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"100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "fad3a25235b9448b94450b285a255bda": { + "f2ac84087dc44396ba038fe66dda6f94": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7448,7 +7382,7 @@ "text_color": null } }, - "fbd7d4ea73254be2b2deaaa3b2315879": { + "f45e40d8fe894c54a435f99ca83a9af5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7501,7 +7435,25 @@ "width": null } }, - "fc6b594ae32e4d7789e07642981bb1cb": { + "f4fca8311ad64e47937de89ecc8e9dab": { + "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 + } + }, + "f64b8b29b1784e22abb666c97681a3dc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7516,16 +7468,42 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6a43e294c6f74c6cb08fd794af7afcd1", - "IPY_MODEL_f177efbf8fcd4299a5c5ef5d2c18ae24", - "IPY_MODEL_e872e61f12c440d89bacf54f54ece5f0" + "IPY_MODEL_dd84a3f49c1c4c929acb96633fa78b1f", + "IPY_MODEL_c630d7221896432296ac4efbd43b54c4", + "IPY_MODEL_31da7fbc4ceb4e4cab95640e5f63a387" ], - "layout": "IPY_MODEL_b95797c6ae9a4df98d457db1eb2fb8cf", + "layout": "IPY_MODEL_a306574ec68049899cf9cc7a5396209d", "tabbable": null, "tooltip": null } }, - "fda1346f54894eeea4d244e48e2db6cc": { + "f8bf518bc6d647f3ab8307b77bad2787": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3d95a4e2520046d8b2e2cf38369b6f03", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_777214f0e66b4c89a70fafc58fae1c98", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "fa1b285a6d474628abba13fe7f5952ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7578,7 +7556,7 @@ "width": null } }, - "ff8a90c9270247469c47cb689ae7fa73": { + "fb083abb666e4d57838a474c630e6824": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -7593,6 +7571,29 @@ "bar_color": null, "description_width": "" } + }, + "ff6312b6a0234759a0f54044414bace6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2562e71fd2f64d07b631e639debc9636", + "placeholder": "​", + "style": "IPY_MODEL_4cd513870c6d47ffa0c1f6c2d8f77251", + "tabbable": null, + "tooltip": null, + "value": "100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 74379a792..741f01a59 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": "2024-08-20T02:17:30.675390Z", - "iopub.status.busy": "2024-08-20T02:17:30.675213Z", - "iopub.status.idle": "2024-08-20T02:17:32.099766Z", - "shell.execute_reply": "2024-08-20T02:17:32.099202Z" + "iopub.execute_input": "2024-08-21T00:42:39.020177Z", + "iopub.status.busy": "2024-08-21T00:42:39.020002Z", + "iopub.status.idle": "2024-08-21T00:42:40.165165Z", + "shell.execute_reply": "2024-08-21T00:42:40.164554Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:17:32.102440Z", - "iopub.status.busy": "2024-08-20T02:17:32.102010Z", - "iopub.status.idle": "2024-08-20T02:17:32.121474Z", - "shell.execute_reply": "2024-08-20T02:17:32.120839Z" + "iopub.execute_input": "2024-08-21T00:42:40.167673Z", + "iopub.status.busy": "2024-08-21T00:42:40.167399Z", + "iopub.status.idle": "2024-08-21T00:42:40.185888Z", + "shell.execute_reply": "2024-08-21T00:42:40.185329Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.124240Z", - "iopub.status.busy": "2024-08-20T02:17:32.123782Z", - "iopub.status.idle": "2024-08-20T02:17:32.161903Z", - "shell.execute_reply": "2024-08-20T02:17:32.161310Z" + "iopub.execute_input": "2024-08-21T00:42:40.188316Z", + "iopub.status.busy": "2024-08-21T00:42:40.187910Z", + "iopub.status.idle": "2024-08-21T00:42:40.224793Z", + "shell.execute_reply": "2024-08-21T00:42:40.224253Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.164301Z", - "iopub.status.busy": "2024-08-20T02:17:32.163892Z", - "iopub.status.idle": "2024-08-20T02:17:32.167503Z", - "shell.execute_reply": "2024-08-20T02:17:32.166961Z" + "iopub.execute_input": "2024-08-21T00:42:40.226826Z", + "iopub.status.busy": "2024-08-21T00:42:40.226427Z", + "iopub.status.idle": "2024-08-21T00:42:40.229894Z", + "shell.execute_reply": "2024-08-21T00:42:40.229350Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.169604Z", - "iopub.status.busy": "2024-08-20T02:17:32.169259Z", - "iopub.status.idle": "2024-08-20T02:17:32.176549Z", - "shell.execute_reply": "2024-08-20T02:17:32.176095Z" + "iopub.execute_input": "2024-08-21T00:42:40.231979Z", + "iopub.status.busy": "2024-08-21T00:42:40.231580Z", + "iopub.status.idle": "2024-08-21T00:42:40.239438Z", + "shell.execute_reply": "2024-08-21T00:42:40.238991Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.178752Z", - "iopub.status.busy": "2024-08-20T02:17:32.178403Z", - "iopub.status.idle": "2024-08-20T02:17:32.181096Z", - "shell.execute_reply": "2024-08-20T02:17:32.180623Z" + "iopub.execute_input": "2024-08-21T00:42:40.241382Z", + "iopub.status.busy": "2024-08-21T00:42:40.241211Z", + "iopub.status.idle": "2024-08-21T00:42:40.243885Z", + "shell.execute_reply": "2024-08-21T00:42:40.243336Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.182992Z", - "iopub.status.busy": "2024-08-20T02:17:32.182822Z", - "iopub.status.idle": "2024-08-20T02:17:35.334471Z", - "shell.execute_reply": "2024-08-20T02:17:35.333927Z" + "iopub.execute_input": "2024-08-21T00:42:40.245971Z", + "iopub.status.busy": "2024-08-21T00:42:40.245666Z", + "iopub.status.idle": "2024-08-21T00:42:43.325922Z", + "shell.execute_reply": "2024-08-21T00:42:43.325277Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:35.337140Z", - "iopub.status.busy": "2024-08-20T02:17:35.336953Z", - "iopub.status.idle": "2024-08-20T02:17:35.346018Z", - "shell.execute_reply": "2024-08-20T02:17:35.345550Z" + "iopub.execute_input": "2024-08-21T00:42:43.328986Z", + "iopub.status.busy": "2024-08-21T00:42:43.328446Z", + "iopub.status.idle": "2024-08-21T00:42:43.337987Z", + "shell.execute_reply": "2024-08-21T00:42:43.337426Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:35.347913Z", - "iopub.status.busy": "2024-08-20T02:17:35.347740Z", - "iopub.status.idle": "2024-08-20T02:17:37.530220Z", - "shell.execute_reply": "2024-08-20T02:17:37.529582Z" + "iopub.execute_input": "2024-08-21T00:42:43.340231Z", + "iopub.status.busy": "2024-08-21T00:42:43.339898Z", + "iopub.status.idle": "2024-08-21T00:42:45.300234Z", + "shell.execute_reply": "2024-08-21T00:42:45.299626Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.532667Z", - "iopub.status.busy": "2024-08-20T02:17:37.532314Z", - "iopub.status.idle": "2024-08-20T02:17:37.551727Z", - "shell.execute_reply": "2024-08-20T02:17:37.551251Z" + "iopub.execute_input": "2024-08-21T00:42:45.302539Z", + "iopub.status.busy": "2024-08-21T00:42:45.302223Z", + "iopub.status.idle": "2024-08-21T00:42:45.320815Z", + "shell.execute_reply": "2024-08-21T00:42:45.320344Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.553782Z", - "iopub.status.busy": "2024-08-20T02:17:37.553599Z", - "iopub.status.idle": "2024-08-20T02:17:37.562552Z", - "shell.execute_reply": "2024-08-20T02:17:37.562008Z" + "iopub.execute_input": "2024-08-21T00:42:45.323090Z", + "iopub.status.busy": "2024-08-21T00:42:45.322698Z", + "iopub.status.idle": "2024-08-21T00:42:45.330546Z", + "shell.execute_reply": "2024-08-21T00:42:45.330098Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.564682Z", - "iopub.status.busy": "2024-08-20T02:17:37.564263Z", - "iopub.status.idle": "2024-08-20T02:17:37.573623Z", - "shell.execute_reply": "2024-08-20T02:17:37.573057Z" + "iopub.execute_input": "2024-08-21T00:42:45.332636Z", + "iopub.status.busy": "2024-08-21T00:42:45.332312Z", + "iopub.status.idle": "2024-08-21T00:42:45.340860Z", + "shell.execute_reply": "2024-08-21T00:42:45.340292Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.575828Z", - "iopub.status.busy": "2024-08-20T02:17:37.575492Z", - "iopub.status.idle": "2024-08-20T02:17:37.583145Z", - "shell.execute_reply": "2024-08-20T02:17:37.582687Z" + "iopub.execute_input": "2024-08-21T00:42:45.342856Z", + "iopub.status.busy": "2024-08-21T00:42:45.342550Z", + "iopub.status.idle": "2024-08-21T00:42:45.350113Z", + "shell.execute_reply": "2024-08-21T00:42:45.349629Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.585205Z", - "iopub.status.busy": "2024-08-20T02:17:37.584870Z", - "iopub.status.idle": "2024-08-20T02:17:37.593427Z", - "shell.execute_reply": "2024-08-20T02:17:37.592949Z" + "iopub.execute_input": "2024-08-21T00:42:45.352205Z", + "iopub.status.busy": "2024-08-21T00:42:45.351869Z", + "iopub.status.idle": "2024-08-21T00:42:45.360446Z", + "shell.execute_reply": "2024-08-21T00:42:45.359939Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.595497Z", - "iopub.status.busy": "2024-08-20T02:17:37.595153Z", - "iopub.status.idle": "2024-08-20T02:17:37.602696Z", - "shell.execute_reply": "2024-08-20T02:17:37.602233Z" + "iopub.execute_input": "2024-08-21T00:42:45.362378Z", + "iopub.status.busy": "2024-08-21T00:42:45.362073Z", + "iopub.status.idle": "2024-08-21T00:42:45.369252Z", + "shell.execute_reply": "2024-08-21T00:42:45.368714Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.604723Z", - "iopub.status.busy": "2024-08-20T02:17:37.604387Z", - "iopub.status.idle": "2024-08-20T02:17:37.611506Z", - "shell.execute_reply": "2024-08-20T02:17:37.611059Z" + "iopub.execute_input": "2024-08-21T00:42:45.371424Z", + "iopub.status.busy": "2024-08-21T00:42:45.371097Z", + "iopub.status.idle": "2024-08-21T00:42:45.378113Z", + "shell.execute_reply": "2024-08-21T00:42:45.377624Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.613742Z", - "iopub.status.busy": "2024-08-20T02:17:37.613408Z", - "iopub.status.idle": "2024-08-20T02:17:37.621320Z", - "shell.execute_reply": "2024-08-20T02:17:37.620872Z" + "iopub.execute_input": "2024-08-21T00:42:45.380217Z", + "iopub.status.busy": "2024-08-21T00:42:45.379888Z", + "iopub.status.idle": "2024-08-21T00:42:45.388315Z", + "shell.execute_reply": "2024-08-21T00:42:45.387730Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index e8c1c1367..944c7f70a 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": "2024-08-20T02:17:40.653105Z", - "iopub.status.busy": "2024-08-20T02:17:40.652539Z", - "iopub.status.idle": "2024-08-20T02:17:43.889364Z", - "shell.execute_reply": "2024-08-20T02:17:43.888668Z" + "iopub.execute_input": "2024-08-21T00:42:48.197839Z", + "iopub.status.busy": "2024-08-21T00:42:48.197661Z", + "iopub.status.idle": "2024-08-21T00:42:50.967466Z", + "shell.execute_reply": "2024-08-21T00:42:50.966936Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:17:43.892128Z", - "iopub.status.busy": "2024-08-20T02:17:43.891640Z", - "iopub.status.idle": "2024-08-20T02:17:43.895466Z", - "shell.execute_reply": "2024-08-20T02:17:43.895014Z" + "iopub.execute_input": "2024-08-21T00:42:50.969963Z", + "iopub.status.busy": "2024-08-21T00:42:50.969666Z", + "iopub.status.idle": "2024-08-21T00:42:50.973228Z", + "shell.execute_reply": "2024-08-21T00:42:50.972662Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.897587Z", - "iopub.status.busy": "2024-08-20T02:17:43.897195Z", - "iopub.status.idle": "2024-08-20T02:17:43.900227Z", - "shell.execute_reply": "2024-08-20T02:17:43.899787Z" + "iopub.execute_input": "2024-08-21T00:42:50.975166Z", + "iopub.status.busy": "2024-08-21T00:42:50.974858Z", + "iopub.status.idle": "2024-08-21T00:42:50.978037Z", + "shell.execute_reply": "2024-08-21T00:42:50.977508Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.902374Z", - "iopub.status.busy": "2024-08-20T02:17:43.902037Z", - "iopub.status.idle": "2024-08-20T02:17:43.948123Z", - "shell.execute_reply": "2024-08-20T02:17:43.947597Z" + "iopub.execute_input": "2024-08-21T00:42:50.980278Z", + "iopub.status.busy": "2024-08-21T00:42:50.979852Z", + "iopub.status.idle": "2024-08-21T00:42:51.018730Z", + "shell.execute_reply": "2024-08-21T00:42:51.018187Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.950404Z", - "iopub.status.busy": "2024-08-20T02:17:43.950033Z", - "iopub.status.idle": "2024-08-20T02:17:43.953987Z", - "shell.execute_reply": "2024-08-20T02:17:43.953498Z" + "iopub.execute_input": "2024-08-21T00:42:51.020813Z", + "iopub.status.busy": "2024-08-21T00:42:51.020467Z", + "iopub.status.idle": "2024-08-21T00:42:51.024267Z", + "shell.execute_reply": "2024-08-21T00:42:51.023800Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'change_pin'}\n" + "Classes: {'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.956096Z", - "iopub.status.busy": "2024-08-20T02:17:43.955753Z", - "iopub.status.idle": "2024-08-20T02:17:43.959032Z", - "shell.execute_reply": "2024-08-20T02:17:43.958569Z" + "iopub.execute_input": "2024-08-21T00:42:51.026219Z", + "iopub.status.busy": "2024-08-21T00:42:51.025887Z", + "iopub.status.idle": "2024-08-21T00:42:51.029160Z", + "shell.execute_reply": "2024-08-21T00:42:51.028719Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.961194Z", - "iopub.status.busy": "2024-08-20T02:17:43.960798Z", - "iopub.status.idle": "2024-08-20T02:17:47.539339Z", - "shell.execute_reply": "2024-08-20T02:17:47.538779Z" + "iopub.execute_input": "2024-08-21T00:42:51.031153Z", + "iopub.status.busy": "2024-08-21T00:42:51.030819Z", + "iopub.status.idle": "2024-08-21T00:42:54.855542Z", + "shell.execute_reply": "2024-08-21T00:42:54.854980Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:47.542112Z", - "iopub.status.busy": "2024-08-20T02:17:47.541704Z", - "iopub.status.idle": "2024-08-20T02:17:48.414944Z", - "shell.execute_reply": "2024-08-20T02:17:48.414342Z" + "iopub.execute_input": "2024-08-21T00:42:54.858207Z", + "iopub.status.busy": "2024-08-21T00:42:54.857857Z", + "iopub.status.idle": "2024-08-21T00:42:55.761897Z", + "shell.execute_reply": "2024-08-21T00:42:55.761312Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:48.418075Z", - "iopub.status.busy": "2024-08-20T02:17:48.417646Z", - "iopub.status.idle": "2024-08-20T02:17:48.420650Z", - "shell.execute_reply": "2024-08-20T02:17:48.420142Z" + "iopub.execute_input": "2024-08-21T00:42:55.764920Z", + "iopub.status.busy": "2024-08-21T00:42:55.764523Z", + "iopub.status.idle": "2024-08-21T00:42:55.767436Z", + "shell.execute_reply": "2024-08-21T00:42:55.766944Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:48.423130Z", - "iopub.status.busy": "2024-08-20T02:17:48.422741Z", - "iopub.status.idle": "2024-08-20T02:17:50.512788Z", - "shell.execute_reply": "2024-08-20T02:17:50.512084Z" + "iopub.execute_input": "2024-08-21T00:42:55.769883Z", + "iopub.status.busy": "2024-08-21T00:42:55.769512Z", + "iopub.status.idle": "2024-08-21T00:42:57.751703Z", + "shell.execute_reply": "2024-08-21T00:42:57.751042Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.516360Z", - "iopub.status.busy": "2024-08-20T02:17:50.515474Z", - "iopub.status.idle": "2024-08-20T02:17:50.559350Z", - "shell.execute_reply": "2024-08-20T02:17:50.558793Z" + "iopub.execute_input": "2024-08-21T00:42:57.756032Z", + "iopub.status.busy": "2024-08-21T00:42:57.754848Z", + "iopub.status.idle": "2024-08-21T00:42:57.780427Z", + "shell.execute_reply": "2024-08-21T00:42:57.779909Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.562005Z", - "iopub.status.busy": "2024-08-20T02:17:50.561575Z", - "iopub.status.idle": "2024-08-20T02:17:50.571250Z", - "shell.execute_reply": "2024-08-20T02:17:50.570668Z" + "iopub.execute_input": "2024-08-21T00:42:57.783964Z", + "iopub.status.busy": "2024-08-21T00:42:57.783026Z", + "iopub.status.idle": "2024-08-21T00:42:57.793469Z", + "shell.execute_reply": "2024-08-21T00:42:57.793043Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.573299Z", - "iopub.status.busy": "2024-08-20T02:17:50.573083Z", - "iopub.status.idle": "2024-08-20T02:17:50.577651Z", - "shell.execute_reply": "2024-08-20T02:17:50.577171Z" + "iopub.execute_input": "2024-08-21T00:42:57.795468Z", + "iopub.status.busy": "2024-08-21T00:42:57.795293Z", + "iopub.status.idle": "2024-08-21T00:42:57.799640Z", + "shell.execute_reply": "2024-08-21T00:42:57.799166Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.579760Z", - "iopub.status.busy": "2024-08-20T02:17:50.579414Z", - "iopub.status.idle": "2024-08-20T02:17:50.585985Z", - "shell.execute_reply": "2024-08-20T02:17:50.585437Z" + "iopub.execute_input": "2024-08-21T00:42:57.801519Z", + "iopub.status.busy": "2024-08-21T00:42:57.801343Z", + "iopub.status.idle": "2024-08-21T00:42:57.807871Z", + "shell.execute_reply": "2024-08-21T00:42:57.807330Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.588204Z", - "iopub.status.busy": "2024-08-20T02:17:50.587870Z", - "iopub.status.idle": "2024-08-20T02:17:50.594858Z", - "shell.execute_reply": "2024-08-20T02:17:50.594371Z" + "iopub.execute_input": "2024-08-21T00:42:57.809785Z", + "iopub.status.busy": "2024-08-21T00:42:57.809618Z", + "iopub.status.idle": "2024-08-21T00:42:57.816205Z", + "shell.execute_reply": "2024-08-21T00:42:57.815747Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.596980Z", - "iopub.status.busy": "2024-08-20T02:17:50.596628Z", - "iopub.status.idle": "2024-08-20T02:17:50.602883Z", - "shell.execute_reply": "2024-08-20T02:17:50.602388Z" + "iopub.execute_input": "2024-08-21T00:42:57.817972Z", + "iopub.status.busy": "2024-08-21T00:42:57.817801Z", + "iopub.status.idle": "2024-08-21T00:42:57.823337Z", + "shell.execute_reply": "2024-08-21T00:42:57.822851Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.605052Z", - "iopub.status.busy": "2024-08-20T02:17:50.604708Z", - "iopub.status.idle": "2024-08-20T02:17:50.613784Z", - "shell.execute_reply": "2024-08-20T02:17:50.613286Z" + "iopub.execute_input": "2024-08-21T00:42:57.825390Z", + "iopub.status.busy": "2024-08-21T00:42:57.825059Z", + "iopub.status.idle": "2024-08-21T00:42:57.833536Z", + "shell.execute_reply": "2024-08-21T00:42:57.832966Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.615869Z", - "iopub.status.busy": "2024-08-20T02:17:50.615531Z", - "iopub.status.idle": "2024-08-20T02:17:50.621241Z", - "shell.execute_reply": "2024-08-20T02:17:50.620655Z" + "iopub.execute_input": "2024-08-21T00:42:57.835599Z", + "iopub.status.busy": "2024-08-21T00:42:57.835187Z", + "iopub.status.idle": "2024-08-21T00:42:57.840572Z", + "shell.execute_reply": "2024-08-21T00:42:57.840031Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.623564Z", - "iopub.status.busy": "2024-08-20T02:17:50.623056Z", - "iopub.status.idle": "2024-08-20T02:17:50.628693Z", - "shell.execute_reply": "2024-08-20T02:17:50.628220Z" + "iopub.execute_input": "2024-08-21T00:42:57.842661Z", + "iopub.status.busy": "2024-08-21T00:42:57.842352Z", + "iopub.status.idle": "2024-08-21T00:42:57.847636Z", + "shell.execute_reply": "2024-08-21T00:42:57.847079Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.630804Z", - "iopub.status.busy": "2024-08-20T02:17:50.630492Z", - "iopub.status.idle": "2024-08-20T02:17:50.634294Z", - "shell.execute_reply": "2024-08-20T02:17:50.633704Z" + "iopub.execute_input": "2024-08-21T00:42:57.849566Z", + "iopub.status.busy": "2024-08-21T00:42:57.849393Z", + "iopub.status.idle": "2024-08-21T00:42:57.852910Z", + "shell.execute_reply": "2024-08-21T00:42:57.852365Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.636366Z", - "iopub.status.busy": "2024-08-20T02:17:50.636088Z", - "iopub.status.idle": "2024-08-20T02:17:50.641705Z", - "shell.execute_reply": "2024-08-20T02:17:50.641097Z" + "iopub.execute_input": "2024-08-21T00:42:57.855166Z", + "iopub.status.busy": "2024-08-21T00:42:57.854784Z", + "iopub.status.idle": "2024-08-21T00:42:57.860086Z", + "shell.execute_reply": "2024-08-21T00:42:57.859637Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index ea12d845c..eb842aff3 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": "2024-08-20T02:17:54.342678Z", - "iopub.status.busy": "2024-08-20T02:17:54.342516Z", - "iopub.status.idle": "2024-08-20T02:17:54.787105Z", - "shell.execute_reply": "2024-08-20T02:17:54.786589Z" + "iopub.execute_input": "2024-08-21T00:43:01.171888Z", + "iopub.status.busy": "2024-08-21T00:43:01.171708Z", + "iopub.status.idle": "2024-08-21T00:43:01.599347Z", + "shell.execute_reply": "2024-08-21T00:43:01.598723Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.789748Z", - "iopub.status.busy": "2024-08-20T02:17:54.789305Z", - "iopub.status.idle": "2024-08-20T02:17:54.923597Z", - "shell.execute_reply": "2024-08-20T02:17:54.923027Z" + "iopub.execute_input": "2024-08-21T00:43:01.602168Z", + "iopub.status.busy": "2024-08-21T00:43:01.601787Z", + "iopub.status.idle": "2024-08-21T00:43:01.731428Z", + "shell.execute_reply": "2024-08-21T00:43:01.730880Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.926017Z", - "iopub.status.busy": "2024-08-20T02:17:54.925610Z", - "iopub.status.idle": "2024-08-20T02:17:54.948848Z", - "shell.execute_reply": "2024-08-20T02:17:54.948254Z" + "iopub.execute_input": "2024-08-21T00:43:01.733775Z", + "iopub.status.busy": "2024-08-21T00:43:01.733383Z", + "iopub.status.idle": "2024-08-21T00:43:01.756845Z", + "shell.execute_reply": "2024-08-21T00:43:01.756289Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.951750Z", - "iopub.status.busy": "2024-08-20T02:17:54.951205Z", - "iopub.status.idle": "2024-08-20T02:17:58.333690Z", - "shell.execute_reply": "2024-08-20T02:17:58.332962Z" + "iopub.execute_input": "2024-08-21T00:43:01.759422Z", + "iopub.status.busy": "2024-08-21T00:43:01.758972Z", + "iopub.status.idle": "2024-08-21T00:43:04.525314Z", + "shell.execute_reply": "2024-08-21T00:43:04.524747Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:58.336453Z", - "iopub.status.busy": "2024-08-20T02:17:58.335863Z", - "iopub.status.idle": "2024-08-20T02:18:06.978320Z", - "shell.execute_reply": "2024-08-20T02:18:06.977689Z" + "iopub.execute_input": "2024-08-21T00:43:04.528021Z", + "iopub.status.busy": "2024-08-21T00:43:04.527475Z", + "iopub.status.idle": "2024-08-21T00:43:13.293687Z", + "shell.execute_reply": "2024-08-21T00:43:13.293070Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:06.980664Z", - "iopub.status.busy": "2024-08-20T02:18:06.980310Z", - "iopub.status.idle": "2024-08-20T02:18:07.140407Z", - "shell.execute_reply": "2024-08-20T02:18:07.139847Z" + "iopub.execute_input": "2024-08-21T00:43:13.296149Z", + "iopub.status.busy": "2024-08-21T00:43:13.295770Z", + "iopub.status.idle": "2024-08-21T00:43:13.454429Z", + "shell.execute_reply": "2024-08-21T00:43:13.453846Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:07.143040Z", - "iopub.status.busy": "2024-08-20T02:18:07.142679Z", - "iopub.status.idle": "2024-08-20T02:18:08.680980Z", - "shell.execute_reply": "2024-08-20T02:18:08.680491Z" + "iopub.execute_input": "2024-08-21T00:43:13.456937Z", + "iopub.status.busy": "2024-08-21T00:43:13.456583Z", + "iopub.status.idle": "2024-08-21T00:43:14.817910Z", + "shell.execute_reply": "2024-08-21T00:43:14.817401Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:08.683287Z", - "iopub.status.busy": "2024-08-20T02:18:08.682919Z", - "iopub.status.idle": "2024-08-20T02:18:08.998377Z", - "shell.execute_reply": "2024-08-20T02:18:08.997764Z" + "iopub.execute_input": "2024-08-21T00:43:14.820072Z", + "iopub.status.busy": "2024-08-21T00:43:14.819706Z", + "iopub.status.idle": "2024-08-21T00:43:15.269488Z", + "shell.execute_reply": "2024-08-21T00:43:15.268851Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.001136Z", - "iopub.status.busy": "2024-08-20T02:18:09.000600Z", - "iopub.status.idle": "2024-08-20T02:18:09.014081Z", - "shell.execute_reply": "2024-08-20T02:18:09.013591Z" + "iopub.execute_input": "2024-08-21T00:43:15.272042Z", + "iopub.status.busy": "2024-08-21T00:43:15.271492Z", + "iopub.status.idle": "2024-08-21T00:43:15.285246Z", + "shell.execute_reply": "2024-08-21T00:43:15.284785Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.016120Z", - "iopub.status.busy": "2024-08-20T02:18:09.015940Z", - "iopub.status.idle": "2024-08-20T02:18:09.039533Z", - "shell.execute_reply": "2024-08-20T02:18:09.038905Z" + "iopub.execute_input": "2024-08-21T00:43:15.287513Z", + "iopub.status.busy": "2024-08-21T00:43:15.287177Z", + "iopub.status.idle": "2024-08-21T00:43:15.307560Z", + "shell.execute_reply": "2024-08-21T00:43:15.306987Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.042064Z", - "iopub.status.busy": "2024-08-20T02:18:09.041579Z", - "iopub.status.idle": "2024-08-20T02:18:09.283734Z", - "shell.execute_reply": "2024-08-20T02:18:09.283109Z" + "iopub.execute_input": "2024-08-21T00:43:15.309775Z", + "iopub.status.busy": "2024-08-21T00:43:15.309322Z", + "iopub.status.idle": "2024-08-21T00:43:15.549642Z", + "shell.execute_reply": "2024-08-21T00:43:15.549085Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.286529Z", - "iopub.status.busy": "2024-08-20T02:18:09.286191Z", - "iopub.status.idle": "2024-08-20T02:18:09.305536Z", - "shell.execute_reply": "2024-08-20T02:18:09.305009Z" + "iopub.execute_input": "2024-08-21T00:43:15.552675Z", + "iopub.status.busy": "2024-08-21T00:43:15.552274Z", + "iopub.status.idle": "2024-08-21T00:43:15.571649Z", + "shell.execute_reply": "2024-08-21T00:43:15.571180Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.307549Z", - "iopub.status.busy": "2024-08-20T02:18:09.307369Z", - "iopub.status.idle": "2024-08-20T02:18:09.476687Z", - "shell.execute_reply": "2024-08-20T02:18:09.476131Z" + "iopub.execute_input": "2024-08-21T00:43:15.573738Z", + "iopub.status.busy": "2024-08-21T00:43:15.573396Z", + "iopub.status.idle": "2024-08-21T00:43:15.740795Z", + "shell.execute_reply": "2024-08-21T00:43:15.740213Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.478997Z", - "iopub.status.busy": "2024-08-20T02:18:09.478812Z", - "iopub.status.idle": "2024-08-20T02:18:09.489242Z", - "shell.execute_reply": "2024-08-20T02:18:09.488671Z" + "iopub.execute_input": "2024-08-21T00:43:15.743277Z", + "iopub.status.busy": "2024-08-21T00:43:15.742918Z", + "iopub.status.idle": "2024-08-21T00:43:15.752623Z", + "shell.execute_reply": "2024-08-21T00:43:15.752133Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.491411Z", - "iopub.status.busy": "2024-08-20T02:18:09.490973Z", - "iopub.status.idle": "2024-08-20T02:18:09.500699Z", - "shell.execute_reply": "2024-08-20T02:18:09.500225Z" + "iopub.execute_input": "2024-08-21T00:43:15.754632Z", + "iopub.status.busy": "2024-08-21T00:43:15.754360Z", + "iopub.status.idle": "2024-08-21T00:43:15.763787Z", + "shell.execute_reply": "2024-08-21T00:43:15.763179Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.502731Z", - "iopub.status.busy": "2024-08-20T02:18:09.502549Z", - "iopub.status.idle": "2024-08-20T02:18:09.529479Z", - "shell.execute_reply": "2024-08-20T02:18:09.528984Z" + "iopub.execute_input": "2024-08-21T00:43:15.765948Z", + "iopub.status.busy": "2024-08-21T00:43:15.765602Z", + "iopub.status.idle": "2024-08-21T00:43:15.791328Z", + "shell.execute_reply": "2024-08-21T00:43:15.790866Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.531581Z", - "iopub.status.busy": "2024-08-20T02:18:09.531237Z", - "iopub.status.idle": "2024-08-20T02:18:09.534052Z", - "shell.execute_reply": "2024-08-20T02:18:09.533586Z" + "iopub.execute_input": "2024-08-21T00:43:15.793538Z", + "iopub.status.busy": "2024-08-21T00:43:15.793210Z", + "iopub.status.idle": "2024-08-21T00:43:15.796045Z", + "shell.execute_reply": "2024-08-21T00:43:15.795529Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.536250Z", - "iopub.status.busy": "2024-08-20T02:18:09.535919Z", - "iopub.status.idle": "2024-08-20T02:18:09.555307Z", - "shell.execute_reply": "2024-08-20T02:18:09.554757Z" + "iopub.execute_input": "2024-08-21T00:43:15.798174Z", + "iopub.status.busy": "2024-08-21T00:43:15.797769Z", + "iopub.status.idle": "2024-08-21T00:43:15.817188Z", + "shell.execute_reply": "2024-08-21T00:43:15.816725Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.557515Z", - "iopub.status.busy": "2024-08-20T02:18:09.557152Z", - "iopub.status.idle": "2024-08-20T02:18:09.561596Z", - "shell.execute_reply": "2024-08-20T02:18:09.561108Z" + "iopub.execute_input": "2024-08-21T00:43:15.819091Z", + "iopub.status.busy": "2024-08-21T00:43:15.818917Z", + "iopub.status.idle": "2024-08-21T00:43:15.823437Z", + "shell.execute_reply": "2024-08-21T00:43:15.822989Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.563658Z", - "iopub.status.busy": "2024-08-20T02:18:09.563335Z", - "iopub.status.idle": "2024-08-20T02:18:09.592021Z", - "shell.execute_reply": "2024-08-20T02:18:09.591552Z" + "iopub.execute_input": "2024-08-21T00:43:15.825477Z", + "iopub.status.busy": "2024-08-21T00:43:15.825162Z", + "iopub.status.idle": "2024-08-21T00:43:15.852907Z", + "shell.execute_reply": "2024-08-21T00:43:15.852367Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.594263Z", - "iopub.status.busy": "2024-08-20T02:18:09.593908Z", - "iopub.status.idle": "2024-08-20T02:18:09.919384Z", - "shell.execute_reply": "2024-08-20T02:18:09.918757Z" + "iopub.execute_input": "2024-08-21T00:43:15.855074Z", + "iopub.status.busy": "2024-08-21T00:43:15.854635Z", + "iopub.status.idle": "2024-08-21T00:43:16.219163Z", + "shell.execute_reply": "2024-08-21T00:43:16.218556Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.921742Z", - "iopub.status.busy": "2024-08-20T02:18:09.921399Z", - "iopub.status.idle": "2024-08-20T02:18:09.924438Z", - "shell.execute_reply": "2024-08-20T02:18:09.923873Z" + "iopub.execute_input": "2024-08-21T00:43:16.221443Z", + "iopub.status.busy": "2024-08-21T00:43:16.221085Z", + "iopub.status.idle": "2024-08-21T00:43:16.224381Z", + "shell.execute_reply": "2024-08-21T00:43:16.223904Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.926626Z", - "iopub.status.busy": "2024-08-20T02:18:09.926286Z", - "iopub.status.idle": "2024-08-20T02:18:09.942337Z", - "shell.execute_reply": "2024-08-20T02:18:09.941698Z" + "iopub.execute_input": "2024-08-21T00:43:16.226481Z", + "iopub.status.busy": "2024-08-21T00:43:16.226086Z", + "iopub.status.idle": "2024-08-21T00:43:16.239073Z", + "shell.execute_reply": "2024-08-21T00:43:16.238514Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.944789Z", - "iopub.status.busy": "2024-08-20T02:18:09.944595Z", - "iopub.status.idle": "2024-08-20T02:18:09.959330Z", - "shell.execute_reply": "2024-08-20T02:18:09.958838Z" + "iopub.execute_input": "2024-08-21T00:43:16.241149Z", + "iopub.status.busy": "2024-08-21T00:43:16.240818Z", + "iopub.status.idle": "2024-08-21T00:43:16.253851Z", + "shell.execute_reply": "2024-08-21T00:43:16.253394Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.961553Z", - "iopub.status.busy": "2024-08-20T02:18:09.961186Z", - "iopub.status.idle": "2024-08-20T02:18:09.971554Z", - "shell.execute_reply": "2024-08-20T02:18:09.971112Z" + "iopub.execute_input": "2024-08-21T00:43:16.255788Z", + "iopub.status.busy": "2024-08-21T00:43:16.255473Z", + "iopub.status.idle": "2024-08-21T00:43:16.265699Z", + "shell.execute_reply": "2024-08-21T00:43:16.265145Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.973798Z", - "iopub.status.busy": "2024-08-20T02:18:09.973436Z", - "iopub.status.idle": "2024-08-20T02:18:09.982920Z", - "shell.execute_reply": "2024-08-20T02:18:09.982407Z" + "iopub.execute_input": "2024-08-21T00:43:16.268027Z", + "iopub.status.busy": "2024-08-21T00:43:16.267560Z", + "iopub.status.idle": "2024-08-21T00:43:16.276845Z", + "shell.execute_reply": "2024-08-21T00:43:16.276285Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.985172Z", - "iopub.status.busy": "2024-08-20T02:18:09.984839Z", - "iopub.status.idle": "2024-08-20T02:18:09.988286Z", - "shell.execute_reply": "2024-08-20T02:18:09.987820Z" + "iopub.execute_input": "2024-08-21T00:43:16.278895Z", + "iopub.status.busy": "2024-08-21T00:43:16.278593Z", + "iopub.status.idle": "2024-08-21T00:43:16.282208Z", + "shell.execute_reply": "2024-08-21T00:43:16.281682Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.990507Z", - "iopub.status.busy": "2024-08-20T02:18:09.990126Z", - "iopub.status.idle": "2024-08-20T02:18:10.042804Z", - "shell.execute_reply": "2024-08-20T02:18:10.042240Z" + "iopub.execute_input": "2024-08-21T00:43:16.284237Z", + "iopub.status.busy": "2024-08-21T00:43:16.283887Z", + "iopub.status.idle": "2024-08-21T00:43:16.335023Z", + "shell.execute_reply": "2024-08-21T00:43:16.334498Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
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 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.045338Z", - "iopub.status.busy": "2024-08-20T02:18:10.044784Z", - "iopub.status.idle": "2024-08-20T02:18:10.052047Z", - "shell.execute_reply": "2024-08-20T02:18:10.051488Z" + "iopub.execute_input": "2024-08-21T00:43:16.337295Z", + "iopub.status.busy": "2024-08-21T00:43:16.336882Z", + "iopub.status.idle": "2024-08-21T00:43:16.342563Z", + "shell.execute_reply": "2024-08-21T00:43:16.342023Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.054394Z", - "iopub.status.busy": "2024-08-20T02:18:10.053951Z", - "iopub.status.idle": "2024-08-20T02:18:10.066067Z", - "shell.execute_reply": "2024-08-20T02:18:10.065481Z" + "iopub.execute_input": "2024-08-21T00:43:16.344534Z", + "iopub.status.busy": "2024-08-21T00:43:16.344225Z", + "iopub.status.idle": "2024-08-21T00:43:16.355308Z", + "shell.execute_reply": "2024-08-21T00:43:16.354768Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.068250Z", - "iopub.status.busy": "2024-08-20T02:18:10.067916Z", - "iopub.status.idle": "2024-08-20T02:18:10.246354Z", - "shell.execute_reply": "2024-08-20T02:18:10.245759Z" + "iopub.execute_input": "2024-08-21T00:43:16.357487Z", + "iopub.status.busy": "2024-08-21T00:43:16.356999Z", + "iopub.status.idle": "2024-08-21T00:43:16.572206Z", + "shell.execute_reply": "2024-08-21T00:43:16.571628Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.248597Z", - "iopub.status.busy": "2024-08-20T02:18:10.248414Z", - "iopub.status.idle": "2024-08-20T02:18:10.256426Z", - "shell.execute_reply": "2024-08-20T02:18:10.255950Z" + "iopub.execute_input": "2024-08-21T00:43:16.574287Z", + "iopub.status.busy": "2024-08-21T00:43:16.574098Z", + "iopub.status.idle": "2024-08-21T00:43:16.582508Z", + "shell.execute_reply": "2024-08-21T00:43:16.581942Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.258571Z", - "iopub.status.busy": "2024-08-20T02:18:10.258299Z", - "iopub.status.idle": "2024-08-20T02:18:10.653195Z", - "shell.execute_reply": "2024-08-20T02:18:10.652443Z" + "iopub.execute_input": "2024-08-21T00:43:16.584541Z", + "iopub.status.busy": "2024-08-21T00:43:16.584367Z", + "iopub.status.idle": "2024-08-21T00:43:17.017635Z", + "shell.execute_reply": "2024-08-21T00:43:17.016929Z" } }, "outputs": [ @@ -3767,25 +3767,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:18:10-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", - "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", + "--2024-08-21 00:43:16-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.109.153, 185.199.108.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", - "Length: 986707 (964K) [application/zip]\r\n", - "Saving to: ‘CIFAR-10-subset.zip’\r\n", - "\r\n", - "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " + "HTTP request sent, awaiting response... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "200 OK\r\n", + "Length: 986707 (964K) [application/zip]\r\n", + "Saving to: ‘CIFAR-10-subset.zip’\r\n", + "\r\n", "\r", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.02s \r\n", "\r\n", - "2024-08-20 02:18:10 (38.2 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-08-21 00:43:16 (39.0 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.655777Z", - "iopub.status.busy": "2024-08-20T02:18:10.655572Z", - "iopub.status.idle": "2024-08-20T02:18:12.615828Z", - "shell.execute_reply": "2024-08-20T02:18:12.615269Z" + "iopub.execute_input": "2024-08-21T00:43:17.020182Z", + "iopub.status.busy": "2024-08-21T00:43:17.019968Z", + "iopub.status.idle": "2024-08-21T00:43:18.913423Z", + "shell.execute_reply": "2024-08-21T00:43:18.912849Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:12.618289Z", - "iopub.status.busy": "2024-08-20T02:18:12.617996Z", - "iopub.status.idle": "2024-08-20T02:18:13.092642Z", - "shell.execute_reply": "2024-08-20T02:18:13.091988Z" + "iopub.execute_input": "2024-08-21T00:43:18.915816Z", + "iopub.status.busy": "2024-08-21T00:43:18.915543Z", + "iopub.status.idle": "2024-08-21T00:43:19.385731Z", + "shell.execute_reply": "2024-08-21T00:43:19.385086Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8bb8c2257ff642589da749e1f2b50557", + "model_id": "67422fd209454e4487fc32db492ca2c1", "version_major": 2, "version_minor": 0 }, @@ -3884,6 +3884,7 @@ "output_type": "stream", "text": [ "Removing dark, blurry from potential issues in the dataset as it exceeds max_prevalence=0.1\n", + "Finding spurious correlation issues in the dataset ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n", "No issues found in the data. Good job!\n", @@ -3897,9 +3898,9 @@ "\n", "\n", "\n", - "Here is a summary of spurious correlations between image features like 'dark_score', 'blurry_score', etc., and class labels detected in the data.\n", + "Here is a summary of spurious correlations between image features (like 'dark_score', 'blurry_score', etc.) and class labels detected in the data.\n", "\n", - "A lower score for each property implies a higher correlation of that property with the class labels.\n", + "A lower score implies a higher likelihood of a spurious correlation between that property and the class labels.\n", "\n", "\n", "property score\n", @@ -3950,10 +3951,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:13.096544Z", - "iopub.status.busy": "2024-08-20T02:18:13.095622Z", - "iopub.status.idle": "2024-08-20T02:18:13.113469Z", - "shell.execute_reply": "2024-08-20T02:18:13.112926Z" + "iopub.execute_input": "2024-08-21T00:43:19.388156Z", + "iopub.status.busy": "2024-08-21T00:43:19.387960Z", + "iopub.status.idle": "2024-08-21T00:43:19.402565Z", + "shell.execute_reply": "2024-08-21T00:43:19.402004Z" } }, "outputs": [ @@ -3961,7 +3962,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Correlation scores for image properties:\n" + "Label uncorrelatedness scores for image properties:\n" ] }, { @@ -3992,13 +3993,13 @@ " \n", " \n", " 0\n", - " dark_score\n", - " 0.000\n", + " odd_size_score\n", + " 0.500\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.180\n", + " odd_aspect_ratio_score\n", + " 0.500\n", " \n", " \n", " 2\n", @@ -4007,18 +4008,18 @@ " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " light_score\n", + " 0.180\n", " \n", " \n", " 4\n", - " odd_size_score\n", + " grayscale_score\n", " 0.500\n", " \n", " \n", " 5\n", - " grayscale_score\n", - " 0.500\n", + " dark_score\n", + " 0.000\n", " \n", " \n", " 6\n", @@ -4031,12 +4032,12 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.000\n", - "1 light_score 0.180\n", + "0 odd_size_score 0.500\n", + "1 odd_aspect_ratio_score 0.500\n", "2 low_information_score 0.015\n", - "3 odd_aspect_ratio_score 0.500\n", - "4 odd_size_score 0.500\n", - "5 grayscale_score 0.500\n", + "3 light_score 0.180\n", + "4 grayscale_score 0.500\n", + "5 dark_score 0.000\n", "6 blurry_score 0.015" ] }, @@ -4072,35 +4073,35 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.237196\n", " True\n", + " 0.237196\n", " \n", " \n", " 1\n", - " 0.197229\n", " True\n", + " 0.197229\n", " \n", " \n", " 2\n", - " 0.254188\n", " True\n", + " 0.254188\n", " \n", " \n", " 3\n", - " 0.229170\n", " True\n", + " 0.229170\n", " \n", " \n", " 4\n", - " 0.208907\n", " True\n", + " 0.208907\n", " \n", " \n", " ...\n", @@ -4109,28 +4110,28 @@ " \n", " \n", " 195\n", - " 0.793840\n", " False\n", + " 0.793840\n", " \n", " \n", " 196\n", - " 1.000000\n", " False\n", + " 1.000000\n", " \n", " \n", " 197\n", - " 0.971560\n", " False\n", + " 0.971560\n", " \n", " \n", " 198\n", - " 0.862236\n", " False\n", + " 0.862236\n", " \n", " \n", " 199\n", - " 0.973533\n", " False\n", + " 0.973533\n", " \n", " \n", "\n", @@ -4138,18 +4139,18 @@ "" ], "text/plain": [ - " dark_score is_dark_issue\n", - "0 0.237196 True\n", - "1 0.197229 True\n", - "2 0.254188 True\n", - "3 0.229170 True\n", - "4 0.208907 True\n", - ".. ... ...\n", - "195 0.793840 False\n", - "196 1.000000 False\n", - "197 0.971560 False\n", - "198 0.862236 False\n", - "199 0.973533 False\n", + " is_dark_issue dark_score\n", + "0 True 0.237196\n", + "1 True 0.197229\n", + "2 True 0.254188\n", + "3 True 0.229170\n", + "4 True 0.208907\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", "\n", "[200 rows x 2 columns]" ] @@ -4161,10 +4162,10 @@ "source": [ "from IPython.display import display\n", "\n", - "# Get the correlation scores for image properties\n", - "correlation_scores = lab._correlations_df\n", - "print(\"Correlation scores for image properties:\")\n", - "display(correlation_scores)\n", + "# Get scores for label uncorrelatedness with image properties\n", + "label_uncorrelatedness_scores = lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for image properties:\")\n", + "display(label_uncorrelatedness_scores)\n", "\n", "# Get image-specific issues\n", "issue_name = \"dark\"\n", @@ -4177,19 +4178,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n", - "\n", "Interpreting the results:\n", "\n", - "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", + "1. **Label Uncorrelatedness Scores**: The `label_uncorrelatedness_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n", "\n", - "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", + "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the label_uncorrelatedness_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", "\n", "For real-world datasets, pay attention to:\n", "\n", - "- Properties with notably low scores in the correlation_scores DataFrame\n", + "- Properties with notably low scores in the label_uncorrelatedness_scores DataFrame\n", "- Prevalent issues in the image_issues DataFrame\n", "\n", "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n", @@ -4197,9 +4195,68 @@ "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`." ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-21T00:43:19.404760Z", + "iopub.status.busy": "2024-08-21T00:43:19.404582Z", + "iopub.status.idle": "2024-08-21T00:43:19.550076Z", + "shell.execute_reply": "2024-08-21T00:43:19.549530Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_scores_labels(lab, property=\"dark_score\"):\n", + " \"\"\"\n", + " Plots the scores of image-specific properties like 'dark_score', 'blurry_score', etc. \n", + " against labels for each instance in the dataset using 'Datalab' object.\n", + "\n", + " Parameters:\n", + " -----------\n", + " lab : 'Datalab' object\n", + " \n", + " property : str, optional\n", + " The name of the property to be plotted against the labels.\n", + " \n", + " Returns:\n", + " --------\n", + " None\n", + " This function does not return any value. It generates a plot of the specified \n", + " property against the labels.\n", + " \"\"\"\n", + " issues_copy = lab.issues.copy()\n", + " issues_copy[\"label\"] = lab.labels\n", + " issues_copy.boxplot(column=[property], by=\"label\")\n", + "\n", + "# Plotting 'dark_score' value of each instance in the dataset against class label\n", + "plot_scores_labels(lab, \"dark_score\")" + ] + }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "The above plot illustrates the distribution of dark scores across class labels. In this dataset, 100 images from the `Frog` class (Class 0 in the plot) have been darkened, while 100 images from the `Truck` class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the 'darkness' feature and the class labels: `Frog` images are dark, whereas `Truck` images are not. We can see that the `dark_score` values between the two classes are non-overlapping. This characteristic of the dataset is identified by `Datalab`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n", "\n", @@ -4208,14 +4265,15 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:13.117053Z", - "iopub.status.busy": "2024-08-20T02:18:13.116150Z", - "iopub.status.idle": "2024-08-20T02:18:13.628191Z", - "shell.execute_reply": "2024-08-20T02:18:13.627646Z" - } + "iopub.execute_input": "2024-08-21T00:43:19.552545Z", + "iopub.status.busy": "2024-08-21T00:43:19.552159Z", + "iopub.status.idle": "2024-08-21T00:43:20.077354Z", + "shell.execute_reply": "2024-08-21T00:43:20.076712Z" + }, + "nbsphinx": "hidden" }, "outputs": [ { @@ -4229,7 +4287,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84c47a9fab904f3d8908756c3b332bed", + "model_id": "d3c19809a6204f20ac5cac77fe50ad95", "version_major": 2, "version_minor": 0 }, @@ -4244,9 +4302,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "Finding spurious correlation issues in the dataset ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n", - "Correlation scores for original dataset:\n" + "Label uncorrelatedness scores for original dataset:\n" ] }, { @@ -4277,13 +4336,13 @@ " \n", " \n", " 0\n", - " dark_score\n", - " 0.300\n", + " odd_size_score\n", + " 0.500\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.415\n", + " odd_aspect_ratio_score\n", + " 0.500\n", " \n", " \n", " 2\n", @@ -4292,18 +4351,18 @@ " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " light_score\n", + " 0.415\n", " \n", " \n", " 4\n", - " odd_size_score\n", + " grayscale_score\n", " 0.500\n", " \n", " \n", " 5\n", - " grayscale_score\n", - " 0.500\n", + " dark_score\n", + " 0.300\n", " \n", " \n", " 6\n", @@ -4316,12 +4375,12 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.300\n", - "1 light_score 0.415\n", + "0 odd_size_score 0.500\n", + "1 odd_aspect_ratio_score 0.500\n", "2 low_information_score 0.325\n", - "3 odd_aspect_ratio_score 0.500\n", - "4 odd_size_score 0.500\n", - "5 grayscale_score 0.500\n", + "3 light_score 0.415\n", + "4 grayscale_score 0.500\n", + "5 dark_score 0.300\n", "6 blurry_score 0.335" ] }, @@ -4357,35 +4416,35 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.797509\n", " False\n", + " 0.797509\n", " \n", " \n", " 1\n", - " 0.663760\n", " False\n", + " 0.663760\n", " \n", " \n", " 2\n", - " 0.849826\n", " False\n", + " 0.849826\n", " \n", " \n", " 3\n", - " 0.773951\n", " False\n", + " 0.773951\n", " \n", " \n", " 4\n", - " 0.699518\n", " False\n", + " 0.699518\n", " \n", " \n", " ...\n", @@ -4394,28 +4453,28 @@ " \n", " \n", " 195\n", - " 0.793840\n", " False\n", + " 0.793840\n", " \n", " \n", " 196\n", - " 1.000000\n", " False\n", + " 1.000000\n", " \n", " \n", " 197\n", - " 0.971560\n", " False\n", + " 0.971560\n", " \n", " \n", " 198\n", - " 0.862236\n", " False\n", + " 0.862236\n", " \n", " \n", " 199\n", - " 0.973533\n", " False\n", + " 0.973533\n", " \n", " \n", "\n", @@ -4423,18 +4482,18 @@ "" ], "text/plain": [ - " dark_score is_dark_issue\n", - "0 0.797509 False\n", - "1 0.663760 False\n", - "2 0.849826 False\n", - "3 0.773951 False\n", - "4 0.699518 False\n", - ".. ... ...\n", - "195 0.793840 False\n", - "196 1.000000 False\n", - "197 0.971560 False\n", - "198 0.862236 False\n", - "199 0.973533 False\n", + " is_dark_issue dark_score\n", + "0 False 0.797509\n", + "1 False 0.663760\n", + "2 False 0.849826\n", + "3 False 0.773951\n", + "4 False 0.699518\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", "\n", "[200 rows x 2 columns]" ] @@ -4453,8 +4512,8 @@ "original_lab.find_issues()\n", "\n", "# Compare correlation scores\n", - "original_scores = original_lab._correlations_df\n", - "print(\"Correlation scores for original dataset:\")\n", + "original_scores = original_lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for original dataset:\")\n", "display(original_scores)\n", "\n", "# Compare image-specific issues\n", @@ -4465,15 +4524,55 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "When comparing the results:\n", "\n", - "1. Look for differences in the correlation scores, especially for the 'dark' property.\n", + "1. Look for differences in the label uncorrelatedness scores, especially for the 'dark' property.\n", "2. Compare the number and types of image-specific issues detected.\n", "\n", "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. This comparison highlights how spurious correlations can be detected by `Datalab`." ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-21T00:43:20.079729Z", + "iopub.status.busy": "2024-08-21T00:43:20.079373Z", + "iopub.status.idle": "2024-08-21T00:43:20.227878Z", + "shell.execute_reply": "2024-08-21T00:43:20.227303Z" + }, + "nbsphinx": "hidden" + }, + "outputs": [ + { + "data": { + "image/png": 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"iopub.execute_input": "2024-08-20T02:18:17.805542Z", - "iopub.status.busy": "2024-08-20T02:18:17.805087Z", - "iopub.status.idle": "2024-08-20T02:18:19.246385Z", - "shell.execute_reply": "2024-08-20T02:18:19.245826Z" + "iopub.execute_input": "2024-08-21T00:43:24.975874Z", + "iopub.status.busy": "2024-08-21T00:43:24.975689Z", + "iopub.status.idle": "2024-08-21T00:43:26.130072Z", + "shell.execute_reply": "2024-08-21T00:43:26.129440Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.248964Z", - "iopub.status.busy": "2024-08-20T02:18:19.248482Z", - "iopub.status.idle": "2024-08-20T02:18:19.251383Z", - "shell.execute_reply": "2024-08-20T02:18:19.250938Z" + "iopub.execute_input": "2024-08-21T00:43:26.132687Z", + "iopub.status.busy": "2024-08-21T00:43:26.132421Z", + "iopub.status.idle": "2024-08-21T00:43:26.135360Z", + "shell.execute_reply": "2024-08-21T00:43:26.134813Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.253494Z", - "iopub.status.busy": "2024-08-20T02:18:19.253140Z", - "iopub.status.idle": "2024-08-20T02:18:19.265298Z", - "shell.execute_reply": "2024-08-20T02:18:19.264725Z" + "iopub.execute_input": "2024-08-21T00:43:26.137797Z", + "iopub.status.busy": "2024-08-21T00:43:26.137353Z", + "iopub.status.idle": "2024-08-21T00:43:26.149676Z", + "shell.execute_reply": "2024-08-21T00:43:26.149205Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.267347Z", - "iopub.status.busy": "2024-08-20T02:18:19.267029Z", - "iopub.status.idle": "2024-08-20T02:18:24.466640Z", - "shell.execute_reply": "2024-08-20T02:18:24.466146Z" + "iopub.execute_input": "2024-08-21T00:43:26.151609Z", + "iopub.status.busy": "2024-08-21T00:43:26.151435Z", + "iopub.status.idle": "2024-08-21T00:43:30.893896Z", + "shell.execute_reply": "2024-08-21T00:43:30.893274Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index be294f45e..f92c95774 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:26.824446Z", - "iopub.status.busy": "2024-08-20T02:18:26.824273Z", - "iopub.status.idle": "2024-08-20T02:18:28.260323Z", - "shell.execute_reply": "2024-08-20T02:18:28.259751Z" + "iopub.execute_input": "2024-08-21T00:43:33.124706Z", + "iopub.status.busy": "2024-08-21T00:43:33.124531Z", + "iopub.status.idle": "2024-08-21T00:43:34.258108Z", + "shell.execute_reply": "2024-08-21T00:43:34.257543Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:28.262978Z", - "iopub.status.busy": "2024-08-20T02:18:28.262672Z", - "iopub.status.idle": "2024-08-20T02:18:28.266248Z", - "shell.execute_reply": "2024-08-20T02:18:28.265673Z" + "iopub.execute_input": "2024-08-21T00:43:34.260791Z", + "iopub.status.busy": "2024-08-21T00:43:34.260358Z", + "iopub.status.idle": "2024-08-21T00:43:34.263745Z", + "shell.execute_reply": "2024-08-21T00:43:34.263180Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:28.268522Z", - "iopub.status.busy": "2024-08-20T02:18:28.268073Z", - "iopub.status.idle": "2024-08-20T02:18:31.923865Z", - "shell.execute_reply": "2024-08-20T02:18:31.923205Z" + "iopub.execute_input": "2024-08-21T00:43:34.265950Z", + "iopub.status.busy": "2024-08-21T00:43:34.265625Z", + "iopub.status.idle": "2024-08-21T00:43:37.615038Z", + "shell.execute_reply": "2024-08-21T00:43:37.614383Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:31.927361Z", - "iopub.status.busy": "2024-08-20T02:18:31.926431Z", - "iopub.status.idle": "2024-08-20T02:18:31.972856Z", - "shell.execute_reply": "2024-08-20T02:18:31.972092Z" + "iopub.execute_input": "2024-08-21T00:43:37.618146Z", + "iopub.status.busy": "2024-08-21T00:43:37.617403Z", + "iopub.status.idle": "2024-08-21T00:43:37.660445Z", + "shell.execute_reply": "2024-08-21T00:43:37.659796Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:31.975898Z", - "iopub.status.busy": "2024-08-20T02:18:31.975497Z", - "iopub.status.idle": "2024-08-20T02:18:32.024339Z", - "shell.execute_reply": "2024-08-20T02:18:32.023682Z" + "iopub.execute_input": "2024-08-21T00:43:37.663149Z", + "iopub.status.busy": "2024-08-21T00:43:37.662756Z", + "iopub.status.idle": "2024-08-21T00:43:37.699432Z", + "shell.execute_reply": "2024-08-21T00:43:37.698818Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.027241Z", - "iopub.status.busy": "2024-08-20T02:18:32.026842Z", - "iopub.status.idle": "2024-08-20T02:18:32.029883Z", - "shell.execute_reply": "2024-08-20T02:18:32.029410Z" + "iopub.execute_input": "2024-08-21T00:43:37.702287Z", + "iopub.status.busy": "2024-08-21T00:43:37.701804Z", + "iopub.status.idle": "2024-08-21T00:43:37.705054Z", + "shell.execute_reply": "2024-08-21T00:43:37.704587Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.031947Z", - "iopub.status.busy": "2024-08-20T02:18:32.031634Z", - "iopub.status.idle": "2024-08-20T02:18:32.034472Z", - "shell.execute_reply": "2024-08-20T02:18:32.034036Z" + "iopub.execute_input": "2024-08-21T00:43:37.707228Z", + "iopub.status.busy": "2024-08-21T00:43:37.706791Z", + "iopub.status.idle": "2024-08-21T00:43:37.709625Z", + "shell.execute_reply": "2024-08-21T00:43:37.709171Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.036493Z", - "iopub.status.busy": "2024-08-20T02:18:32.036308Z", - "iopub.status.idle": "2024-08-20T02:18:32.059976Z", - "shell.execute_reply": "2024-08-20T02:18:32.059445Z" + "iopub.execute_input": "2024-08-21T00:43:37.711757Z", + "iopub.status.busy": "2024-08-21T00:43:37.711365Z", + "iopub.status.idle": "2024-08-21T00:43:37.738363Z", + "shell.execute_reply": "2024-08-21T00:43:37.737812Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d82d5feb5be34f61b1a570c3dd0179b0", + "model_id": "e88b11d2fc2c4e1dabf9d5ab29054a5a", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2f0c54e98dd4c02b9cf7548afd53f0d", + "model_id": "7ce54cb5c4f340b7bb0a813fe148c2ac", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.065764Z", - "iopub.status.busy": "2024-08-20T02:18:32.065412Z", - "iopub.status.idle": "2024-08-20T02:18:32.071940Z", - "shell.execute_reply": "2024-08-20T02:18:32.071514Z" + "iopub.execute_input": "2024-08-21T00:43:37.744690Z", + "iopub.status.busy": "2024-08-21T00:43:37.744352Z", + "iopub.status.idle": "2024-08-21T00:43:37.751032Z", + "shell.execute_reply": "2024-08-21T00:43:37.750612Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.074109Z", - "iopub.status.busy": "2024-08-20T02:18:32.073667Z", - "iopub.status.idle": "2024-08-20T02:18:32.077079Z", - "shell.execute_reply": "2024-08-20T02:18:32.076644Z" + "iopub.execute_input": "2024-08-21T00:43:37.753066Z", + "iopub.status.busy": "2024-08-21T00:43:37.752736Z", + "iopub.status.idle": "2024-08-21T00:43:37.756637Z", + "shell.execute_reply": "2024-08-21T00:43:37.756202Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.079272Z", - "iopub.status.busy": "2024-08-20T02:18:32.078890Z", - "iopub.status.idle": "2024-08-20T02:18:32.085244Z", - "shell.execute_reply": "2024-08-20T02:18:32.084783Z" + "iopub.execute_input": "2024-08-21T00:43:37.758695Z", + "iopub.status.busy": "2024-08-21T00:43:37.758365Z", + "iopub.status.idle": "2024-08-21T00:43:37.764764Z", + "shell.execute_reply": "2024-08-21T00:43:37.764295Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.087316Z", - "iopub.status.busy": "2024-08-20T02:18:32.086982Z", - "iopub.status.idle": "2024-08-20T02:18:32.132301Z", - "shell.execute_reply": "2024-08-20T02:18:32.131651Z" + "iopub.execute_input": "2024-08-21T00:43:37.766873Z", + "iopub.status.busy": "2024-08-21T00:43:37.766456Z", + "iopub.status.idle": "2024-08-21T00:43:37.807572Z", + "shell.execute_reply": "2024-08-21T00:43:37.806830Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.135193Z", - "iopub.status.busy": "2024-08-20T02:18:32.134697Z", - "iopub.status.idle": "2024-08-20T02:18:32.180547Z", - "shell.execute_reply": "2024-08-20T02:18:32.179925Z" + "iopub.execute_input": "2024-08-21T00:43:37.810644Z", + "iopub.status.busy": "2024-08-21T00:43:37.810107Z", + "iopub.status.idle": "2024-08-21T00:43:37.853337Z", + "shell.execute_reply": "2024-08-21T00:43:37.852723Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.183314Z", - "iopub.status.busy": "2024-08-20T02:18:32.182928Z", - "iopub.status.idle": "2024-08-20T02:18:32.318818Z", - "shell.execute_reply": "2024-08-20T02:18:32.318227Z" + "iopub.execute_input": "2024-08-21T00:43:37.856040Z", + "iopub.status.busy": "2024-08-21T00:43:37.855648Z", + "iopub.status.idle": "2024-08-21T00:43:37.987449Z", + "shell.execute_reply": "2024-08-21T00:43:37.986885Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.321628Z", - "iopub.status.busy": "2024-08-20T02:18:32.321051Z", - "iopub.status.idle": "2024-08-20T02:18:35.406655Z", - "shell.execute_reply": "2024-08-20T02:18:35.405994Z" + "iopub.execute_input": "2024-08-21T00:43:37.990134Z", + "iopub.status.busy": "2024-08-21T00:43:37.989581Z", + "iopub.status.idle": "2024-08-21T00:43:40.945105Z", + "shell.execute_reply": "2024-08-21T00:43:40.944425Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.409123Z", - "iopub.status.busy": "2024-08-20T02:18:35.408927Z", - "iopub.status.idle": "2024-08-20T02:18:35.471131Z", - "shell.execute_reply": "2024-08-20T02:18:35.470526Z" + "iopub.execute_input": "2024-08-21T00:43:40.947439Z", + "iopub.status.busy": "2024-08-21T00:43:40.947245Z", + "iopub.status.idle": "2024-08-21T00:43:41.002087Z", + "shell.execute_reply": "2024-08-21T00:43:41.001550Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.473400Z", - "iopub.status.busy": "2024-08-20T02:18:35.473199Z", - "iopub.status.idle": "2024-08-20T02:18:35.520888Z", - "shell.execute_reply": "2024-08-20T02:18:35.520350Z" + "iopub.execute_input": "2024-08-21T00:43:41.004111Z", + "iopub.status.busy": "2024-08-21T00:43:41.003920Z", + "iopub.status.idle": "2024-08-21T00:43:41.046065Z", + "shell.execute_reply": "2024-08-21T00:43:41.045584Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "1f15f0b7", + "id": "a7cb8e5a", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "cc67f639", + "id": "ce44f993", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "5a601c1a", + "id": "1f258ff1", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "b72d4fd7", + "id": "4e739e39", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.523105Z", - "iopub.status.busy": "2024-08-20T02:18:35.522924Z", - "iopub.status.idle": "2024-08-20T02:18:35.530530Z", - "shell.execute_reply": "2024-08-20T02:18:35.530069Z" + "iopub.execute_input": "2024-08-21T00:43:41.048111Z", + "iopub.status.busy": "2024-08-21T00:43:41.047923Z", + "iopub.status.idle": "2024-08-21T00:43:41.055443Z", + "shell.execute_reply": "2024-08-21T00:43:41.054977Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "dfcac1fa", + "id": "2f5c2abd", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. 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"iopub.execute_input": "2024-08-20T02:18:39.079038Z", - "iopub.status.busy": "2024-08-20T02:18:39.078872Z", - "iopub.status.idle": "2024-08-20T02:18:40.521690Z", - "shell.execute_reply": "2024-08-20T02:18:40.521131Z" + "iopub.execute_input": "2024-08-21T00:43:44.323922Z", + "iopub.status.busy": "2024-08-21T00:43:44.323504Z", + "iopub.status.idle": "2024-08-21T00:43:45.482662Z", + "shell.execute_reply": "2024-08-21T00:43:45.482106Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.524199Z", - "iopub.status.busy": "2024-08-20T02:18:40.523738Z", - "iopub.status.idle": "2024-08-20T02:18:40.527321Z", - "shell.execute_reply": "2024-08-20T02:18:40.526891Z" + "iopub.execute_input": "2024-08-21T00:43:45.485461Z", + "iopub.status.busy": "2024-08-21T00:43:45.484927Z", + "iopub.status.idle": "2024-08-21T00:43:45.488591Z", + "shell.execute_reply": "2024-08-21T00:43:45.488149Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.529321Z", - "iopub.status.busy": "2024-08-20T02:18:40.529040Z", - "iopub.status.idle": "2024-08-20T02:18:40.830470Z", - "shell.execute_reply": "2024-08-20T02:18:40.829983Z" + "iopub.execute_input": "2024-08-21T00:43:45.490643Z", + "iopub.status.busy": "2024-08-21T00:43:45.490301Z", + "iopub.status.idle": "2024-08-21T00:43:45.766813Z", + "shell.execute_reply": "2024-08-21T00:43:45.766258Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.832627Z", - "iopub.status.busy": "2024-08-20T02:18:40.832279Z", - "iopub.status.idle": "2024-08-20T02:18:40.838038Z", - "shell.execute_reply": "2024-08-20T02:18:40.837573Z" + "iopub.execute_input": "2024-08-21T00:43:45.769098Z", + "iopub.status.busy": "2024-08-21T00:43:45.768752Z", + "iopub.status.idle": "2024-08-21T00:43:45.774608Z", + "shell.execute_reply": "2024-08-21T00:43:45.774168Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.840027Z", - "iopub.status.busy": "2024-08-20T02:18:40.839707Z", - "iopub.status.idle": "2024-08-20T02:18:40.846682Z", - "shell.execute_reply": "2024-08-20T02:18:40.846218Z" + "iopub.execute_input": "2024-08-21T00:43:45.776485Z", + "iopub.status.busy": "2024-08-21T00:43:45.776314Z", + "iopub.status.idle": "2024-08-21T00:43:45.783218Z", + "shell.execute_reply": "2024-08-21T00:43:45.782653Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.848706Z", - "iopub.status.busy": "2024-08-20T02:18:40.848382Z", - "iopub.status.idle": "2024-08-20T02:18:40.853255Z", - "shell.execute_reply": "2024-08-20T02:18:40.852680Z" + "iopub.execute_input": "2024-08-21T00:43:45.785398Z", + "iopub.status.busy": "2024-08-21T00:43:45.785009Z", + "iopub.status.idle": "2024-08-21T00:43:45.789740Z", + "shell.execute_reply": "2024-08-21T00:43:45.789307Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.855222Z", - "iopub.status.busy": "2024-08-20T02:18:40.854921Z", - "iopub.status.idle": "2024-08-20T02:18:40.860716Z", - "shell.execute_reply": "2024-08-20T02:18:40.860248Z" + "iopub.execute_input": "2024-08-21T00:43:45.791748Z", + "iopub.status.busy": "2024-08-21T00:43:45.791456Z", + "iopub.status.idle": "2024-08-21T00:43:45.797389Z", + "shell.execute_reply": "2024-08-21T00:43:45.796810Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.862798Z", - "iopub.status.busy": "2024-08-20T02:18:40.862460Z", - "iopub.status.idle": "2024-08-20T02:18:40.866494Z", - "shell.execute_reply": "2024-08-20T02:18:40.865924Z" + "iopub.execute_input": "2024-08-21T00:43:45.799517Z", + "iopub.status.busy": "2024-08-21T00:43:45.799174Z", + "iopub.status.idle": "2024-08-21T00:43:45.803248Z", + "shell.execute_reply": "2024-08-21T00:43:45.802813Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.868698Z", - "iopub.status.busy": "2024-08-20T02:18:40.868378Z", - "iopub.status.idle": "2024-08-20T02:18:40.933865Z", - "shell.execute_reply": "2024-08-20T02:18:40.933191Z" + "iopub.execute_input": "2024-08-21T00:43:45.805238Z", + "iopub.status.busy": "2024-08-21T00:43:45.804914Z", + "iopub.status.idle": "2024-08-21T00:43:45.870308Z", + "shell.execute_reply": "2024-08-21T00:43:45.869741Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - 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"iopub.execute_input": "2024-08-20T02:18:43.351804Z", - "iopub.status.busy": "2024-08-20T02:18:43.350840Z", - "iopub.status.idle": "2024-08-20T02:18:43.384316Z", - "shell.execute_reply": "2024-08-20T02:18:43.383808Z" + "iopub.execute_input": "2024-08-21T00:43:48.105802Z", + "iopub.status.busy": "2024-08-21T00:43:48.104884Z", + "iopub.status.idle": "2024-08-21T00:43:48.133986Z", + "shell.execute_reply": "2024-08-21T00:43:48.133511Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.387211Z", - "iopub.status.busy": "2024-08-20T02:18:43.386773Z", - "iopub.status.idle": "2024-08-20T02:18:43.934525Z", - "shell.execute_reply": "2024-08-20T02:18:43.933974Z" + "iopub.execute_input": "2024-08-21T00:43:48.137043Z", + "iopub.status.busy": "2024-08-21T00:43:48.136416Z", + "iopub.status.idle": "2024-08-21T00:43:48.664903Z", + "shell.execute_reply": "2024-08-21T00:43:48.664340Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.937351Z", - "iopub.status.busy": "2024-08-20T02:18:43.936985Z", - "iopub.status.idle": "2024-08-20T02:18:44.067674Z", - "shell.execute_reply": "2024-08-20T02:18:44.067055Z" + "iopub.execute_input": "2024-08-21T00:43:48.668839Z", + "iopub.status.busy": "2024-08-21T00:43:48.667904Z", + "iopub.status.idle": "2024-08-21T00:43:48.799618Z", + "shell.execute_reply": "2024-08-21T00:43:48.799015Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.071135Z", - "iopub.status.busy": "2024-08-20T02:18:44.070004Z", - "iopub.status.idle": "2024-08-20T02:18:44.079002Z", - "shell.execute_reply": "2024-08-20T02:18:44.078493Z" + "iopub.execute_input": "2024-08-21T00:43:48.802587Z", + "iopub.status.busy": "2024-08-21T00:43:48.802251Z", + "iopub.status.idle": "2024-08-21T00:43:48.808846Z", + "shell.execute_reply": "2024-08-21T00:43:48.808361Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.082548Z", - "iopub.status.busy": "2024-08-20T02:18:44.081615Z", - "iopub.status.idle": "2024-08-20T02:18:44.089637Z", - "shell.execute_reply": "2024-08-20T02:18:44.089115Z" + "iopub.execute_input": "2024-08-21T00:43:48.811963Z", + "iopub.status.busy": "2024-08-21T00:43:48.810885Z", + "iopub.status.idle": "2024-08-21T00:43:48.818883Z", + "shell.execute_reply": "2024-08-21T00:43:48.818393Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.093142Z", - "iopub.status.busy": "2024-08-20T02:18:44.092209Z", - "iopub.status.idle": "2024-08-20T02:18:44.099634Z", - "shell.execute_reply": "2024-08-20T02:18:44.099137Z" + "iopub.execute_input": "2024-08-21T00:43:48.822356Z", + "iopub.status.busy": "2024-08-21T00:43:48.821424Z", + "iopub.status.idle": "2024-08-21T00:43:48.828749Z", + "shell.execute_reply": "2024-08-21T00:43:48.828250Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.103125Z", - "iopub.status.busy": "2024-08-20T02:18:44.102204Z", - "iopub.status.idle": "2024-08-20T02:18:44.108338Z", - "shell.execute_reply": "2024-08-20T02:18:44.107839Z" + "iopub.execute_input": "2024-08-21T00:43:48.832200Z", + "iopub.status.busy": "2024-08-21T00:43:48.831242Z", + "iopub.status.idle": "2024-08-21T00:43:48.837286Z", + "shell.execute_reply": "2024-08-21T00:43:48.836796Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.110735Z", - "iopub.status.busy": "2024-08-20T02:18:44.110563Z", - "iopub.status.idle": "2024-08-20T02:18:44.115117Z", - "shell.execute_reply": "2024-08-20T02:18:44.114675Z" + "iopub.execute_input": "2024-08-21T00:43:48.840754Z", + "iopub.status.busy": "2024-08-21T00:43:48.839819Z", + "iopub.status.idle": "2024-08-21T00:43:48.845405Z", + "shell.execute_reply": "2024-08-21T00:43:48.844997Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.117018Z", - "iopub.status.busy": "2024-08-20T02:18:44.116864Z", - "iopub.status.idle": "2024-08-20T02:18:44.191518Z", - "shell.execute_reply": "2024-08-20T02:18:44.190902Z" + "iopub.execute_input": "2024-08-21T00:43:48.848112Z", + "iopub.status.busy": "2024-08-21T00:43:48.847491Z", + "iopub.status.idle": "2024-08-21T00:43:48.923292Z", + "shell.execute_reply": "2024-08-21T00:43:48.922762Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.195789Z", - "iopub.status.busy": "2024-08-20T02:18:44.195385Z", - "iopub.status.idle": "2024-08-20T02:18:44.204480Z", - "shell.execute_reply": "2024-08-20T02:18:44.203923Z" + "iopub.execute_input": "2024-08-21T00:43:48.926332Z", + "iopub.status.busy": "2024-08-21T00:43:48.925595Z", + "iopub.status.idle": "2024-08-21T00:43:48.935839Z", + "shell.execute_reply": "2024-08-21T00:43:48.935360Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.207795Z", - "iopub.status.busy": "2024-08-20T02:18:44.206903Z", - "iopub.status.idle": "2024-08-20T02:18:44.210712Z", - "shell.execute_reply": "2024-08-20T02:18:44.210291Z" + "iopub.execute_input": "2024-08-21T00:43:48.938763Z", + "iopub.status.busy": "2024-08-21T00:43:48.938336Z", + "iopub.status.idle": "2024-08-21T00:43:48.941682Z", + "shell.execute_reply": "2024-08-21T00:43:48.941148Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.213385Z", - "iopub.status.busy": "2024-08-20T02:18:44.212803Z", - "iopub.status.idle": "2024-08-20T02:18:44.223231Z", - "shell.execute_reply": "2024-08-20T02:18:44.222657Z" + "iopub.execute_input": "2024-08-21T00:43:48.943690Z", + "iopub.status.busy": "2024-08-21T00:43:48.943523Z", + "iopub.status.idle": "2024-08-21T00:43:48.953778Z", + "shell.execute_reply": "2024-08-21T00:43:48.953320Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.225400Z", - "iopub.status.busy": "2024-08-20T02:18:44.225210Z", - "iopub.status.idle": "2024-08-20T02:18:44.231940Z", - "shell.execute_reply": "2024-08-20T02:18:44.231250Z" + "iopub.execute_input": "2024-08-21T00:43:48.955857Z", + "iopub.status.busy": "2024-08-21T00:43:48.955527Z", + "iopub.status.idle": "2024-08-21T00:43:48.961904Z", + "shell.execute_reply": "2024-08-21T00:43:48.961466Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.234067Z", - "iopub.status.busy": "2024-08-20T02:18:44.233893Z", - "iopub.status.idle": "2024-08-20T02:18:44.237411Z", - "shell.execute_reply": "2024-08-20T02:18:44.236700Z" + "iopub.execute_input": "2024-08-21T00:43:48.963753Z", + "iopub.status.busy": "2024-08-21T00:43:48.963585Z", + "iopub.status.idle": "2024-08-21T00:43:48.966870Z", + "shell.execute_reply": "2024-08-21T00:43:48.966412Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.239332Z", - "iopub.status.busy": "2024-08-20T02:18:44.239159Z", - "iopub.status.idle": "2024-08-20T02:18:48.319121Z", - "shell.execute_reply": "2024-08-20T02:18:48.318603Z" + "iopub.execute_input": "2024-08-21T00:43:48.968877Z", + "iopub.status.busy": "2024-08-21T00:43:48.968552Z", + "iopub.status.idle": "2024-08-21T00:43:52.986642Z", + "shell.execute_reply": "2024-08-21T00:43:52.986089Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:48.322465Z", - "iopub.status.busy": "2024-08-20T02:18:48.321697Z", - "iopub.status.idle": "2024-08-20T02:18:48.325214Z", - "shell.execute_reply": "2024-08-20T02:18:48.324785Z" + "iopub.execute_input": "2024-08-21T00:43:52.989780Z", + "iopub.status.busy": "2024-08-21T00:43:52.988877Z", + "iopub.status.idle": "2024-08-21T00:43:52.993710Z", + "shell.execute_reply": "2024-08-21T00:43:52.993266Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:48.327316Z", - "iopub.status.busy": "2024-08-20T02:18:48.327002Z", - "iopub.status.idle": "2024-08-20T02:18:48.329876Z", - "shell.execute_reply": "2024-08-20T02:18:48.329469Z" + "iopub.execute_input": "2024-08-21T00:43:52.995821Z", + "iopub.status.busy": "2024-08-21T00:43:52.995483Z", + "iopub.status.idle": "2024-08-21T00:43:52.998348Z", + "shell.execute_reply": "2024-08-21T00:43:52.997886Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index c1a97fbf9..6ff79093e 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": "2024-08-20T02:18:51.681853Z", - "iopub.status.busy": "2024-08-20T02:18:51.681681Z", - "iopub.status.idle": "2024-08-20T02:18:53.129044Z", - "shell.execute_reply": "2024-08-20T02:18:53.128476Z" + "iopub.execute_input": "2024-08-21T00:43:56.140800Z", + "iopub.status.busy": "2024-08-21T00:43:56.140620Z", + "iopub.status.idle": "2024-08-21T00:43:57.343557Z", + "shell.execute_reply": "2024-08-21T00:43:57.343015Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:18:53.131558Z", - "iopub.status.busy": "2024-08-20T02:18:53.131088Z", - "iopub.status.idle": "2024-08-20T02:18:53.134587Z", - "shell.execute_reply": "2024-08-20T02:18:53.134019Z" + "iopub.execute_input": "2024-08-21T00:43:57.346298Z", + "iopub.status.busy": "2024-08-21T00:43:57.345733Z", + "iopub.status.idle": "2024-08-21T00:43:57.530661Z", + "shell.execute_reply": "2024-08-21T00:43:57.530087Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.136980Z", - "iopub.status.busy": "2024-08-20T02:18:53.136531Z", - "iopub.status.idle": "2024-08-20T02:18:53.148739Z", - "shell.execute_reply": "2024-08-20T02:18:53.148175Z" + "iopub.execute_input": "2024-08-21T00:43:57.533119Z", + "iopub.status.busy": "2024-08-21T00:43:57.532920Z", + "iopub.status.idle": "2024-08-21T00:43:57.544404Z", + "shell.execute_reply": "2024-08-21T00:43:57.543955Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.150948Z", - "iopub.status.busy": "2024-08-20T02:18:53.150659Z", - "iopub.status.idle": "2024-08-20T02:18:53.391414Z", - "shell.execute_reply": "2024-08-20T02:18:53.390934Z" + "iopub.execute_input": "2024-08-21T00:43:57.546292Z", + "iopub.status.busy": "2024-08-21T00:43:57.546113Z", + "iopub.status.idle": "2024-08-21T00:43:57.781144Z", + "shell.execute_reply": "2024-08-21T00:43:57.780517Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.393846Z", - "iopub.status.busy": "2024-08-20T02:18:53.393517Z", - "iopub.status.idle": "2024-08-20T02:18:53.419044Z", - "shell.execute_reply": "2024-08-20T02:18:53.418597Z" + "iopub.execute_input": "2024-08-21T00:43:57.783496Z", + "iopub.status.busy": "2024-08-21T00:43:57.783061Z", + "iopub.status.idle": "2024-08-21T00:43:57.809126Z", + "shell.execute_reply": "2024-08-21T00:43:57.808529Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.421156Z", - "iopub.status.busy": "2024-08-20T02:18:53.420796Z", - "iopub.status.idle": "2024-08-20T02:18:55.596000Z", - "shell.execute_reply": "2024-08-20T02:18:55.595347Z" + "iopub.execute_input": "2024-08-21T00:43:57.811664Z", + "iopub.status.busy": "2024-08-21T00:43:57.811098Z", + "iopub.status.idle": "2024-08-21T00:43:59.890782Z", + "shell.execute_reply": "2024-08-21T00:43:59.890150Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:55.598602Z", - "iopub.status.busy": "2024-08-20T02:18:55.598063Z", - "iopub.status.idle": "2024-08-20T02:18:55.616511Z", - "shell.execute_reply": "2024-08-20T02:18:55.616058Z" + "iopub.execute_input": "2024-08-21T00:43:59.893622Z", + "iopub.status.busy": "2024-08-21T00:43:59.892889Z", + "iopub.status.idle": "2024-08-21T00:43:59.910894Z", + "shell.execute_reply": "2024-08-21T00:43:59.910318Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:55.618658Z", - "iopub.status.busy": "2024-08-20T02:18:55.618308Z", - "iopub.status.idle": "2024-08-20T02:18:57.218343Z", - "shell.execute_reply": "2024-08-20T02:18:57.217657Z" + "iopub.execute_input": "2024-08-21T00:43:59.913118Z", + "iopub.status.busy": "2024-08-21T00:43:59.912629Z", + "iopub.status.idle": "2024-08-21T00:44:01.485996Z", + "shell.execute_reply": "2024-08-21T00:44:01.485331Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.221005Z", - "iopub.status.busy": "2024-08-20T02:18:57.220309Z", - "iopub.status.idle": "2024-08-20T02:18:57.234346Z", - "shell.execute_reply": "2024-08-20T02:18:57.233776Z" + "iopub.execute_input": "2024-08-21T00:44:01.488797Z", + "iopub.status.busy": "2024-08-21T00:44:01.488082Z", + "iopub.status.idle": "2024-08-21T00:44:01.501841Z", + "shell.execute_reply": "2024-08-21T00:44:01.501318Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.236411Z", - "iopub.status.busy": "2024-08-20T02:18:57.236099Z", - "iopub.status.idle": "2024-08-20T02:18:57.319189Z", - "shell.execute_reply": "2024-08-20T02:18:57.318628Z" + "iopub.execute_input": "2024-08-21T00:44:01.503912Z", + "iopub.status.busy": "2024-08-21T00:44:01.503642Z", + "iopub.status.idle": "2024-08-21T00:44:01.590531Z", + "shell.execute_reply": "2024-08-21T00:44:01.589895Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.321670Z", - "iopub.status.busy": "2024-08-20T02:18:57.321188Z", - "iopub.status.idle": "2024-08-20T02:18:57.536110Z", - "shell.execute_reply": "2024-08-20T02:18:57.535559Z" + "iopub.execute_input": "2024-08-21T00:44:01.592709Z", + "iopub.status.busy": "2024-08-21T00:44:01.592483Z", + "iopub.status.idle": "2024-08-21T00:44:01.806762Z", + "shell.execute_reply": "2024-08-21T00:44:01.806173Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.538284Z", - "iopub.status.busy": "2024-08-20T02:18:57.538098Z", - "iopub.status.idle": "2024-08-20T02:18:57.555022Z", - "shell.execute_reply": "2024-08-20T02:18:57.554545Z" + "iopub.execute_input": "2024-08-21T00:44:01.809115Z", + "iopub.status.busy": "2024-08-21T00:44:01.808792Z", + "iopub.status.idle": "2024-08-21T00:44:01.825931Z", + "shell.execute_reply": "2024-08-21T00:44:01.825443Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.557201Z", - "iopub.status.busy": "2024-08-20T02:18:57.556872Z", - "iopub.status.idle": "2024-08-20T02:18:57.566684Z", - "shell.execute_reply": "2024-08-20T02:18:57.566222Z" + "iopub.execute_input": "2024-08-21T00:44:01.828060Z", + "iopub.status.busy": "2024-08-21T00:44:01.827758Z", + "iopub.status.idle": "2024-08-21T00:44:01.837905Z", + "shell.execute_reply": "2024-08-21T00:44:01.837364Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.568776Z", - "iopub.status.busy": "2024-08-20T02:18:57.568456Z", - "iopub.status.idle": "2024-08-20T02:18:57.661709Z", - "shell.execute_reply": "2024-08-20T02:18:57.661129Z" + "iopub.execute_input": "2024-08-21T00:44:01.839879Z", + "iopub.status.busy": "2024-08-21T00:44:01.839580Z", + "iopub.status.idle": "2024-08-21T00:44:01.935954Z", + "shell.execute_reply": "2024-08-21T00:44:01.935375Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.664109Z", - "iopub.status.busy": "2024-08-20T02:18:57.663766Z", - "iopub.status.idle": "2024-08-20T02:18:57.807373Z", - "shell.execute_reply": "2024-08-20T02:18:57.806736Z" + "iopub.execute_input": "2024-08-21T00:44:01.938342Z", + "iopub.status.busy": "2024-08-21T00:44:01.937987Z", + "iopub.status.idle": "2024-08-21T00:44:02.078263Z", + "shell.execute_reply": "2024-08-21T00:44:02.077615Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.809598Z", - "iopub.status.busy": "2024-08-20T02:18:57.809366Z", - "iopub.status.idle": "2024-08-20T02:18:57.813238Z", - "shell.execute_reply": "2024-08-20T02:18:57.812677Z" + "iopub.execute_input": "2024-08-21T00:44:02.080783Z", + "iopub.status.busy": "2024-08-21T00:44:02.080438Z", + "iopub.status.idle": "2024-08-21T00:44:02.084408Z", + "shell.execute_reply": "2024-08-21T00:44:02.083813Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.815295Z", - "iopub.status.busy": "2024-08-20T02:18:57.815004Z", - "iopub.status.idle": "2024-08-20T02:18:57.818743Z", - "shell.execute_reply": "2024-08-20T02:18:57.818208Z" + "iopub.execute_input": "2024-08-21T00:44:02.086440Z", + "iopub.status.busy": "2024-08-21T00:44:02.086169Z", + "iopub.status.idle": "2024-08-21T00:44:02.090052Z", + "shell.execute_reply": "2024-08-21T00:44:02.089491Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.820752Z", - "iopub.status.busy": "2024-08-20T02:18:57.820413Z", - "iopub.status.idle": "2024-08-20T02:18:57.857122Z", - "shell.execute_reply": "2024-08-20T02:18:57.856626Z" + "iopub.execute_input": "2024-08-21T00:44:02.092030Z", + "iopub.status.busy": "2024-08-21T00:44:02.091746Z", + "iopub.status.idle": "2024-08-21T00:44:02.128325Z", + "shell.execute_reply": "2024-08-21T00:44:02.127730Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.859267Z", - "iopub.status.busy": "2024-08-20T02:18:57.858911Z", - "iopub.status.idle": "2024-08-20T02:18:57.899267Z", - "shell.execute_reply": "2024-08-20T02:18:57.898774Z" + "iopub.execute_input": "2024-08-21T00:44:02.130318Z", + "iopub.status.busy": "2024-08-21T00:44:02.130002Z", + "iopub.status.idle": "2024-08-21T00:44:02.171110Z", + "shell.execute_reply": "2024-08-21T00:44:02.170530Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.901394Z", - "iopub.status.busy": "2024-08-20T02:18:57.901043Z", - "iopub.status.idle": "2024-08-20T02:18:58.003043Z", - "shell.execute_reply": "2024-08-20T02:18:58.002311Z" + "iopub.execute_input": "2024-08-21T00:44:02.173124Z", + "iopub.status.busy": "2024-08-21T00:44:02.172810Z", + "iopub.status.idle": "2024-08-21T00:44:02.280606Z", + "shell.execute_reply": "2024-08-21T00:44:02.279967Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.006068Z", - "iopub.status.busy": "2024-08-20T02:18:58.005525Z", - "iopub.status.idle": "2024-08-20T02:18:58.110250Z", - "shell.execute_reply": "2024-08-20T02:18:58.109590Z" + "iopub.execute_input": "2024-08-21T00:44:02.283100Z", + "iopub.status.busy": "2024-08-21T00:44:02.282915Z", + "iopub.status.idle": "2024-08-21T00:44:02.392853Z", + "shell.execute_reply": "2024-08-21T00:44:02.392282Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.112453Z", - "iopub.status.busy": "2024-08-20T02:18:58.112218Z", - "iopub.status.idle": "2024-08-20T02:18:58.323254Z", - "shell.execute_reply": "2024-08-20T02:18:58.322659Z" + "iopub.execute_input": "2024-08-21T00:44:02.395138Z", + "iopub.status.busy": "2024-08-21T00:44:02.394895Z", + "iopub.status.idle": "2024-08-21T00:44:02.630946Z", + "shell.execute_reply": "2024-08-21T00:44:02.630356Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.325554Z", - "iopub.status.busy": "2024-08-20T02:18:58.325088Z", - "iopub.status.idle": "2024-08-20T02:18:58.549365Z", - "shell.execute_reply": "2024-08-20T02:18:58.548707Z" + "iopub.execute_input": "2024-08-21T00:44:02.633172Z", + "iopub.status.busy": "2024-08-21T00:44:02.632982Z", + "iopub.status.idle": "2024-08-21T00:44:02.858035Z", + "shell.execute_reply": "2024-08-21T00:44:02.857391Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.551633Z", - "iopub.status.busy": "2024-08-20T02:18:58.551390Z", - "iopub.status.idle": "2024-08-20T02:18:58.557774Z", - "shell.execute_reply": "2024-08-20T02:18:58.557277Z" + "iopub.execute_input": "2024-08-21T00:44:02.860575Z", + "iopub.status.busy": "2024-08-21T00:44:02.860188Z", + "iopub.status.idle": "2024-08-21T00:44:02.866265Z", + "shell.execute_reply": "2024-08-21T00:44:02.865703Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.559834Z", - "iopub.status.busy": "2024-08-20T02:18:58.559507Z", - "iopub.status.idle": "2024-08-20T02:18:58.775893Z", - "shell.execute_reply": "2024-08-20T02:18:58.775301Z" + "iopub.execute_input": "2024-08-21T00:44:02.868556Z", + "iopub.status.busy": "2024-08-21T00:44:02.868207Z", + "iopub.status.idle": "2024-08-21T00:44:03.086365Z", + "shell.execute_reply": "2024-08-21T00:44:03.085768Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.778369Z", - "iopub.status.busy": "2024-08-20T02:18:58.777982Z", - "iopub.status.idle": "2024-08-20T02:18:59.833777Z", - "shell.execute_reply": "2024-08-20T02:18:59.833215Z" + "iopub.execute_input": "2024-08-21T00:44:03.088869Z", + "iopub.status.busy": "2024-08-21T00:44:03.088487Z", + "iopub.status.idle": "2024-08-21T00:44:04.135646Z", + "shell.execute_reply": "2024-08-21T00:44:04.135093Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 95d3b75d7..073024f68 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": "2024-08-20T02:19:03.522991Z", - "iopub.status.busy": "2024-08-20T02:19:03.522832Z", - "iopub.status.idle": "2024-08-20T02:19:04.942768Z", - "shell.execute_reply": "2024-08-20T02:19:04.942212Z" + "iopub.execute_input": "2024-08-21T00:44:08.536925Z", + "iopub.status.busy": "2024-08-21T00:44:08.536756Z", + "iopub.status.idle": "2024-08-21T00:44:09.687224Z", + "shell.execute_reply": "2024-08-21T00:44:09.686734Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:04.945469Z", - "iopub.status.busy": "2024-08-20T02:19:04.944979Z", - "iopub.status.idle": "2024-08-20T02:19:04.948001Z", - "shell.execute_reply": "2024-08-20T02:19:04.947540Z" + "iopub.execute_input": "2024-08-21T00:44:09.689870Z", + "iopub.status.busy": "2024-08-21T00:44:09.689408Z", + "iopub.status.idle": "2024-08-21T00:44:09.692521Z", + "shell.execute_reply": "2024-08-21T00:44:09.692073Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:04.950109Z", - "iopub.status.busy": "2024-08-20T02:19:04.949813Z", - "iopub.status.idle": "2024-08-20T02:19:04.957738Z", - "shell.execute_reply": "2024-08-20T02:19:04.957121Z" + "iopub.execute_input": "2024-08-21T00:44:09.694579Z", + "iopub.status.busy": "2024-08-21T00:44:09.694245Z", + "iopub.status.idle": "2024-08-21T00:44:09.702175Z", + "shell.execute_reply": "2024-08-21T00:44:09.701716Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:04.959689Z", - "iopub.status.busy": "2024-08-20T02:19:04.959510Z", - "iopub.status.idle": "2024-08-20T02:19:05.007494Z", - "shell.execute_reply": "2024-08-20T02:19:05.006846Z" + "iopub.execute_input": "2024-08-21T00:44:09.704223Z", + "iopub.status.busy": "2024-08-21T00:44:09.703803Z", + "iopub.status.idle": "2024-08-21T00:44:09.750825Z", + "shell.execute_reply": "2024-08-21T00:44:09.750317Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.009955Z", - "iopub.status.busy": "2024-08-20T02:19:05.009768Z", - "iopub.status.idle": "2024-08-20T02:19:05.026808Z", - "shell.execute_reply": "2024-08-20T02:19:05.026271Z" + "iopub.execute_input": "2024-08-21T00:44:09.753056Z", + "iopub.status.busy": "2024-08-21T00:44:09.752765Z", + "iopub.status.idle": "2024-08-21T00:44:09.769433Z", + "shell.execute_reply": "2024-08-21T00:44:09.768993Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.028834Z", - "iopub.status.busy": "2024-08-20T02:19:05.028656Z", - "iopub.status.idle": "2024-08-20T02:19:05.032389Z", - "shell.execute_reply": "2024-08-20T02:19:05.031915Z" + "iopub.execute_input": "2024-08-21T00:44:09.771586Z", + "iopub.status.busy": "2024-08-21T00:44:09.771104Z", + "iopub.status.idle": "2024-08-21T00:44:09.774951Z", + "shell.execute_reply": "2024-08-21T00:44:09.774438Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.034336Z", - "iopub.status.busy": "2024-08-20T02:19:05.034163Z", - "iopub.status.idle": "2024-08-20T02:19:05.052377Z", - "shell.execute_reply": "2024-08-20T02:19:05.051801Z" + "iopub.execute_input": "2024-08-21T00:44:09.777032Z", + "iopub.status.busy": "2024-08-21T00:44:09.776653Z", + "iopub.status.idle": "2024-08-21T00:44:09.792096Z", + "shell.execute_reply": "2024-08-21T00:44:09.791502Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.054757Z", - "iopub.status.busy": "2024-08-20T02:19:05.054411Z", - "iopub.status.idle": "2024-08-20T02:19:05.081157Z", - "shell.execute_reply": "2024-08-20T02:19:05.080588Z" + "iopub.execute_input": "2024-08-21T00:44:09.794285Z", + "iopub.status.busy": "2024-08-21T00:44:09.793887Z", + "iopub.status.idle": "2024-08-21T00:44:09.819917Z", + "shell.execute_reply": "2024-08-21T00:44:09.819351Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.083440Z", - "iopub.status.busy": "2024-08-20T02:19:05.083115Z", - "iopub.status.idle": "2024-08-20T02:19:07.231377Z", - "shell.execute_reply": "2024-08-20T02:19:07.230719Z" + "iopub.execute_input": "2024-08-21T00:44:09.822110Z", + "iopub.status.busy": "2024-08-21T00:44:09.821731Z", + "iopub.status.idle": "2024-08-21T00:44:11.811688Z", + "shell.execute_reply": "2024-08-21T00:44:11.811036Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.234251Z", - "iopub.status.busy": "2024-08-20T02:19:07.233645Z", - "iopub.status.idle": "2024-08-20T02:19:07.240565Z", - "shell.execute_reply": "2024-08-20T02:19:07.240009Z" + "iopub.execute_input": "2024-08-21T00:44:11.814185Z", + "iopub.status.busy": "2024-08-21T00:44:11.813766Z", + "iopub.status.idle": "2024-08-21T00:44:11.820401Z", + "shell.execute_reply": "2024-08-21T00:44:11.819834Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.242853Z", - "iopub.status.busy": "2024-08-20T02:19:07.242453Z", - "iopub.status.idle": "2024-08-20T02:19:07.254962Z", - "shell.execute_reply": "2024-08-20T02:19:07.254495Z" + "iopub.execute_input": "2024-08-21T00:44:11.822461Z", + "iopub.status.busy": "2024-08-21T00:44:11.822116Z", + "iopub.status.idle": "2024-08-21T00:44:11.835628Z", + "shell.execute_reply": "2024-08-21T00:44:11.835052Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.256913Z", - "iopub.status.busy": "2024-08-20T02:19:07.256739Z", - "iopub.status.idle": "2024-08-20T02:19:07.263271Z", - "shell.execute_reply": "2024-08-20T02:19:07.262692Z" + "iopub.execute_input": "2024-08-21T00:44:11.837751Z", + "iopub.status.busy": "2024-08-21T00:44:11.837353Z", + "iopub.status.idle": "2024-08-21T00:44:11.843662Z", + "shell.execute_reply": "2024-08-21T00:44:11.843107Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.265460Z", - "iopub.status.busy": "2024-08-20T02:19:07.265040Z", - "iopub.status.idle": "2024-08-20T02:19:07.267720Z", - "shell.execute_reply": "2024-08-20T02:19:07.267271Z" + "iopub.execute_input": "2024-08-21T00:44:11.845794Z", + "iopub.status.busy": "2024-08-21T00:44:11.845414Z", + "iopub.status.idle": "2024-08-21T00:44:11.848002Z", + "shell.execute_reply": "2024-08-21T00:44:11.847541Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.269736Z", - "iopub.status.busy": "2024-08-20T02:19:07.269397Z", - "iopub.status.idle": "2024-08-20T02:19:07.272660Z", - "shell.execute_reply": "2024-08-20T02:19:07.272134Z" + "iopub.execute_input": "2024-08-21T00:44:11.849966Z", + "iopub.status.busy": "2024-08-21T00:44:11.849626Z", + "iopub.status.idle": "2024-08-21T00:44:11.852992Z", + "shell.execute_reply": "2024-08-21T00:44:11.852471Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.274827Z", - "iopub.status.busy": "2024-08-20T02:19:07.274428Z", - "iopub.status.idle": "2024-08-20T02:19:07.277013Z", - "shell.execute_reply": "2024-08-20T02:19:07.276590Z" + "iopub.execute_input": "2024-08-21T00:44:11.855027Z", + "iopub.status.busy": "2024-08-21T00:44:11.854692Z", + "iopub.status.idle": "2024-08-21T00:44:11.857204Z", + "shell.execute_reply": "2024-08-21T00:44:11.856762Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.279076Z", - "iopub.status.busy": "2024-08-20T02:19:07.278677Z", - "iopub.status.idle": "2024-08-20T02:19:07.282955Z", - "shell.execute_reply": "2024-08-20T02:19:07.282393Z" + "iopub.execute_input": "2024-08-21T00:44:11.859154Z", + "iopub.status.busy": "2024-08-21T00:44:11.858846Z", + "iopub.status.idle": "2024-08-21T00:44:11.862989Z", + "shell.execute_reply": "2024-08-21T00:44:11.862452Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.285399Z", - "iopub.status.busy": "2024-08-20T02:19:07.284844Z", - "iopub.status.idle": "2024-08-20T02:19:07.313264Z", - "shell.execute_reply": "2024-08-20T02:19:07.312797Z" + "iopub.execute_input": "2024-08-21T00:44:11.865075Z", + "iopub.status.busy": "2024-08-21T00:44:11.864770Z", + "iopub.status.idle": "2024-08-21T00:44:11.893446Z", + "shell.execute_reply": "2024-08-21T00:44:11.892897Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.315544Z", - "iopub.status.busy": "2024-08-20T02:19:07.315122Z", - "iopub.status.idle": "2024-08-20T02:19:07.319938Z", - "shell.execute_reply": "2024-08-20T02:19:07.319454Z" + "iopub.execute_input": "2024-08-21T00:44:11.895680Z", + "iopub.status.busy": "2024-08-21T00:44:11.895363Z", + "iopub.status.idle": "2024-08-21T00:44:11.899971Z", + "shell.execute_reply": "2024-08-21T00:44:11.899406Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 0045fc3d5..55be504a5 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": "2024-08-20T02:19:10.411320Z", - "iopub.status.busy": "2024-08-20T02:19:10.411152Z", - "iopub.status.idle": "2024-08-20T02:19:11.855342Z", - "shell.execute_reply": "2024-08-20T02:19:11.854698Z" + "iopub.execute_input": "2024-08-21T00:44:14.676045Z", + "iopub.status.busy": "2024-08-21T00:44:14.675498Z", + "iopub.status.idle": "2024-08-21T00:44:15.874773Z", + "shell.execute_reply": "2024-08-21T00:44:15.874228Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:11.858020Z", - "iopub.status.busy": "2024-08-20T02:19:11.857673Z", - "iopub.status.idle": "2024-08-20T02:19:11.877980Z", - "shell.execute_reply": "2024-08-20T02:19:11.877379Z" + "iopub.execute_input": "2024-08-21T00:44:15.877397Z", + "iopub.status.busy": "2024-08-21T00:44:15.876909Z", + "iopub.status.idle": "2024-08-21T00:44:16.071071Z", + "shell.execute_reply": "2024-08-21T00:44:16.070463Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:11.880496Z", - "iopub.status.busy": "2024-08-20T02:19:11.880001Z", - "iopub.status.idle": "2024-08-20T02:19:11.893035Z", - "shell.execute_reply": "2024-08-20T02:19:11.892560Z" + "iopub.execute_input": "2024-08-21T00:44:16.073651Z", + "iopub.status.busy": "2024-08-21T00:44:16.073324Z", + "iopub.status.idle": "2024-08-21T00:44:16.087152Z", + "shell.execute_reply": "2024-08-21T00:44:16.086700Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:11.895218Z", - "iopub.status.busy": "2024-08-20T02:19:11.894778Z", - "iopub.status.idle": "2024-08-20T02:19:14.510745Z", - "shell.execute_reply": "2024-08-20T02:19:14.510214Z" + "iopub.execute_input": "2024-08-21T00:44:16.089136Z", + "iopub.status.busy": "2024-08-21T00:44:16.088951Z", + "iopub.status.idle": "2024-08-21T00:44:18.704832Z", + "shell.execute_reply": "2024-08-21T00:44:18.704201Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:14.512935Z", - "iopub.status.busy": "2024-08-20T02:19:14.512739Z", - "iopub.status.idle": "2024-08-20T02:19:15.874543Z", - "shell.execute_reply": "2024-08-20T02:19:15.873988Z" + "iopub.execute_input": "2024-08-21T00:44:18.707234Z", + "iopub.status.busy": "2024-08-21T00:44:18.706898Z", + "iopub.status.idle": "2024-08-21T00:44:20.047215Z", + "shell.execute_reply": "2024-08-21T00:44:20.046656Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:15.876840Z", - "iopub.status.busy": "2024-08-20T02:19:15.876649Z", - "iopub.status.idle": "2024-08-20T02:19:15.880654Z", - "shell.execute_reply": "2024-08-20T02:19:15.880101Z" + "iopub.execute_input": "2024-08-21T00:44:20.049763Z", + "iopub.status.busy": "2024-08-21T00:44:20.049407Z", + "iopub.status.idle": "2024-08-21T00:44:20.053503Z", + "shell.execute_reply": "2024-08-21T00:44:20.052933Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:15.882794Z", - "iopub.status.busy": "2024-08-20T02:19:15.882352Z", - "iopub.status.idle": "2024-08-20T02:19:18.024351Z", - "shell.execute_reply": "2024-08-20T02:19:18.023702Z" + "iopub.execute_input": "2024-08-21T00:44:20.055507Z", + "iopub.status.busy": "2024-08-21T00:44:20.055225Z", + "iopub.status.idle": "2024-08-21T00:44:22.125480Z", + "shell.execute_reply": "2024-08-21T00:44:22.124862Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:18.026941Z", - "iopub.status.busy": "2024-08-20T02:19:18.026511Z", - "iopub.status.idle": "2024-08-20T02:19:18.035086Z", - "shell.execute_reply": "2024-08-20T02:19:18.034546Z" + "iopub.execute_input": "2024-08-21T00:44:22.128137Z", + "iopub.status.busy": "2024-08-21T00:44:22.127601Z", + "iopub.status.idle": "2024-08-21T00:44:22.135349Z", + "shell.execute_reply": "2024-08-21T00:44:22.134822Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:18.037295Z", - "iopub.status.busy": "2024-08-20T02:19:18.036946Z", - "iopub.status.idle": "2024-08-20T02:19:20.557491Z", - "shell.execute_reply": "2024-08-20T02:19:20.556891Z" + "iopub.execute_input": "2024-08-21T00:44:22.137450Z", + "iopub.status.busy": "2024-08-21T00:44:22.137123Z", + "iopub.status.idle": "2024-08-21T00:44:24.871164Z", + "shell.execute_reply": "2024-08-21T00:44:24.870540Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.559617Z", - "iopub.status.busy": "2024-08-20T02:19:20.559437Z", - "iopub.status.idle": "2024-08-20T02:19:20.563117Z", - "shell.execute_reply": "2024-08-20T02:19:20.562661Z" + "iopub.execute_input": "2024-08-21T00:44:24.873731Z", + "iopub.status.busy": "2024-08-21T00:44:24.873390Z", + "iopub.status.idle": "2024-08-21T00:44:24.877293Z", + "shell.execute_reply": "2024-08-21T00:44:24.876833Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.565077Z", - "iopub.status.busy": "2024-08-20T02:19:20.564763Z", - "iopub.status.idle": "2024-08-20T02:19:20.568388Z", - "shell.execute_reply": "2024-08-20T02:19:20.567836Z" + "iopub.execute_input": "2024-08-21T00:44:24.879356Z", + "iopub.status.busy": "2024-08-21T00:44:24.879021Z", + "iopub.status.idle": "2024-08-21T00:44:24.882335Z", + "shell.execute_reply": "2024-08-21T00:44:24.881908Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.570408Z", - "iopub.status.busy": "2024-08-20T02:19:20.570101Z", - "iopub.status.idle": "2024-08-20T02:19:20.573344Z", - "shell.execute_reply": "2024-08-20T02:19:20.572863Z" + "iopub.execute_input": "2024-08-21T00:44:24.884241Z", + "iopub.status.busy": "2024-08-21T00:44:24.884064Z", + "iopub.status.idle": "2024-08-21T00:44:24.887414Z", + "shell.execute_reply": "2024-08-21T00:44:24.886955Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 87cb82783..32d0f5ddc 