[docs] def set_knn_graph(
-
self, features: npt.NDArray, find_issues_kwargs: Dict[str, Any]
+
self, features: Optional[npt.NDArray], find_issues_kwargs: Dict[str, Any]
) -> csr_matrix:
knn_graph = self._process_knn_graph_from_inputs(find_issues_kwargs)
old_knn_metric = self.datalab.get_info("statistics").get("knn_metric")
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index a132cb157..dc99451a9 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 dc5572679..6e0038c03 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 8dfca67f0..09574dfc5 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 2f34a8eb5..4233b3e37 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 697727369..571720f79 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 26d56baa9..b57d1a78d 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 536dae745..ad443f917 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 50e70f883..08c79ed22 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 fd7976fd9..8d8828558 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 032d057c8..6f4547d32 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 190b0dd97..57a1151bb 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 60e2a6eff..a34982b84 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -111,7 +111,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 1fa4cb9fb..30972450d 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index 8edb029f9..b4659acf4 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index 23b41967e..55f94f538 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\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 66cd8098d..3d9a15212 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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
index 68132f3c5..439f42c6b 100644
--- a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
+++ b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
@@ -606,7 +606,7 @@
underperforming_group
-find_issues(features, pred_probs[, cluster_ids])
|
+
find_issues(pred_probs[, features, cluster_ids])
|
Finds occurrences of this particular issue in the dataset. |
set_knn_graph(features, find_issues_kwargs)
|
@@ -683,7 +683,7 @@ underperforming_group[source]
Finds occurrences of this particular issue in the dataset.
Computes the issues and summary dataframes. Calls collect_info to compute the info dict.
diff --git a/master/searchindex.js b/master/searchindex.js
index ff6c4093f..d7c758ac5 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/datalab/datalab", "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", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", 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"tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", 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"module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[19, "issue-manager"], [20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[19, "registered-issue-managers"]], "Unregistered issue managers": [[19, "unregistered-issue-managers"]], "ML task-specific issue managers": [[19, "ml-task-specific-issue-managers"]], "label": [[21, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "noniid": [[22, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[23, "null"]], "outlier": [[24, "module-cleanlab.datalab.internal.issue_manager.outlier"], [44, "module-cleanlab.internal.outlier"], [60, "module-cleanlab.outlier"]], "regression": [[25, "regression"], [62, "regression"]], "Priority Order for finding issues:": [[26, null]], "underperforming_group": [[27, "underperforming-group"]], "report": [[28, "report"]], "task": [[29, "task"]], "dataset": [[31, "module-cleanlab.dataset"], [52, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[32, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[33, "module-cleanlab.experimental.coteaching"]], "experimental": [[34, "experimental"]], "label_issues_batched": [[35, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[36, "module-cleanlab.experimental.mnist_pytorch"]], "filter": [[37, "module-cleanlab.filter"], [53, "module-cleanlab.multilabel_classification.filter"], [56, "filter"], [65, "filter"], [69, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[39, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[40, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[41, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[42, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[43, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[45, "module-cleanlab.internal.token_classification_utils"]], "util": [[46, "module-cleanlab.internal.util"]], "validation": [[47, "module-cleanlab.internal.validation"]], "fasttext": [[48, "fasttext"]], "models": [[49, "models"]], "keras": [[50, "module-cleanlab.models.keras"]], "multiannotator": [[51, "module-cleanlab.multiannotator"]], "multilabel_classification": [[54, "multilabel-classification"]], "rank": [[55, "module-cleanlab.multilabel_classification.rank"], [58, "module-cleanlab.object_detection.rank"], [61, "module-cleanlab.rank"], [67, "module-cleanlab.segmentation.rank"], [71, "module-cleanlab.token_classification.rank"]], "object_detection": [[57, "object-detection"]], "summary": [[59, "summary"], [68, "module-cleanlab.segmentation.summary"], [72, "module-cleanlab.token_classification.summary"]], "regression.learn": [[63, "module-cleanlab.regression.learn"]], "regression.rank": [[64, "module-cleanlab.regression.rank"]], "segmentation": [[66, "segmentation"]], "token_classification": [[70, "token-classification"]], "cleanlab open-source documentation": [[73, "cleanlab-open-source-documentation"]], "Quickstart": [[73, "quickstart"]], "1. Install cleanlab": [[73, "install-cleanlab"]], "2. Find common issues in your data": [[73, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[73, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[73, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[73, "improve-your-data-via-many-other-techniques"]], "Contributing": [[73, "contributing"]], "Easy Mode": [[73, "easy-mode"], [79, "Easy-Mode"], [80, "Easy-Mode"], [83, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[74, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[74, "function-and-class-name-changes"]], "Module name changes": [[74, "module-name-changes"]], "New modules": [[74, "new-modules"]], "Removed modules": [[74, "removed-modules"]], "Common argument and variable name changes": [[74, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[75, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[75, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[75, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[75, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[75, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[75, "5.-Use-cleanlab-to-find-label-issues"], [79, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[76, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[76, "Install-and-import-required-dependencies"]], "Create and load the data": [[76, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[76, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[76, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[76, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[76, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[76, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[76, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[77, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[77, "1.-Install-and-import-required-dependencies"], [83, "1.-Install-and-import-required-dependencies"], [86, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[77, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[77, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[77, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[77, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[77, "Get-additional-information"]], "Near duplicate issues": [[77, "Near-duplicate-issues"], [83, "Near-duplicate-issues"]], "Datalab Tutorials": [[78, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[79, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[79, "1.-Install-required-dependencies"], [80, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"]], "2. Load and process the data": [[79, "2.-Load-and-process-the-data"], [91, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[79, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[79, "Label-issues"], [80, "Label-issues"], [83, "Label-issues"]], "Outlier issues": [[79, "Outlier-issues"], [80, "Outlier-issues"], [83, "Outlier-issues"]], "Near-duplicate issues": [[79, "Near-duplicate-issues"], [80, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[80, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[80, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[80, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[80, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[80, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[81, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[81, "Install-dependencies-and-import-them"], [84, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[81, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[81, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[82, "FAQ"]], "What data can cleanlab detect issues in?": [[82, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[82, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[82, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[82, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[82, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[82, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[82, "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?": [[82, "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?": [[82, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[82, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[82, "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?": [[82, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[82, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[82, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[83, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[83, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[83, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[83, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[83, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[83, "7.-Use-cleanlab-to-find-issues"]], "View report": [[83, "View-report"]], "View most likely examples with label errors": [[83, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[83, "View-most-severe-outliers"]], "View sets of near duplicate images": [[83, "View-sets-of-near-duplicate-images"]], "Dark images": [[83, "Dark-images"]], "View top examples of dark images": [[83, "View-top-examples-of-dark-images"]], "Low information images": [[83, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[84, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[84, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[84, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[84, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[84, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[84, "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.": [[84, "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": [[84, "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": [[84, "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!": [[84, "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": [[84, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[84, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[84, "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)": [[84, "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:": [[84, "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": [[84, "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.": [[84, "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.": [[84, "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.": [[84, "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.": [[84, "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?": [[84, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[84, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[85, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[86, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[86, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[86, "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": [[86, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[86, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[86, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[86, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[86, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[86, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[87, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[87, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[87, "2.-Format-data,-labels,-and-model-predictions"], [88, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[87, "3.