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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index cdc6cafbf..1443f0bca 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index a278c7b85..7e9d16cce 100644
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diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
index 25a686165..d045f2500 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:53.786288Z",
- "iopub.status.busy": "2024-08-26T15:49:53.786078Z",
- "iopub.status.idle": "2024-08-26T15:49:55.058310Z",
- "shell.execute_reply": "2024-08-26T15:49:55.057679Z"
+ "iopub.execute_input": "2024-08-28T20:04:42.119805Z",
+ "iopub.status.busy": "2024-08-28T20:04:42.119627Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.356727Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.356102Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.061371Z",
- "iopub.status.busy": "2024-08-26T15:49:55.060806Z",
- "iopub.status.idle": "2024-08-26T15:49:55.079140Z",
- "shell.execute_reply": "2024-08-26T15:49:55.078680Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.359225Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.358943Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.377369Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.376770Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.081404Z",
- "iopub.status.busy": "2024-08-26T15:49:55.080983Z",
- "iopub.status.idle": "2024-08-26T15:49:55.275955Z",
- "shell.execute_reply": "2024-08-26T15:49:55.275375Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.379836Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.379355Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.522623Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.522044Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.313930Z",
- "iopub.status.busy": "2024-08-26T15:49:55.313329Z",
- "iopub.status.idle": "2024-08-26T15:49:55.317364Z",
- "shell.execute_reply": "2024-08-26T15:49:55.316909Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.553029Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.552834Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.556519Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.556047Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.319414Z",
- "iopub.status.busy": "2024-08-26T15:49:55.319068Z",
- "iopub.status.idle": "2024-08-26T15:49:55.327715Z",
- "shell.execute_reply": "2024-08-26T15:49:55.327122Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.558453Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.558283Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.566371Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.565935Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.329976Z",
- "iopub.status.busy": "2024-08-26T15:49:55.329562Z",
- "iopub.status.idle": "2024-08-26T15:49:55.332413Z",
- "shell.execute_reply": "2024-08-26T15:49:55.331825Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.568470Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.568293Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.570807Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.570342Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.334498Z",
- "iopub.status.busy": "2024-08-26T15:49:55.334155Z",
- "iopub.status.idle": "2024-08-26T15:49:55.859656Z",
- "shell.execute_reply": "2024-08-26T15:49:55.859123Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.572686Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.572515Z",
+ "iopub.status.idle": "2024-08-28T20:04:44.093962Z",
+ "shell.execute_reply": "2024-08-28T20:04:44.093422Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.862132Z",
- "iopub.status.busy": "2024-08-26T15:49:55.861942Z",
- "iopub.status.idle": "2024-08-26T15:49:57.828703Z",
- "shell.execute_reply": "2024-08-26T15:49:57.828113Z"
+ "iopub.execute_input": "2024-08-28T20:04:44.096377Z",
+ "iopub.status.busy": "2024-08-28T20:04:44.096155Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.009078Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.008425Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.831725Z",
- "iopub.status.busy": "2024-08-26T15:49:57.830860Z",
- "iopub.status.idle": "2024-08-26T15:49:57.841628Z",
- "shell.execute_reply": "2024-08-26T15:49:57.841073Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.011848Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.011198Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.021914Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.021384Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.843831Z",
- "iopub.status.busy": "2024-08-26T15:49:57.843444Z",
- "iopub.status.idle": "2024-08-26T15:49:57.847541Z",
- "shell.execute_reply": "2024-08-26T15:49:57.846968Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.024105Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.023682Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.027945Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.027378Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.849507Z",
- "iopub.status.busy": "2024-08-26T15:49:57.849202Z",
- "iopub.status.idle": "2024-08-26T15:49:57.858293Z",
- "shell.execute_reply": "2024-08-26T15:49:57.857744Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.030083Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.029775Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.038705Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.038223Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.860454Z",
- "iopub.status.busy": "2024-08-26T15:49:57.860152Z",
- "iopub.status.idle": "2024-08-26T15:49:57.972126Z",
- "shell.execute_reply": "2024-08-26T15:49:57.971546Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.040729Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.040398Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.151924Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.151398Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.974328Z",
- "iopub.status.busy": "2024-08-26T15:49:57.973926Z",
- "iopub.status.idle": "2024-08-26T15:49:57.976598Z",
- "shell.execute_reply": "2024-08-26T15:49:57.976151Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.154071Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.153792Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.156683Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.156129Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.978573Z",
- "iopub.status.busy": "2024-08-26T15:49:57.978261Z",
- "iopub.status.idle": "2024-08-26T15:50:00.065786Z",
- "shell.execute_reply": "2024-08-26T15:50:00.064976Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.158754Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.158318Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.235464Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.234799Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:00.069072Z",
- "iopub.status.busy": "2024-08-26T15:50:00.068250Z",
- "iopub.status.idle": "2024-08-26T15:50:00.079734Z",
- "shell.execute_reply": "2024-08-26T15:50:00.079177Z"
+ "iopub.execute_input": "2024-08-28T20:04:48.238628Z",
+ "iopub.status.busy": "2024-08-28T20:04:48.237802Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.248832Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.248357Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:00.081968Z",
- "iopub.status.busy": "2024-08-26T15:50:00.081512Z",
- "iopub.status.idle": "2024-08-26T15:50:00.307993Z",
- "shell.execute_reply": "2024-08-26T15:50:00.307364Z"
+ "iopub.execute_input": "2024-08-28T20:04:48.250827Z",
+ "iopub.status.busy": "2024-08-28T20:04:48.250644Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.292922Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.292468Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index 8c5c26b42..3d9566c81 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:03.569360Z",
- "iopub.status.busy": "2024-08-26T15:50:03.569180Z",
- "iopub.status.idle": "2024-08-26T15:50:06.407526Z",
- "shell.execute_reply": "2024-08-26T15:50:06.406949Z"
+ "iopub.execute_input": "2024-08-28T20:04:51.549919Z",
+ "iopub.status.busy": "2024-08-28T20:04:51.549750Z",
+ "iopub.status.idle": "2024-08-28T20:04:54.603829Z",
+ "shell.execute_reply": "2024-08-28T20:04:54.603164Z"
},
"nbsphinx": "hidden"
},
@@ -135,7 +135,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:06.410122Z",
- "iopub.status.busy": "2024-08-26T15:50:06.409772Z",
- "iopub.status.idle": "2024-08-26T15:50:06.413458Z",
- "shell.execute_reply": "2024-08-26T15:50:06.412873Z"
+ "iopub.execute_input": "2024-08-28T20:04:54.606360Z",
+ "iopub.status.busy": "2024-08-28T20:04:54.606051Z",
+ "iopub.status.idle": "2024-08-28T20:04:54.609561Z",
+ "shell.execute_reply": "2024-08-28T20:04:54.609105Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:06.415650Z",
- "iopub.status.busy": "2024-08-26T15:50:06.415319Z",
- "iopub.status.idle": "2024-08-26T15:50:06.418479Z",
- "shell.execute_reply": "2024-08-26T15:50:06.417934Z"
+ "iopub.execute_input": "2024-08-28T20:04:54.611731Z",
+ "iopub.status.busy": "2024-08-28T20:04:54.611374Z",
+ "iopub.status.idle": "2024-08-28T20:04:54.614331Z",
+ "shell.execute_reply": "2024-08-28T20:04:54.613882Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
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@@ -342,7 +342,7 @@
"output_type": "stream",
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"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'getting_spare_card', 'change_pin', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'card_about_to_expire', 'visa_or_mastercard', 'cancel_transfer'}\n"
+ "Classes: {'apple_pay_or_google_pay', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'cancel_transfer', 'supported_cards_and_currencies', 'getting_spare_card', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone'}\n"
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+ "ff735b03eb3d4c8385ba7ab965db9e17": {
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@@ -3639,6 +3621,24 @@
"visibility": null,
"width": null
}
+ },
+ "ffa4d1ac015a4234b2e990611468fefd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
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},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index db407c8a3..6911d1cef 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:18.375949Z",
- "iopub.status.busy": "2024-08-26T15:50:18.375420Z",
- "iopub.status.idle": "2024-08-26T15:50:23.859019Z",
- "shell.execute_reply": "2024-08-26T15:50:23.858417Z"
+ "iopub.execute_input": "2024-08-28T20:05:07.495227Z",
+ "iopub.status.busy": "2024-08-28T20:05:07.494741Z",
+ "iopub.status.idle": "2024-08-28T20:05:12.965294Z",
+ "shell.execute_reply": "2024-08-28T20:05:12.964724Z"
},
"nbsphinx": "hidden"
},
@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:23.863948Z"
+ "iopub.execute_input": "2024-08-28T20:05:12.967811Z",
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+ "shell.execute_reply": "2024-08-28T20:05:12.970254Z"
},
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},
@@ -157,10 +157,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:23.870358Z"
+ "iopub.execute_input": "2024-08-28T20:05:12.972551Z",
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+ "shell.execute_reply": "2024-08-28T20:05:12.976599Z"
},
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@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
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- "iopub.status.idle": "2024-08-26T15:50:25.664909Z",
- "shell.execute_reply": "2024-08-26T15:50:25.664194Z"
+ "iopub.execute_input": "2024-08-28T20:05:12.979087Z",
+ "iopub.status.busy": "2024-08-28T20:05:12.978759Z",
+ "iopub.status.idle": "2024-08-28T20:05:14.689169Z",
+ "shell.execute_reply": "2024-08-28T20:05:14.688459Z"
},
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@@ -242,10 +242,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:25.678115Z"
+ "iopub.execute_input": "2024-08-28T20:05:14.691661Z",
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+ "shell.execute_reply": "2024-08-28T20:05:14.704037Z"
},
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"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
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@@ -380,10 +380,10 @@
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"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:27.173138Z"
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@@ -474,10 +474,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:27.194686Z"
+ "iopub.execute_input": "2024-08-28T20:05:16.709023Z",
+ "iopub.status.busy": "2024-08-28T20:05:16.708689Z",
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+ "shell.execute_reply": "2024-08-28T20:05:16.726572Z"
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"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
"execution_count": 10,
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- "shell.execute_reply": "2024-08-26T15:50:27.199873Z"
+ "iopub.execute_input": "2024-08-28T20:05:16.729067Z",
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@@ -582,10 +582,10 @@
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+ "iopub.execute_input": "2024-08-28T20:05:16.733878Z",
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@@ -617,10 +617,10 @@
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- "shell.execute_reply": "2024-08-26T15:50:41.588990Z"
+ "iopub.execute_input": "2024-08-28T20:05:30.830643Z",
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@@ -680,10 +680,10 @@
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@@ -717,10 +717,10 @@
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@@ -767,10 +767,10 @@
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@@ -807,10 +807,10 @@
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@@ -870,10 +870,10 @@
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@@ -977,10 +977,10 @@
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@@ -1253,10 +1253,10 @@
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@@ -1297,10 +1297,10 @@
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@@ -1348,10 +1348,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 273aaa0cb..241f4fc04 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index 62eda8c4b..99a008a84 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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@@ -91,7 +91,7 @@
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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+ "shell.execute_reply": "2024-08-28T20:05:47.188252Z"
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@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
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+ "shell.execute_reply": "2024-08-28T20:05:47.199394Z"
}
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"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:57.959729Z",
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},
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},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index f0bfcffa1..d0e0da8b4 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
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- "shell.execute_reply": "2024-08-26T15:51:03.844700Z"
+ "iopub.execute_input": "2024-08-28T20:05:49.952557Z",
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+ "shell.execute_reply": "2024-08-28T20:05:52.912996Z"
},
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@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
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- "shell.execute_reply": "2024-08-26T15:51:03.850759Z"
+ "iopub.execute_input": "2024-08-28T20:05:52.916168Z",
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+ "shell.execute_reply": "2024-08-28T20:05:52.918704Z"
