CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(),\n",
" n_boot=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(),\n", - " n_boot=1)
LinearRegression()
LinearRegression()
LinearRegression()
LinearRegression()
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
"""
import warnings
+from typing import Optional, Tuple, Union
+
import numpy as np
import pandas as pd
-from typing import Union, Tuple
-from cleanlab.typing import DatasetLike, LabelLike
-from cleanlab.internal.validation import labels_to_array
from cleanlab.internal.constants import FLOATING_POINT_COMPARISON, TINY_VALUE
+from cleanlab.internal.validation import labels_to_array
+from cleanlab.typing import DatasetLike, LabelLike
[docs]def remove_noise_from_class(noise_matrix, class_without_noise) -> np.ndarray:
@@ -733,7 +734,7 @@ Source code for cleanlab.internal.util
return x
-[docs]def value_counts(x, *, num_classes=None, multi_label=False) -> np.ndarray:
+[docs]def value_counts(x, *, num_classes: Optional[int] = None, multi_label=False) -> np.ndarray:
"""Returns an np.ndarray of shape (K, 1), with the
value counts for every unique item in the labels list/array,
where K is the number of unique entries in labels.
@@ -768,17 +769,27 @@ Source code for cleanlab.internal.util
if multi_label:
x = [z for lst in x for z in lst] # Flatten
unique_classes, counts = np.unique(x, return_counts=True)
+
+ # Early exit if num_classes is not provided or redundant
if num_classes is None or num_classes == len(unique_classes):
return counts
+
# Else, there are missing classes
- if num_classes <= max(unique_classes):
- raise ValueError(f"Required: num_classes > max(x), but {num_classes} <= {max(x)}.")
+ labels_are_integers = np.issubdtype(np.array(x).dtype, np.integer)
+ if labels_are_integers and num_classes <= np.max(unique_classes):
+ raise ValueError(f"Required: num_classes > max(x), but {num_classes} <= {np.max(x)}.")
+
# Add zero counts for all missing classes in [0, 1,..., num_classes-1]
- # multi_label=False regardless because x was flattened.
- missing_classes = get_missing_classes(x, num_classes=num_classes, multi_label=False)
- missing_counts = [(z, 0) for z in missing_classes]
+ total_counts = np.zeros(num_classes, dtype=int)
+ # Fill in counts for classes that are present.
+ # If labels are integers, unique_classes can be used directly as indices to place counts
+ # into the correct positions in total_counts array.
+ # If labels are strings, use a slice to fill counts sequentially since strings do not map to indices.
+ count_ids = unique_classes if labels_are_integers else slice(len(unique_classes))
+ total_counts[count_ids] = counts
+
# Return counts with zeros for all missing classes.
- return np.array(list(zip(*sorted(list(zip(unique_classes, counts)) + missing_counts)))[1])
+ return total_counts
[docs]def value_counts_fill_missing_classes(x, num_classes, *, multi_label=False) -> np.ndarray:
@@ -1175,9 +1186,7 @@ Source code for cleanlab.internal.util
or ``len(pre_X)`` if buffer_size cannot be determined, or None if no ShuffleDataset found.
"""
try:
- from tensorflow.python.data.ops.dataset_ops import (
- ShuffleDataset,
- )
+ from tensorflow.python.data.ops.dataset_ops import ShuffleDataset
X_inputs = [X]
while len(X_inputs) == 1:
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index bff523c4a..af4fa1a0e 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 fd60e477f..7358210c0 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 9929144cd..e4f6f24c6 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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/data_monitor.ipynb b/master/_sources/tutorials/datalab/data_monitor.ipynb
index c8028845b..b047fcc74 100644
--- a/master/_sources/tutorials/datalab/data_monitor.ipynb
+++ b/master/_sources/tutorials/datalab/data_monitor.ipynb
@@ -83,7 +83,7 @@
"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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 7547dcb2e..374f8c635 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 dfa321169..e6eac65a1 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 fca506acb..579502709 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 73408c22d..38c63fccd 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 8594e4f29..ccb91890a 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 8d76d666d..7d7e4d455 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 b1c79e544..53a51ab17 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 cdfa538fe..1da9fe082 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 8a439c67e..e8fa753d8 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 139acf2da..c613c004e 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 607913619..f6a8aec81 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 75423ff33..a25ab3f03 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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 3fc326dde..a1480e328 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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/count.html b/master/cleanlab/count.html
index d5e201007..1cd5b2d1b 100644
--- a/master/cleanlab/count.html
+++ b/master/cleanlab/count.html
@@ -976,7 +976,7 @@
-
-cleanlab.count.estimate_confident_joint_and_cv_pred_proba(X, labels, clf=LogisticRegression(), *, cv_n_folds=5, thresholds=None, seed=None, calibrate=True, clf_kwargs={}, validation_func=None)[source]#
+cleanlab.count.estimate_confident_joint_and_cv_pred_proba(X, labels, clf=LogisticRegression(multi_class='auto'), *, cv_n_folds=5, thresholds=None, seed=None, calibrate=True, clf_kwargs={}, validation_func=None)[source]#
Estimates P(labels, y), the confident counts of the latent
joint distribution of true and noisy labels
using observed labels and predicted probabilities pred_probs.