-Use-cleanlab-to-find-label-issues"], [88, "3.-Use-cleanlab-to-find-label-issues"], [92, "3.-Use-cleanlab-to-find-label-issues"], [95, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[87, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[87, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[87, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[87, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[87, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[88, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[88, "1.-Install-required-dependencies-and-download-data"], [92, "1.-Install-required-dependencies-and-download-data"], [95, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[88, "Get-label-quality-scores"], [92, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[88, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[88, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[88, "Other-uses-of-visualize"]], "Exploratory data analysis": [[88, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[89, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[89, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[89, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[89, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[89, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[89, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[90, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[90, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[90, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[91, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[91, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[91, "4.-Train-a-more-robust-model-from-noisy-labels"], [94, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[91, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[92, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[92, "2.-Get-data,-labels,-and-pred_probs"], [95, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[92, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[92, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[92, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[93, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[93, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[93, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[94, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[94, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[95, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[95, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[95, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[95, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[95, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info 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"cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() 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Define a classification model and compute out-of-sample predicted probabilities": [[80, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[80, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[80, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[81, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[81, "Install-dependencies-and-import-them"], [84, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[81, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[81, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[82, "FAQ"]], "What data can cleanlab detect issues in?": [[82, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[82, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[82, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[82, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[82, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[82, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[82, "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?": [[82, "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?": [[82, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[82, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[82, "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?": [[82, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[82, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[82, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[83, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[83, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[83, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[83, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[83, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[83, "7.-Use-cleanlab-to-find-issues"]], "View report": [[83, "View-report"]], "View most likely examples with label errors": [[83, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[83, "View-most-severe-outliers"]], "View sets of near duplicate images": [[83, "View-sets-of-near-duplicate-images"]], "Dark images": [[83, "Dark-images"]], "View top examples of dark images": [[83, "View-top-examples-of-dark-images"]], "Low information images": [[83, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[84, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[84, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[84, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[84, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[84, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[84, "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.": [[84, "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": [[84, "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": [[84, "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!": [[84, "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": [[84, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[84, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[84, "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)": [[84, "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:": [[84, "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": [[84, "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.": [[84, "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.": [[84, "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.": [[84, "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.": [[84, "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?": [[84, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[84, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[85, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[86, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[86, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[86, "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": [[86, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[86, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[86, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[86, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[86, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[86, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[87, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[87, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[87, "2.-Format-data,-labels,-and-model-predictions"], [88, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[87, "3.-Use-cleanlab-to-find-label-issues"], [88, "3.-Use-cleanlab-to-find-label-issues"], [92, "3.-Use-cleanlab-to-find-label-issues"], [95, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[87, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[87, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[87, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[87, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[87, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[88, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[88, "1.-Install-required-dependencies-and-download-data"], [92, "1.-Install-required-dependencies-and-download-data"], [95, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[88, "Get-label-quality-scores"], [92, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[88, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[88, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[88, "Other-uses-of-visualize"]], "Exploratory data analysis": [[88, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[89, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[89, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[89, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[89, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[89, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[89, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[90, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[90, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[90, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[91, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[91, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[91, "4.-Train-a-more-robust-model-from-noisy-labels"], [94, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[91, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[92, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[92, "2.-Get-data,-labels,-and-pred_probs"], [95, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[92, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[92, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[92, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[93, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[93, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[93, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[94, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[94, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[95, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[95, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[95, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[95, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[95, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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"cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[10, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[10, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[10, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[10, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[10, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[11, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[21, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[22, 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"cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[51, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[51, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[51, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[52, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[52, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[52, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module 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"get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[61, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[62, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[63, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[63, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[63, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.fit"]], 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(cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[64, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[64, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[65, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[65, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[66, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[67, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[67, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[67, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[68, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[69, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[69, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[70, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[71, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[71, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[71, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[72, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[72, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[72, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[72, 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diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 5e5b16b62..e4566892b 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1275,7 +1275,7 @@ 5. Use cleanlab to find label issues
| 34848 |
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True |
+ 0.203922 |
| 50270 |
- 0.204588 |
True |
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@@ -3261,7 +3287,7 @@
Easy Mode which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data
issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!