}
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@@ -152,17 +152,17 @@
"execution_count": 3,
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+ "shell.execute_reply": "2024-08-28T20:05:56.015125Z"
}
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+ "model_id": "f546543a4b994e7c947e3ddab9ef35fd",
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@@ -176,7 +176,7 @@
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+ "model_id": "f90499b00cc0489c9b7f21169ba0b4bd",
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@@ -190,7 +190,7 @@
{
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+ "model_id": "99d28f10e86b47028f586cf991c46491",
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@@ -204,7 +204,7 @@
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+ "model_id": "9ae89310be0a4a419eb65241c7fa071c",
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@@ -218,7 +218,7 @@
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+ "model_id": "312c9d3a255542eeb5612377fe2e80e5",
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@@ -260,10 +260,10 @@
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}
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@@ -288,17 +288,17 @@
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+ "model_id": "d19a5e1c9c5f401fb90b639fdd9f1b70",
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@@ -336,10 +336,10 @@
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@@ -372,10 +372,10 @@
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+ "shell.execute_reply": "2024-08-28T20:06:26.298928Z"
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@@ -413,10 +413,10 @@
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+ "iopub.execute_input": "2024-08-28T20:06:26.301409Z",
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+ "shell.execute_reply": "2024-08-28T20:06:26.304711Z"
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@@ -553,10 +553,10 @@
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@@ -681,10 +681,10 @@
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- "shell.execute_reply": "2024-08-26T15:51:38.792296Z"
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+ "shell.execute_reply": "2024-08-28T20:06:26.343385Z"
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@@ -721,10 +721,10 @@
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+ "iopub.execute_input": "2024-08-28T20:06:26.346604Z",
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+ "shell.execute_reply": "2024-08-28T20:06:59.759497Z"
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"outputs": [
@@ -740,21 +740,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.144\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.860\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.930\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.838\n",
"Computing feature embeddings ...\n"
]
},
{
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@@ -775,7 +775,7 @@
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@@ -798,21 +798,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.995\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.827\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.984\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.861\n",
"Computing feature embeddings ...\n"
]
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- "model_id": "5dd4ac73beaa4c909b6d006c9b6b289f",
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@@ -833,7 +833,7 @@
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@@ -856,21 +856,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.056\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.896\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.832\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.601\n",
"Computing feature embeddings ...\n"
]
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@@ -891,7 +891,7 @@
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@@ -970,10 +970,10 @@
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- "shell.execute_reply": "2024-08-26T15:52:13.500140Z"
+ "iopub.execute_input": "2024-08-28T20:06:59.762672Z",
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+ "shell.execute_reply": "2024-08-28T20:06:59.778380Z"
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@@ -998,10 +998,10 @@
"execution_count": 13,
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- "iopub.execute_input": "2024-08-26T15:52:13.503031Z",
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- "shell.execute_reply": "2024-08-26T15:52:14.010336Z"
+ "iopub.execute_input": "2024-08-28T20:06:59.781644Z",
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+ "iopub.status.idle": "2024-08-28T20:07:00.232620Z",
+ "shell.execute_reply": "2024-08-28T20:07:00.231966Z"
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@@ -1021,10 +1021,10 @@
"execution_count": 14,
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- "iopub.status.idle": "2024-08-26T15:54:07.250429Z",
- "shell.execute_reply": "2024-08-26T15:54:07.249850Z"
+ "iopub.execute_input": "2024-08-28T20:07:00.235053Z",
+ "iopub.status.busy": "2024-08-28T20:07:00.234870Z",
+ "iopub.status.idle": "2024-08-28T20:08:50.130089Z",
+ "shell.execute_reply": "2024-08-28T20:08:50.129424Z"
}
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@@ -1063,7 +1063,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a0e6ab9c19e34b8e9bbe62ee43bd8c35",
+ "model_id": "dccb84fb19484d4980a7659b3d7a2270",
"version_major": 2,
"version_minor": 0
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@@ -1109,10 +1109,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:54:07.253184Z",
- "iopub.status.busy": "2024-08-26T15:54:07.252602Z",
- "iopub.status.idle": "2024-08-26T15:54:07.719555Z",
- "shell.execute_reply": "2024-08-26T15:54:07.718967Z"
+ "iopub.execute_input": "2024-08-28T20:08:50.132488Z",
+ "iopub.status.busy": "2024-08-28T20:08:50.132077Z",
+ "iopub.status.idle": "2024-08-28T20:08:50.588895Z",
+ "shell.execute_reply": "2024-08-28T20:08:50.588329Z"
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"outputs": [
@@ -1258,10 +1258,10 @@
"execution_count": 16,
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- "iopub.execute_input": "2024-08-26T15:54:07.722577Z",
- "iopub.status.busy": "2024-08-26T15:54:07.722057Z",
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- "shell.execute_reply": "2024-08-26T15:54:07.784657Z"
+ "iopub.execute_input": "2024-08-28T20:08:50.591803Z",
+ "iopub.status.busy": "2024-08-28T20:08:50.591296Z",
+ "iopub.status.idle": "2024-08-28T20:08:50.653079Z",
+ "shell.execute_reply": "2024-08-28T20:08:50.652532Z"
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"outputs": [
@@ -1365,10 +1365,10 @@
"execution_count": 17,
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- "iopub.execute_input": "2024-08-26T15:54:07.787526Z",
- "iopub.status.busy": "2024-08-26T15:54:07.787195Z",
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- "shell.execute_reply": "2024-08-26T15:54:07.795642Z"
+ "iopub.execute_input": "2024-08-28T20:08:50.655358Z",
+ "iopub.status.busy": "2024-08-28T20:08:50.654879Z",
+ "iopub.status.idle": "2024-08-28T20:08:50.663334Z",
+ "shell.execute_reply": "2024-08-28T20:08:50.662785Z"
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"outputs": [
@@ -1498,10 +1498,10 @@
"execution_count": 18,
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- "shell.execute_reply": "2024-08-26T15:54:07.802450Z"
+ "iopub.execute_input": "2024-08-28T20:08:50.665441Z",
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+ "IPY_MODEL_36050e8d2e9e44a3a4ed1600bdd36831"
],
- "layout": "IPY_MODEL_cd61e02259094a979ac8fdee8d778a4a",
+ "layout": "IPY_MODEL_0e64e21c42624612ab3f8a532e796075",
"tabbable": null,
"tooltip": null
}
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index dc624f661..e1622782b 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
@@ -73,10 +73,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:14.109304Z",
- "iopub.status.busy": "2024-08-26T15:54:14.108881Z",
- "iopub.status.idle": "2024-08-26T15:54:15.281952Z",
- "shell.execute_reply": "2024-08-26T15:54:15.281381Z"
+ "iopub.execute_input": "2024-08-28T20:08:57.484968Z",
+ "iopub.status.busy": "2024-08-28T20:08:57.484796Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.631433Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.630894Z"
},
"nbsphinx": "hidden"
},
@@ -86,7 +86,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -111,10 +111,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.284532Z",
- "iopub.status.busy": "2024-08-26T15:54:15.284089Z",
- "iopub.status.idle": "2024-08-26T15:54:15.302497Z",
- "shell.execute_reply": "2024-08-26T15:54:15.301896Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.633842Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.633571Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.651793Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.651341Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.304895Z",
- "iopub.status.busy": "2024-08-26T15:54:15.304508Z",
- "iopub.status.idle": "2024-08-26T15:54:15.325953Z",
- "shell.execute_reply": "2024-08-26T15:54:15.325390Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.654138Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.653629Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.690305Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.689852Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.328080Z",
- "iopub.status.busy": "2024-08-26T15:54:15.327751Z",
- "iopub.status.idle": "2024-08-26T15:54:15.331370Z",
- "shell.execute_reply": "2024-08-26T15:54:15.330867Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.692190Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.692019Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.695887Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.695430Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.333595Z",
- "iopub.status.busy": "2024-08-26T15:54:15.333139Z",
- "iopub.status.idle": "2024-08-26T15:54:15.341092Z",
- "shell.execute_reply": "2024-08-26T15:54:15.340517Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.698037Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.697703Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.704998Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.704569Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.343311Z",
- "iopub.status.busy": "2024-08-26T15:54:15.343000Z",
- "iopub.status.idle": "2024-08-26T15:54:15.346105Z",
- "shell.execute_reply": "2024-08-26T15:54:15.345645Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.707067Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.706731Z",
+ "iopub.status.idle": "2024-08-28T20:08:58.709330Z",
+ "shell.execute_reply": "2024-08-28T20:08:58.708870Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:15.348163Z",
- "iopub.status.busy": "2024-08-26T15:54:15.347822Z",
- "iopub.status.idle": "2024-08-26T15:54:18.449602Z",
- "shell.execute_reply": "2024-08-26T15:54:18.449016Z"
+ "iopub.execute_input": "2024-08-28T20:08:58.711286Z",
+ "iopub.status.busy": "2024-08-28T20:08:58.710953Z",
+ "iopub.status.idle": "2024-08-28T20:09:01.878117Z",
+ "shell.execute_reply": "2024-08-28T20:09:01.877469Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:18.452379Z",
- "iopub.status.busy": "2024-08-26T15:54:18.452168Z",
- "iopub.status.idle": "2024-08-26T15:54:18.461665Z",
- "shell.execute_reply": "2024-08-26T15:54:18.461066Z"
+ "iopub.execute_input": "2024-08-28T20:09:01.881086Z",
+ "iopub.status.busy": "2024-08-28T20:09:01.880632Z",
+ "iopub.status.idle": "2024-08-28T20:09:01.890402Z",
+ "shell.execute_reply": "2024-08-28T20:09:01.889852Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:18.463964Z",
- "iopub.status.busy": "2024-08-26T15:54:18.463672Z",
- "iopub.status.idle": "2024-08-26T15:54:20.596861Z",
- "shell.execute_reply": "2024-08-26T15:54:20.596179Z"
+ "iopub.execute_input": "2024-08-28T20:09:01.892748Z",
+ "iopub.status.busy": "2024-08-28T20:09:01.892545Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.897249Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.896593Z"
}
},
"outputs": [
@@ -476,10 +476,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.599365Z",
- "iopub.status.busy": "2024-08-26T15:54:20.598980Z",
- "iopub.status.idle": "2024-08-26T15:54:20.619735Z",
- "shell.execute_reply": "2024-08-26T15:54:20.619219Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.899722Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.899310Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.918088Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.917623Z"
},
"scrolled": true
},
@@ -609,10 +609,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.621986Z",
- "iopub.status.busy": "2024-08-26T15:54:20.621626Z",
- "iopub.status.idle": "2024-08-26T15:54:20.629738Z",
- "shell.execute_reply": "2024-08-26T15:54:20.629246Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.920225Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.919882Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.927652Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.927063Z"
}
},
"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.631922Z",
- "iopub.status.busy": "2024-08-26T15:54:20.631599Z",
- "iopub.status.idle": "2024-08-26T15:54:20.640869Z",
- "shell.execute_reply": "2024-08-26T15:54:20.640300Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.929691Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.929386Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.938398Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.937878Z"
}
},
"outputs": [
@@ -848,10 +848,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.642988Z",
- "iopub.status.busy": "2024-08-26T15:54:20.642648Z",
- "iopub.status.idle": "2024-08-26T15:54:20.650592Z",
- "shell.execute_reply": "2024-08-26T15:54:20.650087Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.940404Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.940098Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.947710Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.947261Z"
}
},
"outputs": [
@@ -965,10 +965,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.652766Z",
- "iopub.status.busy": "2024-08-26T15:54:20.652433Z",
- "iopub.status.idle": "2024-08-26T15:54:20.661662Z",
- "shell.execute_reply": "2024-08-26T15:54:20.661085Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.949811Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.949417Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.958627Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.958084Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.663894Z",
- "iopub.status.busy": "2024-08-26T15:54:20.663442Z",
- "iopub.status.idle": "2024-08-26T15:54:20.671133Z",
- "shell.execute_reply": "2024-08-26T15:54:20.670552Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.960754Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.960427Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.967656Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.967111Z"
}
},
"outputs": [
@@ -1197,10 +1197,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.673323Z",
- "iopub.status.busy": "2024-08-26T15:54:20.672984Z",
- "iopub.status.idle": "2024-08-26T15:54:20.680708Z",
- "shell.execute_reply": "2024-08-26T15:54:20.680120Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.969647Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.969334Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.976478Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.976037Z"
}
},
"outputs": [
@@ -1306,10 +1306,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:20.682929Z",
- "iopub.status.busy": "2024-08-26T15:54:20.682566Z",
- "iopub.status.idle": "2024-08-26T15:54:20.690842Z",
