",
+ "iopub.status.idle": "2024-07-02T12:06:06.680772Z",
+ "shell.execute_reply": "2024-07-02T12:06:06.680290Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:27.830888Z",
- "iopub.status.busy": "2024-07-01T15:07:27.830547Z",
- "iopub.status.idle": "2024-07-01T15:07:28.275927Z",
- "shell.execute_reply": "2024-07-01T15:07:28.275306Z"
+ "iopub.execute_input": "2024-07-02T12:06:06.682984Z",
+ "iopub.status.busy": "2024-07-02T12:06:06.682675Z",
+ "iopub.status.idle": "2024-07-02T12:06:07.129389Z",
+ "shell.execute_reply": "2024-07-02T12:06:07.128744Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:28.278580Z",
- "iopub.status.busy": "2024-07-01T15:07:28.278386Z",
- "iopub.status.idle": "2024-07-01T15:07:28.478171Z",
- "shell.execute_reply": "2024-07-01T15:07:28.477537Z"
+ "iopub.execute_input": "2024-07-02T12:06:07.132269Z",
+ "iopub.status.busy": "2024-07-02T12:06:07.132092Z",
+ "iopub.status.idle": "2024-07-02T12:06:07.345651Z",
+ "shell.execute_reply": "2024-07-02T12:06:07.345066Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:28.481029Z",
- "iopub.status.busy": "2024-07-01T15:07:28.480514Z",
- "iopub.status.idle": "2024-07-01T15:07:28.679630Z",
- "shell.execute_reply": "2024-07-01T15:07:28.679032Z"
+ "iopub.execute_input": "2024-07-02T12:06:07.347943Z",
+ "iopub.status.busy": "2024-07-02T12:06:07.347569Z",
+ "iopub.status.idle": "2024-07-02T12:06:07.545897Z",
+ "shell.execute_reply": "2024-07-02T12:06:07.545303Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:28.681815Z",
- "iopub.status.busy": "2024-07-01T15:07:28.681633Z",
- "iopub.status.idle": "2024-07-01T15:07:28.684760Z",
- "shell.execute_reply": "2024-07-01T15:07:28.684215Z"
+ "iopub.execute_input": "2024-07-02T12:06:07.548054Z",
+ "iopub.status.busy": "2024-07-02T12:06:07.547721Z",
+ "iopub.status.idle": "2024-07-02T12:06:07.550610Z",
+ "shell.execute_reply": "2024-07-02T12:06:07.550172Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:28.686736Z",
- "iopub.status.busy": "2024-07-01T15:07:28.686404Z",
- "iopub.status.idle": "2024-07-01T15:07:29.599883Z",
- "shell.execute_reply": "2024-07-01T15:07:29.599378Z"
+ "iopub.execute_input": "2024-07-02T12:06:07.552606Z",
+ "iopub.status.busy": "2024-07-02T12:06:07.552209Z",
+ "iopub.status.idle": "2024-07-02T12:06:08.545283Z",
+ "shell.execute_reply": "2024-07-02T12:06:08.544691Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:29.602491Z",
- "iopub.status.busy": "2024-07-01T15:07:29.602156Z",
- "iopub.status.idle": "2024-07-01T15:07:29.724845Z",
- "shell.execute_reply": "2024-07-01T15:07:29.724400Z"
+ "iopub.execute_input": "2024-07-02T12:06:08.550100Z",
+ "iopub.status.busy": "2024-07-02T12:06:08.549675Z",
+ "iopub.status.idle": "2024-07-02T12:06:08.692703Z",
+ "shell.execute_reply": "2024-07-02T12:06:08.692222Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:29.727049Z",
- "iopub.status.busy": "2024-07-01T15:07:29.726723Z",
- "iopub.status.idle": "2024-07-01T15:07:29.857120Z",
- "shell.execute_reply": "2024-07-01T15:07:29.856610Z"
+ "iopub.execute_input": "2024-07-02T12:06:08.694865Z",
+ "iopub.status.busy": "2024-07-02T12:06:08.694525Z",
+ "iopub.status.idle": "2024-07-02T12:06:08.829794Z",
+ "shell.execute_reply": "2024-07-02T12:06:08.829310Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:29.859645Z",
- "iopub.status.busy": "2024-07-01T15:07:29.859295Z",
- "iopub.status.idle": "2024-07-01T15:07:30.599850Z",
- "shell.execute_reply": "2024-07-01T15:07:30.599307Z"
+ "iopub.execute_input": "2024-07-02T12:06:08.832030Z",
+ "iopub.status.busy": "2024-07-02T12:06:08.831714Z",
+ "iopub.status.idle": "2024-07-02T12:06:09.569943Z",
+ "shell.execute_reply": "2024-07-02T12:06:09.569367Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:30.602022Z",
- "iopub.status.busy": "2024-07-01T15:07:30.601697Z",
- "iopub.status.idle": "2024-07-01T15:07:30.605345Z",
- "shell.execute_reply": "2024-07-01T15:07:30.604899Z"
+ "iopub.execute_input": "2024-07-02T12:06:09.572191Z",
+ "iopub.status.busy": "2024-07-02T12:06:09.571856Z",
+ "iopub.status.idle": "2024-07-02T12:06:09.575442Z",
+ "shell.execute_reply": "2024-07-02T12:06:09.575034Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index de1ca9206..12c6da264 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": "2024-07-01T15:07:32.630513Z",
- "iopub.status.busy": "2024-07-01T15:07:32.630022Z",
- "iopub.status.idle": "2024-07-01T15:07:35.339624Z",
- "shell.execute_reply": "2024-07-01T15:07:35.338990Z"
+ "iopub.execute_input": "2024-07-02T12:06:11.678697Z",
+ "iopub.status.busy": "2024-07-02T12:06:11.678521Z",
+ "iopub.status.idle": "2024-07-02T12:06:14.408240Z",
+ "shell.execute_reply": "2024-07-02T12:06:14.407674Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:35.342314Z",
- "iopub.status.busy": "2024-07-01T15:07:35.341942Z",
- "iopub.status.idle": "2024-07-01T15:07:35.677074Z",
- "shell.execute_reply": "2024-07-01T15:07:35.676543Z"
+ "iopub.execute_input": "2024-07-02T12:06:14.410934Z",
+ "iopub.status.busy": "2024-07-02T12:06:14.410443Z",
+ "iopub.status.idle": "2024-07-02T12:06:14.735244Z",
+ "shell.execute_reply": "2024-07-02T12:06:14.734679Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:35.679709Z",
- "iopub.status.busy": "2024-07-01T15:07:35.679377Z",
- "iopub.status.idle": "2024-07-01T15:07:35.683566Z",
- "shell.execute_reply": "2024-07-01T15:07:35.683132Z"
+ "iopub.execute_input": "2024-07-02T12:06:14.737835Z",
+ "iopub.status.busy": "2024-07-02T12:06:14.737360Z",
+ "iopub.status.idle": "2024-07-02T12:06:14.741543Z",
+ "shell.execute_reply": "2024-07-02T12:06:14.741013Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:35.685686Z",
- "iopub.status.busy": "2024-07-01T15:07:35.685365Z",
- "iopub.status.idle": "2024-07-01T15:07:42.517988Z",
- "shell.execute_reply": "2024-07-01T15:07:42.517428Z"
+ "iopub.execute_input": "2024-07-02T12:06:14.743746Z",
+ "iopub.status.busy": "2024-07-02T12:06:14.743385Z",
+ "iopub.status.idle": "2024-07-02T12:06:25.921071Z",
+ "shell.execute_reply": "2024-07-02T12:06:25.920486Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
- " 0%| | 786432/170498071 [00:00<00:21, 7820176.68it/s]"
+ " 0%| | 458752/170498071 [00:00<00:37, 4550205.38it/s]"
]
},
{
@@ -260,7 +260,7 @@
"output_type": "stream",
"text": [
"\r",
- " 3%|▎ | 4980736/170498071 [00:00<00:05, 27792797.45it/s]"
+ " 2%|▏ | 2686976/170498071 [00:00<00:11, 14867624.00it/s]"
]
},
{
@@ -268,7 +268,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▋ | 10944512/170498071 [00:00<00:03, 42298222.53it/s]"
+ " 3%|▎ | 4915200/170498071 [00:00<00:09, 18176569.25it/s]"
]
},
{
@@ -276,7 +276,7 @@
"output_type": "stream",
"text": [
"\r",
- " 10%|▉ | 16449536/170498071 [00:00<00:03, 47151386.97it/s]"
+ " 4%|▍ | 7110656/170498071 [00:00<00:08, 19525356.25it/s]"
]
},
{
@@ -284,7 +284,7 @@
"output_type": "stream",
"text": [
"\r",
- " 12%|█▏ | 21168128/170498071 [00:00<00:03, 41939697.62it/s]"
+ " 5%|▌ | 9273344/170498071 [00:00<00:08, 20138060.31it/s]"
]
},
{
@@ -292,7 +292,7 @@
"output_type": "stream",
"text": [
"\r",
- " 15%|█▍ | 25460736/170498071 [00:00<00:04, 36250418.01it/s]"
+ " 7%|▋ | 11468800/170498071 [00:00<00:07, 20583296.62it/s]"
]
},
{
@@ -300,7 +300,7 @@
"output_type": "stream",
"text": [
"\r",
- " 17%|█▋ | 29261824/170498071 [00:00<00:04, 31518178.43it/s]"
+ " 8%|▊ | 13565952/170498071 [00:00<00:07, 20618122.34it/s]"
]
},
{
@@ -308,7 +308,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▉ | 32604160/170498071 [00:00<00:04, 29639430.93it/s]"
+ " 9%|▉ | 15695872/170498071 [00:00<00:07, 20684064.34it/s]"
]
},
{
@@ -316,7 +316,7 @@
"output_type": "stream",
"text": [
"\r",
- " 21%|██ | 35684352/170498071 [00:01<00:04, 28240309.80it/s]"
+ " 10%|█ | 17793024/170498071 [00:00<00:07, 20210099.70it/s]"
]
},
{
@@ -324,7 +324,7 @@
"output_type": "stream",
"text": [
"\r",
- " 23%|██▎ | 38600704/170498071 [00:01<00:04, 27686377.59it/s]"
+ " 12%|█▏ | 19857408/170498071 [00:01<00:07, 20157298.26it/s]"
]
},
{
@@ -332,7 +332,7 @@
"output_type": "stream",
"text": [
"\r",
- " 24%|██▍ | 41451520/170498071 [00:01<00:04, 27880418.29it/s]"
+ " 13%|█▎ | 21889024/170498071 [00:01<00:07, 19580366.36it/s]"
]
},
{
@@ -340,7 +340,7 @@
"output_type": "stream",
"text": [
"\r",
- " 26%|██▌ | 44302336/170498071 [00:01<00:04, 27339381.04it/s]"
+ " 14%|█▍ | 23887872/170498071 [00:01<00:07, 19689752.59it/s]"
]
},
{
@@ -348,7 +348,7 @@
"output_type": "stream",
"text": [
"\r",
- " 28%|██▊ | 47153152/170498071 [00:01<00:04, 27588406.47it/s]"
+ " 15%|█▌ | 26148864/170498071 [00:01<00:07, 20522936.05it/s]"
]
},
{
@@ -356,7 +356,7 @@
"output_type": "stream",
"text": [
"\r",
- " 29%|██▉ | 49938432/170498071 [00:01<00:04, 27519027.46it/s]"
+ " 17%|█▋ | 28901376/170498071 [00:01<00:06, 22488420.49it/s]"
]
},
{
@@ -364,7 +364,7 @@
"output_type": "stream",
"text": [
"\r",
- " 31%|███ | 52723712/170498071 [00:01<00:04, 26308629.52it/s]"
+ " 18%|█▊ | 31260672/170498071 [00:01<00:06, 22666713.87it/s]"
]
},
{
@@ -372,7 +372,7 @@
"output_type": "stream",
"text": [
"\r",
- " 32%|███▏ | 55377920/170498071 [00:01<00:04, 25864377.41it/s]"
+ " 20%|█▉ | 33783808/170498071 [00:01<00:05, 23420171.66it/s]"
]
},
{
@@ -380,7 +380,7 @@
"output_type": "stream",
"text": [
"\r",
- " 35%|███▍ | 58884096/170498071 [00:01<00:03, 28360977.09it/s]"
+ " 21%|██ | 36143104/170498071 [00:01<00:05, 23367837.44it/s]"
]
},
{
@@ -388,7 +388,7 @@
"output_type": "stream",
"text": [
"\r",
- " 37%|███▋ | 62717952/170498071 [00:02<00:03, 31182568.80it/s]"
+ " 23%|██▎ | 38567936/170498071 [00:01<00:05, 23628433.32it/s]"
]
},
{
@@ -396,7 +396,7 @@
"output_type": "stream",
"text": [
"\r",
- " 39%|███▉ | 66846720/170498071 [00:02<00:03, 34093308.02it/s]"
+ " 24%|██▍ | 40992768/170498071 [00:01<00:05, 23729287.62it/s]"
]
},
{
@@ -404,7 +404,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▏ | 71303168/170498071 [00:02<00:02, 37046963.01it/s]"
+ " 25%|██▌ | 43384832/170498071 [00:02<00:05, 23481823.27it/s]"
]
},
{
@@ -412,7 +412,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▍ | 76513280/170498071 [00:02<00:02, 41418183.98it/s]"
+ " 27%|██▋ | 45809664/170498071 [00:02<00:05, 23565140.27it/s]"
]
},
{
@@ -420,7 +420,7 @@
"output_type": "stream",
"text": [
"\r",
- " 47%|████▋ | 80871424/170498071 [00:02<00:02, 42002402.02it/s]"
+ " 28%|██▊ | 48168960/170498071 [00:02<00:05, 22124551.71it/s]"
]
},
{
@@ -428,7 +428,7 @@
"output_type": "stream",
"text": [
"\r",
- " 50%|█████ | 85721088/170498071 [00:02<00:01, 43883828.77it/s]"
+ " 30%|██▉ | 50429952/170498071 [00:02<00:05, 21597165.25it/s]"
]
},
{
@@ -436,7 +436,7 @@
"output_type": "stream",
"text": [
"\r",
- " 53%|█████▎ | 90144768/170498071 [00:02<00:01, 43748354.63it/s]"
+ " 31%|███ | 52625408/170498071 [00:02<00:05, 21122055.47it/s]"
]
},
{
@@ -444,7 +444,7 @@
"output_type": "stream",
"text": [
"\r",
- " 56%|█████▌ | 94633984/170498071 [00:02<00:01, 43921848.70it/s]"
+ " 32%|███▏ | 54755328/170498071 [00:02<00:05, 20674704.10it/s]"
]
},
{
@@ -452,7 +452,7 @@
"output_type": "stream",
"text": [
"\r",
- " 58%|█████▊ | 99057664/170498071 [00:02<00:01, 43385313.39it/s]"
+ " 33%|███▎ | 56852480/170498071 [00:02<00:05, 20193072.76it/s]"
]
},
{
@@ -460,7 +460,7 @@
"output_type": "stream",
"text": [
"\r",
- " 61%|██████ | 104136704/170498071 [00:02<00:01, 45560088.81it/s]"
+ " 35%|███▍ | 59015168/170498071 [00:02<00:05, 20474965.70it/s]"