diff --git a/master/tutorials/image.ipynb b/master/tutorials/image.ipynb
index 69ce591e4..1f800a1d6 100644
--- a/master/tutorials/image.ipynb
+++ b/master/tutorials/image.ipynb
@@ -71,10 +71,10 @@
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@@ -358,10 +358,10 @@
"execution_count": 7,
"metadata": {
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- "shell.execute_reply": "2024-02-12T22:44:55.142476Z"
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@@ -399,10 +399,10 @@
"execution_count": 8,
"metadata": {
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- "shell.execute_reply": "2024-02-12T22:44:55.147871Z"
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@@ -539,10 +539,10 @@
"execution_count": 9,
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@@ -667,10 +667,10 @@
"execution_count": 10,
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}
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@@ -707,10 +707,10 @@
"execution_count": 11,
"metadata": {
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- "shell.execute_reply": "2024-02-12T22:45:27.673664Z"
+ "iopub.execute_input": "2024-02-12T23:51:51.980815Z",
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}
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"outputs": [
@@ -726,14 +726,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.817\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.773\n"
]
},
{
"name": "stdout",
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- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.679\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.440\n",
"Computing feature embeddings ...\n"
]
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@@ -750,7 +750,7 @@
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+ " 5%|▌ | 2/40 [00:00<00:01, 19.48it/s]"
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+ " 22%|██▎ | 9/40 [00:00<00:00, 47.14it/s]"
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+ " 40%|████ | 16/40 [00:00<00:00, 54.36it/s]"
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+ " 57%|█████▊ | 23/40 [00:00<00:00, 57.75it/s]"
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+ " 72%|███████▎ | 29/40 [00:00<00:00, 53.55it/s]"
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+ " 88%|████████▊ | 35/40 [00:00<00:00, 54.02it/s]"
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+ "100%|██████████| 40/40 [00:00<00:00, 52.34it/s]"
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+ " 5%|▌ | 2/40 [00:00<00:02, 17.28it/s]"
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+ " 22%|██▎ | 9/40 [00:00<00:00, 45.42it/s]"
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+ " 38%|███▊ | 15/40 [00:00<00:00, 49.97it/s]"
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"output_type": "stream",
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+ " 52%|█████▎ | 21/40 [00:00<00:00, 51.44it/s]"
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+ " 70%|███████ | 28/40 [00:00<00:00, 55.46it/s]"
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@@ -868,7 +868,7 @@
"output_type": "stream",
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+ " 90%|█████████ | 36/40 [00:00<00:00, 61.79it/s]"
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{
@@ -876,36 +876,36 @@
"output_type": "stream",
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+ "100%|██████████| 40/40 [00:00<00:00, 55.50it/s]"
]
},
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "\n"
+ "\n",
+ "Training on fold: 2 ...\n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\n",
- "Training on fold: 2 ...\n"
+ "\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.830\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.625\n"
]
},
{
"name": "stdout",
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- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.590\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.389\n",
"Computing feature embeddings ...\n"
]
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+ " 2%|▎ | 1/40 [00:00<00:04, 8.53it/s]"
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+ " 20%|██ | 8/40 [00:00<00:00, 41.57it/s]"
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+ " 38%|███▊ | 15/40 [00:00<00:00, 53.26it/s]"
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+ " 55%|█████▌ | 22/40 [00:00<00:00, 59.37it/s]"
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+ " 72%|███████▎ | 29/40 [00:00<00:00, 60.26it/s]"
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@@ -962,7 +962,7 @@
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+ " 92%|█████████▎| 37/40 [00:00<00:00, 65.41it/s]"
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+ "100%|██████████| 40/40 [00:00<00:00, 57.75it/s]"
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+ " 2%|▎ | 1/40 [00:00<00:04, 9.07it/s]"
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"output_type": "stream",
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+ " 18%|█▊ | 7/40 [00:00<00:00, 36.33it/s]"
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"output_type": "stream",
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+ " 35%|███▌ | 14/40 [00:00<00:00, 49.06it/s]"
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"output_type": "stream",
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+ " 55%|█████▌ | 22/40 [00:00<00:00, 57.78it/s]"
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"output_type": "stream",
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+ " 72%|███████▎ | 29/40 [00:00<00:00, 61.67it/s]"
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"output_type": "stream",
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+ " 92%|█████████▎| 37/40 [00:00<00:00, 67.09it/s]"
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"output_type": "stream",
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+ "100%|██████████| 40/40 [00:00<00:00, 57.88it/s]"
]
},
{
@@ -1070,14 +1070,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.789\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.594\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.561\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.414\n",
"Computing feature embeddings ...\n"
]
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+ " 2%|▎ | 1/40 [00:00<00:03, 9.99it/s]"
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+ " 20%|██ | 8/40 [00:00<00:00, 40.73it/s]"