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": "2024-08-20T02:19:23.360886Z", - "iopub.status.busy": "2024-08-20T02:19:23.360718Z", - "iopub.status.idle": "2024-08-20T02:19:24.792942Z", - "shell.execute_reply": "2024-08-20T02:19:24.792381Z" + "iopub.execute_input": "2024-08-21T00:44:27.446117Z", + "iopub.status.busy": "2024-08-21T00:44:27.445691Z", + "iopub.status.idle": "2024-08-21T00:44:28.649409Z", + "shell.execute_reply": "2024-08-21T00:44:28.648789Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:24.795672Z", - "iopub.status.busy": "2024-08-20T02:19:24.795198Z", - "iopub.status.idle": "2024-08-20T02:19:25.977880Z", - "shell.execute_reply": "2024-08-20T02:19:25.977145Z" + "iopub.execute_input": "2024-08-21T00:44:28.652026Z", + "iopub.status.busy": "2024-08-21T00:44:28.651696Z", + "iopub.status.idle": "2024-08-21T00:44:29.855030Z", + "shell.execute_reply": "2024-08-21T00:44:29.854307Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.980582Z", - "iopub.status.busy": "2024-08-20T02:19:25.980189Z", - "iopub.status.idle": "2024-08-20T02:19:25.983474Z", - "shell.execute_reply": "2024-08-20T02:19:25.983045Z" + "iopub.execute_input": "2024-08-21T00:44:29.857690Z", + "iopub.status.busy": "2024-08-21T00:44:29.857268Z", + "iopub.status.idle": "2024-08-21T00:44:29.860479Z", + "shell.execute_reply": "2024-08-21T00:44:29.859998Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.985536Z", - "iopub.status.busy": "2024-08-20T02:19:25.985218Z", - "iopub.status.idle": "2024-08-20T02:19:25.992912Z", - "shell.execute_reply": "2024-08-20T02:19:25.992481Z" + "iopub.execute_input": "2024-08-21T00:44:29.862482Z", + "iopub.status.busy": "2024-08-21T00:44:29.862134Z", + "iopub.status.idle": "2024-08-21T00:44:29.868426Z", + "shell.execute_reply": "2024-08-21T00:44:29.868003Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.995066Z", - "iopub.status.busy": "2024-08-20T02:19:25.994673Z", - "iopub.status.idle": "2024-08-20T02:19:26.314231Z", - "shell.execute_reply": "2024-08-20T02:19:26.313627Z" + "iopub.execute_input": "2024-08-21T00:44:29.870503Z", + "iopub.status.busy": "2024-08-21T00:44:29.870145Z", + "iopub.status.idle": "2024-08-21T00:44:30.361705Z", + "shell.execute_reply": "2024-08-21T00:44:30.361008Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.317208Z", - "iopub.status.busy": "2024-08-20T02:19:26.317004Z", - "iopub.status.idle": "2024-08-20T02:19:26.322547Z", - "shell.execute_reply": "2024-08-20T02:19:26.322090Z" + "iopub.execute_input": "2024-08-21T00:44:30.364838Z", + "iopub.status.busy": "2024-08-21T00:44:30.364463Z", + "iopub.status.idle": "2024-08-21T00:44:30.370026Z", + "shell.execute_reply": "2024-08-21T00:44:30.369463Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.324528Z", - "iopub.status.busy": "2024-08-20T02:19:26.324198Z", - "iopub.status.idle": "2024-08-20T02:19:26.328062Z", - "shell.execute_reply": "2024-08-20T02:19:26.327504Z" + "iopub.execute_input": "2024-08-21T00:44:30.372147Z", + "iopub.status.busy": "2024-08-21T00:44:30.371809Z", + "iopub.status.idle": "2024-08-21T00:44:30.375913Z", + "shell.execute_reply": "2024-08-21T00:44:30.375349Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.330057Z", - "iopub.status.busy": "2024-08-20T02:19:26.329758Z", - "iopub.status.idle": "2024-08-20T02:19:27.344062Z", - "shell.execute_reply": "2024-08-20T02:19:27.343427Z" + "iopub.execute_input": "2024-08-21T00:44:30.378099Z", + "iopub.status.busy": "2024-08-21T00:44:30.377758Z", + "iopub.status.idle": "2024-08-21T00:44:31.233478Z", + "shell.execute_reply": "2024-08-21T00:44:31.232810Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.346665Z", - "iopub.status.busy": "2024-08-20T02:19:27.346299Z", - "iopub.status.idle": "2024-08-20T02:19:27.553795Z", - "shell.execute_reply": "2024-08-20T02:19:27.553289Z" + "iopub.execute_input": "2024-08-21T00:44:31.236084Z", + "iopub.status.busy": "2024-08-21T00:44:31.235599Z", + "iopub.status.idle": "2024-08-21T00:44:31.476832Z", + "shell.execute_reply": "2024-08-21T00:44:31.476343Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.555986Z", - "iopub.status.busy": "2024-08-20T02:19:27.555679Z", - "iopub.status.idle": "2024-08-20T02:19:27.559876Z", - "shell.execute_reply": "2024-08-20T02:19:27.559433Z" + "iopub.execute_input": "2024-08-21T00:44:31.479096Z", + "iopub.status.busy": "2024-08-21T00:44:31.478660Z", + "iopub.status.idle": "2024-08-21T00:44:31.483153Z", + "shell.execute_reply": "2024-08-21T00:44:31.482587Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.561898Z", - "iopub.status.busy": "2024-08-20T02:19:27.561565Z", - "iopub.status.idle": "2024-08-20T02:19:27.928742Z", - "shell.execute_reply": "2024-08-20T02:19:27.928127Z" + "iopub.execute_input": "2024-08-21T00:44:31.485319Z", + "iopub.status.busy": "2024-08-21T00:44:31.484888Z", + "iopub.status.idle": "2024-08-21T00:44:31.942164Z", + "shell.execute_reply": "2024-08-21T00:44:31.941558Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.931148Z", - "iopub.status.busy": "2024-08-20T02:19:27.930962Z", - "iopub.status.idle": "2024-08-20T02:19:28.265530Z", - "shell.execute_reply": "2024-08-20T02:19:28.264985Z" + "iopub.execute_input": "2024-08-21T00:44:31.945300Z", + "iopub.status.busy": "2024-08-21T00:44:31.945115Z", + "iopub.status.idle": "2024-08-21T00:44:32.281383Z", + "shell.execute_reply": "2024-08-21T00:44:32.280733Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:28.267564Z", - "iopub.status.busy": "2024-08-20T02:19:28.267388Z", - "iopub.status.idle": "2024-08-20T02:19:28.634288Z", - "shell.execute_reply": "2024-08-20T02:19:28.633645Z" + "iopub.execute_input": "2024-08-21T00:44:32.283744Z", + "iopub.status.busy": "2024-08-21T00:44:32.283409Z", + "iopub.status.idle": "2024-08-21T00:44:32.647591Z", + "shell.execute_reply": "2024-08-21T00:44:32.647010Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:28.637582Z", - "iopub.status.busy": "2024-08-20T02:19:28.637364Z", - "iopub.status.idle": "2024-08-20T02:19:29.079339Z", - "shell.execute_reply": "2024-08-20T02:19:29.078714Z" + "iopub.execute_input": "2024-08-21T00:44:32.650921Z", + "iopub.status.busy": "2024-08-21T00:44:32.650725Z", + "iopub.status.idle": "2024-08-21T00:44:33.090696Z", + "shell.execute_reply": "2024-08-21T00:44:33.090049Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.083840Z", - "iopub.status.busy": "2024-08-20T02:19:29.083451Z", - "iopub.status.idle": "2024-08-20T02:19:29.516951Z", - "shell.execute_reply": "2024-08-20T02:19:29.516329Z" + "iopub.execute_input": "2024-08-21T00:44:33.095146Z", + "iopub.status.busy": "2024-08-21T00:44:33.094951Z", + "iopub.status.idle": "2024-08-21T00:44:33.515097Z", + "shell.execute_reply": "2024-08-21T00:44:33.514471Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.519252Z", - "iopub.status.busy": "2024-08-20T02:19:29.518938Z", - "iopub.status.idle": "2024-08-20T02:19:29.737047Z", - "shell.execute_reply": "2024-08-20T02:19:29.736513Z" + "iopub.execute_input": "2024-08-21T00:44:33.518293Z", + "iopub.status.busy": "2024-08-21T00:44:33.518095Z", + "iopub.status.idle": "2024-08-21T00:44:33.709538Z", + "shell.execute_reply": "2024-08-21T00:44:33.708870Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.739475Z", - "iopub.status.busy": "2024-08-20T02:19:29.739154Z", - "iopub.status.idle": "2024-08-20T02:19:29.938318Z", - "shell.execute_reply": "2024-08-20T02:19:29.937762Z" + "iopub.execute_input": "2024-08-21T00:44:33.712115Z", + "iopub.status.busy": "2024-08-21T00:44:33.711896Z", + "iopub.status.idle": "2024-08-21T00:44:33.894449Z", + "shell.execute_reply": "2024-08-21T00:44:33.893894Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.941047Z", - "iopub.status.busy": "2024-08-20T02:19:29.940662Z", - "iopub.status.idle": "2024-08-20T02:19:29.943743Z", - "shell.execute_reply": "2024-08-20T02:19:29.943177Z" + "iopub.execute_input": "2024-08-21T00:44:33.897345Z", + "iopub.status.busy": "2024-08-21T00:44:33.896986Z", + "iopub.status.idle": "2024-08-21T00:44:33.899743Z", + "shell.execute_reply": "2024-08-21T00:44:33.899308Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.945776Z", - "iopub.status.busy": "2024-08-20T02:19:29.945606Z", - "iopub.status.idle": "2024-08-20T02:19:30.990728Z", - "shell.execute_reply": "2024-08-20T02:19:30.990184Z" + "iopub.execute_input": "2024-08-21T00:44:33.901739Z", + "iopub.status.busy": "2024-08-21T00:44:33.901417Z", + "iopub.status.idle": "2024-08-21T00:44:34.864741Z", + "shell.execute_reply": "2024-08-21T00:44:34.864144Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:30.993374Z", - "iopub.status.busy": "2024-08-20T02:19:30.993177Z", - "iopub.status.idle": "2024-08-20T02:19:31.135358Z", - "shell.execute_reply": "2024-08-20T02:19:31.134850Z" + "iopub.execute_input": "2024-08-21T00:44:34.867449Z", + "iopub.status.busy": "2024-08-21T00:44:34.867242Z", + "iopub.status.idle": "2024-08-21T00:44:35.005594Z", + "shell.execute_reply": "2024-08-21T00:44:35.005140Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:31.137483Z", - "iopub.status.busy": "2024-08-20T02:19:31.137182Z", - "iopub.status.idle": "2024-08-20T02:19:31.361162Z", - "shell.execute_reply": "2024-08-20T02:19:31.360532Z" + "iopub.execute_input": "2024-08-21T00:44:35.007763Z", + "iopub.status.busy": "2024-08-21T00:44:35.007407Z", + "iopub.status.idle": "2024-08-21T00:44:35.145240Z", + "shell.execute_reply": "2024-08-21T00:44:35.144745Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:31.363266Z", - "iopub.status.busy": "2024-08-20T02:19:31.363077Z", - "iopub.status.idle": "2024-08-20T02:19:32.051327Z", - "shell.execute_reply": "2024-08-20T02:19:32.050669Z" + "iopub.execute_input": "2024-08-21T00:44:35.147522Z", + "iopub.status.busy": "2024-08-21T00:44:35.147162Z", + "iopub.status.idle": "2024-08-21T00:44:35.837392Z", + "shell.execute_reply": "2024-08-21T00:44:35.836743Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:32.053777Z", - "iopub.status.busy": "2024-08-20T02:19:32.053483Z", - "iopub.status.idle": "2024-08-20T02:19:32.057205Z", - "shell.execute_reply": "2024-08-20T02:19:32.056645Z" + "iopub.execute_input": "2024-08-21T00:44:35.839731Z", + "iopub.status.busy": "2024-08-21T00:44:35.839542Z", + "iopub.status.idle": "2024-08-21T00:44:35.843337Z", + "shell.execute_reply": "2024-08-21T00:44:35.842774Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index d44506520..121ffa164 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": "2024-08-20T02:19:34.499280Z", - "iopub.status.busy": "2024-08-20T02:19:34.498882Z", - "iopub.status.idle": "2024-08-20T02:19:37.717028Z", - "shell.execute_reply": "2024-08-20T02:19:37.716470Z" + "iopub.execute_input": "2024-08-21T00:44:38.066360Z", + "iopub.status.busy": "2024-08-21T00:44:38.066183Z", + "iopub.status.idle": "2024-08-21T00:44:40.896600Z", + "shell.execute_reply": "2024-08-21T00:44:40.896031Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:37.719892Z", - "iopub.status.busy": "2024-08-20T02:19:37.719263Z", - "iopub.status.idle": "2024-08-20T02:19:37.738735Z", - "shell.execute_reply": "2024-08-20T02:19:37.738151Z" + "iopub.execute_input": "2024-08-21T00:44:40.899402Z", + "iopub.status.busy": "2024-08-21T00:44:40.898897Z", + "iopub.status.idle": "2024-08-21T00:44:41.226395Z", + "shell.execute_reply": "2024-08-21T00:44:41.225910Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:37.741325Z", - "iopub.status.busy": "2024-08-20T02:19:37.740862Z", - "iopub.status.idle": "2024-08-20T02:19:37.745006Z", - "shell.execute_reply": "2024-08-20T02:19:37.744463Z" + "iopub.execute_input": "2024-08-21T00:44:41.228949Z", + "iopub.status.busy": "2024-08-21T00:44:41.228477Z", + "iopub.status.idle": "2024-08-21T00:44:41.232327Z", + "shell.execute_reply": "2024-08-21T00:44:41.231896Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:37.747360Z", - "iopub.status.busy": "2024-08-20T02:19:37.747036Z", - "iopub.status.idle": "2024-08-20T02:19:42.256454Z", - "shell.execute_reply": "2024-08-20T02:19:42.255864Z" + "iopub.execute_input": "2024-08-21T00:44:41.234396Z", + "iopub.status.busy": "2024-08-21T00:44:41.234010Z", + "iopub.status.idle": "2024-08-21T00:44:46.003284Z", + "shell.execute_reply": "2024-08-21T00:44:46.002701Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 851968/170498071 [00:00<00:21, 7726348.04it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8249212.65it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10715136/170498071 [00:00<00:02, 58990173.15it/s]" + " 5%|▌ | 9306112/170498071 [00:00<00:03, 50615669.56it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 21692416/170498071 [00:00<00:01, 81629044.14it/s]" + " 10%|█ | 17891328/170498071 [00:00<00:02, 66243488.74it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32604160/170498071 [00:00<00:01, 92256260.34it/s]" + " 16%|█▋ | 27983872/170498071 [00:00<00:01, 79535198.51it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 44007424/170498071 [00:00<00:01, 100003938.21it/s]" + " 21%|██ | 36208640/170498071 [00:00<00:01, 80500379.19it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54886400/170498071 [00:00<00:01, 102923099.77it/s]" + " 27%|██▋ | 46039040/170498071 [00:00<00:01, 86481939.37it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 65667072/170498071 [00:00<00:01, 104452371.65it/s]" + " 32%|███▏ | 54755328/170498071 [00:00<00:01, 86441499.46it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 76972032/170498071 [00:00<00:00, 107058916.63it/s]" + " 38%|███▊ | 64061440/170498071 [00:00<00:01, 88437234.98it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 87719936/170498071 [00:00<00:00, 106652264.44it/s]" + " 43%|████▎ | 72941568/170498071 [00:00<00:01, 88504042.73it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 98435072/170498071 [00:01<00:00, 104959293.28it/s]" + " 48%|████▊ | 81920000/170498071 [00:01<00:00, 88819896.51it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 109477888/170498071 [00:01<00:00, 106599779.14it/s]" + " 53%|█████▎ | 91160576/170498071 [00:01<00:00, 89878347.40it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 120160256/170498071 [00:01<00:00, 105376043.64it/s]" + " 59%|█████▉ | 100171776/170498071 [00:01<00:00, 88552034.31it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130711552/170498071 [00:01<00:00, 100681796.58it/s]" + " 64%|██████▍ | 109608960/170498071 [00:01<00:00, 90217856.34it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141819904/170498071 [00:01<00:00, 103650641.61it/s]" + " 70%|██████▉ | 118652928/170498071 [00:01<00:00, 88798720.02it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 152240128/170498071 [00:01<00:00, 102641067.98it/s]" + " 75%|███████▌ | 128450560/170498071 [00:01<00:00, 91496573.36it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162562048/170498071 [00:01<00:00, 100813373.82it/s]" + " 81%|████████ | 137625600/170498071 [00:01<00:00, 89580266.26it/s]" ] }, { @@ -380,7 +380,31 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 99039292.87it/s] " + " 86%|████████▋ | 147128320/170498071 [00:01<00:00, 91113760.17it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 156270592/170498071 [00:01<00:00, 89857702.55it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 97%|█████████▋| 165773312/170498071 [00:01<00:00, 91343882.90it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:01<00:00, 85531172.07it/s]" ] }, { @@ -498,10 +522,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.258763Z", - "iopub.status.busy": "2024-08-20T02:19:42.258577Z", - "iopub.status.idle": "2024-08-20T02:19:42.263590Z", - "shell.execute_reply": "2024-08-20T02:19:42.263140Z" + "iopub.execute_input": "2024-08-21T00:44:46.005425Z", + "iopub.status.busy": "2024-08-21T00:44:46.005245Z", + "iopub.status.idle": "2024-08-21T00:44:46.010091Z", + "shell.execute_reply": "2024-08-21T00:44:46.009632Z" }, "nbsphinx": "hidden" }, @@ -552,10 +576,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.265560Z", - "iopub.status.busy": "2024-08-20T02:19:42.265239Z", - "iopub.status.idle": "2024-08-20T02:19:42.792402Z", - "shell.execute_reply": "2024-08-20T02:19:42.791889Z" + "iopub.execute_input": "2024-08-21T00:44:46.012218Z", + "iopub.status.busy": "2024-08-21T00:44:46.011792Z", + "iopub.status.idle": "2024-08-21T00:44:46.552461Z", + "shell.execute_reply": "2024-08-21T00:44:46.551864Z" } }, "outputs": [ @@ -588,10 +612,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.794517Z", - "iopub.status.busy": "2024-08-20T02:19:42.794333Z", - "iopub.status.idle": "2024-08-20T02:19:43.311733Z", - "shell.execute_reply": "2024-08-20T02:19:43.311064Z" + "iopub.execute_input": "2024-08-21T00:44:46.554759Z", + "iopub.status.busy": "2024-08-21T00:44:46.554411Z", + "iopub.status.idle": "2024-08-21T00:44:47.061482Z", + "shell.execute_reply": "2024-08-21T00:44:47.060868Z" } }, "outputs": [ @@ -629,10 +653,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:43.313896Z", - "iopub.status.busy": "2024-08-20T02:19:43.313703Z", - "iopub.status.idle": "2024-08-20T02:19:43.317387Z", - "shell.execute_reply": "2024-08-20T02:19:43.316911Z" + "iopub.execute_input": "2024-08-21T00:44:47.063597Z", + "iopub.status.busy": "2024-08-21T00:44:47.063395Z", + "iopub.status.idle": "2024-08-21T00:44:47.067074Z", + "shell.execute_reply": "2024-08-21T00:44:47.066614Z" } }, "outputs": [], @@ -655,17 +679,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:43.319247Z", - "iopub.status.busy": "2024-08-20T02:19:43.319077Z", - "iopub.status.idle": "2024-08-20T02:19:55.699364Z", - "shell.execute_reply": "2024-08-20T02:19:55.698752Z" + "iopub.execute_input": "2024-08-21T00:44:47.069202Z", + "iopub.status.busy": "2024-08-21T00:44:47.068736Z", + "iopub.status.idle": "2024-08-21T00:44:59.482222Z", + "shell.execute_reply": "2024-08-21T00:44:59.481608Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "642acd4483d444aba595b064e7e139f2", + "model_id": "2aece906d5414d149f1b677c20ee62ea", "version_major": 2, "version_minor": 0 }, @@ -724,10 +748,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:55.702022Z", - "iopub.status.busy": "2024-08-20T02:19:55.701836Z", - "iopub.status.idle": "2024-08-20T02:19:57.881011Z", - "shell.execute_reply": "2024-08-20T02:19:57.880414Z" + "iopub.execute_input": "2024-08-21T00:44:59.484767Z", + "iopub.status.busy": "2024-08-21T00:44:59.484373Z", + "iopub.status.idle": "2024-08-21T00:45:01.580531Z", + "shell.execute_reply": "2024-08-21T00:45:01.579978Z" } }, "outputs": [ @@ -771,10 +795,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:57.883556Z", - "iopub.status.busy": "2024-08-20T02:19:57.883182Z", - "iopub.status.idle": "2024-08-20T02:19:58.113830Z", - "shell.execute_reply": "2024-08-20T02:19:58.113258Z" + "iopub.execute_input": "2024-08-21T00:45:01.583216Z", + "iopub.status.busy": "2024-08-21T00:45:01.582715Z", + "iopub.status.idle": "2024-08-21T00:45:01.835720Z", + "shell.execute_reply": "2024-08-21T00:45:01.835061Z" } }, "outputs": [ @@ -810,10 +834,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:58.116207Z", - "iopub.status.busy": "2024-08-20T02:19:58.116028Z", - "iopub.status.idle": "2024-08-20T02:19:58.775793Z", - "shell.execute_reply": "2024-08-20T02:19:58.775175Z" + "iopub.execute_input": "2024-08-21T00:45:01.838410Z", + "iopub.status.busy": "2024-08-21T00:45:01.838179Z", + "iopub.status.idle": "2024-08-21T00:45:02.484647Z", + "shell.execute_reply": "2024-08-21T00:45:02.484044Z" } }, "outputs": [ @@ -863,10 +887,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:58.778247Z", - "iopub.status.busy": "2024-08-20T02:19:58.778067Z", - "iopub.status.idle": "2024-08-20T02:19:59.073646Z", - "shell.execute_reply": "2024-08-20T02:19:59.073079Z" + "iopub.execute_input": "2024-08-21T00:45:02.487053Z", + "iopub.status.busy": "2024-08-21T00:45:02.486880Z", + "iopub.status.idle": "2024-08-21T00:45:02.775155Z", + "shell.execute_reply": "2024-08-21T00:45:02.774552Z" } }, "outputs": [ @@ -914,10 +938,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.075669Z", - "iopub.status.busy": "2024-08-20T02:19:59.075494Z", - "iopub.status.idle": "2024-08-20T02:19:59.313635Z", - "shell.execute_reply": "2024-08-20T02:19:59.313009Z" + "iopub.execute_input": "2024-08-21T00:45:02.777542Z", + "iopub.status.busy": "2024-08-21T00:45:02.777200Z", + "iopub.status.idle": "2024-08-21T00:45:03.004237Z", + "shell.execute_reply": "2024-08-21T00:45:03.003698Z" } }, "outputs": [ @@ -973,10 +997,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.316178Z", - "iopub.status.busy": "2024-08-20T02:19:59.315831Z", - "iopub.status.idle": "2024-08-20T02:19:59.397580Z", - "shell.execute_reply": "2024-08-20T02:19:59.396927Z" + "iopub.execute_input": "2024-08-21T00:45:03.006715Z", + "iopub.status.busy": "2024-08-21T00:45:03.006213Z", + "iopub.status.idle": "2024-08-21T00:45:03.079353Z", + "shell.execute_reply": "2024-08-21T00:45:03.078862Z" } }, "outputs": [], @@ -997,10 +1021,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.400410Z", - "iopub.status.busy": "2024-08-20T02:19:59.399851Z", - "iopub.status.idle": "2024-08-20T02:20:09.496202Z", - "shell.execute_reply": "2024-08-20T02:20:09.495583Z" + "iopub.execute_input": "2024-08-21T00:45:03.081841Z", + "iopub.status.busy": "2024-08-21T00:45:03.081489Z", + "iopub.status.idle": "2024-08-21T00:45:13.396780Z", + "shell.execute_reply": "2024-08-21T00:45:13.396110Z" } }, "outputs": [ @@ -1037,10 +1061,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:09.498840Z", - "iopub.status.busy": "2024-08-20T02:20:09.498416Z", - "iopub.status.idle": "2024-08-20T02:20:11.827863Z", - "shell.execute_reply": "2024-08-20T02:20:11.827207Z" + "iopub.execute_input": "2024-08-21T00:45:13.399549Z", + "iopub.status.busy": "2024-08-21T00:45:13.398957Z", + "iopub.status.idle": "2024-08-21T00:45:15.614825Z", + "shell.execute_reply": "2024-08-21T00:45:15.614327Z" } }, "outputs": [ @@ -1071,10 +1095,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:11.830805Z", - "iopub.status.busy": "2024-08-20T02:20:11.830187Z", - "iopub.status.idle": "2024-08-20T02:20:12.036080Z", - "shell.execute_reply": "2024-08-20T02:20:12.035582Z" + "iopub.execute_input": "2024-08-21T00:45:15.617532Z", + "iopub.status.busy": "2024-08-21T00:45:15.616897Z", + "iopub.status.idle": "2024-08-21T00:45:15.830910Z", + "shell.execute_reply": "2024-08-21T00:45:15.830305Z" } }, "outputs": [], @@ -1088,10 +1112,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:12.038552Z", - "iopub.status.busy": "2024-08-20T02:20:12.038185Z", - "iopub.status.idle": "2024-08-20T02:20:12.041256Z", - "shell.execute_reply": "2024-08-20T02:20:12.040807Z" + "iopub.execute_input": "2024-08-21T00:45:15.833496Z", + "iopub.status.busy": "2024-08-21T00:45:15.833180Z", + "iopub.status.idle": "2024-08-21T00:45:15.836370Z", + "shell.execute_reply": "2024-08-21T00:45:15.835883Z" } }, "outputs": [], @@ -1129,10 +1153,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:12.043442Z", - "iopub.status.busy": "2024-08-20T02:20:12.043116Z", - "iopub.status.idle": "2024-08-20T02:20:12.051808Z", - "shell.execute_reply": "2024-08-20T02:20:12.051353Z" + "iopub.execute_input": "2024-08-21T00:45:15.838493Z", + "iopub.status.busy": "2024-08-21T00:45:15.838071Z", + "iopub.status.idle": "2024-08-21T00:45:15.846417Z", + "shell.execute_reply": "2024-08-21T00:45:15.845889Z" }, "nbsphinx": "hidden" }, @@ -1177,7 +1201,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"tooltip": null - } - }, - "a673b7221a2a4306a9d68253bd529778": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "tooltip": null, + "value": 102469840.0 } }, - "b2d6e40503a2481bbf4ebb46fcfd3dde": { + "45d23e20356141f8b282232e7a970845": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1394,56 +1485,7 @@ "width": null } }, - "b8d81524ba4c4b99ab6d0d6217cf18c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2b9840e8eb314e8ba7b76bccb5b7d86b", - "placeholder": "​", - "style": "IPY_MODEL_169d58f4735041f7a00a218864eaf538", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "c8851ece38b7471fb2bda870c287b422": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_482db617aa45471dbd4788d5ea3eaf8d", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a673b7221a2a4306a9d68253bd529778", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "f1c1be1a57e44c73a36e7a8d3ac16f26": { + "728c44e458d84e538ef30537097e692f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1496,25 +1538,7 @@ "width": null } }, - "f8338a6f5da64d57b669fd0a68596611": { - "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 - } - }, - "f91634ed9c494ec4a95bbc683c0d5570": { + "7a2cd802a8d24314a46149b1d522ec92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1529,12 +1553,12 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b2d6e40503a2481bbf4ebb46fcfd3dde", + "layout": "IPY_MODEL_45d23e20356141f8b282232e7a970845", "placeholder": "​", - "style": "IPY_MODEL_f8338a6f5da64d57b669fd0a68596611", + "style": "IPY_MODEL_2278bbdcd34d42c685310e394def87da", "tabbable": null, "tooltip": null, - "value": " 102M/102M [00:00<00:00, 269MB/s]" + "value": "model.safetensors: 100%" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index c266f0fdb..e8381488b 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": "2024-08-20T02:20:16.211959Z", - "iopub.status.busy": "2024-08-20T02:20:16.211775Z", - "iopub.status.idle": "2024-08-20T02:20:17.625109Z", - "shell.execute_reply": "2024-08-20T02:20:17.624532Z" + "iopub.execute_input": "2024-08-21T00:45:20.030885Z", + "iopub.status.busy": "2024-08-21T00:45:20.030716Z", + "iopub.status.idle": "2024-08-21T00:45:21.238274Z", + "shell.execute_reply": "2024-08-21T00:45:21.237657Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:20:17.627810Z", - "iopub.status.busy": "2024-08-20T02:20:17.627188Z", - "iopub.status.idle": "2024-08-20T02:20:17.646081Z", - "shell.execute_reply": "2024-08-20T02:20:17.645467Z" + "iopub.execute_input": "2024-08-21T00:45:21.240674Z", + "iopub.status.busy": "2024-08-21T00:45:21.240417Z", + "iopub.status.idle": "2024-08-21T00:45:21.258244Z", + "shell.execute_reply": "2024-08-21T00:45:21.257816Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.648526Z", - "iopub.status.busy": "2024-08-20T02:20:17.648175Z", - "iopub.status.idle": "2024-08-20T02:20:17.651475Z", - "shell.execute_reply": "2024-08-20T02:20:17.651004Z" + "iopub.execute_input": "2024-08-21T00:45:21.260491Z", + "iopub.status.busy": "2024-08-21T00:45:21.259990Z", + "iopub.status.idle": "2024-08-21T00:45:21.262962Z", + "shell.execute_reply": "2024-08-21T00:45:21.262506Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.653391Z", - "iopub.status.busy": "2024-08-20T02:20:17.653196Z", - "iopub.status.idle": "2024-08-20T02:20:17.738201Z", - "shell.execute_reply": "2024-08-20T02:20:17.737589Z" + "iopub.execute_input": "2024-08-21T00:45:21.264900Z", + "iopub.status.busy": "2024-08-21T00:45:21.264725Z", + "iopub.status.idle": "2024-08-21T00:45:21.356509Z", + "shell.execute_reply": "2024-08-21T00:45:21.356026Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.740445Z", - "iopub.status.busy": "2024-08-20T02:20:17.740254Z", - "iopub.status.idle": "2024-08-20T02:20:17.744806Z", - "shell.execute_reply": "2024-08-20T02:20:17.744337Z" + "iopub.execute_input": "2024-08-21T00:45:21.358702Z", + "iopub.status.busy": "2024-08-21T00:45:21.358375Z", + "iopub.status.idle": "2024-08-21T00:45:21.538107Z", + "shell.execute_reply": "2024-08-21T00:45:21.537485Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.746909Z", - "iopub.status.busy": "2024-08-20T02:20:17.746590Z", - "iopub.status.idle": "2024-08-20T02:20:17.995978Z", - "shell.execute_reply": "2024-08-20T02:20:17.995401Z" + "iopub.execute_input": "2024-08-21T00:45:21.540685Z", + "iopub.status.busy": "2024-08-21T00:45:21.540349Z", + "iopub.status.idle": "2024-08-21T00:45:21.783605Z", + "shell.execute_reply": "2024-08-21T00:45:21.783082Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.998260Z", - "iopub.status.busy": "2024-08-20T02:20:17.997904Z", - "iopub.status.idle": "2024-08-20T02:20:18.002417Z", - "shell.execute_reply": "2024-08-20T02:20:18.001833Z" + "iopub.execute_input": "2024-08-21T00:45:21.785804Z", + "iopub.status.busy": "2024-08-21T00:45:21.785616Z", + "iopub.status.idle": "2024-08-21T00:45:21.790197Z", + "shell.execute_reply": "2024-08-21T00:45:21.789742Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:18.004416Z", - "iopub.status.busy": "2024-08-20T02:20:18.004107Z", - "iopub.status.idle": "2024-08-20T02:20:18.010552Z", - "shell.execute_reply": "2024-08-20T02:20:18.010097Z" + "iopub.execute_input": "2024-08-21T00:45:21.792282Z", + "iopub.status.busy": "2024-08-21T00:45:21.791871Z", + "iopub.status.idle": "2024-08-21T00:45:21.798042Z", + "shell.execute_reply": "2024-08-21T00:45:21.797612Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:18.012700Z", - "iopub.status.busy": "2024-08-20T02:20:18.012394Z", - "iopub.status.idle": "2024-08-20T02:20:18.015066Z", - "shell.execute_reply": "2024-08-20T02:20:18.014531Z" + "iopub.execute_input": "2024-08-21T00:45:21.800226Z", + "iopub.status.busy": "2024-08-21T00:45:21.799851Z", + "iopub.status.idle": "2024-08-21T00:45:21.802377Z", + "shell.execute_reply": "2024-08-21T00:45:21.801941Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:18.016989Z", - "iopub.status.busy": "2024-08-20T02:20:18.016723Z", - "iopub.status.idle": "2024-08-20T02:20:27.069419Z", - "shell.execute_reply": "2024-08-20T02:20:27.068809Z" + "iopub.execute_input": "2024-08-21T00:45:21.804417Z", + "iopub.status.busy": "2024-08-21T00:45:21.803994Z", + "iopub.status.idle": "2024-08-21T00:45:30.720019Z", + "shell.execute_reply": "2024-08-21T00:45:30.719430Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.072424Z", - "iopub.status.busy": "2024-08-20T02:20:27.071784Z", - "iopub.status.idle": "2024-08-20T02:20:27.078857Z", - "shell.execute_reply": "2024-08-20T02:20:27.078386Z" + "iopub.execute_input": "2024-08-21T00:45:30.722952Z", + "iopub.status.busy": "2024-08-21T00:45:30.722298Z", + "iopub.status.idle": "2024-08-21T00:45:30.730061Z", + "shell.execute_reply": "2024-08-21T00:45:30.729593Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.080771Z", - "iopub.status.busy": "2024-08-20T02:20:27.080596Z", - "iopub.status.idle": "2024-08-20T02:20:27.084327Z", - "shell.execute_reply": "2024-08-20T02:20:27.083864Z" + "iopub.execute_input": "2024-08-21T00:45:30.732078Z", + "iopub.status.busy": "2024-08-21T00:45:30.731875Z", + "iopub.status.idle": "2024-08-21T00:45:30.735786Z", + "shell.execute_reply": "2024-08-21T00:45:30.735212Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.086155Z", - "iopub.status.busy": "2024-08-20T02:20:27.085985Z", - "iopub.status.idle": "2024-08-20T02:20:27.088961Z", - "shell.execute_reply": "2024-08-20T02:20:27.088413Z" + "iopub.execute_input": "2024-08-21T00:45:30.737738Z", + "iopub.status.busy": "2024-08-21T00:45:30.737562Z", + "iopub.status.idle": "2024-08-21T00:45:30.740611Z", + "shell.execute_reply": "2024-08-21T00:45:30.740093Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.090982Z", - "iopub.status.busy": "2024-08-20T02:20:27.090808Z", - "iopub.status.idle": "2024-08-20T02:20:27.093742Z", - "shell.execute_reply": "2024-08-20T02:20:27.093293Z" + "iopub.execute_input": "2024-08-21T00:45:30.742610Z", + "iopub.status.busy": "2024-08-21T00:45:30.742345Z", + "iopub.status.idle": "2024-08-21T00:45:30.745340Z", + "shell.execute_reply": "2024-08-21T00:45:30.744896Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.095505Z", - "iopub.status.busy": "2024-08-20T02:20:27.095333Z", - "iopub.status.idle": "2024-08-20T02:20:27.103648Z", - "shell.execute_reply": "2024-08-20T02:20:27.103189Z" + "iopub.execute_input": "2024-08-21T00:45:30.747324Z", + "iopub.status.busy": "2024-08-21T00:45:30.746923Z", + "iopub.status.idle": "2024-08-21T00:45:30.754976Z", + "shell.execute_reply": "2024-08-21T00:45:30.754402Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.105701Z", - "iopub.status.busy": "2024-08-20T02:20:27.105524Z", - "iopub.status.idle": "2024-08-20T02:20:27.108032Z", - "shell.execute_reply": "2024-08-20T02:20:27.107562Z" + "iopub.execute_input": "2024-08-21T00:45:30.756957Z", + "iopub.status.busy": "2024-08-21T00:45:30.756628Z", + "iopub.status.idle": "2024-08-21T00:45:30.759233Z", + "shell.execute_reply": "2024-08-21T00:45:30.758769Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.109992Z", - "iopub.status.busy": "2024-08-20T02:20:27.109810Z", - "iopub.status.idle": "2024-08-20T02:20:27.243262Z", - "shell.execute_reply": "2024-08-20T02:20:27.242644Z" + "iopub.execute_input": "2024-08-21T00:45:30.761134Z", + "iopub.status.busy": "2024-08-21T00:45:30.760955Z", + "iopub.status.idle": "2024-08-21T00:45:30.893602Z", + "shell.execute_reply": "2024-08-21T00:45:30.892927Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.245552Z", - "iopub.status.busy": "2024-08-20T02:20:27.245355Z", - "iopub.status.idle": "2024-08-20T02:20:27.361934Z", - "shell.execute_reply": "2024-08-20T02:20:27.361332Z" + "iopub.execute_input": "2024-08-21T00:45:30.896298Z", + "iopub.status.busy": "2024-08-21T00:45:30.895880Z", + "iopub.status.idle": "2024-08-21T00:45:31.004776Z", + "shell.execute_reply": "2024-08-21T00:45:31.004239Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.364305Z", - "iopub.status.busy": "2024-08-20T02:20:27.364121Z", - "iopub.status.idle": "2024-08-20T02:20:27.869112Z", - "shell.execute_reply": "2024-08-20T02:20:27.868560Z" + "iopub.execute_input": "2024-08-21T00:45:31.007132Z", + "iopub.status.busy": "2024-08-21T00:45:31.006711Z", + "iopub.status.idle": "2024-08-21T00:45:31.507243Z", + "shell.execute_reply": "2024-08-21T00:45:31.506612Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.871683Z", - "iopub.status.busy": "2024-08-20T02:20:27.871465Z", - "iopub.status.idle": "2024-08-20T02:20:27.968366Z", - "shell.execute_reply": "2024-08-20T02:20:27.967776Z" + "iopub.execute_input": "2024-08-21T00:45:31.510047Z", + "iopub.status.busy": "2024-08-21T00:45:31.509565Z", + "iopub.status.idle": "2024-08-21T00:45:31.607269Z", + "shell.execute_reply": "2024-08-21T00:45:31.606674Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.970537Z", - "iopub.status.busy": "2024-08-20T02:20:27.970355Z", - "iopub.status.idle": "2024-08-20T02:20:27.978993Z", - "shell.execute_reply": "2024-08-20T02:20:27.978433Z" + "iopub.execute_input": "2024-08-21T00:45:31.609387Z", + "iopub.status.busy": "2024-08-21T00:45:31.609207Z", + "iopub.status.idle": "2024-08-21T00:45:31.617879Z", + "shell.execute_reply": "2024-08-21T00:45:31.617346Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.980976Z", - "iopub.status.busy": "2024-08-20T02:20:27.980669Z", - "iopub.status.idle": "2024-08-20T02:20:27.983505Z", - "shell.execute_reply": "2024-08-20T02:20:27.982960Z" + "iopub.execute_input": "2024-08-21T00:45:31.619747Z", + "iopub.status.busy": "2024-08-21T00:45:31.619574Z", + "iopub.status.idle": "2024-08-21T00:45:31.622204Z", + "shell.execute_reply": "2024-08-21T00:45:31.621752Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:27.985617Z", - "iopub.status.busy": "2024-08-20T02:20:27.985178Z", - "iopub.status.idle": "2024-08-20T02:20:33.646162Z", - "shell.execute_reply": "2024-08-20T02:20:33.645558Z" + "iopub.execute_input": "2024-08-21T00:45:31.624156Z", + "iopub.status.busy": "2024-08-21T00:45:31.623983Z", + "iopub.status.idle": "2024-08-21T00:45:37.204902Z", + "shell.execute_reply": "2024-08-21T00:45:37.204285Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:33.648626Z", - "iopub.status.busy": "2024-08-20T02:20:33.648210Z", - "iopub.status.idle": "2024-08-20T02:20:33.657502Z", - "shell.execute_reply": "2024-08-20T02:20:33.656918Z" + "iopub.execute_input": "2024-08-21T00:45:37.207254Z", + "iopub.status.busy": "2024-08-21T00:45:37.206859Z", + "iopub.status.idle": "2024-08-21T00:45:37.215288Z", + "shell.execute_reply": "2024-08-21T00:45:37.214729Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:33.659776Z", - "iopub.status.busy": "2024-08-20T02:20:33.659372Z", - "iopub.status.idle": "2024-08-20T02:20:33.724158Z", - "shell.execute_reply": "2024-08-20T02:20:33.723533Z" + "iopub.execute_input": "2024-08-21T00:45:37.217502Z", + "iopub.status.busy": "2024-08-21T00:45:37.217153Z", + "iopub.status.idle": "2024-08-21T00:45:37.285251Z", + "shell.execute_reply": "2024-08-21T00:45:37.284642Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 4ac3af7d6..7b6da3e55 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:36.888231Z", - "iopub.status.busy": "2024-08-20T02:20:36.888053Z", - "iopub.status.idle": "2024-08-20T02:20:38.537307Z", - "shell.execute_reply": "2024-08-20T02:20:38.536594Z" + "iopub.execute_input": "2024-08-21T00:45:40.466811Z", + "iopub.status.busy": "2024-08-21T00:45:40.466640Z", + "iopub.status.idle": "2024-08-21T00:45:42.883520Z", + "shell.execute_reply": "2024-08-21T00:45:42.882755Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:38.540226Z", - "iopub.status.busy": "2024-08-20T02:20:38.539748Z", - "iopub.status.idle": "2024-08-20T02:22:01.964653Z", - "shell.execute_reply": "2024-08-20T02:22:01.963964Z" + "iopub.execute_input": "2024-08-21T00:45:42.886008Z", + "iopub.status.busy": "2024-08-21T00:45:42.885816Z", + "iopub.status.idle": "2024-08-21T00:46:48.432528Z", + "shell.execute_reply": "2024-08-21T00:46:48.431818Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:01.967334Z", - "iopub.status.busy": "2024-08-20T02:22:01.966936Z", - "iopub.status.idle": "2024-08-20T02:22:03.406051Z", - "shell.execute_reply": "2024-08-20T02:22:03.405489Z" + "iopub.execute_input": "2024-08-21T00:46:48.435091Z", + "iopub.status.busy": "2024-08-21T00:46:48.434899Z", + "iopub.status.idle": "2024-08-21T00:46:49.596397Z", + "shell.execute_reply": "2024-08-21T00:46:49.595894Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.408486Z", - "iopub.status.busy": "2024-08-20T02:22:03.408194Z", - "iopub.status.idle": "2024-08-20T02:22:03.411443Z", - "shell.execute_reply": "2024-08-20T02:22:03.410981Z" + "iopub.execute_input": "2024-08-21T00:46:49.598800Z", + "iopub.status.busy": "2024-08-21T00:46:49.598484Z", + "iopub.status.idle": "2024-08-21T00:46:49.601969Z", + "shell.execute_reply": "2024-08-21T00:46:49.601513Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.413581Z", - "iopub.status.busy": "2024-08-20T02:22:03.413241Z", - "iopub.status.idle": "2024-08-20T02:22:03.417640Z", - "shell.execute_reply": "2024-08-20T02:22:03.417037Z" + "iopub.execute_input": "2024-08-21T00:46:49.604008Z", + "iopub.status.busy": "2024-08-21T00:46:49.603652Z", + "iopub.status.idle": "2024-08-21T00:46:49.607367Z", + "shell.execute_reply": "2024-08-21T00:46:49.606937Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.419956Z", - "iopub.status.busy": "2024-08-20T02:22:03.419536Z", - "iopub.status.idle": "2024-08-20T02:22:03.423128Z", - "shell.execute_reply": "2024-08-20T02:22:03.422694Z" + "iopub.execute_input": "2024-08-21T00:46:49.609599Z", + "iopub.status.busy": "2024-08-21T00:46:49.609187Z", + "iopub.status.idle": "2024-08-21T00:46:49.612699Z", + "shell.execute_reply": "2024-08-21T00:46:49.612255Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.425108Z", - "iopub.status.busy": "2024-08-20T02:22:03.424794Z", - "iopub.status.idle": "2024-08-20T02:22:03.427666Z", - "shell.execute_reply": "2024-08-20T02:22:03.427206Z" + "iopub.execute_input": "2024-08-21T00:46:49.614723Z", + "iopub.status.busy": "2024-08-21T00:46:49.614324Z", + "iopub.status.idle": "2024-08-21T00:46:49.617188Z", + "shell.execute_reply": "2024-08-21T00:46:49.616743Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.429508Z", - "iopub.status.busy": "2024-08-20T02:22:03.429330Z", - "iopub.status.idle": "2024-08-20T02:22:41.737114Z", - "shell.execute_reply": "2024-08-20T02:22:41.736373Z" + "iopub.execute_input": "2024-08-21T00:46:49.619230Z", + "iopub.status.busy": "2024-08-21T00:46:49.618837Z", + "iopub.status.idle": "2024-08-21T00:47:27.817727Z", + "shell.execute_reply": "2024-08-21T00:47:27.816997Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20304eeec37340f3bf2a39a2a20b2a67", + "model_id": "5990bcf3d14946b580823f2cc6231b4d", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c7558622fee406090521d6bf9363191", + "model_id": "e932bfd1a1e2432aa3d1c447df10a9f1", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:41.739813Z", - "iopub.status.busy": "2024-08-20T02:22:41.739590Z", - "iopub.status.idle": "2024-08-20T02:22:42.185977Z", - "shell.execute_reply": "2024-08-20T02:22:42.185387Z" + "iopub.execute_input": "2024-08-21T00:47:27.820727Z", + "iopub.status.busy": "2024-08-21T00:47:27.820316Z", + "iopub.status.idle": "2024-08-21T00:47:28.498355Z", + "shell.execute_reply": "2024-08-21T00:47:28.497855Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:42.188223Z", - "iopub.status.busy": "2024-08-20T02:22:42.187806Z", - "iopub.status.idle": "2024-08-20T02:22:45.261325Z", - "shell.execute_reply": "2024-08-20T02:22:45.260739Z" + "iopub.execute_input": "2024-08-21T00:47:28.500634Z", + "iopub.status.busy": "2024-08-21T00:47:28.500194Z", + "iopub.status.idle": "2024-08-21T00:47:31.527168Z", + "shell.execute_reply": "2024-08-21T00:47:31.526595Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:45.263692Z", - "iopub.status.busy": "2024-08-20T02:22:45.263239Z", - "iopub.status.idle": "2024-08-20T02:23:18.319788Z", - "shell.execute_reply": "2024-08-20T02:23:18.319233Z" + "iopub.execute_input": "2024-08-21T00:47:31.529441Z", + "iopub.status.busy": "2024-08-21T00:47:31.529101Z", + "iopub.status.idle": "2024-08-21T00:48:04.004533Z", + "shell.execute_reply": "2024-08-21T00:48:04.004033Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8201274110a4756b2e8dcd5b6c33705", + "model_id": "5297291313004a85837ee0d01806ef8b", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:18.322035Z", - "iopub.status.busy": "2024-08-20T02:23:18.321611Z", - "iopub.status.idle": "2024-08-20T02:23:33.160852Z", - "shell.execute_reply": "2024-08-20T02:23:33.160296Z" + "iopub.execute_input": "2024-08-21T00:48:04.006657Z", + "iopub.status.busy": "2024-08-21T00:48:04.006345Z", + "iopub.status.idle": "2024-08-21T00:48:19.210511Z", + "shell.execute_reply": "2024-08-21T00:48:19.209851Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - 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"iopub.execute_input": "2024-08-20T02:23:47.189790Z", - "iopub.status.busy": "2024-08-20T02:23:47.189361Z", - "iopub.status.idle": "2024-08-20T02:23:48.403988Z", - "shell.execute_reply": "2024-08-20T02:23:48.403347Z" + "iopub.execute_input": "2024-08-21T00:48:33.131555Z", + "iopub.status.busy": "2024-08-21T00:48:33.131391Z", + "iopub.status.idle": "2024-08-21T00:48:34.477099Z", + "shell.execute_reply": "2024-08-21T00:48:34.476468Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:23:47-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-08-21 00:48:33-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.99, 2400:52e0:1a00::1068:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.\r\n" + "169.150.236.104, 2400:52e0:1a00::1070:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.104|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" ] }, { @@ -122,9 +129,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.63MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-08-20 02:23:47 (5.63 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-08-21 00:48:33 (6.33 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -136,24 +143,24 @@ "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 " + " inflating: data/train.txt " ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r\n" + "\r\n", + " inflating: data/valid.txt \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:23:47-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.69.145, 3.5.9.145, 3.5.25.176, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.69.145|:443... connected.\r\n", + "--2024-08-21 00:48:34-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.63.33, 52.217.95.209, 3.5.27.229, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.63.33|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,9 +181,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 103MB/s in 0.2s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.09s \r\n", "\r\n", - "2024-08-20 02:23:48 (103 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-08-21 00:48:34 (182 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -193,10 +200,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:48.406808Z", - "iopub.status.busy": "2024-08-20T02:23:48.406400Z", - "iopub.status.idle": "2024-08-20T02:23:50.001165Z", - "shell.execute_reply": "2024-08-20T02:23:50.000519Z" + "iopub.execute_input": "2024-08-21T00:48:34.479751Z", + "iopub.status.busy": "2024-08-21T00:48:34.479377Z", + "iopub.status.idle": "2024-08-21T00:48:35.780789Z", + "shell.execute_reply": "2024-08-21T00:48:35.780239Z" }, "nbsphinx": "hidden" }, @@ -207,7 +214,7 @@ "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -233,10 +240,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.004031Z", - "iopub.status.busy": "2024-08-20T02:23:50.003445Z", - "iopub.status.idle": "2024-08-20T02:23:50.007127Z", - "shell.execute_reply": "2024-08-20T02:23:50.006582Z" + "iopub.execute_input": "2024-08-21T00:48:35.783186Z", + "iopub.status.busy": "2024-08-21T00:48:35.782857Z", + "iopub.status.idle": "2024-08-21T00:48:35.786293Z", + "shell.execute_reply": "2024-08-21T00:48:35.785824Z" } }, "outputs": [], @@ -286,10 +293,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.009438Z", - "iopub.status.busy": "2024-08-20T02:23:50.009017Z", - "iopub.status.idle": "2024-08-20T02:23:50.012211Z", - "shell.execute_reply": "2024-08-20T02:23:50.011731Z" + "iopub.execute_input": "2024-08-21T00:48:35.788435Z", + "iopub.status.busy": "2024-08-21T00:48:35.788082Z", + "iopub.status.idle": "2024-08-21T00:48:35.791210Z", + "shell.execute_reply": "2024-08-21T00:48:35.790645Z" }, "nbsphinx": "hidden" }, @@ -307,10 +314,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.014371Z", - "iopub.status.busy": "2024-08-20T02:23:50.013919Z", - "iopub.status.idle": "2024-08-20T02:23:59.245717Z", - "shell.execute_reply": "2024-08-20T02:23:59.245080Z" + "iopub.execute_input": "2024-08-21T00:48:35.793110Z", + "iopub.status.busy": "2024-08-21T00:48:35.792936Z", + "iopub.status.idle": "2024-08-21T00:48:44.878008Z", + "shell.execute_reply": "2024-08-21T00:48:44.877430Z" } }, "outputs": [], @@ -384,10 +391,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.248529Z", - "iopub.status.busy": "2024-08-20T02:23:59.248146Z", - "iopub.status.idle": "2024-08-20T02:23:59.253721Z", - "shell.execute_reply": "2024-08-20T02:23:59.253260Z" + "iopub.execute_input": "2024-08-21T00:48:44.880606Z", + "iopub.status.busy": "2024-08-21T00:48:44.880228Z", + "iopub.status.idle": "2024-08-21T00:48:44.885938Z", + "shell.execute_reply": "2024-08-21T00:48:44.885464Z" }, "nbsphinx": "hidden" }, @@ -427,10 +434,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.255898Z", - "iopub.status.busy": "2024-08-20T02:23:59.255557Z", - "iopub.status.idle": "2024-08-20T02:23:59.636940Z", - "shell.execute_reply": "2024-08-20T02:23:59.636377Z" + "iopub.execute_input": "2024-08-21T00:48:44.887970Z", + "iopub.status.busy": "2024-08-21T00:48:44.887607Z", + "iopub.status.idle": "2024-08-21T00:48:45.236413Z", + "shell.execute_reply": "2024-08-21T00:48:45.235748Z" } }, "outputs": [], @@ -467,10 +474,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.639442Z", - "iopub.status.busy": "2024-08-20T02:23:59.639072Z", - "iopub.status.idle": "2024-08-20T02:23:59.643596Z", - "shell.execute_reply": "2024-08-20T02:23:59.643126Z" + "iopub.execute_input": "2024-08-21T00:48:45.238797Z", + "iopub.status.busy": "2024-08-21T00:48:45.238594Z", + "iopub.status.idle": "2024-08-21T00:48:45.242741Z", + "shell.execute_reply": "2024-08-21T00:48:45.242293Z" } }, "outputs": [ @@ -542,10 +549,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.645735Z", - "iopub.status.busy": "2024-08-20T02:23:59.645420Z", - "iopub.status.idle": "2024-08-20T02:24:02.450516Z", - "shell.execute_reply": "2024-08-20T02:24:02.449822Z" + "iopub.execute_input": "2024-08-21T00:48:45.244879Z", + "iopub.status.busy": "2024-08-21T00:48:45.244540Z", + "iopub.status.idle": "2024-08-21T00:48:47.886104Z", + "shell.execute_reply": "2024-08-21T00:48:47.885240Z" } }, "outputs": [], @@ -567,10 +574,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.453944Z", - "iopub.status.busy": "2024-08-20T02:24:02.453026Z", - "iopub.status.idle": "2024-08-20T02:24:02.457210Z", - "shell.execute_reply": "2024-08-20T02:24:02.456664Z" + "iopub.execute_input": "2024-08-21T00:48:47.889759Z", + "iopub.status.busy": "2024-08-21T00:48:47.888760Z", + "iopub.status.idle": "2024-08-21T00:48:47.893081Z", + "shell.execute_reply": "2024-08-21T00:48:47.892524Z" } }, "outputs": [ @@ -606,10 +613,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.459333Z", - "iopub.status.busy": "2024-08-20T02:24:02.458986Z", - "iopub.status.idle": "2024-08-20T02:24:02.464337Z", - "shell.execute_reply": "2024-08-20T02:24:02.463806Z" + "iopub.execute_input": "2024-08-21T00:48:47.895029Z", + "iopub.status.busy": "2024-08-21T00:48:47.894719Z", + "iopub.status.idle": "2024-08-21T00:48:47.900629Z", + "shell.execute_reply": "2024-08-21T00:48:47.900158Z" } }, "outputs": [ @@ -787,10 +794,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.466242Z", - "iopub.status.busy": "2024-08-20T02:24:02.466067Z", - "iopub.status.idle": "2024-08-20T02:24:02.492938Z", - "shell.execute_reply": "2024-08-20T02:24:02.492457Z" + "iopub.execute_input": "2024-08-21T00:48:47.902736Z", + "iopub.status.busy": "2024-08-21T00:48:47.902396Z", + "iopub.status.idle": "2024-08-21T00:48:47.928872Z", + "shell.execute_reply": "2024-08-21T00:48:47.928414Z" } }, "outputs": [ @@ -892,10 +899,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.495013Z", - "iopub.status.busy": "2024-08-20T02:24:02.494699Z", - "iopub.status.idle": "2024-08-20T02:24:02.499367Z", - "shell.execute_reply": "2024-08-20T02:24:02.498819Z" + "iopub.execute_input": "2024-08-21T00:48:47.931025Z", + "iopub.status.busy": "2024-08-21T00:48:47.930625Z", + "iopub.status.idle": "2024-08-21T00:48:47.935094Z", + "shell.execute_reply": "2024-08-21T00:48:47.934549Z" } }, "outputs": [ @@ -969,10 +976,10 @@ "id": "db0b5179", "metadata": { "execution": { - 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Source code for cleanlab.datalab.datalab