]
},
{
@@ -468,7 +468,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▍ | 108724224/170498071 [00:03<00:01, 43798020.18it/s]"
+ " 36%|███▌ | 61440000/170498071 [00:02<00:05, 21428625.80it/s]"
]
},
{
@@ -476,7 +476,7 @@
"output_type": "stream",
"text": [
"\r",
- " 67%|██████▋ | 113573888/170498071 [00:03<00:01, 45129830.46it/s]"
+ " 37%|███▋ | 63602688/170498071 [00:03<00:05, 20984454.80it/s]"
]
},
{
@@ -484,7 +484,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|██████▉ | 118554624/170498071 [00:03<00:01, 46326838.71it/s]"
+ " 39%|███▊ | 65732608/170498071 [00:03<00:05, 20040214.69it/s]"
]
},
{
@@ -492,7 +492,7 @@
"output_type": "stream",
"text": [
"\r",
- " 72%|███████▏ | 123207680/170498071 [00:03<00:01, 46221454.72it/s]"
+ " 40%|███▉ | 67764224/170498071 [00:03<00:05, 19617119.66it/s]"
]
},
{
@@ -500,7 +500,7 @@
"output_type": "stream",
"text": [
"\r",
- " 75%|███████▍ | 127860736/170498071 [00:03<00:00, 45331471.71it/s]"
+ " 41%|████ | 69763072/170498071 [00:03<00:05, 19368566.16it/s]"
]
},
{
@@ -508,7 +508,7 @@
"output_type": "stream",
"text": [
"\r",
- " 78%|███████▊ | 132775936/170498071 [00:03<00:00, 46431225.49it/s]"
+ " 42%|████▏ | 71729152/170498071 [00:03<00:05, 18942200.76it/s]"
]
},
{
@@ -516,7 +516,7 @@
"output_type": "stream",
"text": [
"\r",
- " 81%|████████ | 137756672/170498071 [00:03<00:00, 47136533.89it/s]"
+ " 43%|████▎ | 73760768/170498071 [00:03<00:05, 19136506.47it/s]"
]
},
{
@@ -524,7 +524,7 @@
"output_type": "stream",
"text": [
"\r",
- " 84%|████████▍ | 142966784/170498071 [00:03<00:00, 48566305.03it/s]"
+ " 44%|████▍ | 75694080/170498071 [00:03<00:05, 18546539.77it/s]"
]
},
{
@@ -532,7 +532,7 @@
"output_type": "stream",
"text": [
"\r",
- " 87%|████████▋ | 147849216/170498071 [00:03<00:00, 47837503.41it/s]"
+ " 46%|████▌ | 77856768/170498071 [00:03<00:04, 19310897.00it/s]"
]
},
{
@@ -540,7 +540,7 @@
"output_type": "stream",
"text": [
"\r",
- " 90%|█████████ | 153649152/170498071 [00:03<00:00, 50699005.24it/s]"
+ " 47%|████▋ | 79855616/170498071 [00:03<00:04, 19370411.60it/s]"
]
},
{
@@ -548,7 +548,7 @@
"output_type": "stream",
"text": [
"\r",
- " 94%|█████████▍| 160006144/170498071 [00:04<00:00, 54457105.34it/s]"
+ " 48%|████▊ | 81821696/170498071 [00:03<00:04, 18841681.57it/s]"
]
},
{
@@ -556,7 +556,7 @@
"output_type": "stream",
"text": [
"\r",
- " 98%|█████████▊| 166297600/170498071 [00:04<00:00, 56798743.02it/s]"
+ " 49%|████▉ | 83722240/170498071 [00:04<00:04, 18578900.08it/s]"
]
},
{
@@ -564,7 +564,343 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 170498071/170498071 [00:04<00:00, 39742139.91it/s]"
+ " 50%|█████ | 85590016/170498071 [00:04<00:04, 18310455.55it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 51%|█████▏ | 87425024/170498071 [00:04<00:04, 17994534.24it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 52%|█████▏ | 89227264/170498071 [00:04<00:04, 17969991.24it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 53%|█████▎ | 91029504/170498071 [00:04<00:04, 17885343.83it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 54%|█████▍ | 92864512/170498071 [00:04<00:04, 17966202.49it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 56%|█████▌ | 95223808/170498071 [00:04<00:03, 19459620.88it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 57%|█████▋ | 97583104/170498071 [00:04<00:03, 20637975.50it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 59%|█████▊ | 99909632/170498071 [00:04<00:03, 21306263.38it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 60%|█████▉ | 102072320/170498071 [00:05<00:03, 21151929.09it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 61%|██████▏ | 104464384/170498071 [00:05<00:03, 21792656.59it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 63%|██████▎ | 106659840/170498071 [00:05<00:02, 21386725.65it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 64%|██████▍ | 108920832/170498071 [00:05<00:02, 21710014.23it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 65%|██████▌ | 111116288/170498071 [00:05<00:02, 21241167.89it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 67%|██████▋ | 113541120/170498071 [00:05<00:02, 22049083.16it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 68%|██████▊ | 115769344/170498071 [00:05<00:02, 21067777.78it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 69%|██████▉ | 118030336/170498071 [00:05<00:02, 21474442.53it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 70%|███████ | 120193024/170498071 [00:05<00:02, 19823274.12it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 72%|███████▏ | 122224640/170498071 [00:06<00:02, 19471183.62it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 73%|███████▎ | 124190720/170498071 [00:06<00:02, 17873496.55it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 74%|███████▍ | 126025728/170498071 [00:06<00:02, 16817889.13it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 75%|███████▍ | 127762432/170498071 [00:06<00:02, 16186952.66it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 76%|███████▌ | 129400832/170498071 [00:06<00:02, 15789183.38it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 77%|███████▋ | 131006464/170498071 [00:06<00:02, 15482944.36it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 78%|███████▊ | 132579328/170498071 [00:06<00:02, 15098811.97it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 79%|███████▊ | 134119424/170498071 [00:06<00:02, 14977124.11it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 80%|███████▉ | 135626752/170498071 [00:06<00:02, 14929116.66it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 80%|████████ | 137134080/170498071 [00:07<00:02, 14648969.73it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 81%|████████▏ | 138608640/170498071 [00:07<00:02, 14671163.55it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 82%|████████▏ | 140083200/170498071 [00:07<00:02, 14686569.65it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 83%|████████▎ | 141885440/170498071 [00:07<00:01, 15554076.95it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 84%|████████▍ | 143589376/170498071 [00:07<00:01, 15854513.49it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 85%|████████▌ | 145391616/170498071 [00:07<00:01, 16350344.79it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 86%|████████▋ | 147128320/170498071 [00:07<00:01, 16559020.07it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 87%|████████▋ | 149061632/170498071 [00:07<00:01, 17232449.65it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 88%|████████▊ | 150863872/170498071 [00:07<00:01, 17447581.45it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 90%|████████▉ | 152633344/170498071 [00:07<00:01, 17476767.38it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 91%|█████████ | 155058176/170498071 [00:08<00:00, 19434356.44it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 93%|█████████▎| 157712384/170498071 [00:08<00:00, 21544683.73it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 94%|█████████▍| 160661504/170498071 [00:08<00:00, 23832060.44it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 96%|█████████▌| 163545088/170498071 [00:08<00:00, 25225731.12it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 98%|█████████▊| 166854656/170498071 [00:08<00:00, 27373989.44it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "100%|█████████▉| 169672704/170498071 [00:08<00:00, 27582174.15it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "100%|██████████| 170498071/170498071 [00:08<00:00, 19884004.38it/s]"
]
},
{
@@ -682,10 +1018,10 @@
"id": "9b64e0aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:42.520241Z",
- "iopub.status.busy": "2024-07-01T15:07:42.519920Z",
- "iopub.status.idle": "2024-07-01T15:07:42.524659Z",
- "shell.execute_reply": "2024-07-01T15:07:42.524205Z"
+ "iopub.execute_input": "2024-07-02T12:06:25.923304Z",
+ "iopub.status.busy": "2024-07-02T12:06:25.922962Z",
+ "iopub.status.idle": "2024-07-02T12:06:25.927532Z",
+ "shell.execute_reply": "2024-07-02T12:06:25.927116Z"
},
"nbsphinx": "hidden"
},
@@ -736,10 +1072,10 @@
"id": "a00aa3ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:42.526805Z",
- "iopub.status.busy": "2024-07-01T15:07:42.526366Z",
- "iopub.status.idle": "2024-07-01T15:07:43.068980Z",
- "shell.execute_reply": "2024-07-01T15:07:43.068376Z"
+ "iopub.execute_input": "2024-07-02T12:06:25.929617Z",
+ "iopub.status.busy": "2024-07-02T12:06:25.929294Z",
+ "iopub.status.idle": "2024-07-02T12:06:26.466020Z",
+ "shell.execute_reply": "2024-07-02T12:06:26.465500Z"
}
},
"outputs": [
@@ -772,10 +1108,10 @@
"id": "41e5cb6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:43.071278Z",
- "iopub.status.busy": "2024-07-01T15:07:43.070855Z",
- "iopub.status.idle": "2024-07-01T15:07:43.585255Z",
- "shell.execute_reply": "2024-07-01T15:07:43.584665Z"
+ "iopub.execute_input": "2024-07-02T12:06:26.468274Z",
+ "iopub.status.busy": "2024-07-02T12:06:26.467846Z",
+ "iopub.status.idle": "2024-07-02T12:06:26.973804Z",
+ "shell.execute_reply": "2024-07-02T12:06:26.973190Z"
}
},
"outputs": [
@@ -813,10 +1149,10 @@
"id": "1cf25354",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:43.587754Z",
- "iopub.status.busy": "2024-07-01T15:07:43.587325Z",
- "iopub.status.idle": "2024-07-01T15:07:43.591527Z",
- "shell.execute_reply": "2024-07-01T15:07:43.591015Z"
+ "iopub.execute_input": "2024-07-02T12:06:26.976024Z",
+ "iopub.status.busy": "2024-07-02T12:06:26.975702Z",
+ "iopub.status.idle": "2024-07-02T12:06:26.979191Z",
+ "shell.execute_reply": "2024-07-02T12:06:26.978654Z"
}
},
"outputs": [],
@@ -839,17 +1175,17 @@
"id": "85a58d41",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:43.593739Z",
- "iopub.status.busy": "2024-07-01T15:07:43.593377Z",
- "iopub.status.idle": "2024-07-01T15:07:56.035606Z",
- "shell.execute_reply": "2024-07-01T15:07:56.035038Z"
+ "iopub.execute_input": "2024-07-02T12:06:26.981120Z",
+ "iopub.status.busy": "2024-07-02T12:06:26.980808Z",
+ "iopub.status.idle": "2024-07-02T12:06:39.219368Z",
+ "shell.execute_reply": "2024-07-02T12:06:39.218785Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6e0af51d1d7c41f6b28e94e107a2e2dd",
+ "model_id": "e62048d58b1a436fa16544b9ecbd1a17",
"version_major": 2,
"version_minor": 0
},
@@ -908,10 +1244,10 @@
"id": "feb0f519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:56.037942Z",
- "iopub.status.busy": "2024-07-01T15:07:56.037579Z",
- "iopub.status.idle": "2024-07-01T15:07:58.157290Z",
- "shell.execute_reply": "2024-07-01T15:07:58.156719Z"
+ "iopub.execute_input": "2024-07-02T12:06:39.221701Z",
+ "iopub.status.busy": "2024-07-02T12:06:39.221327Z",
+ "iopub.status.idle": "2024-07-02T12:06:41.264255Z",
+ "shell.execute_reply": "2024-07-02T12:06:41.263645Z"
}
},
"outputs": [
@@ -955,10 +1291,10 @@
"id": "089d5860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:58.160021Z",
- "iopub.status.busy": "2024-07-01T15:07:58.159723Z",
- "iopub.status.idle": "2024-07-01T15:07:58.413397Z",
- "shell.execute_reply": "2024-07-01T15:07:58.412322Z"
+ "iopub.execute_input": "2024-07-02T12:06:41.266829Z",
+ "iopub.status.busy": "2024-07-02T12:06:41.266301Z",
+ "iopub.status.idle": "2024-07-02T12:06:41.492927Z",
+ "shell.execute_reply": "2024-07-02T12:06:41.492268Z"
}
},
"outputs": [
@@ -994,10 +1330,10 @@