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+ " 38%|███▊ | 15/40 [00:00<00:00, 52.94it/s]"
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"output_type": "stream",
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+ " 55%|█████▌ | 22/40 [00:00<00:00, 57.73it/s]"
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+ " 75%|███████▌ | 30/40 [00:00<00:00, 62.43it/s]"
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+ "100%|██████████| 40/40 [00:00<00:00, 58.99it/s]"
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+ " 2%|▎ | 1/40 [00:00<00:04, 9.52it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 18%|█▊ | 7/40 [00:00<00:00, 38.56it/s]"
]
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{
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"output_type": "stream",
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+ " 35%|███▌ | 14/40 [00:00<00:00, 51.15it/s]"
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{
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"output_type": "stream",
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+ " 52%|█████▎ | 21/40 [00:00<00:00, 57.29it/s]"
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"output_type": "stream",
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+ " 70%|███████ | 28/40 [00:00<00:00, 60.00it/s]"
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@@ -1204,7 +1212,7 @@
"output_type": "stream",
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+ " 90%|█████████ | 36/40 [00:00<00:00, 65.08it/s]"
]
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{
@@ -1212,7 +1220,7 @@
"output_type": "stream",
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+ "100%|██████████| 40/40 [00:00<00:00, 57.49it/s]"
]
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{
@@ -1289,10 +1297,10 @@
"execution_count": 12,
"metadata": {
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- "shell.execute_reply": "2024-02-12T22:45:27.691828Z"
+ "iopub.execute_input": "2024-02-12T23:52:23.495654Z",
+ "iopub.status.busy": "2024-02-12T23:52:23.495270Z",
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+ "shell.execute_reply": "2024-02-12T23:52:23.510158Z"
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"outputs": [],
@@ -1317,10 +1325,10 @@
"execution_count": 13,
"metadata": {
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- "shell.execute_reply": "2024-02-12T22:45:28.160543Z"
+ "iopub.execute_input": "2024-02-12T23:52:23.512509Z",
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+ "shell.execute_reply": "2024-02-12T23:52:23.943177Z"
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"outputs": [],
@@ -1340,10 +1348,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.status.idle": "2024-02-12T22:48:54.291763Z",
- "shell.execute_reply": "2024-02-12T22:48:54.291105Z"
+ "iopub.execute_input": "2024-02-12T23:52:23.946059Z",
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+ "iopub.status.idle": "2024-02-12T23:55:47.777354Z",
+ "shell.execute_reply": "2024-02-12T23:55:47.776802Z"
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"outputs": [
@@ -1389,7 +1397,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7eeea8c1775c427982135e3e26276a66",
+ "model_id": "c44a3a93af7b497c8dd54e04f2398ff3",
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"version_minor": 0
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@@ -1428,10 +1436,10 @@
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- "iopub.status.idle": "2024-02-12T22:48:54.982960Z",
- "shell.execute_reply": "2024-02-12T22:48:54.982388Z"
+ "iopub.execute_input": "2024-02-12T23:55:47.780109Z",
+ "iopub.status.busy": "2024-02-12T23:55:47.779325Z",
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+ "shell.execute_reply": "2024-02-12T23:55:48.449924Z"
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"outputs": [
@@ -1580,10 +1588,10 @@
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@@ -2517,47 +2525,47 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
" \n",
" \n",
" | 34848 | \n",
- " 0.203922 | \n",
" True | \n",
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\n",
" \n",
" | 50270 | \n",
- " 0.204588 | \n",
" True | \n",
+ " 0.204588 | \n",
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\n",
" \n",
" | 3936 | \n",
- " 0.213098 | \n",
" True | \n",
+ " 0.213098 | \n",
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\n",
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- " 0.217686 | \n",
" True | \n",
+ " 0.217686 | \n",
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\n",
" \n",
" | 8094 | \n",
- " 0.230118 | \n",
" True | \n",
+ " 0.230118 | \n",
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\n",
" \n",
"\n",
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"
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- "34848 0.203922 True\n",
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- "733 0.217686 True\n",
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diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb
index aaaa6b078..c7ccb86c3 100644
--- a/master/tutorials/indepth_overview.ipynb
+++ b/master/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
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@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
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+ "iopub.status.idle": "2024-02-12T23:55:59.842655Z",
+ "shell.execute_reply": "2024-02-12T23:55:59.842038Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2218,10 +2218,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:06.516462Z",
- "iopub.status.busy": "2024-02-12T22:49:06.516269Z",
- "iopub.status.idle": "2024-02-12T22:49:06.522329Z",
- "shell.execute_reply": "2024-02-12T22:49:06.521896Z"
+ "iopub.execute_input": "2024-02-12T23:55:59.845039Z",
+ "iopub.status.busy": "2024-02-12T23:55:59.844653Z",
+ "iopub.status.idle": "2024-02-12T23:55:59.850413Z",
+ "shell.execute_reply": "2024-02-12T23:55:59.849954Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2275,10 +2275,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:06.524470Z",
- "iopub.status.busy": "2024-02-12T22:49:06.524170Z",
- "iopub.status.idle": "2024-02-12T22:49:06.740727Z",
- "shell.execute_reply": "2024-02-12T22:49:06.740021Z"
+ "iopub.execute_input": "2024-02-12T23:55:59.852474Z",
+ "iopub.status.busy": "2024-02-12T23:55:59.852080Z",