@@ -1045,7 +1045,7 @@
-
-cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba(X, labels, clf=LogisticRegression(), *, cv_n_folds=5, thresholds=None, converge_latent_estimates=False, py_method='cnt', seed=None, clf_kwargs={}, validation_func=None)[source]#
+cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba(X, labels, clf=LogisticRegression(multi_class='auto'), *, cv_n_folds=5, thresholds=None, converge_latent_estimates=False, py_method='cnt', seed=None, clf_kwargs={}, validation_func=None)[source]#
This function computes the out-of-sample predicted
probability P(label=k|x) for every example x in X using cross
validation while also computing the confident counts noise
@@ -1109,7 +1109,7 @@
-
-cleanlab.count.estimate_cv_predicted_probabilities(X, labels, clf=LogisticRegression(), *, cv_n_folds=5, seed=None, clf_kwargs={}, validation_func=None)[source]#
+cleanlab.count.estimate_cv_predicted_probabilities(X, labels, clf=LogisticRegression(multi_class='auto'), *, cv_n_folds=5, seed=None, clf_kwargs={}, validation_func=None)[source]#
This function computes the out-of-sample predicted
probability [P(label=k|x)] for every example in X using cross
validation. Output is a np.ndarray of shape (N, K) where N is
@@ -1148,7 +1148,7 @@
-
-cleanlab.count.estimate_noise_matrices(X, labels, clf=LogisticRegression(), *, cv_n_folds=5, thresholds=None, converge_latent_estimates=True, seed=None, clf_kwargs={}, validation_func=None)[source]#
+cleanlab.count.estimate_noise_matrices(X, labels, clf=LogisticRegression(multi_class='auto'), *, cv_n_folds=5, thresholds=None, converge_latent_estimates=True, seed=None, clf_kwargs={}, validation_func=None)[source]#
Estimates the noise_matrix of shape (K, K). This is the
fraction of examples in every class, labeled as every other class. The
noise_matrix is a conditional probability matrix for P(label=k_s|true_label=k_y).
diff --git a/master/searchindex.js b/master/searchindex.js
index e6c51e9ef..820a1d57d 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/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", 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Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [105, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [105, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "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": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "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": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [105, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[89, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[89, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [100, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[89, "2.-Create-and-load-the-data-(can-skip-these-details)"], [91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. 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Looking for both label issues and outliers": [[89, "8.-Looking-for-both-label-issues-and-outliers"]], "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"]], "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)"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [98, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "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?": [[97, "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?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[97, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[98, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[98, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[98, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[98, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[98, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[98, "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.": [[98, "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": [[98, "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": [[98, "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!": [[98, "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": [[98, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[98, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[98, "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)": [[98, "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:": [[98, "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": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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?": [[98, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[98, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[99, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[100, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[100, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[100, "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": [[100, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[100, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[100, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[100, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[100, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[100, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[101, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[101, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[101, "2.-Format-data,-labels,-and-model-predictions"], [102, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[101, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [106, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[101, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[101, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[101, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[101, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[101, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[102, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[102, "1.-Install-required-dependencies-and-download-data"], [106, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[102, "Get-label-quality-scores"], [106, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[102, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[102, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[102, "Other-uses-of-visualize"]], "Exploratory data analysis": [[102, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[103, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[103, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[103, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[103, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[103, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[103, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[104, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[104, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[104, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[105, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[105, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[105, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[106, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[106, "2.-Get-data,-labels,-and-pred_probs"], [107, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[106, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[106, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[106, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[107, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[107, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[107, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[107, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[107, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[56, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[56, 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"round_preserving_sum() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[57, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[59, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[60, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[60, 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"compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, 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module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, 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\ No newline at end of file