"id": "78b1951c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:58.415968Z",
- "iopub.status.busy": "2024-07-01T15:07:58.415746Z",
- "iopub.status.idle": "2024-07-01T15:07:59.087448Z",
- "shell.execute_reply": "2024-07-01T15:07:59.086838Z"
+ "iopub.execute_input": "2024-07-02T12:06:41.495155Z",
+ "iopub.status.busy": "2024-07-02T12:06:41.494971Z",
+ "iopub.status.idle": "2024-07-02T12:06:42.143408Z",
+ "shell.execute_reply": "2024-07-02T12:06:42.142827Z"
}
},
"outputs": [
@@ -1047,10 +1383,10 @@
"id": "e9dff81b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:59.090441Z",
- "iopub.status.busy": "2024-07-01T15:07:59.090110Z",
- "iopub.status.idle": "2024-07-01T15:07:59.428255Z",
- "shell.execute_reply": "2024-07-01T15:07:59.427685Z"
+ "iopub.execute_input": "2024-07-02T12:06:42.145875Z",
+ "iopub.status.busy": "2024-07-02T12:06:42.145693Z",
+ "iopub.status.idle": "2024-07-02T12:06:42.443716Z",
+ "shell.execute_reply": "2024-07-02T12:06:42.443121Z"
}
},
"outputs": [
@@ -1098,10 +1434,10 @@
"id": "616769f8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:59.430501Z",
- "iopub.status.busy": "2024-07-01T15:07:59.430156Z",
- "iopub.status.idle": "2024-07-01T15:07:59.674251Z",
- "shell.execute_reply": "2024-07-01T15:07:59.673618Z"
+ "iopub.execute_input": "2024-07-02T12:06:42.445959Z",
+ "iopub.status.busy": "2024-07-02T12:06:42.445765Z",
+ "iopub.status.idle": "2024-07-02T12:06:42.675040Z",
+ "shell.execute_reply": "2024-07-02T12:06:42.674459Z"
}
},
"outputs": [
@@ -1157,10 +1493,10 @@
"id": "40fed4ef",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:59.676994Z",
- "iopub.status.busy": "2024-07-01T15:07:59.676767Z",
- "iopub.status.idle": "2024-07-01T15:07:59.764794Z",
- "shell.execute_reply": "2024-07-01T15:07:59.764299Z"
+ "iopub.execute_input": "2024-07-02T12:06:42.677732Z",
+ "iopub.status.busy": "2024-07-02T12:06:42.677210Z",
+ "iopub.status.idle": "2024-07-02T12:06:42.745827Z",
+ "shell.execute_reply": "2024-07-02T12:06:42.745362Z"
}
},
"outputs": [],
@@ -1181,10 +1517,10 @@
"id": "89f9db72",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:07:59.767244Z",
- "iopub.status.busy": "2024-07-01T15:07:59.766860Z",
- "iopub.status.idle": "2024-07-01T15:08:10.069096Z",
- "shell.execute_reply": "2024-07-01T15:08:10.068139Z"
+ "iopub.execute_input": "2024-07-02T12:06:42.748346Z",
+ "iopub.status.busy": "2024-07-02T12:06:42.748025Z",
+ "iopub.status.idle": "2024-07-02T12:06:52.686113Z",
+ "shell.execute_reply": "2024-07-02T12:06:52.685493Z"
}
},
"outputs": [
@@ -1221,10 +1557,10 @@
"id": "874c885a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:10.071585Z",
- "iopub.status.busy": "2024-07-01T15:08:10.071346Z",
- "iopub.status.idle": "2024-07-01T15:08:12.388556Z",
- "shell.execute_reply": "2024-07-01T15:08:12.388050Z"
+ "iopub.execute_input": "2024-07-02T12:06:52.688740Z",
+ "iopub.status.busy": "2024-07-02T12:06:52.688263Z",
+ "iopub.status.idle": "2024-07-02T12:06:54.757637Z",
+ "shell.execute_reply": "2024-07-02T12:06:54.757095Z"
}
},
"outputs": [
@@ -1255,10 +1591,10 @@
"id": "e110fc4b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:12.391205Z",
- "iopub.status.busy": "2024-07-01T15:08:12.390795Z",
- "iopub.status.idle": "2024-07-01T15:08:12.593036Z",
- "shell.execute_reply": "2024-07-01T15:08:12.592540Z"
+ "iopub.execute_input": "2024-07-02T12:06:54.760248Z",
+ "iopub.status.busy": "2024-07-02T12:06:54.759634Z",
+ "iopub.status.idle": "2024-07-02T12:06:54.964477Z",
+ "shell.execute_reply": "2024-07-02T12:06:54.963957Z"
}
},
"outputs": [],
@@ -1272,10 +1608,10 @@
"id": "85b60cbf",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:12.595509Z",
- "iopub.status.busy": "2024-07-01T15:08:12.595162Z",
- "iopub.status.idle": "2024-07-01T15:08:12.598275Z",
- "shell.execute_reply": "2024-07-01T15:08:12.597815Z"
+ "iopub.execute_input": "2024-07-02T12:06:54.966866Z",
+ "iopub.status.busy": "2024-07-02T12:06:54.966507Z",
+ "iopub.status.idle": "2024-07-02T12:06:54.969693Z",
+ "shell.execute_reply": "2024-07-02T12:06:54.969165Z"
}
},
"outputs": [],
@@ -1297,10 +1633,10 @@
"id": "17f96fa6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:12.600173Z",
- "iopub.status.busy": "2024-07-01T15:08:12.599856Z",
- "iopub.status.idle": "2024-07-01T15:08:12.608170Z",
- "shell.execute_reply": "2024-07-01T15:08:12.607771Z"
+ "iopub.execute_input": "2024-07-02T12:06:54.971890Z",
+ "iopub.status.busy": "2024-07-02T12:06:54.971573Z",
+ "iopub.status.idle": "2024-07-02T12:06:54.979664Z",
+ "shell.execute_reply": "2024-07-02T12:06:54.979125Z"
},
"nbsphinx": "hidden"
},
@@ -1345,7 +1681,60 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "2727be2db0d2497f9fb52dc8697095a2": {
+ "038d1dec855f4a5d8a895b8c5ca8a543": {
+ "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
+ }
+ },
+ "189964aceefe49698fa8fa689efdba0f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1360,55 +1749,74 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_c995655b1f814ffb9d408cda2ffbc566",
+ "layout": "IPY_MODEL_7f36baa4eaa845949d5ad61b24217bd2",
"placeholder": "",
- "style": "IPY_MODEL_f100e8e508354e3a998f09e41481fe4a",
+ "style": "IPY_MODEL_50adf2f382654575992aa00abedb3fda",
"tabbable": null,
"tooltip": null,
- "value": " 102M/102M [00:00<00:00, 311MB/s]"
+ "value": " 102M/102M [00:00<00:00, 291MB/s]"
}
},
- "56a99359b74d41799a5e62b4bf03f499": {
+ "32782ba639b74ba19d535e6b9e43df2f": {
"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
}
},
- "6e0af51d1d7c41f6b28e94e107a2e2dd": {
+ "50adf2f382654575992aa00abedb3fda": {
"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
+ }
+ },
+ "55c2a3ff8e46463392cbdc7feacce684": {
+ "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_bbb9fac4ce02410c9a7cf006ad612d8a",
- "IPY_MODEL_beaa49c5ac66403896b3c555d2a06c91",
- "IPY_MODEL_2727be2db0d2497f9fb52dc8697095a2"
- ],
- "layout": "IPY_MODEL_b4c1215d79fe4c049a90606906d2692c",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_038d1dec855f4a5d8a895b8c5ca8a543",
+ "placeholder": "",
+ "style": "IPY_MODEL_32782ba639b74ba19d535e6b9e43df2f",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "model.safetensors: 100%"
}
},
- "847a911faef949b68edb66c688cd3c4b": {
+ "7f36baa4eaa845949d5ad61b24217bd2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1461,7 +1869,23 @@
"width": null
}
},
- "b4c1215d79fe4c049a90606906d2692c": {
+ "9c339ec47e3249839dd034d9f3c0f0bd": {
+ "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": ""
+ }
+ },
+ "d0f48ceb51424194a566927347c5e11d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1514,30 +1938,7 @@
"width": null
}
},
- "bbb9fac4ce02410c9a7cf006ad612d8a": {
- "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_847a911faef949b68edb66c688cd3c4b",
- "placeholder": "",
- "style": "IPY_MODEL_e010df9fbdef472aa0c2e3f8a393bb55",
- "tabbable": null,
- "tooltip": null,
- "value": "model.safetensors: 100%"
- }
- },
- "beaa49c5ac66403896b3c555d2a06c91": {
+ "e2efb59d0f4740bb8af23c2fd00116b3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "FloatProgressModel",
@@ -1553,70 +1954,41 @@
"bar_style": "success",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_ce770c3a1d5d42a98a4d28d04bc1c7d7",
+ "layout": "IPY_MODEL_f2d6b576288e4f7fbed42581aafbf977",
"max": 102469840.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_56a99359b74d41799a5e62b4bf03f499",
+ "style": "IPY_MODEL_9c339ec47e3249839dd034d9f3c0f0bd",
"tabbable": null,
"tooltip": null,
"value": 102469840.0
}
},
- "c995655b1f814ffb9d408cda2ffbc566": {
- "model_module": "@jupyter-widgets/base",
+ "e62048d58b1a436fa16544b9ecbd1a17": {
+ "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/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_55c2a3ff8e46463392cbdc7feacce684",
+ "IPY_MODEL_e2efb59d0f4740bb8af23c2fd00116b3",
+ "IPY_MODEL_189964aceefe49698fa8fa689efdba0f"
+ ],
+ "layout": "IPY_MODEL_d0f48ceb51424194a566927347c5e11d",
+ "tabbable": null,
+ "tooltip": null
}
},
- "ce770c3a1d5d42a98a4d28d04bc1c7d7": {
+ "f2d6b576288e4f7fbed42581aafbf977": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1668,42 +2040,6 @@
"visibility": null,
"width": null
}
- },
- "e010df9fbdef472aa0c2e3f8a393bb55": {
- "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
- }
- },
- "f100e8e508354e3a998f09e41481fe4a": {
- "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/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 46926446f..75e02e92c 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": "2024-07-01T15:08:16.815662Z",
- "iopub.status.busy": "2024-07-01T15:08:16.815212Z",
- "iopub.status.idle": "2024-07-01T15:08:18.061264Z",
- "shell.execute_reply": "2024-07-01T15:08:18.060688Z"
+ "iopub.execute_input": "2024-07-02T12:06:59.101052Z",
+ "iopub.status.busy": "2024-07-02T12:06:59.100876Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.258136Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.257587Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:08:18.064012Z",
- "iopub.status.busy": "2024-07-01T15:08:18.063557Z",
- "iopub.status.idle": "2024-07-01T15:08:18.081205Z",
- "shell.execute_reply": "2024-07-01T15:08:18.080744Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.260745Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.260339Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.277570Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.277011Z"
}
},
"outputs": [],
@@ -164,10 +164,10 @@
"id": "284dc264",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.083492Z",
- "iopub.status.busy": "2024-07-01T15:08:18.083103Z",
- "iopub.status.idle": "2024-07-01T15:08:18.086376Z",
- "shell.execute_reply": "2024-07-01T15:08:18.085828Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.280398Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.279700Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.283630Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.282919Z"
},
"nbsphinx": "hidden"
},
@@ -198,10 +198,10 @@
"id": "0f7450db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.088481Z",
- "iopub.status.busy": "2024-07-01T15:08:18.088156Z",
- "iopub.status.idle": "2024-07-01T15:08:18.174903Z",
- "shell.execute_reply": "2024-07-01T15:08:18.174413Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.286415Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.285840Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.351880Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.350456Z"
}
},
"outputs": [
@@ -374,10 +374,10 @@
"id": "55513fed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.177216Z",
- "iopub.status.busy": "2024-07-01T15:08:18.176851Z",
- "iopub.status.idle": "2024-07-01T15:08:18.363421Z",
- "shell.execute_reply": "2024-07-01T15:08:18.362759Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.354191Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.353874Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.543757Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.543276Z"
},
"nbsphinx": "hidden"
},
@@ -417,10 +417,10 @@
"id": "df5a0f59",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.366181Z",
- "iopub.status.busy": "2024-07-01T15:08:18.365737Z",
- "iopub.status.idle": "2024-07-01T15:08:18.613007Z",
- "shell.execute_reply": "2024-07-01T15:08:18.612399Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.545894Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.545559Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.784978Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.784416Z"