+ "iopub.status.idle": "2024-02-12T23:56:00.071774Z",
+ "shell.execute_reply": "2024-02-12T23:56:00.071205Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2325,10 +2325,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:06.743180Z",
- "iopub.status.busy": "2024-02-12T22:49:06.742849Z",
- "iopub.status.idle": "2024-02-12T22:49:07.830568Z",
- "shell.execute_reply": "2024-02-12T22:49:07.829930Z"
+ "iopub.execute_input": "2024-02-12T23:56:00.074077Z",
+ "iopub.status.busy": "2024-02-12T23:56:00.073754Z",
+ "iopub.status.idle": "2024-02-12T23:56:01.156090Z",
+ "shell.execute_reply": "2024-02-12T23:56:01.155450Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb
index a8731c237..707556a1f 100644
--- a/master/tutorials/multiannotator.ipynb
+++ b/master/tutorials/multiannotator.ipynb
@@ -89,10 +89,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:11.327098Z",
- "iopub.status.busy": "2024-02-12T22:49:11.326930Z",
- "iopub.status.idle": "2024-02-12T22:49:12.371035Z",
- "shell.execute_reply": "2024-02-12T22:49:12.370449Z"
+ "iopub.execute_input": "2024-02-12T23:56:04.538038Z",
+ "iopub.status.busy": "2024-02-12T23:56:04.537630Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.578886Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.578336Z"
},
"nbsphinx": "hidden"
},
@@ -102,7 +102,7 @@
"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@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -136,10 +136,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.373689Z",
- "iopub.status.busy": "2024-02-12T22:49:12.373162Z",
- "iopub.status.idle": "2024-02-12T22:49:12.376283Z",
- "shell.execute_reply": "2024-02-12T22:49:12.375854Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.581551Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.581138Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.584685Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.584263Z"
}
},
"outputs": [],
@@ -264,10 +264,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.378463Z",
- "iopub.status.busy": "2024-02-12T22:49:12.378020Z",
- "iopub.status.idle": "2024-02-12T22:49:12.385700Z",
- "shell.execute_reply": "2024-02-12T22:49:12.385291Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.586771Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.586447Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.594060Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.593629Z"
},
"nbsphinx": "hidden"
},
@@ -351,10 +351,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.387657Z",
- "iopub.status.busy": "2024-02-12T22:49:12.387343Z",
- "iopub.status.idle": "2024-02-12T22:49:12.433505Z",
- "shell.execute_reply": "2024-02-12T22:49:12.432990Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.595992Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.595672Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.642907Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.642415Z"
}
},
"outputs": [],
@@ -380,10 +380,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.435717Z",
- "iopub.status.busy": "2024-02-12T22:49:12.435537Z",
- "iopub.status.idle": "2024-02-12T22:49:12.452948Z",
- "shell.execute_reply": "2024-02-12T22:49:12.452435Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.645075Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.644741Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.661843Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.661343Z"
}
},
"outputs": [
@@ -598,10 +598,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.454912Z",
- "iopub.status.busy": "2024-02-12T22:49:12.454742Z",
- "iopub.status.idle": "2024-02-12T22:49:12.458402Z",
- "shell.execute_reply": "2024-02-12T22:49:12.457979Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.663789Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.663465Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.667196Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.666678Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.460570Z",
- "iopub.status.busy": "2024-02-12T22:49:12.460187Z",
- "iopub.status.idle": "2024-02-12T22:49:12.490002Z",
- "shell.execute_reply": "2024-02-12T22:49:12.489582Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.669251Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.668941Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.695637Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.695211Z"
}
},
"outputs": [],
@@ -699,10 +699,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.492004Z",
- "iopub.status.busy": "2024-02-12T22:49:12.491823Z",
- "iopub.status.idle": "2024-02-12T22:49:12.517574Z",
- "shell.execute_reply": "2024-02-12T22:49:12.517147Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.697621Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.697287Z",
+ "iopub.status.idle": "2024-02-12T23:56:05.723892Z",
+ "shell.execute_reply": "2024-02-12T23:56:05.723447Z"
}
},
"outputs": [],
@@ -739,10 +739,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:12.519566Z",
- "iopub.status.busy": "2024-02-12T22:49:12.519393Z",
- "iopub.status.idle": "2024-02-12T22:49:14.258314Z",
- "shell.execute_reply": "2024-02-12T22:49:14.257760Z"
+ "iopub.execute_input": "2024-02-12T23:56:05.726040Z",
+ "iopub.status.busy": "2024-02-12T23:56:05.725708Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.441080Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.440542Z"
}
},
"outputs": [],
@@ -772,10 +772,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.260696Z",
- "iopub.status.busy": "2024-02-12T22:49:14.260402Z",
- "iopub.status.idle": "2024-02-12T22:49:14.267174Z",
- "shell.execute_reply": "2024-02-12T22:49:14.266719Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.443609Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.443156Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.449967Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.449505Z"
},
"scrolled": true
},
@@ -886,10 +886,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.269182Z",
- "iopub.status.busy": "2024-02-12T22:49:14.269013Z",
- "iopub.status.idle": "2024-02-12T22:49:14.281566Z",
- "shell.execute_reply": "2024-02-12T22:49:14.281123Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.451895Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.451573Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.464138Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.463588Z"
}
},
"outputs": [