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Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [105, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [105, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "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": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "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": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [105, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[89, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[89, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [100, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[89, "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": [[89, "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": [[89, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[89, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[89, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "7. Finding outliers in new data": [[89, "7.-Finding-outliers-in-new-data"]], "8. Looking for both label issues and outliers": [[89, "8.-Looking-for-both-label-issues-and-outliers"]], "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"]], "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. 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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)"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [98, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "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?": [[97, "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?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[97, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[98, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[98, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[98, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[98, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[98, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[98, "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.": [[98, "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": [[98, "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": [[98, "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!": [[98, "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": [[98, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[98, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[98, "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)": [[98, "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:": [[98, "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": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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?": [[98, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[98, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[99, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[100, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[100, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[100, "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": [[100, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[100, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[100, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[100, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[100, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[100, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[101, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[101, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[101, "2.-Format-data,-labels,-and-model-predictions"], [102, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[101, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [106, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[101, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[101, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[101, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[101, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[101, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[102, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[102, "1.-Install-required-dependencies-and-download-data"], [106, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[102, "Get-label-quality-scores"], [106, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[102, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[102, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[102, "Other-uses-of-visualize"]], "Exploratory data analysis": [[102, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[103, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[103, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[103, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[103, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[103, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[103, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[104, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[104, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[104, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[105, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[105, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[105, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[106, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[106, "2.-Get-data,-labels,-and-pred_probs"], [107, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[106, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[106, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[106, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[107, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[107, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[107, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[107, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[107, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[50, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[51, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[51, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[51, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[51, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[51, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[52, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[52, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[52, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[52, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[52, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[53, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[53, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[54, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[54, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[54, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[55, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[55, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[56, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[57, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[59, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[60, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[61, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[62, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[63, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[64, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[65, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[66, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[66, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[67, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[68, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[72, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[73, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[73, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[73, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[74, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[74, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[75, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[75, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[76, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[77, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[78, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[82, "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 eee718530..fa91c6081 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": "2024-05-15T04:10:06.173743Z",