}
},
"outputs": [
@@ -456,10 +456,10 @@
"id": "7af78a8a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.615413Z",
- "iopub.status.busy": "2024-07-01T15:08:18.615032Z",
- "iopub.status.idle": "2024-07-01T15:08:18.619703Z",
- "shell.execute_reply": "2024-07-01T15:08:18.619083Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.787127Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.786946Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.791220Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.790792Z"
}
},
"outputs": [],
@@ -477,10 +477,10 @@
"id": "9556c624",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.621922Z",
- "iopub.status.busy": "2024-07-01T15:08:18.621693Z",
- "iopub.status.idle": "2024-07-01T15:08:18.629203Z",
- "shell.execute_reply": "2024-07-01T15:08:18.628679Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.793213Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.792887Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.798368Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.797958Z"
}
},
"outputs": [],
@@ -527,10 +527,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.631920Z",
- "iopub.status.busy": "2024-07-01T15:08:18.631513Z",
- "iopub.status.idle": "2024-07-01T15:08:18.634527Z",
- "shell.execute_reply": "2024-07-01T15:08:18.633964Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.800409Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.800087Z",
+ "iopub.status.idle": "2024-07-02T12:07:00.802550Z",
+ "shell.execute_reply": "2024-07-02T12:07:00.802117Z"
}
},
"outputs": [],
@@ -545,10 +545,10 @@
"id": "7e1b7860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:18.636916Z",
- "iopub.status.busy": "2024-07-01T15:08:18.636464Z",
- "iopub.status.idle": "2024-07-01T15:08:27.681165Z",
- "shell.execute_reply": "2024-07-01T15:08:27.680590Z"
+ "iopub.execute_input": "2024-07-02T12:07:00.804548Z",
+ "iopub.status.busy": "2024-07-02T12:07:00.804231Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.170648Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.170087Z"
}
},
"outputs": [],
@@ -572,10 +572,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.683915Z",
- "iopub.status.busy": "2024-07-01T15:08:27.683447Z",
- "iopub.status.idle": "2024-07-01T15:08:27.691061Z",
- "shell.execute_reply": "2024-07-01T15:08:27.690544Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.173635Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.172986Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.180628Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.180165Z"
}
},
"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.693260Z",
- "iopub.status.busy": "2024-07-01T15:08:27.692915Z",
- "iopub.status.idle": "2024-07-01T15:08:27.696508Z",
- "shell.execute_reply": "2024-07-01T15:08:27.696074Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.182718Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.182401Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.186064Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.185614Z"
}
},
"outputs": [],
@@ -696,10 +696,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.698591Z",
- "iopub.status.busy": "2024-07-01T15:08:27.698265Z",
- "iopub.status.idle": "2024-07-01T15:08:27.701394Z",
- "shell.execute_reply": "2024-07-01T15:08:27.700844Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.188065Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.187765Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.191124Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.190682Z"
}
},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.703468Z",
- "iopub.status.busy": "2024-07-01T15:08:27.703137Z",
- "iopub.status.idle": "2024-07-01T15:08:27.706217Z",
- "shell.execute_reply": "2024-07-01T15:08:27.705750Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.193018Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.192715Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.195753Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.195211Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.708232Z",
- "iopub.status.busy": "2024-07-01T15:08:27.707899Z",
- "iopub.status.idle": "2024-07-01T15:08:27.715999Z",
- "shell.execute_reply": "2024-07-01T15:08:27.715525Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.197818Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.197511Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.205619Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.205180Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.718018Z",
- "iopub.status.busy": "2024-07-01T15:08:27.717680Z",
- "iopub.status.idle": "2024-07-01T15:08:27.720242Z",
- "shell.execute_reply": "2024-07-01T15:08:27.719798Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.207503Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.207209Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.209820Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.209307Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.722277Z",
- "iopub.status.busy": "2024-07-01T15:08:27.721939Z",
- "iopub.status.idle": "2024-07-01T15:08:27.850295Z",
- "shell.execute_reply": "2024-07-01T15:08:27.849694Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.211933Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.211620Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.330539Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.329946Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.852484Z",
- "iopub.status.busy": "2024-07-01T15:08:27.852300Z",
- "iopub.status.idle": "2024-07-01T15:08:27.955847Z",
- "shell.execute_reply": "2024-07-01T15:08:27.955257Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.332913Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.332537Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.439546Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.438879Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:27.958252Z",
- "iopub.status.busy": "2024-07-01T15:08:27.957880Z",
- "iopub.status.idle": "2024-07-01T15:08:28.451750Z",
- "shell.execute_reply": "2024-07-01T15:08:28.451203Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.441953Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.441731Z",
+ "iopub.status.idle": "2024-07-02T12:07:09.926340Z",
+ "shell.execute_reply": "2024-07-02T12:07:09.925811Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:28.454335Z",
- "iopub.status.busy": "2024-07-01T15:08:28.454151Z",
- "iopub.status.idle": "2024-07-01T15:08:28.527356Z",
- "shell.execute_reply": "2024-07-01T15:08:28.526736Z"
+ "iopub.execute_input": "2024-07-02T12:07:09.928918Z",
+ "iopub.status.busy": "2024-07-02T12:07:09.928531Z",
+ "iopub.status.idle": "2024-07-02T12:07:10.007223Z",
+ "shell.execute_reply": "2024-07-02T12:07:10.006669Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:28.529697Z",
- "iopub.status.busy": "2024-07-01T15:08:28.529341Z",
- "iopub.status.idle": "2024-07-01T15:08:28.538428Z",
- "shell.execute_reply": "2024-07-01T15:08:28.537958Z"
+ "iopub.execute_input": "2024-07-02T12:07:10.009492Z",
+ "iopub.status.busy": "2024-07-02T12:07:10.009118Z",
+ "iopub.status.idle": "2024-07-02T12:07:10.017415Z",
+ "shell.execute_reply": "2024-07-02T12:07:10.016968Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:28.540454Z",
- "iopub.status.busy": "2024-07-01T15:08:28.540269Z",
- "iopub.status.idle": "2024-07-01T15:08:28.542883Z",
- "shell.execute_reply": "2024-07-01T15:08:28.542447Z"
+ "iopub.execute_input": "2024-07-02T12:07:10.019396Z",
+ "iopub.status.busy": "2024-07-02T12:07:10.019069Z",
+ "iopub.status.idle": "2024-07-02T12:07:10.021767Z",
+ "shell.execute_reply": "2024-07-02T12:07:10.021319Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:28.544877Z",
- "iopub.status.busy": "2024-07-01T15:08:28.544701Z",
- "iopub.status.idle": "2024-07-01T15:08:33.972038Z",
- "shell.execute_reply": "2024-07-01T15:08:33.971430Z"
+ "iopub.execute_input": "2024-07-02T12:07:10.023754Z",
+ "iopub.status.busy": "2024-07-02T12:07:10.023447Z",
+ "iopub.status.idle": "2024-07-02T12:07:15.333825Z",
+ "shell.execute_reply": "2024-07-02T12:07:15.333229Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:33.974153Z",
- "iopub.status.busy": "2024-07-01T15:08:33.973956Z",
- "iopub.status.idle": "2024-07-01T15:08:33.982773Z",
- "shell.execute_reply": "2024-07-01T15:08:33.982320Z"
+ "iopub.execute_input": "2024-07-02T12:07:15.336220Z",
+ "iopub.status.busy": "2024-07-02T12:07:15.335826Z",
+ "iopub.status.idle": "2024-07-02T12:07:15.344270Z",
+ "shell.execute_reply": "2024-07-02T12:07:15.343811Z"
}
},
"outputs": [
@@ -1376,10 +1376,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:33.984722Z",
- "iopub.status.busy": "2024-07-01T15:08:33.984546Z",
- "iopub.status.idle": "2024-07-01T15:08:34.049986Z",
- "shell.execute_reply": "2024-07-01T15:08:34.049478Z"
+ "iopub.execute_input": "2024-07-02T12:07:15.346339Z",
+ "iopub.status.busy": "2024-07-02T12:07:15.346012Z",
+ "iopub.status.idle": "2024-07-02T12:07:15.414442Z",
+ "shell.execute_reply": "2024-07-02T12:07:15.413948Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 3b1c6435d..fdafb004b 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": "2024-07-01T15:08:37.513049Z",
- "iopub.status.busy": "2024-07-01T15:08:37.512824Z",
- "iopub.status.idle": "2024-07-01T15:08:39.014630Z",
- "shell.execute_reply": "2024-07-01T15:08:39.013915Z"
+ "iopub.execute_input": "2024-07-02T12:07:18.593560Z",
+ "iopub.status.busy": "2024-07-02T12:07:18.593400Z",
+ "iopub.status.idle": "2024-07-02T12:07:20.263944Z",
+ "shell.execute_reply": "2024-07-02T12:07:20.263270Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:08:39.017371Z",
- "iopub.status.busy": "2024-07-01T15:08:39.017127Z",
- "iopub.status.idle": "2024-07-01T15:09:39.584116Z",
- "shell.execute_reply": "2024-07-01T15:09:39.583459Z"
+ "iopub.execute_input": "2024-07-02T12:07:20.266581Z",
+ "iopub.status.busy": "2024-07-02T12:07:20.266205Z",
+ "iopub.status.idle": "2024-07-02T12:08:06.109041Z",
+ "shell.execute_reply": "2024-07-02T12:08:06.108401Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:09:39.586690Z",
- "iopub.status.busy": "2024-07-01T15:09:39.586340Z",
- "iopub.status.idle": "2024-07-01T15:09:40.720146Z",
- "shell.execute_reply": "2024-07-01T15:09:40.719576Z"
+ "iopub.execute_input": "2024-07-02T12:08:06.111457Z",
+ "iopub.status.busy": "2024-07-02T12:08:06.111270Z",
+ "iopub.status.idle": "2024-07-02T12:08:07.194905Z",
+ "shell.execute_reply": "2024-07-02T12:08:07.194300Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:09:40.722658Z",
- "iopub.status.busy": "2024-07-01T15:09:40.722386Z",
- "iopub.status.idle": "2024-07-01T15:09:40.725657Z",
- "shell.execute_reply": "2024-07-01T15:09:40.725218Z"
+ "iopub.execute_input": "2024-07-02T12:08:07.197493Z",
+ "iopub.status.busy": "2024-07-02T12:08:07.197237Z",
+ "iopub.status.idle": "2024-07-02T12:08:07.200309Z",
+ "shell.execute_reply": "2024-07-02T12:08:07.199874Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:09:40.727650Z",
- "iopub.status.busy": "2024-07-01T15:09:40.727470Z",
- "iopub.status.idle": "2024-07-01T15:09:40.731254Z",
- "shell.execute_reply": "2024-07-01T15:09:40.730747Z"
+ "iopub.execute_input": "2024-07-02T12:08:07.202276Z",