@@ -1139,10 +1139,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.283654Z",
- "iopub.status.busy": "2024-02-12T22:49:14.283391Z",
- "iopub.status.idle": "2024-02-12T22:49:14.289606Z",
- "shell.execute_reply": "2024-02-12T22:49:14.289172Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.465960Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.465790Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.472235Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.471788Z"
},
"scrolled": true
},
@@ -1316,10 +1316,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.291635Z",
- "iopub.status.busy": "2024-02-12T22:49:14.291386Z",
- "iopub.status.idle": "2024-02-12T22:49:14.294083Z",
- "shell.execute_reply": "2024-02-12T22:49:14.293677Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.474190Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.473885Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.476603Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.476049Z"
}
},
"outputs": [],
@@ -1341,10 +1341,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.296112Z",
- "iopub.status.busy": "2024-02-12T22:49:14.295801Z",
- "iopub.status.idle": "2024-02-12T22:49:14.299071Z",
- "shell.execute_reply": "2024-02-12T22:49:14.298561Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.478499Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.478209Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.481667Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.481127Z"
},
"scrolled": true
},
@@ -1396,10 +1396,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.301134Z",
- "iopub.status.busy": "2024-02-12T22:49:14.300817Z",
- "iopub.status.idle": "2024-02-12T22:49:14.303249Z",
- "shell.execute_reply": "2024-02-12T22:49:14.302831Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.483781Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.483461Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.485919Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.485483Z"
}
},
"outputs": [],
@@ -1423,10 +1423,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.305180Z",
- "iopub.status.busy": "2024-02-12T22:49:14.304871Z",
- "iopub.status.idle": "2024-02-12T22:49:14.309081Z",
- "shell.execute_reply": "2024-02-12T22:49:14.308563Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.488011Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.487693Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.491554Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.491034Z"
}
},
"outputs": [
@@ -1481,10 +1481,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.311113Z",
- "iopub.status.busy": "2024-02-12T22:49:14.310799Z",
- "iopub.status.idle": "2024-02-12T22:49:14.339130Z",
- "shell.execute_reply": "2024-02-12T22:49:14.338700Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.493639Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.493306Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.522407Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.521930Z"
}
},
"outputs": [],
@@ -1527,10 +1527,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:14.341393Z",
- "iopub.status.busy": "2024-02-12T22:49:14.341075Z",
- "iopub.status.idle": "2024-02-12T22:49:14.345755Z",
- "shell.execute_reply": "2024-02-12T22:49:14.345217Z"
+ "iopub.execute_input": "2024-02-12T23:56:07.524554Z",
+ "iopub.status.busy": "2024-02-12T23:56:07.524222Z",
+ "iopub.status.idle": "2024-02-12T23:56:07.528683Z",
+ "shell.execute_reply": "2024-02-12T23:56:07.528255Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb
index 57e7ea255..25d4e4304 100644
--- a/master/tutorials/multilabel_classification.ipynb
+++ b/master/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:17.137846Z",
- "iopub.status.busy": "2024-02-12T22:49:17.137670Z",
- "iopub.status.idle": "2024-02-12T22:49:18.258972Z",
- "shell.execute_reply": "2024-02-12T22:49:18.258419Z"
+ "iopub.execute_input": "2024-02-12T23:56:10.240468Z",
+ "iopub.status.busy": "2024-02-12T23:56:10.240301Z",
+ "iopub.status.idle": "2024-02-12T23:56:11.320917Z",
+ "shell.execute_reply": "2024-02-12T23:56:11.320373Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -105,10 +105,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:18.261671Z",
- "iopub.status.busy": "2024-02-12T22:49:18.261219Z",
- "iopub.status.idle": "2024-02-12T22:49:18.457380Z",
- "shell.execute_reply": "2024-02-12T22:49:18.456833Z"
+ "iopub.execute_input": "2024-02-12T23:56:11.323289Z",
+ "iopub.status.busy": "2024-02-12T23:56:11.323027Z",
+ "iopub.status.idle": "2024-02-12T23:56:11.513590Z",
+ "shell.execute_reply": "2024-02-12T23:56:11.513001Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:18.460133Z",
- "iopub.status.busy": "2024-02-12T22:49:18.459571Z",
- "iopub.status.idle": "2024-02-12T22:49:18.472669Z",
- "shell.execute_reply": "2024-02-12T22:49:18.472164Z"
+ "iopub.execute_input": "2024-02-12T23:56:11.516308Z",
+ "iopub.status.busy": "2024-02-12T23:56:11.515954Z",
+ "iopub.status.idle": "2024-02-12T23:56:11.528800Z",
+ "shell.execute_reply": "2024-02-12T23:56:11.528260Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:18.474828Z",
- "iopub.status.busy": "2024-02-12T22:49:18.474498Z",
- "iopub.status.idle": "2024-02-12T22:49:21.113443Z",
- "shell.execute_reply": "2024-02-12T22:49:21.112936Z"
+ "iopub.execute_input": "2024-02-12T23:56:11.531020Z",
+ "iopub.status.busy": "2024-02-12T23:56:11.530698Z",
+ "iopub.status.idle": "2024-02-12T23:56:14.158976Z",
+ "shell.execute_reply": "2024-02-12T23:56:14.158514Z"
}
},
"outputs": [
@@ -452,10 +452,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:21.115686Z",
- "iopub.status.busy": "2024-02-12T22:49:21.115348Z",
- "iopub.status.idle": "2024-02-12T22:49:22.470789Z",
- "shell.execute_reply": "2024-02-12T22:49:22.470212Z"
+ "iopub.execute_input": "2024-02-12T23:56:14.161097Z",
+ "iopub.status.busy": "2024-02-12T23:56:14.160804Z",
+ "iopub.status.idle": "2024-02-12T23:56:15.499117Z",
+ "shell.execute_reply": "2024-02-12T23:56:15.498486Z"
}
},
"outputs": [],
@@ -497,10 +497,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:22.473099Z",
- "iopub.status.busy": "2024-02-12T22:49:22.472794Z",
- "iopub.status.idle": "2024-02-12T22:49:22.476749Z",
- "shell.execute_reply": "2024-02-12T22:49:22.476196Z"
+ "iopub.execute_input": "2024-02-12T23:56:15.501657Z",
+ "iopub.status.busy": "2024-02-12T23:56:15.501305Z",
+ "iopub.status.idle": "2024-02-12T23:56:15.505246Z",