- "shell.execute_reply": "2024-08-26T15:54:20.690326Z"
+ "iopub.execute_input": "2024-08-28T20:09:03.978405Z",
+ "iopub.status.busy": "2024-08-28T20:09:03.978234Z",
+ "iopub.status.idle": "2024-08-28T20:09:03.986709Z",
+ "shell.execute_reply": "2024-08-28T20:09:03.986282Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 5d4d24b3a..a79eb7b3d 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:23.767329Z",
- "iopub.status.busy": "2024-08-26T15:54:23.766904Z",
- "iopub.status.idle": "2024-08-26T15:54:26.717164Z",
- "shell.execute_reply": "2024-08-26T15:54:26.716517Z"
+ "iopub.execute_input": "2024-08-28T20:09:06.667772Z",
+ "iopub.status.busy": "2024-08-28T20:09:06.667589Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.446715Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.446202Z"
},
"nbsphinx": "hidden"
},
@@ -96,7 +96,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.720039Z",
- "iopub.status.busy": "2024-08-26T15:54:26.719548Z",
- "iopub.status.idle": "2024-08-26T15:54:26.722953Z",
- "shell.execute_reply": "2024-08-26T15:54:26.722453Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.449561Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.449015Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.452229Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.451779Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.725073Z",
- "iopub.status.busy": "2024-08-26T15:54:26.724682Z",
- "iopub.status.idle": "2024-08-26T15:54:26.727880Z",
- "shell.execute_reply": "2024-08-26T15:54:26.727424Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.454188Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.453882Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.457040Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.456484Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.729884Z",
- "iopub.status.busy": "2024-08-26T15:54:26.729547Z",
- "iopub.status.idle": "2024-08-26T15:54:26.751412Z",
- "shell.execute_reply": "2024-08-26T15:54:26.750890Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.458927Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.458746Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.500755Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.500292Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.753620Z",
- "iopub.status.busy": "2024-08-26T15:54:26.753261Z",
- "iopub.status.idle": "2024-08-26T15:54:26.756908Z",
- "shell.execute_reply": "2024-08-26T15:54:26.756385Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.502650Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.502470Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.506380Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.505920Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'cancel_transfer', 'getting_spare_card', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'change_pin'}\n"
+ "Classes: {'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged', 'getting_spare_card', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'cancel_transfer', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.758907Z",
- "iopub.status.busy": "2024-08-26T15:54:26.758575Z",
- "iopub.status.idle": "2024-08-26T15:54:26.761810Z",
- "shell.execute_reply": "2024-08-26T15:54:26.761254Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.508323Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.508145Z",
+ "iopub.status.idle": "2024-08-28T20:09:09.511367Z",
+ "shell.execute_reply": "2024-08-28T20:09:09.510908Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:26.763957Z",
- "iopub.status.busy": "2024-08-26T15:54:26.763625Z",
- "iopub.status.idle": "2024-08-26T15:54:31.044703Z",
- "shell.execute_reply": "2024-08-26T15:54:31.044116Z"
+ "iopub.execute_input": "2024-08-28T20:09:09.513290Z",
+ "iopub.status.busy": "2024-08-28T20:09:09.513119Z",
+ "iopub.status.idle": "2024-08-28T20:09:13.327602Z",
+ "shell.execute_reply": "2024-08-28T20:09:13.327019Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:31.047500Z",
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- "iopub.status.idle": "2024-08-26T15:54:31.975680Z",
- "shell.execute_reply": "2024-08-26T15:54:31.975010Z"
+ "iopub.execute_input": "2024-08-28T20:09:13.330344Z",
+ "iopub.status.busy": "2024-08-28T20:09:13.330103Z",
+ "iopub.status.idle": "2024-08-28T20:09:14.248319Z",
+ "shell.execute_reply": "2024-08-28T20:09:14.247698Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:31.979040Z",
- "iopub.status.busy": "2024-08-26T15:54:31.978560Z",
- "iopub.status.idle": "2024-08-26T15:54:31.981693Z",
- "shell.execute_reply": "2024-08-26T15:54:31.981174Z"
+ "iopub.execute_input": "2024-08-28T20:09:14.251336Z",
+ "iopub.status.busy": "2024-08-28T20:09:14.250917Z",
+ "iopub.status.idle": "2024-08-28T20:09:14.254017Z",
+ "shell.execute_reply": "2024-08-28T20:09:14.253514Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:31.985036Z",
- "iopub.status.busy": "2024-08-26T15:54:31.984064Z",
- "iopub.status.idle": "2024-08-26T15:54:34.095091Z",
- "shell.execute_reply": "2024-08-26T15:54:34.094037Z"
+ "iopub.execute_input": "2024-08-28T20:09:14.256490Z",
+ "iopub.status.busy": "2024-08-28T20:09:14.256096Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.217332Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.216596Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.099316Z",
- "iopub.status.busy": "2024-08-26T15:54:34.098088Z",
- "iopub.status.idle": "2024-08-26T15:54:34.124948Z",
- "shell.execute_reply": "2024-08-26T15:54:34.124391Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.220230Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.219803Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.243903Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.243330Z"
},
"scrolled": true
},
@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.128745Z",
- "iopub.status.busy": "2024-08-26T15:54:34.127878Z",
- "iopub.status.idle": "2024-08-26T15:54:34.137226Z",
- "shell.execute_reply": "2024-08-26T15:54:34.136493Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.246452Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.246037Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.255941Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.255493Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.139822Z",
- "iopub.status.busy": "2024-08-26T15:54:34.139392Z",
- "iopub.status.idle": "2024-08-26T15:54:34.143990Z",
- "shell.execute_reply": "2024-08-26T15:54:34.143494Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.258230Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.257870Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.262445Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.262017Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.146004Z",
- "iopub.status.busy": "2024-08-26T15:54:34.145827Z",
- "iopub.status.idle": "2024-08-26T15:54:34.152551Z",
- "shell.execute_reply": "2024-08-26T15:54:34.152042Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.264669Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.264314Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.270741Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.270305Z"
}
},
"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.154784Z",
- "iopub.status.busy": "2024-08-26T15:54:34.154411Z",
- "iopub.status.idle": "2024-08-26T15:54:34.162610Z",
- "shell.execute_reply": "2024-08-26T15:54:34.162016Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.273002Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.272647Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.279067Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.278632Z"
}
},
"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.165047Z",
- "iopub.status.busy": "2024-08-26T15:54:34.164702Z",
- "iopub.status.idle": "2024-08-26T15:54:34.170937Z",
- "shell.execute_reply": "2024-08-26T15:54:34.170346Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.281168Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.280836Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.286515Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.285987Z"
}
},
"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.173239Z",
- "iopub.status.busy": "2024-08-26T15:54:34.172892Z",
- "iopub.status.idle": "2024-08-26T15:54:34.181824Z",
- "shell.execute_reply": "2024-08-26T15:54:34.181227Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.288678Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.288295Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.296683Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.296125Z"
}
},
"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.184176Z",
- "iopub.status.busy": "2024-08-26T15:54:34.183819Z",
- "iopub.status.idle": "2024-08-26T15:54:34.189592Z",
- "shell.execute_reply": "2024-08-26T15:54:34.189013Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.298685Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.298350Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.303747Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.303176Z"
}
},
"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.191826Z",
- "iopub.status.busy": "2024-08-26T15:54:34.191485Z",
- "iopub.status.idle": "2024-08-26T15:54:34.197135Z",
- "shell.execute_reply": "2024-08-26T15:54:34.196593Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.305874Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.305529Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.311138Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.310579Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.199323Z",
- "iopub.status.busy": "2024-08-26T15:54:34.198999Z",
- "iopub.status.idle": "2024-08-26T15:54:34.202390Z",
- "shell.execute_reply": "2024-08-26T15:54:34.201850Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.313281Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.312953Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.316148Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.315629Z"
}
},
"outputs": [
@@ -1449,10 +1449,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:34.204586Z",
- "iopub.status.busy": "2024-08-26T15:54:34.204241Z",
- "iopub.status.idle": "2024-08-26T15:54:34.209327Z",
- "shell.execute_reply": "2024-08-26T15:54:34.208871Z"
+ "iopub.execute_input": "2024-08-28T20:09:16.318258Z",
+ "iopub.status.busy": "2024-08-28T20:09:16.317859Z",
+ "iopub.status.idle": "2024-08-28T20:09:16.322985Z",
+ "shell.execute_reply": "2024-08-28T20:09:16.322533Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index 31cb3f500..871dee6a0 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:38.759112Z",
- "iopub.status.busy": "2024-08-26T15:54:38.758919Z",
- "iopub.status.idle": "2024-08-26T15:54:39.215433Z",
- "shell.execute_reply": "2024-08-26T15:54:39.214907Z"
+ "iopub.execute_input": "2024-08-28T20:09:19.771331Z",
+ "iopub.status.busy": "2024-08-28T20:09:19.771170Z",
+ "iopub.status.idle": "2024-08-28T20:09:20.203947Z",
+ "shell.execute_reply": "2024-08-28T20:09:20.203422Z"
}
},
"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:39.218396Z",
- "iopub.status.busy": "2024-08-26T15:54:39.217877Z",
- "iopub.status.idle": "2024-08-26T15:54:39.353724Z",
- "shell.execute_reply": "2024-08-26T15:54:39.353143Z"
+ "iopub.execute_input": "2024-08-28T20:09:20.206510Z",
+ "iopub.status.busy": "2024-08-28T20:09:20.206092Z",
+ "iopub.status.idle": "2024-08-28T20:09:20.338932Z",
+ "shell.execute_reply": "2024-08-28T20:09:20.338305Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:39.356009Z",
- "iopub.status.busy": "2024-08-26T15:54:39.355760Z",
- "iopub.status.idle": "2024-08-26T15:54:39.380390Z",
- "shell.execute_reply": "2024-08-26T15:54:39.379777Z"
+ "iopub.execute_input": "2024-08-28T20:09:20.341381Z",
+ "iopub.status.busy": "2024-08-28T20:09:20.340976Z",
+ "iopub.status.idle": "2024-08-28T20:09:20.363703Z",
+ "shell.execute_reply": "2024-08-28T20:09:20.363138Z"
}
},
"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:39.383260Z",
- "iopub.status.busy": "2024-08-26T15:54:39.382742Z",
- "iopub.status.idle": "2024-08-26T15:54:42.387338Z",
- "shell.execute_reply": "2024-08-26T15:54:42.386595Z"
+ "iopub.execute_input": "2024-08-28T20:09:20.366713Z",
+ "iopub.status.busy": "2024-08-28T20:09:20.366163Z",
+ "iopub.status.idle": "2024-08-28T20:09:23.163182Z",
+ "shell.execute_reply": "2024-08-28T20:09:23.162494Z"
}
},
"outputs": [
@@ -235,7 +235,7 @@
"Finding class_imbalance issues ...\n",
"Finding underperforming_group issues ...\n",
"\n",
- "Audit complete. 524 issues found in the dataset.\n"
+ "Audit complete. 523 issues found in the dataset.\n"
]
},
{
@@ -280,13 +280,13 @@
"
\n",
" 2 | \n",
" outlier | \n",
- " 0.356925 | \n",
- " 363 | \n",
+ " 0.356959 | \n",
+ " 362 | \n",
"
\n",
" \n",
" 3 | \n",
" near_duplicate | \n",
- " 0.619581 | \n",
+ " 0.619565 | \n",
" 108 | \n",
"
\n",
" \n",
@@ -315,8 +315,8 @@
" issue_type score num_issues\n",
"0 null 1.000000 0\n",
"1 label 0.991400 52\n",
- "2 outlier 0.356925 363\n",
- "3 near_duplicate 0.619581 108\n",
+ "2 outlier 0.356959 362\n",
+ "3 near_duplicate 0.619565 108\n",
"4 non_iid 0.000000 1\n",
"5 class_imbalance 0.500000 0\n",
"6 underperforming_group 0.651838 0"
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:42.390104Z",
- "iopub.status.busy": "2024-08-26T15:54:42.389566Z",
- "iopub.status.idle": "2024-08-26T15:54:52.329674Z",
- "shell.execute_reply": "2024-08-26T15:54:52.329133Z"
+ "iopub.execute_input": "2024-08-28T20:09:23.166128Z",
+ "iopub.status.busy": "2024-08-28T20:09:23.165571Z",
+ "iopub.status.idle": "2024-08-28T20:09:32.198950Z",
+ "shell.execute_reply": "2024-08-28T20:09:32.198342Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:52.332096Z",
- "iopub.status.busy": "2024-08-26T15:54:52.331674Z",
- "iopub.status.idle": "2024-08-26T15:54:52.493633Z",
- "shell.execute_reply": "2024-08-26T15:54:52.492929Z"
+ "iopub.execute_input": "2024-08-28T20:09:32.201273Z",
+ "iopub.status.busy": "2024-08-28T20:09:32.200874Z",
+ "iopub.status.idle": "2024-08-28T20:09:32.359974Z",
+ "shell.execute_reply": "2024-08-28T20:09:32.359321Z"
}
},
"outputs": [],
@@ -838,10 +838,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:52.496177Z",
- "iopub.status.busy": "2024-08-26T15:54:52.495969Z",
- "iopub.status.idle": "2024-08-26T15:54:53.925137Z",
- "shell.execute_reply": "2024-08-26T15:54:53.924517Z"
+ "iopub.execute_input": "2024-08-28T20:09:32.362593Z",
+ "iopub.status.busy": "2024-08-28T20:09:32.362241Z",
+ "iopub.status.idle": "2024-08-28T20:09:33.706259Z",
+ "shell.execute_reply": "2024-08-28T20:09:33.705764Z"
}
},
"outputs": [
@@ -1000,10 +1000,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:53.927691Z",
- "iopub.status.busy": "2024-08-26T15:54:53.927274Z",
- "iopub.status.idle": "2024-08-26T15:54:54.361405Z",
- "shell.execute_reply": "2024-08-26T15:54:54.360761Z"
+ "iopub.execute_input": "2024-08-28T20:09:33.708514Z",
+ "iopub.status.busy": "2024-08-28T20:09:33.708155Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.142519Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.141938Z"
}
},
"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:54.364105Z",
- "iopub.status.busy": "2024-08-26T15:54:54.363404Z",
- "iopub.status.idle": "2024-08-26T15:54:54.377500Z",
- "shell.execute_reply": "2024-08-26T15:54:54.377021Z"
+ "iopub.execute_input": "2024-08-28T20:09:34.144941Z",
+ "iopub.status.busy": "2024-08-28T20:09:34.144511Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.157689Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.157230Z"
}
},
"outputs": [],
@@ -1115,10 +1115,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:54.379842Z",
- "iopub.status.busy": "2024-08-26T15:54:54.379476Z",
- "iopub.status.idle": "2024-08-26T15:54:54.398245Z",
- "shell.execute_reply": "2024-08-26T15:54:54.397742Z"