+ "iopub.status.busy": "2024-07-02T12:08:07.202097Z",
+ "iopub.status.idle": "2024-07-02T12:08:07.205874Z",
+ "shell.execute_reply": "2024-07-02T12:08:07.205417Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:09:40.733340Z",
- "iopub.status.busy": "2024-07-01T15:09:40.733016Z",
- "iopub.status.idle": "2024-07-01T15:09:40.736638Z",
- "shell.execute_reply": "2024-07-01T15:09:40.736162Z"
+ "iopub.execute_input": "2024-07-02T12:08:07.207818Z",
+ "iopub.status.busy": "2024-07-02T12:08:07.207520Z",
+ "iopub.status.idle": "2024-07-02T12:08:07.211075Z",
+ "shell.execute_reply": "2024-07-02T12:08:07.210551Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:09:40.738709Z",
- "iopub.status.busy": "2024-07-01T15:09:40.738283Z",
- "iopub.status.idle": "2024-07-01T15:09:40.741139Z",
- "shell.execute_reply": "2024-07-01T15:09:40.740716Z"
+ "iopub.execute_input": "2024-07-02T12:08:07.213131Z",
+ "iopub.status.busy": "2024-07-02T12:08:07.212769Z",
+ "iopub.status.idle": "2024-07-02T12:08:07.215484Z",
+ "shell.execute_reply": "2024-07-02T12:08:07.215039Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:09:40.743089Z",
- "iopub.status.busy": "2024-07-01T15:09:40.742768Z",
- "iopub.status.idle": "2024-07-01T15:10:14.851046Z",
- "shell.execute_reply": "2024-07-01T15:10:14.850360Z"
+ "iopub.execute_input": "2024-07-02T12:08:07.217418Z",
+ "iopub.status.busy": "2024-07-02T12:08:07.217121Z",
+ "iopub.status.idle": "2024-07-02T12:08:41.707148Z",
+ "shell.execute_reply": "2024-07-02T12:08:41.706563Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e93b88c996c44feeb3673439eaaea41d",
+ "model_id": "9e20fdede857444e8054f80d2f1060d4",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "cae016dc953549ce807817682c42dc87",
+ "model_id": "301ab18342ea43859b3e69cf6784234e",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:10:14.853695Z",
- "iopub.status.busy": "2024-07-01T15:10:14.853439Z",
- "iopub.status.idle": "2024-07-01T15:10:15.523116Z",
- "shell.execute_reply": "2024-07-01T15:10:15.522614Z"
+ "iopub.execute_input": "2024-07-02T12:08:41.710056Z",
+ "iopub.status.busy": "2024-07-02T12:08:41.709655Z",
+ "iopub.status.idle": "2024-07-02T12:08:42.388632Z",
+ "shell.execute_reply": "2024-07-02T12:08:42.388139Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:10:15.525482Z",
- "iopub.status.busy": "2024-07-01T15:10:15.525022Z",
- "iopub.status.idle": "2024-07-01T15:10:18.415066Z",
- "shell.execute_reply": "2024-07-01T15:10:18.414464Z"
+ "iopub.execute_input": "2024-07-02T12:08:42.390931Z",
+ "iopub.status.busy": "2024-07-02T12:08:42.390474Z",
+ "iopub.status.idle": "2024-07-02T12:08:45.214722Z",
+ "shell.execute_reply": "2024-07-02T12:08:45.214183Z"
}
},
"outputs": [
@@ -519,17 +519,17 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:10:18.417219Z",
- "iopub.status.busy": "2024-07-01T15:10:18.417035Z",
- "iopub.status.idle": "2024-07-01T15:10:50.808150Z",
- "shell.execute_reply": "2024-07-01T15:10:50.807678Z"
+ "iopub.execute_input": "2024-07-02T12:08:45.217043Z",
+ "iopub.status.busy": "2024-07-02T12:08:45.216683Z",
+ "iopub.status.idle": "2024-07-02T12:09:17.125267Z",
+ "shell.execute_reply": "2024-07-02T12:09:17.124709Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "731093637bac464aa707d2bcbb8b8fa8",
+ "model_id": "bd3e5cdb83b549b9ac1d29639e5d5848",
"version_major": 2,
"version_minor": 0
},
@@ -769,10 +769,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:10:50.810430Z",
- "iopub.status.busy": "2024-07-01T15:10:50.810008Z",
- "iopub.status.idle": "2024-07-01T15:11:05.045003Z",
- "shell.execute_reply": "2024-07-01T15:11:05.044449Z"
+ "iopub.execute_input": "2024-07-02T12:09:17.127732Z",
+ "iopub.status.busy": "2024-07-02T12:09:17.127284Z",
+ "iopub.status.idle": "2024-07-02T12:09:31.678319Z",
+ "shell.execute_reply": "2024-07-02T12:09:31.677670Z"
}
},
"outputs": [],
@@ -786,10 +786,10 @@
"id": "716c74f3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:05.047402Z",
- "iopub.status.busy": "2024-07-01T15:11:05.047124Z",
- "iopub.status.idle": "2024-07-01T15:11:08.765062Z",
- "shell.execute_reply": "2024-07-01T15:11:08.764520Z"
+ "iopub.execute_input": "2024-07-02T12:09:31.680982Z",
+ "iopub.status.busy": "2024-07-02T12:09:31.680776Z",
+ "iopub.status.idle": "2024-07-02T12:09:35.425388Z",
+ "shell.execute_reply": "2024-07-02T12:09:35.424766Z"
}
},
"outputs": [
@@ -858,17 +858,17 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:08.767118Z",
- "iopub.status.busy": "2024-07-01T15:11:08.766928Z",
- "iopub.status.idle": "2024-07-01T15:11:10.139794Z",
- "shell.execute_reply": "2024-07-01T15:11:10.139251Z"
+ "iopub.execute_input": "2024-07-02T12:09:35.427710Z",
+ "iopub.status.busy": "2024-07-02T12:09:35.427361Z",
+ "iopub.status.idle": "2024-07-02T12:09:36.906817Z",
+ "shell.execute_reply": "2024-07-02T12:09:36.906253Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "94d7b260381b4b7da5eca25e123790e6",
+ "model_id": "f7bb7e722917409d87abfe3e6a57fae6",
"version_major": 2,
"version_minor": 0
},
@@ -898,10 +898,10 @@
"id": "390780a1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:10.142331Z",
- "iopub.status.busy": "2024-07-01T15:11:10.141916Z",
- "iopub.status.idle": "2024-07-01T15:11:10.170330Z",
- "shell.execute_reply": "2024-07-01T15:11:10.169827Z"
+ "iopub.execute_input": "2024-07-02T12:09:36.909119Z",
+ "iopub.status.busy": "2024-07-02T12:09:36.908767Z",
+ "iopub.status.idle": "2024-07-02T12:09:36.938376Z",
+ "shell.execute_reply": "2024-07-02T12:09:36.937849Z"
}
},
"outputs": [],
@@ -915,10 +915,10 @@
"id": "933d6ef0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:10.172647Z",
- "iopub.status.busy": "2024-07-01T15:11:10.172452Z",
- "iopub.status.idle": "2024-07-01T15:11:16.278293Z",
- "shell.execute_reply": "2024-07-01T15:11:16.277810Z"
+ "iopub.execute_input": "2024-07-02T12:09:36.940953Z",
+ "iopub.status.busy": "2024-07-02T12:09:36.940583Z",
+ "iopub.status.idle": "2024-07-02T12:09:42.990503Z",
+ "shell.execute_reply": "2024-07-02T12:09:42.989906Z"
}
},
"outputs": [
@@ -991,10 +991,10 @@
"id": "86bac686",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:16.280555Z",
- "iopub.status.busy": "2024-07-01T15:11:16.280188Z",
- "iopub.status.idle": "2024-07-01T15:11:16.335969Z",
- "shell.execute_reply": "2024-07-01T15:11:16.335497Z"
+ "iopub.execute_input": "2024-07-02T12:09:42.992659Z",
+ "iopub.status.busy": "2024-07-02T12:09:42.992472Z",
+ "iopub.status.idle": "2024-07-02T12:09:43.049918Z",
+ "shell.execute_reply": "2024-07-02T12:09:43.049421Z"
},
"nbsphinx": "hidden"
},
@@ -1038,23 +1038,25 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0ce80ae00bf44c64b8d09bc5ac656e27": {
+ "004250ad803f48e690d7de9d8df2a5d4": {
"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
}
},
- "0d422e6ef3524fe18196d3c8b173b4b6": {
+ "09ba332d59f94952875cd79ebffa12b3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1069,15 +1071,121 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_58f2dea7d21a44d288433745eff48fb5",
+ "layout": "IPY_MODEL_292d8527f19545118904c48eb804b159",
"placeholder": "",
- "style": "IPY_MODEL_a6f5a4d370df4f5ab60b6aa6323dfe0c",
+ "style": "IPY_MODEL_ebe541c7a5ed4c939a5f7b993c9dee23",
"tabbable": null,
"tooltip": null,
- "value": "number of examples processed for estimating thresholds: 100%"
+ "value": " 30/30 [00:01<00:00, 20.84it/s]"
+ }
+ },
+ "14f7f9d4ea3e4f77a067725d5a561423": {
+ "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
+ }
+ },
+ "1c5deedf3c2d452d9820d81060a3a997": {
+ "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
}
},
- "0fba3cd87f5e4a30858fae2027c96f2f": {
+ "292d8527f19545118904c48eb804b159": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1130,7 +1238,7 @@
"width": null
}
},
- "120e14c159a940bebfa008af4846e1af": {
+ "2b3a352a3891401497f4005cb5fc04d1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1145,41 +1253,85 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_d7d60f679eea43e79193e07ed20fc5aa",
+ "layout": "IPY_MODEL_1c5deedf3c2d452d9820d81060a3a997",
"placeholder": "",
- "style": "IPY_MODEL_2d90c5dd6d1848b38f0aa931986b4799",
+ "style": "IPY_MODEL_c90d5255c6884dd0acaca4ca4a0be555",
"tabbable": null,
"tooltip": null,
- "value": " 30/30 [00:00<00:00, 768.06it/s]"
+ "value": "100%"
}
},
- "1a7a00c51cf841f9960806391ff7b8c0": {
+ "2cda7dd9aaa748a58027afd119786f13": {
"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_4bb2732aa35d440a84e89690b8a0f610",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_a88b229f72f34e9a8c5e66a993f56a21",
+ "layout": "IPY_MODEL_3bc90b1caf7040428b193fe64f002cf0",
+ "placeholder": "",
+ "style": "IPY_MODEL_004250ad803f48e690d7de9d8df2a5d4",
"tabbable": null,
"tooltip": null,
- "value": 30.0
+ "value": "number of examples processed for estimating thresholds: 100%"
+ }
+ },
+ "2d192adc8c47477a9a6eecca9e36e444": {
+ "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_5c44620b24814621897a324f8628f9b0",
+ "placeholder": "",
+ "style": "IPY_MODEL_8c0884f36e9a43fcb603fc1d8a5ac45d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 4997683/4997683 [00:31<00:00, 155900.40it/s]"
+ }
+ },
+ "301ab18342ea43859b3e69cf6784234e": {
+ "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_689cb1d684d746f081730b6005754954",
+ "IPY_MODEL_87cc943b387f45588eebeaa6c9ffd2b2",
+ "IPY_MODEL_621b8d69d18c4672a45b4f9791a34f0e"
+ ],
+ "layout": "IPY_MODEL_39fca7453c7a49f9b90660089493b14b",
+ "tabbable": null,
+ "tooltip": null
}
},
- "1d6b185c42f944568fbf955268ac9154": {
+ "39fca7453c7a49f9b90660089493b14b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1232,48 +1384,76 @@
"width": null
}
},
- "2535e62f9cf1494fb6cc24a0ac2907b2": {
- "model_module": "@jupyter-widgets/controls",
+ "3bc90b1caf7040428b193fe64f002cf0": {
+ "model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
- "model_name": "HTMLModel",
+ "model_name": "LayoutModel",
"state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
+ "_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
+ "_model_name": "LayoutModel",
"_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_1d6b185c42f944568fbf955268ac9154",
- "placeholder": "",
- "style": "IPY_MODEL_880d91c28adf4495881e409632e6f2c0",
- "tabbable": null,
- "tooltip": null,
- "value": "number of examples processed for checking labels: 100%"
+ "_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
}
},
- "2d90c5dd6d1848b38f0aa931986b4799": {
+ "40f6e020e4814cb8b0192d390bc249e5": {
"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": ""
}
},
- "3889edbb1fe4425ebf7afefb82fe5802": {
+ "443d77bb1d4e408f9a9d727916a4a908": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1326,7 +1506,7 @@
"width": null
}
},
- "470506ccc55441d4a3335d76966e3c9a": {
+ "46d41e19a14142bbad7d9d0b73fd7a54": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -1344,7 +1524,7 @@
"text_color": null
}
},
- "4bb2732aa35d440a84e89690b8a0f610": {
+ "50d3a7dfb3964711a029df0085e31f7b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1397,7 +1577,7 @@
"width": null
}
},
- "58f2dea7d21a44d288433745eff48fb5": {
+ "5148cb9775f14bd194e8f971e6975671": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1450,43 +1630,76 @@
"width": null
}
},
- "5ab028ba9f524306aa0c67b32b1e5513": {
+ "58e7f42a53f94f1bbd0867761587546d": {
"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": ""
}
},