- "iopub.status.busy": "2024-05-15T04:10:06.173250Z",
- "iopub.status.idle": "2024-05-15T04:10:07.404368Z",
- "shell.execute_reply": "2024-05-15T04:10:07.403746Z"
+ "iopub.execute_input": "2024-05-21T21:34:19.348374Z",
+ "iopub.status.busy": "2024-05-21T21:34:19.347806Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.736841Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.736177Z"
},
"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@ca3892994e7f40b237b467761f72acf3786e8666\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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": "2024-05-15T04:10:07.407186Z",
- "iopub.status.busy": "2024-05-15T04:10:07.406649Z",
- "iopub.status.idle": "2024-05-15T04:10:07.426894Z",
- "shell.execute_reply": "2024-05-15T04:10:07.426427Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.739922Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.739376Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.762166Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.761647Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:07.429234Z",
- "iopub.status.busy": "2024-05-15T04:10:07.428925Z",
- "iopub.status.idle": "2024-05-15T04:10:07.644053Z",
- "shell.execute_reply": "2024-05-15T04:10:07.643441Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.764996Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.764560Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.914987Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.914388Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:07.675044Z",
- "iopub.status.busy": "2024-05-15T04:10:07.674559Z",
- "iopub.status.idle": "2024-05-15T04:10:07.678414Z",
- "shell.execute_reply": "2024-05-15T04:10:07.677922Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.948682Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.948153Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.952352Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.951735Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:07.680598Z",
- "iopub.status.busy": "2024-05-15T04:10:07.680269Z",
- "iopub.status.idle": "2024-05-15T04:10:07.688360Z",
- "shell.execute_reply": "2024-05-15T04:10:07.687911Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.954823Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.954378Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.964089Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.963578Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:07.690466Z",
- "iopub.status.busy": "2024-05-15T04:10:07.690155Z",
- "iopub.status.idle": "2024-05-15T04:10:07.692657Z",
- "shell.execute_reply": "2024-05-15T04:10:07.692233Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.966832Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.966585Z",
+ "iopub.status.idle": "2024-05-21T21:34:20.969686Z",
+ "shell.execute_reply": "2024-05-21T21:34:20.969193Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:07.694473Z",
- "iopub.status.busy": "2024-05-15T04:10:07.694301Z",
- "iopub.status.idle": "2024-05-15T04:10:08.217222Z",
- "shell.execute_reply": "2024-05-15T04:10:08.216672Z"
+ "iopub.execute_input": "2024-05-21T21:34:20.971694Z",
+ "iopub.status.busy": "2024-05-21T21:34:20.971508Z",
+ "iopub.status.idle": "2024-05-21T21:34:21.506645Z",
+ "shell.execute_reply": "2024-05-21T21:34:21.506046Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:08.219521Z",
- "iopub.status.busy": "2024-05-15T04:10:08.219326Z",
- "iopub.status.idle": "2024-05-15T04:10:09.891770Z",
- "shell.execute_reply": "2024-05-15T04:10:09.891133Z"
+ "iopub.execute_input": "2024-05-21T21:34:21.509485Z",
+ "iopub.status.busy": "2024-05-21T21:34:21.509113Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.414430Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.413812Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:09.894298Z",
- "iopub.status.busy": "2024-05-15T04:10:09.893747Z",
- "iopub.status.idle": "2024-05-15T04:10:09.903692Z",
- "shell.execute_reply": "2024-05-15T04:10:09.903163Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.417397Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.416667Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.427663Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.427144Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:09.905886Z",
- "iopub.status.busy": "2024-05-15T04:10:09.905510Z",
- "iopub.status.idle": "2024-05-15T04:10:09.909563Z",
- "shell.execute_reply": "2024-05-15T04:10:09.909041Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.430046Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.429676Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.434407Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.433784Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:09.911810Z",
- "iopub.status.busy": "2024-05-15T04:10:09.911416Z",
- "iopub.status.idle": "2024-05-15T04:10:09.918245Z",
- "shell.execute_reply": "2024-05-15T04:10:09.917841Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.436778Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.436347Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.444154Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.443598Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:09.920184Z",
- "iopub.status.busy": "2024-05-15T04:10:09.919844Z",
- "iopub.status.idle": "2024-05-15T04:10:10.030863Z",
- "shell.execute_reply": "2024-05-15T04:10:10.030329Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.446674Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.446288Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.560935Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.560327Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:10.033170Z",
- "iopub.status.busy": "2024-05-15T04:10:10.032829Z",
- "iopub.status.idle": "2024-05-15T04:10:10.035522Z",
- "shell.execute_reply": "2024-05-15T04:10:10.035091Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.563414Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.562955Z",
+ "iopub.status.idle": "2024-05-21T21:34:23.566168Z",
+ "shell.execute_reply": "2024-05-21T21:34:23.565633Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:10.037549Z",
- "iopub.status.busy": "2024-05-15T04:10:10.037238Z",
- "iopub.status.idle": "2024-05-15T04:10:12.073731Z",
- "shell.execute_reply": "2024-05-15T04:10:12.073023Z"
+ "iopub.execute_input": "2024-05-21T21:34:23.568585Z",
+ "iopub.status.busy": "2024-05-21T21:34:23.568123Z",
+ "iopub.status.idle": "2024-05-21T21:34:25.690853Z",
+ "shell.execute_reply": "2024-05-21T21:34:25.690211Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:12.076907Z",
- "iopub.status.busy": "2024-05-15T04:10:12.076050Z",
- "iopub.status.idle": "2024-05-15T04:10:12.087995Z",
- "shell.execute_reply": "2024-05-15T04:10:12.087414Z"
+ "iopub.execute_input": "2024-05-21T21:34:25.694215Z",
+ "iopub.status.busy": "2024-05-21T21:34:25.693272Z",
+ "iopub.status.idle": "2024-05-21T21:34:25.706550Z",
+ "shell.execute_reply": "2024-05-21T21:34:25.705913Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T04:10:12.090134Z",
- "iopub.status.busy": "2024-05-15T04:10:12.089722Z",
- "iopub.status.idle": "2024-05-15T04:10:12.167809Z",
- "shell.execute_reply": "2024-05-15T04:10:12.167228Z"
+ "iopub.execute_input": "2024-05-21T21:34:25.709115Z",
+ "iopub.status.busy": "2024-05-21T21:34:25.708694Z",
+ "iopub.status.idle": "2024-05-21T21:34:25.749027Z",
+ "shell.execute_reply": "2024-05-21T21:34:25.748531Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index ec9a641bf..d0ea33992 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -798,7 +798,7 @@ 2. Load and format the text dataset
This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'supported_cards_and_currencies', 'change_pin', 'card_payment_fee_charged', 'card_about_to_expire', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'apple_pay_or_google_pay'}
+Classes: {'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'cancel_transfer', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'supported_cards_and_currencies', 'apple_pay_or_google_pay'}
Let’s print the first example in the train set.