 )
 from cleanlab.datalab.internal.serialize import _Serializer
 from cleanlab.datalab.internal.task import Task
-from cleanlab.datalab.internal.spurious_correlation import SpuriousCorrelations
 
 if TYPE_CHECKING:  # pragma: no cover
     import numpy.typing as npt
@@ -746,7 +745,6 @@ 
Source code for cleanlab.datalab.datalab
         self.cleanlab_version = cleanlab.version.__version__
         self.verbosity = verbosity
         self._imagelab = create_imagelab(dataset=self.data, image_key=image_key)
-        self._correlations_df = pd.DataFrame(columns=["property", "score"])
 
         # Create the builder for DataIssues
         builder = _DataIssuesBuilder(self._data)
@@ -1034,7 +1032,6 @@ 
Source code for cleanlab.datalab.datalab
             show_summary_score=show_summary_score,
             show_all_issues=show_all_issues,
             imagelab=self._imagelab,
-            correlations_df=self._correlations_df,
         )
         reporter.report(num_examples=num_examples)

 
@@ -1251,60 +1248,7 @@ 
Source code for cleanlab.datalab.datalab
         datalab = _Serializer.deserialize(path=path, data=data)
         load_message = f"Datalab loaded from folder: {path}"
         print(load_message)
-        return datalab

-
-    def _spurious_correlation(self) -> pd.DataFrame:
-        """
-        Assess potential spurious correlations in issue severity scores.
-
-        This method calculates scores indicating the likelihood of spurious correlations
-        for various issue severity scores in the dataset, as estimated by the `find_issues()` method.
-        Currently, it focuses on severity scores related to image attributes.
-        If `find_issues()` has not been called, it raises a ValueError.
-
-        Returns
-        -------
-        `correlations_df` : pandas.DataFrame
-            A DataFrame containing the calculated correlations for each property, excluding 'class_imbalance_score'.
-            The DataFrame includes:
-            - 'property' : str
-                The name of the property.
-            - 'score' : float
-                The spurious correlation score (between 0 and 1) for the property,
-                where a low score indicates a higher likelihood of spurious correlation,
-                and a high score indicates a lower likelihood.
-
-        Raises
-        ------
-        ValueError
-            If the issues have not been identified (i.e., `find_issues()` has not been called).
-
-        Notes
-        -----
-        This method currently focuses on image-related severity scores, with potential for future expansions.
-        """
-        try:
-            issues = self.get_issues()
-        except ValueError:
-            raise ValueError(
-                "Please call find_issues() before proceeding with finding Spurious Correlations"
-            )
-
-        if not all(
-            default_cleanvision_issue + "_score" in issues.columns.tolist()
-            for default_cleanvision_issue in DEFAULT_CLEANVISION_ISSUES.keys()
-        ):
-            raise ValueError("All vision issue scores are not computed by get_issues() method")
-
-        cleanvision_issues_columns = [
-            default_cleanvision_issue + "_score"
-            for default_cleanvision_issue in DEFAULT_CLEANVISION_ISSUES.keys()
-        ]
-        issues_score_data = issues[cleanvision_issues_columns]
-        property_correlations = SpuriousCorrelations(data=issues_score_data, labels=self.labels)
-        correlations_df = property_correlations.calculate_correlations()
-
-        return correlations_df

+        return datalab
diff --git a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst index ee6f09fe8..783bc4250 100644 --- a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst +++ b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst @@ -101,7 +101,7 @@ Some metadata about label issues is stored in the `issues` attribute of the Data Let's look at one way to access this information. .. testcode:: - + lab.get_issues("label").sort_values("label_score").head(5) The output will look something like this: @@ -118,7 +118,7 @@ The output will look something like this: ``is_label_issue`` ~~~~~~~~~~~~~~~~~~ -A boolean column that flags examples with label issues. +A boolean column that flags examples with label issues. If `True`, the example is estimated to have a label issue. If `False`, the example is estimated to not have a label issue. @@ -194,7 +194,7 @@ A boolean column, where `True` indicates that an example is identified as an out ``outlier_score`` ~~~~~~~~~~~~~~~~~ -A numeric column with scores between 0 and 1. +A numeric column with scores between 0 and 1. A smaller value for an example indicates that it is less common or typical in the dataset, suggesting that it is more likely to be an outlier. @@ -236,12 +236,12 @@ The output will look something like this: .. testoutput:: - is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor - 36 True 0.066009 [11, 80] 0.003906 - 11 True 0.066009 [36] 0.003906 - 80 True 0.093245 [36] 0.005599 - 27 False 0.156720 [] 0.009751 - 72 False 0.156720 [] 0.009751 + is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor + 36 True 0.066009 [11, 80] 0.003906 + 11 True 0.066009 [36] 0.003906 + 80 True 0.093245 [36] 0.005599 + 27 False 0.156720 [] 0.009751 + 72 False 0.156720 [] 0.009751 ``is_near_duplicate_issue`` @@ -399,6 +399,76 @@ To detect these issues, simply specify the `image_key` argument in :py:meth:`~cl This functionality currently works only with Hugging Face datasets. You can convert other local dataset formats into a Hugging Face dataset by following `this guide `_. More information on these image-specific issues is available in the `CleanVision package `_ . +Spurious Correlations between image-specific properties and labels +------------------------------------------------------------------ + +Based on the :ref:`image properties discussed earlier `, Datalab can also look for spurious correlations between image properties and the labels in the dataset. +These are unintended relationships between irrelevant features in images and the given labels, which ML models may easily exploit during training without learning the relevant features. +Once deployed, such models would consistently fail to generalize on unseen data where these spurious correlations most likely don't hold. + +Spurious correlations may arise in the dataset due to various reasons, such as: + +- Images for certain classes might be consistently captured under specific environmental conditions. +- Preprocessing techniques applied to the data might introduce systematic differences across classes. +- Objects of different classes may be systematically photographed in particular ways. + +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. + +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: + +.. code:: + + lab.get_info("spurious_correlations")["correlations_df"] + + +The output will look something like this: + +.. testoutput:: + + property score + 0 blurry_score 0.559 + 1 dark_score 0.808 + 2 light_score 0.723 + 3 odd_size_score 0.957 + 4 odd_aspect_ratio_score 0.835 + 5 grayscale_score 0.003 # Likely to be spuriously correlated with the labels + 6 low_information_score 0.688 + + +.. warning:: + + Note that the label uncorrelatedness scores are *not* stored in the `issues` attribute of Datalab. + +``property`` +~~~~~~~~~~~~ + +A categorical column that identifies specific image-related characteristics assessed for potential spurious correlations with the class labels. Each entry in this column represents a distinct property of the images, such as blurriness, darkness, or grayscale, which may or may not be correlated with the labels. + +``score`` +~~~~~~~~~ + +A numeric column that gives the level of label uncorrelatedness for each image-specific property computed while calling `lab.find_issues()`. The score lies between 0 and 1. The lower the score for an image-property, the more correlated the image-property is with the given labels. + +.. tip:: + + This type of issue has the issue name `"spurious_correlations"`. + + Run a check for this particular kind of issue by calling :py:meth:`Datalab.find_issues() ` like so: + + .. code-block:: python + + # `lab` is a Datalab instance + lab.find_issues(..., issue_types = {"spurious_correlations": {}}) + + + Underperforming Group Issue --------------------------- diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index 6c4abfb70..b68260d38 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 e86ca813f..836f9471a 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 f46e4ba03..efa7401c2 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 059b3572f..c59d384e4 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 2908897d6..e900209e7 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 256ff2972..4106ce143 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 81529d474..f2d873dc7 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/workflows.ipynb b/master/_sources/tutorials/datalab/workflows.ipynb index ccd7d003e..462ca3b79 100644 --- a/master/_sources/tutorials/datalab/workflows.ipynb +++ b/master/_sources/tutorials/datalab/workflows.ipynb @@ -1450,10 +1450,10 @@ "source": [ "from IPython.display import display\n", "\n", - "# Get the correlation scores for image properties\n", - "correlation_scores = lab._correlations_df\n", - "print(\"Correlation scores for image properties:\")\n", - "display(correlation_scores)\n", + "# Get scores for label uncorrelatedness with image properties\n", + "label_uncorrelatedness_scores = lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for image properties:\")\n", + "display(label_uncorrelatedness_scores)\n", "\n", "# Get image-specific issues\n", "issue_name = \"dark\"\n", @@ -1466,19 +1466,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n", - "\n", "Interpreting the results:\n", "\n", - "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", + "1. **Label Uncorrelatedness Scores**: The `label_uncorrelatedness_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n", "\n", - "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", + "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the label_uncorrelatedness_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", "\n", "For real-world datasets, pay attention to:\n", "\n", - "- Properties with notably low scores in the correlation_scores DataFrame\n", + "- Properties with notably low scores in the label_uncorrelatedness_scores DataFrame\n", "- Prevalent issues in the image_issues DataFrame\n", "\n", "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n", @@ -1486,9 +1483,50 @@ "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_scores_labels(lab, property=\"dark_score\"):\n", + " \"\"\"\n", + " Plots the scores of image-specific properties like 'dark_score', 'blurry_score', etc. \n", + " against labels for each instance in the dataset using 'Datalab' object.\n", + "\n", + " Parameters:\n", + " -----------\n", + " lab : 'Datalab' object\n", + " \n", + " property : str, optional\n", + " The name of the property to be plotted against the labels.\n", + " \n", + " Returns:\n", + " --------\n", + " None\n", + " This function does not return any value. It generates a plot of the specified \n", + " property against the labels.\n", + " \"\"\"\n", + " issues_copy = lab.issues.copy()\n", + " issues_copy[\"label\"] = lab.labels\n", + " issues_copy.boxplot(column=[property], by=\"label\")\n", + "\n", + "# Plotting 'dark_score' value of each instance in the dataset against class label\n", + "plot_scores_labels(lab, \"dark_score\")" + ] + }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "The above plot illustrates the distribution of dark scores across class labels. In this dataset, 100 images from the `Frog` class (Class 0 in the plot) have been darkened, while 100 images from the `Truck` class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the 'darkness' feature and the class labels: `Frog` images are dark, whereas `Truck` images are not. We can see that the `dark_score` values between the two classes are non-overlapping. This characteristic of the dataset is identified by `Datalab`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n", "\n", @@ -1498,7 +1536,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "nbsphinx": "hidden" + }, "outputs": [], "source": [ "# Load the original dataset\n", @@ -1510,8 +1550,8 @@ "original_lab.find_issues()\n", "\n", "# Compare correlation scores\n", - "original_scores = original_lab._correlations_df\n", - "print(\"Correlation scores for original dataset:\")\n", + "original_scores = original_lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for original dataset:\")\n", "display(original_scores)\n", "\n", "# Compare image-specific issues\n", @@ -1522,15 +1562,38 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "When comparing the results:\n", "\n", - "1. Look for differences in the correlation scores, especially for the 'dark' property.\n", + "1. Look for differences in the label uncorrelatedness scores, especially for the 'dark' property.\n", "2. Compare the number and types of image-specific issues detected.\n", "\n", "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. This comparison highlights how spurious correlations can be detected by `Datalab`." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "nbsphinx": "hidden" + }, + "outputs": [], + "source": [ + "# Plotting 'dark_score' value of each instance in the original dataset against class label\n", + "plot_scores_labels(original_lab, \"dark_score\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbsphinx": "hidden" + }, + "source": [ + "The above plot illustrates the distribution of dark scores across class labels. In this dataset, 100 images each from the classes `Frog` (Class 0 in the plot) and `Truck` (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. There is no apparent spurious correlation with respect to the 'darkness' feature and class labels. We can see that the `dark_score` values between the two classes are highly overlapping." + ] } ], "metadata": { diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index be8265249..635147e61 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 a1479fe19..47a1e50a1 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 eed222f81..e7e3a5555 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 494af573e..138add5f4 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 f27a2c1a0..7c4abb62d 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 141d59f05..153e4084d 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 ac364caa8..45eff23b3 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 6b578cffe..5d26c7f28 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 02af03482..2bcb0527c 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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 fcf356a23..6da5356ed 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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/cleanlab/datalab/guide/index.html b/master/cleanlab/datalab/guide/index.html index f3e6320c4..0f81ffb54 100644 --- a/master/cleanlab/datalab/guide/index.html +++ b/master/cleanlab/datalab/guide/index.html @@ -650,6 +650,7 @@

Image-specific Issuesthis guide. More information on these image-specific issues is available in the CleanVision package .

+
+

Spurious Correlations between image-specific properties and labels#

+

Based on the image properties discussed earlier, Datalab can also look for spurious correlations between image properties and the labels in the dataset. +These are unintended relationships between irrelevant features in images and the given labels, which ML models may easily exploit during training without learning the relevant features. +Once deployed, such models would consistently fail to generalize on unseen data where these spurious correlations most likely don’t hold.

+

Spurious correlations may arise in the dataset due to various reasons, such as:

+
    +

  • Images for certain classes might be consistently captured under specific environmental conditions.

    • +

  • Preprocessing techniques applied to the data might introduce systematic differences across classes.

    • +

  • Objects of different classes may be systematically photographed in particular ways.

    • +
+

Spurious Correlations are checked for when Datalab is initialized for an image dataset with the image_key keyword argument, +after checking for 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.

+

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:

+
lab.get_info("spurious_correlations")["correlations_df"]
+
+
+

The output will look something like this:

+
                     property         score
+0                blurry_score          0.559
+1                  dark_score          0.808
+2                 light_score          0.723
+3              odd_size_score          0.957
+4      odd_aspect_ratio_score          0.835
+5             grayscale_score          0.003  # Likely to be spuriously correlated with the labels
+6       low_information_score          0.688
+
+
+
+

Warning

+

Note that the label uncorrelatedness scores are not stored in the issues attribute of Datalab.

+
+
+

property#

+

A categorical column that identifies specific image-related characteristics assessed for potential spurious correlations with the class labels. Each entry in this column represents a distinct property of the images, such as blurriness, darkness, or grayscale, which may or may not be correlated with the labels.

+
+
+

score#

+

A numeric column that gives the level of label uncorrelatedness for each image-specific property computed while calling lab.find_issues(). The score lies between 0 and 1. The lower the score for an image-property, the more correlated the image-property is with the given labels.

+
+

Tip

+

This type of issue has the issue name "spurious_correlations".

+

Run a check for this particular kind of issue by calling Datalab.find_issues() like so:

+
# `lab` is a Datalab instance
+lab.find_issues(..., issue_types = {"spurious_correlations": {}})
+
+
+
+
+

Underperforming Group Issue#

An underperforming group refers to a cluster of similar examples (i.e. a slice) in the dataset for which the ML model predictions are poor. The examples in this underperforming group may have noisy labels or feature values, or the trained ML model may not have learned how to properly handle them (consider collecting more data from this subpopulation or up-weighting the existing data from this group).

@@ -1554,6 +1608,11 @@

Cleanlab Studio (Easy Mode)Image-specific Issues +
  • Spurious Correlations between image-specific properties and labels +
  • Underperforming Group Issue
    • is_underperforming_group_issue
    • underperforming_group_score
      • diff --git a/master/objects.inv b/master/objects.inv index eef5aeabda3ad4c644ebe4c21175a15df5f48bc1..0a12d8188cbdd4cbaeb194ad561e1488afdcda61 100644 GIT binary patch delta 28768 zcmV)tK$pL!t^(w)0CcaZ`(&i)pRlT~#D`g+s!iKq(DS z;BbM;uLzMzs~ffB+9r@ow@#pJ-*p^_aLZW)X9A0Wvk1B@f|dn)<74Q^^Q7&1`{;F$ zEnLPKfB3=LHVP2E-TP+Wh!o-PNSNs6bB#R4`*ZhO@D9G2kM|=`e=bLVMVJxgEN2F} z0B%N@G2ZfE#?WY_da_A4(=>drw>=IM?~<@UYgEAd&&sTOWN$uP1|RFcM&a&INsoBZ zCv?zC9k5UMj{VIZxk0OOP?dv5A{HzEZs(9>tK=|N56Z_*!>+JjiW zX%{M-_m8{RMd%Ooe}A;mrJ$;1Sp+(?ss@JZ5MF4cVdQ;wqlHR*|BfV}{-zW5=zsIgFsG#1}yF^}e zo~Ns~iWqhEA#xWMQ3-VG>&*@`s1$OhgVzP*)gFQ_fjd1D9K{cDLjFjRCq+9D+7vES zrBA9+^Ep@Xe?72fH=mSCx>d<*Q*DLTX|HlB$7*|Rxq=2d&3AyzndW}RS1PsAAlsI7 ztCIgVtUcz+sT`~AzYXgT!2@JUnh#|q=^*GUs>W6&0?zZb>4w;AvNp%);LYG6xQW`T zOTxdEZkPE9q4`6gXkW;`LKHj8;| zg%4mPY+)V0eQMtRWY(9w_oD;#AwvWINV z!LOkXf8f*D_%c3y7#PdhvkY??MTTfhsbdhOb++w9tOcUD)?UZUGA|x=mi09WUDESE z6d^{cv9tV$Y8u1z&r(_4fxBYl)oKRWqaB(}EvvLCCtrpu&|N~4D$Zf22F#SuuNBl? 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[[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": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "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"]], "4. (Optional) Compare with a Dataset Without Spurious Correlations": [[95, "4.-(Optional)-Compare-with-a-Dataset-Without-Spurious-Correlations"]], "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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"Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, 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"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": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "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. 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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. 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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"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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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 fd425ea02..644be8c52 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": "2024-08-20T02:13:14.190249Z", - "iopub.status.busy": "2024-08-20T02:13:14.189893Z", - "iopub.status.idle": "2024-08-20T02:13:15.741967Z", - "shell.execute_reply": "2024-08-20T02:13:15.741315Z" + "iopub.execute_input": "2024-08-21T00:38:23.829302Z", + "iopub.status.busy": "2024-08-21T00:38:23.829129Z", + "iopub.status.idle": "2024-08-21T00:38:25.080193Z", + "shell.execute_reply": "2024-08-21T00:38:25.079552Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:13:15.744709Z", - "iopub.status.busy": "2024-08-20T02:13:15.744377Z", - "iopub.status.idle": "2024-08-20T02:13:15.765049Z", - "shell.execute_reply": "2024-08-20T02:13:15.764575Z" + "iopub.execute_input": "2024-08-21T00:38:25.082901Z", + "iopub.status.busy": "2024-08-21T00:38:25.082445Z", + "iopub.status.idle": "2024-08-21T00:38:25.100491Z", + "shell.execute_reply": "2024-08-21T00:38:25.099905Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:15.767526Z", - "iopub.status.busy": "2024-08-20T02:13:15.767219Z", - "iopub.status.idle": "2024-08-20T02:13:15.909588Z", - "shell.execute_reply": "2024-08-20T02:13:15.908955Z" + "iopub.execute_input": "2024-08-21T00:38:25.102735Z", + "iopub.status.busy": "2024-08-21T00:38:25.102340Z", + "iopub.status.idle": "2024-08-21T00:38:25.258889Z", + "shell.execute_reply": "2024-08-21T00:38:25.258314Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:15.941170Z", - "iopub.status.busy": "2024-08-20T02:13:15.940735Z", - "iopub.status.idle": "2024-08-20T02:13:15.944576Z", - "shell.execute_reply": "2024-08-20T02:13:15.944095Z" + "iopub.execute_input": "2024-08-21T00:38:25.290166Z", + "iopub.status.busy": "2024-08-21T00:38:25.289738Z", + "iopub.status.idle": "2024-08-21T00:38:25.293435Z", + "shell.execute_reply": "2024-08-21T00:38:25.292975Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:15.946688Z", - "iopub.status.busy": "2024-08-20T02:13:15.946342Z", - "iopub.status.idle": "2024-08-20T02:13:15.954811Z", - "shell.execute_reply": "2024-08-20T02:13:15.954360Z" + "iopub.execute_input": "2024-08-21T00:38:25.295476Z", + "iopub.status.busy": "2024-08-21T00:38:25.295164Z", + "iopub.status.idle": "2024-08-21T00:38:25.303395Z", + "shell.execute_reply": "2024-08-21T00:38:25.302840Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:15.957011Z", - "iopub.status.busy": "2024-08-20T02:13:15.956687Z", - "iopub.status.idle": "2024-08-20T02:13:15.959396Z", - "shell.execute_reply": "2024-08-20T02:13:15.958850Z" + "iopub.execute_input": "2024-08-21T00:38:25.305638Z", + "iopub.status.busy": "2024-08-21T00:38:25.305312Z", + "iopub.status.idle": "2024-08-21T00:38:25.307821Z", + "shell.execute_reply": "2024-08-21T00:38:25.307370Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:15.961590Z", - "iopub.status.busy": "2024-08-20T02:13:15.961269Z", - "iopub.status.idle": "2024-08-20T02:13:16.492235Z", - "shell.execute_reply": "2024-08-20T02:13:16.491677Z" + "iopub.execute_input": "2024-08-21T00:38:25.309939Z", + "iopub.status.busy": "2024-08-21T00:38:25.309500Z", + "iopub.status.idle": "2024-08-21T00:38:25.827776Z", + "shell.execute_reply": "2024-08-21T00:38:25.827237Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:16.494794Z", - "iopub.status.busy": "2024-08-20T02:13:16.494399Z", - "iopub.status.idle": "2024-08-20T02:13:18.632020Z", - "shell.execute_reply": "2024-08-20T02:13:18.631415Z" + "iopub.execute_input": "2024-08-21T00:38:25.830272Z", + "iopub.status.busy": "2024-08-21T00:38:25.829915Z", + "iopub.status.idle": "2024-08-21T00:38:27.730582Z", + "shell.execute_reply": "2024-08-21T00:38:27.729902Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.634886Z", - "iopub.status.busy": "2024-08-20T02:13:18.634055Z", - "iopub.status.idle": "2024-08-20T02:13:18.644480Z", - "shell.execute_reply": "2024-08-20T02:13:18.643943Z" + "iopub.execute_input": "2024-08-21T00:38:27.733405Z", + "iopub.status.busy": "2024-08-21T00:38:27.732797Z", + "iopub.status.idle": "2024-08-21T00:38:27.743240Z", + "shell.execute_reply": "2024-08-21T00:38:27.742797Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.646750Z", - "iopub.status.busy": "2024-08-20T02:13:18.646452Z", - "iopub.status.idle": "2024-08-20T02:13:18.650757Z", - "shell.execute_reply": "2024-08-20T02:13:18.650211Z" + "iopub.execute_input": "2024-08-21T00:38:27.745348Z", + "iopub.status.busy": "2024-08-21T00:38:27.745054Z", + "iopub.status.idle": "2024-08-21T00:38:27.749349Z", + "shell.execute_reply": "2024-08-21T00:38:27.748910Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.653087Z", - "iopub.status.busy": "2024-08-20T02:13:18.652746Z", - "iopub.status.idle": "2024-08-20T02:13:18.659774Z", - "shell.execute_reply": "2024-08-20T02:13:18.659338Z" + "iopub.execute_input": "2024-08-21T00:38:27.751347Z", + "iopub.status.busy": "2024-08-21T00:38:27.751019Z", + "iopub.status.idle": "2024-08-21T00:38:27.759589Z", + "shell.execute_reply": "2024-08-21T00:38:27.759174Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.661673Z", - "iopub.status.busy": "2024-08-20T02:13:18.661496Z", - "iopub.status.idle": "2024-08-20T02:13:18.775965Z", - "shell.execute_reply": "2024-08-20T02:13:18.775376Z" + "iopub.execute_input": "2024-08-21T00:38:27.761623Z", + "iopub.status.busy": "2024-08-21T00:38:27.761294Z", + "iopub.status.idle": "2024-08-21T00:38:27.873862Z", + "shell.execute_reply": "2024-08-21T00:38:27.873367Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.778214Z", - "iopub.status.busy": "2024-08-20T02:13:18.777806Z", - "iopub.status.idle": "2024-08-20T02:13:18.780766Z", - "shell.execute_reply": "2024-08-20T02:13:18.780193Z" + "iopub.execute_input": "2024-08-21T00:38:27.876020Z", + "iopub.status.busy": "2024-08-21T00:38:27.875661Z", + "iopub.status.idle": "2024-08-21T00:38:27.878456Z", + "shell.execute_reply": "2024-08-21T00:38:27.877996Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:18.782931Z", - "iopub.status.busy": "2024-08-20T02:13:18.782591Z", - "iopub.status.idle": "2024-08-20T02:13:21.085811Z", - "shell.execute_reply": "2024-08-20T02:13:21.085149Z" + "iopub.execute_input": "2024-08-21T00:38:27.880370Z", + "iopub.status.busy": "2024-08-21T00:38:27.880037Z", + "iopub.status.idle": "2024-08-21T00:38:30.018806Z", + "shell.execute_reply": "2024-08-21T00:38:30.018038Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:21.088966Z", - "iopub.status.busy": "2024-08-20T02:13:21.088109Z", - "iopub.status.idle": "2024-08-20T02:13:21.100164Z", - "shell.execute_reply": "2024-08-20T02:13:21.099584Z" + "iopub.execute_input": "2024-08-21T00:38:30.022393Z", + "iopub.status.busy": "2024-08-21T00:38:30.021364Z", + "iopub.status.idle": "2024-08-21T00:38:30.033059Z", + "shell.execute_reply": "2024-08-21T00:38:30.032580Z" } }, "outputs": [ @@ -786,10 +786,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:21.102404Z", - "iopub.status.busy": "2024-08-20T02:13:21.102072Z", - "iopub.status.idle": "2024-08-20T02:13:21.144817Z", - "shell.execute_reply": "2024-08-20T02:13:21.144212Z" + "iopub.execute_input": "2024-08-21T00:38:30.035104Z", + "iopub.status.busy": "2024-08-21T00:38:30.034922Z", + "iopub.status.idle": "2024-08-21T00:38:30.076162Z", + "shell.execute_reply": "2024-08-21T00:38:30.075625Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index 4a0a588a5..c71da7fb6 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 
       This dataset has 10 classes.
-Classes: {'card_payment_fee_charged', 'cancel_transfer', 'getting_spare_card', 'visa_or_mastercard', 'change_pin', 'card_about_to_expire', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed'}
+Classes: {'card_payment_fee_charged', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard'}

      Let’s print the first example in the train set.