+ "shell.execute_reply": "2024-02-12T23:56:15.504700Z"
}
},
"outputs": [
@@ -542,10 +542,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:22.478756Z",
- "iopub.status.busy": "2024-02-12T22:49:22.478452Z",
- "iopub.status.idle": "2024-02-12T22:49:24.288753Z",
- "shell.execute_reply": "2024-02-12T22:49:24.288102Z"
+ "iopub.execute_input": "2024-02-12T23:56:15.507214Z",
+ "iopub.status.busy": "2024-02-12T23:56:15.506912Z",
+ "iopub.status.idle": "2024-02-12T23:56:17.247126Z",
+ "shell.execute_reply": "2024-02-12T23:56:17.246558Z"
}
},
"outputs": [
@@ -592,10 +592,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:24.291487Z",
- "iopub.status.busy": "2024-02-12T22:49:24.290920Z",
- "iopub.status.idle": "2024-02-12T22:49:24.298783Z",
- "shell.execute_reply": "2024-02-12T22:49:24.298297Z"
+ "iopub.execute_input": "2024-02-12T23:56:17.249916Z",
+ "iopub.status.busy": "2024-02-12T23:56:17.249208Z",
+ "iopub.status.idle": "2024-02-12T23:56:17.256754Z",
+ "shell.execute_reply": "2024-02-12T23:56:17.256235Z"
}
},
"outputs": [
@@ -631,10 +631,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:24.300968Z",
- "iopub.status.busy": "2024-02-12T22:49:24.300576Z",
- "iopub.status.idle": "2024-02-12T22:49:26.891347Z",
- "shell.execute_reply": "2024-02-12T22:49:26.890823Z"
+ "iopub.execute_input": "2024-02-12T23:56:17.258904Z",
+ "iopub.status.busy": "2024-02-12T23:56:17.258582Z",
+ "iopub.status.idle": "2024-02-12T23:56:19.821797Z",
+ "shell.execute_reply": "2024-02-12T23:56:19.821210Z"
}
},
"outputs": [
@@ -669,10 +669,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:26.893420Z",
- "iopub.status.busy": "2024-02-12T22:49:26.893225Z",
- "iopub.status.idle": "2024-02-12T22:49:26.896998Z",
- "shell.execute_reply": "2024-02-12T22:49:26.896539Z"
+ "iopub.execute_input": "2024-02-12T23:56:19.823998Z",
+ "iopub.status.busy": "2024-02-12T23:56:19.823698Z",
+ "iopub.status.idle": "2024-02-12T23:56:19.827135Z",
+ "shell.execute_reply": "2024-02-12T23:56:19.826622Z"
}
},
"outputs": [
@@ -719,10 +719,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:26.898924Z",
- "iopub.status.busy": "2024-02-12T22:49:26.898746Z",
- "iopub.status.idle": "2024-02-12T22:49:26.902892Z",
- "shell.execute_reply": "2024-02-12T22:49:26.902428Z"
+ "iopub.execute_input": "2024-02-12T23:56:19.829148Z",
+ "iopub.status.busy": "2024-02-12T23:56:19.828830Z",
+ "iopub.status.idle": "2024-02-12T23:56:19.832857Z",
+ "shell.execute_reply": "2024-02-12T23:56:19.832422Z"
}
},
"outputs": [],
@@ -750,10 +750,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:26.904728Z",
- "iopub.status.busy": "2024-02-12T22:49:26.904551Z",
- "iopub.status.idle": "2024-02-12T22:49:26.907736Z",
- "shell.execute_reply": "2024-02-12T22:49:26.907290Z"
+ "iopub.execute_input": "2024-02-12T23:56:19.834802Z",
+ "iopub.status.busy": "2024-02-12T23:56:19.834481Z",
+ "iopub.status.idle": "2024-02-12T23:56:19.837391Z",
+ "shell.execute_reply": "2024-02-12T23:56:19.836962Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb
index 780640890..e80a8f1e8 100644
--- a/master/tutorials/object_detection.ipynb
+++ b/master/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:29.412355Z",
- "iopub.status.busy": "2024-02-12T22:49:29.412182Z",
- "iopub.status.idle": "2024-02-12T22:49:30.526526Z",
- "shell.execute_reply": "2024-02-12T22:49:30.525887Z"
+ "iopub.execute_input": "2024-02-12T23:56:22.140899Z",
+ "iopub.status.busy": "2024-02-12T23:56:22.140709Z",
+ "iopub.status.idle": "2024-02-12T23:56:23.228656Z",
+ "shell.execute_reply": "2024-02-12T23:56:23.228102Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@bc22a6a4a967464be8c6133854b75af6acc10f7b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@b25d54f3802a5ca6f34c12f616928f1f7cde206d\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -109,10 +109,10 @@
"id": "c90449c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:30.528990Z",
- "iopub.status.busy": "2024-02-12T22:49:30.528713Z",
- "iopub.status.idle": "2024-02-12T22:49:33.180612Z",
- "shell.execute_reply": "2024-02-12T22:49:33.179832Z"
+ "iopub.execute_input": "2024-02-12T23:56:23.231351Z",
+ "iopub.status.busy": "2024-02-12T23:56:23.230862Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.303894Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.303203Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.183324Z",
- "iopub.status.busy": "2024-02-12T22:49:33.183127Z",
- "iopub.status.idle": "2024-02-12T22:49:33.186465Z",
- "shell.execute_reply": "2024-02-12T22:49:33.185898Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.306476Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.306007Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.309229Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.308792Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.188663Z",
- "iopub.status.busy": "2024-02-12T22:49:33.188286Z",
- "iopub.status.idle": "2024-02-12T22:49:33.194879Z",
- "shell.execute_reply": "2024-02-12T22:49:33.194329Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.311203Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.311026Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.317765Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.317326Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.196918Z",
- "iopub.status.busy": "2024-02-12T22:49:33.196600Z",
- "iopub.status.idle": "2024-02-12T22:49:33.683030Z",
- "shell.execute_reply": "2024-02-12T22:49:33.682447Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.319762Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.319435Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.802961Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.802339Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.685697Z",
- "iopub.status.busy": "2024-02-12T22:49:33.685346Z",
- "iopub.status.idle": "2024-02-12T22:49:33.690668Z",
- "shell.execute_reply": "2024-02-12T22:49:33.690108Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.805323Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.804884Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.810173Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.809617Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.692667Z",
- "iopub.status.busy": "2024-02-12T22:49:33.692303Z",
- "iopub.status.idle": "2024-02-12T22:49:33.696167Z",