@@ -861,43 +861,43 @@ 2. Load and format the text dataset
-
+
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.
+/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.
warnings.warn(
Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
Training on fold: 1 ... -epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.733 -epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.692 +epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.247 +epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 5.039 Computing feature embeddings ...
Training on fold: 2 ... -epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.742 -epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.465 +epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.156 +epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.978 Computing feature embeddings ...
Training on fold: 3 ... -epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.676 -epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.428 +epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.216 +epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.030 Computing feature embeddings ...
This dataset has 10 classes.
-Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'visa_or_mastercard', 'supported_cards_and_currencies'}
+Classes: {'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer', 'beneficiary_not_allowed', 'getting_spare_card', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire'}
Let’s view the i-th example in the dataset:
diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 39446ce70..2976b39bc 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:46.683349Z", - "iopub.status.busy": "2024-05-15T04:16:46.683184Z", - "iopub.status.idle": "2024-05-15T04:16:49.229530Z", - "shell.execute_reply": "2024-05-15T04:16:49.228907Z" + "iopub.execute_input": "2024-05-21T21:41:13.153824Z", + "iopub.status.busy": "2024-05-21T21:41:13.153471Z", + "iopub.status.idle": "2024-05-21T21:41:15.927681Z", + "shell.execute_reply": "2024-05-21T21:41:15.927126Z" }, "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@ca3892994e7f40b237b467761f72acf3786e8666\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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": "2024-05-15T04:16:49.232213Z", - "iopub.status.busy": "2024-05-15T04:16:49.231913Z", - "iopub.status.idle": "2024-05-15T04:16:49.235101Z", - "shell.execute_reply": "2024-05-15T04:16:49.234688Z" + "iopub.execute_input": "2024-05-21T21:41:15.930478Z", + "iopub.status.busy": "2024-05-21T21:41:15.930015Z", + "iopub.status.idle": "2024-05-21T21:41:15.933366Z", + "shell.execute_reply": "2024-05-21T21:41:15.932941Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:49.237223Z", - "iopub.status.busy": "2024-05-15T04:16:49.236835Z", - "iopub.status.idle": "2024-05-15T04:16:49.239775Z", - "shell.execute_reply": "2024-05-15T04:16:49.239345Z" + "iopub.execute_input": "2024-05-21T21:41:15.935477Z", + "iopub.status.busy": "2024-05-21T21:41:15.935095Z", + "iopub.status.idle": "2024-05-21T21:41:15.938269Z", + "shell.execute_reply": "2024-05-21T21:41:15.937741Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:49.241652Z", - "iopub.status.busy": "2024-05-15T04:16:49.241479Z", - "iopub.status.idle": "2024-05-15T04:16:49.270473Z", - "shell.execute_reply": "2024-05-15T04:16:49.269990Z" + "iopub.execute_input": "2024-05-21T21:41:15.940351Z", + "iopub.status.busy": "2024-05-21T21:41:15.940022Z", + "iopub.status.idle": "2024-05-21T21:41:16.010477Z", + "shell.execute_reply": "2024-05-21T21:41:16.009850Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:49.272783Z", - "iopub.status.busy": "2024-05-15T04:16:49.272433Z", - "iopub.status.idle": "2024-05-15T04:16:49.276058Z", - "shell.execute_reply": "2024-05-15T04:16:49.275537Z" + "iopub.execute_input": "2024-05-21T21:41:16.012947Z", + "iopub.status.busy": "2024-05-21T21:41:16.012510Z", + "iopub.status.idle": "2024-05-21T21:41:16.016299Z", + "shell.execute_reply": "2024-05-21T21:41:16.015755Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'visa_or_mastercard', 