- "620edc866142421db8ff2794fa79153f": {
- "model_module": "@jupyter-widgets/controls",
+ "5c44620b24814621897a324f8628f9b0": {
+ "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
}
},
- "645ee85b81c0443e9b9b94ea8c6e87fd": {
+ "621b8d69d18c4672a45b4f9791a34f0e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1501,65 +1714,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_7e82ec8863a94f5d8e6065f6c467c1ba",
+ "layout": "IPY_MODEL_8d802ab243544f9d9299452347a70569",
"placeholder": "",
- "style": "IPY_MODEL_470506ccc55441d4a3335d76966e3c9a",
- "tabbable": null,
- "tooltip": null,
- "value": "images processed using softmin: 100%"
- }
- },
- "722e544a67304c41a49646191b43f64e": {
- "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_8dfb6569b11b4e579ab5f1679787b922",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_f781332402924b45849cdca8f6978ed3",
+ "style": "IPY_MODEL_9c2434fab6d1467eb5531b5dd54033e7",
"tabbable": null,
"tooltip": null,
- "value": 30.0
- }
- },
- "731093637bac464aa707d2bcbb8b8fa8": {
- "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_9b08851d974b4b75b2758e2517483e68",
- "IPY_MODEL_a7e45bb52c5c49fd91aef928ec848223",
- "IPY_MODEL_8df97aa7051e4cac814f7092dcecc401"
- ],
- "layout": "IPY_MODEL_998f6677b81d4e86955affe5f63bdf7b",
- "tabbable": null,
- "tooltip": null
+ "value": " 30/30 [00:22<00:00, 1.35it/s]"
}
},
- "742abd1e12e941f0ade6da7a2b14d0d0": {
+ "689cb1d684d746f081730b6005754954": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLModel",
@@ -1574,41 +1737,15 @@
"_view_name": "HTMLView",
"description": "",
"description_allow_html": false,
- "layout": "IPY_MODEL_f6a973b86f594108bb655affcc8de322",
+ "layout": "IPY_MODEL_14f7f9d4ea3e4f77a067725d5a561423",
"placeholder": "",
- "style": "IPY_MODEL_620edc866142421db8ff2794fa79153f",
- "tabbable": null,
- "tooltip": null,
- "value": " 30/30 [00:01<00:00, 22.07it/s]"
- }
- },
- "754ca065e07f4e7eb99baf4c7cf19ce4": {
- "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_b8a70293c3224c66a3f27168557b9ba8",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_ec84cd07627b443a8c8ce0e1f099c72c",
+ "style": "IPY_MODEL_df3ac9cfe3ae41ceb9de218f2d91ba0b",
"tabbable": null,
"tooltip": null,
- "value": 30.0
+ "value": "number of examples processed for checking labels: 100%"
}
},
- "7e82ec8863a94f5d8e6065f6c467c1ba": {
+ "6ec1d65e68c649a287aa126587fe0f81": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1661,25 +1798,7 @@
"width": null
}
},
- "880d91c28adf4495881e409632e6f2c0": {
- "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
- }
- },
- "88577bae8f0943e6a91fd0f7eb9523ad": {
+ "6fad649441314d58a415b41b259d3ca9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1732,30 +1851,77 @@
"width": null
}
},
- "8df97aa7051e4cac814f7092dcecc401": {
+ "7fe63f20c00f4319a0964ba731ec434b": {
"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_b53c3a56cde9432497a763625dc6e49a",
- "placeholder": "",
- "style": "IPY_MODEL_a513f46efc83419b8f4e22c3b2c064d2",
+ "layout": "IPY_MODEL_acd636f3fd2248ec95449072f24f9e7c",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_58e7f42a53f94f1bbd0867761587546d",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 30.0
+ }
+ },
+ "87cc943b387f45588eebeaa6c9ffd2b2": {
+ "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_b345e277b7bf4c62b4c637851b6c0214",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_aa5c0233f9e24cfb94a975b0bdddc4f5",
"tabbable": null,
"tooltip": null,
- "value": " 4997683/4997683 [00:32<00:00, 156404.46it/s]"
+ "value": 30.0
+ }
+ },
+ "8c0884f36e9a43fcb603fc1d8a5ac45d": {
+ "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
}
},
- "8dfb6569b11b4e579ab5f1679787b922": {
+ "8d802ab243544f9d9299452347a70569": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1808,7 +1974,25 @@
"width": null
}
},
- "94d7b260381b4b7da5eca25e123790e6": {
+ "9c2434fab6d1467eb5531b5dd54033e7": {
+ "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
+ }
+ },
+ "9e20fdede857444e8054f80d2f1060d4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -1823,16 +2007,32 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_645ee85b81c0443e9b9b94ea8c6e87fd",
- "IPY_MODEL_1a7a00c51cf841f9960806391ff7b8c0",
- "IPY_MODEL_742abd1e12e941f0ade6da7a2b14d0d0"
+ "IPY_MODEL_2cda7dd9aaa748a58027afd119786f13",
+ "IPY_MODEL_7fe63f20c00f4319a0964ba731ec434b",
+ "IPY_MODEL_cec723cee8f249199133eca7dc012de4"
],
- "layout": "IPY_MODEL_0fba3cd87f5e4a30858fae2027c96f2f",
+ "layout": "IPY_MODEL_443d77bb1d4e408f9a9d727916a4a908",
"tabbable": null,
"tooltip": null
}
},
- "998f6677b81d4e86955affe5f63bdf7b": {
+ "aa5c0233f9e24cfb94a975b0bdddc4f5": {
+ "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": ""
+ }
+ },
+ "acd636f3fd2248ec95449072f24f9e7c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1885,30 +2085,7 @@
"width": null
}
},
- "9b08851d974b4b75b2758e2517483e68": {
- "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_b8bda17c986d4f7fa6a7b2aaf2a0be0a",
- "placeholder": "",
- "style": "IPY_MODEL_e3090d513b8a48f380607cc86cd1895f",
- "tabbable": null,
- "tooltip": null,
- "value": "100%"
- }
- },
- "a4188823d8964bafbc43b207d512faeb": {
+ "b345e277b7bf4c62b4c637851b6c0214": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -1961,115 +2138,14 @@
"width": null
}
},
- "a513f46efc83419b8f4e22c3b2c064d2": {
- "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
- }
- },
- "a6f5a4d370df4f5ab60b6aa6323dfe0c": {
- "model_module": "@jupyter-widgets/controls",
+ "b3df8f2c00a04d5b832de9fc959e9aae": {
+ "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",
- "_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
- }
- },
- "a7e45bb52c5c49fd91aef928ec848223": {
- "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_88577bae8f0943e6a91fd0f7eb9523ad",
- "max": 4997683.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_0ce80ae00bf44c64b8d09bc5ac656e27",
- "tabbable": null,
- "tooltip": null,
- "value": 4997683.0
- }
- },
- "a88b229f72f34e9a8c5e66a993f56a21": {
- "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": ""
- }
- },
- "aa1f2aad145f4f339c1cd410fe718472": {
- "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_f5afe4dbdebe45929344b9d66f6e48a8",
- "placeholder": "",
- "style": "IPY_MODEL_5ab028ba9f524306aa0c67b32b1e5513",
- "tabbable": null,
- "tooltip": null,
- "value": " 30/30 [00:21<00:00, 1.33it/s]"
- }
- },
- "b53c3a56cde9432497a763625dc6e49a": {
- "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": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
@@ -2115,113 +2191,30 @@
"width": null
}
},
- "b8a70293c3224c66a3f27168557b9ba8": {
- "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
- }
- },
- "b8bda17c986d4f7fa6a7b2aaf2a0be0a": {
- "model_module": "@jupyter-widgets/base",
+ "ba6883ab9881467ba869997da1c9ea0e": {
+ "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/base",
+ "_view_module": "@jupyter-widgets/controls",
"_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": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_6fad649441314d58a415b41b259d3ca9",
+ "placeholder": "",
+ "style": "IPY_MODEL_46d41e19a14142bbad7d9d0b73fd7a54",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "images processed using softmin: 100%"
}
},
- "cae016dc953549ce807817682c42dc87": {
+ "bd3e5cdb83b549b9ac1d29639e5d5848": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HBoxModel",
@@ -2236,69 +2229,16 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_2535e62f9cf1494fb6cc24a0ac2907b2",
- "IPY_MODEL_722e544a67304c41a49646191b43f64e",
- "IPY_MODEL_aa1f2aad145f4f339c1cd410fe718472"
+ "IPY_MODEL_2b3a352a3891401497f4005cb5fc04d1",
+ "IPY_MODEL_d0ee84f27abf457094d435faffe772aa",
+ "IPY_MODEL_2d192adc8c47477a9a6eecca9e36e444"
],
- "layout": "IPY_MODEL_a4188823d8964bafbc43b207d512faeb",
+ "layout": "IPY_MODEL_d13c21dbbe954346b0c709b6d940db68",
"tabbable": null,
"tooltip": null
}
},
- "d7d60f679eea43e79193e07ed20fc5aa": {
- "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
- }
- },
- "e3090d513b8a48f380607cc86cd1895f": {
+ "c90d5255c6884dd0acaca4ca4a0be555": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "HTMLStyleModel",
@@ -2316,47 +2256,56 @@
"text_color": null
}
},
- "e93b88c996c44feeb3673439eaaea41d": {
+ "cec723cee8f249199133eca7dc012de4": {
"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_0d422e6ef3524fe18196d3c8b173b4b6",
- "IPY_MODEL_754ca065e07f4e7eb99baf4c7cf19ce4",
- "IPY_MODEL_120e14c159a940bebfa008af4846e1af"
- ],
- "layout": "IPY_MODEL_3889edbb1fe4425ebf7afefb82fe5802",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_5148cb9775f14bd194e8f971e6975671",
+ "placeholder": "",
+ "style": "IPY_MODEL_e1313619907446f882ce04af77ca7ac9",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": " 30/30 [00:00<00:00, 789.00it/s]"
}
},
- "ec84cd07627b443a8c8ce0e1f099c72c": {
+ "d0ee84f27abf457094d435faffe772aa": {
"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_6ec1d65e68c649a287aa126587fe0f81",
+ "max": 4997683.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_40f6e020e4814cb8b0192d390bc249e5",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 4997683.0
}
},
- "f5afe4dbdebe45929344b9d66f6e48a8": {
+ "d13c21dbbe954346b0c709b6d940db68": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2409,60 +2358,87 @@
"width": null
}
},
- "f6a973b86f594108bb655affcc8de322": {
- "model_module": "@jupyter-widgets/base",
+ "dbe2204bfeee42de9c8c9d92d9dc0eb7": {
+ "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_b3df8f2c00a04d5b832de9fc959e9aae",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_f6b0d85730d34497bc3daf8d027415bc",
+ "tabbable": null,
+ "tooltip": null,
+ "value": 30.0
+ }
+ },
+ "df3ac9cfe3ae41ceb9de218f2d91ba0b": {
+ "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
}
},
- "f781332402924b45849cdca8f6978ed3": {
+ "e1313619907446f882ce04af77ca7ac9": {
+ "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
+ }
+ },
+ "ebe541c7a5ed4c939a5f7b993c9dee23": {
+ "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
+ }
+ },
+ "f6b0d85730d34497bc3daf8d027415bc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "2.0.0",
"model_name": "ProgressStyleModel",
@@ -2477,6 +2453,30 @@
"bar_color": null,
"description_width": ""
}
+ },
+ "f7bb7e722917409d87abfe3e6a57fae6": {
+ "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_ba6883ab9881467ba869997da1c9ea0e",
+ "IPY_MODEL_dbe2204bfeee42de9c8c9d92d9dc0eb7",
+ "IPY_MODEL_09ba332d59f94952875cd79ebffa12b3"
+ ],
+ "layout": "IPY_MODEL_50d3a7dfb3964711a029df0085e31f7b",
+ "tabbable": null,
+ "tooltip": null
+ }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 8650ebc00..2f967cbe9 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": "2024-07-01T15:11:18.503218Z",
- "iopub.status.busy": "2024-07-01T15:11:18.502735Z",
- "iopub.status.idle": "2024-07-01T15:11:19.975527Z",
- "shell.execute_reply": "2024-07-01T15:11:19.974839Z"
+ "iopub.execute_input": "2024-07-02T12:09:45.418874Z",
+ "iopub.status.busy": "2024-07-02T12:09:45.418417Z",
+ "iopub.status.idle": "2024-07-02T12:09:46.521891Z",
+ "shell.execute_reply": "2024-07-02T12:09:46.521319Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-07-01 15:11:18-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-07-02 12:09:45-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,15 +94,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "169.150.236.98, 2400:52e0:1a00::871:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... "