+ "iopub.execute_input": "2024-08-28T20:09:34.159753Z",
+ "iopub.status.busy": "2024-08-28T20:09:34.159414Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.180259Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.179666Z"
}
},
"outputs": [],
@@ -1146,10 +1146,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:54.400658Z",
- "iopub.status.busy": "2024-08-26T15:54:54.400452Z",
- "iopub.status.idle": "2024-08-26T15:54:54.629761Z",
- "shell.execute_reply": "2024-08-26T15:54:54.629093Z"
+ "iopub.execute_input": "2024-08-28T20:09:34.182439Z",
+ "iopub.status.busy": "2024-08-28T20:09:34.182121Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.410667Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.410121Z"
}
},
"outputs": [],
@@ -1189,10 +1189,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:54.632497Z",
- "iopub.status.busy": "2024-08-26T15:54:54.632295Z",
- "iopub.status.idle": "2024-08-26T15:54:54.651943Z",
- "shell.execute_reply": "2024-08-26T15:54:54.651403Z"
+ "iopub.execute_input": "2024-08-28T20:09:34.413358Z",
+ "iopub.status.busy": "2024-08-28T20:09:34.412952Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.432227Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.431652Z"
}
},
"outputs": [
@@ -1390,10 +1390,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:54:54.654296Z",
- "iopub.status.busy": "2024-08-26T15:54:54.653851Z",
- "iopub.status.idle": "2024-08-26T15:54:54.833100Z",
- "shell.execute_reply": "2024-08-26T15:54:54.832491Z"
+ "iopub.execute_input": "2024-08-28T20:09:34.434365Z",
+ "iopub.status.busy": "2024-08-28T20:09:34.434181Z",
+ "iopub.status.idle": "2024-08-28T20:09:34.602467Z",
+ "shell.execute_reply": "2024-08-28T20:09:34.601833Z"
}
},
"outputs": [
@@ -1460,10 +1460,10 @@
"execution_count": 14,
"metadata": {
"execution": {
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@@ -3551,10 +3551,10 @@
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"name": "stdout",
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- "--2024-08-26 15:54:55-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n",
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"Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n",
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@@ -4231,10 +4238,10 @@
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@@ -4447,35 +4454,35 @@
" \n",
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@@ -4572,10 +4579,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index e035bdf3d..07d4a084d 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
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@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index cd7e6f125..4a4e30da4 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
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diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
index facf398f8..19ee6dad6 100644
--- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
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"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
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+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
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@@ -449,10 +449,10 @@
"id": "46275634-da56-4e58-9061-8108be2b585d",
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@@ -488,10 +488,10 @@
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@@ -506,10 +506,10 @@
"id": "7ac47c3d-9e87-45b7-9064-bfa45578872e",
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"outputs": [
@@ -609,10 +609,10 @@
"id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b",
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"outputs": [
@@ -724,10 +724,10 @@
"id": "b68e0418-86cf-431f-9107-2dd0a310ca42",
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@@ -931,10 +931,10 @@
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"outputs": [
@@ -968,10 +968,10 @@
"id": "e72320ec-7792-4347-b2fb-630f2519127c",
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"outputs": [
@@ -1005,10 +1005,10 @@
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"outputs": [
@@ -1205,10 +1205,10 @@
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@@ -1234,10 +1234,10 @@
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"outputs": [
@@ -1711,10 +1711,10 @@
"id": "044c0eb1-299a-4851-b1bf-268d5bce56c1",
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@@ -1738,10 +1738,10 @@
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"outputs": [
@@ -1953,10 +1953,10 @@
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"outputs": [
@@ -2073,10 +2073,10 @@
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@@ -2090,10 +2090,10 @@
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"outputs": [],
@@ -2117,10 +2117,10 @@
"id": "5ce2d89f-e832-448d-bfac-9941da15c895",
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"outputs": [
@@ -2160,10 +2160,10 @@
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"outputs": [],
@@ -2194,10 +2194,10 @@
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"outputs": [
@@ -2408,10 +2408,10 @@
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"outputs": [
@@ -2441,10 +2441,10 @@
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"outputs": [
@@ -2477,10 +2477,10 @@
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"outputs": [
@@ -2513,10 +2513,10 @@
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"outputs": [
@@ -2542,10 +2542,10 @@
"id": "08080458-0cd7-447d-80e6-384cb8d31eaf",
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@@ -2569,10 +2569,10 @@
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"outputs": [
@@ -3052,10 +3052,10 @@
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"outputs": [
@@ -3111,10 +3111,10 @@
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+ "shell.execute_reply": "2024-08-28T20:10:08.283055Z"
}
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"outputs": [],
@@ -3150,10 +3150,10 @@
"id": "941ab2a6",
"metadata": {
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- "iopub.status.idle": "2024-08-26T15:55:30.547035Z",
- "shell.execute_reply": "2024-08-26T15:55:30.546403Z"
+ "iopub.execute_input": "2024-08-28T20:10:08.285641Z",
+ "iopub.status.busy": "2024-08-28T20:10:08.285369Z",
+ "iopub.status.idle": "2024-08-28T20:10:08.296591Z",
+ "shell.execute_reply": "2024-08-28T20:10:08.296036Z"
}
},
"outputs": [],
@@ -3261,10 +3261,10 @@
"id": "50666fb9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:30.549375Z",
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- "iopub.status.idle": "2024-08-26T15:55:30.556029Z",
- "shell.execute_reply": "2024-08-26T15:55:30.555553Z"
+ "iopub.execute_input": "2024-08-28T20:10:08.298817Z",
+ "iopub.status.busy": "2024-08-28T20:10:08.298509Z",
+ "iopub.status.idle": "2024-08-28T20:10:08.305024Z",
+ "shell.execute_reply": "2024-08-28T20:10:08.304495Z"
},
"nbsphinx": "hidden"
},
@@ -3346,10 +3346,10 @@
"id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:30.558185Z",
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- "iopub.status.idle": "2024-08-26T15:55:30.561412Z",
- "shell.execute_reply": "2024-08-26T15:55:30.560822Z"
+ "iopub.execute_input": "2024-08-28T20:10:08.307009Z",
+ "iopub.status.busy": "2024-08-28T20:10:08.306698Z",
+ "iopub.status.idle": "2024-08-28T20:10:08.309906Z",
+ "shell.execute_reply": "2024-08-28T20:10:08.309410Z"
}
},
"outputs": [],
@@ -3373,10 +3373,10 @@
"id": "ce1c0ada-88b1-4654-b43f-3c0b59002979",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:30.563623Z",
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- "shell.execute_reply": "2024-08-26T15:55:34.647124Z"
+ "iopub.execute_input": "2024-08-28T20:10:08.311873Z",
+ "iopub.status.busy": "2024-08-28T20:10:08.311702Z",
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+ "shell.execute_reply": "2024-08-28T20:10:12.336527Z"
}
},
"outputs": [
@@ -3419,10 +3419,10 @@
"id": "3f572acf-31c3-4874-9100-451796e35b06",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:34.650677Z",
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- "iopub.status.idle": "2024-08-26T15:55:34.653991Z",
- "shell.execute_reply": "2024-08-26T15:55:34.653587Z"
+ "iopub.execute_input": "2024-08-28T20:10:12.339575Z",
+ "iopub.status.busy": "2024-08-28T20:10:12.339168Z",
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+ "shell.execute_reply": "2024-08-28T20:10:12.341777Z"
}
},
"outputs": [
@@ -3460,10 +3460,10 @@
"id": "6a025a88",
"metadata": {
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- "shell.execute_reply": "2024-08-26T15:55:34.657946Z"
+ "iopub.execute_input": "2024-08-28T20:10:12.344378Z",
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+ "iopub.status.idle": "2024-08-28T20:10:12.346559Z",
+ "shell.execute_reply": "2024-08-28T20:10:12.346169Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index ecc184176..94545a8e2 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:38.086130Z",
- "iopub.status.busy": "2024-08-26T15:55:38.085966Z",
- "iopub.status.idle": "2024-08-26T15:55:39.311143Z",
- "shell.execute_reply": "2024-08-26T15:55:39.310564Z"
+ "iopub.execute_input": "2024-08-28T20:10:15.415169Z",
+ "iopub.status.busy": "2024-08-28T20:10:15.414998Z",
+ "iopub.status.idle": "2024-08-28T20:10:16.612302Z",
+ "shell.execute_reply": "2024-08-28T20:10:16.611666Z"
},
"nbsphinx": "hidden"
},
@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -95,10 +95,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:39.313573Z",
- "iopub.status.busy": "2024-08-26T15:55:39.313197Z",
- "iopub.status.idle": "2024-08-26T15:55:39.493648Z",
- "shell.execute_reply": "2024-08-26T15:55:39.493028Z"
+ "iopub.execute_input": "2024-08-28T20:10:16.614807Z",
+ "iopub.status.busy": "2024-08-28T20:10:16.614546Z",
+ "iopub.status.idle": "2024-08-28T20:10:16.795267Z",
+ "shell.execute_reply": "2024-08-28T20:10:16.794632Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-08-26T15:55:39.496034Z",
- "iopub.status.idle": "2024-08-26T15:55:39.508251Z",
- "shell.execute_reply": "2024-08-26T15:55:39.507813Z"
+ "iopub.execute_input": "2024-08-28T20:10:16.797857Z",
+ "iopub.status.busy": "2024-08-28T20:10:16.797433Z",
+ "iopub.status.idle": "2024-08-28T20:10:16.809711Z",
+ "shell.execute_reply": "2024-08-28T20:10:16.809292Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:39.510455Z",
- "iopub.status.busy": "2024-08-26T15:55:39.509993Z",
- "iopub.status.idle": "2024-08-26T15:55:39.748733Z",
- "shell.execute_reply": "2024-08-26T15:55:39.748087Z"
+ "iopub.execute_input": "2024-08-28T20:10:16.811782Z",
+ "iopub.status.busy": "2024-08-28T20:10:16.811369Z",
+ "iopub.status.idle": "2024-08-28T20:10:17.046442Z",
+ "shell.execute_reply": "2024-08-28T20:10:17.045868Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:39.751243Z",
- "iopub.status.busy": "2024-08-26T15:55:39.750850Z",
- "iopub.status.idle": "2024-08-26T15:55:39.777570Z",
- "shell.execute_reply": "2024-08-26T15:55:39.777078Z"
+ "iopub.execute_input": "2024-08-28T20:10:17.048796Z",
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+ "shell.execute_reply": "2024-08-28T20:10:17.073959Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:55:39.779657Z",
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- "shell.execute_reply": "2024-08-26T15:55:41.934417Z"
+ "iopub.execute_input": "2024-08-28T20:10:17.076615Z",
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+ "shell.execute_reply": "2024-08-28T20:10:19.167790Z"
}
},
"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-08-26T15:55:41.955557Z"
+ "iopub.execute_input": "2024-08-28T20:10:19.170900Z",
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+ "shell.execute_reply": "2024-08-28T20:10:19.187864Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
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- "shell.execute_reply": "2024-08-26T15:55:43.589949Z"
+ "iopub.execute_input": "2024-08-28T20:10:19.190470Z",
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},
"id": "AaHC5MRKjruT"
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@@ -729,10 +729,10 @@
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},
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@@ -781,10 +781,10 @@
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},
"id": "Db8YHnyVjruU"
},
@@ -891,10 +891,10 @@
"execution_count": 11,
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- "shell.execute_reply": "2024-08-26T15:55:43.913315Z"
+ "iopub.execute_input": "2024-08-28T20:10:20.879051Z",
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},
"id": "iJqAHuS2jruV"
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@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
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+ "iopub.execute_input": "2024-08-28T20:10:21.088578Z",
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},
"id": "PcPTZ_JJG3Cx"
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@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
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},
"id": "0lonvOYvjruV"
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@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
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+ "iopub.execute_input": "2024-08-28T20:10:21.118973Z",
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},
"id": "MfqTCa3kjruV"
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@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
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+ "iopub.execute_input": "2024-08-28T20:10:21.211242Z",
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},
"id": "9ZtWAYXqMAPL"
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@@ -1697,10 +1697,10 @@
"execution_count": 16,
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+ "iopub.execute_input": "2024-08-28T20:10:21.358196Z",
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},
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@@ -1738,10 +1738,10 @@
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@@ -1796,10 +1796,10 @@
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@@ -1850,10 +1850,10 @@
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@@ -1922,10 +1922,10 @@
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"id": "g5LHhhuqFbXK"
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@@ -1957,10 +1957,10 @@
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"execution": {
- "iopub.execute_input": "2024-08-26T15:55:44.406534Z",
- "iopub.status.busy": "2024-08-26T15:55:44.406130Z",
- "iopub.status.idle": "2024-08-26T15:55:44.519957Z",
- "shell.execute_reply": "2024-08-26T15:55:44.519299Z"
+ "iopub.execute_input": "2024-08-28T20:10:21.559918Z",
+ "iopub.status.busy": "2024-08-28T20:10:21.559498Z",
+ "iopub.status.idle": "2024-08-28T20:10:21.664012Z",
+ "shell.execute_reply": "2024-08-28T20:10:21.663415Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:44.522219Z",
- "iopub.status.busy": "2024-08-26T15:55:44.521957Z",
- "iopub.status.idle": "2024-08-26T15:55:44.735309Z",
- "shell.execute_reply": "2024-08-26T15:55:44.734712Z"
+ "iopub.execute_input": "2024-08-28T20:10:21.666332Z",
+ "iopub.status.busy": "2024-08-28T20:10:21.666086Z",
+ "iopub.status.idle": "2024-08-28T20:10:21.883555Z",
+ "shell.execute_reply": "2024-08-28T20:10:21.882993Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:44.737523Z",
- "iopub.status.busy": "2024-08-26T15:55:44.737321Z",
- "iopub.status.idle": "2024-08-26T15:55:44.980688Z",
- "shell.execute_reply": "2024-08-26T15:55:44.980009Z"
+ "iopub.execute_input": "2024-08-28T20:10:21.885941Z",
+ "iopub.status.busy": "2024-08-28T20:10:21.885493Z",
+ "iopub.status.idle": "2024-08-28T20:10:22.093141Z",
+ "shell.execute_reply": "2024-08-28T20:10:22.092561Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:44.983243Z",