'supported_cards_and_currencies'}\n" + "Classes: {'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer', 'beneficiary_not_allowed', 'getting_spare_card', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:49.277949Z", - "iopub.status.busy": "2024-05-15T04:16:49.277775Z", - "iopub.status.idle": "2024-05-15T04:16:49.280996Z", - "shell.execute_reply": "2024-05-15T04:16:49.280525Z" + "iopub.execute_input": "2024-05-21T21:41:16.018597Z", + "iopub.status.busy": "2024-05-21T21:41:16.018189Z", + "iopub.status.idle": "2024-05-21T21:41:16.021415Z", + "shell.execute_reply": "2024-05-21T21:41:16.020899Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:49.283047Z", - "iopub.status.busy": "2024-05-15T04:16:49.282658Z", - "iopub.status.idle": "2024-05-15T04:16:53.072812Z", - "shell.execute_reply": "2024-05-15T04:16:53.072267Z" + "iopub.execute_input": "2024-05-21T21:41:16.023377Z", + "iopub.status.busy": "2024-05-21T21:41:16.023091Z", + "iopub.status.idle": "2024-05-21T21:41:19.780695Z", + "shell.execute_reply": "2024-05-21T21:41:19.780053Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:53.075457Z", - "iopub.status.busy": "2024-05-15T04:16:53.075074Z", - "iopub.status.idle": "2024-05-15T04:16:53.947959Z", - "shell.execute_reply": "2024-05-15T04:16:53.947405Z" + "iopub.execute_input": "2024-05-21T21:41:19.783849Z", + "iopub.status.busy": "2024-05-21T21:41:19.783433Z", + "iopub.status.idle": "2024-05-21T21:41:20.662734Z", + "shell.execute_reply": "2024-05-21T21:41:20.662143Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:53.950855Z", - "iopub.status.busy": "2024-05-15T04:16:53.950466Z", - "iopub.status.idle": "2024-05-15T04:16:53.953320Z", - "shell.execute_reply": "2024-05-15T04:16:53.952842Z" + "iopub.execute_input": "2024-05-21T21:41:20.666701Z", + "iopub.status.busy": "2024-05-21T21:41:20.665714Z", + "iopub.status.idle": "2024-05-21T21:41:20.669900Z", + "shell.execute_reply": "2024-05-21T21:41:20.669394Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:53.955668Z", - "iopub.status.busy": "2024-05-15T04:16:53.955288Z", - "iopub.status.idle": "2024-05-15T04:16:55.461004Z", - "shell.execute_reply": "2024-05-15T04:16:55.460396Z" + "iopub.execute_input": "2024-05-21T21:41:20.673549Z", + "iopub.status.busy": "2024-05-21T21:41:20.672603Z", + "iopub.status.idle": "2024-05-21T21:41:22.259118Z", + "shell.execute_reply": "2024-05-21T21:41:22.258520Z" }, "scrolled": true }, @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.463910Z", - "iopub.status.busy": "2024-05-15T04:16:55.463366Z", - "iopub.status.idle": "2024-05-15T04:16:55.486583Z", - "shell.execute_reply": "2024-05-15T04:16:55.486099Z" + "iopub.execute_input": "2024-05-21T21:41:22.262878Z", + "iopub.status.busy": "2024-05-21T21:41:22.261509Z", + "iopub.status.idle": "2024-05-21T21:41:22.288182Z", + "shell.execute_reply": "2024-05-21T21:41:22.287638Z" }, "scrolled": true }, @@ -666,10 +666,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.489128Z", - "iopub.status.busy": "2024-05-15T04:16:55.488808Z", - "iopub.status.idle": "2024-05-15T04:16:55.498016Z", - "shell.execute_reply": "2024-05-15T04:16:55.497538Z" + "iopub.execute_input": "2024-05-21T21:41:22.291914Z", + "iopub.status.busy": "2024-05-21T21:41:22.290976Z", + "iopub.status.idle": "2024-05-21T21:41:22.302925Z", + "shell.execute_reply": "2024-05-21T21:41:22.302403Z" }, "scrolled": true }, @@ -779,10 +779,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.500611Z", - "iopub.status.busy": "2024-05-15T04:16:55.500238Z", - "iopub.status.idle": "2024-05-15T04:16:55.504743Z", - "shell.execute_reply": "2024-05-15T04:16:55.504264Z" + "iopub.execute_input": "2024-05-21T21:41:22.306554Z", + "iopub.status.busy": "2024-05-21T21:41:22.305634Z", + "iopub.status.idle": "2024-05-21T21:41:22.312354Z", + "shell.execute_reply": "2024-05-21T21:41:22.311758Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.507080Z", - "iopub.status.busy": "2024-05-15T04:16:55.506876Z", - "iopub.status.idle": "2024-05-15T04:16:55.514520Z", - "shell.execute_reply": "2024-05-15T04:16:55.513987Z" + "iopub.execute_input": "2024-05-21T21:41:22.314633Z", + "iopub.status.busy": "2024-05-21T21:41:22.314451Z", + "iopub.status.idle": "2024-05-21T21:41:22.321616Z", + "shell.execute_reply": "2024-05-21T21:41:22.321154Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.516644Z", - "iopub.status.busy": "2024-05-15T04:16:55.516256Z", - "iopub.status.idle": "2024-05-15T04:16:55.522631Z", - "shell.execute_reply": "2024-05-15T04:16:55.522100Z" + "iopub.execute_input": "2024-05-21T21:41:22.323653Z", + "iopub.status.busy": "2024-05-21T21:41:22.323330Z", + "iopub.status.idle": "2024-05-21T21:41:22.331115Z", + "shell.execute_reply": "2024-05-21T21:41:22.330680Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.524321Z", - "iopub.status.busy": "2024-05-15T04:16:55.524155Z", - "iopub.status.idle": "2024-05-15T04:16:55.529786Z", - "shell.execute_reply": "2024-05-15T04:16:55.529252Z" + "iopub.execute_input": "2024-05-21T21:41:22.333055Z", + "iopub.status.busy": "2024-05-21T21:41:22.332874Z", + "iopub.status.idle": "2024-05-21T21:41:22.338602Z", + "shell.execute_reply": "2024-05-21T21:41:22.338162Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.531693Z", - "iopub.status.busy": "2024-05-15T04:16:55.531406Z", - "iopub.status.idle": "2024-05-15T04:16:55.539627Z", - "shell.execute_reply": "2024-05-15T04:16:55.539088Z" + "iopub.execute_input": "2024-05-21T21:41:22.340521Z", + "iopub.status.busy": "2024-05-21T21:41:22.340351Z", + "iopub.status.idle": "2024-05-21T21:41:22.349181Z", + "shell.execute_reply": "2024-05-21T21:41:22.348633Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.541589Z", - "iopub.status.busy": "2024-05-15T04:16:55.541267Z", - "iopub.status.idle": "2024-05-15T04:16:55.546620Z", - "shell.execute_reply": "2024-05-15T04:16:55.546165Z" + "iopub.execute_input": "2024-05-21T21:41:22.351237Z", + "iopub.status.busy": "2024-05-21T21:41:22.350923Z", + "iopub.status.idle": "2024-05-21T21:41:22.356456Z", + "shell.execute_reply": "2024-05-21T21:41:22.355998Z" } }, "outputs": [ @@ -1322,10 +1322,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.548574Z", - "iopub.status.busy": "2024-05-15T04:16:55.548189Z", - "iopub.status.idle": "2024-05-15T04:16:55.553517Z", - "shell.execute_reply": "2024-05-15T04:16:55.552984Z" + "iopub.execute_input": "2024-05-21T21:41:22.358319Z", + "iopub.status.busy": "2024-05-21T21:41:22.358144Z", + "iopub.status.idle": "2024-05-21T21:41:22.363766Z", + "shell.execute_reply": "2024-05-21T21:41:22.363309Z" } }, "outputs": [ @@ -1404,10 +1404,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.555420Z", - "iopub.status.busy": "2024-05-15T04:16:55.555136Z", - "iopub.status.idle": "2024-05-15T04:16:55.558531Z", - "shell.execute_reply": "2024-05-15T04:16:55.558110Z" + "iopub.execute_input": "2024-05-21T21:41:22.365807Z", + "iopub.status.busy": "2024-05-21T21:41:22.365482Z", + "iopub.status.idle": "2024-05-21T21:41:22.369123Z", + "shell.execute_reply": "2024-05-21T21:41:22.368679Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:55.560607Z", - "iopub.status.busy": "2024-05-15T04:16:55.560298Z", - "iopub.status.idle": "2024-05-15T04:16:55.565081Z", - "shell.execute_reply": "2024-05-15T04:16:55.564660Z" + "iopub.execute_input": "2024-05-21T21:41:22.371324Z", + "iopub.status.busy": "2024-05-21T21:41:22.370934Z", + "iopub.status.idle": "2024-05-21T21:41:22.376213Z", + "shell.execute_reply": "2024-05-21T21:41:22.375675Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 098994f5a..c0f97712b 