+ "185.93.1.249, 2400:52e0:1a00::871:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|185.93.1.249|:443... connected.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -123,9 +122,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-07-02 12:09:45 (6.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -145,9 +144,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-07-01 15:11:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.28.244, 3.5.24.72, 52.217.13.252, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.28.244|:443... connected.\r\n",
+ "--2024-07-02 12:09:46-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.236.81, 16.182.109.113, 3.5.9.115, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.236.81|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -168,17 +167,9 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 35%[======> ] 5.78M 28.9MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 100%[===================>] 16.26M 52.3MB/s in 0.3s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-07-01 15:11:19 (52.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-07-02 12:09:46 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -195,10 +186,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:19.978352Z",
- "iopub.status.busy": "2024-07-01T15:11:19.977882Z",
- "iopub.status.idle": "2024-07-01T15:11:21.215995Z",
- "shell.execute_reply": "2024-07-01T15:11:21.215505Z"
+ "iopub.execute_input": "2024-07-02T12:09:46.524639Z",
+ "iopub.status.busy": "2024-07-02T12:09:46.524272Z",
+ "iopub.status.idle": "2024-07-02T12:09:47.827762Z",
+ "shell.execute_reply": "2024-07-02T12:09:47.827179Z"
},
"nbsphinx": "hidden"
},
@@ -209,7 +200,7 @@
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -235,10 +226,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:21.218513Z",
- "iopub.status.busy": "2024-07-01T15:11:21.218132Z",
- "iopub.status.idle": "2024-07-01T15:11:21.221470Z",
- "shell.execute_reply": "2024-07-01T15:11:21.221045Z"
+ "iopub.execute_input": "2024-07-02T12:09:47.830413Z",
+ "iopub.status.busy": "2024-07-02T12:09:47.829987Z",
+ "iopub.status.idle": "2024-07-02T12:09:47.833472Z",
+ "shell.execute_reply": "2024-07-02T12:09:47.833017Z"
}
},
"outputs": [],
@@ -288,10 +279,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:21.223694Z",
- "iopub.status.busy": "2024-07-01T15:11:21.223257Z",
- "iopub.status.idle": "2024-07-01T15:11:21.226332Z",
- "shell.execute_reply": "2024-07-01T15:11:21.225848Z"
+ "iopub.execute_input": "2024-07-02T12:09:47.835687Z",
+ "iopub.status.busy": "2024-07-02T12:09:47.835327Z",
+ "iopub.status.idle": "2024-07-02T12:09:47.838382Z",
+ "shell.execute_reply": "2024-07-02T12:09:47.837903Z"
},
"nbsphinx": "hidden"
},
@@ -309,10 +300,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:21.228084Z",
- "iopub.status.busy": "2024-07-01T15:11:21.227917Z",
- "iopub.status.idle": "2024-07-01T15:11:30.310755Z",
- "shell.execute_reply": "2024-07-01T15:11:30.310211Z"
+ "iopub.execute_input": "2024-07-02T12:09:47.840488Z",
+ "iopub.status.busy": "2024-07-02T12:09:47.840076Z",
+ "iopub.status.idle": "2024-07-02T12:09:56.981305Z",
+ "shell.execute_reply": "2024-07-02T12:09:56.980685Z"
}
},
"outputs": [],
@@ -386,10 +377,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:30.313310Z",
- "iopub.status.busy": "2024-07-01T15:11:30.313004Z",
- "iopub.status.idle": "2024-07-01T15:11:30.318459Z",
- "shell.execute_reply": "2024-07-01T15:11:30.318009Z"
+ "iopub.execute_input": "2024-07-02T12:09:56.983968Z",
+ "iopub.status.busy": "2024-07-02T12:09:56.983751Z",
+ "iopub.status.idle": "2024-07-02T12:09:56.989422Z",
+ "shell.execute_reply": "2024-07-02T12:09:56.988975Z"
},
"nbsphinx": "hidden"
},
@@ -429,10 +420,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:30.320517Z",
- "iopub.status.busy": "2024-07-01T15:11:30.320198Z",
- "iopub.status.idle": "2024-07-01T15:11:30.659248Z",
- "shell.execute_reply": "2024-07-01T15:11:30.658770Z"
+ "iopub.execute_input": "2024-07-02T12:09:56.991449Z",
+ "iopub.status.busy": "2024-07-02T12:09:56.991142Z",
+ "iopub.status.idle": "2024-07-02T12:09:57.333959Z",
+ "shell.execute_reply": "2024-07-02T12:09:57.333418Z"
}
},
"outputs": [],
@@ -469,10 +460,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:30.661698Z",
- "iopub.status.busy": "2024-07-01T15:11:30.661301Z",
- "iopub.status.idle": "2024-07-01T15:11:30.665925Z",
- "shell.execute_reply": "2024-07-01T15:11:30.665448Z"
+ "iopub.execute_input": "2024-07-02T12:09:57.336408Z",
+ "iopub.status.busy": "2024-07-02T12:09:57.336047Z",
+ "iopub.status.idle": "2024-07-02T12:09:57.340566Z",
+ "shell.execute_reply": "2024-07-02T12:09:57.340088Z"
}
},
"outputs": [
@@ -544,10 +535,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:30.667958Z",
- "iopub.status.busy": "2024-07-01T15:11:30.667632Z",
- "iopub.status.idle": "2024-07-01T15:11:33.481219Z",
- "shell.execute_reply": "2024-07-01T15:11:33.480521Z"
+ "iopub.execute_input": "2024-07-02T12:09:57.342536Z",
+ "iopub.status.busy": "2024-07-02T12:09:57.342207Z",
+ "iopub.status.idle": "2024-07-02T12:09:59.889796Z",
+ "shell.execute_reply": "2024-07-02T12:09:59.889167Z"
}
},
"outputs": [],
@@ -569,10 +560,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:33.484626Z",
- "iopub.status.busy": "2024-07-01T15:11:33.483787Z",
- "iopub.status.idle": "2024-07-01T15:11:33.488254Z",
- "shell.execute_reply": "2024-07-01T15:11:33.487491Z"
+ "iopub.execute_input": "2024-07-02T12:09:59.892826Z",
+ "iopub.status.busy": "2024-07-02T12:09:59.892074Z",
+ "iopub.status.idle": "2024-07-02T12:09:59.896257Z",
+ "shell.execute_reply": "2024-07-02T12:09:59.895794Z"
}
},
"outputs": [
@@ -608,10 +599,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:33.490509Z",
- "iopub.status.busy": "2024-07-01T15:11:33.490170Z",
- "iopub.status.idle": "2024-07-01T15:11:33.496148Z",
- "shell.execute_reply": "2024-07-01T15:11:33.495591Z"
+ "iopub.execute_input": "2024-07-02T12:09:59.898108Z",
+ "iopub.status.busy": "2024-07-02T12:09:59.897930Z",
+ "iopub.status.idle": "2024-07-02T12:09:59.903451Z",
+ "shell.execute_reply": "2024-07-02T12:09:59.902896Z"
}
},
"outputs": [
@@ -789,10 +780,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:33.498276Z",
- "iopub.status.busy": "2024-07-01T15:11:33.497940Z",
- "iopub.status.idle": "2024-07-01T15:11:33.525403Z",
- "shell.execute_reply": "2024-07-01T15:11:33.524817Z"
+ "iopub.execute_input": "2024-07-02T12:09:59.905627Z",
+ "iopub.status.busy": "2024-07-02T12:09:59.905242Z",
+ "iopub.status.idle": "2024-07-02T12:09:59.932087Z",
+ "shell.execute_reply": "2024-07-02T12:09:59.931495Z"
}
},
"outputs": [
@@ -894,10 +885,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:33.527747Z",
- "iopub.status.busy": "2024-07-01T15:11:33.527322Z",
- "iopub.status.idle": "2024-07-01T15:11:33.532159Z",
- "shell.execute_reply": "2024-07-01T15:11:33.531610Z"
+ "iopub.execute_input": "2024-07-02T12:09:59.934435Z",
+ "iopub.status.busy": "2024-07-02T12:09:59.934079Z",
+ "iopub.status.idle": "2024-07-02T12:09:59.939450Z",
+ "shell.execute_reply": "2024-07-02T12:09:59.938896Z"
}
},
"outputs": [
@@ -971,10 +962,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:33.534578Z",
- "iopub.status.busy": "2024-07-01T15:11:33.534006Z",
- "iopub.status.idle": "2024-07-01T15:11:34.915561Z",
- "shell.execute_reply": "2024-07-01T15:11:34.914971Z"
+ "iopub.execute_input": "2024-07-02T12:09:59.941692Z",
+ "iopub.status.busy": "2024-07-02T12:09:59.941362Z",
+ "iopub.status.idle": "2024-07-02T12:10:01.337767Z",
+ "shell.execute_reply": "2024-07-02T12:10:01.337179Z"
}
},
"outputs": [
@@ -1146,10 +1137,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:11:34.917877Z",
- "iopub.status.busy": "2024-07-01T15:11:34.917550Z",
- "iopub.status.idle": "2024-07-01T15:11:34.921657Z",
- "shell.execute_reply": "2024-07-01T15:11:34.921191Z"
+ "iopub.execute_input": "2024-07-02T12:10:01.339986Z",
+ "iopub.status.busy": "2024-07-02T12:10:01.339664Z",
+ "iopub.status.idle": "2024-07-02T12:10:01.343749Z",
+ "shell.execute_reply": "2024-07-02T12:10:01.343244Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree
index bcc71851e..56f3b4982 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 49658f8e3..23aebfda4 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 e4c47ce00..8402a8744 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 fd49abec4..9d2401ea6 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 f27a35b4c..cb14f0ca8 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 8f2157fbd..5ee4aa04a 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 f288175e0..f82d8d8ce 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 0aac48e0b..20c2362aa 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 40134de46..12c4a09fb 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 760690942..0f4f6ff70 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 e67183aaa..d13650311 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 3fe24834b..b47372bab 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 b0e93f410..6cd8f62ce 100644
Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ
diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree
index 6428a59c9..5d501d388 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 3f8ef0c26..779172c32 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 e03ae1aaa..b63288b8e 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 0da910c90..4c4cceacc 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 91d81d859..902271f2c 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 2c4d181cb..e37583260 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 580a61d62..fb404519c 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 d7fd3bc72..bb1e7ebf4 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 20cfb5da3..19a3e019f 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 ad65bcbbd..db7874a9d 100644
Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 6bd0ac215..9699fd59f 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 d6b47ffef..af77ff1a5 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 d52a1f814..b012c3b83 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 744175c39..40b596a7c 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 4b8205657..6d03ae333 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 2ba045367..ac39104cf 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 51da2d9a6..3e5552460 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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/workflows.ipynb b/master/_sources/tutorials/datalab/workflows.ipynb
index 6bd4ee5cf..0a17e353b 100644
--- a/master/_sources/tutorials/datalab/workflows.ipynb
+++ b/master/_sources/tutorials/datalab/workflows.ipynb
@@ -1331,22 +1331,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Find Spurious Correlation between Vision Dataset features and class labels\n",
+ "## Identify Spurious Correlations in Image Datasets\n",
"\n",
- "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n",
+ "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n",
+ "These correlations could lead to unreliable model predictions and poor generalization.\n",
"\n",
- "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models."