      @@ -880,43 +880,43 @@

      2. Load and format the text dataset
      -
      +
      -
      +
      -
      +
      -
      +
      -
      +
      -
      +
      -
      +
      @@ -1219,7 +1219,7 @@

      Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

      The modern AI pipeline automated with Cleanlab Studio

    • diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index b254946e7..39a2b9ea5 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:25.297540Z", - "iopub.status.busy": "2024-08-20T02:13:25.297364Z", - "iopub.status.idle": "2024-08-20T02:13:29.014190Z", - "shell.execute_reply": "2024-08-20T02:13:29.013610Z" + "iopub.execute_input": "2024-08-21T00:38:33.330886Z", + "iopub.status.busy": "2024-08-21T00:38:33.330718Z", + "iopub.status.idle": "2024-08-21T00:38:36.484677Z", + "shell.execute_reply": "2024-08-21T00:38:36.484040Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:13:29.016881Z", - "iopub.status.busy": "2024-08-20T02:13:29.016376Z", - "iopub.status.idle": "2024-08-20T02:13:29.019927Z", - "shell.execute_reply": "2024-08-20T02:13:29.019442Z" + "iopub.execute_input": "2024-08-21T00:38:36.487289Z", + "iopub.status.busy": "2024-08-21T00:38:36.486833Z", + "iopub.status.idle": "2024-08-21T00:38:36.490269Z", + "shell.execute_reply": "2024-08-21T00:38:36.489799Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.022049Z", - "iopub.status.busy": "2024-08-20T02:13:29.021711Z", - "iopub.status.idle": "2024-08-20T02:13:29.025287Z", - "shell.execute_reply": "2024-08-20T02:13:29.024806Z" + "iopub.execute_input": "2024-08-21T00:38:36.492216Z", + "iopub.status.busy": "2024-08-21T00:38:36.491870Z", + "iopub.status.idle": "2024-08-21T00:38:36.494763Z", + "shell.execute_reply": "2024-08-21T00:38:36.494336Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.027335Z", - "iopub.status.busy": "2024-08-20T02:13:29.026994Z", - "iopub.status.idle": "2024-08-20T02:13:29.075882Z", - "shell.execute_reply": "2024-08-20T02:13:29.075323Z" + "iopub.execute_input": "2024-08-21T00:38:36.496912Z", + "iopub.status.busy": "2024-08-21T00:38:36.496575Z", + "iopub.status.idle": "2024-08-21T00:38:36.571627Z", + "shell.execute_reply": "2024-08-21T00:38:36.571055Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.078179Z", - "iopub.status.busy": "2024-08-20T02:13:29.077812Z", - "iopub.status.idle": "2024-08-20T02:13:29.081326Z", - "shell.execute_reply": "2024-08-20T02:13:29.080863Z" + "iopub.execute_input": "2024-08-21T00:38:36.573906Z", + "iopub.status.busy": "2024-08-21T00:38:36.573550Z", + "iopub.status.idle": "2024-08-21T00:38:36.577167Z", + "shell.execute_reply": "2024-08-21T00:38:36.576719Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.083377Z", - "iopub.status.busy": "2024-08-20T02:13:29.083026Z", - "iopub.status.idle": "2024-08-20T02:13:29.086409Z", - "shell.execute_reply": "2024-08-20T02:13:29.085871Z" + "iopub.execute_input": "2024-08-21T00:38:36.579100Z", + "iopub.status.busy": "2024-08-21T00:38:36.578767Z", + "iopub.status.idle": "2024-08-21T00:38:36.581977Z", + "shell.execute_reply": "2024-08-21T00:38:36.581476Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_payment_fee_charged', 'cancel_transfer', 'getting_spare_card', 'visa_or_mastercard', 'change_pin', 'card_about_to_expire', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed'}\n" + "Classes: {'card_payment_fee_charged', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.088580Z", - "iopub.status.busy": "2024-08-20T02:13:29.088248Z", - "iopub.status.idle": "2024-08-20T02:13:29.091546Z", - "shell.execute_reply": "2024-08-20T02:13:29.091076Z" + "iopub.execute_input": "2024-08-21T00:38:36.584048Z", + "iopub.status.busy": "2024-08-21T00:38:36.583623Z", + "iopub.status.idle": "2024-08-21T00:38:36.586832Z", + "shell.execute_reply": "2024-08-21T00:38:36.586300Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.093715Z", - "iopub.status.busy": "2024-08-20T02:13:29.093277Z", - "iopub.status.idle": "2024-08-20T02:13:29.096626Z", - "shell.execute_reply": "2024-08-20T02:13:29.096091Z" + "iopub.execute_input": "2024-08-21T00:38:36.589029Z", + "iopub.status.busy": "2024-08-21T00:38:36.588703Z", + "iopub.status.idle": "2024-08-21T00:38:36.591835Z", + "shell.execute_reply": "2024-08-21T00:38:36.591373Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:29.098647Z", - "iopub.status.busy": "2024-08-20T02:13:29.098331Z", - "iopub.status.idle": "2024-08-20T02:13:33.247807Z", - "shell.execute_reply": "2024-08-20T02:13:33.247159Z" + "iopub.execute_input": "2024-08-21T00:38:36.593876Z", + "iopub.status.busy": "2024-08-21T00:38:36.593536Z", + "iopub.status.idle": "2024-08-21T00:38:40.983007Z", + "shell.execute_reply": "2024-08-21T00:38:40.982346Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93bc055fa52a41b2950890a95f668b90", + "model_id": "dd7949c1606c4f2e812fdd8feba321ae", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "096dc29eb7324ce69b2cb344b4ffa096", + "model_id": "2b46b6b742b140a6ae76b5e2138240c1", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ed05c40d2fa482887cb58e68a24a7dd", + "model_id": "a2aeb2f3cd514c72a5ef14cb7caf2cc3", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "351e2dae192e4a1fb0d9836dae7b8263", + "model_id": "f51effc7e938409b8c53ba4884203b53", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6b32367c5e54c2abfe2fcac9a3c9e2e", + "model_id": "a784357269da4d7db70a9fe43b14d88d", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:46.597071Z", - "iopub.status.busy": "2024-08-20T02:13:46.596714Z", - "iopub.status.idle": "2024-08-20T02:13:46.599826Z", - "shell.execute_reply": "2024-08-20T02:13:46.599363Z" + "iopub.execute_input": "2024-08-21T00:38:54.115118Z", + "iopub.status.busy": "2024-08-21T00:38:54.114765Z", + "iopub.status.idle": "2024-08-21T00:38:54.117966Z", + "shell.execute_reply": "2024-08-21T00:38:54.117531Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:46.601832Z", - "iopub.status.busy": "2024-08-20T02:13:46.601652Z", - "iopub.status.idle": "2024-08-20T02:13:46.606793Z", - "shell.execute_reply": "2024-08-20T02:13:46.606362Z" + "iopub.execute_input": "2024-08-21T00:38:54.120021Z", + "iopub.status.busy": "2024-08-21T00:38:54.119669Z", + "iopub.status.idle": "2024-08-21T00:38:54.124399Z", + "shell.execute_reply": "2024-08-21T00:38:54.123965Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-08-20T02:13:46.608751Z", - "iopub.status.busy": "2024-08-20T02:13:46.608576Z", - "iopub.status.idle": "2024-08-20T02:13:48.269878Z", - "shell.execute_reply": "2024-08-20T02:13:48.269047Z" + "iopub.execute_input": "2024-08-21T00:38:54.126535Z", + "iopub.status.busy": "2024-08-21T00:38:54.126266Z", + "iopub.status.idle": "2024-08-21T00:38:55.795303Z", + "shell.execute_reply": "2024-08-21T00:38:55.794501Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-08-20T02:13:48.272728Z", - "iopub.status.busy": "2024-08-20T02:13:48.272510Z", - "iopub.status.idle": "2024-08-20T02:13:48.283370Z", - "shell.execute_reply": "2024-08-20T02:13:48.282888Z" + "iopub.execute_input": "2024-08-21T00:38:55.798105Z", + "iopub.status.busy": "2024-08-21T00:38:55.797897Z", + "iopub.status.idle": "2024-08-21T00:38:55.809089Z", + "shell.execute_reply": "2024-08-21T00:38:55.808523Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:48.285601Z", - "iopub.status.busy": "2024-08-20T02:13:48.285208Z", - "iopub.status.idle": "2024-08-20T02:13:48.291129Z", - "shell.execute_reply": "2024-08-20T02:13:48.290644Z" + "iopub.execute_input": "2024-08-21T00:38:55.811394Z", + "iopub.status.busy": "2024-08-21T00:38:55.811062Z", + "iopub.status.idle": "2024-08-21T00:38:55.818505Z", + "shell.execute_reply": "2024-08-21T00:38:55.817935Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-08-20T02:13:48.293361Z", - "iopub.status.busy": "2024-08-20T02:13:48.293147Z", - "iopub.status.idle": "2024-08-20T02:13:48.779094Z", - "shell.execute_reply": "2024-08-20T02:13:48.778533Z" + "iopub.execute_input": "2024-08-21T00:38:55.820636Z", + "iopub.status.busy": "2024-08-21T00:38:55.820214Z", + "iopub.status.idle": "2024-08-21T00:38:56.495601Z", + "shell.execute_reply": "2024-08-21T00:38:56.495094Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:48.781311Z", - "iopub.status.busy": "2024-08-20T02:13:48.781074Z", - "iopub.status.idle": "2024-08-20T02:13:49.455972Z", - "shell.execute_reply": "2024-08-20T02:13:49.455320Z" + "iopub.execute_input": "2024-08-21T00:38:56.498001Z", + "iopub.status.busy": "2024-08-21T00:38:56.497507Z", + "iopub.status.idle": "2024-08-21T00:38:57.206792Z", + "shell.execute_reply": "2024-08-21T00:38:57.206297Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-08-20T02:13:49.458422Z", - "iopub.status.busy": "2024-08-20T02:13:49.458234Z", - "iopub.status.idle": "2024-08-20T02:13:49.476805Z", - "shell.execute_reply": "2024-08-20T02:13:49.476263Z" + "iopub.execute_input": "2024-08-21T00:38:57.209391Z", + "iopub.status.busy": "2024-08-21T00:38:57.208907Z", + "iopub.status.idle": "2024-08-21T00:38:57.227422Z", + "shell.execute_reply": "2024-08-21T00:38:57.226967Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:49.478880Z", - "iopub.status.busy": "2024-08-20T02:13:49.478553Z", - "iopub.status.idle": "2024-08-20T02:13:49.481706Z", - "shell.execute_reply": "2024-08-20T02:13:49.481242Z" + "iopub.execute_input": "2024-08-21T00:38:57.229421Z", + "iopub.status.busy": "2024-08-21T00:38:57.229123Z", + "iopub.status.idle": "2024-08-21T00:38:57.232237Z", + "shell.execute_reply": "2024-08-21T00:38:57.231762Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:13:49.483600Z", - "iopub.status.busy": "2024-08-20T02:13:49.483307Z", - "iopub.status.idle": "2024-08-20T02:14:04.209189Z", - "shell.execute_reply": "2024-08-20T02:14:04.208571Z" + "iopub.execute_input": "2024-08-21T00:38:57.234020Z", + "iopub.status.busy": "2024-08-21T00:38:57.233865Z", + "iopub.status.idle": "2024-08-21T00:39:11.766292Z", + "shell.execute_reply": "2024-08-21T00:39:11.765732Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-08-20T02:14:04.212003Z", - "iopub.status.busy": "2024-08-20T02:14:04.211645Z", - "iopub.status.idle": "2024-08-20T02:14:04.215446Z", - "shell.execute_reply": "2024-08-20T02:14:04.214943Z" + "iopub.execute_input": "2024-08-21T00:39:11.769092Z", + "iopub.status.busy": "2024-08-21T00:39:11.768633Z", + "iopub.status.idle": "2024-08-21T00:39:11.772767Z", + "shell.execute_reply": "2024-08-21T00:39:11.772281Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:04.217455Z", - "iopub.status.busy": "2024-08-20T02:14:04.217280Z", - "iopub.status.idle": "2024-08-20T02:14:04.939210Z", - "shell.execute_reply": "2024-08-20T02:14:04.938601Z" + "iopub.execute_input": "2024-08-21T00:39:11.774876Z", + "iopub.status.busy": "2024-08-21T00:39:11.774517Z", + "iopub.status.idle": "2024-08-21T00:39:12.518278Z", + "shell.execute_reply": "2024-08-21T00:39:12.517696Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-08-20T02:14:04.942267Z", - "iopub.status.busy": "2024-08-20T02:14:04.941852Z", - "iopub.status.idle": "2024-08-20T02:14:04.946876Z", - "shell.execute_reply": "2024-08-20T02:14:04.946360Z" + "iopub.execute_input": "2024-08-21T00:39:12.521953Z", + "iopub.status.busy": "2024-08-21T00:39:12.520997Z", + "iopub.status.idle": "2024-08-21T00:39:12.527737Z", + "shell.execute_reply": "2024-08-21T00:39:12.527232Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:04.949489Z", - "iopub.status.busy": "2024-08-20T02:14:04.949079Z", - "iopub.status.idle": "2024-08-20T02:14:05.062400Z", - "shell.execute_reply": "2024-08-20T02:14:05.061706Z" + "iopub.execute_input": "2024-08-21T00:39:12.531310Z", + "iopub.status.busy": "2024-08-21T00:39:12.530381Z", + "iopub.status.idle": "2024-08-21T00:39:12.643117Z", + "shell.execute_reply": "2024-08-21T00:39:12.642433Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:05.065011Z", - "iopub.status.busy": "2024-08-20T02:14:05.064625Z", - "iopub.status.idle": "2024-08-20T02:14:05.077492Z", - "shell.execute_reply": "2024-08-20T02:14:05.077005Z" + "iopub.execute_input": "2024-08-21T00:39:12.645771Z", + "iopub.status.busy": "2024-08-21T00:39:12.645187Z", + "iopub.status.idle": "2024-08-21T00:39:12.657945Z", + "shell.execute_reply": "2024-08-21T00:39:12.657478Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:05.079641Z", - "iopub.status.busy": "2024-08-20T02:14:05.079323Z", - 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"iopub.status.busy": "2024-08-20T02:14:05.095539Z", - "iopub.status.idle": "2024-08-20T02:14:05.101435Z", - "shell.execute_reply": "2024-08-20T02:14:05.100846Z" + "iopub.execute_input": "2024-08-21T00:39:12.676026Z", + "iopub.status.busy": "2024-08-21T00:39:12.675661Z", + "iopub.status.idle": "2024-08-21T00:39:12.681320Z", + "shell.execute_reply": "2024-08-21T00:39:12.680745Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-08-20T02:14:05.103654Z", - "iopub.status.busy": "2024-08-20T02:14:05.103331Z", - "iopub.status.idle": "2024-08-20T02:14:05.217593Z", - "shell.execute_reply": "2024-08-20T02:14:05.216991Z" + "iopub.execute_input": "2024-08-21T00:39:12.683436Z", + "iopub.status.busy": "2024-08-21T00:39:12.683094Z", + "iopub.status.idle": "2024-08-21T00:39:12.793769Z", + "shell.execute_reply": "2024-08-21T00:39:12.793288Z" }, "id": "ff1NFVlDoysO", "outputId": 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"version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 442884cae..cd95fafc8 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:09.631310Z", - "iopub.status.busy": "2024-08-20T02:14:09.630813Z", - "iopub.status.idle": "2024-08-20T02:14:11.103762Z", - "shell.execute_reply": "2024-08-20T02:14:11.103186Z" + "iopub.execute_input": "2024-08-21T00:39:17.056989Z", + "iopub.status.busy": "2024-08-21T00:39:17.056816Z", + "iopub.status.idle": "2024-08-21T00:39:18.254226Z", + "shell.execute_reply": "2024-08-21T00:39:18.253679Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:11.106401Z", - "iopub.status.busy": "2024-08-20T02:14:11.106094Z", - "iopub.status.idle": "2024-08-20T02:14:11.109292Z", - "shell.execute_reply": "2024-08-20T02:14:11.108792Z" + "iopub.execute_input": "2024-08-21T00:39:18.256790Z", + "iopub.status.busy": "2024-08-21T00:39:18.256394Z", + "iopub.status.idle": "2024-08-21T00:39:18.259452Z", + "shell.execute_reply": "2024-08-21T00:39:18.258894Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:11.111555Z", - "iopub.status.busy": "2024-08-20T02:14:11.111217Z", - "iopub.status.idle": "2024-08-20T02:14:11.119696Z", - "shell.execute_reply": "2024-08-20T02:14:11.119234Z" + "iopub.execute_input": "2024-08-21T00:39:18.261966Z", + "iopub.status.busy": "2024-08-21T00:39:18.261485Z", + "iopub.status.idle": "2024-08-21T00:39:18.270205Z", + "shell.execute_reply": "2024-08-21T00:39:18.269650Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:11.121764Z", - "iopub.status.busy": "2024-08-20T02:14:11.121432Z", - "iopub.status.idle": "2024-08-20T02:14:11.126387Z", - "shell.execute_reply": "2024-08-20T02:14:11.125955Z" + "iopub.execute_input": "2024-08-21T00:39:18.272341Z", + "iopub.status.busy": "2024-08-21T00:39:18.272022Z", + "iopub.status.idle": "2024-08-21T00:39:18.277180Z", + "shell.execute_reply": "2024-08-21T00:39:18.276618Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:11.128529Z", - "iopub.status.busy": "2024-08-20T02:14:11.128125Z", - "iopub.status.idle": "2024-08-20T02:14:11.136093Z", - "shell.execute_reply": "2024-08-20T02:14:11.135510Z" + "iopub.execute_input": "2024-08-21T00:39:18.279328Z", + "iopub.status.busy": "2024-08-21T00:39:18.278990Z", + "iopub.status.idle": "2024-08-21T00:39:18.464242Z", + "shell.execute_reply": "2024-08-21T00:39:18.463710Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:11.138313Z", - "iopub.status.busy": "2024-08-20T02:14:11.137860Z", - "iopub.status.idle": "2024-08-20T02:14:11.514567Z", - "shell.execute_reply": "2024-08-20T02:14:11.513985Z" + "iopub.execute_input": "2024-08-21T00:39:18.466893Z", + "iopub.status.busy": "2024-08-21T00:39:18.466445Z", + "iopub.status.idle": "2024-08-21T00:39:18.841190Z", + "shell.execute_reply": 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null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 12169.96 examples/s]" + "value": 132.0 } }, - "4487f6f64fd94b3cb5c3c17b8220113b": { + "a8e5ae2e343f4958ae1452652ca72421": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1649,25 +1637,53 @@ "width": null } }, - "a15664cb56d948b8a7fc666198715958": { + "a8f6a3da63c94dcfa8e0ed1268333e6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f82e92d5a42b4742a6928bfc3c436454", + "placeholder": "​", + "style": "IPY_MODEL_0a27653d59324f22a363402374ee5004", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13191.84 examples/s]" } }, - "bf99dfbdaff34107be9d5cb677d00e81": { + "b2ce6bcc658e4dddb4e66225d107c979": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c2e541c6e0834f8a8fae58f4f0e6dcf9", + "placeholder": "​", + "style": "IPY_MODEL_f1a8f33efc7747b8bcd74da075a1892d", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "c2e541c6e0834f8a8fae58f4f0e6dcf9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1720,23 +1736,25 @@ "width": null } }, - "d43b7af756e14f66b961078063ac1ede": { + "f1a8f33efc7747b8bcd74da075a1892d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f486c9ac3dcc46059aea4ed02ced34c6": { + "f82e92d5a42b4742a6928bfc3c436454": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1788,24 +1806,6 @@ "visibility": null, "width": null } - }, - "f997cd87c3c248a79bb5302e023796a7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index b93509401..ba9fe5565 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:16.901911Z", - "iopub.status.busy": "2024-08-20T02:14:16.901739Z", - "iopub.status.idle": "2024-08-20T02:14:18.364026Z", - "shell.execute_reply": "2024-08-20T02:14:18.363510Z" + "iopub.execute_input": "2024-08-21T00:39:23.980569Z", + "iopub.status.busy": "2024-08-21T00:39:23.980411Z", + "iopub.status.idle": "2024-08-21T00:39:25.175906Z", + "shell.execute_reply": "2024-08-21T00:39:25.175359Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.366625Z", - "iopub.status.busy": "2024-08-20T02:14:18.366169Z", - "iopub.status.idle": "2024-08-20T02:14:18.369251Z", - "shell.execute_reply": "2024-08-20T02:14:18.368758Z" + "iopub.execute_input": "2024-08-21T00:39:25.178380Z", + "iopub.status.busy": "2024-08-21T00:39:25.177978Z", + "iopub.status.idle": "2024-08-21T00:39:25.180854Z", + "shell.execute_reply": "2024-08-21T00:39:25.180393Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.371308Z", - "iopub.status.busy": "2024-08-20T02:14:18.371127Z", - "iopub.status.idle": "2024-08-20T02:14:18.379946Z", - "shell.execute_reply": "2024-08-20T02:14:18.379495Z" + "iopub.execute_input": "2024-08-21T00:39:25.182850Z", + "iopub.status.busy": "2024-08-21T00:39:25.182673Z", + "iopub.status.idle": "2024-08-21T00:39:25.192070Z", + "shell.execute_reply": "2024-08-21T00:39:25.191561Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.381762Z", - "iopub.status.busy": "2024-08-20T02:14:18.381587Z", - "iopub.status.idle": "2024-08-20T02:14:18.386252Z", - "shell.execute_reply": "2024-08-20T02:14:18.385823Z" + "iopub.execute_input": "2024-08-21T00:39:25.193872Z", + "iopub.status.busy": "2024-08-21T00:39:25.193695Z", + "iopub.status.idle": "2024-08-21T00:39:25.198516Z", + "shell.execute_reply": "2024-08-21T00:39:25.197972Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.388398Z", - "iopub.status.busy": "2024-08-20T02:14:18.388057Z", - "iopub.status.idle": "2024-08-20T02:14:18.396690Z", - "shell.execute_reply": "2024-08-20T02:14:18.396199Z" + "iopub.execute_input": "2024-08-21T00:39:25.200635Z", + "iopub.status.busy": "2024-08-21T00:39:25.200458Z", + "iopub.status.idle": "2024-08-21T00:39:25.383481Z", + "shell.execute_reply": "2024-08-21T00:39:25.382877Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.398709Z", - "iopub.status.busy": "2024-08-20T02:14:18.398370Z", - "iopub.status.idle": "2024-08-20T02:14:18.774250Z", - "shell.execute_reply": "2024-08-20T02:14:18.773669Z" + "iopub.execute_input": "2024-08-21T00:39:25.385899Z", + "iopub.status.busy": "2024-08-21T00:39:25.385675Z", + "iopub.status.idle": "2024-08-21T00:39:25.759439Z", + "shell.execute_reply": "2024-08-21T00:39:25.758831Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.776433Z", - "iopub.status.busy": "2024-08-20T02:14:18.776078Z", - "iopub.status.idle": "2024-08-20T02:14:18.778743Z", - "shell.execute_reply": "2024-08-20T02:14:18.778316Z" + "iopub.execute_input": "2024-08-21T00:39:25.761666Z", + "iopub.status.busy": "2024-08-21T00:39:25.761337Z", + "iopub.status.idle": "2024-08-21T00:39:25.764207Z", + "shell.execute_reply": "2024-08-21T00:39:25.763630Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.780701Z", - "iopub.status.busy": "2024-08-20T02:14:18.780429Z", - "iopub.status.idle": "2024-08-20T02:14:18.890959Z", - "shell.execute_reply": "2024-08-20T02:14:18.890474Z" + "iopub.execute_input": "2024-08-21T00:39:25.766292Z", + "iopub.status.busy": "2024-08-21T00:39:25.766112Z", + "iopub.status.idle": "2024-08-21T00:39:25.800111Z", + "shell.execute_reply": "2024-08-21T00:39:25.799663Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:18.892980Z", - "iopub.status.busy": "2024-08-20T02:14:18.892796Z", - "iopub.status.idle": "2024-08-20T02:14:20.999564Z", - "shell.execute_reply": "2024-08-20T02:14:20.998967Z" + "iopub.execute_input": "2024-08-21T00:39:25.802098Z", + "iopub.status.busy": "2024-08-21T00:39:25.801927Z", + "iopub.status.idle": "2024-08-21T00:39:27.874005Z", + "shell.execute_reply": "2024-08-21T00:39:27.873352Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.002285Z", - "iopub.status.busy": "2024-08-20T02:14:21.001645Z", - "iopub.status.idle": "2024-08-20T02:14:21.021940Z", - "shell.execute_reply": "2024-08-20T02:14:21.021435Z" + "iopub.execute_input": "2024-08-21T00:39:27.876509Z", + "iopub.status.busy": "2024-08-21T00:39:27.876188Z", + "iopub.status.idle": "2024-08-21T00:39:27.894832Z", + "shell.execute_reply": "2024-08-21T00:39:27.894359Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.024123Z", - "iopub.status.busy": "2024-08-20T02:14:21.023809Z", - "iopub.status.idle": "2024-08-20T02:14:21.031035Z", - "shell.execute_reply": "2024-08-20T02:14:21.030516Z" + "iopub.execute_input": "2024-08-21T00:39:27.896911Z", + "iopub.status.busy": "2024-08-21T00:39:27.896731Z", + "iopub.status.idle": "2024-08-21T00:39:27.903109Z", + "shell.execute_reply": "2024-08-21T00:39:27.902682Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.033200Z", - "iopub.status.busy": "2024-08-20T02:14:21.033017Z", - "iopub.status.idle": "2024-08-20T02:14:21.039166Z", - "shell.execute_reply": "2024-08-20T02:14:21.038534Z" + "iopub.execute_input": "2024-08-21T00:39:27.904960Z", + "iopub.status.busy": "2024-08-21T00:39:27.904786Z", + "iopub.status.idle": "2024-08-21T00:39:27.910643Z", + "shell.execute_reply": "2024-08-21T00:39:27.910192Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.041209Z", - "iopub.status.busy": "2024-08-20T02:14:21.041020Z", - "iopub.status.idle": "2024-08-20T02:14:21.051654Z", - "shell.execute_reply": "2024-08-20T02:14:21.051068Z" + "iopub.execute_input": "2024-08-21T00:39:27.912650Z", + "iopub.status.busy": "2024-08-21T00:39:27.912260Z", + "iopub.status.idle": "2024-08-21T00:39:27.922499Z", + "shell.execute_reply": "2024-08-21T00:39:27.921943Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.053899Z", - "iopub.status.busy": "2024-08-20T02:14:21.053551Z", - "iopub.status.idle": "2024-08-20T02:14:21.062658Z", - "shell.execute_reply": "2024-08-20T02:14:21.062079Z" + "iopub.execute_input": "2024-08-21T00:39:27.924654Z", + "iopub.status.busy": "2024-08-21T00:39:27.924252Z", + "iopub.status.idle": "2024-08-21T00:39:27.933462Z", + "shell.execute_reply": "2024-08-21T00:39:27.932887Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.064906Z", - "iopub.status.busy": "2024-08-20T02:14:21.064569Z", - "iopub.status.idle": "2024-08-20T02:14:21.071291Z", - "shell.execute_reply": "2024-08-20T02:14:21.070783Z" + "iopub.execute_input": "2024-08-21T00:39:27.935608Z", + "iopub.status.busy": "2024-08-21T00:39:27.935209Z", + "iopub.status.idle": "2024-08-21T00:39:27.942032Z", + "shell.execute_reply": "2024-08-21T00:39:27.941536Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.073343Z", - "iopub.status.busy": "2024-08-20T02:14:21.072998Z", - "iopub.status.idle": "2024-08-20T02:14:21.082039Z", - "shell.execute_reply": "2024-08-20T02:14:21.081586Z" + "iopub.execute_input": "2024-08-21T00:39:27.944151Z", + "iopub.status.busy": "2024-08-21T00:39:27.943805Z", + "iopub.status.idle": "2024-08-21T00:39:27.952822Z", + "shell.execute_reply": "2024-08-21T00:39:27.952264Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:21.084116Z", - "iopub.status.busy": "2024-08-20T02:14:21.083792Z", - "iopub.status.idle": "2024-08-20T02:14:21.100152Z", - "shell.execute_reply": "2024-08-20T02:14:21.099567Z" + "iopub.execute_input": "2024-08-21T00:39:27.954983Z", + "iopub.status.busy": "2024-08-21T00:39:27.954660Z", + "iopub.status.idle": "2024-08-21T00:39:27.970717Z", + "shell.execute_reply": "2024-08-21T00:39:27.970282Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 3655619e3..9ac0e60ed 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -727,31 +727,31 @@

    2. Fetch and normalize the Fashion-MNIST dataset

    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    [3]:
@@ -806,7 +806,7 @@ 
    2. Fetch and normalize the Fashion-MNIST dataset
    -
    +

    Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.

    @@ -1055,8 +1055,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.207
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.694
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.798
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.853
 Computing feature embeddings ...

    @@ -1064,7 +1064,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1078,7 +1078,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1087,8 +1087,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.161
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.733
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.923
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.625
 Computing feature embeddings ...

    @@ -1096,7 +1096,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1110,7 +1110,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1119,8 +1119,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.006
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.826
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.897
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.677
 Computing feature embeddings ...

    @@ -1128,7 +1128,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1142,7 +1142,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1204,7 +1204,7 @@

    7. Use cleanlab to find issues

    -
    +
    @@ -1212,6 +1212,7 @@

    7. Use cleanlab to find issues 
     Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1
+Finding spurious correlation issues in the dataset ...
 
 Audit complete. 7714 issues found in the dataset.

    @@ -2041,35 +2042,35 @@

    Low information images - low_information_score is_low_information_issue + low_information_score 53050 - 0.067975 True + 0.067975 40875 - 0.089929 True + 0.089929 9594 - 0.092601 True + 0.092601 34825 - 0.107744 True + 0.107744 37530 - 0.108516 True + 0.108516 @@ -2097,7 +2098,7 @@

    Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

    diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index a1a667c7d..8733ed39c 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:23.925032Z", - "iopub.status.busy": "2024-08-20T02:14:23.924856Z", - "iopub.status.idle": "2024-08-20T02:14:27.016805Z", - "shell.execute_reply": "2024-08-20T02:14:27.016161Z" + "iopub.execute_input": "2024-08-21T00:39:30.707451Z", + "iopub.status.busy": "2024-08-21T00:39:30.707266Z", + "iopub.status.idle": "2024-08-21T00:39:33.654586Z", + "shell.execute_reply": "2024-08-21T00:39:33.653966Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:27.019513Z", - "iopub.status.busy": "2024-08-20T02:14:27.019217Z", - "iopub.status.idle": "2024-08-20T02:14:27.023375Z", - "shell.execute_reply": "2024-08-20T02:14:27.022936Z" + "iopub.execute_input": "2024-08-21T00:39:33.657326Z", + "iopub.status.busy": "2024-08-21T00:39:33.657023Z", + "iopub.status.idle": "2024-08-21T00:39:33.660735Z", + "shell.execute_reply": "2024-08-21T00:39:33.660181Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:27.025468Z", - "iopub.status.busy": "2024-08-20T02:14:27.025101Z", - "iopub.status.idle": "2024-08-20T02:14:29.012079Z", - "shell.execute_reply": "2024-08-20T02:14:29.011486Z" + "iopub.execute_input": "2024-08-21T00:39:33.662880Z", + "iopub.status.busy": "2024-08-21T00:39:33.662538Z", + "iopub.status.idle": "2024-08-21T00:39:36.748194Z", + "shell.execute_reply": "2024-08-21T00:39:36.747710Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd6b09e86f8c40f38a15de31fd966867", + "model_id": "a20e61f2133d4a678bd2b2e50042b1f3", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0b797b78688457792e471764aadcac5", + "model_id": "463d739b98d0435d9baf793cf87f15e3", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d40a68c9c6774397bdea2fbe6bb9e2cb", + "model_id": "3f585e537a3d44e192823e0223eb4127", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aabff29de5e044afa3f214ce59f8d064", + "model_id": "0d0a8ce6b3874ba39d794c1add79af3a", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc6b594ae32e4d7789e07642981bb1cb", + "model_id": "5916bec07eac422cab70586495cde9c8", "version_major": 2, "version_minor": 0 }, @@ -260,10 +260,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:29.014307Z", - "iopub.status.busy": "2024-08-20T02:14:29.013979Z", - "iopub.status.idle": "2024-08-20T02:14:29.017756Z", - "shell.execute_reply": "2024-08-20T02:14:29.017217Z" + "iopub.execute_input": "2024-08-21T00:39:36.750537Z", + "iopub.status.busy": "2024-08-21T00:39:36.750183Z", + "iopub.status.idle": "2024-08-21T00:39:36.753899Z", + "shell.execute_reply": "2024-08-21T00:39:36.753423Z" } }, "outputs": [ @@ -288,17 +288,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:29.019769Z", - "iopub.status.busy": "2024-08-20T02:14:29.019421Z", - "iopub.status.idle": "2024-08-20T02:14:40.711282Z", - "shell.execute_reply": "2024-08-20T02:14:40.710695Z" + "iopub.execute_input": "2024-08-21T00:39:36.755910Z", + "iopub.status.busy": "2024-08-21T00:39:36.755571Z", + "iopub.status.idle": "2024-08-21T00:39:48.255444Z", + "shell.execute_reply": "2024-08-21T00:39:48.254912Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5faa54627d394ff5be9347e2a821a5d5", + "model_id": "c59aab2b6a7e4c4c980d67c2b0a3e630", "version_major": 2, "version_minor": 0 }, @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:40.714147Z", - "iopub.status.busy": "2024-08-20T02:14:40.713749Z", - "iopub.status.idle": "2024-08-20T02:14:59.249566Z", - "shell.execute_reply": "2024-08-20T02:14:59.248980Z" + "iopub.execute_input": "2024-08-21T00:39:48.257988Z", + "iopub.status.busy": "2024-08-21T00:39:48.257756Z", + "iopub.status.idle": "2024-08-21T00:40:07.067772Z", + "shell.execute_reply": "2024-08-21T00:40:07.067241Z" } }, "outputs": [], @@ -372,10 +372,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:59.252407Z", - "iopub.status.busy": "2024-08-20T02:14:59.251972Z", - "iopub.status.idle": "2024-08-20T02:14:59.256948Z", - "shell.execute_reply": "2024-08-20T02:14:59.256404Z" + "iopub.execute_input": "2024-08-21T00:40:07.070526Z", + "iopub.status.busy": "2024-08-21T00:40:07.070190Z", + "iopub.status.idle": "2024-08-21T00:40:07.075111Z", + "shell.execute_reply": "2024-08-21T00:40:07.074534Z" } }, "outputs": [], @@ -413,10 +413,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:59.259100Z", - "iopub.status.busy": "2024-08-20T02:14:59.258704Z", - "iopub.status.idle": "2024-08-20T02:14:59.262833Z", - "shell.execute_reply": "2024-08-20T02:14:59.262295Z" + "iopub.execute_input": "2024-08-21T00:40:07.077043Z", + "iopub.status.busy": "2024-08-21T00:40:07.076750Z", + "iopub.status.idle": "2024-08-21T00:40:07.080992Z", + "shell.execute_reply": "2024-08-21T00:40:07.080444Z" }, "nbsphinx": "hidden" }, @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:59.265013Z", - "iopub.status.busy": "2024-08-20T02:14:59.264751Z", - "iopub.status.idle": "2024-08-20T02:14:59.273828Z", - "shell.execute_reply": "2024-08-20T02:14:59.273254Z" + "iopub.execute_input": "2024-08-21T00:40:07.083423Z", + "iopub.status.busy": "2024-08-21T00:40:07.083014Z", + "iopub.status.idle": "2024-08-21T00:40:07.091943Z", + "shell.execute_reply": "2024-08-21T00:40:07.091414Z" }, "nbsphinx": "hidden" }, @@ -681,10 +681,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:59.276087Z", - "iopub.status.busy": "2024-08-20T02:14:59.275664Z", - "iopub.status.idle": "2024-08-20T02:14:59.303828Z", - "shell.execute_reply": "2024-08-20T02:14:59.303212Z" + "iopub.execute_input": "2024-08-21T00:40:07.093993Z", + "iopub.status.busy": "2024-08-21T00:40:07.093589Z", + "iopub.status.idle": "2024-08-21T00:40:07.120316Z", + "shell.execute_reply": "2024-08-21T00:40:07.119719Z" } }, "outputs": [], @@ -721,10 +721,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:14:59.306556Z", - "iopub.status.busy": "2024-08-20T02:14:59.306131Z", - "iopub.status.idle": "2024-08-20T02:15:33.542504Z", - "shell.execute_reply": "2024-08-20T02:15:33.541888Z" + "iopub.execute_input": "2024-08-21T00:40:07.122525Z", + "iopub.status.busy": "2024-08-21T00:40:07.122249Z", + "iopub.status.idle": "2024-08-21T00:40:40.782324Z", + "shell.execute_reply": "2024-08-21T00:40:40.781725Z" } }, "outputs": [ @@ -740,21 +740,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.207\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.798\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.694\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.853\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de4a40cbeae5420eaa285c1e15ff5c2b", + "model_id": "81607a3e18ca4baeb1b8f8f0aa78fd4b", "version_major": 2, "version_minor": 0 }, @@ -775,7 +775,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45ddc6839105425c957fa8cc282296cd", + "model_id": "e18ca4639102462dac92c3c5a84ab0f0", "version_major": 2, "version_minor": 0 }, @@ -798,21 +798,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.161\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.923\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.733\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.625\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df490d58807c40f381869a6efc0f5427", + "model_id": "c115bb12c8ec4ff9a7eb01faca32c461", "version_major": 2, "version_minor": 0 }, @@ -833,7 +833,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56ca127f2df5437f87fbbafdc821e5c3", + "model_id": "65d287d8c3144fc2b78a43f0bbd2cad9", "version_major": 2, "version_minor": 0 }, @@ -856,21 +856,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.006\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.897\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.826\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.677\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c04185b16fe24db68ca78fe46f88c7f6", + "model_id": "18d825b02ab240c8b132e33d7d8f86e8", "version_major": 2, "version_minor": 0 }, @@ -891,7 +891,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "441cfdef564242b99b8b1cc22c5d0f4a", + "model_id": "f64b8b29b1784e22abb666c97681a3dc", "version_major": 2, "version_minor": 0 }, @@ -970,10 +970,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:15:33.545264Z", - "iopub.status.busy": "2024-08-20T02:15:33.544679Z", - "iopub.status.idle": "2024-08-20T02:15:33.562115Z", - "shell.execute_reply": "2024-08-20T02:15:33.561639Z" + "iopub.execute_input": "2024-08-21T00:40:40.785014Z", + "iopub.status.busy": "2024-08-21T00:40:40.784590Z", + "iopub.status.idle": "2024-08-21T00:40:40.802127Z", + "shell.execute_reply": "2024-08-21T00:40:40.801583Z" } }, "outputs": [], @@ -998,10 +998,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:15:33.564262Z", - "iopub.status.busy": "2024-08-20T02:15:33.564075Z", - "iopub.status.idle": "2024-08-20T02:15:34.044704Z", - "shell.execute_reply": "2024-08-20T02:15:34.044079Z" + "iopub.execute_input": "2024-08-21T00:40:40.804293Z", + "iopub.status.busy": "2024-08-21T00:40:40.803995Z", + "iopub.status.idle": "2024-08-21T00:40:41.274993Z", + "shell.execute_reply": "2024-08-21T00:40:41.274435Z" } }, "outputs": [], @@ -1021,10 +1021,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:15:34.047591Z", - "iopub.status.busy": "2024-08-20T02:15:34.047128Z", - "iopub.status.idle": "2024-08-20T02:17:24.286699Z", - "shell.execute_reply": "2024-08-20T02:17:24.286013Z" + "iopub.execute_input": "2024-08-21T00:40:41.277482Z", + "iopub.status.busy": "2024-08-21T00:40:41.277118Z", + "iopub.status.idle": "2024-08-21T00:42:32.523063Z", + "shell.execute_reply": "2024-08-21T00:42:32.522397Z" } }, "outputs": [ @@ -1063,7 +1063,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1976bceab224f84a094c80cde4e8466", + "model_id": "d2f20d6f0ba44123bab8b7d235b6515d", "version_major": 2, "version_minor": 0 }, @@ -1078,7 +1078,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n" + "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n", + "Finding spurious correlation issues in the dataset ...\n" ] }, { @@ -1108,10 +1109,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:24.289218Z", - "iopub.status.busy": "2024-08-20T02:17:24.288839Z", - "iopub.status.idle": "2024-08-20T02:17:24.758407Z", - "shell.execute_reply": "2024-08-20T02:17:24.757697Z" + "iopub.execute_input": "2024-08-21T00:42:32.525907Z", + "iopub.status.busy": "2024-08-21T00:42:32.525282Z", + "iopub.status.idle": "2024-08-21T00:42:32.984283Z", + "shell.execute_reply": "2024-08-21T00:42:32.983705Z" } }, "outputs": [ @@ -1257,10 +1258,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:24.760793Z", - "iopub.status.busy": "2024-08-20T02:17:24.760383Z", - "iopub.status.idle": "2024-08-20T02:17:24.822881Z", - "shell.execute_reply": "2024-08-20T02:17:24.822298Z" + "iopub.execute_input": "2024-08-21T00:42:32.986800Z", + "iopub.status.busy": "2024-08-21T00:42:32.986351Z", + "iopub.status.idle": "2024-08-21T00:42:33.048656Z", + "shell.execute_reply": "2024-08-21T00:42:33.048105Z" } }, "outputs": [ @@ -1364,10 +1365,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:24.825273Z", - "iopub.status.busy": "2024-08-20T02:17:24.824880Z", - "iopub.status.idle": "2024-08-20T02:17:24.834267Z", - "shell.execute_reply": "2024-08-20T02:17:24.833809Z" + "iopub.execute_input": "2024-08-21T00:42:33.050934Z", + "iopub.status.busy": "2024-08-21T00:42:33.050587Z", + "iopub.status.idle": "2024-08-21T00:42:33.059646Z", + "shell.execute_reply": "2024-08-21T00:42:33.059194Z" } }, "outputs": [ @@ -1497,10 +1498,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:24.836217Z", - "iopub.status.busy": "2024-08-20T02:17:24.836040Z", - "iopub.status.idle": "2024-08-20T02:17:24.840828Z", - "shell.execute_reply": "2024-08-20T02:17:24.840359Z" + "iopub.execute_input": "2024-08-21T00:42:33.061792Z", + "iopub.status.busy": "2024-08-21T00:42:33.061531Z", + "iopub.status.idle": "2024-08-21T00:42:33.066696Z", + "shell.execute_reply": "2024-08-21T00:42:33.066258Z" }, "nbsphinx": "hidden" }, @@ -1546,10 +1547,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:24.843031Z", - "iopub.status.busy": "2024-08-20T02:17:24.842608Z", - "iopub.status.idle": "2024-08-20T02:17:25.361966Z", - "shell.execute_reply": "2024-08-20T02:17:25.361360Z" + "iopub.execute_input": "2024-08-21T00:42:33.068703Z", + "iopub.status.busy": "2024-08-21T00:42:33.068372Z", + "iopub.status.idle": "2024-08-21T00:42:33.570790Z", + "shell.execute_reply": "2024-08-21T00:42:33.570202Z" } }, "outputs": [ @@ -1584,10 +1585,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:25.364423Z", - "iopub.status.busy": "2024-08-20T02:17:25.364053Z", - "iopub.status.idle": "2024-08-20T02:17:25.372684Z", - "shell.execute_reply": "2024-08-20T02:17:25.372122Z" + "iopub.execute_input": "2024-08-21T00:42:33.573203Z", + "iopub.status.busy": "2024-08-21T00:42:33.572793Z", + "iopub.status.idle": "2024-08-21T00:42:33.581715Z", + "shell.execute_reply": "2024-08-21T00:42:33.581137Z" } }, "outputs": [ @@ -1754,10 +1755,10 @@ "execution_count": 21, "metadata": { "execution": { - 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"iopub.execute_input": "2024-08-20T02:17:26.889720Z", - "iopub.status.busy": "2024-08-20T02:17:26.889548Z", - "iopub.status.idle": "2024-08-20T02:17:26.897595Z", - "shell.execute_reply": "2024-08-20T02:17:26.897104Z" + "iopub.execute_input": "2024-08-21T00:42:35.086568Z", + "iopub.status.busy": "2024-08-21T00:42:35.086393Z", + "iopub.status.idle": "2024-08-21T00:42:35.094375Z", + "shell.execute_reply": "2024-08-21T00:42:35.093906Z" } }, "outputs": [ @@ -2410,47 +2411,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "

    " ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2471,10 +2472,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:26.899613Z", - "iopub.status.busy": "2024-08-20T02:17:26.899267Z", - "iopub.status.idle": "2024-08-20T02:17:27.096728Z", - "shell.execute_reply": "2024-08-20T02:17:27.096135Z" + "iopub.execute_input": "2024-08-21T00:42:35.096382Z", + "iopub.status.busy": "2024-08-21T00:42:35.096048Z", + "iopub.status.idle": "2024-08-21T00:42:35.290511Z", + "shell.execute_reply": "2024-08-21T00:42:35.289937Z" } }, "outputs": [ @@ -2514,10 +2515,10 @@ "execution_count": 31, "metadata": { "execution": { - 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"iopub.execute_input": "2024-08-20T02:17:30.675390Z", - "iopub.status.busy": "2024-08-20T02:17:30.675213Z", - "iopub.status.idle": "2024-08-20T02:17:32.099766Z", - "shell.execute_reply": "2024-08-20T02:17:32.099202Z" + "iopub.execute_input": "2024-08-21T00:42:39.020177Z", + "iopub.status.busy": "2024-08-21T00:42:39.020002Z", + "iopub.status.idle": "2024-08-21T00:42:40.165165Z", + "shell.execute_reply": "2024-08-21T00:42:40.164554Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:17:32.102440Z", - "iopub.status.busy": "2024-08-20T02:17:32.102010Z", - "iopub.status.idle": "2024-08-20T02:17:32.121474Z", - "shell.execute_reply": "2024-08-20T02:17:32.120839Z" + "iopub.execute_input": "2024-08-21T00:42:40.167673Z", + "iopub.status.busy": "2024-08-21T00:42:40.167399Z", + "iopub.status.idle": "2024-08-21T00:42:40.185888Z", + "shell.execute_reply": "2024-08-21T00:42:40.185329Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.124240Z", - "iopub.status.busy": "2024-08-20T02:17:32.123782Z", - "iopub.status.idle": "2024-08-20T02:17:32.161903Z", - "shell.execute_reply": "2024-08-20T02:17:32.161310Z" + "iopub.execute_input": "2024-08-21T00:42:40.188316Z", + "iopub.status.busy": "2024-08-21T00:42:40.187910Z", + "iopub.status.idle": "2024-08-21T00:42:40.224793Z", + "shell.execute_reply": "2024-08-21T00:42:40.224253Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.164301Z", - "iopub.status.busy": "2024-08-20T02:17:32.163892Z", - "iopub.status.idle": "2024-08-20T02:17:32.167503Z", - "shell.execute_reply": "2024-08-20T02:17:32.166961Z" + "iopub.execute_input": "2024-08-21T00:42:40.226826Z", + "iopub.status.busy": "2024-08-21T00:42:40.226427Z", + "iopub.status.idle": "2024-08-21T00:42:40.229894Z", + "shell.execute_reply": "2024-08-21T00:42:40.229350Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.169604Z", - "iopub.status.busy": "2024-08-20T02:17:32.169259Z", - "iopub.status.idle": "2024-08-20T02:17:32.176549Z", - "shell.execute_reply": "2024-08-20T02:17:32.176095Z" + "iopub.execute_input": "2024-08-21T00:42:40.231979Z", + "iopub.status.busy": "2024-08-21T00:42:40.231580Z", + "iopub.status.idle": "2024-08-21T00:42:40.239438Z", + "shell.execute_reply": "2024-08-21T00:42:40.238991Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.178752Z", - "iopub.status.busy": "2024-08-20T02:17:32.178403Z", - "iopub.status.idle": "2024-08-20T02:17:32.181096Z", - "shell.execute_reply": "2024-08-20T02:17:32.180623Z" + "iopub.execute_input": "2024-08-21T00:42:40.241382Z", + "iopub.status.busy": "2024-08-21T00:42:40.241211Z", + "iopub.status.idle": "2024-08-21T00:42:40.243885Z", + "shell.execute_reply": "2024-08-21T00:42:40.243336Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:32.182992Z", - "iopub.status.busy": "2024-08-20T02:17:32.182822Z", - "iopub.status.idle": "2024-08-20T02:17:35.334471Z", - "shell.execute_reply": "2024-08-20T02:17:35.333927Z" + "iopub.execute_input": "2024-08-21T00:42:40.245971Z", + "iopub.status.busy": "2024-08-21T00:42:40.245666Z", + "iopub.status.idle": "2024-08-21T00:42:43.325922Z", + "shell.execute_reply": "2024-08-21T00:42:43.325277Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:35.337140Z", - "iopub.status.busy": "2024-08-20T02:17:35.336953Z", - "iopub.status.idle": "2024-08-20T02:17:35.346018Z", - "shell.execute_reply": "2024-08-20T02:17:35.345550Z" + "iopub.execute_input": "2024-08-21T00:42:43.328986Z", + "iopub.status.busy": "2024-08-21T00:42:43.328446Z", + "iopub.status.idle": "2024-08-21T00:42:43.337987Z", + "shell.execute_reply": "2024-08-21T00:42:43.337426Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:35.347913Z", - "iopub.status.busy": "2024-08-20T02:17:35.347740Z", - "iopub.status.idle": "2024-08-20T02:17:37.530220Z", - "shell.execute_reply": "2024-08-20T02:17:37.529582Z" + "iopub.execute_input": "2024-08-21T00:42:43.340231Z", + "iopub.status.busy": "2024-08-21T00:42:43.339898Z", + "iopub.status.idle": "2024-08-21T00:42:45.300234Z", + "shell.execute_reply": "2024-08-21T00:42:45.299626Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.532667Z", - "iopub.status.busy": "2024-08-20T02:17:37.532314Z", - "iopub.status.idle": "2024-08-20T02:17:37.551727Z", - "shell.execute_reply": "2024-08-20T02:17:37.551251Z" + "iopub.execute_input": "2024-08-21T00:42:45.302539Z", + "iopub.status.busy": "2024-08-21T00:42:45.302223Z", + "iopub.status.idle": "2024-08-21T00:42:45.320815Z", + "shell.execute_reply": "2024-08-21T00:42:45.320344Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.553782Z", - "iopub.status.busy": "2024-08-20T02:17:37.553599Z", - "iopub.status.idle": "2024-08-20T02:17:37.562552Z", - "shell.execute_reply": "2024-08-20T02:17:37.562008Z" + "iopub.execute_input": "2024-08-21T00:42:45.323090Z", + "iopub.status.busy": "2024-08-21T00:42:45.322698Z", + "iopub.status.idle": "2024-08-21T00:42:45.330546Z", + "shell.execute_reply": "2024-08-21T00:42:45.330098Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.564682Z", - "iopub.status.busy": "2024-08-20T02:17:37.564263Z", - "iopub.status.idle": "2024-08-20T02:17:37.573623Z", - "shell.execute_reply": "2024-08-20T02:17:37.573057Z" + "iopub.execute_input": "2024-08-21T00:42:45.332636Z", + "iopub.status.busy": "2024-08-21T00:42:45.332312Z", + "iopub.status.idle": "2024-08-21T00:42:45.340860Z", + "shell.execute_reply": "2024-08-21T00:42:45.340292Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.575828Z", - "iopub.status.busy": "2024-08-20T02:17:37.575492Z", - "iopub.status.idle": "2024-08-20T02:17:37.583145Z", - "shell.execute_reply": "2024-08-20T02:17:37.582687Z" + "iopub.execute_input": "2024-08-21T00:42:45.342856Z", + "iopub.status.busy": "2024-08-21T00:42:45.342550Z", + "iopub.status.idle": "2024-08-21T00:42:45.350113Z", + "shell.execute_reply": "2024-08-21T00:42:45.349629Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.585205Z", - "iopub.status.busy": "2024-08-20T02:17:37.584870Z", - "iopub.status.idle": "2024-08-20T02:17:37.593427Z", - "shell.execute_reply": "2024-08-20T02:17:37.592949Z" + "iopub.execute_input": "2024-08-21T00:42:45.352205Z", + "iopub.status.busy": "2024-08-21T00:42:45.351869Z", + "iopub.status.idle": "2024-08-21T00:42:45.360446Z", + "shell.execute_reply": "2024-08-21T00:42:45.359939Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.595497Z", - "iopub.status.busy": "2024-08-20T02:17:37.595153Z", - "iopub.status.idle": "2024-08-20T02:17:37.602696Z", - "shell.execute_reply": "2024-08-20T02:17:37.602233Z" + "iopub.execute_input": "2024-08-21T00:42:45.362378Z", + "iopub.status.busy": "2024-08-21T00:42:45.362073Z", + "iopub.status.idle": "2024-08-21T00:42:45.369252Z", + "shell.execute_reply": "2024-08-21T00:42:45.368714Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.604723Z", - "iopub.status.busy": "2024-08-20T02:17:37.604387Z", - "iopub.status.idle": "2024-08-20T02:17:37.611506Z", - "shell.execute_reply": "2024-08-20T02:17:37.611059Z" + "iopub.execute_input": "2024-08-21T00:42:45.371424Z", + "iopub.status.busy": "2024-08-21T00:42:45.371097Z", + "iopub.status.idle": "2024-08-21T00:42:45.378113Z", + "shell.execute_reply": "2024-08-21T00:42:45.377624Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:37.613742Z", - "iopub.status.busy": "2024-08-20T02:17:37.613408Z", - "iopub.status.idle": "2024-08-20T02:17:37.621320Z", - "shell.execute_reply": "2024-08-20T02:17:37.620872Z" + "iopub.execute_input": "2024-08-21T00:42:45.380217Z", + "iopub.status.busy": "2024-08-21T00:42:45.379888Z", + "iopub.status.idle": "2024-08-21T00:42:45.388315Z", + "shell.execute_reply": "2024-08-21T00:42:45.387730Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index de73d6518..447c41a86 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -791,7 +791,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'getting_spare_card', 'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'change_pin'}
+Classes: {'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies'}