- "iopub.status.busy": "2024-08-26T15:55:44.982856Z",
- "iopub.status.idle": "2024-08-26T15:55:44.989124Z",
- "shell.execute_reply": "2024-08-26T15:55:44.988663Z"
+ "iopub.execute_input": "2024-08-28T20:10:22.095719Z",
+ "iopub.status.busy": "2024-08-28T20:10:22.095201Z",
+ "iopub.status.idle": "2024-08-28T20:10:22.101800Z",
+ "shell.execute_reply": "2024-08-28T20:10:22.101231Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:44.991199Z",
- "iopub.status.busy": "2024-08-26T15:55:44.990866Z",
- "iopub.status.idle": "2024-08-26T15:55:45.206648Z",
- "shell.execute_reply": "2024-08-26T15:55:45.206056Z"
+ "iopub.execute_input": "2024-08-28T20:10:22.104004Z",
+ "iopub.status.busy": "2024-08-28T20:10:22.103656Z",
+ "iopub.status.idle": "2024-08-28T20:10:22.317385Z",
+ "shell.execute_reply": "2024-08-28T20:10:22.316794Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:45.208912Z",
- "iopub.status.busy": "2024-08-26T15:55:45.208710Z",
- "iopub.status.idle": "2024-08-26T15:55:46.311061Z",
- "shell.execute_reply": "2024-08-26T15:55:46.310458Z"
+ "iopub.execute_input": "2024-08-28T20:10:22.319795Z",
+ "iopub.status.busy": "2024-08-28T20:10:22.319339Z",
+ "iopub.status.idle": "2024-08-28T20:10:23.373257Z",
+ "shell.execute_reply": "2024-08-28T20:10:23.372707Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index b1f7815f0..182ef924d 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:50.101189Z",
- "iopub.status.busy": "2024-08-26T15:55:50.100749Z",
- "iopub.status.idle": "2024-08-26T15:55:51.329967Z",
- "shell.execute_reply": "2024-08-26T15:55:51.329400Z"
+ "iopub.execute_input": "2024-08-28T20:10:27.766693Z",
+ "iopub.status.busy": "2024-08-28T20:10:27.766517Z",
+ "iopub.status.idle": "2024-08-28T20:10:28.915327Z",
+ "shell.execute_reply": "2024-08-28T20:10:28.914713Z"
},
"nbsphinx": "hidden"
},
@@ -101,7 +101,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.332983Z",
- "iopub.status.busy": "2024-08-26T15:55:51.332429Z",
- "iopub.status.idle": "2024-08-26T15:55:51.335909Z",
- "shell.execute_reply": "2024-08-26T15:55:51.335324Z"
+ "iopub.execute_input": "2024-08-28T20:10:28.917979Z",
+ "iopub.status.busy": "2024-08-28T20:10:28.917699Z",
+ "iopub.status.idle": "2024-08-28T20:10:28.920819Z",
+ "shell.execute_reply": "2024-08-28T20:10:28.920356Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.338118Z",
- "iopub.status.busy": "2024-08-26T15:55:51.337791Z",
- "iopub.status.idle": "2024-08-26T15:55:51.346087Z",
- "shell.execute_reply": "2024-08-26T15:55:51.345575Z"
+ "iopub.execute_input": "2024-08-28T20:10:28.922962Z",
+ "iopub.status.busy": "2024-08-28T20:10:28.922646Z",
+ "iopub.status.idle": "2024-08-28T20:10:28.930551Z",
+ "shell.execute_reply": "2024-08-28T20:10:28.930005Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.348269Z",
- "iopub.status.busy": "2024-08-26T15:55:51.347912Z",
- "iopub.status.idle": "2024-08-26T15:55:51.397438Z",
- "shell.execute_reply": "2024-08-26T15:55:51.396901Z"
+ "iopub.execute_input": "2024-08-28T20:10:28.932525Z",
+ "iopub.status.busy": "2024-08-28T20:10:28.932211Z",
+ "iopub.status.idle": "2024-08-28T20:10:28.979093Z",
+ "shell.execute_reply": "2024-08-28T20:10:28.978502Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.400009Z",
- "iopub.status.busy": "2024-08-26T15:55:51.399623Z",
- "iopub.status.idle": "2024-08-26T15:55:51.418118Z",
- "shell.execute_reply": "2024-08-26T15:55:51.417489Z"
+ "iopub.execute_input": "2024-08-28T20:10:28.985872Z",
+ "iopub.status.busy": "2024-08-28T20:10:28.985450Z",
+ "iopub.status.idle": "2024-08-28T20:10:29.002587Z",
+ "shell.execute_reply": "2024-08-28T20:10:29.002005Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.420616Z",
- "iopub.status.busy": "2024-08-26T15:55:51.420228Z",
- "iopub.status.idle": "2024-08-26T15:55:51.424652Z",
- "shell.execute_reply": "2024-08-26T15:55:51.424134Z"
+ "iopub.execute_input": "2024-08-28T20:10:29.004731Z",
+ "iopub.status.busy": "2024-08-28T20:10:29.004300Z",
+ "iopub.status.idle": "2024-08-28T20:10:29.008197Z",
+ "shell.execute_reply": "2024-08-28T20:10:29.007752Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.427131Z",
- "iopub.status.busy": "2024-08-26T15:55:51.426753Z",
- "iopub.status.idle": "2024-08-26T15:55:51.441388Z",
- "shell.execute_reply": "2024-08-26T15:55:51.440867Z"
+ "iopub.execute_input": "2024-08-28T20:10:29.010364Z",
+ "iopub.status.busy": "2024-08-28T20:10:29.009943Z",
+ "iopub.status.idle": "2024-08-28T20:10:29.026608Z",
+ "shell.execute_reply": "2024-08-28T20:10:29.026045Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.443816Z",
- "iopub.status.busy": "2024-08-26T15:55:51.443412Z",
- "iopub.status.idle": "2024-08-26T15:55:51.470951Z",
- "shell.execute_reply": "2024-08-26T15:55:51.470409Z"
+ "iopub.execute_input": "2024-08-28T20:10:29.028736Z",
+ "iopub.status.busy": "2024-08-28T20:10:29.028426Z",
+ "iopub.status.idle": "2024-08-28T20:10:29.055143Z",
+ "shell.execute_reply": "2024-08-28T20:10:29.054600Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:51.473560Z",
- "iopub.status.busy": "2024-08-26T15:55:51.473170Z",
- "iopub.status.idle": "2024-08-26T15:55:53.572478Z",
- "shell.execute_reply": "2024-08-26T15:55:53.571934Z"
+ "iopub.execute_input": "2024-08-28T20:10:29.057327Z",
+ "iopub.status.busy": "2024-08-28T20:10:29.057014Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.002405Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.001830Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.575148Z",
- "iopub.status.busy": "2024-08-26T15:55:53.574805Z",
- "iopub.status.idle": "2024-08-26T15:55:53.581965Z",
- "shell.execute_reply": "2024-08-26T15:55:53.581495Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.005159Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.004615Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.012724Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.012195Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.583931Z",
- "iopub.status.busy": "2024-08-26T15:55:53.583750Z",
- "iopub.status.idle": "2024-08-26T15:55:53.597950Z",
- "shell.execute_reply": "2024-08-26T15:55:53.597508Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.014935Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.014554Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.028506Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.028046Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.600192Z",
- "iopub.status.busy": "2024-08-26T15:55:53.599844Z",
- "iopub.status.idle": "2024-08-26T15:55:53.606325Z",
- "shell.execute_reply": "2024-08-26T15:55:53.605753Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.030456Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.030187Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.036558Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.036100Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.608524Z",
- "iopub.status.busy": "2024-08-26T15:55:53.608203Z",
- "iopub.status.idle": "2024-08-26T15:55:53.611057Z",
- "shell.execute_reply": "2024-08-26T15:55:53.610487Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.038612Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.038278Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.040841Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.040408Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.613133Z",
- "iopub.status.busy": "2024-08-26T15:55:53.612727Z",
- "iopub.status.idle": "2024-08-26T15:55:53.616493Z",
- "shell.execute_reply": "2024-08-26T15:55:53.615926Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.042840Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.042526Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.045765Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.045253Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.618748Z",
- "iopub.status.busy": "2024-08-26T15:55:53.618292Z",
- "iopub.status.idle": "2024-08-26T15:55:53.620917Z",
- "shell.execute_reply": "2024-08-26T15:55:53.620475Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.047816Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.047469Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.049975Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.049552Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.622763Z",
- "iopub.status.busy": "2024-08-26T15:55:53.622591Z",
- "iopub.status.idle": "2024-08-26T15:55:53.626806Z",
- "shell.execute_reply": "2024-08-26T15:55:53.626334Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.051779Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.051611Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.055321Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.054835Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.628797Z",
- "iopub.status.busy": "2024-08-26T15:55:53.628623Z",
- "iopub.status.idle": "2024-08-26T15:55:53.657166Z",
- "shell.execute_reply": "2024-08-26T15:55:53.656656Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.057500Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.057113Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.085687Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.085122Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:53.659591Z",
- "iopub.status.busy": "2024-08-26T15:55:53.659398Z",
- "iopub.status.idle": "2024-08-26T15:55:53.664302Z",
- "shell.execute_reply": "2024-08-26T15:55:53.663840Z"
+ "iopub.execute_input": "2024-08-28T20:10:31.087938Z",
+ "iopub.status.busy": "2024-08-28T20:10:31.087519Z",
+ "iopub.status.idle": "2024-08-28T20:10:31.092195Z",
+ "shell.execute_reply": "2024-08-28T20:10:31.091636Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 5c10fb5e6..4d9dceb0f 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:56.766882Z",
- "iopub.status.busy": "2024-08-26T15:55:56.766428Z",
- "iopub.status.idle": "2024-08-26T15:55:58.038153Z",
- "shell.execute_reply": "2024-08-26T15:55:58.037589Z"
+ "iopub.execute_input": "2024-08-28T20:10:33.930965Z",
+ "iopub.status.busy": "2024-08-28T20:10:33.930785Z",
+ "iopub.status.idle": "2024-08-28T20:10:35.153533Z",
+ "shell.execute_reply": "2024-08-28T20:10:35.152975Z"
},
"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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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": "2024-08-26T15:55:58.040800Z",
- "iopub.status.busy": "2024-08-26T15:55:58.040343Z",
- "iopub.status.idle": "2024-08-26T15:55:58.241293Z",
- "shell.execute_reply": "2024-08-26T15:55:58.240703Z"
+ "iopub.execute_input": "2024-08-28T20:10:35.156153Z",
+ "iopub.status.busy": "2024-08-28T20:10:35.155689Z",
+ "iopub.status.idle": "2024-08-28T20:10:35.354611Z",
+ "shell.execute_reply": "2024-08-28T20:10:35.354050Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:58.243890Z",
- "iopub.status.busy": "2024-08-26T15:55:58.243560Z",
- "iopub.status.idle": "2024-08-26T15:55:58.257402Z",
- "shell.execute_reply": "2024-08-26T15:55:58.256901Z"
+ "iopub.execute_input": "2024-08-28T20:10:35.357320Z",
+ "iopub.status.busy": "2024-08-28T20:10:35.356844Z",
+ "iopub.status.idle": "2024-08-28T20:10:35.370491Z",
+ "shell.execute_reply": "2024-08-28T20:10:35.370031Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:55:58.259462Z",
- "iopub.status.busy": "2024-08-26T15:55:58.259112Z",
- "iopub.status.idle": "2024-08-26T15:56:00.987646Z",
- "shell.execute_reply": "2024-08-26T15:56:00.987007Z"
+ "iopub.execute_input": "2024-08-28T20:10:35.372636Z",
+ "iopub.status.busy": "2024-08-28T20:10:35.372216Z",
+ "iopub.status.idle": "2024-08-28T20:10:38.005651Z",
+ "shell.execute_reply": "2024-08-28T20:10:38.005141Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:00.990103Z",
- "iopub.status.busy": "2024-08-26T15:56:00.989893Z",
- "iopub.status.idle": "2024-08-26T15:56:02.372407Z",
- "shell.execute_reply": "2024-08-26T15:56:02.371693Z"
+ "iopub.execute_input": "2024-08-28T20:10:38.007913Z",
+ "iopub.status.busy": "2024-08-28T20:10:38.007631Z",
+ "iopub.status.idle": "2024-08-28T20:10:39.351557Z",
+ "shell.execute_reply": "2024-08-28T20:10:39.350978Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:02.375314Z",
- "iopub.status.busy": "2024-08-26T15:56:02.374897Z",
- "iopub.status.idle": "2024-08-26T15:56:02.378930Z",
- "shell.execute_reply": "2024-08-26T15:56:02.378306Z"
+ "iopub.execute_input": "2024-08-28T20:10:39.354056Z",
+ "iopub.status.busy": "2024-08-28T20:10:39.353851Z",
+ "iopub.status.idle": "2024-08-28T20:10:39.358053Z",
+ "shell.execute_reply": "2024-08-28T20:10:39.357549Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:02.381153Z",
- "iopub.status.busy": "2024-08-26T15:56:02.380797Z",
- "iopub.status.idle": "2024-08-26T15:56:04.546007Z",
- "shell.execute_reply": "2024-08-26T15:56:04.545338Z"
+ "iopub.execute_input": "2024-08-28T20:10:39.360196Z",
+ "iopub.status.busy": "2024-08-28T20:10:39.359778Z",
+ "iopub.status.idle": "2024-08-28T20:10:41.461777Z",
+ "shell.execute_reply": "2024-08-28T20:10:41.461112Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:04.549003Z",
- "iopub.status.busy": "2024-08-26T15:56:04.548276Z",
- "iopub.status.idle": "2024-08-26T15:56:04.556518Z",
- "shell.execute_reply": "2024-08-26T15:56:04.556052Z"
+ "iopub.execute_input": "2024-08-28T20:10:41.464117Z",
+ "iopub.status.busy": "2024-08-28T20:10:41.463808Z",
+ "iopub.status.idle": "2024-08-28T20:10:41.472128Z",
+ "shell.execute_reply": "2024-08-28T20:10:41.471653Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:04.558584Z",
- "iopub.status.busy": "2024-08-26T15:56:04.558246Z",
- "iopub.status.idle": "2024-08-26T15:56:07.362260Z",
- "shell.execute_reply": "2024-08-26T15:56:07.361630Z"
+ "iopub.execute_input": "2024-08-28T20:10:41.474128Z",
+ "iopub.status.busy": "2024-08-28T20:10:41.473948Z",
+ "iopub.status.idle": "2024-08-28T20:10:44.212444Z",
+ "shell.execute_reply": "2024-08-28T20:10:44.211902Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:07.364609Z",
- "iopub.status.busy": "2024-08-26T15:56:07.364404Z",
- "iopub.status.idle": "2024-08-26T15:56:07.368177Z",
- "shell.execute_reply": "2024-08-26T15:56:07.367638Z"
+ "iopub.execute_input": "2024-08-28T20:10:44.214662Z",
+ "iopub.status.busy": "2024-08-28T20:10:44.214321Z",
+ "iopub.status.idle": "2024-08-28T20:10:44.218007Z",
+ "shell.execute_reply": "2024-08-28T20:10:44.217522Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:07.370130Z",
- "iopub.status.busy": "2024-08-26T15:56:07.369942Z",
- "iopub.status.idle": "2024-08-26T15:56:07.373364Z",
- "shell.execute_reply": "2024-08-26T15:56:07.372878Z"
+ "iopub.execute_input": "2024-08-28T20:10:44.219925Z",
+ "iopub.status.busy": "2024-08-28T20:10:44.219752Z",
+ "iopub.status.idle": "2024-08-28T20:10:44.223252Z",
+ "shell.execute_reply": "2024-08-28T20:10:44.222701Z"
}
},
"outputs": [],
@@ -769,10 +769,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:07.375274Z",
- "iopub.status.busy": "2024-08-26T15:56:07.375092Z",
- "iopub.status.idle": "2024-08-26T15:56:07.378537Z",
- "shell.execute_reply": "2024-08-26T15:56:07.378053Z"
+ "iopub.execute_input": "2024-08-28T20:10:44.225306Z",
+ "iopub.status.busy": "2024-08-28T20:10:44.225013Z",
+ "iopub.status.idle": "2024-08-28T20:10:44.228262Z",
+ "shell.execute_reply": "2024-08-28T20:10:44.227703Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 5911b8286..0ae33f70b 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:10.340207Z",
- "iopub.status.busy": "2024-08-26T15:56:10.340030Z",
- "iopub.status.idle": "2024-08-26T15:56:11.624749Z",
- "shell.execute_reply": "2024-08-26T15:56:11.624166Z"
+ "iopub.execute_input": "2024-08-28T20:10:46.815104Z",
+ "iopub.status.busy": "2024-08-28T20:10:46.814934Z",
+ "iopub.status.idle": "2024-08-28T20:10:48.021695Z",
+ "shell.execute_reply": "2024-08-28T20:10:48.021151Z"
},
"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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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": "2024-08-26T15:56:11.627256Z",