- "shell.execute_reply": "2024-02-12T22:49:33.695665Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.812189Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.812028Z",
+ "iopub.status.idle": "2024-02-12T23:56:24.815640Z",
+ "shell.execute_reply": "2024-02-12T23:56:24.815111Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:33.698014Z",
- "iopub.status.busy": "2024-02-12T22:49:33.697830Z",
- "iopub.status.idle": "2024-02-12T22:49:34.370682Z",
- "shell.execute_reply": "2024-02-12T22:49:34.370018Z"
+ "iopub.execute_input": "2024-02-12T23:56:24.817678Z",
+ "iopub.status.busy": "2024-02-12T23:56:24.817338Z",
+ "iopub.status.idle": "2024-02-12T23:56:25.518766Z",
+ "shell.execute_reply": "2024-02-12T23:56:25.518129Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:34.372860Z",
- "iopub.status.busy": "2024-02-12T22:49:34.372665Z",
- "iopub.status.idle": "2024-02-12T22:49:34.541716Z",
- "shell.execute_reply": "2024-02-12T22:49:34.541246Z"
+ "iopub.execute_input": "2024-02-12T23:56:25.521164Z",
+ "iopub.status.busy": "2024-02-12T23:56:25.520797Z",
+ "iopub.status.idle": "2024-02-12T23:56:25.667693Z",
+ "shell.execute_reply": "2024-02-12T23:56:25.667126Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:34.543729Z",
- "iopub.status.busy": "2024-02-12T22:49:34.543542Z",
- "iopub.status.idle": "2024-02-12T22:49:34.547818Z",
- "shell.execute_reply": "2024-02-12T22:49:34.547385Z"
+ "iopub.execute_input": "2024-02-12T23:56:25.669798Z",
+ "iopub.status.busy": "2024-02-12T23:56:25.669479Z",
+ "iopub.status.idle": "2024-02-12T23:56:25.673810Z",
+ "shell.execute_reply": "2024-02-12T23:56:25.673249Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:34.549616Z",
- "iopub.status.busy": "2024-02-12T22:49:34.549442Z",
- "iopub.status.idle": "2024-02-12T22:49:35.003592Z",
- "shell.execute_reply": "2024-02-12T22:49:35.003069Z"
+ "iopub.execute_input": "2024-02-12T23:56:25.675766Z",
+ "iopub.status.busy": "2024-02-12T23:56:25.675467Z",
+ "iopub.status.idle": "2024-02-12T23:56:26.120953Z",
+ "shell.execute_reply": "2024-02-12T23:56:26.120360Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:35.005867Z",
- "iopub.status.busy": "2024-02-12T22:49:35.005681Z",
- "iopub.status.idle": "2024-02-12T22:49:35.340134Z",
- "shell.execute_reply": "2024-02-12T22:49:35.339527Z"
+ "iopub.execute_input": "2024-02-12T23:56:26.123845Z",
+ "iopub.status.busy": "2024-02-12T23:56:26.123481Z",
+ "iopub.status.idle": "2024-02-12T23:56:26.454695Z",
+ "shell.execute_reply": "2024-02-12T23:56:26.454102Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:35.342655Z",
- "iopub.status.busy": "2024-02-12T22:49:35.342175Z",
- "iopub.status.idle": "2024-02-12T22:49:35.687614Z",
- "shell.execute_reply": "2024-02-12T22:49:35.687041Z"
+ "iopub.execute_input": "2024-02-12T23:56:26.457118Z",
+ "iopub.status.busy": "2024-02-12T23:56:26.456922Z",
+ "iopub.status.idle": "2024-02-12T23:56:26.818521Z",
+ "shell.execute_reply": "2024-02-12T23:56:26.817933Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:35.689838Z",
- "iopub.status.busy": "2024-02-12T22:49:35.689432Z",
- "iopub.status.idle": "2024-02-12T22:49:36.105407Z",
- "shell.execute_reply": "2024-02-12T22:49:36.104863Z"
+ "iopub.execute_input": "2024-02-12T23:56:26.821330Z",
+ "iopub.status.busy": "2024-02-12T23:56:26.820948Z",
+ "iopub.status.idle": "2024-02-12T23:56:27.236356Z",
+ "shell.execute_reply": "2024-02-12T23:56:27.235774Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:36.109633Z",
- "iopub.status.busy": "2024-02-12T22:49:36.109271Z",
- "iopub.status.idle": "2024-02-12T22:49:36.557639Z",
- "shell.execute_reply": "2024-02-12T22:49:36.557070Z"
+ "iopub.execute_input": "2024-02-12T23:56:27.240859Z",
+ "iopub.status.busy": "2024-02-12T23:56:27.240397Z",
+ "iopub.status.idle": "2024-02-12T23:56:27.662840Z",
+ "shell.execute_reply": "2024-02-12T23:56:27.662274Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:36.560788Z",
- "iopub.status.busy": "2024-02-12T22:49:36.560443Z",
- "iopub.status.idle": "2024-02-12T22:49:36.777646Z",
- "shell.execute_reply": "2024-02-12T22:49:36.777086Z"
+ "iopub.execute_input": "2024-02-12T23:56:27.665109Z",
+ "iopub.status.busy": "2024-02-12T23:56:27.664778Z",
+ "iopub.status.idle": "2024-02-12T23:56:27.878959Z",
+ "shell.execute_reply": "2024-02-12T23:56:27.878412Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:36.779941Z",
- "iopub.status.busy": "2024-02-12T22:49:36.779591Z",
- "iopub.status.idle": "2024-02-12T22:49:36.978942Z",
- "shell.execute_reply": "2024-02-12T22:49:36.978426Z"
+ "iopub.execute_input": "2024-02-12T23:56:27.881237Z",
+ "iopub.status.busy": "2024-02-12T23:56:27.880881Z",
+ "iopub.status.idle": "2024-02-12T23:56:28.080478Z",
+ "shell.execute_reply": "2024-02-12T23:56:28.079892Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:36.981446Z",
- "iopub.status.busy": "2024-02-12T22:49:36.981110Z",
- "iopub.status.idle": "2024-02-12T22:49:36.983896Z",
- "shell.execute_reply": "2024-02-12T22:49:36.983476Z"
+ "iopub.execute_input": "2024-02-12T23:56:28.083155Z",
+ "iopub.status.busy": "2024-02-12T23:56:28.082956Z",
+ "iopub.status.idle": "2024-02-12T23:56:28.086231Z",
+ "shell.execute_reply": "2024-02-12T23:56:28.085665Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T22:49:36.985865Z",
- "iopub.status.busy": "2024-02-12T22:49:36.985551Z",
- "iopub.status.idle": "2024-02-12T22:49:37.936717Z",
- "shell.execute_reply": "2024-02-12T22:49:37.936159Z"
+ "iopub.execute_input": "2024-02-12T23:56:28.088300Z",
+ "iopub.status.busy": "2024-02-12T23:56:28.087899Z",
+ "iopub.status.idle": "2024-02-12T23:56:28.970362Z",
+ "shell.execute_reply": "2024-02-12T23:56:28.969773Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
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}
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"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
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"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
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+ "shell.execute_reply": "2024-02-12T23:56:29.941084Z"
}
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"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
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},
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diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html
index 94a3f2528..dbda754b3 100644
--- a/master/tutorials/outliers.html
+++ b/master/tutorials/outliers.html
@@ -731,16 +731,16 @@