    Let’s view the i-th example in the dataset:

    diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index e8c1c1367..944c7f70a 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:40.653105Z", - "iopub.status.busy": "2024-08-20T02:17:40.652539Z", - "iopub.status.idle": "2024-08-20T02:17:43.889364Z", - "shell.execute_reply": "2024-08-20T02:17:43.888668Z" + "iopub.execute_input": "2024-08-21T00:42:48.197839Z", + "iopub.status.busy": "2024-08-21T00:42:48.197661Z", + "iopub.status.idle": "2024-08-21T00:42:50.967466Z", + "shell.execute_reply": "2024-08-21T00:42:50.966936Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:17:43.892128Z", - "iopub.status.busy": "2024-08-20T02:17:43.891640Z", - "iopub.status.idle": "2024-08-20T02:17:43.895466Z", - "shell.execute_reply": "2024-08-20T02:17:43.895014Z" + "iopub.execute_input": "2024-08-21T00:42:50.969963Z", + "iopub.status.busy": "2024-08-21T00:42:50.969666Z", + "iopub.status.idle": "2024-08-21T00:42:50.973228Z", + "shell.execute_reply": "2024-08-21T00:42:50.972662Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.897587Z", - "iopub.status.busy": "2024-08-20T02:17:43.897195Z", - "iopub.status.idle": "2024-08-20T02:17:43.900227Z", - "shell.execute_reply": "2024-08-20T02:17:43.899787Z" + "iopub.execute_input": "2024-08-21T00:42:50.975166Z", + "iopub.status.busy": "2024-08-21T00:42:50.974858Z", + "iopub.status.idle": "2024-08-21T00:42:50.978037Z", + "shell.execute_reply": "2024-08-21T00:42:50.977508Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.902374Z", - "iopub.status.busy": "2024-08-20T02:17:43.902037Z", - "iopub.status.idle": "2024-08-20T02:17:43.948123Z", - "shell.execute_reply": "2024-08-20T02:17:43.947597Z" + "iopub.execute_input": "2024-08-21T00:42:50.980278Z", + "iopub.status.busy": "2024-08-21T00:42:50.979852Z", + "iopub.status.idle": "2024-08-21T00:42:51.018730Z", + "shell.execute_reply": "2024-08-21T00:42:51.018187Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.950404Z", - "iopub.status.busy": "2024-08-20T02:17:43.950033Z", - "iopub.status.idle": "2024-08-20T02:17:43.953987Z", - "shell.execute_reply": "2024-08-20T02:17:43.953498Z" + "iopub.execute_input": "2024-08-21T00:42:51.020813Z", + "iopub.status.busy": "2024-08-21T00:42:51.020467Z", + "iopub.status.idle": "2024-08-21T00:42:51.024267Z", + "shell.execute_reply": "2024-08-21T00:42:51.023800Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'change_pin'}\n" + "Classes: {'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.956096Z", - "iopub.status.busy": "2024-08-20T02:17:43.955753Z", - "iopub.status.idle": "2024-08-20T02:17:43.959032Z", - "shell.execute_reply": "2024-08-20T02:17:43.958569Z" + "iopub.execute_input": "2024-08-21T00:42:51.026219Z", + "iopub.status.busy": "2024-08-21T00:42:51.025887Z", + "iopub.status.idle": "2024-08-21T00:42:51.029160Z", + "shell.execute_reply": "2024-08-21T00:42:51.028719Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:43.961194Z", - "iopub.status.busy": "2024-08-20T02:17:43.960798Z", - "iopub.status.idle": "2024-08-20T02:17:47.539339Z", - "shell.execute_reply": "2024-08-20T02:17:47.538779Z" + "iopub.execute_input": "2024-08-21T00:42:51.031153Z", + "iopub.status.busy": "2024-08-21T00:42:51.030819Z", + "iopub.status.idle": "2024-08-21T00:42:54.855542Z", + "shell.execute_reply": "2024-08-21T00:42:54.854980Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:47.542112Z", - "iopub.status.busy": "2024-08-20T02:17:47.541704Z", - "iopub.status.idle": "2024-08-20T02:17:48.414944Z", - "shell.execute_reply": "2024-08-20T02:17:48.414342Z" + "iopub.execute_input": "2024-08-21T00:42:54.858207Z", + "iopub.status.busy": "2024-08-21T00:42:54.857857Z", + "iopub.status.idle": "2024-08-21T00:42:55.761897Z", + "shell.execute_reply": "2024-08-21T00:42:55.761312Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:48.418075Z", - "iopub.status.busy": "2024-08-20T02:17:48.417646Z", - "iopub.status.idle": "2024-08-20T02:17:48.420650Z", - "shell.execute_reply": "2024-08-20T02:17:48.420142Z" + "iopub.execute_input": "2024-08-21T00:42:55.764920Z", + "iopub.status.busy": "2024-08-21T00:42:55.764523Z", + "iopub.status.idle": "2024-08-21T00:42:55.767436Z", + "shell.execute_reply": "2024-08-21T00:42:55.766944Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:48.423130Z", - "iopub.status.busy": "2024-08-20T02:17:48.422741Z", - "iopub.status.idle": "2024-08-20T02:17:50.512788Z", - "shell.execute_reply": "2024-08-20T02:17:50.512084Z" + "iopub.execute_input": "2024-08-21T00:42:55.769883Z", + "iopub.status.busy": "2024-08-21T00:42:55.769512Z", + "iopub.status.idle": "2024-08-21T00:42:57.751703Z", + "shell.execute_reply": "2024-08-21T00:42:57.751042Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.516360Z", - "iopub.status.busy": "2024-08-20T02:17:50.515474Z", - "iopub.status.idle": "2024-08-20T02:17:50.559350Z", - "shell.execute_reply": "2024-08-20T02:17:50.558793Z" + "iopub.execute_input": "2024-08-21T00:42:57.756032Z", + "iopub.status.busy": "2024-08-21T00:42:57.754848Z", + "iopub.status.idle": "2024-08-21T00:42:57.780427Z", + "shell.execute_reply": "2024-08-21T00:42:57.779909Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.562005Z", - "iopub.status.busy": "2024-08-20T02:17:50.561575Z", - "iopub.status.idle": "2024-08-20T02:17:50.571250Z", - "shell.execute_reply": "2024-08-20T02:17:50.570668Z" + "iopub.execute_input": "2024-08-21T00:42:57.783964Z", + "iopub.status.busy": "2024-08-21T00:42:57.783026Z", + "iopub.status.idle": "2024-08-21T00:42:57.793469Z", + "shell.execute_reply": "2024-08-21T00:42:57.793043Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.573299Z", - "iopub.status.busy": "2024-08-20T02:17:50.573083Z", - "iopub.status.idle": "2024-08-20T02:17:50.577651Z", - "shell.execute_reply": "2024-08-20T02:17:50.577171Z" + "iopub.execute_input": "2024-08-21T00:42:57.795468Z", + "iopub.status.busy": "2024-08-21T00:42:57.795293Z", + "iopub.status.idle": "2024-08-21T00:42:57.799640Z", + "shell.execute_reply": "2024-08-21T00:42:57.799166Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.579760Z", - "iopub.status.busy": "2024-08-20T02:17:50.579414Z", - "iopub.status.idle": "2024-08-20T02:17:50.585985Z", - "shell.execute_reply": "2024-08-20T02:17:50.585437Z" + "iopub.execute_input": "2024-08-21T00:42:57.801519Z", + "iopub.status.busy": "2024-08-21T00:42:57.801343Z", + "iopub.status.idle": "2024-08-21T00:42:57.807871Z", + "shell.execute_reply": "2024-08-21T00:42:57.807330Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.588204Z", - "iopub.status.busy": "2024-08-20T02:17:50.587870Z", - "iopub.status.idle": "2024-08-20T02:17:50.594858Z", - "shell.execute_reply": "2024-08-20T02:17:50.594371Z" + "iopub.execute_input": "2024-08-21T00:42:57.809785Z", + "iopub.status.busy": "2024-08-21T00:42:57.809618Z", + "iopub.status.idle": "2024-08-21T00:42:57.816205Z", + "shell.execute_reply": "2024-08-21T00:42:57.815747Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.596980Z", - "iopub.status.busy": "2024-08-20T02:17:50.596628Z", - "iopub.status.idle": "2024-08-20T02:17:50.602883Z", - "shell.execute_reply": "2024-08-20T02:17:50.602388Z" + "iopub.execute_input": "2024-08-21T00:42:57.817972Z", + "iopub.status.busy": "2024-08-21T00:42:57.817801Z", + "iopub.status.idle": "2024-08-21T00:42:57.823337Z", + "shell.execute_reply": "2024-08-21T00:42:57.822851Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.605052Z", - "iopub.status.busy": "2024-08-20T02:17:50.604708Z", - "iopub.status.idle": "2024-08-20T02:17:50.613784Z", - "shell.execute_reply": "2024-08-20T02:17:50.613286Z" + "iopub.execute_input": "2024-08-21T00:42:57.825390Z", + "iopub.status.busy": "2024-08-21T00:42:57.825059Z", + "iopub.status.idle": "2024-08-21T00:42:57.833536Z", + "shell.execute_reply": "2024-08-21T00:42:57.832966Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.615869Z", - "iopub.status.busy": "2024-08-20T02:17:50.615531Z", - "iopub.status.idle": "2024-08-20T02:17:50.621241Z", - "shell.execute_reply": "2024-08-20T02:17:50.620655Z" + "iopub.execute_input": "2024-08-21T00:42:57.835599Z", + "iopub.status.busy": "2024-08-21T00:42:57.835187Z", + "iopub.status.idle": "2024-08-21T00:42:57.840572Z", + "shell.execute_reply": "2024-08-21T00:42:57.840031Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.623564Z", - "iopub.status.busy": "2024-08-20T02:17:50.623056Z", - "iopub.status.idle": "2024-08-20T02:17:50.628693Z", - "shell.execute_reply": "2024-08-20T02:17:50.628220Z" + "iopub.execute_input": "2024-08-21T00:42:57.842661Z", + "iopub.status.busy": "2024-08-21T00:42:57.842352Z", + "iopub.status.idle": "2024-08-21T00:42:57.847636Z", + "shell.execute_reply": "2024-08-21T00:42:57.847079Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.630804Z", - "iopub.status.busy": "2024-08-20T02:17:50.630492Z", - "iopub.status.idle": "2024-08-20T02:17:50.634294Z", - "shell.execute_reply": "2024-08-20T02:17:50.633704Z" + "iopub.execute_input": "2024-08-21T00:42:57.849566Z", + "iopub.status.busy": "2024-08-21T00:42:57.849393Z", + "iopub.status.idle": "2024-08-21T00:42:57.852910Z", + "shell.execute_reply": "2024-08-21T00:42:57.852365Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:50.636366Z", - "iopub.status.busy": "2024-08-20T02:17:50.636088Z", - "iopub.status.idle": "2024-08-20T02:17:50.641705Z", - "shell.execute_reply": "2024-08-20T02:17:50.641097Z" + "iopub.execute_input": "2024-08-21T00:42:57.855166Z", + "iopub.status.busy": "2024-08-21T00:42:57.854784Z", + "iopub.status.idle": "2024-08-21T00:42:57.860086Z", + "shell.execute_reply": "2024-08-21T00:42:57.859637Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index d94142c5e..1e34b68e5 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -3140,224 +3140,224 @@

    6. (Optional) Visualize the Results - +
    - - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
     AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
    8nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
    @@ -3503,8 +3503,8 @@

    1. Load the Dataset 
    ---2024-08-20 02:18:10--  https://s.cleanlab.ai/CIFAR-10-subset.zip
-Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...
+--2024-08-21 00:43:16--  https://s.cleanlab.ai/CIFAR-10-subset.zip
+Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.109.153, 185.199.108.153, ...
 Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 986707 (964K) [application/zip]
@@ -3512,7 +3512,7 @@ 
    1. Load the Dataset
    -
    +
    @@ -3590,6 +3590,7 @@

    2. Run Datalab Analysis 
     Removing dark, blurry from potential issues in the dataset as it exceeds max_prevalence=0.1
+Finding spurious correlation issues in the dataset ...
 
 Audit complete. 0 issues found in the dataset.
 No issues found in the data. Good job!
@@ -3603,9 +3604,9 @@ 
    2. Run Datalab Analysis3. Interpret the Results
    from IPython.display import display
 
-# Get the correlation scores for image properties
-correlation_scores = lab._correlations_df
-print("Correlation scores for image properties:")
-display(correlation_scores)
+# Get scores for label uncorrelatedness with image properties
+label_uncorrelatedness_scores = lab.get_info("spurious_correlations")["correlations_df"]
+print("Label uncorrelatedness scores for image properties:")
+display(label_uncorrelatedness_scores)
 
 # Get image-specific issues
 issue_name = "dark"
@@ -3653,7 +3654,7 @@ 
    3. Interpret the Results
 
    -Correlation scores for image properties:
+Label uncorrelatedness scores for image properties:
    @@ -3685,13 +3686,13 @@

    3. Interpret the Results - dark_score is_dark_issue + dark_score 0 - 0.237196 True + 0.237196 1 - 0.197229 True + 0.197229 2 - 0.254188 True + 0.254188 3 - 0.229170 True + 0.229170 4 - 0.208907 True + 0.208907 ... @@ -3790,275 +3791,91 @@

    3. Interpret the Results_correlations_df attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.

    -

    Interpreting the results:

      -

    1. Correlation Scores: The correlation_scores DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.

      2. +

    2. Label Uncorrelatedness Scores: The label_uncorrelatedness_scores DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.

    3. Image-Specific Issues: The image_issues DataFrame provides details on detected image-specific problems, including the issue type and affected samples.

    -

    In our CIFAR-10 subset example, you should see that the ‘dark’ property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the ‘frog’ class). This is due to our artificial darkening of these images to demonstrate the concept.

    +

    In our CIFAR-10 subset example, you should see that the ‘dark’ property has a low score in the label_uncorrelatedness_scores, indicating a strong correlation with one of the classes (likely the ‘frog’ class). This is due to our artificial darkening of these images to demonstrate the concept.

    For real-world datasets, pay attention to:

      -

    • Properties with notably low scores in the correlation_scores DataFrame

      • +

    • Properties with notably low scores in the label_uncorrelatedness_scores DataFrame

    • Prevalent issues in the image_issues DataFrame

    These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.

    Note: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of lab.report().

    - -
    -

    4. (Optional) Compare with a Dataset Without Spurious Correlations#

    -

    To understand the impact of spurious correlations, it can be helpful to compare our results with a dataset that doesn’t have artificially introduced biases. In this case, we’ll use the original CIFAR-10 subset.

    [37]:
    -
    # Load the original dataset
-original_data_dir = "CIFAR-10-subset/original_images"
-original_dataset = load_image_dataset(original_data_dir)
+
    def plot_scores_labels(lab, property="dark_score"):
+    """
+    Plots the scores of image-specific properties like 'dark_score', 'blurry_score', etc.
+    against labels for each instance in the dataset using 'Datalab' object.
 
-# Create a new Datalab instance and run analysis
-original_lab = Datalab(data=original_dataset, label_name="label", image_key="image")
-original_lab.find_issues()
+    Parameters:
+    -----------
+    lab : 'Datalab' object
 
-# Compare correlation scores
-original_scores = original_lab._correlations_df
-print("Correlation scores for original dataset:")
-display(original_scores)
+    property : str, optional
+        The name of the property to be plotted against the labels.
 
-# Compare image-specific issues
-original_issues = original_lab.get_issues("dark")
-print("\nImage-specific issues in original dataset:")
-display(original_issues)
-
    
-
    
-
    -
    -
    -
    -
    -
    -Finding class_imbalance issues ...
-Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    +    Returns:
+    --------
+    None
+        This function does not return any value. It generates a plot of the specified
+        property against the labels.
+    """
+    issues_copy = lab.issues.copy()
+    issues_copy["label"] = lab.labels
+    issues_copy.boxplot(column=[property], by="label")
 
-Audit complete. 0 issues found in the dataset.
-Correlation scores for original dataset:
-
    +# Plotting 'dark_score' value of each instance in the dataset against class label +plot_scores_labels(lab, "dark_score") +
    -
    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    propertyscore
    0dark_score0.300
    1light_score0.415
    2low_information_score0.325
    3odd_aspect_ratio_score0.500
    4odd_size_score0.500
    5grayscale_score0.500
    6blurry_score0.335
    -
    -
    -
    +
    -
    -
-Image-specific issues in original dataset:
-
    +../../_images/tutorials_datalab_workflows_86_0.png
    -
    -
    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    dark_scoreis_dark_issue
    00.797509False
    10.663760False
    20.849826False
    30.773951False
    40.699518False
    .........
    1950.793840False
    1961.000000False
    1970.971560False
    1980.862236False
    1990.973533False
    -

    200 rows × 2 columns

    -
    -

    When comparing the results:

    -
      -

    1. Look for differences in the correlation scores, especially for the ‘dark’ property.

      2. -

    2. Compare the number and types of image-specific issues detected.

      3. -
    -

    You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. This comparison highlights how spurious correlations can be detected by Datalab.

    +

    The above plot illustrates the distribution of dark scores across class labels. In this dataset, 100 images from the Frog class (Class 0 in the plot) have been darkened, while 100 images from the Truck class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the ‘darkness’ feature and the class labels: Frog images are dark, whereas Truck images are not. We can see that the dark_score values between the two +classes are non-overlapping. This characteristic of the dataset is identified by Datalab.

    @@ -4196,7 +4013,6 @@

    4. (Optional) Compare with a Dataset Without Spurious Correlations1. Load the Dataset
  • 2. Run Datalab Analysis
  • 3. Interpret the Results
    • -
  • 4. (Optional) Compare with a Dataset Without Spurious Correlations
    • diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index ea12d845c..eb842aff3 100644 --- a/master/tutorials/datalab/workflows.ipynb +++ b/master/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.342678Z", - "iopub.status.busy": "2024-08-20T02:17:54.342516Z", - "iopub.status.idle": "2024-08-20T02:17:54.787105Z", - "shell.execute_reply": "2024-08-20T02:17:54.786589Z" + "iopub.execute_input": "2024-08-21T00:43:01.171888Z", + "iopub.status.busy": "2024-08-21T00:43:01.171708Z", + "iopub.status.idle": "2024-08-21T00:43:01.599347Z", + "shell.execute_reply": "2024-08-21T00:43:01.598723Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.789748Z", - "iopub.status.busy": "2024-08-20T02:17:54.789305Z", - "iopub.status.idle": "2024-08-20T02:17:54.923597Z", - "shell.execute_reply": "2024-08-20T02:17:54.923027Z" + "iopub.execute_input": "2024-08-21T00:43:01.602168Z", + "iopub.status.busy": "2024-08-21T00:43:01.601787Z", + "iopub.status.idle": "2024-08-21T00:43:01.731428Z", + "shell.execute_reply": "2024-08-21T00:43:01.730880Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.926017Z", - "iopub.status.busy": "2024-08-20T02:17:54.925610Z", - "iopub.status.idle": "2024-08-20T02:17:54.948848Z", - "shell.execute_reply": "2024-08-20T02:17:54.948254Z" + "iopub.execute_input": "2024-08-21T00:43:01.733775Z", + "iopub.status.busy": "2024-08-21T00:43:01.733383Z", + "iopub.status.idle": "2024-08-21T00:43:01.756845Z", + "shell.execute_reply": "2024-08-21T00:43:01.756289Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:54.951750Z", - "iopub.status.busy": "2024-08-20T02:17:54.951205Z", - "iopub.status.idle": "2024-08-20T02:17:58.333690Z", - "shell.execute_reply": "2024-08-20T02:17:58.332962Z" + "iopub.execute_input": "2024-08-21T00:43:01.759422Z", + "iopub.status.busy": "2024-08-21T00:43:01.758972Z", + "iopub.status.idle": "2024-08-21T00:43:04.525314Z", + "shell.execute_reply": "2024-08-21T00:43:04.524747Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:17:58.336453Z", - "iopub.status.busy": "2024-08-20T02:17:58.335863Z", - "iopub.status.idle": "2024-08-20T02:18:06.978320Z", - "shell.execute_reply": "2024-08-20T02:18:06.977689Z" + "iopub.execute_input": "2024-08-21T00:43:04.528021Z", + "iopub.status.busy": "2024-08-21T00:43:04.527475Z", + "iopub.status.idle": "2024-08-21T00:43:13.293687Z", + "shell.execute_reply": "2024-08-21T00:43:13.293070Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:06.980664Z", - "iopub.status.busy": "2024-08-20T02:18:06.980310Z", - "iopub.status.idle": "2024-08-20T02:18:07.140407Z", - "shell.execute_reply": "2024-08-20T02:18:07.139847Z" + "iopub.execute_input": "2024-08-21T00:43:13.296149Z", + "iopub.status.busy": "2024-08-21T00:43:13.295770Z", + "iopub.status.idle": "2024-08-21T00:43:13.454429Z", + "shell.execute_reply": "2024-08-21T00:43:13.453846Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:07.143040Z", - "iopub.status.busy": "2024-08-20T02:18:07.142679Z", - "iopub.status.idle": "2024-08-20T02:18:08.680980Z", - "shell.execute_reply": "2024-08-20T02:18:08.680491Z" + "iopub.execute_input": "2024-08-21T00:43:13.456937Z", + "iopub.status.busy": "2024-08-21T00:43:13.456583Z", + "iopub.status.idle": "2024-08-21T00:43:14.817910Z", + "shell.execute_reply": "2024-08-21T00:43:14.817401Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:08.683287Z", - "iopub.status.busy": "2024-08-20T02:18:08.682919Z", - "iopub.status.idle": "2024-08-20T02:18:08.998377Z", - "shell.execute_reply": "2024-08-20T02:18:08.997764Z" + "iopub.execute_input": "2024-08-21T00:43:14.820072Z", + "iopub.status.busy": "2024-08-21T00:43:14.819706Z", + "iopub.status.idle": "2024-08-21T00:43:15.269488Z", + "shell.execute_reply": "2024-08-21T00:43:15.268851Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.001136Z", - "iopub.status.busy": "2024-08-20T02:18:09.000600Z", - "iopub.status.idle": "2024-08-20T02:18:09.014081Z", - "shell.execute_reply": "2024-08-20T02:18:09.013591Z" + "iopub.execute_input": "2024-08-21T00:43:15.272042Z", + "iopub.status.busy": "2024-08-21T00:43:15.271492Z", + "iopub.status.idle": "2024-08-21T00:43:15.285246Z", + "shell.execute_reply": "2024-08-21T00:43:15.284785Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.016120Z", - "iopub.status.busy": "2024-08-20T02:18:09.015940Z", - "iopub.status.idle": "2024-08-20T02:18:09.039533Z", - "shell.execute_reply": "2024-08-20T02:18:09.038905Z" + "iopub.execute_input": "2024-08-21T00:43:15.287513Z", + "iopub.status.busy": "2024-08-21T00:43:15.287177Z", + "iopub.status.idle": "2024-08-21T00:43:15.307560Z", + "shell.execute_reply": "2024-08-21T00:43:15.306987Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.042064Z", - "iopub.status.busy": "2024-08-20T02:18:09.041579Z", - "iopub.status.idle": "2024-08-20T02:18:09.283734Z", - "shell.execute_reply": "2024-08-20T02:18:09.283109Z" + "iopub.execute_input": "2024-08-21T00:43:15.309775Z", + "iopub.status.busy": "2024-08-21T00:43:15.309322Z", + "iopub.status.idle": "2024-08-21T00:43:15.549642Z", + "shell.execute_reply": "2024-08-21T00:43:15.549085Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.286529Z", - "iopub.status.busy": "2024-08-20T02:18:09.286191Z", - "iopub.status.idle": "2024-08-20T02:18:09.305536Z", - "shell.execute_reply": "2024-08-20T02:18:09.305009Z" + "iopub.execute_input": "2024-08-21T00:43:15.552675Z", + "iopub.status.busy": "2024-08-21T00:43:15.552274Z", + "iopub.status.idle": "2024-08-21T00:43:15.571649Z", + "shell.execute_reply": "2024-08-21T00:43:15.571180Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.307549Z", - "iopub.status.busy": "2024-08-20T02:18:09.307369Z", - "iopub.status.idle": "2024-08-20T02:18:09.476687Z", - "shell.execute_reply": "2024-08-20T02:18:09.476131Z" + "iopub.execute_input": "2024-08-21T00:43:15.573738Z", + "iopub.status.busy": "2024-08-21T00:43:15.573396Z", + "iopub.status.idle": "2024-08-21T00:43:15.740795Z", + "shell.execute_reply": "2024-08-21T00:43:15.740213Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.478997Z", - "iopub.status.busy": "2024-08-20T02:18:09.478812Z", - "iopub.status.idle": "2024-08-20T02:18:09.489242Z", - "shell.execute_reply": "2024-08-20T02:18:09.488671Z" + "iopub.execute_input": "2024-08-21T00:43:15.743277Z", + "iopub.status.busy": "2024-08-21T00:43:15.742918Z", + "iopub.status.idle": "2024-08-21T00:43:15.752623Z", + "shell.execute_reply": "2024-08-21T00:43:15.752133Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.491411Z", - "iopub.status.busy": "2024-08-20T02:18:09.490973Z", - "iopub.status.idle": "2024-08-20T02:18:09.500699Z", - "shell.execute_reply": "2024-08-20T02:18:09.500225Z" + "iopub.execute_input": "2024-08-21T00:43:15.754632Z", + "iopub.status.busy": "2024-08-21T00:43:15.754360Z", + "iopub.status.idle": "2024-08-21T00:43:15.763787Z", + "shell.execute_reply": "2024-08-21T00:43:15.763179Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.502731Z", - "iopub.status.busy": "2024-08-20T02:18:09.502549Z", - "iopub.status.idle": "2024-08-20T02:18:09.529479Z", - "shell.execute_reply": "2024-08-20T02:18:09.528984Z" + "iopub.execute_input": "2024-08-21T00:43:15.765948Z", + "iopub.status.busy": "2024-08-21T00:43:15.765602Z", + "iopub.status.idle": "2024-08-21T00:43:15.791328Z", + "shell.execute_reply": "2024-08-21T00:43:15.790866Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.531581Z", - "iopub.status.busy": "2024-08-20T02:18:09.531237Z", - "iopub.status.idle": "2024-08-20T02:18:09.534052Z", - "shell.execute_reply": "2024-08-20T02:18:09.533586Z" + "iopub.execute_input": "2024-08-21T00:43:15.793538Z", + "iopub.status.busy": "2024-08-21T00:43:15.793210Z", + "iopub.status.idle": "2024-08-21T00:43:15.796045Z", + "shell.execute_reply": "2024-08-21T00:43:15.795529Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.536250Z", - "iopub.status.busy": "2024-08-20T02:18:09.535919Z", - "iopub.status.idle": "2024-08-20T02:18:09.555307Z", - "shell.execute_reply": "2024-08-20T02:18:09.554757Z" + "iopub.execute_input": "2024-08-21T00:43:15.798174Z", + "iopub.status.busy": "2024-08-21T00:43:15.797769Z", + "iopub.status.idle": "2024-08-21T00:43:15.817188Z", + "shell.execute_reply": "2024-08-21T00:43:15.816725Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.557515Z", - "iopub.status.busy": "2024-08-20T02:18:09.557152Z", - "iopub.status.idle": "2024-08-20T02:18:09.561596Z", - "shell.execute_reply": "2024-08-20T02:18:09.561108Z" + "iopub.execute_input": "2024-08-21T00:43:15.819091Z", + "iopub.status.busy": "2024-08-21T00:43:15.818917Z", + "iopub.status.idle": "2024-08-21T00:43:15.823437Z", + "shell.execute_reply": "2024-08-21T00:43:15.822989Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.563658Z", - "iopub.status.busy": "2024-08-20T02:18:09.563335Z", - "iopub.status.idle": "2024-08-20T02:18:09.592021Z", - "shell.execute_reply": "2024-08-20T02:18:09.591552Z" + "iopub.execute_input": "2024-08-21T00:43:15.825477Z", + "iopub.status.busy": "2024-08-21T00:43:15.825162Z", + "iopub.status.idle": "2024-08-21T00:43:15.852907Z", + "shell.execute_reply": "2024-08-21T00:43:15.852367Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.594263Z", - "iopub.status.busy": "2024-08-20T02:18:09.593908Z", - "iopub.status.idle": "2024-08-20T02:18:09.919384Z", - "shell.execute_reply": "2024-08-20T02:18:09.918757Z" + "iopub.execute_input": "2024-08-21T00:43:15.855074Z", + "iopub.status.busy": "2024-08-21T00:43:15.854635Z", + "iopub.status.idle": "2024-08-21T00:43:16.219163Z", + "shell.execute_reply": "2024-08-21T00:43:16.218556Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.921742Z", - "iopub.status.busy": "2024-08-20T02:18:09.921399Z", - "iopub.status.idle": "2024-08-20T02:18:09.924438Z", - "shell.execute_reply": "2024-08-20T02:18:09.923873Z" + "iopub.execute_input": "2024-08-21T00:43:16.221443Z", + "iopub.status.busy": "2024-08-21T00:43:16.221085Z", + "iopub.status.idle": "2024-08-21T00:43:16.224381Z", + "shell.execute_reply": "2024-08-21T00:43:16.223904Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.926626Z", - "iopub.status.busy": "2024-08-20T02:18:09.926286Z", - "iopub.status.idle": "2024-08-20T02:18:09.942337Z", - "shell.execute_reply": "2024-08-20T02:18:09.941698Z" + "iopub.execute_input": "2024-08-21T00:43:16.226481Z", + "iopub.status.busy": "2024-08-21T00:43:16.226086Z", + "iopub.status.idle": "2024-08-21T00:43:16.239073Z", + "shell.execute_reply": "2024-08-21T00:43:16.238514Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.944789Z", - "iopub.status.busy": "2024-08-20T02:18:09.944595Z", - "iopub.status.idle": "2024-08-20T02:18:09.959330Z", - "shell.execute_reply": "2024-08-20T02:18:09.958838Z" + "iopub.execute_input": "2024-08-21T00:43:16.241149Z", + "iopub.status.busy": "2024-08-21T00:43:16.240818Z", + "iopub.status.idle": "2024-08-21T00:43:16.253851Z", + "shell.execute_reply": "2024-08-21T00:43:16.253394Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.961553Z", - "iopub.status.busy": "2024-08-20T02:18:09.961186Z", - "iopub.status.idle": "2024-08-20T02:18:09.971554Z", - "shell.execute_reply": "2024-08-20T02:18:09.971112Z" + "iopub.execute_input": "2024-08-21T00:43:16.255788Z", + "iopub.status.busy": "2024-08-21T00:43:16.255473Z", + "iopub.status.idle": "2024-08-21T00:43:16.265699Z", + "shell.execute_reply": "2024-08-21T00:43:16.265145Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.973798Z", - "iopub.status.busy": "2024-08-20T02:18:09.973436Z", - "iopub.status.idle": "2024-08-20T02:18:09.982920Z", - "shell.execute_reply": "2024-08-20T02:18:09.982407Z" + "iopub.execute_input": "2024-08-21T00:43:16.268027Z", + "iopub.status.busy": "2024-08-21T00:43:16.267560Z", + "iopub.status.idle": "2024-08-21T00:43:16.276845Z", + "shell.execute_reply": "2024-08-21T00:43:16.276285Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.985172Z", - "iopub.status.busy": "2024-08-20T02:18:09.984839Z", - "iopub.status.idle": "2024-08-20T02:18:09.988286Z", - "shell.execute_reply": "2024-08-20T02:18:09.987820Z" + "iopub.execute_input": "2024-08-21T00:43:16.278895Z", + "iopub.status.busy": "2024-08-21T00:43:16.278593Z", + "iopub.status.idle": "2024-08-21T00:43:16.282208Z", + "shell.execute_reply": "2024-08-21T00:43:16.281682Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:09.990507Z", - "iopub.status.busy": "2024-08-20T02:18:09.990126Z", - "iopub.status.idle": "2024-08-20T02:18:10.042804Z", - "shell.execute_reply": "2024-08-20T02:18:10.042240Z" + "iopub.execute_input": "2024-08-21T00:43:16.284237Z", + "iopub.status.busy": "2024-08-21T00:43:16.283887Z", + "iopub.status.idle": "2024-08-21T00:43:16.335023Z", + "shell.execute_reply": "2024-08-21T00:43:16.334498Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - 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    \n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.045338Z", - "iopub.status.busy": "2024-08-20T02:18:10.044784Z", - "iopub.status.idle": "2024-08-20T02:18:10.052047Z", - "shell.execute_reply": "2024-08-20T02:18:10.051488Z" + "iopub.execute_input": "2024-08-21T00:43:16.337295Z", + "iopub.status.busy": "2024-08-21T00:43:16.336882Z", + "iopub.status.idle": "2024-08-21T00:43:16.342563Z", + "shell.execute_reply": "2024-08-21T00:43:16.342023Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.054394Z", - "iopub.status.busy": "2024-08-20T02:18:10.053951Z", - "iopub.status.idle": "2024-08-20T02:18:10.066067Z", - "shell.execute_reply": "2024-08-20T02:18:10.065481Z" + "iopub.execute_input": "2024-08-21T00:43:16.344534Z", + "iopub.status.busy": "2024-08-21T00:43:16.344225Z", + "iopub.status.idle": "2024-08-21T00:43:16.355308Z", + "shell.execute_reply": "2024-08-21T00:43:16.354768Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.068250Z", - "iopub.status.busy": "2024-08-20T02:18:10.067916Z", - "iopub.status.idle": "2024-08-20T02:18:10.246354Z", - "shell.execute_reply": "2024-08-20T02:18:10.245759Z" + "iopub.execute_input": "2024-08-21T00:43:16.357487Z", + "iopub.status.busy": "2024-08-21T00:43:16.356999Z", + "iopub.status.idle": "2024-08-21T00:43:16.572206Z", + "shell.execute_reply": "2024-08-21T00:43:16.571628Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.248597Z", - "iopub.status.busy": "2024-08-20T02:18:10.248414Z", - "iopub.status.idle": "2024-08-20T02:18:10.256426Z", - "shell.execute_reply": "2024-08-20T02:18:10.255950Z" + "iopub.execute_input": "2024-08-21T00:43:16.574287Z", + "iopub.status.busy": "2024-08-21T00:43:16.574098Z", + "iopub.status.idle": "2024-08-21T00:43:16.582508Z", + "shell.execute_reply": "2024-08-21T00:43:16.581942Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.258571Z", - "iopub.status.busy": "2024-08-20T02:18:10.258299Z", - "iopub.status.idle": "2024-08-20T02:18:10.653195Z", - "shell.execute_reply": "2024-08-20T02:18:10.652443Z" + "iopub.execute_input": "2024-08-21T00:43:16.584541Z", + "iopub.status.busy": "2024-08-21T00:43:16.584367Z", + "iopub.status.idle": "2024-08-21T00:43:17.017635Z", + "shell.execute_reply": "2024-08-21T00:43:17.016929Z" } }, "outputs": [ @@ -3767,25 +3767,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:18:10-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", - "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", + "--2024-08-21 00:43:16-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.109.153, 185.199.108.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", - "Length: 986707 (964K) [application/zip]\r\n", - "Saving to: ‘CIFAR-10-subset.zip’\r\n", - "\r\n", - "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " + "HTTP request sent, awaiting response... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "200 OK\r\n", + "Length: 986707 (964K) [application/zip]\r\n", + "Saving to: ‘CIFAR-10-subset.zip’\r\n", + "\r\n", "\r", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.02s \r\n", "\r\n", - "2024-08-20 02:18:10 (38.2 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-08-21 00:43:16 (39.0 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:10.655777Z", - "iopub.status.busy": "2024-08-20T02:18:10.655572Z", - "iopub.status.idle": "2024-08-20T02:18:12.615828Z", - "shell.execute_reply": "2024-08-20T02:18:12.615269Z" + "iopub.execute_input": "2024-08-21T00:43:17.020182Z", + "iopub.status.busy": "2024-08-21T00:43:17.019968Z", + "iopub.status.idle": "2024-08-21T00:43:18.913423Z", + "shell.execute_reply": "2024-08-21T00:43:18.912849Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:12.618289Z", - "iopub.status.busy": "2024-08-20T02:18:12.617996Z", - "iopub.status.idle": "2024-08-20T02:18:13.092642Z", - "shell.execute_reply": "2024-08-20T02:18:13.091988Z" + "iopub.execute_input": "2024-08-21T00:43:18.915816Z", + "iopub.status.busy": "2024-08-21T00:43:18.915543Z", + "iopub.status.idle": "2024-08-21T00:43:19.385731Z", + "shell.execute_reply": "2024-08-21T00:43:19.385086Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8bb8c2257ff642589da749e1f2b50557", + "model_id": "67422fd209454e4487fc32db492ca2c1", "version_major": 2, "version_minor": 0 }, @@ -3884,6 +3884,7 @@ "output_type": "stream", "text": [ "Removing dark, blurry from potential issues in the dataset as it exceeds max_prevalence=0.1\n", + "Finding spurious correlation issues in the dataset ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n", "No issues found in the data. Good job!\n", @@ -3897,9 +3898,9 @@ "\n", "\n", "\n", - "Here is a summary of spurious correlations between image features like 'dark_score', 'blurry_score', etc., and class labels detected in the data.\n", + "Here is a summary of spurious correlations between image features (like 'dark_score', 'blurry_score', etc.) and class labels detected in the data.\n", "\n", - "A lower score for each property implies a higher correlation of that property with the class labels.\n", + "A lower score implies a higher likelihood of a spurious correlation between that property and the class labels.\n", "\n", "\n", "property score\n", @@ -3950,10 +3951,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:13.096544Z", - "iopub.status.busy": "2024-08-20T02:18:13.095622Z", - "iopub.status.idle": "2024-08-20T02:18:13.113469Z", - "shell.execute_reply": "2024-08-20T02:18:13.112926Z" + "iopub.execute_input": "2024-08-21T00:43:19.388156Z", + "iopub.status.busy": "2024-08-21T00:43:19.387960Z", + "iopub.status.idle": "2024-08-21T00:43:19.402565Z", + "shell.execute_reply": "2024-08-21T00:43:19.402004Z" } }, "outputs": [ @@ -3961,7 +3962,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Correlation scores for image properties:\n" + "Label uncorrelatedness scores for image properties:\n" ] }, { @@ -3992,13 +3993,13 @@ " \n", " \n", " 0\n", - " dark_score\n", - " 0.000\n", + " odd_size_score\n", + " 0.500\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.180\n", + " odd_aspect_ratio_score\n", + " 0.500\n", " \n", " \n", " 2\n", @@ -4007,18 +4008,18 @@ " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " light_score\n", + " 0.180\n", " \n", " \n", " 4\n", - " odd_size_score\n", + " grayscale_score\n", " 0.500\n", " \n", " \n", " 5\n", - " grayscale_score\n", - " 0.500\n", + " dark_score\n", + " 0.000\n", " \n", " \n", " 6\n", @@ -4031,12 +4032,12 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.000\n", - "1 light_score 0.180\n", + "0 odd_size_score 0.500\n", + "1 odd_aspect_ratio_score 0.500\n", "2 low_information_score 0.015\n", - "3 odd_aspect_ratio_score 0.500\n", - "4 odd_size_score 0.500\n", - "5 grayscale_score 0.500\n", + "3 light_score 0.180\n", + "4 grayscale_score 0.500\n", + "5 dark_score 0.000\n", "6 blurry_score 0.015" ] }, @@ -4072,35 +4073,35 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.237196\n", " True\n", + " 0.237196\n", " \n", " \n", " 1\n", - " 0.197229\n", " True\n", + " 0.197229\n", " \n", " \n", " 2\n", - " 0.254188\n", " True\n", + " 0.254188\n", " \n", " \n", " 3\n", - " 0.229170\n", " True\n", + " 0.229170\n", " \n", " \n", " 4\n", - " 0.208907\n", " True\n", + " 0.208907\n", " \n", " \n", " ...\n", @@ -4109,28 +4110,28 @@ " \n", " \n", " 195\n", - " 0.793840\n", " False\n", + " 0.793840\n", " \n", " \n", " 196\n", - " 1.000000\n", " False\n", + " 1.000000\n", " \n", " \n", " 197\n", - " 0.971560\n", " False\n", + " 0.971560\n", " \n", " \n", " 198\n", - " 0.862236\n", " False\n", + " 0.862236\n", " \n", " \n", " 199\n", - " 0.973533\n", " False\n", + " 0.973533\n", " \n", " \n", "\n", @@ -4138,18 +4139,18 @@ "

    " ], "text/plain": [ - " dark_score is_dark_issue\n", - "0 0.237196 True\n", - "1 0.197229 True\n", - "2 0.254188 True\n", - "3 0.229170 True\n", - "4 0.208907 True\n", - ".. ... ...\n", - "195 0.793840 False\n", - "196 1.000000 False\n", - "197 0.971560 False\n", - "198 0.862236 False\n", - "199 0.973533 False\n", + " is_dark_issue dark_score\n", + "0 True 0.237196\n", + "1 True 0.197229\n", + "2 True 0.254188\n", + "3 True 0.229170\n", + "4 True 0.208907\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", "\n", "[200 rows x 2 columns]" ] @@ -4161,10 +4162,10 @@ "source": [ "from IPython.display import display\n", "\n", - "# Get the correlation scores for image properties\n", - "correlation_scores = lab._correlations_df\n", - "print(\"Correlation scores for image properties:\")\n", - "display(correlation_scores)\n", + "# Get scores for label uncorrelatedness with image properties\n", + "label_uncorrelatedness_scores = lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for image properties:\")\n", + "display(label_uncorrelatedness_scores)\n", "\n", "# Get image-specific issues\n", "issue_name = \"dark\"\n", @@ -4177,19 +4178,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n", - "\n", "Interpreting the results:\n", "\n", - "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", + "1. **Label Uncorrelatedness Scores**: The `label_uncorrelatedness_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n", "\n", - "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", + "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the label_uncorrelatedness_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", "\n", "For real-world datasets, pay attention to:\n", "\n", - "- Properties with notably low scores in the correlation_scores DataFrame\n", + "- Properties with notably low scores in the label_uncorrelatedness_scores DataFrame\n", "- Prevalent issues in the image_issues DataFrame\n", "\n", "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n", @@ -4197,9 +4195,68 @@ "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`." ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-21T00:43:19.404760Z", + "iopub.status.busy": "2024-08-21T00:43:19.404582Z", + "iopub.status.idle": "2024-08-21T00:43:19.550076Z", + "shell.execute_reply": "2024-08-21T00:43:19.549530Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_scores_labels(lab, property=\"dark_score\"):\n", + " \"\"\"\n", + " Plots the scores of image-specific properties like 'dark_score', 'blurry_score', etc. \n", + " against labels for each instance in the dataset using 'Datalab' object.\n", + "\n", + " Parameters:\n", + " -----------\n", + " lab : 'Datalab' object\n", + " \n", + " property : str, optional\n", + " The name of the property to be plotted against the labels.\n", + " \n", + " Returns:\n", + " --------\n", + " None\n", + " This function does not return any value. It generates a plot of the specified \n", + " property against the labels.\n", + " \"\"\"\n", + " issues_copy = lab.issues.copy()\n", + " issues_copy[\"label\"] = lab.labels\n", + " issues_copy.boxplot(column=[property], by=\"label\")\n", + "\n", + "# Plotting 'dark_score' value of each instance in the dataset against class label\n", + "plot_scores_labels(lab, \"dark_score\")" + ] + }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "The above plot illustrates the distribution of dark scores across class labels. In this dataset, 100 images from the `Frog` class (Class 0 in the plot) have been darkened, while 100 images from the `Truck` class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the 'darkness' feature and the class labels: `Frog` images are dark, whereas `Truck` images are not. We can see that the `dark_score` values between the two classes are non-overlapping. This characteristic of the dataset is identified by `Datalab`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n", "\n", @@ -4208,14 +4265,15 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:13.117053Z", - "iopub.status.busy": "2024-08-20T02:18:13.116150Z", - "iopub.status.idle": "2024-08-20T02:18:13.628191Z", - "shell.execute_reply": "2024-08-20T02:18:13.627646Z" - } + "iopub.execute_input": "2024-08-21T00:43:19.552545Z", + "iopub.status.busy": "2024-08-21T00:43:19.552159Z", + "iopub.status.idle": "2024-08-21T00:43:20.077354Z", + "shell.execute_reply": "2024-08-21T00:43:20.076712Z" + }, + "nbsphinx": "hidden" }, "outputs": [ { @@ -4229,7 +4287,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84c47a9fab904f3d8908756c3b332bed", + "model_id": "d3c19809a6204f20ac5cac77fe50ad95", "version_major": 2, "version_minor": 0 }, @@ -4244,9 +4302,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "Finding spurious correlation issues in the dataset ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n", - "Correlation scores for original dataset:\n" + "Label uncorrelatedness scores for original dataset:\n" ] }, { @@ -4277,13 +4336,13 @@ " \n", " \n", " 0\n", - " dark_score\n", - " 0.300\n", + " odd_size_score\n", + " 0.500\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.415\n", + " odd_aspect_ratio_score\n", + " 0.500\n", " \n", " \n", " 2\n", @@ -4292,18 +4351,18 @@ " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " light_score\n", + " 0.415\n", " \n", " \n", " 4\n", - " odd_size_score\n", + " grayscale_score\n", " 0.500\n", " \n", " \n", " 5\n", - " grayscale_score\n", - " 0.500\n", + " dark_score\n", + " 0.300\n", " \n", " \n", " 6\n", @@ -4316,12 +4375,12 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.300\n", - "1 light_score 0.415\n", + "0 odd_size_score 0.500\n", + "1 odd_aspect_ratio_score 0.500\n", "2 low_information_score 0.325\n", - "3 odd_aspect_ratio_score 0.500\n", - "4 odd_size_score 0.500\n", - "5 grayscale_score 0.500\n", + "3 light_score 0.415\n", + "4 grayscale_score 0.500\n", + "5 dark_score 0.300\n", "6 blurry_score 0.335" ] }, @@ -4357,35 +4416,35 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.797509\n", " False\n", + " 0.797509\n", " \n", " \n", " 1\n", - " 0.663760\n", " False\n", + " 0.663760\n", " \n", " \n", " 2\n", - " 0.849826\n", " False\n", + " 0.849826\n", " \n", " \n", " 3\n", - " 0.773951\n", " False\n", + " 0.773951\n", " \n", " \n", " 4\n", - " 0.699518\n", " False\n", + " 0.699518\n", " \n", " \n", " ...\n", @@ -4394,28 +4453,28 @@ " \n", " \n", " 195\n", - " 0.793840\n", " False\n", + " 0.793840\n", " \n", " \n", " 196\n", - " 1.000000\n", " False\n", + " 1.000000\n", " \n", " \n", " 197\n", - " 0.971560\n", " False\n", + " 0.971560\n", " \n", " \n", " 198\n", - " 0.862236\n", " False\n", + " 0.862236\n", " \n", " \n", " 199\n", - " 0.973533\n", " False\n", + " 0.973533\n", " \n", " \n", "\n", @@ -4423,18 +4482,18 @@ "
    " ], "text/plain": [ - " dark_score is_dark_issue\n", - "0 0.797509 False\n", - "1 0.663760 False\n", - "2 0.849826 False\n", - "3 0.773951 False\n", - "4 0.699518 False\n", - ".. ... ...\n", - "195 0.793840 False\n", - "196 1.000000 False\n", - "197 0.971560 False\n", - "198 0.862236 False\n", - "199 0.973533 False\n", + " is_dark_issue dark_score\n", + "0 False 0.797509\n", + "1 False 0.663760\n", + "2 False 0.849826\n", + "3 False 0.773951\n", + "4 False 0.699518\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", "\n", "[200 rows x 2 columns]" ] @@ -4453,8 +4512,8 @@ "original_lab.find_issues()\n", "\n", "# Compare correlation scores\n", - "original_scores = original_lab._correlations_df\n", - "print(\"Correlation scores for original dataset:\")\n", + "original_scores = original_lab.get_info(\"spurious_correlations\")[\"correlations_df\"]\n", + "print(\"Label uncorrelatedness scores for original dataset:\")\n", "display(original_scores)\n", "\n", "# Compare image-specific issues\n", @@ -4465,15 +4524,55 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "nbsphinx": "hidden" + }, "source": [ "When comparing the results:\n", "\n", - "1. Look for differences in the correlation scores, especially for the 'dark' property.\n", + "1. Look for differences in the label uncorrelatedness scores, especially for the 'dark' property.\n", "2. Compare the number and types of image-specific issues detected.\n", "\n", "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. This comparison highlights how spurious correlations can be detected by `Datalab`." ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-21T00:43:20.079729Z", + "iopub.status.busy": "2024-08-21T00:43:20.079373Z", + "iopub.status.idle": "2024-08-21T00:43:20.227878Z", + "shell.execute_reply": "2024-08-21T00:43:20.227303Z" + }, + "nbsphinx": "hidden" + }, + "outputs": [ + { + "data": { + "image/png": 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"iopub.execute_input": "2024-08-20T02:18:17.805542Z", - "iopub.status.busy": "2024-08-20T02:18:17.805087Z", - "iopub.status.idle": "2024-08-20T02:18:19.246385Z", - "shell.execute_reply": "2024-08-20T02:18:19.245826Z" + "iopub.execute_input": "2024-08-21T00:43:24.975874Z", + "iopub.status.busy": "2024-08-21T00:43:24.975689Z", + "iopub.status.idle": "2024-08-21T00:43:26.130072Z", + "shell.execute_reply": "2024-08-21T00:43:26.129440Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.248964Z", - "iopub.status.busy": "2024-08-20T02:18:19.248482Z", - "iopub.status.idle": "2024-08-20T02:18:19.251383Z", - "shell.execute_reply": "2024-08-20T02:18:19.250938Z" + "iopub.execute_input": "2024-08-21T00:43:26.132687Z", + "iopub.status.busy": "2024-08-21T00:43:26.132421Z", + "iopub.status.idle": "2024-08-21T00:43:26.135360Z", + "shell.execute_reply": "2024-08-21T00:43:26.134813Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.253494Z", - "iopub.status.busy": "2024-08-20T02:18:19.253140Z", - "iopub.status.idle": "2024-08-20T02:18:19.265298Z", - "shell.execute_reply": "2024-08-20T02:18:19.264725Z" + "iopub.execute_input": "2024-08-21T00:43:26.137797Z", + "iopub.status.busy": "2024-08-21T00:43:26.137353Z", + "iopub.status.idle": "2024-08-21T00:43:26.149676Z", + "shell.execute_reply": "2024-08-21T00:43:26.149205Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:19.267347Z", - "iopub.status.busy": "2024-08-20T02:18:19.267029Z", - "iopub.status.idle": "2024-08-20T02:18:24.466640Z", - "shell.execute_reply": "2024-08-20T02:18:24.466146Z" + "iopub.execute_input": "2024-08-21T00:43:26.151609Z", + "iopub.status.busy": "2024-08-21T00:43:26.151435Z", + "iopub.status.idle": "2024-08-21T00:43:30.893896Z", + "shell.execute_reply": "2024-08-21T00:43:30.893274Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 0b8cd97f3..506e7c76d 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -831,13 +831,13 @@