- "iopub.status.busy": "2024-08-26T15:56:11.626951Z",
- "iopub.status.idle": "2024-08-26T15:56:14.401848Z",
- "shell.execute_reply": "2024-08-26T15:56:14.401149Z"
+ "iopub.execute_input": "2024-08-28T20:10:48.024013Z",
+ "iopub.status.busy": "2024-08-28T20:10:48.023761Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.277515Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.276826Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.404488Z",
- "iopub.status.busy": "2024-08-26T15:56:14.404277Z",
- "iopub.status.idle": "2024-08-26T15:56:14.407583Z",
- "shell.execute_reply": "2024-08-26T15:56:14.407130Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.280063Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.279861Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.283240Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.282780Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.409784Z",
- "iopub.status.busy": "2024-08-26T15:56:14.409456Z",
- "iopub.status.idle": "2024-08-26T15:56:14.416283Z",
- "shell.execute_reply": "2024-08-26T15:56:14.415707Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.285108Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.284938Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.291323Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.290907Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.418739Z",
- "iopub.status.busy": "2024-08-26T15:56:14.418202Z",
- "iopub.status.idle": "2024-08-26T15:56:14.927903Z",
- "shell.execute_reply": "2024-08-26T15:56:14.927290Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.293431Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.293110Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.783591Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.782027Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.930602Z",
- "iopub.status.busy": "2024-08-26T15:56:14.930405Z",
- "iopub.status.idle": "2024-08-26T15:56:14.935854Z",
- "shell.execute_reply": "2024-08-26T15:56:14.935409Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.786202Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.785747Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.791315Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.790873Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.937858Z",
- "iopub.status.busy": "2024-08-26T15:56:14.937674Z",
- "iopub.status.idle": "2024-08-26T15:56:14.941832Z",
- "shell.execute_reply": "2024-08-26T15:56:14.941270Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.793221Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.792959Z",
+ "iopub.status.idle": "2024-08-28T20:10:49.796741Z",
+ "shell.execute_reply": "2024-08-28T20:10:49.796193Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:14.944226Z",
- "iopub.status.busy": "2024-08-26T15:56:14.943744Z",
- "iopub.status.idle": "2024-08-26T15:56:15.859004Z",
- "shell.execute_reply": "2024-08-26T15:56:15.858309Z"
+ "iopub.execute_input": "2024-08-28T20:10:49.798650Z",
+ "iopub.status.busy": "2024-08-28T20:10:49.798471Z",
+ "iopub.status.idle": "2024-08-28T20:10:50.700257Z",
+ "shell.execute_reply": "2024-08-28T20:10:50.699669Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:15.861666Z",
- "iopub.status.busy": "2024-08-26T15:56:15.861235Z",
- "iopub.status.idle": "2024-08-26T15:56:16.069556Z",
- "shell.execute_reply": "2024-08-26T15:56:16.069040Z"
+ "iopub.execute_input": "2024-08-28T20:10:50.702769Z",
+ "iopub.status.busy": "2024-08-28T20:10:50.702385Z",
+ "iopub.status.idle": "2024-08-28T20:10:50.906003Z",
+ "shell.execute_reply": "2024-08-28T20:10:50.905441Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:16.071703Z",
- "iopub.status.busy": "2024-08-26T15:56:16.071511Z",
- "iopub.status.idle": "2024-08-26T15:56:16.076159Z",
- "shell.execute_reply": "2024-08-26T15:56:16.075690Z"
+ "iopub.execute_input": "2024-08-28T20:10:50.908257Z",
+ "iopub.status.busy": "2024-08-28T20:10:50.907925Z",
+ "iopub.status.idle": "2024-08-28T20:10:50.912230Z",
+ "shell.execute_reply": "2024-08-28T20:10:50.911705Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:16.078253Z",
- "iopub.status.busy": "2024-08-26T15:56:16.077918Z",
- "iopub.status.idle": "2024-08-26T15:56:16.556663Z",
- "shell.execute_reply": "2024-08-26T15:56:16.556007Z"
+ "iopub.execute_input": "2024-08-28T20:10:50.914229Z",
+ "iopub.status.busy": "2024-08-28T20:10:50.913934Z",
+ "iopub.status.idle": "2024-08-28T20:10:51.372403Z",
+ "shell.execute_reply": "2024-08-28T20:10:51.371788Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:16.559624Z",
- "iopub.status.busy": "2024-08-26T15:56:16.559248Z",
- "iopub.status.idle": "2024-08-26T15:56:16.898201Z",
- "shell.execute_reply": "2024-08-26T15:56:16.897622Z"
+ "iopub.execute_input": "2024-08-28T20:10:51.375629Z",
+ "iopub.status.busy": "2024-08-28T20:10:51.375236Z",
+ "iopub.status.idle": "2024-08-28T20:10:51.682387Z",
+ "shell.execute_reply": "2024-08-28T20:10:51.681777Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:16.900962Z",
- "iopub.status.busy": "2024-08-26T15:56:16.900766Z",
- "iopub.status.idle": "2024-08-26T15:56:17.272375Z",
- "shell.execute_reply": "2024-08-26T15:56:17.271736Z"
+ "iopub.execute_input": "2024-08-28T20:10:51.685393Z",
+ "iopub.status.busy": "2024-08-28T20:10:51.685043Z",
+ "iopub.status.idle": "2024-08-28T20:10:52.047573Z",
+ "shell.execute_reply": "2024-08-28T20:10:52.046954Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:17.275685Z",
- "iopub.status.busy": "2024-08-26T15:56:17.275315Z",
- "iopub.status.idle": "2024-08-26T15:56:17.708365Z",
- "shell.execute_reply": "2024-08-26T15:56:17.707796Z"
+ "iopub.execute_input": "2024-08-28T20:10:52.050958Z",
+ "iopub.status.busy": "2024-08-28T20:10:52.050427Z",
+ "iopub.status.idle": "2024-08-28T20:10:52.490967Z",
+ "shell.execute_reply": "2024-08-28T20:10:52.490360Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:17.713051Z",
- "iopub.status.busy": "2024-08-26T15:56:17.712669Z",
- "iopub.status.idle": "2024-08-26T15:56:18.145788Z",
- "shell.execute_reply": "2024-08-26T15:56:18.145119Z"
+ "iopub.execute_input": "2024-08-28T20:10:52.495742Z",
+ "iopub.status.busy": "2024-08-28T20:10:52.495348Z",
+ "iopub.status.idle": "2024-08-28T20:10:52.944831Z",
+ "shell.execute_reply": "2024-08-28T20:10:52.944216Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:18.149293Z",
- "iopub.status.busy": "2024-08-26T15:56:18.148772Z",
- "iopub.status.idle": "2024-08-26T15:56:18.346020Z",
- "shell.execute_reply": "2024-08-26T15:56:18.345257Z"
+ "iopub.execute_input": "2024-08-28T20:10:52.947220Z",
+ "iopub.status.busy": "2024-08-28T20:10:52.946860Z",
+ "iopub.status.idle": "2024-08-28T20:10:53.160258Z",
+ "shell.execute_reply": "2024-08-28T20:10:53.159728Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:18.348953Z",
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- "shell.execute_reply": "2024-08-26T15:56:18.533465Z"
+ "iopub.execute_input": "2024-08-28T20:10:53.162365Z",
+ "iopub.status.busy": "2024-08-28T20:10:53.162188Z",
+ "iopub.status.idle": "2024-08-28T20:10:53.362551Z",
+ "shell.execute_reply": "2024-08-28T20:10:53.362026Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:56:18.536855Z",
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- "iopub.status.idle": "2024-08-26T15:56:18.539344Z",
- "shell.execute_reply": "2024-08-26T15:56:18.538884Z"
+ "iopub.execute_input": "2024-08-28T20:10:53.365064Z",
+ "iopub.status.busy": "2024-08-28T20:10:53.364728Z",
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+ "shell.execute_reply": "2024-08-28T20:10:53.367227Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:56:18.541453Z",
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- "shell.execute_reply": "2024-08-26T15:56:19.476978Z"
+ "iopub.execute_input": "2024-08-28T20:10:53.369654Z",
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}
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"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:56:19.480700Z",
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- "shell.execute_reply": "2024-08-26T15:56:19.635531Z"
+ "iopub.execute_input": "2024-08-28T20:10:54.315772Z",
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+ "shell.execute_reply": "2024-08-28T20:10:54.506821Z"
}
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"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:19.638250Z",
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- "iopub.status.idle": "2024-08-26T15:56:19.786441Z",
- "shell.execute_reply": "2024-08-26T15:56:19.785787Z"
+ "iopub.execute_input": "2024-08-28T20:10:54.509938Z",
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+ "shell.execute_reply": "2024-08-28T20:10:54.732826Z"
}
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"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
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+ "shell.execute_reply": "2024-08-28T20:10:55.346567Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:20.474731Z",
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- "shell.execute_reply": "2024-08-26T15:56:20.477783Z"
+ "iopub.execute_input": "2024-08-28T20:10:55.349252Z",
+ "iopub.status.busy": "2024-08-28T20:10:55.349063Z",
+ "iopub.status.idle": "2024-08-28T20:10:55.352859Z",
+ "shell.execute_reply": "2024-08-28T20:10:55.352398Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 62016d498..eee3d8de7 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:22.809306Z",
- "iopub.status.busy": "2024-08-26T15:56:22.809121Z",
- "iopub.status.idle": "2024-08-26T15:56:25.923468Z",
- "shell.execute_reply": "2024-08-26T15:56:25.922782Z"
+ "iopub.execute_input": "2024-08-28T20:10:57.723058Z",
+ "iopub.status.busy": "2024-08-28T20:10:57.722897Z",
+ "iopub.status.idle": "2024-08-28T20:11:00.537065Z",
+ "shell.execute_reply": "2024-08-28T20:11:00.536472Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"id": "4396f544",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:25.926138Z",
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- "iopub.status.idle": "2024-08-26T15:56:26.295753Z",
- "shell.execute_reply": "2024-08-26T15:56:26.295060Z"
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+ "shell.execute_reply": "2024-08-28T20:11:00.856533Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:56:26.298531Z",
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- "iopub.status.idle": "2024-08-26T15:56:26.302951Z",
- "shell.execute_reply": "2024-08-26T15:56:26.302336Z"
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+ "shell.execute_reply": "2024-08-28T20:11:00.863279Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
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- "shell.execute_reply": "2024-08-26T15:56:34.247391Z"
+ "iopub.execute_input": "2024-08-28T20:11:00.866137Z",
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+ "iopub.status.idle": "2024-08-28T20:11:05.612330Z",
+ "shell.execute_reply": "2024-08-28T20:11:05.611754Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 1%| | 917504/170498071 [00:00<00:21, 7809760.44it/s]"
]
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@@ -260,7 +260,7 @@
"output_type": "stream",
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+ " 3%|▎ | 5079040/170498071 [00:00<00:06, 26343456.81it/s]"
]
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"output_type": "stream",
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+ " 6%|▌ | 10158080/170498071 [00:00<00:04, 37052814.35it/s]"
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+ " 10%|▉ | 16384000/170498071 [00:00<00:03, 46686975.20it/s]"
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+ " 14%|█▍ | 24543232/170498071 [00:00<00:02, 59011646.68it/s]"
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+ " 19%|█▉ | 33193984/170498071 [00:00<00:02, 68241152.96it/s]"
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+ " 33%|███▎ | 55738368/170498071 [00:00<00:01, 92555163.57it/s]"
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+ " 39%|███▉ | 66748416/170498071 [00:00<00:01, 98007788.92it/s]"
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+ " 45%|████▌ | 77332480/170498071 [00:01<00:00, 100399566.26it/s]"
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{
@@ -698,10 +514,10 @@
"id": "9b64e0aa",
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+ "shell.execute_reply": "2024-08-28T20:11:05.618484Z"
},
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},
@@ -752,10 +568,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index cb48944b8..01a953e2d 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:10.353933Z",
- "iopub.status.busy": "2024-08-26T15:57:10.353544Z",
- "iopub.status.idle": "2024-08-26T15:57:11.706462Z",
- "shell.execute_reply": "2024-08-26T15:57:11.705786Z"
+ "iopub.execute_input": "2024-08-28T20:11:39.599355Z",
+ "iopub.status.busy": "2024-08-28T20:11:39.599170Z",
+ "iopub.status.idle": "2024-08-28T20:11:40.799564Z",
+ "shell.execute_reply": "2024-08-28T20:11:40.798993Z"
},
"nbsphinx": "hidden"
},
@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -142,10 +142,10 @@
"id": "4fb10b8f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:11.709553Z",
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- "iopub.status.idle": "2024-08-26T15:57:11.728637Z",
- "shell.execute_reply": "2024-08-26T15:57:11.728052Z"
+ "iopub.execute_input": "2024-08-28T20:11:40.802223Z",
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+ "iopub.status.idle": "2024-08-28T20:11:40.819536Z",
+ "shell.execute_reply": "2024-08-28T20:11:40.819063Z"
}
},
"outputs": [],
@@ -164,10 +164,10 @@
"id": "284dc264",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:11.731676Z",
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- "iopub.status.idle": "2024-08-26T15:57:11.734952Z",
- "shell.execute_reply": "2024-08-26T15:57:11.734419Z"
+ "iopub.execute_input": "2024-08-28T20:11:40.821690Z",
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+ "iopub.status.idle": "2024-08-28T20:11:40.824363Z",
+ "shell.execute_reply": "2024-08-28T20:11:40.823776Z"
},
"nbsphinx": "hidden"
},
@@ -198,10 +198,10 @@
"id": "0f7450db",
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- "iopub.status.busy": "2024-08-26T15:57:11.736848Z",
- "iopub.status.idle": "2024-08-26T15:57:11.873269Z",
- "shell.execute_reply": "2024-08-26T15:57:11.872516Z"
+ "iopub.execute_input": "2024-08-28T20:11:40.826351Z",
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+ "iopub.status.idle": "2024-08-28T20:11:40.916158Z",
+ "shell.execute_reply": "2024-08-28T20:11:40.915614Z"
}
},
"outputs": [
@@ -374,10 +374,10 @@
"id": "55513fed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:11.876290Z",
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- "iopub.status.idle": "2024-08-26T15:57:12.074479Z",
- "shell.execute_reply": "2024-08-26T15:57:12.073882Z"
+ "iopub.execute_input": "2024-08-28T20:11:40.918419Z",
+ "iopub.status.busy": "2024-08-28T20:11:40.917993Z",
+ "iopub.status.idle": "2024-08-28T20:11:41.100340Z",
+ "shell.execute_reply": "2024-08-28T20:11:41.099717Z"
},
"nbsphinx": "hidden"
},
@@ -417,10 +417,10 @@
"id": "df5a0f59",
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- "iopub.status.busy": "2024-08-26T15:57:12.076625Z",
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- "shell.execute_reply": "2024-08-26T15:57:12.336363Z"
+ "iopub.execute_input": "2024-08-28T20:11:41.103082Z",
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+ "shell.execute_reply": "2024-08-28T20:11:41.315570Z"
}
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"outputs": [
@@ -456,10 +456,10 @@
"id": "7af78a8a",
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:57:12.339363Z",
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- "shell.execute_reply": "2024-08-26T15:57:12.343036Z"