+ "\n",
+ "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n",
+ "\n",
+ "- Darkness\n",
+ "- Blurriness\n",
+ "- Aspect ratio anomalies\n",
+ "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n",
+ "\n",
+ "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 1. Load the dataset\n",
+ "### 1. Load the Dataset\n",
+ "\n",
+ "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n",
"\n",
- "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n",
+ "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n",
+ "- The correlation scores range from 0 to 1, where:\n",
+ " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n",
+ " - Scores close to 1 suggest little to no correlation between the property and class labels.\n",
+ "- By introducing this bias, we expect to see:\n",
+ " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n",
+ " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n",
"\n",
- "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height."
+ "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels."
]
},
{
@@ -1402,7 +1419,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find vision-related issues"
+ "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find image-related issues"
]
},
{
@@ -1523,9 +1540,9 @@
"transformed_property_scores = get_property_scores(transformed_dataset)\n",
"\n",
"# Displaying the scores dataframe\n",
- "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n",
+ "display(Markdown(\"### Image-specific property scores in the original dataset\"))\n",
"display(standard_property_scores)\n",
- "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n",
+ "display(Markdown(\"### Image-specific property scores in the transformed dataset\"))\n",
"display(transformed_property_scores)\n",
"\n",
"# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index ce8be3372..7af5e7f6e 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 7d89efb39..e036f973f 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 ddf90b86b..56543bad0 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 542b9cfd4..348a544a8 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 79a87033f..2d80f1068 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 4f7e0427a..b6dbc6271 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 c0d2c1d07..fe223cf83 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 e65d991e6..f5ced067f 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 2777086e9..f02e0094c 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/objects.inv b/master/objects.inv
index 0a47f7ce7..a324b74dd 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/searchindex.js b/master/searchindex.js
index 2c6f7b4df..47f457c5d 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/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/fasttext", "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/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/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/fasttext.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/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", "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", "fasttext", "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", "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, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 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, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "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, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 96, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 96, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 96, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 96, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106, 108], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106, 108], "signific": [10, 94, 95, 96, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 96, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 96, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 96, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106, 108], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 96, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106, 108], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "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, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": [37, 96], "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103, 108], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": 62, "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": [62, 96], "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 96, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 96, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103, 104], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "getting_spare_card": [88, 95], "visa_or_mastercard": [88, 95], "lost_or_stolen_phon": [88, 95], "card_about_to_expir": [88, 95], "card_payment_fee_charg": [88, 95], "beneficiary_not_allow": [88, 95], "supported_cards_and_curr": [88, 95], "apple_pay_or_google_pai": [88, 95], "change_pin": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106, 108], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "749": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "439": 92, "493": 92, "060": 92, "851": 92, "330": [92, 103], "505": 92, "491": 92, "476": 92, "340": 92, "739": 92, "328": [92, 103], "310": 92, "490": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": 95, "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "523": 96, "991400": 96, "356958": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "mislead": 96, "pinpoint": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "enhanc": [96, 99, 101, 103], "alter": [96, 98], "darken": 96, "reduct": 96, "aspect": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "101603839": 96, "38it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": [97, 108], "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103, 108], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103, 108], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": [99, 108], "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": 103, "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": [103, 108], "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "anomali": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "39742139": 104, "91it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "871": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "71mb": 108, "mb": 108, "directori": 108, "inflat": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "3mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 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"], [16, 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": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [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.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 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, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 96, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "between": 96, "vision": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "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"], [19, "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"]], "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"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "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?": [[98, "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?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "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?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "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.": [[99, "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": [[99, "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": [[99, "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!": [[99, "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": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "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)": [[99, "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:": [[99, "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": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "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": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "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.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "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"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "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)": [[52, "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)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "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/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/fasttext", "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/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/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/fasttext.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/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", "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", "fasttext", "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", "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, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 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, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 96, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 96, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "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, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 96, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 96, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 96, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 96, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 96, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10, 96], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 96, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "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, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": 37, "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92, 96], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": [62, 96], "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": 62, "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103, 108], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103], "08": [82, 96, 99, 103, 104, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95, 96], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103, 108], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106, 108], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "card_about_to_expir": [88, 95], "lost_or_stolen_phon": [88, 95], "getting_spare_card": [88, 95], "change_pin": [88, 95], "card_payment_fee_charg": [88, 95], "supported_cards_and_curr": [88, 95], "beneficiary_not_allow": [88, 95], "visa_or_mastercard": [88, 95], "apple_pay_or_google_pai": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103, 108], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106, 108], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103, 108], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103, 108], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "801": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "468": 92, "493": 92, "060": 92, "793": 92, "330": [92, 103], "505": 92, "570": 92, "476": 92, "340": 92, "822": 92, "328": [92, 103], "310": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103, 108], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103, 108], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": [95, 96], "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "524": 96, "991400": 96, "356924": 96, "363": 96, "619581": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "aspect": 96, "anomali": [96, 103], "enhanc": [96, 99, 101, 103], "artifici": 96, "alter": [96, 98], "darken": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "setup": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "69520911": 96, "78it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103, 108], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103, 108], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103, 108], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": 99, "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106, 108], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": [103, 108], "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": 103, "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "19884004": 104, "38it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "871": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "kb": 108, "mb": 108, "directori": 108, "inflat": 108, "182": 108, "113": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 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"], [16, 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": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [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.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 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, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "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"], [19, "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"]], "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"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "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?": [[98, "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?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "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?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "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.": [[99, "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": [[99, "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": [[99, "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!": [[99, "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": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "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)": [[99, "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:": [[99, "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": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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.": [[99, "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?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "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": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "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.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "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"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "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)": [[52, "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)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "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 835b9297f..0c56c3881 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": "2024-07-01T15:01:38.704463Z",
- "iopub.status.busy": "2024-07-01T15:01:38.704282Z",
- "iopub.status.idle": "2024-07-01T15:01:39.968773Z",
- "shell.execute_reply": "2024-07-01T15:01:39.968140Z"
+ "iopub.execute_input": "2024-07-02T12:00:24.117516Z",
+ "iopub.status.busy": "2024-07-02T12:00:24.117048Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.333194Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.332647Z"
},
"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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:01:39.971457Z",
- "iopub.status.busy": "2024-07-01T15:01:39.971069Z",
- "iopub.status.idle": "2024-07-01T15:01:39.990015Z",
- "shell.execute_reply": "2024-07-01T15:01:39.989387Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.335570Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.335300Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.352966Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.352544Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:39.992806Z",
- "iopub.status.busy": "2024-07-01T15:01:39.992402Z",
- "iopub.status.idle": "2024-07-01T15:01:40.303536Z",
- "shell.execute_reply": "2024-07-01T15:01:40.302965Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.355177Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.354929Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.498882Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.498315Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.336204Z",
- "iopub.status.busy": "2024-07-01T15:01:40.335666Z",
- "iopub.status.idle": "2024-07-01T15:01:40.340138Z",
- "shell.execute_reply": "2024-07-01T15:01:40.339623Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.528732Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.528329Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.532259Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.531790Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.342354Z",
- "iopub.status.busy": "2024-07-01T15:01:40.342145Z",
- "iopub.status.idle": "2024-07-01T15:01:40.351148Z",
- "shell.execute_reply": "2024-07-01T15:01:40.350569Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.534236Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.534064Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.542721Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.542178Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.353562Z",
- "iopub.status.busy": "2024-07-01T15:01:40.353231Z",
- "iopub.status.idle": "2024-07-01T15:01:40.356046Z",
- "shell.execute_reply": "2024-07-01T15:01:40.355491Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.544841Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.544667Z",
+ "iopub.status.idle": "2024-07-02T12:00:25.547142Z",
+ "shell.execute_reply": "2024-07-02T12:00:25.546723Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.358053Z",
- "iopub.status.busy": "2024-07-01T15:01:40.357874Z",
- "iopub.status.idle": "2024-07-01T15:01:40.885000Z",
- "shell.execute_reply": "2024-07-01T15:01:40.884377Z"
+ "iopub.execute_input": "2024-07-02T12:00:25.549121Z",
+ "iopub.status.busy": "2024-07-02T12:00:25.548952Z",
+ "iopub.status.idle": "2024-07-02T12:00:26.069775Z",
+ "shell.execute_reply": "2024-07-02T12:00:26.069166Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:40.887806Z",
- "iopub.status.busy": "2024-07-01T15:01:40.887346Z",
- "iopub.status.idle": "2024-07-01T15:01:42.858439Z",
- "shell.execute_reply": "2024-07-01T15:01:42.857751Z"
+ "iopub.execute_input": "2024-07-02T12:00:26.072294Z",
+ "iopub.status.busy": "2024-07-02T12:00:26.072111Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.964122Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.963476Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.861505Z",
- "iopub.status.busy": "2024-07-01T15:01:42.860685Z",
- "iopub.status.idle": "2024-07-01T15:01:42.872129Z",
- "shell.execute_reply": "2024-07-01T15:01:42.871534Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.966793Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.966128Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.975803Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.975266Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.874722Z",
- "iopub.status.busy": "2024-07-01T15:01:42.874312Z",
- "iopub.status.idle": "2024-07-01T15:01:42.879185Z",
- "shell.execute_reply": "2024-07-01T15:01:42.878651Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.977956Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.977648Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.981829Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.981303Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.881719Z",
- "iopub.status.busy": "2024-07-01T15:01:42.881293Z",
- "iopub.status.idle": "2024-07-01T15:01:42.890936Z",
- "shell.execute_reply": "2024-07-01T15:01:42.890441Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.984025Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.983701Z",
+ "iopub.status.idle": "2024-07-02T12:00:27.990825Z",
+ "shell.execute_reply": "2024-07-02T12:00:27.990380Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:42.893152Z",
- "iopub.status.busy": "2024-07-01T15:01:42.892940Z",
- "iopub.status.idle": "2024-07-01T15:01:43.010191Z",
- "shell.execute_reply": "2024-07-01T15:01:43.009566Z"
+ "iopub.execute_input": "2024-07-02T12:00:27.992803Z",
+ "iopub.status.busy": "2024-07-02T12:00:27.992505Z",
+ "iopub.status.idle": "2024-07-02T12:00:28.104238Z",
+ "shell.execute_reply": "2024-07-02T12:00:28.103750Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:43.012877Z",
- "iopub.status.busy": "2024-07-01T15:01:43.012678Z",
- "iopub.status.idle": "2024-07-01T15:01:43.015881Z",
- "shell.execute_reply": "2024-07-01T15:01:43.015414Z"
+ "iopub.execute_input": "2024-07-02T12:00:28.106465Z",
+ "iopub.status.busy": "2024-07-02T12:00:28.106127Z",
+ "iopub.status.idle": "2024-07-02T12:00:28.108811Z",
+ "shell.execute_reply": "2024-07-02T12:00:28.108400Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:43.017749Z",
- "iopub.status.busy": "2024-07-01T15:01:43.017574Z",
- "iopub.status.idle": "2024-07-01T15:01:45.116344Z",
- "shell.execute_reply": "2024-07-01T15:01:45.115698Z"
+ "iopub.execute_input": "2024-07-02T12:00:28.110759Z",
+ "iopub.status.busy": "2024-07-02T12:00:28.110457Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.104044Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.103432Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:45.119290Z",
- "iopub.status.busy": "2024-07-01T15:01:45.118731Z",
- "iopub.status.idle": "2024-07-01T15:01:45.130593Z",
- "shell.execute_reply": "2024-07-01T15:01:45.130118Z"
+ "iopub.execute_input": "2024-07-02T12:00:30.106906Z",
+ "iopub.status.busy": "2024-07-02T12:00:30.106328Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.117548Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.117099Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-01T15:01:45.132594Z",
- "iopub.status.busy": "2024-07-01T15:01:45.132413Z",
- "iopub.status.idle": "2024-07-01T15:01:45.200709Z",
- "shell.execute_reply": "2024-07-01T15:01:45.200202Z"
+ "iopub.execute_input": "2024-07-02T12:00:30.119573Z",
+ "iopub.status.busy": "2024-07-02T12:00:30.119249Z",
+ "iopub.status.idle": "2024-07-02T12:00:30.150922Z",
+ "shell.execute_reply": "2024-07-02T12:00:30.150454Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 87f58e815..c0155c6ba 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 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 |
@@ -3473,14 +3473,36 @@ 3. (Optional) Visualize class imbalance issues