    How can I find label issues in big datasets with limited memory?
    -
    +
    -
    +
    @@ -1702,7 +1702,7 @@

    Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

    Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai

    diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index be294f45e..f92c95774 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:26.824446Z", - "iopub.status.busy": "2024-08-20T02:18:26.824273Z", - "iopub.status.idle": "2024-08-20T02:18:28.260323Z", - "shell.execute_reply": "2024-08-20T02:18:28.259751Z" + "iopub.execute_input": "2024-08-21T00:43:33.124706Z", + "iopub.status.busy": "2024-08-21T00:43:33.124531Z", + "iopub.status.idle": "2024-08-21T00:43:34.258108Z", + "shell.execute_reply": "2024-08-21T00:43:34.257543Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:28.262978Z", - "iopub.status.busy": "2024-08-20T02:18:28.262672Z", - "iopub.status.idle": "2024-08-20T02:18:28.266248Z", - "shell.execute_reply": "2024-08-20T02:18:28.265673Z" + "iopub.execute_input": "2024-08-21T00:43:34.260791Z", + "iopub.status.busy": "2024-08-21T00:43:34.260358Z", + "iopub.status.idle": "2024-08-21T00:43:34.263745Z", + "shell.execute_reply": "2024-08-21T00:43:34.263180Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:28.268522Z", - "iopub.status.busy": "2024-08-20T02:18:28.268073Z", - "iopub.status.idle": "2024-08-20T02:18:31.923865Z", - "shell.execute_reply": "2024-08-20T02:18:31.923205Z" + "iopub.execute_input": "2024-08-21T00:43:34.265950Z", + "iopub.status.busy": "2024-08-21T00:43:34.265625Z", + "iopub.status.idle": "2024-08-21T00:43:37.615038Z", + "shell.execute_reply": "2024-08-21T00:43:37.614383Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:31.927361Z", - "iopub.status.busy": "2024-08-20T02:18:31.926431Z", - "iopub.status.idle": "2024-08-20T02:18:31.972856Z", - "shell.execute_reply": "2024-08-20T02:18:31.972092Z" + "iopub.execute_input": "2024-08-21T00:43:37.618146Z", + "iopub.status.busy": "2024-08-21T00:43:37.617403Z", + "iopub.status.idle": "2024-08-21T00:43:37.660445Z", + "shell.execute_reply": "2024-08-21T00:43:37.659796Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:31.975898Z", - "iopub.status.busy": "2024-08-20T02:18:31.975497Z", - "iopub.status.idle": "2024-08-20T02:18:32.024339Z", - "shell.execute_reply": "2024-08-20T02:18:32.023682Z" + "iopub.execute_input": "2024-08-21T00:43:37.663149Z", + "iopub.status.busy": "2024-08-21T00:43:37.662756Z", + "iopub.status.idle": "2024-08-21T00:43:37.699432Z", + "shell.execute_reply": "2024-08-21T00:43:37.698818Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.027241Z", - "iopub.status.busy": "2024-08-20T02:18:32.026842Z", - "iopub.status.idle": "2024-08-20T02:18:32.029883Z", - "shell.execute_reply": "2024-08-20T02:18:32.029410Z" + "iopub.execute_input": "2024-08-21T00:43:37.702287Z", + "iopub.status.busy": "2024-08-21T00:43:37.701804Z", + "iopub.status.idle": "2024-08-21T00:43:37.705054Z", + "shell.execute_reply": "2024-08-21T00:43:37.704587Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.031947Z", - "iopub.status.busy": "2024-08-20T02:18:32.031634Z", - "iopub.status.idle": "2024-08-20T02:18:32.034472Z", - "shell.execute_reply": "2024-08-20T02:18:32.034036Z" + "iopub.execute_input": "2024-08-21T00:43:37.707228Z", + "iopub.status.busy": "2024-08-21T00:43:37.706791Z", + "iopub.status.idle": "2024-08-21T00:43:37.709625Z", + "shell.execute_reply": "2024-08-21T00:43:37.709171Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.036493Z", - "iopub.status.busy": "2024-08-20T02:18:32.036308Z", - "iopub.status.idle": "2024-08-20T02:18:32.059976Z", - "shell.execute_reply": "2024-08-20T02:18:32.059445Z" + "iopub.execute_input": "2024-08-21T00:43:37.711757Z", + "iopub.status.busy": "2024-08-21T00:43:37.711365Z", + "iopub.status.idle": "2024-08-21T00:43:37.738363Z", + "shell.execute_reply": "2024-08-21T00:43:37.737812Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d82d5feb5be34f61b1a570c3dd0179b0", + "model_id": "e88b11d2fc2c4e1dabf9d5ab29054a5a", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2f0c54e98dd4c02b9cf7548afd53f0d", + "model_id": "7ce54cb5c4f340b7bb0a813fe148c2ac", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.065764Z", - "iopub.status.busy": "2024-08-20T02:18:32.065412Z", - "iopub.status.idle": "2024-08-20T02:18:32.071940Z", - "shell.execute_reply": "2024-08-20T02:18:32.071514Z" + "iopub.execute_input": "2024-08-21T00:43:37.744690Z", + "iopub.status.busy": "2024-08-21T00:43:37.744352Z", + "iopub.status.idle": "2024-08-21T00:43:37.751032Z", + "shell.execute_reply": "2024-08-21T00:43:37.750612Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.074109Z", - "iopub.status.busy": "2024-08-20T02:18:32.073667Z", - "iopub.status.idle": "2024-08-20T02:18:32.077079Z", - "shell.execute_reply": "2024-08-20T02:18:32.076644Z" + "iopub.execute_input": "2024-08-21T00:43:37.753066Z", + "iopub.status.busy": "2024-08-21T00:43:37.752736Z", + "iopub.status.idle": "2024-08-21T00:43:37.756637Z", + "shell.execute_reply": "2024-08-21T00:43:37.756202Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.079272Z", - "iopub.status.busy": "2024-08-20T02:18:32.078890Z", - "iopub.status.idle": "2024-08-20T02:18:32.085244Z", - "shell.execute_reply": "2024-08-20T02:18:32.084783Z" + "iopub.execute_input": "2024-08-21T00:43:37.758695Z", + "iopub.status.busy": "2024-08-21T00:43:37.758365Z", + "iopub.status.idle": "2024-08-21T00:43:37.764764Z", + "shell.execute_reply": "2024-08-21T00:43:37.764295Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.087316Z", - "iopub.status.busy": "2024-08-20T02:18:32.086982Z", - "iopub.status.idle": "2024-08-20T02:18:32.132301Z", - "shell.execute_reply": "2024-08-20T02:18:32.131651Z" + "iopub.execute_input": "2024-08-21T00:43:37.766873Z", + "iopub.status.busy": "2024-08-21T00:43:37.766456Z", + "iopub.status.idle": "2024-08-21T00:43:37.807572Z", + "shell.execute_reply": "2024-08-21T00:43:37.806830Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.135193Z", - "iopub.status.busy": "2024-08-20T02:18:32.134697Z", - "iopub.status.idle": "2024-08-20T02:18:32.180547Z", - "shell.execute_reply": "2024-08-20T02:18:32.179925Z" + "iopub.execute_input": "2024-08-21T00:43:37.810644Z", + "iopub.status.busy": "2024-08-21T00:43:37.810107Z", + "iopub.status.idle": "2024-08-21T00:43:37.853337Z", + "shell.execute_reply": "2024-08-21T00:43:37.852723Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.183314Z", - "iopub.status.busy": "2024-08-20T02:18:32.182928Z", - "iopub.status.idle": "2024-08-20T02:18:32.318818Z", - "shell.execute_reply": "2024-08-20T02:18:32.318227Z" + "iopub.execute_input": "2024-08-21T00:43:37.856040Z", + "iopub.status.busy": "2024-08-21T00:43:37.855648Z", + "iopub.status.idle": "2024-08-21T00:43:37.987449Z", + "shell.execute_reply": "2024-08-21T00:43:37.986885Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:32.321628Z", - "iopub.status.busy": "2024-08-20T02:18:32.321051Z", - "iopub.status.idle": "2024-08-20T02:18:35.406655Z", - "shell.execute_reply": "2024-08-20T02:18:35.405994Z" + "iopub.execute_input": "2024-08-21T00:43:37.990134Z", + "iopub.status.busy": "2024-08-21T00:43:37.989581Z", + "iopub.status.idle": "2024-08-21T00:43:40.945105Z", + "shell.execute_reply": "2024-08-21T00:43:40.944425Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.409123Z", - "iopub.status.busy": "2024-08-20T02:18:35.408927Z", - "iopub.status.idle": "2024-08-20T02:18:35.471131Z", - "shell.execute_reply": "2024-08-20T02:18:35.470526Z" + "iopub.execute_input": "2024-08-21T00:43:40.947439Z", + "iopub.status.busy": "2024-08-21T00:43:40.947245Z", + "iopub.status.idle": "2024-08-21T00:43:41.002087Z", + "shell.execute_reply": "2024-08-21T00:43:41.001550Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.473400Z", - "iopub.status.busy": "2024-08-20T02:18:35.473199Z", - "iopub.status.idle": "2024-08-20T02:18:35.520888Z", - "shell.execute_reply": "2024-08-20T02:18:35.520350Z" + "iopub.execute_input": "2024-08-21T00:43:41.004111Z", + "iopub.status.busy": "2024-08-21T00:43:41.003920Z", + "iopub.status.idle": "2024-08-21T00:43:41.046065Z", + "shell.execute_reply": "2024-08-21T00:43:41.045584Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "1f15f0b7", + "id": "a7cb8e5a", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "cc67f639", + "id": "ce44f993", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "5a601c1a", + "id": "1f258ff1", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "b72d4fd7", + "id": "4e739e39", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.523105Z", - "iopub.status.busy": "2024-08-20T02:18:35.522924Z", - "iopub.status.idle": "2024-08-20T02:18:35.530530Z", - "shell.execute_reply": "2024-08-20T02:18:35.530069Z" + "iopub.execute_input": "2024-08-21T00:43:41.048111Z", + "iopub.status.busy": "2024-08-21T00:43:41.047923Z", + "iopub.status.idle": "2024-08-21T00:43:41.055443Z", + "shell.execute_reply": "2024-08-21T00:43:41.054977Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "dfcac1fa", + "id": "2f5c2abd", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "34d13b9d", + "id": "33fc0025", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.532400Z", - "iopub.status.busy": "2024-08-20T02:18:35.532228Z", - "iopub.status.idle": "2024-08-20T02:18:35.552090Z", - "shell.execute_reply": "2024-08-20T02:18:35.551600Z" + "iopub.execute_input": "2024-08-21T00:43:41.057268Z", + "iopub.status.busy": "2024-08-21T00:43:41.057097Z", + "iopub.status.idle": "2024-08-21T00:43:41.075807Z", + "shell.execute_reply": "2024-08-21T00:43:41.075358Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "61354613", + "id": "1c822d98", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:35.554430Z", - "iopub.status.busy": "2024-08-20T02:18:35.553975Z", - "iopub.status.idle": "2024-08-20T02:18:35.557509Z", - "shell.execute_reply": "2024-08-20T02:18:35.557025Z" + "iopub.execute_input": "2024-08-21T00:43:41.077653Z", + "iopub.status.busy": "2024-08-21T00:43:41.077482Z", + "iopub.status.idle": "2024-08-21T00:43:41.080541Z", + "shell.execute_reply": "2024-08-21T00:43:41.079995Z" } }, "outputs": [ @@ -1622,7 +1622,23 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"iopub.execute_input": "2024-08-20T02:18:39.079038Z", - "iopub.status.busy": "2024-08-20T02:18:39.078872Z", - "iopub.status.idle": "2024-08-20T02:18:40.521690Z", - "shell.execute_reply": "2024-08-20T02:18:40.521131Z" + "iopub.execute_input": "2024-08-21T00:43:44.323922Z", + "iopub.status.busy": "2024-08-21T00:43:44.323504Z", + "iopub.status.idle": "2024-08-21T00:43:45.482662Z", + "shell.execute_reply": "2024-08-21T00:43:45.482106Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.524199Z", - "iopub.status.busy": "2024-08-20T02:18:40.523738Z", - "iopub.status.idle": "2024-08-20T02:18:40.527321Z", - "shell.execute_reply": "2024-08-20T02:18:40.526891Z" + "iopub.execute_input": "2024-08-21T00:43:45.485461Z", + "iopub.status.busy": "2024-08-21T00:43:45.484927Z", + "iopub.status.idle": "2024-08-21T00:43:45.488591Z", + "shell.execute_reply": "2024-08-21T00:43:45.488149Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.529321Z", - "iopub.status.busy": "2024-08-20T02:18:40.529040Z", - "iopub.status.idle": "2024-08-20T02:18:40.830470Z", - "shell.execute_reply": "2024-08-20T02:18:40.829983Z" + "iopub.execute_input": "2024-08-21T00:43:45.490643Z", + "iopub.status.busy": "2024-08-21T00:43:45.490301Z", + "iopub.status.idle": "2024-08-21T00:43:45.766813Z", + "shell.execute_reply": "2024-08-21T00:43:45.766258Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.832627Z", - "iopub.status.busy": "2024-08-20T02:18:40.832279Z", - "iopub.status.idle": "2024-08-20T02:18:40.838038Z", - "shell.execute_reply": "2024-08-20T02:18:40.837573Z" + "iopub.execute_input": "2024-08-21T00:43:45.769098Z", + "iopub.status.busy": "2024-08-21T00:43:45.768752Z", + "iopub.status.idle": "2024-08-21T00:43:45.774608Z", + "shell.execute_reply": "2024-08-21T00:43:45.774168Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.840027Z", - "iopub.status.busy": "2024-08-20T02:18:40.839707Z", - "iopub.status.idle": "2024-08-20T02:18:40.846682Z", - "shell.execute_reply": "2024-08-20T02:18:40.846218Z" + "iopub.execute_input": "2024-08-21T00:43:45.776485Z", + "iopub.status.busy": "2024-08-21T00:43:45.776314Z", + "iopub.status.idle": "2024-08-21T00:43:45.783218Z", + "shell.execute_reply": "2024-08-21T00:43:45.782653Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.848706Z", - "iopub.status.busy": "2024-08-20T02:18:40.848382Z", - "iopub.status.idle": "2024-08-20T02:18:40.853255Z", - "shell.execute_reply": "2024-08-20T02:18:40.852680Z" + "iopub.execute_input": "2024-08-21T00:43:45.785398Z", + "iopub.status.busy": "2024-08-21T00:43:45.785009Z", + "iopub.status.idle": "2024-08-21T00:43:45.789740Z", + "shell.execute_reply": "2024-08-21T00:43:45.789307Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.855222Z", - "iopub.status.busy": "2024-08-20T02:18:40.854921Z", - "iopub.status.idle": "2024-08-20T02:18:40.860716Z", - "shell.execute_reply": "2024-08-20T02:18:40.860248Z" + "iopub.execute_input": "2024-08-21T00:43:45.791748Z", + "iopub.status.busy": "2024-08-21T00:43:45.791456Z", + "iopub.status.idle": "2024-08-21T00:43:45.797389Z", + "shell.execute_reply": "2024-08-21T00:43:45.796810Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.862798Z", - "iopub.status.busy": "2024-08-20T02:18:40.862460Z", - "iopub.status.idle": "2024-08-20T02:18:40.866494Z", - "shell.execute_reply": "2024-08-20T02:18:40.865924Z" + "iopub.execute_input": "2024-08-21T00:43:45.799517Z", + "iopub.status.busy": "2024-08-21T00:43:45.799174Z", + "iopub.status.idle": "2024-08-21T00:43:45.803248Z", + "shell.execute_reply": "2024-08-21T00:43:45.802813Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.868698Z", - "iopub.status.busy": "2024-08-20T02:18:40.868378Z", - "iopub.status.idle": "2024-08-20T02:18:40.933865Z", - "shell.execute_reply": "2024-08-20T02:18:40.933191Z" + "iopub.execute_input": "2024-08-21T00:43:45.805238Z", + "iopub.status.busy": "2024-08-21T00:43:45.804914Z", + "iopub.status.idle": "2024-08-21T00:43:45.870308Z", + "shell.execute_reply": "2024-08-21T00:43:45.869741Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.936337Z", - "iopub.status.busy": "2024-08-20T02:18:40.936127Z", - "iopub.status.idle": "2024-08-20T02:18:40.946629Z", - "shell.execute_reply": "2024-08-20T02:18:40.946140Z" + "iopub.execute_input": "2024-08-21T00:43:45.873236Z", + "iopub.status.busy": "2024-08-21T00:43:45.872722Z", + "iopub.status.idle": "2024-08-21T00:43:45.883273Z", + "shell.execute_reply": "2024-08-21T00:43:45.882789Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:40.949049Z", - "iopub.status.busy": "2024-08-20T02:18:40.948678Z", - "iopub.status.idle": "2024-08-20T02:18:40.968181Z", - "shell.execute_reply": "2024-08-20T02:18:40.967688Z" + "iopub.execute_input": "2024-08-21T00:43:45.886413Z", + "iopub.status.busy": "2024-08-21T00:43:45.885359Z", + "iopub.status.idle": "2024-08-21T00:43:45.908212Z", + "shell.execute_reply": "2024-08-21T00:43:45.907674Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - 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"iopub.execute_input": "2024-08-20T02:18:41.129468Z", - "iopub.status.busy": "2024-08-20T02:18:41.129152Z", - "iopub.status.idle": "2024-08-20T02:18:43.315688Z", - "shell.execute_reply": "2024-08-20T02:18:43.314995Z" + "iopub.execute_input": "2024-08-21T00:43:46.080126Z", + "iopub.status.busy": "2024-08-21T00:43:46.079767Z", + "iopub.status.idle": "2024-08-21T00:43:48.070424Z", + "shell.execute_reply": "2024-08-21T00:43:48.069820Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.319532Z", - "iopub.status.busy": "2024-08-20T02:18:43.318405Z", - "iopub.status.idle": "2024-08-20T02:18:43.333322Z", - "shell.execute_reply": "2024-08-20T02:18:43.332786Z" + "iopub.execute_input": "2024-08-21T00:43:48.074100Z", + "iopub.status.busy": "2024-08-21T00:43:48.072890Z", + "iopub.status.idle": "2024-08-21T00:43:48.087651Z", + "shell.execute_reply": "2024-08-21T00:43:48.087161Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.336884Z", - "iopub.status.busy": "2024-08-20T02:18:43.335952Z", - "iopub.status.idle": "2024-08-20T02:18:43.339988Z", - "shell.execute_reply": "2024-08-20T02:18:43.339477Z" + "iopub.execute_input": "2024-08-21T00:43:48.091142Z", + "iopub.status.busy": "2024-08-21T00:43:48.090218Z", + "iopub.status.idle": "2024-08-21T00:43:48.094330Z", + "shell.execute_reply": "2024-08-21T00:43:48.093698Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.343505Z", - "iopub.status.busy": "2024-08-20T02:18:43.342554Z", - "iopub.status.idle": "2024-08-20T02:18:43.348202Z", - "shell.execute_reply": "2024-08-20T02:18:43.347701Z" + "iopub.execute_input": "2024-08-21T00:43:48.097756Z", + "iopub.status.busy": "2024-08-21T00:43:48.096840Z", + "iopub.status.idle": "2024-08-21T00:43:48.102318Z", + "shell.execute_reply": "2024-08-21T00:43:48.101826Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.351804Z", - "iopub.status.busy": "2024-08-20T02:18:43.350840Z", - "iopub.status.idle": "2024-08-20T02:18:43.384316Z", - "shell.execute_reply": "2024-08-20T02:18:43.383808Z" + "iopub.execute_input": "2024-08-21T00:43:48.105802Z", + "iopub.status.busy": "2024-08-21T00:43:48.104884Z", + "iopub.status.idle": "2024-08-21T00:43:48.133986Z", + "shell.execute_reply": "2024-08-21T00:43:48.133511Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.387211Z", - "iopub.status.busy": "2024-08-20T02:18:43.386773Z", - "iopub.status.idle": "2024-08-20T02:18:43.934525Z", - "shell.execute_reply": "2024-08-20T02:18:43.933974Z" + "iopub.execute_input": "2024-08-21T00:43:48.137043Z", + "iopub.status.busy": "2024-08-21T00:43:48.136416Z", + "iopub.status.idle": "2024-08-21T00:43:48.664903Z", + "shell.execute_reply": "2024-08-21T00:43:48.664340Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:43.937351Z", - "iopub.status.busy": "2024-08-20T02:18:43.936985Z", - "iopub.status.idle": "2024-08-20T02:18:44.067674Z", - "shell.execute_reply": "2024-08-20T02:18:44.067055Z" + "iopub.execute_input": "2024-08-21T00:43:48.668839Z", + "iopub.status.busy": "2024-08-21T00:43:48.667904Z", + "iopub.status.idle": "2024-08-21T00:43:48.799618Z", + "shell.execute_reply": "2024-08-21T00:43:48.799015Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.071135Z", - "iopub.status.busy": "2024-08-20T02:18:44.070004Z", - "iopub.status.idle": "2024-08-20T02:18:44.079002Z", - "shell.execute_reply": "2024-08-20T02:18:44.078493Z" + "iopub.execute_input": "2024-08-21T00:43:48.802587Z", + "iopub.status.busy": "2024-08-21T00:43:48.802251Z", + "iopub.status.idle": "2024-08-21T00:43:48.808846Z", + "shell.execute_reply": "2024-08-21T00:43:48.808361Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.082548Z", - "iopub.status.busy": "2024-08-20T02:18:44.081615Z", - "iopub.status.idle": "2024-08-20T02:18:44.089637Z", - "shell.execute_reply": "2024-08-20T02:18:44.089115Z" + "iopub.execute_input": "2024-08-21T00:43:48.811963Z", + "iopub.status.busy": "2024-08-21T00:43:48.810885Z", + "iopub.status.idle": "2024-08-21T00:43:48.818883Z", + "shell.execute_reply": "2024-08-21T00:43:48.818393Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.093142Z", - "iopub.status.busy": "2024-08-20T02:18:44.092209Z", - "iopub.status.idle": "2024-08-20T02:18:44.099634Z", - "shell.execute_reply": "2024-08-20T02:18:44.099137Z" + "iopub.execute_input": "2024-08-21T00:43:48.822356Z", + "iopub.status.busy": "2024-08-21T00:43:48.821424Z", + "iopub.status.idle": "2024-08-21T00:43:48.828749Z", + "shell.execute_reply": "2024-08-21T00:43:48.828250Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.103125Z", - "iopub.status.busy": "2024-08-20T02:18:44.102204Z", - "iopub.status.idle": "2024-08-20T02:18:44.108338Z", - "shell.execute_reply": "2024-08-20T02:18:44.107839Z" + "iopub.execute_input": "2024-08-21T00:43:48.832200Z", + "iopub.status.busy": "2024-08-21T00:43:48.831242Z", + "iopub.status.idle": "2024-08-21T00:43:48.837286Z", + "shell.execute_reply": "2024-08-21T00:43:48.836796Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.110735Z", - "iopub.status.busy": "2024-08-20T02:18:44.110563Z", - "iopub.status.idle": "2024-08-20T02:18:44.115117Z", - "shell.execute_reply": "2024-08-20T02:18:44.114675Z" + "iopub.execute_input": "2024-08-21T00:43:48.840754Z", + "iopub.status.busy": "2024-08-21T00:43:48.839819Z", + "iopub.status.idle": "2024-08-21T00:43:48.845405Z", + "shell.execute_reply": "2024-08-21T00:43:48.844997Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.117018Z", - "iopub.status.busy": "2024-08-20T02:18:44.116864Z", - "iopub.status.idle": "2024-08-20T02:18:44.191518Z", - "shell.execute_reply": "2024-08-20T02:18:44.190902Z" + "iopub.execute_input": "2024-08-21T00:43:48.848112Z", + "iopub.status.busy": "2024-08-21T00:43:48.847491Z", + "iopub.status.idle": "2024-08-21T00:43:48.923292Z", + "shell.execute_reply": "2024-08-21T00:43:48.922762Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.195789Z", - "iopub.status.busy": "2024-08-20T02:18:44.195385Z", - "iopub.status.idle": "2024-08-20T02:18:44.204480Z", - "shell.execute_reply": "2024-08-20T02:18:44.203923Z" + "iopub.execute_input": "2024-08-21T00:43:48.926332Z", + "iopub.status.busy": "2024-08-21T00:43:48.925595Z", + "iopub.status.idle": "2024-08-21T00:43:48.935839Z", + "shell.execute_reply": "2024-08-21T00:43:48.935360Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.207795Z", - "iopub.status.busy": "2024-08-20T02:18:44.206903Z", - "iopub.status.idle": "2024-08-20T02:18:44.210712Z", - "shell.execute_reply": "2024-08-20T02:18:44.210291Z" + "iopub.execute_input": "2024-08-21T00:43:48.938763Z", + "iopub.status.busy": "2024-08-21T00:43:48.938336Z", + "iopub.status.idle": "2024-08-21T00:43:48.941682Z", + "shell.execute_reply": "2024-08-21T00:43:48.941148Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.213385Z", - "iopub.status.busy": "2024-08-20T02:18:44.212803Z", - "iopub.status.idle": "2024-08-20T02:18:44.223231Z", - "shell.execute_reply": "2024-08-20T02:18:44.222657Z" + "iopub.execute_input": "2024-08-21T00:43:48.943690Z", + "iopub.status.busy": "2024-08-21T00:43:48.943523Z", + "iopub.status.idle": "2024-08-21T00:43:48.953778Z", + "shell.execute_reply": "2024-08-21T00:43:48.953320Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.225400Z", - "iopub.status.busy": "2024-08-20T02:18:44.225210Z", - "iopub.status.idle": "2024-08-20T02:18:44.231940Z", - "shell.execute_reply": "2024-08-20T02:18:44.231250Z" + "iopub.execute_input": "2024-08-21T00:43:48.955857Z", + "iopub.status.busy": "2024-08-21T00:43:48.955527Z", + "iopub.status.idle": "2024-08-21T00:43:48.961904Z", + "shell.execute_reply": "2024-08-21T00:43:48.961466Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.234067Z", - "iopub.status.busy": "2024-08-20T02:18:44.233893Z", - "iopub.status.idle": "2024-08-20T02:18:44.237411Z", - "shell.execute_reply": "2024-08-20T02:18:44.236700Z" + "iopub.execute_input": "2024-08-21T00:43:48.963753Z", + "iopub.status.busy": "2024-08-21T00:43:48.963585Z", + "iopub.status.idle": "2024-08-21T00:43:48.966870Z", + "shell.execute_reply": "2024-08-21T00:43:48.966412Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:44.239332Z", - "iopub.status.busy": "2024-08-20T02:18:44.239159Z", - "iopub.status.idle": "2024-08-20T02:18:48.319121Z", - "shell.execute_reply": "2024-08-20T02:18:48.318603Z" + "iopub.execute_input": "2024-08-21T00:43:48.968877Z", + "iopub.status.busy": "2024-08-21T00:43:48.968552Z", + "iopub.status.idle": "2024-08-21T00:43:52.986642Z", + "shell.execute_reply": "2024-08-21T00:43:52.986089Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:48.322465Z", - "iopub.status.busy": "2024-08-20T02:18:48.321697Z", - "iopub.status.idle": "2024-08-20T02:18:48.325214Z", - "shell.execute_reply": "2024-08-20T02:18:48.324785Z" + "iopub.execute_input": "2024-08-21T00:43:52.989780Z", + "iopub.status.busy": "2024-08-21T00:43:52.988877Z", + "iopub.status.idle": "2024-08-21T00:43:52.993710Z", + "shell.execute_reply": "2024-08-21T00:43:52.993266Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:48.327316Z", - "iopub.status.busy": "2024-08-20T02:18:48.327002Z", - "iopub.status.idle": "2024-08-20T02:18:48.329876Z", - "shell.execute_reply": "2024-08-20T02:18:48.329469Z" + "iopub.execute_input": "2024-08-21T00:43:52.995821Z", + "iopub.status.busy": "2024-08-21T00:43:52.995483Z", + "iopub.status.idle": "2024-08-21T00:43:52.998348Z", + "shell.execute_reply": "2024-08-21T00:43:52.997886Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index c1a97fbf9..6ff79093e 100644 --- a/master/tutorials/indepth_overview.ipynb +++ b/master/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:51.681853Z", - "iopub.status.busy": "2024-08-20T02:18:51.681681Z", - "iopub.status.idle": "2024-08-20T02:18:53.129044Z", - "shell.execute_reply": "2024-08-20T02:18:53.128476Z" + "iopub.execute_input": "2024-08-21T00:43:56.140800Z", + "iopub.status.busy": "2024-08-21T00:43:56.140620Z", + "iopub.status.idle": "2024-08-21T00:43:57.343557Z", + "shell.execute_reply": "2024-08-21T00:43:57.343015Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:18:53.131558Z", - "iopub.status.busy": "2024-08-20T02:18:53.131088Z", - "iopub.status.idle": "2024-08-20T02:18:53.134587Z", - "shell.execute_reply": "2024-08-20T02:18:53.134019Z" + "iopub.execute_input": "2024-08-21T00:43:57.346298Z", + "iopub.status.busy": "2024-08-21T00:43:57.345733Z", + "iopub.status.idle": "2024-08-21T00:43:57.530661Z", + "shell.execute_reply": "2024-08-21T00:43:57.530087Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.136980Z", - "iopub.status.busy": "2024-08-20T02:18:53.136531Z", - "iopub.status.idle": "2024-08-20T02:18:53.148739Z", - "shell.execute_reply": "2024-08-20T02:18:53.148175Z" + "iopub.execute_input": "2024-08-21T00:43:57.533119Z", + "iopub.status.busy": "2024-08-21T00:43:57.532920Z", + "iopub.status.idle": "2024-08-21T00:43:57.544404Z", + "shell.execute_reply": "2024-08-21T00:43:57.543955Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.150948Z", - "iopub.status.busy": "2024-08-20T02:18:53.150659Z", - "iopub.status.idle": "2024-08-20T02:18:53.391414Z", - "shell.execute_reply": "2024-08-20T02:18:53.390934Z" + "iopub.execute_input": "2024-08-21T00:43:57.546292Z", + "iopub.status.busy": "2024-08-21T00:43:57.546113Z", + "iopub.status.idle": "2024-08-21T00:43:57.781144Z", + "shell.execute_reply": "2024-08-21T00:43:57.780517Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.393846Z", - "iopub.status.busy": "2024-08-20T02:18:53.393517Z", - "iopub.status.idle": "2024-08-20T02:18:53.419044Z", - "shell.execute_reply": "2024-08-20T02:18:53.418597Z" + "iopub.execute_input": "2024-08-21T00:43:57.783496Z", + "iopub.status.busy": "2024-08-21T00:43:57.783061Z", + "iopub.status.idle": "2024-08-21T00:43:57.809126Z", + "shell.execute_reply": "2024-08-21T00:43:57.808529Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:53.421156Z", - "iopub.status.busy": "2024-08-20T02:18:53.420796Z", - "iopub.status.idle": "2024-08-20T02:18:55.596000Z", - "shell.execute_reply": "2024-08-20T02:18:55.595347Z" + "iopub.execute_input": "2024-08-21T00:43:57.811664Z", + "iopub.status.busy": "2024-08-21T00:43:57.811098Z", + "iopub.status.idle": "2024-08-21T00:43:59.890782Z", + "shell.execute_reply": "2024-08-21T00:43:59.890150Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:55.598602Z", - "iopub.status.busy": "2024-08-20T02:18:55.598063Z", - "iopub.status.idle": "2024-08-20T02:18:55.616511Z", - "shell.execute_reply": "2024-08-20T02:18:55.616058Z" + "iopub.execute_input": "2024-08-21T00:43:59.893622Z", + "iopub.status.busy": "2024-08-21T00:43:59.892889Z", + "iopub.status.idle": "2024-08-21T00:43:59.910894Z", + "shell.execute_reply": "2024-08-21T00:43:59.910318Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:55.618658Z", - "iopub.status.busy": "2024-08-20T02:18:55.618308Z", - "iopub.status.idle": "2024-08-20T02:18:57.218343Z", - "shell.execute_reply": "2024-08-20T02:18:57.217657Z" + "iopub.execute_input": "2024-08-21T00:43:59.913118Z", + "iopub.status.busy": "2024-08-21T00:43:59.912629Z", + "iopub.status.idle": "2024-08-21T00:44:01.485996Z", + "shell.execute_reply": "2024-08-21T00:44:01.485331Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.221005Z", - "iopub.status.busy": "2024-08-20T02:18:57.220309Z", - "iopub.status.idle": "2024-08-20T02:18:57.234346Z", - "shell.execute_reply": "2024-08-20T02:18:57.233776Z" + "iopub.execute_input": "2024-08-21T00:44:01.488797Z", + "iopub.status.busy": "2024-08-21T00:44:01.488082Z", + "iopub.status.idle": "2024-08-21T00:44:01.501841Z", + "shell.execute_reply": "2024-08-21T00:44:01.501318Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.236411Z", - "iopub.status.busy": "2024-08-20T02:18:57.236099Z", - "iopub.status.idle": "2024-08-20T02:18:57.319189Z", - "shell.execute_reply": "2024-08-20T02:18:57.318628Z" + "iopub.execute_input": "2024-08-21T00:44:01.503912Z", + "iopub.status.busy": "2024-08-21T00:44:01.503642Z", + "iopub.status.idle": "2024-08-21T00:44:01.590531Z", + "shell.execute_reply": "2024-08-21T00:44:01.589895Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.321670Z", - "iopub.status.busy": "2024-08-20T02:18:57.321188Z", - "iopub.status.idle": "2024-08-20T02:18:57.536110Z", - "shell.execute_reply": "2024-08-20T02:18:57.535559Z" + "iopub.execute_input": "2024-08-21T00:44:01.592709Z", + "iopub.status.busy": "2024-08-21T00:44:01.592483Z", + "iopub.status.idle": "2024-08-21T00:44:01.806762Z", + "shell.execute_reply": "2024-08-21T00:44:01.806173Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.538284Z", - "iopub.status.busy": "2024-08-20T02:18:57.538098Z", - "iopub.status.idle": "2024-08-20T02:18:57.555022Z", - "shell.execute_reply": "2024-08-20T02:18:57.554545Z" + "iopub.execute_input": "2024-08-21T00:44:01.809115Z", + "iopub.status.busy": "2024-08-21T00:44:01.808792Z", + "iopub.status.idle": "2024-08-21T00:44:01.825931Z", + "shell.execute_reply": "2024-08-21T00:44:01.825443Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.557201Z", - "iopub.status.busy": "2024-08-20T02:18:57.556872Z", - "iopub.status.idle": "2024-08-20T02:18:57.566684Z", - "shell.execute_reply": "2024-08-20T02:18:57.566222Z" + "iopub.execute_input": "2024-08-21T00:44:01.828060Z", + "iopub.status.busy": "2024-08-21T00:44:01.827758Z", + "iopub.status.idle": "2024-08-21T00:44:01.837905Z", + "shell.execute_reply": "2024-08-21T00:44:01.837364Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.568776Z", - "iopub.status.busy": "2024-08-20T02:18:57.568456Z", - "iopub.status.idle": "2024-08-20T02:18:57.661709Z", - "shell.execute_reply": "2024-08-20T02:18:57.661129Z" + "iopub.execute_input": "2024-08-21T00:44:01.839879Z", + "iopub.status.busy": "2024-08-21T00:44:01.839580Z", + "iopub.status.idle": "2024-08-21T00:44:01.935954Z", + "shell.execute_reply": "2024-08-21T00:44:01.935375Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.664109Z", - "iopub.status.busy": "2024-08-20T02:18:57.663766Z", - "iopub.status.idle": "2024-08-20T02:18:57.807373Z", - "shell.execute_reply": "2024-08-20T02:18:57.806736Z" + "iopub.execute_input": "2024-08-21T00:44:01.938342Z", + "iopub.status.busy": "2024-08-21T00:44:01.937987Z", + "iopub.status.idle": "2024-08-21T00:44:02.078263Z", + "shell.execute_reply": "2024-08-21T00:44:02.077615Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.809598Z", - "iopub.status.busy": "2024-08-20T02:18:57.809366Z", - "iopub.status.idle": "2024-08-20T02:18:57.813238Z", - "shell.execute_reply": "2024-08-20T02:18:57.812677Z" + "iopub.execute_input": "2024-08-21T00:44:02.080783Z", + "iopub.status.busy": "2024-08-21T00:44:02.080438Z", + "iopub.status.idle": "2024-08-21T00:44:02.084408Z", + "shell.execute_reply": "2024-08-21T00:44:02.083813Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.815295Z", - "iopub.status.busy": "2024-08-20T02:18:57.815004Z", - "iopub.status.idle": "2024-08-20T02:18:57.818743Z", - "shell.execute_reply": "2024-08-20T02:18:57.818208Z" + "iopub.execute_input": "2024-08-21T00:44:02.086440Z", + "iopub.status.busy": "2024-08-21T00:44:02.086169Z", + "iopub.status.idle": "2024-08-21T00:44:02.090052Z", + "shell.execute_reply": "2024-08-21T00:44:02.089491Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.820752Z", - "iopub.status.busy": "2024-08-20T02:18:57.820413Z", - "iopub.status.idle": "2024-08-20T02:18:57.857122Z", - "shell.execute_reply": "2024-08-20T02:18:57.856626Z" + "iopub.execute_input": "2024-08-21T00:44:02.092030Z", + "iopub.status.busy": "2024-08-21T00:44:02.091746Z", + "iopub.status.idle": "2024-08-21T00:44:02.128325Z", + "shell.execute_reply": "2024-08-21T00:44:02.127730Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.859267Z", - "iopub.status.busy": "2024-08-20T02:18:57.858911Z", - "iopub.status.idle": "2024-08-20T02:18:57.899267Z", - "shell.execute_reply": "2024-08-20T02:18:57.898774Z" + "iopub.execute_input": "2024-08-21T00:44:02.130318Z", + "iopub.status.busy": "2024-08-21T00:44:02.130002Z", + "iopub.status.idle": "2024-08-21T00:44:02.171110Z", + "shell.execute_reply": "2024-08-21T00:44:02.170530Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:57.901394Z", - "iopub.status.busy": "2024-08-20T02:18:57.901043Z", - "iopub.status.idle": "2024-08-20T02:18:58.003043Z", - "shell.execute_reply": "2024-08-20T02:18:58.002311Z" + "iopub.execute_input": "2024-08-21T00:44:02.173124Z", + "iopub.status.busy": "2024-08-21T00:44:02.172810Z", + "iopub.status.idle": "2024-08-21T00:44:02.280606Z", + "shell.execute_reply": "2024-08-21T00:44:02.279967Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.006068Z", - "iopub.status.busy": "2024-08-20T02:18:58.005525Z", - "iopub.status.idle": "2024-08-20T02:18:58.110250Z", - "shell.execute_reply": "2024-08-20T02:18:58.109590Z" + "iopub.execute_input": "2024-08-21T00:44:02.283100Z", + "iopub.status.busy": "2024-08-21T00:44:02.282915Z", + "iopub.status.idle": "2024-08-21T00:44:02.392853Z", + "shell.execute_reply": "2024-08-21T00:44:02.392282Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.112453Z", - "iopub.status.busy": "2024-08-20T02:18:58.112218Z", - "iopub.status.idle": "2024-08-20T02:18:58.323254Z", - "shell.execute_reply": "2024-08-20T02:18:58.322659Z" + "iopub.execute_input": "2024-08-21T00:44:02.395138Z", + "iopub.status.busy": "2024-08-21T00:44:02.394895Z", + "iopub.status.idle": "2024-08-21T00:44:02.630946Z", + "shell.execute_reply": "2024-08-21T00:44:02.630356Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.325554Z", - "iopub.status.busy": "2024-08-20T02:18:58.325088Z", - "iopub.status.idle": "2024-08-20T02:18:58.549365Z", - "shell.execute_reply": "2024-08-20T02:18:58.548707Z" + "iopub.execute_input": "2024-08-21T00:44:02.633172Z", + "iopub.status.busy": "2024-08-21T00:44:02.632982Z", + "iopub.status.idle": "2024-08-21T00:44:02.858035Z", + "shell.execute_reply": "2024-08-21T00:44:02.857391Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.551633Z", - "iopub.status.busy": "2024-08-20T02:18:58.551390Z", - "iopub.status.idle": "2024-08-20T02:18:58.557774Z", - "shell.execute_reply": "2024-08-20T02:18:58.557277Z" + "iopub.execute_input": "2024-08-21T00:44:02.860575Z", + "iopub.status.busy": "2024-08-21T00:44:02.860188Z", + "iopub.status.idle": "2024-08-21T00:44:02.866265Z", + "shell.execute_reply": "2024-08-21T00:44:02.865703Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.559834Z", - "iopub.status.busy": "2024-08-20T02:18:58.559507Z", - "iopub.status.idle": "2024-08-20T02:18:58.775893Z", - "shell.execute_reply": "2024-08-20T02:18:58.775301Z" + "iopub.execute_input": "2024-08-21T00:44:02.868556Z", + "iopub.status.busy": "2024-08-21T00:44:02.868207Z", + "iopub.status.idle": "2024-08-21T00:44:03.086365Z", + "shell.execute_reply": "2024-08-21T00:44:03.085768Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:18:58.778369Z", - "iopub.status.busy": "2024-08-20T02:18:58.777982Z", - "iopub.status.idle": "2024-08-20T02:18:59.833777Z", - "shell.execute_reply": "2024-08-20T02:18:59.833215Z" + "iopub.execute_input": "2024-08-21T00:44:03.088869Z", + "iopub.status.busy": "2024-08-21T00:44:03.088487Z", + "iopub.status.idle": "2024-08-21T00:44:04.135646Z", + "shell.execute_reply": "2024-08-21T00:44:04.135093Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 95d3b75d7..073024f68 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:03.522991Z", - "iopub.status.busy": "2024-08-20T02:19:03.522832Z", - "iopub.status.idle": "2024-08-20T02:19:04.942768Z", - "shell.execute_reply": "2024-08-20T02:19:04.942212Z" + "iopub.execute_input": "2024-08-21T00:44:08.536925Z", + "iopub.status.busy": "2024-08-21T00:44:08.536756Z", + "iopub.status.idle": "2024-08-21T00:44:09.687224Z", + "shell.execute_reply": "2024-08-21T00:44:09.686734Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:04.945469Z", - "iopub.status.busy": "2024-08-20T02:19:04.944979Z", - "iopub.status.idle": "2024-08-20T02:19:04.948001Z", - "shell.execute_reply": "2024-08-20T02:19:04.947540Z" + "iopub.execute_input": "2024-08-21T00:44:09.689870Z", + "iopub.status.busy": "2024-08-21T00:44:09.689408Z", + "iopub.status.idle": "2024-08-21T00:44:09.692521Z", + "shell.execute_reply": "2024-08-21T00:44:09.692073Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:04.950109Z", - "iopub.status.busy": "2024-08-20T02:19:04.949813Z", - "iopub.status.idle": "2024-08-20T02:19:04.957738Z", - "shell.execute_reply": "2024-08-20T02:19:04.957121Z" + "iopub.execute_input": "2024-08-21T00:44:09.694579Z", + "iopub.status.busy": "2024-08-21T00:44:09.694245Z", + "iopub.status.idle": "2024-08-21T00:44:09.702175Z", + "shell.execute_reply": "2024-08-21T00:44:09.701716Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:04.959689Z", - "iopub.status.busy": "2024-08-20T02:19:04.959510Z", - "iopub.status.idle": "2024-08-20T02:19:05.007494Z", - "shell.execute_reply": "2024-08-20T02:19:05.006846Z" + "iopub.execute_input": "2024-08-21T00:44:09.704223Z", + "iopub.status.busy": "2024-08-21T00:44:09.703803Z", + "iopub.status.idle": "2024-08-21T00:44:09.750825Z", + "shell.execute_reply": "2024-08-21T00:44:09.750317Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.009955Z", - "iopub.status.busy": "2024-08-20T02:19:05.009768Z", - "iopub.status.idle": "2024-08-20T02:19:05.026808Z", - "shell.execute_reply": "2024-08-20T02:19:05.026271Z" + "iopub.execute_input": "2024-08-21T00:44:09.753056Z", + "iopub.status.busy": "2024-08-21T00:44:09.752765Z", + "iopub.status.idle": "2024-08-21T00:44:09.769433Z", + "shell.execute_reply": "2024-08-21T00:44:09.768993Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.028834Z", - "iopub.status.busy": "2024-08-20T02:19:05.028656Z", - "iopub.status.idle": "2024-08-20T02:19:05.032389Z", - "shell.execute_reply": "2024-08-20T02:19:05.031915Z" + "iopub.execute_input": "2024-08-21T00:44:09.771586Z", + "iopub.status.busy": "2024-08-21T00:44:09.771104Z", + "iopub.status.idle": "2024-08-21T00:44:09.774951Z", + "shell.execute_reply": "2024-08-21T00:44:09.774438Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.034336Z", - "iopub.status.busy": "2024-08-20T02:19:05.034163Z", - "iopub.status.idle": "2024-08-20T02:19:05.052377Z", - "shell.execute_reply": "2024-08-20T02:19:05.051801Z" + "iopub.execute_input": "2024-08-21T00:44:09.777032Z", + "iopub.status.busy": "2024-08-21T00:44:09.776653Z", + "iopub.status.idle": "2024-08-21T00:44:09.792096Z", + "shell.execute_reply": "2024-08-21T00:44:09.791502Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.054757Z", - "iopub.status.busy": "2024-08-20T02:19:05.054411Z", - "iopub.status.idle": "2024-08-20T02:19:05.081157Z", - "shell.execute_reply": "2024-08-20T02:19:05.080588Z" + "iopub.execute_input": "2024-08-21T00:44:09.794285Z", + "iopub.status.busy": "2024-08-21T00:44:09.793887Z", + "iopub.status.idle": "2024-08-21T00:44:09.819917Z", + "shell.execute_reply": "2024-08-21T00:44:09.819351Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:05.083440Z", - "iopub.status.busy": "2024-08-20T02:19:05.083115Z", - "iopub.status.idle": "2024-08-20T02:19:07.231377Z", - "shell.execute_reply": "2024-08-20T02:19:07.230719Z" + "iopub.execute_input": "2024-08-21T00:44:09.822110Z", + "iopub.status.busy": "2024-08-21T00:44:09.821731Z", + "iopub.status.idle": "2024-08-21T00:44:11.811688Z", + "shell.execute_reply": "2024-08-21T00:44:11.811036Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.234251Z", - "iopub.status.busy": "2024-08-20T02:19:07.233645Z", - "iopub.status.idle": "2024-08-20T02:19:07.240565Z", - "shell.execute_reply": "2024-08-20T02:19:07.240009Z" + "iopub.execute_input": "2024-08-21T00:44:11.814185Z", + "iopub.status.busy": "2024-08-21T00:44:11.813766Z", + "iopub.status.idle": "2024-08-21T00:44:11.820401Z", + "shell.execute_reply": "2024-08-21T00:44:11.819834Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.242853Z", - "iopub.status.busy": "2024-08-20T02:19:07.242453Z", - "iopub.status.idle": "2024-08-20T02:19:07.254962Z", - "shell.execute_reply": "2024-08-20T02:19:07.254495Z" + "iopub.execute_input": "2024-08-21T00:44:11.822461Z", + "iopub.status.busy": "2024-08-21T00:44:11.822116Z", + "iopub.status.idle": "2024-08-21T00:44:11.835628Z", + "shell.execute_reply": "2024-08-21T00:44:11.835052Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.256913Z", - "iopub.status.busy": "2024-08-20T02:19:07.256739Z", - "iopub.status.idle": "2024-08-20T02:19:07.263271Z", - "shell.execute_reply": "2024-08-20T02:19:07.262692Z" + "iopub.execute_input": "2024-08-21T00:44:11.837751Z", + "iopub.status.busy": "2024-08-21T00:44:11.837353Z", + "iopub.status.idle": "2024-08-21T00:44:11.843662Z", + "shell.execute_reply": "2024-08-21T00:44:11.843107Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.265460Z", - "iopub.status.busy": "2024-08-20T02:19:07.265040Z", - "iopub.status.idle": "2024-08-20T02:19:07.267720Z", - "shell.execute_reply": "2024-08-20T02:19:07.267271Z" + "iopub.execute_input": "2024-08-21T00:44:11.845794Z", + "iopub.status.busy": "2024-08-21T00:44:11.845414Z", + "iopub.status.idle": "2024-08-21T00:44:11.848002Z", + "shell.execute_reply": "2024-08-21T00:44:11.847541Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.269736Z", - "iopub.status.busy": "2024-08-20T02:19:07.269397Z", - "iopub.status.idle": "2024-08-20T02:19:07.272660Z", - "shell.execute_reply": "2024-08-20T02:19:07.272134Z" + "iopub.execute_input": "2024-08-21T00:44:11.849966Z", + "iopub.status.busy": "2024-08-21T00:44:11.849626Z", + "iopub.status.idle": "2024-08-21T00:44:11.852992Z", + "shell.execute_reply": "2024-08-21T00:44:11.852471Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.274827Z", - "iopub.status.busy": "2024-08-20T02:19:07.274428Z", - "iopub.status.idle": "2024-08-20T02:19:07.277013Z", - "shell.execute_reply": "2024-08-20T02:19:07.276590Z" + "iopub.execute_input": "2024-08-21T00:44:11.855027Z", + "iopub.status.busy": "2024-08-21T00:44:11.854692Z", + "iopub.status.idle": "2024-08-21T00:44:11.857204Z", + "shell.execute_reply": "2024-08-21T00:44:11.856762Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.279076Z", - "iopub.status.busy": "2024-08-20T02:19:07.278677Z", - "iopub.status.idle": "2024-08-20T02:19:07.282955Z", - "shell.execute_reply": "2024-08-20T02:19:07.282393Z" + "iopub.execute_input": "2024-08-21T00:44:11.859154Z", + "iopub.status.busy": "2024-08-21T00:44:11.858846Z", + "iopub.status.idle": "2024-08-21T00:44:11.862989Z", + "shell.execute_reply": "2024-08-21T00:44:11.862452Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.285399Z", - "iopub.status.busy": "2024-08-20T02:19:07.284844Z", - "iopub.status.idle": "2024-08-20T02:19:07.313264Z", - "shell.execute_reply": "2024-08-20T02:19:07.312797Z" + "iopub.execute_input": "2024-08-21T00:44:11.865075Z", + "iopub.status.busy": "2024-08-21T00:44:11.864770Z", + "iopub.status.idle": "2024-08-21T00:44:11.893446Z", + "shell.execute_reply": "2024-08-21T00:44:11.892897Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:07.315544Z", - "iopub.status.busy": "2024-08-20T02:19:07.315122Z", - "iopub.status.idle": "2024-08-20T02:19:07.319938Z", - "shell.execute_reply": "2024-08-20T02:19:07.319454Z" + "iopub.execute_input": "2024-08-21T00:44:11.895680Z", + "iopub.status.busy": "2024-08-21T00:44:11.895363Z", + "iopub.status.idle": "2024-08-21T00:44:11.899971Z", + "shell.execute_reply": "2024-08-21T00:44:11.899406Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 0045fc3d5..55be504a5 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:10.411320Z", - "iopub.status.busy": "2024-08-20T02:19:10.411152Z", - "iopub.status.idle": "2024-08-20T02:19:11.855342Z", - "shell.execute_reply": "2024-08-20T02:19:11.854698Z" + "iopub.execute_input": "2024-08-21T00:44:14.676045Z", + "iopub.status.busy": "2024-08-21T00:44:14.675498Z", + "iopub.status.idle": "2024-08-21T00:44:15.874773Z", + "shell.execute_reply": "2024-08-21T00:44:15.874228Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:11.858020Z", - "iopub.status.busy": "2024-08-20T02:19:11.857673Z", - "iopub.status.idle": "2024-08-20T02:19:11.877980Z", - "shell.execute_reply": "2024-08-20T02:19:11.877379Z" + "iopub.execute_input": "2024-08-21T00:44:15.877397Z", + "iopub.status.busy": "2024-08-21T00:44:15.876909Z", + "iopub.status.idle": "2024-08-21T00:44:16.071071Z", + "shell.execute_reply": "2024-08-21T00:44:16.070463Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:11.880496Z", - "iopub.status.busy": "2024-08-20T02:19:11.880001Z", - "iopub.status.idle": "2024-08-20T02:19:11.893035Z", - "shell.execute_reply": "2024-08-20T02:19:11.892560Z" + "iopub.execute_input": "2024-08-21T00:44:16.073651Z", + "iopub.status.busy": "2024-08-21T00:44:16.073324Z", + "iopub.status.idle": "2024-08-21T00:44:16.087152Z", + "shell.execute_reply": "2024-08-21T00:44:16.086700Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:11.895218Z", - "iopub.status.busy": "2024-08-20T02:19:11.894778Z", - "iopub.status.idle": "2024-08-20T02:19:14.510745Z", - "shell.execute_reply": "2024-08-20T02:19:14.510214Z" + "iopub.execute_input": "2024-08-21T00:44:16.089136Z", + "iopub.status.busy": "2024-08-21T00:44:16.088951Z", + "iopub.status.idle": "2024-08-21T00:44:18.704832Z", + "shell.execute_reply": "2024-08-21T00:44:18.704201Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:14.512935Z", - "iopub.status.busy": "2024-08-20T02:19:14.512739Z", - "iopub.status.idle": "2024-08-20T02:19:15.874543Z", - "shell.execute_reply": "2024-08-20T02:19:15.873988Z" + "iopub.execute_input": "2024-08-21T00:44:18.707234Z", + "iopub.status.busy": "2024-08-21T00:44:18.706898Z", + "iopub.status.idle": "2024-08-21T00:44:20.047215Z", + "shell.execute_reply": "2024-08-21T00:44:20.046656Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:15.876840Z", - "iopub.status.busy": "2024-08-20T02:19:15.876649Z", - "iopub.status.idle": "2024-08-20T02:19:15.880654Z", - "shell.execute_reply": "2024-08-20T02:19:15.880101Z" + "iopub.execute_input": "2024-08-21T00:44:20.049763Z", + "iopub.status.busy": "2024-08-21T00:44:20.049407Z", + "iopub.status.idle": "2024-08-21T00:44:20.053503Z", + "shell.execute_reply": "2024-08-21T00:44:20.052933Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:15.882794Z", - "iopub.status.busy": "2024-08-20T02:19:15.882352Z", - "iopub.status.idle": "2024-08-20T02:19:18.024351Z", - "shell.execute_reply": "2024-08-20T02:19:18.023702Z" + "iopub.execute_input": "2024-08-21T00:44:20.055507Z", + "iopub.status.busy": "2024-08-21T00:44:20.055225Z", + "iopub.status.idle": "2024-08-21T00:44:22.125480Z", + "shell.execute_reply": "2024-08-21T00:44:22.124862Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:18.026941Z", - "iopub.status.busy": "2024-08-20T02:19:18.026511Z", - "iopub.status.idle": "2024-08-20T02:19:18.035086Z", - "shell.execute_reply": "2024-08-20T02:19:18.034546Z" + "iopub.execute_input": "2024-08-21T00:44:22.128137Z", + "iopub.status.busy": "2024-08-21T00:44:22.127601Z", + "iopub.status.idle": "2024-08-21T00:44:22.135349Z", + "shell.execute_reply": "2024-08-21T00:44:22.134822Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:18.037295Z", - "iopub.status.busy": "2024-08-20T02:19:18.036946Z", - "iopub.status.idle": "2024-08-20T02:19:20.557491Z", - "shell.execute_reply": "2024-08-20T02:19:20.556891Z" + "iopub.execute_input": "2024-08-21T00:44:22.137450Z", + "iopub.status.busy": "2024-08-21T00:44:22.137123Z", + "iopub.status.idle": "2024-08-21T00:44:24.871164Z", + "shell.execute_reply": "2024-08-21T00:44:24.870540Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.559617Z", - "iopub.status.busy": "2024-08-20T02:19:20.559437Z", - "iopub.status.idle": "2024-08-20T02:19:20.563117Z", - "shell.execute_reply": "2024-08-20T02:19:20.562661Z" + "iopub.execute_input": "2024-08-21T00:44:24.873731Z", + "iopub.status.busy": "2024-08-21T00:44:24.873390Z", + "iopub.status.idle": "2024-08-21T00:44:24.877293Z", + "shell.execute_reply": "2024-08-21T00:44:24.876833Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.565077Z", - "iopub.status.busy": "2024-08-20T02:19:20.564763Z", - "iopub.status.idle": "2024-08-20T02:19:20.568388Z", - "shell.execute_reply": "2024-08-20T02:19:20.567836Z" + "iopub.execute_input": "2024-08-21T00:44:24.879356Z", + "iopub.status.busy": "2024-08-21T00:44:24.879021Z", + "iopub.status.idle": "2024-08-21T00:44:24.882335Z", + "shell.execute_reply": "2024-08-21T00:44:24.881908Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:20.570408Z", - "iopub.status.busy": "2024-08-20T02:19:20.570101Z", - "iopub.status.idle": "2024-08-20T02:19:20.573344Z", - "shell.execute_reply": "2024-08-20T02:19:20.572863Z" + "iopub.execute_input": "2024-08-21T00:44:24.884241Z", + "iopub.status.busy": "2024-08-21T00:44:24.884064Z", + "iopub.status.idle": "2024-08-21T00:44:24.887414Z", + "shell.execute_reply": "2024-08-21T00:44:24.886955Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 87cb82783..32d0f5ddc 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:23.360886Z", - "iopub.status.busy": "2024-08-20T02:19:23.360718Z", - "iopub.status.idle": "2024-08-20T02:19:24.792942Z", - "shell.execute_reply": "2024-08-20T02:19:24.792381Z" + "iopub.execute_input": "2024-08-21T00:44:27.446117Z", + "iopub.status.busy": "2024-08-21T00:44:27.445691Z", + "iopub.status.idle": "2024-08-21T00:44:28.649409Z", + "shell.execute_reply": "2024-08-21T00:44:28.648789Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:24.795672Z", - "iopub.status.busy": "2024-08-20T02:19:24.795198Z", - "iopub.status.idle": "2024-08-20T02:19:25.977880Z", - "shell.execute_reply": "2024-08-20T02:19:25.977145Z" + "iopub.execute_input": "2024-08-21T00:44:28.652026Z", + "iopub.status.busy": "2024-08-21T00:44:28.651696Z", + "iopub.status.idle": "2024-08-21T00:44:29.855030Z", + "shell.execute_reply": "2024-08-21T00:44:29.854307Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.980582Z", - "iopub.status.busy": "2024-08-20T02:19:25.980189Z", - "iopub.status.idle": "2024-08-20T02:19:25.983474Z", - "shell.execute_reply": "2024-08-20T02:19:25.983045Z" + "iopub.execute_input": "2024-08-21T00:44:29.857690Z", + "iopub.status.busy": "2024-08-21T00:44:29.857268Z", + "iopub.status.idle": "2024-08-21T00:44:29.860479Z", + "shell.execute_reply": "2024-08-21T00:44:29.859998Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.985536Z", - "iopub.status.busy": "2024-08-20T02:19:25.985218Z", - "iopub.status.idle": "2024-08-20T02:19:25.992912Z", - "shell.execute_reply": "2024-08-20T02:19:25.992481Z" + "iopub.execute_input": "2024-08-21T00:44:29.862482Z", + "iopub.status.busy": "2024-08-21T00:44:29.862134Z", + "iopub.status.idle": "2024-08-21T00:44:29.868426Z", + "shell.execute_reply": "2024-08-21T00:44:29.868003Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:25.995066Z", - "iopub.status.busy": "2024-08-20T02:19:25.994673Z", - "iopub.status.idle": "2024-08-20T02:19:26.314231Z", - "shell.execute_reply": "2024-08-20T02:19:26.313627Z" + "iopub.execute_input": "2024-08-21T00:44:29.870503Z", + "iopub.status.busy": "2024-08-21T00:44:29.870145Z", + "iopub.status.idle": "2024-08-21T00:44:30.361705Z", + "shell.execute_reply": "2024-08-21T00:44:30.361008Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.317208Z", - "iopub.status.busy": "2024-08-20T02:19:26.317004Z", - "iopub.status.idle": "2024-08-20T02:19:26.322547Z", - "shell.execute_reply": "2024-08-20T02:19:26.322090Z" + "iopub.execute_input": "2024-08-21T00:44:30.364838Z", + "iopub.status.busy": "2024-08-21T00:44:30.364463Z", + "iopub.status.idle": "2024-08-21T00:44:30.370026Z", + "shell.execute_reply": "2024-08-21T00:44:30.369463Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.324528Z", - "iopub.status.busy": "2024-08-20T02:19:26.324198Z", - "iopub.status.idle": "2024-08-20T02:19:26.328062Z", - "shell.execute_reply": "2024-08-20T02:19:26.327504Z" + "iopub.execute_input": "2024-08-21T00:44:30.372147Z", + "iopub.status.busy": "2024-08-21T00:44:30.371809Z", + "iopub.status.idle": "2024-08-21T00:44:30.375913Z", + "shell.execute_reply": "2024-08-21T00:44:30.375349Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:26.330057Z", - "iopub.status.busy": "2024-08-20T02:19:26.329758Z", - "iopub.status.idle": "2024-08-20T02:19:27.344062Z", - "shell.execute_reply": "2024-08-20T02:19:27.343427Z" + "iopub.execute_input": "2024-08-21T00:44:30.378099Z", + "iopub.status.busy": "2024-08-21T00:44:30.377758Z", + "iopub.status.idle": "2024-08-21T00:44:31.233478Z", + "shell.execute_reply": "2024-08-21T00:44:31.232810Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.346665Z", - "iopub.status.busy": "2024-08-20T02:19:27.346299Z", - "iopub.status.idle": "2024-08-20T02:19:27.553795Z", - "shell.execute_reply": "2024-08-20T02:19:27.553289Z" + "iopub.execute_input": "2024-08-21T00:44:31.236084Z", + "iopub.status.busy": "2024-08-21T00:44:31.235599Z", + "iopub.status.idle": "2024-08-21T00:44:31.476832Z", + "shell.execute_reply": "2024-08-21T00:44:31.476343Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.555986Z", - "iopub.status.busy": "2024-08-20T02:19:27.555679Z", - "iopub.status.idle": "2024-08-20T02:19:27.559876Z", - "shell.execute_reply": "2024-08-20T02:19:27.559433Z" + "iopub.execute_input": "2024-08-21T00:44:31.479096Z", + "iopub.status.busy": "2024-08-21T00:44:31.478660Z", + "iopub.status.idle": "2024-08-21T00:44:31.483153Z", + "shell.execute_reply": "2024-08-21T00:44:31.482587Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.561898Z", - "iopub.status.busy": "2024-08-20T02:19:27.561565Z", - "iopub.status.idle": "2024-08-20T02:19:27.928742Z", - "shell.execute_reply": "2024-08-20T02:19:27.928127Z" + "iopub.execute_input": "2024-08-21T00:44:31.485319Z", + "iopub.status.busy": "2024-08-21T00:44:31.484888Z", + "iopub.status.idle": "2024-08-21T00:44:31.942164Z", + "shell.execute_reply": "2024-08-21T00:44:31.941558Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:27.931148Z", - "iopub.status.busy": "2024-08-20T02:19:27.930962Z", - "iopub.status.idle": "2024-08-20T02:19:28.265530Z", - "shell.execute_reply": "2024-08-20T02:19:28.264985Z" + "iopub.execute_input": "2024-08-21T00:44:31.945300Z", + "iopub.status.busy": "2024-08-21T00:44:31.945115Z", + "iopub.status.idle": "2024-08-21T00:44:32.281383Z", + "shell.execute_reply": "2024-08-21T00:44:32.280733Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:28.267564Z", - "iopub.status.busy": "2024-08-20T02:19:28.267388Z", - "iopub.status.idle": "2024-08-20T02:19:28.634288Z", - "shell.execute_reply": "2024-08-20T02:19:28.633645Z" + "iopub.execute_input": "2024-08-21T00:44:32.283744Z", + "iopub.status.busy": "2024-08-21T00:44:32.283409Z", + "iopub.status.idle": "2024-08-21T00:44:32.647591Z", + "shell.execute_reply": "2024-08-21T00:44:32.647010Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:28.637582Z", - "iopub.status.busy": "2024-08-20T02:19:28.637364Z", - "iopub.status.idle": "2024-08-20T02:19:29.079339Z", - "shell.execute_reply": "2024-08-20T02:19:29.078714Z" + "iopub.execute_input": "2024-08-21T00:44:32.650921Z", + "iopub.status.busy": "2024-08-21T00:44:32.650725Z", + "iopub.status.idle": "2024-08-21T00:44:33.090696Z", + "shell.execute_reply": "2024-08-21T00:44:33.090049Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.083840Z", - "iopub.status.busy": "2024-08-20T02:19:29.083451Z", - "iopub.status.idle": "2024-08-20T02:19:29.516951Z", - "shell.execute_reply": "2024-08-20T02:19:29.516329Z" + "iopub.execute_input": "2024-08-21T00:44:33.095146Z", + "iopub.status.busy": "2024-08-21T00:44:33.094951Z", + "iopub.status.idle": "2024-08-21T00:44:33.515097Z", + "shell.execute_reply": "2024-08-21T00:44:33.514471Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.519252Z", - "iopub.status.busy": "2024-08-20T02:19:29.518938Z", - "iopub.status.idle": "2024-08-20T02:19:29.737047Z", - "shell.execute_reply": "2024-08-20T02:19:29.736513Z" + "iopub.execute_input": "2024-08-21T00:44:33.518293Z", + "iopub.status.busy": "2024-08-21T00:44:33.518095Z", + "iopub.status.idle": "2024-08-21T00:44:33.709538Z", + "shell.execute_reply": "2024-08-21T00:44:33.708870Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.739475Z", - "iopub.status.busy": "2024-08-20T02:19:29.739154Z", - "iopub.status.idle": "2024-08-20T02:19:29.938318Z", - "shell.execute_reply": "2024-08-20T02:19:29.937762Z" + "iopub.execute_input": "2024-08-21T00:44:33.712115Z", + "iopub.status.busy": "2024-08-21T00:44:33.711896Z", + "iopub.status.idle": "2024-08-21T00:44:33.894449Z", + "shell.execute_reply": "2024-08-21T00:44:33.893894Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.941047Z", - "iopub.status.busy": "2024-08-20T02:19:29.940662Z", - "iopub.status.idle": "2024-08-20T02:19:29.943743Z", - "shell.execute_reply": "2024-08-20T02:19:29.943177Z" + "iopub.execute_input": "2024-08-21T00:44:33.897345Z", + "iopub.status.busy": "2024-08-21T00:44:33.896986Z", + "iopub.status.idle": "2024-08-21T00:44:33.899743Z", + "shell.execute_reply": "2024-08-21T00:44:33.899308Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:29.945776Z", - "iopub.status.busy": "2024-08-20T02:19:29.945606Z", - "iopub.status.idle": "2024-08-20T02:19:30.990728Z", - "shell.execute_reply": "2024-08-20T02:19:30.990184Z" + "iopub.execute_input": "2024-08-21T00:44:33.901739Z", + "iopub.status.busy": "2024-08-21T00:44:33.901417Z", + "iopub.status.idle": "2024-08-21T00:44:34.864741Z", + "shell.execute_reply": "2024-08-21T00:44:34.864144Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:30.993374Z", - "iopub.status.busy": "2024-08-20T02:19:30.993177Z", - "iopub.status.idle": "2024-08-20T02:19:31.135358Z", - "shell.execute_reply": "2024-08-20T02:19:31.134850Z" + "iopub.execute_input": "2024-08-21T00:44:34.867449Z", + "iopub.status.busy": "2024-08-21T00:44:34.867242Z", + "iopub.status.idle": "2024-08-21T00:44:35.005594Z", + "shell.execute_reply": "2024-08-21T00:44:35.005140Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:31.137483Z", - "iopub.status.busy": "2024-08-20T02:19:31.137182Z", - "iopub.status.idle": "2024-08-20T02:19:31.361162Z", - "shell.execute_reply": "2024-08-20T02:19:31.360532Z" + "iopub.execute_input": "2024-08-21T00:44:35.007763Z", + "iopub.status.busy": "2024-08-21T00:44:35.007407Z", + "iopub.status.idle": "2024-08-21T00:44:35.145240Z", + "shell.execute_reply": "2024-08-21T00:44:35.144745Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:31.363266Z", - "iopub.status.busy": "2024-08-20T02:19:31.363077Z", - "iopub.status.idle": "2024-08-20T02:19:32.051327Z", - "shell.execute_reply": "2024-08-20T02:19:32.050669Z" + "iopub.execute_input": "2024-08-21T00:44:35.147522Z", + "iopub.status.busy": "2024-08-21T00:44:35.147162Z", + "iopub.status.idle": "2024-08-21T00:44:35.837392Z", + "shell.execute_reply": "2024-08-21T00:44:35.836743Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:32.053777Z", - "iopub.status.busy": "2024-08-20T02:19:32.053483Z", - "iopub.status.idle": "2024-08-20T02:19:32.057205Z", - "shell.execute_reply": "2024-08-20T02:19:32.056645Z" + "iopub.execute_input": "2024-08-21T00:44:35.839731Z", + "iopub.status.busy": "2024-08-21T00:44:35.839542Z", + "iopub.status.idle": "2024-08-21T00:44:35.843337Z", + "shell.execute_reply": "2024-08-21T00:44:35.842774Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 96cf5c422..67a9ff17c 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