+ "iopub.execute_input": "2024-08-28T20:11:41.318444Z",
+ "iopub.status.busy": "2024-08-28T20:11:41.318149Z",
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+ "shell.execute_reply": "2024-08-28T20:11:41.322231Z"
}
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"outputs": [],
@@ -477,10 +477,10 @@
"id": "9556c624",
"metadata": {
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- "iopub.execute_input": "2024-08-26T15:57:12.345634Z",
- "iopub.status.busy": "2024-08-26T15:57:12.345270Z",
- "iopub.status.idle": "2024-08-26T15:57:12.352146Z",
- "shell.execute_reply": "2024-08-26T15:57:12.351523Z"
+ "iopub.execute_input": "2024-08-28T20:11:41.324707Z",
+ "iopub.status.busy": "2024-08-28T20:11:41.324370Z",
+ "iopub.status.idle": "2024-08-28T20:11:41.330461Z",
+ "shell.execute_reply": "2024-08-28T20:11:41.330018Z"
}
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@@ -527,10 +527,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:12.357092Z",
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- "shell.execute_reply": "2024-08-26T15:57:12.359374Z"
+ "iopub.execute_input": "2024-08-28T20:11:41.332672Z",
+ "iopub.status.busy": "2024-08-28T20:11:41.332335Z",
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+ "shell.execute_reply": "2024-08-28T20:11:41.335030Z"
}
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@@ -545,10 +545,10 @@
"id": "7e1b7860",
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"execution": {
- "iopub.execute_input": "2024-08-26T15:57:12.362474Z",
- "iopub.status.busy": "2024-08-26T15:57:12.362043Z",
- "iopub.status.idle": "2024-08-26T15:57:21.815400Z",
- "shell.execute_reply": "2024-08-26T15:57:21.814683Z"
+ "iopub.execute_input": "2024-08-28T20:11:41.337553Z",
+ "iopub.status.busy": "2024-08-28T20:11:41.337227Z",
+ "iopub.status.idle": "2024-08-28T20:11:50.266507Z",
+ "shell.execute_reply": "2024-08-28T20:11:50.265931Z"
}
},
"outputs": [],
@@ -572,10 +572,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:57:21.818598Z",
- "iopub.status.busy": "2024-08-26T15:57:21.817970Z",
- "iopub.status.idle": "2024-08-26T15:57:21.825776Z",
- "shell.execute_reply": "2024-08-26T15:57:21.825153Z"
+ "iopub.execute_input": "2024-08-28T20:11:50.269479Z",
+ "iopub.status.busy": "2024-08-28T20:11:50.268821Z",
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+ "shell.execute_reply": "2024-08-28T20:11:50.275976Z"
}
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"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
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"execution": {
- "iopub.execute_input": "2024-08-26T15:57:21.828092Z",
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- "shell.execute_reply": "2024-08-26T15:57:21.831555Z"
+ "iopub.execute_input": "2024-08-28T20:11:50.278693Z",
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}
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@@ -696,10 +696,10 @@
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- "shell.execute_reply": "2024-08-26T15:57:21.836818Z"
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}
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@@ -734,10 +734,10 @@
"id": "00949977",
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@@ -756,10 +756,10 @@
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- "shell.execute_reply": "2024-08-26T15:57:21.853255Z"
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@@ -883,10 +883,10 @@
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@@ -921,10 +921,10 @@
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@@ -963,10 +963,10 @@
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@@ -1022,10 +1022,10 @@
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@@ -1041,10 +1041,10 @@
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@@ -1079,10 +1079,10 @@
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@@ -1189,10 +1189,10 @@
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@@ -1217,10 +1217,10 @@
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@@ -1264,10 +1264,10 @@
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"outputs": [
@@ -1392,10 +1392,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index e74549c5b..61af56b03 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
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- "shell.execute_reply": "2024-08-26T15:57:36.243657Z"
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+ "shell.execute_reply": "2024-08-28T20:12:01.983231Z"
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"outputs": [],
@@ -79,10 +79,10 @@
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@@ -97,10 +97,10 @@
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"dependencies = [\"cleanlab\"]\n",
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"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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 704630cc8..3cd5ab8f7 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
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- "iopub.status.idle": "2024-08-26T16:03:54.228374Z",
- "shell.execute_reply": "2024-08-26T16:03:54.227662Z"
+ "iopub.execute_input": "2024-08-28T20:14:49.935375Z",
+ "iopub.status.busy": "2024-08-28T20:14:49.935021Z",
+ "iopub.status.idle": "2024-08-28T20:14:51.191268Z",
+ "shell.execute_reply": "2024-08-28T20:14:51.190654Z"
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@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-08-26 16:03:52-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-08-28 20:14:49-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,8 +94,8 @@
"name": "stdout",
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- "169.150.249.163, 2400:52e0:1a01::1115:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.249.163|:443... connected.\r\n"
+ "185.93.1.243, 2400:52e0:1a00::871:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|185.93.1.243|:443... connected.\r\n"
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{
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- "conll2003.zip 100%[===================>] 959.94K 6.11MB/s in 0.2s \r\n",
- "\r\n",
- "2024-08-26 16:03:52 (6.11 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
- "\r\n",
- "mkdir: cannot create directory ‘data’: File exists\r\n"
+ "conll2003.zip 100%[===================>] 959.94K 4.87MB/s in 0.2s \r\n",
+ "\r\n"
]
},
{
"name": "stdout",
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"text": [
- "Archive: conll2003.zip\r\n",
- " inflating: data/metadata \r\n",
- " inflating: data/test.txt \r\n",
- " inflating: data/train.txt \r\n",
- " inflating: data/valid.txt \r\n"
+ "2024-08-28 20:14:50 (4.87 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "\r\n",
+ "mkdir: cannot create directory ‘data’: File exists\r\n"
]
},
{
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"text": [
- "--2024-08-26 16:03:52-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.216.209, 52.217.230.177, 52.217.116.41, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.216.209|:443... "
+ "Archive: conll2003.zip\r\n",
+ " inflating: data/metadata \r\n",
+ " inflating: data/test.txt \r\n",
+ " inflating: data/train.txt \r\n",
+ " inflating: data/valid.txt "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "connected.\r\n"
+ "\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "--2024-08-28 20:14:50-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.16.156, 52.217.10.132, 3.5.30.217, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.16.156|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
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@@ -180,33 +180,9 @@
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- "pred_probs.npz 7%[> ] 1.25M 3.11MB/s "
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 49%[========> ] 7.97M 13.2MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 100%[===================>] 16.26M 21.5MB/s in 0.8s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-08-26 16:03:54 (21.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-08-28 20:14:51 (125 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
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@@ -223,10 +199,10 @@
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+ "shell.execute_reply": "2024-08-28T20:14:52.471143Z"
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@@ -237,7 +213,7 @@
"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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -263,10 +239,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:03:55.562075Z",
- "iopub.status.busy": "2024-08-26T16:03:55.561623Z",
- "iopub.status.idle": "2024-08-26T16:03:55.564854Z",
- "shell.execute_reply": "2024-08-26T16:03:55.564374Z"
+ "iopub.execute_input": "2024-08-28T20:14:52.474110Z",
+ "iopub.status.busy": "2024-08-28T20:14:52.473822Z",
+ "iopub.status.idle": "2024-08-28T20:14:52.477401Z",
+ "shell.execute_reply": "2024-08-28T20:14:52.476829Z"
}
},
"outputs": [],
@@ -316,10 +292,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:03:55.567011Z",
- "iopub.status.busy": "2024-08-26T16:03:55.566646Z",
- "iopub.status.idle": "2024-08-26T16:03:55.569740Z",
- "shell.execute_reply": "2024-08-26T16:03:55.569184Z"
+ "iopub.execute_input": "2024-08-28T20:14:52.479601Z",
+ "iopub.status.busy": "2024-08-28T20:14:52.479161Z",
+ "iopub.status.idle": "2024-08-28T20:14:52.482135Z",
+ "shell.execute_reply": "2024-08-28T20:14:52.481690Z"
},
"nbsphinx": "hidden"
},
@@ -337,10 +313,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:03:55.571809Z",
- "iopub.status.busy": "2024-08-26T16:03:55.571484Z",
- "iopub.status.idle": "2024-08-26T16:04:04.593614Z",
- "shell.execute_reply": "2024-08-26T16:04:04.593000Z"
+ "iopub.execute_input": "2024-08-28T20:14:52.484091Z",
+ "iopub.status.busy": "2024-08-28T20:14:52.483910Z",
+ "iopub.status.idle": "2024-08-28T20:15:01.459104Z",
+ "shell.execute_reply": "2024-08-28T20:15:01.458495Z"
}
},
"outputs": [],
@@ -414,10 +390,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:04.596217Z",
- "iopub.status.busy": "2024-08-26T16:04:04.596011Z",
- "iopub.status.idle": "2024-08-26T16:04:04.601781Z",
- "shell.execute_reply": "2024-08-26T16:04:04.601301Z"
+ "iopub.execute_input": "2024-08-28T20:15:01.461836Z",
+ "iopub.status.busy": "2024-08-28T20:15:01.461325Z",
+ "iopub.status.idle": "2024-08-28T20:15:01.467064Z",
+ "shell.execute_reply": "2024-08-28T20:15:01.466528Z"
},
"nbsphinx": "hidden"
},
@@ -457,10 +433,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:04.603874Z",
- "iopub.status.busy": "2024-08-26T16:04:04.603537Z",
- "iopub.status.idle": "2024-08-26T16:04:04.965244Z",
- "shell.execute_reply": "2024-08-26T16:04:04.964692Z"
+ "iopub.execute_input": "2024-08-28T20:15:01.469080Z",
+ "iopub.status.busy": "2024-08-28T20:15:01.468761Z",
+ "iopub.status.idle": "2024-08-28T20:15:01.830066Z",
+ "shell.execute_reply": "2024-08-28T20:15:01.829505Z"
}
},
"outputs": [],
@@ -497,10 +473,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:04.967937Z",
- "iopub.status.busy": "2024-08-26T16:04:04.967492Z",
- "iopub.status.idle": "2024-08-26T16:04:04.972633Z",
- "shell.execute_reply": "2024-08-26T16:04:04.972006Z"
+ "iopub.execute_input": "2024-08-28T20:15:01.832550Z",
+ "iopub.status.busy": "2024-08-28T20:15:01.832175Z",
+ "iopub.status.idle": "2024-08-28T20:15:01.836735Z",
+ "shell.execute_reply": "2024-08-28T20:15:01.836255Z"
}
},
"outputs": [
@@ -572,10 +548,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:04.975197Z",
- "iopub.status.busy": "2024-08-26T16:04:04.974808Z",
- "iopub.status.idle": "2024-08-26T16:04:07.698648Z",
- "shell.execute_reply": "2024-08-26T16:04:07.697909Z"
+ "iopub.execute_input": "2024-08-28T20:15:01.838927Z",
+ "iopub.status.busy": "2024-08-28T20:15:01.838595Z",
+ "iopub.status.idle": "2024-08-28T20:15:04.486155Z",
+ "shell.execute_reply": "2024-08-28T20:15:04.485339Z"
}
},
"outputs": [],
@@ -597,10 +573,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:07.701619Z",
- "iopub.status.busy": "2024-08-26T16:04:07.701017Z",
- "iopub.status.idle": "2024-08-26T16:04:07.704967Z",
- "shell.execute_reply": "2024-08-26T16:04:07.704464Z"
+ "iopub.execute_input": "2024-08-28T20:15:04.489549Z",
+ "iopub.status.busy": "2024-08-28T20:15:04.488660Z",
+ "iopub.status.idle": "2024-08-28T20:15:04.492922Z",
+ "shell.execute_reply": "2024-08-28T20:15:04.492365Z"
}
},
"outputs": [
@@ -636,10 +612,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:07.707094Z",
- "iopub.status.busy": "2024-08-26T16:04:07.706758Z",
- "iopub.status.idle": "2024-08-26T16:04:07.711843Z",
- "shell.execute_reply": "2024-08-26T16:04:07.711300Z"
+ "iopub.execute_input": "2024-08-28T20:15:04.495072Z",
+ "iopub.status.busy": "2024-08-28T20:15:04.494615Z",
+ "iopub.status.idle": "2024-08-28T20:15:04.500167Z",
+ "shell.execute_reply": "2024-08-28T20:15:04.499692Z"
}
},
"outputs": [
@@ -817,10 +793,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:07.714045Z",
- "iopub.status.busy": "2024-08-26T16:04:07.713708Z",
- "iopub.status.idle": "2024-08-26T16:04:07.740657Z",
- "shell.execute_reply": "2024-08-26T16:04:07.740074Z"
+ "iopub.execute_input": "2024-08-28T20:15:04.502053Z",
+ "iopub.status.busy": "2024-08-28T20:15:04.501871Z",
+ "iopub.status.idle": "2024-08-28T20:15:04.528763Z",
+ "shell.execute_reply": "2024-08-28T20:15:04.528326Z"
}
},
"outputs": [
@@ -922,10 +898,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:07.743029Z",
- "iopub.status.busy": "2024-08-26T16:04:07.742589Z",
- "iopub.status.idle": "2024-08-26T16:04:07.747982Z",
- "shell.execute_reply": "2024-08-26T16:04:07.747370Z"
+ "iopub.execute_input": "2024-08-28T20:15:04.530900Z",
+ "iopub.status.busy": "2024-08-28T20:15:04.530584Z",
+ "iopub.status.idle": "2024-08-28T20:15:04.535158Z",
+ "shell.execute_reply": "2024-08-28T20:15:04.534669Z"
}
},
"outputs": [
@@ -999,10 +975,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:07.750138Z",
- "iopub.status.busy": "2024-08-26T16:04:07.749956Z",
- "iopub.status.idle": "2024-08-26T16:04:09.204508Z",
- "shell.execute_reply": "2024-08-26T16:04:09.203868Z"
+ "iopub.execute_input": "2024-08-28T20:15:04.537227Z",
+ "iopub.status.busy": "2024-08-28T20:15:04.536914Z",
+ "iopub.status.idle": "2024-08-28T20:15:05.945438Z",
+ "shell.execute_reply": "2024-08-28T20:15:05.944845Z"
}
},
"outputs": [
@@ -1174,10 +1150,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T16:04:09.206914Z",
- "iopub.status.busy": "2024-08-26T16:04:09.206702Z",
- "iopub.status.idle": "2024-08-26T16:04:09.211093Z",
- "shell.execute_reply": "2024-08-26T16:04:09.210480Z"
+ "iopub.execute_input": "2024-08-28T20:15:05.947574Z",
+ "iopub.status.busy": "2024-08-28T20:15:05.947384Z",
+ "iopub.status.idle": "2024-08-28T20:15:05.951417Z",
+ "shell.execute_reply": "2024-08-28T20:15:05.950877Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree
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index b66f8a452..35662ec16 100644
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diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree
index 2253ad2c3..14ec088ea 100644
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diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
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diff --git a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
index 7d627b6a7..c3eaabfa7 100644
--- a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
+++ b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst
@@ -415,8 +415,9 @@ Spurious correlations may arise in the dataset due to various reasons, such as:
Spurious Correlations are checked for when Datalab is initialized for an image dataset with the `image_key` keyword argument,
after checking for :ref:`Image-specific Issues ` where the image properties are computed.