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:58.731090Z", - "iopub.status.busy": "2024-05-15T04:16:58.730921Z", - "iopub.status.idle": "2024-05-15T04:16:59.811081Z", - "shell.execute_reply": "2024-05-15T04:16:59.810532Z" + "iopub.execute_input": "2024-05-21T21:41:25.785770Z", + "iopub.status.busy": "2024-05-21T21:41:25.785598Z", + "iopub.status.idle": "2024-05-21T21:41:26.959965Z", + "shell.execute_reply": "2024-05-21T21:41:26.959317Z" }, "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@ca3892994e7f40b237b467761f72acf3786e8666\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@c13f32a2a6c1d6f3166d760b60f805c94d9f54fe\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": "2024-05-15T04:16:59.813676Z", - "iopub.status.busy": "2024-05-15T04:16:59.813166Z", - "iopub.status.idle": "2024-05-15T04:16:59.816022Z", - "shell.execute_reply": "2024-05-15T04:16:59.815490Z" + "iopub.execute_input": "2024-05-21T21:41:26.962293Z", + "iopub.status.busy": "2024-05-21T21:41:26.962007Z", + "iopub.status.idle": "2024-05-21T21:41:26.964973Z", + "shell.execute_reply": "2024-05-21T21:41:26.964404Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:59.818099Z", - "iopub.status.busy": "2024-05-15T04:16:59.817891Z", - "iopub.status.idle": "2024-05-15T04:16:59.829764Z", - "shell.execute_reply": "2024-05-15T04:16:59.829241Z" + "iopub.execute_input": "2024-05-21T21:41:26.967153Z", + "iopub.status.busy": "2024-05-21T21:41:26.966971Z", + "iopub.status.idle": "2024-05-21T21:41:26.979452Z", + "shell.execute_reply": "2024-05-21T21:41:26.979018Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T04:16:59.831905Z", - "iopub.status.busy": "2024-05-15T04:16:59.831586Z", - "iopub.status.idle": "2024-05-15T04:17:05.056802Z", - "shell.execute_reply": "2024-05-15T04:17:05.056349Z" + "iopub.execute_input": "2024-05-21T21:41:26.981381Z", + "iopub.status.busy": "2024-05-21T21:41:26.981205Z", + "iopub.status.idle": "2024-05-21T21:41:31.160055Z", + "shell.execute_reply": "2024-05-21T21:41:31.159574Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index c3122443d..805bc234a 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -812,13 +812,13 @@CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(),
n_boot=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(), - n_boot=1)
LinearRegression()
LinearRegression()
LinearRegression()
LinearRegression()
With Datalab:
Datalab runs CleanLearning under the hood when looking for label issues in regression datasets. Here’s how you can achieve the same behavior as calling CleanLearning.find_label_issues() in the code above using Datalab:
-/tmp/ipykernel_7875/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_7709/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values
Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai
CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(),\n",
" n_boot=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. CleanLearning(include_aleatoric_uncertainty=False, model=LinearRegression(),\n", - " n_boot=1)
LinearRegression()
LinearRegression()
LinearRegression()
LinearRegression()
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
LogisticRegression(random_state=0)
-100%|██████████| 170498071/170498071 [00:02<00:00, 81183422.80it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 107058699.01it/s]
Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().
---2024-05-15 04:21:25-- https://data.deepai.org/conll2003.zip
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+--2024-05-21 21:45:57-- https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.250, 2400:52e0:1a00::894:1
Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.
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mkdir: cannot create directory ‘data’: File exists
Archive: conll2003.zip
@@ -708,16 +708,16 @@ 1. Install required dependencies and download data