-Each image property is assigned a label uncorrelatedness score for the entire dataset. The lower the score, the more likely the property is to be spuriously correlated with the labels.
-Consider reviewing the relationship between the image property and the labels if the corresponding label uncorrelatedness score is low.
+Each image property (e.g. darkness/brightness) is assigned a label uncorrelatedness score for the entire dataset. The lower the score, the more strongly the property is correlated with the class labels, across images of the dataset. This score is mathematically defined as: 1 minus the relative accuracy improvement in predicting the labels based solely on this image property value (relative to always predicting the most common overall class).
+
+Consider reviewing the relationship between images with high and low values of this property and the labels if the corresponding label uncorrelatedness score is low, because ML models trained on this dataset may latch onto the spurious correlation and fail to generalize.
This issue type is more about the overall dataset vs. individual data points and will only be highlighted by Datalab in its report, if any such troublesome image properties are found.
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 3ce45e534..4ed4c3ce4 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index 31ac70bc5..8508a3088 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index 9718656c6..62fdb0d46 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 6026e1b4d..c954d6400 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 e69cee73c..9121afc09 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 4268ddcce..fb2403dc9 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 5a0359197..33cca72ef 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 1feb459c2..2215c8453 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb
index 94593ec3d..ad40ed93a 100644
--- a/master/_sources/tutorials/improving_ml_performance.ipynb
+++ b/master/_sources/tutorials/improving_ml_performance.ipynb
@@ -67,7 +67,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 3dcc3b0b7..5186f103b 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 c081269e9..f618b89f2 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 9def01431..cd9f8e279 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 5dc5e8485..de38f2058 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 5170e2d93..df9ea0623 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 331dbcb58..5f474f512 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 5da17b46c..210dc3748 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\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 67990f0eb..7f74c760a 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@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html
index f6a998a40..3314badda 100644
--- a/master/cleanlab/datalab/guide/issue_type_description.html
+++ b/master/cleanlab/datalab/guide/issue_type_description.html
@@ -1148,8 +1148,8 @@ Each image property is assigned a label uncorrelatedness score for the entire dataset. The lower the score, the more likely the property is to be spuriously correlated with the labels.
-Consider reviewing the relationship between the image property and the labels if the corresponding label uncorrelatedness score is low.
+Each image property (e.g. darkness/brightness) is assigned a label uncorrelatedness score for the entire dataset. The lower the score, the more strongly the property is correlated with the class labels, across images of the dataset. This score is mathematically defined as: 1 minus the relative accuracy improvement in predicting the labels based solely on this image property value (relative to always predicting the most common overall class).
+Consider reviewing the relationship between images with high and low values of this property and the labels if the corresponding label uncorrelatedness score is low, because ML models trained on this dataset may latch onto the spurious correlation and fail to generalize.
This issue type is more about the overall dataset vs. individual data points and will only be highlighted by Datalab in its report, if any such troublesome image properties are found.
Metadata about spurious correlations is stored in the info
attribute of the Datalab object.
It can be accessed like so:
diff --git a/master/searchindex.js b/master/searchindex.js
index 3c74fb4dc..0ca2f8cad 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", 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"tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/improving_ml_performance.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "Improving ML Performance via Data Curation with Train vs Test Splits", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing 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"predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, 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task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [70, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [72, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [62, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [63, "module-cleanlab.multilabel_classification.filter"], [66, "filter"], [75, "filter"], [79, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": 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"regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[83, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "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. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[86, "Spending-too-much-time-on-data-quality?"], [87, "Spending-too-much-time-on-data-quality?"], [90, "Spending-too-much-time-on-data-quality?"], [93, "Spending-too-much-time-on-data-quality?"], [94, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [99, "Spending-too-much-time-on-data-quality?"], [102, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [105, "spending-too-much-time-on-data-quality"], [106, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module 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\ No newline at end of file
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Install cleanlab": [[83, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[83, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "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. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. 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Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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"module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[66, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[67, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[68, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[72, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[73, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[73, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[73, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[74, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[74, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[75, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[75, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[76, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[77, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[78, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 25a686165..d045f2500 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:53.786288Z",
- "iopub.status.busy": "2024-08-26T15:49:53.786078Z",
- "iopub.status.idle": "2024-08-26T15:49:55.058310Z",
- "shell.execute_reply": "2024-08-26T15:49:55.057679Z"
+ "iopub.execute_input": "2024-08-28T20:04:42.119805Z",
+ "iopub.status.busy": "2024-08-28T20:04:42.119627Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.356727Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.356102Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@894a33971fd8cf99254476de4c8b68d2f685b130\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@4cf5c9dccc966516e38d398aa18db514a3e89bef\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.061371Z",
- "iopub.status.busy": "2024-08-26T15:49:55.060806Z",
- "iopub.status.idle": "2024-08-26T15:49:55.079140Z",
- "shell.execute_reply": "2024-08-26T15:49:55.078680Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.359225Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.358943Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.377369Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.376770Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.081404Z",
- "iopub.status.busy": "2024-08-26T15:49:55.080983Z",
- "iopub.status.idle": "2024-08-26T15:49:55.275955Z",
- "shell.execute_reply": "2024-08-26T15:49:55.275375Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.379836Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.379355Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.522623Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.522044Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.313930Z",
- "iopub.status.busy": "2024-08-26T15:49:55.313329Z",
- "iopub.status.idle": "2024-08-26T15:49:55.317364Z",
- "shell.execute_reply": "2024-08-26T15:49:55.316909Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.553029Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.552834Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.556519Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.556047Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.319414Z",
- "iopub.status.busy": "2024-08-26T15:49:55.319068Z",
- "iopub.status.idle": "2024-08-26T15:49:55.327715Z",
- "shell.execute_reply": "2024-08-26T15:49:55.327122Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.558453Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.558283Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.566371Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.565935Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.329976Z",
- "iopub.status.busy": "2024-08-26T15:49:55.329562Z",
- "iopub.status.idle": "2024-08-26T15:49:55.332413Z",
- "shell.execute_reply": "2024-08-26T15:49:55.331825Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.568470Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.568293Z",
+ "iopub.status.idle": "2024-08-28T20:04:43.570807Z",
+ "shell.execute_reply": "2024-08-28T20:04:43.570342Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.334498Z",
- "iopub.status.busy": "2024-08-26T15:49:55.334155Z",
- "iopub.status.idle": "2024-08-26T15:49:55.859656Z",
- "shell.execute_reply": "2024-08-26T15:49:55.859123Z"
+ "iopub.execute_input": "2024-08-28T20:04:43.572686Z",
+ "iopub.status.busy": "2024-08-28T20:04:43.572515Z",
+ "iopub.status.idle": "2024-08-28T20:04:44.093962Z",
+ "shell.execute_reply": "2024-08-28T20:04:44.093422Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:55.862132Z",
- "iopub.status.busy": "2024-08-26T15:49:55.861942Z",
- "iopub.status.idle": "2024-08-26T15:49:57.828703Z",
- "shell.execute_reply": "2024-08-26T15:49:57.828113Z"
+ "iopub.execute_input": "2024-08-28T20:04:44.096377Z",
+ "iopub.status.busy": "2024-08-28T20:04:44.096155Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.009078Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.008425Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.831725Z",
- "iopub.status.busy": "2024-08-26T15:49:57.830860Z",
- "iopub.status.idle": "2024-08-26T15:49:57.841628Z",
- "shell.execute_reply": "2024-08-26T15:49:57.841073Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.011848Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.011198Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.021914Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.021384Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.843831Z",
- "iopub.status.busy": "2024-08-26T15:49:57.843444Z",
- "iopub.status.idle": "2024-08-26T15:49:57.847541Z",
- "shell.execute_reply": "2024-08-26T15:49:57.846968Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.024105Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.023682Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.027945Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.027378Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.849507Z",
- "iopub.status.busy": "2024-08-26T15:49:57.849202Z",
- "iopub.status.idle": "2024-08-26T15:49:57.858293Z",
- "shell.execute_reply": "2024-08-26T15:49:57.857744Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.030083Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.029775Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.038705Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.038223Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.860454Z",
- "iopub.status.busy": "2024-08-26T15:49:57.860152Z",
- "iopub.status.idle": "2024-08-26T15:49:57.972126Z",
- "shell.execute_reply": "2024-08-26T15:49:57.971546Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.040729Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.040398Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.151924Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.151398Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.974328Z",
- "iopub.status.busy": "2024-08-26T15:49:57.973926Z",
- "iopub.status.idle": "2024-08-26T15:49:57.976598Z",
- "shell.execute_reply": "2024-08-26T15:49:57.976151Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.154071Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.153792Z",
+ "iopub.status.idle": "2024-08-28T20:04:46.156683Z",
+ "shell.execute_reply": "2024-08-28T20:04:46.156129Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:49:57.978573Z",
- "iopub.status.busy": "2024-08-26T15:49:57.978261Z",
- "iopub.status.idle": "2024-08-26T15:50:00.065786Z",
- "shell.execute_reply": "2024-08-26T15:50:00.064976Z"
+ "iopub.execute_input": "2024-08-28T20:04:46.158754Z",
+ "iopub.status.busy": "2024-08-28T20:04:46.158318Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.235464Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.234799Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:00.069072Z",
- "iopub.status.busy": "2024-08-26T15:50:00.068250Z",
- "iopub.status.idle": "2024-08-26T15:50:00.079734Z",
- "shell.execute_reply": "2024-08-26T15:50:00.079177Z"
+ "iopub.execute_input": "2024-08-28T20:04:48.238628Z",
+ "iopub.status.busy": "2024-08-28T20:04:48.237802Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.248832Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.248357Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-26T15:50:00.081968Z",
- "iopub.status.busy": "2024-08-26T15:50:00.081512Z",
- "iopub.status.idle": "2024-08-26T15:50:00.307993Z",
- "shell.execute_reply": "2024-08-26T15:50:00.307364Z"
+ "iopub.execute_input": "2024-08-28T20:04:48.250827Z",
+ "iopub.status.busy": "2024-08-28T20:04:48.250644Z",
+ "iopub.status.idle": "2024-08-28T20:04:48.292922Z",
+ "shell.execute_reply": "2024-08-28T20:04:48.292468Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 3f03b9159..3d2fae52d 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 2. Load and format the text dataset
+
|
- Age |
- Gender |
- Location |
- Annual_Spending |
- Number_of_Transactions |
- Last_Purchase_Date |
- | |
- is_null_issue |
- null_score |
+ Age |
+ Gender |
+ Location |
+ Annual_Spending |
+ Number_of_Transactions |
+ Last_Purchase_Date |
+ | |
+ is_null_issue |
+ null_score |
- 8 |
- nan |
- nan |
- nan |
- nan |
- nan |
- NaT |
- |
- True |
- 0.000000 |
-
-
- 1 |
- nan |
- Female |
- Rural |
- 6421.160000 |
- 5.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 9 |
- nan |
- Male |
- Rural |
- 4655.820000 |
- 1.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 14 |
- nan |
- Male |
- Rural |
- 6790.460000 |
- 3.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 13 |
- nan |
- Male |
- Urban |
- 9167.470000 |
- 4.000000 |
- 2024-01-02 00:00:00 |
- |
- False |
- 0.833333 |
-
-
- 15 |
- nan |
- Other |
- Rural |
- 5327.960000 |
- 8.000000 |
- 2024-01-03 00:00:00 |
- |
- False |
- 0.833333 |
-
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diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb
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