    2. Pre-process the Cifar10 dataset 
    -100%|██████████| 170498071/170498071 [00:01<00:00, 99039292.87it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 85531172.07it/s]

    @@ -910,7 +910,7 @@

    3. Use cleanlab and feature embeddings to find outliers in the data

    -
    +
    @@ -1130,7 +1130,7 @@

    Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

    The modern AI pipeline automated with Cleanlab Studio

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index d44506520..121ffa164 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:34.499280Z", - "iopub.status.busy": "2024-08-20T02:19:34.498882Z", - "iopub.status.idle": "2024-08-20T02:19:37.717028Z", - "shell.execute_reply": "2024-08-20T02:19:37.716470Z" + "iopub.execute_input": "2024-08-21T00:44:38.066360Z", + "iopub.status.busy": "2024-08-21T00:44:38.066183Z", + "iopub.status.idle": "2024-08-21T00:44:40.896600Z", + "shell.execute_reply": "2024-08-21T00:44:40.896031Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:19:37.719892Z", - "iopub.status.busy": "2024-08-20T02:19:37.719263Z", - "iopub.status.idle": "2024-08-20T02:19:37.738735Z", - "shell.execute_reply": "2024-08-20T02:19:37.738151Z" + "iopub.execute_input": "2024-08-21T00:44:40.899402Z", + "iopub.status.busy": "2024-08-21T00:44:40.898897Z", + "iopub.status.idle": "2024-08-21T00:44:41.226395Z", + "shell.execute_reply": "2024-08-21T00:44:41.225910Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:37.741325Z", - "iopub.status.busy": "2024-08-20T02:19:37.740862Z", - "iopub.status.idle": "2024-08-20T02:19:37.745006Z", - "shell.execute_reply": "2024-08-20T02:19:37.744463Z" + "iopub.execute_input": "2024-08-21T00:44:41.228949Z", + "iopub.status.busy": "2024-08-21T00:44:41.228477Z", + "iopub.status.idle": "2024-08-21T00:44:41.232327Z", + "shell.execute_reply": "2024-08-21T00:44:41.231896Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:37.747360Z", - "iopub.status.busy": "2024-08-20T02:19:37.747036Z", - "iopub.status.idle": "2024-08-20T02:19:42.256454Z", - "shell.execute_reply": "2024-08-20T02:19:42.255864Z" + "iopub.execute_input": "2024-08-21T00:44:41.234396Z", + "iopub.status.busy": "2024-08-21T00:44:41.234010Z", + "iopub.status.idle": "2024-08-21T00:44:46.003284Z", + "shell.execute_reply": "2024-08-21T00:44:46.002701Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 851968/170498071 [00:00<00:21, 7726348.04it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8249212.65it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10715136/170498071 [00:00<00:02, 58990173.15it/s]" + " 5%|▌ | 9306112/170498071 [00:00<00:03, 50615669.56it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 21692416/170498071 [00:00<00:01, 81629044.14it/s]" + " 10%|█ | 17891328/170498071 [00:00<00:02, 66243488.74it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32604160/170498071 [00:00<00:01, 92256260.34it/s]" + " 16%|█▋ | 27983872/170498071 [00:00<00:01, 79535198.51it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 44007424/170498071 [00:00<00:01, 100003938.21it/s]" + " 21%|██ | 36208640/170498071 [00:00<00:01, 80500379.19it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54886400/170498071 [00:00<00:01, 102923099.77it/s]" + " 27%|██▋ | 46039040/170498071 [00:00<00:01, 86481939.37it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 65667072/170498071 [00:00<00:01, 104452371.65it/s]" + " 32%|███▏ | 54755328/170498071 [00:00<00:01, 86441499.46it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 76972032/170498071 [00:00<00:00, 107058916.63it/s]" + " 38%|███▊ | 64061440/170498071 [00:00<00:01, 88437234.98it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 87719936/170498071 [00:00<00:00, 106652264.44it/s]" + " 43%|████▎ | 72941568/170498071 [00:00<00:01, 88504042.73it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 98435072/170498071 [00:01<00:00, 104959293.28it/s]" + " 48%|████▊ | 81920000/170498071 [00:01<00:00, 88819896.51it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 109477888/170498071 [00:01<00:00, 106599779.14it/s]" + " 53%|█████▎ | 91160576/170498071 [00:01<00:00, 89878347.40it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 120160256/170498071 [00:01<00:00, 105376043.64it/s]" + " 59%|█████▉ | 100171776/170498071 [00:01<00:00, 88552034.31it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130711552/170498071 [00:01<00:00, 100681796.58it/s]" + " 64%|██████▍ | 109608960/170498071 [00:01<00:00, 90217856.34it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141819904/170498071 [00:01<00:00, 103650641.61it/s]" + " 70%|██████▉ | 118652928/170498071 [00:01<00:00, 88798720.02it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 152240128/170498071 [00:01<00:00, 102641067.98it/s]" + " 75%|███████▌ | 128450560/170498071 [00:01<00:00, 91496573.36it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162562048/170498071 [00:01<00:00, 100813373.82it/s]" + " 81%|████████ | 137625600/170498071 [00:01<00:00, 89580266.26it/s]" ] }, { @@ -380,7 +380,31 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 99039292.87it/s] " + " 86%|████████▋ | 147128320/170498071 [00:01<00:00, 91113760.17it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 156270592/170498071 [00:01<00:00, 89857702.55it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 97%|█████████▋| 165773312/170498071 [00:01<00:00, 91343882.90it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:01<00:00, 85531172.07it/s]" ] }, { @@ -498,10 +522,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.258763Z", - "iopub.status.busy": "2024-08-20T02:19:42.258577Z", - "iopub.status.idle": "2024-08-20T02:19:42.263590Z", - "shell.execute_reply": "2024-08-20T02:19:42.263140Z" + "iopub.execute_input": "2024-08-21T00:44:46.005425Z", + "iopub.status.busy": "2024-08-21T00:44:46.005245Z", + "iopub.status.idle": "2024-08-21T00:44:46.010091Z", + "shell.execute_reply": "2024-08-21T00:44:46.009632Z" }, "nbsphinx": "hidden" }, @@ -552,10 +576,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.265560Z", - "iopub.status.busy": "2024-08-20T02:19:42.265239Z", - "iopub.status.idle": "2024-08-20T02:19:42.792402Z", - "shell.execute_reply": "2024-08-20T02:19:42.791889Z" + "iopub.execute_input": "2024-08-21T00:44:46.012218Z", + "iopub.status.busy": "2024-08-21T00:44:46.011792Z", + "iopub.status.idle": "2024-08-21T00:44:46.552461Z", + "shell.execute_reply": "2024-08-21T00:44:46.551864Z" } }, "outputs": [ @@ -588,10 +612,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:42.794517Z", - "iopub.status.busy": "2024-08-20T02:19:42.794333Z", - "iopub.status.idle": "2024-08-20T02:19:43.311733Z", - "shell.execute_reply": "2024-08-20T02:19:43.311064Z" + "iopub.execute_input": "2024-08-21T00:44:46.554759Z", + "iopub.status.busy": "2024-08-21T00:44:46.554411Z", + "iopub.status.idle": "2024-08-21T00:44:47.061482Z", + "shell.execute_reply": "2024-08-21T00:44:47.060868Z" } }, "outputs": [ @@ -629,10 +653,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:43.313896Z", - "iopub.status.busy": "2024-08-20T02:19:43.313703Z", - "iopub.status.idle": "2024-08-20T02:19:43.317387Z", - "shell.execute_reply": "2024-08-20T02:19:43.316911Z" + "iopub.execute_input": "2024-08-21T00:44:47.063597Z", + "iopub.status.busy": "2024-08-21T00:44:47.063395Z", + "iopub.status.idle": "2024-08-21T00:44:47.067074Z", + "shell.execute_reply": "2024-08-21T00:44:47.066614Z" } }, "outputs": [], @@ -655,17 +679,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:43.319247Z", - "iopub.status.busy": "2024-08-20T02:19:43.319077Z", - "iopub.status.idle": "2024-08-20T02:19:55.699364Z", - "shell.execute_reply": "2024-08-20T02:19:55.698752Z" + "iopub.execute_input": "2024-08-21T00:44:47.069202Z", + "iopub.status.busy": "2024-08-21T00:44:47.068736Z", + "iopub.status.idle": "2024-08-21T00:44:59.482222Z", + "shell.execute_reply": "2024-08-21T00:44:59.481608Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "642acd4483d444aba595b064e7e139f2", + "model_id": "2aece906d5414d149f1b677c20ee62ea", "version_major": 2, "version_minor": 0 }, @@ -724,10 +748,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:55.702022Z", - "iopub.status.busy": "2024-08-20T02:19:55.701836Z", - "iopub.status.idle": "2024-08-20T02:19:57.881011Z", - "shell.execute_reply": "2024-08-20T02:19:57.880414Z" + "iopub.execute_input": "2024-08-21T00:44:59.484767Z", + "iopub.status.busy": "2024-08-21T00:44:59.484373Z", + "iopub.status.idle": "2024-08-21T00:45:01.580531Z", + "shell.execute_reply": "2024-08-21T00:45:01.579978Z" } }, "outputs": [ @@ -771,10 +795,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:57.883556Z", - "iopub.status.busy": "2024-08-20T02:19:57.883182Z", - "iopub.status.idle": "2024-08-20T02:19:58.113830Z", - "shell.execute_reply": "2024-08-20T02:19:58.113258Z" + "iopub.execute_input": "2024-08-21T00:45:01.583216Z", + "iopub.status.busy": "2024-08-21T00:45:01.582715Z", + "iopub.status.idle": "2024-08-21T00:45:01.835720Z", + "shell.execute_reply": "2024-08-21T00:45:01.835061Z" } }, "outputs": [ @@ -810,10 +834,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:58.116207Z", - "iopub.status.busy": "2024-08-20T02:19:58.116028Z", - "iopub.status.idle": "2024-08-20T02:19:58.775793Z", - "shell.execute_reply": "2024-08-20T02:19:58.775175Z" + "iopub.execute_input": "2024-08-21T00:45:01.838410Z", + "iopub.status.busy": "2024-08-21T00:45:01.838179Z", + "iopub.status.idle": "2024-08-21T00:45:02.484647Z", + "shell.execute_reply": "2024-08-21T00:45:02.484044Z" } }, "outputs": [ @@ -863,10 +887,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:58.778247Z", - "iopub.status.busy": "2024-08-20T02:19:58.778067Z", - "iopub.status.idle": "2024-08-20T02:19:59.073646Z", - "shell.execute_reply": "2024-08-20T02:19:59.073079Z" + "iopub.execute_input": "2024-08-21T00:45:02.487053Z", + "iopub.status.busy": "2024-08-21T00:45:02.486880Z", + "iopub.status.idle": "2024-08-21T00:45:02.775155Z", + "shell.execute_reply": "2024-08-21T00:45:02.774552Z" } }, "outputs": [ @@ -914,10 +938,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.075669Z", - "iopub.status.busy": "2024-08-20T02:19:59.075494Z", - "iopub.status.idle": "2024-08-20T02:19:59.313635Z", - "shell.execute_reply": "2024-08-20T02:19:59.313009Z" + "iopub.execute_input": "2024-08-21T00:45:02.777542Z", + "iopub.status.busy": "2024-08-21T00:45:02.777200Z", + "iopub.status.idle": "2024-08-21T00:45:03.004237Z", + "shell.execute_reply": "2024-08-21T00:45:03.003698Z" } }, "outputs": [ @@ -973,10 +997,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.316178Z", - "iopub.status.busy": "2024-08-20T02:19:59.315831Z", - "iopub.status.idle": "2024-08-20T02:19:59.397580Z", - "shell.execute_reply": "2024-08-20T02:19:59.396927Z" + "iopub.execute_input": "2024-08-21T00:45:03.006715Z", + "iopub.status.busy": "2024-08-21T00:45:03.006213Z", + "iopub.status.idle": "2024-08-21T00:45:03.079353Z", + "shell.execute_reply": "2024-08-21T00:45:03.078862Z" } }, "outputs": [], @@ -997,10 +1021,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:19:59.400410Z", - "iopub.status.busy": "2024-08-20T02:19:59.399851Z", - "iopub.status.idle": "2024-08-20T02:20:09.496202Z", - "shell.execute_reply": "2024-08-20T02:20:09.495583Z" + "iopub.execute_input": "2024-08-21T00:45:03.081841Z", + "iopub.status.busy": "2024-08-21T00:45:03.081489Z", + "iopub.status.idle": "2024-08-21T00:45:13.396780Z", + "shell.execute_reply": "2024-08-21T00:45:13.396110Z" } }, "outputs": [ @@ -1037,10 +1061,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:09.498840Z", - "iopub.status.busy": "2024-08-20T02:20:09.498416Z", - "iopub.status.idle": "2024-08-20T02:20:11.827863Z", - "shell.execute_reply": "2024-08-20T02:20:11.827207Z" + "iopub.execute_input": "2024-08-21T00:45:13.399549Z", + "iopub.status.busy": "2024-08-21T00:45:13.398957Z", + "iopub.status.idle": "2024-08-21T00:45:15.614825Z", + "shell.execute_reply": "2024-08-21T00:45:15.614327Z" } }, "outputs": [ @@ -1071,10 +1095,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:11.830805Z", - "iopub.status.busy": "2024-08-20T02:20:11.830187Z", - "iopub.status.idle": "2024-08-20T02:20:12.036080Z", - "shell.execute_reply": "2024-08-20T02:20:12.035582Z" + "iopub.execute_input": "2024-08-21T00:45:15.617532Z", + "iopub.status.busy": "2024-08-21T00:45:15.616897Z", + "iopub.status.idle": "2024-08-21T00:45:15.830910Z", + "shell.execute_reply": "2024-08-21T00:45:15.830305Z" } }, "outputs": [], @@ -1088,10 +1112,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:12.038552Z", - "iopub.status.busy": "2024-08-20T02:20:12.038185Z", - "iopub.status.idle": "2024-08-20T02:20:12.041256Z", - "shell.execute_reply": "2024-08-20T02:20:12.040807Z" + "iopub.execute_input": "2024-08-21T00:45:15.833496Z", + "iopub.status.busy": "2024-08-21T00:45:15.833180Z", + "iopub.status.idle": "2024-08-21T00:45:15.836370Z", + "shell.execute_reply": "2024-08-21T00:45:15.835883Z" } }, "outputs": [], @@ -1129,10 +1153,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:12.043442Z", - "iopub.status.busy": "2024-08-20T02:20:12.043116Z", - "iopub.status.idle": "2024-08-20T02:20:12.051808Z", - "shell.execute_reply": "2024-08-20T02:20:12.051353Z" + "iopub.execute_input": "2024-08-21T00:45:15.838493Z", + "iopub.status.busy": "2024-08-21T00:45:15.838071Z", + "iopub.status.idle": "2024-08-21T00:45:15.846417Z", + "shell.execute_reply": "2024-08-21T00:45:15.845889Z" }, "nbsphinx": "hidden" }, @@ -1177,7 +1201,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2b9840e8eb314e8ba7b76bccb5b7d86b", - "placeholder": "​", - "style": "IPY_MODEL_169d58f4735041f7a00a218864eaf538", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" - } - }, - "c8851ece38b7471fb2bda870c287b422": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_482db617aa45471dbd4788d5ea3eaf8d", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a673b7221a2a4306a9d68253bd529778", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "f1c1be1a57e44c73a36e7a8d3ac16f26": { + "728c44e458d84e538ef30537097e692f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1496,25 +1538,7 @@ "width": null } }, - "f8338a6f5da64d57b669fd0a68596611": { - "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 - } - }, - "f91634ed9c494ec4a95bbc683c0d5570": { + "7a2cd802a8d24314a46149b1d522ec92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1529,12 +1553,12 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b2d6e40503a2481bbf4ebb46fcfd3dde", + "layout": "IPY_MODEL_45d23e20356141f8b282232e7a970845", "placeholder": "​", - "style": "IPY_MODEL_f8338a6f5da64d57b669fd0a68596611", + "style": "IPY_MODEL_2278bbdcd34d42c685310e394def87da", "tabbable": null, "tooltip": null, - "value": " 102M/102M [00:00<00:00, 269MB/s]" + "value": "model.safetensors: 100%" } } }, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index c266f0fdb..e8381488b 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:16.211959Z", - "iopub.status.busy": "2024-08-20T02:20:16.211775Z", - "iopub.status.idle": "2024-08-20T02:20:17.625109Z", - "shell.execute_reply": "2024-08-20T02:20:17.624532Z" + "iopub.execute_input": "2024-08-21T00:45:20.030885Z", + "iopub.status.busy": "2024-08-21T00:45:20.030716Z", + "iopub.status.idle": "2024-08-21T00:45:21.238274Z", + "shell.execute_reply": "2024-08-21T00:45:21.237657Z" }, "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\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": "2024-08-20T02:20:17.627810Z", - "iopub.status.busy": "2024-08-20T02:20:17.627188Z", - "iopub.status.idle": "2024-08-20T02:20:17.646081Z", - "shell.execute_reply": "2024-08-20T02:20:17.645467Z" + "iopub.execute_input": "2024-08-21T00:45:21.240674Z", + "iopub.status.busy": "2024-08-21T00:45:21.240417Z", + "iopub.status.idle": "2024-08-21T00:45:21.258244Z", + "shell.execute_reply": "2024-08-21T00:45:21.257816Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.648526Z", - "iopub.status.busy": "2024-08-20T02:20:17.648175Z", - "iopub.status.idle": "2024-08-20T02:20:17.651475Z", - "shell.execute_reply": "2024-08-20T02:20:17.651004Z" + "iopub.execute_input": "2024-08-21T00:45:21.260491Z", + "iopub.status.busy": "2024-08-21T00:45:21.259990Z", + "iopub.status.idle": "2024-08-21T00:45:21.262962Z", + "shell.execute_reply": "2024-08-21T00:45:21.262506Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.653391Z", - "iopub.status.busy": "2024-08-20T02:20:17.653196Z", - "iopub.status.idle": "2024-08-20T02:20:17.738201Z", - "shell.execute_reply": "2024-08-20T02:20:17.737589Z" + "iopub.execute_input": "2024-08-21T00:45:21.264900Z", + "iopub.status.busy": "2024-08-21T00:45:21.264725Z", + "iopub.status.idle": "2024-08-21T00:45:21.356509Z", + "shell.execute_reply": "2024-08-21T00:45:21.356026Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.740445Z", - "iopub.status.busy": "2024-08-20T02:20:17.740254Z", - "iopub.status.idle": "2024-08-20T02:20:17.744806Z", - "shell.execute_reply": "2024-08-20T02:20:17.744337Z" + "iopub.execute_input": "2024-08-21T00:45:21.358702Z", + "iopub.status.busy": "2024-08-21T00:45:21.358375Z", + "iopub.status.idle": "2024-08-21T00:45:21.538107Z", + "shell.execute_reply": "2024-08-21T00:45:21.537485Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.746909Z", - "iopub.status.busy": "2024-08-20T02:20:17.746590Z", - "iopub.status.idle": "2024-08-20T02:20:17.995978Z", - "shell.execute_reply": "2024-08-20T02:20:17.995401Z" + "iopub.execute_input": "2024-08-21T00:45:21.540685Z", + "iopub.status.busy": "2024-08-21T00:45:21.540349Z", + "iopub.status.idle": "2024-08-21T00:45:21.783605Z", + "shell.execute_reply": "2024-08-21T00:45:21.783082Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:17.998260Z", - "iopub.status.busy": "2024-08-20T02:20:17.997904Z", - "iopub.status.idle": "2024-08-20T02:20:18.002417Z", - "shell.execute_reply": "2024-08-20T02:20:18.001833Z" + "iopub.execute_input": "2024-08-21T00:45:21.785804Z", + "iopub.status.busy": "2024-08-21T00:45:21.785616Z", + "iopub.status.idle": "2024-08-21T00:45:21.790197Z", + "shell.execute_reply": "2024-08-21T00:45:21.789742Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:18.004416Z", - "iopub.status.busy": "2024-08-20T02:20:18.004107Z", - "iopub.status.idle": "2024-08-20T02:20:18.010552Z", - "shell.execute_reply": "2024-08-20T02:20:18.010097Z" + "iopub.execute_input": "2024-08-21T00:45:21.792282Z", + "iopub.status.busy": "2024-08-21T00:45:21.791871Z", + "iopub.status.idle": "2024-08-21T00:45:21.798042Z", + "shell.execute_reply": "2024-08-21T00:45:21.797612Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - 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    3. Use cleanlab to find label issues

    -
    +
    -
    +
    @@ -891,7 +891,7 @@

    Classes which are commonly mislabeled overall

    -
    +
    @@ -1140,7 +1140,7 @@

    Get label quality scores

    -
    +

    Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

    @@ -1196,7 +1196,7 @@

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"_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_422655b6abb04e76ad979c7d6db811b9", "IPY_MODEL_34ce7f1803eb4dadaec339d206a66ec7", "IPY_MODEL_fced5e1f3d504623bfd6adc490f06fc2"], "layout": "IPY_MODEL_8880728d23aa4c138b6db1d8798a792d", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 4ac3af7d6..7b6da3e55 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:36.888231Z", - "iopub.status.busy": "2024-08-20T02:20:36.888053Z", - "iopub.status.idle": "2024-08-20T02:20:38.537307Z", - "shell.execute_reply": "2024-08-20T02:20:38.536594Z" + "iopub.execute_input": "2024-08-21T00:45:40.466811Z", + "iopub.status.busy": "2024-08-21T00:45:40.466640Z", + "iopub.status.idle": "2024-08-21T00:45:42.883520Z", + "shell.execute_reply": "2024-08-21T00:45:42.882755Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:20:38.540226Z", - "iopub.status.busy": "2024-08-20T02:20:38.539748Z", - "iopub.status.idle": "2024-08-20T02:22:01.964653Z", - "shell.execute_reply": "2024-08-20T02:22:01.963964Z" + "iopub.execute_input": "2024-08-21T00:45:42.886008Z", + "iopub.status.busy": "2024-08-21T00:45:42.885816Z", + "iopub.status.idle": "2024-08-21T00:46:48.432528Z", + "shell.execute_reply": "2024-08-21T00:46:48.431818Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:01.967334Z", - "iopub.status.busy": "2024-08-20T02:22:01.966936Z", - "iopub.status.idle": "2024-08-20T02:22:03.406051Z", - "shell.execute_reply": "2024-08-20T02:22:03.405489Z" + "iopub.execute_input": "2024-08-21T00:46:48.435091Z", + "iopub.status.busy": "2024-08-21T00:46:48.434899Z", + "iopub.status.idle": "2024-08-21T00:46:49.596397Z", + "shell.execute_reply": "2024-08-21T00:46:49.595894Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.408486Z", - "iopub.status.busy": "2024-08-20T02:22:03.408194Z", - "iopub.status.idle": "2024-08-20T02:22:03.411443Z", - "shell.execute_reply": "2024-08-20T02:22:03.410981Z" + "iopub.execute_input": "2024-08-21T00:46:49.598800Z", + "iopub.status.busy": "2024-08-21T00:46:49.598484Z", + "iopub.status.idle": "2024-08-21T00:46:49.601969Z", + "shell.execute_reply": "2024-08-21T00:46:49.601513Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.413581Z", - "iopub.status.busy": "2024-08-20T02:22:03.413241Z", - "iopub.status.idle": "2024-08-20T02:22:03.417640Z", - "shell.execute_reply": "2024-08-20T02:22:03.417037Z" + "iopub.execute_input": "2024-08-21T00:46:49.604008Z", + "iopub.status.busy": "2024-08-21T00:46:49.603652Z", + "iopub.status.idle": "2024-08-21T00:46:49.607367Z", + "shell.execute_reply": "2024-08-21T00:46:49.606937Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.419956Z", - "iopub.status.busy": "2024-08-20T02:22:03.419536Z", - "iopub.status.idle": "2024-08-20T02:22:03.423128Z", - "shell.execute_reply": "2024-08-20T02:22:03.422694Z" + "iopub.execute_input": "2024-08-21T00:46:49.609599Z", + "iopub.status.busy": "2024-08-21T00:46:49.609187Z", + "iopub.status.idle": "2024-08-21T00:46:49.612699Z", + "shell.execute_reply": "2024-08-21T00:46:49.612255Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.425108Z", - "iopub.status.busy": "2024-08-20T02:22:03.424794Z", - "iopub.status.idle": "2024-08-20T02:22:03.427666Z", - "shell.execute_reply": "2024-08-20T02:22:03.427206Z" + "iopub.execute_input": "2024-08-21T00:46:49.614723Z", + "iopub.status.busy": "2024-08-21T00:46:49.614324Z", + "iopub.status.idle": "2024-08-21T00:46:49.617188Z", + "shell.execute_reply": "2024-08-21T00:46:49.616743Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:22:03.429508Z", - 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a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index f4d8f6f7e..edd89e062 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

    1. Install required dependencies and download data 
    ---2024-08-20 02:23:47--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 169.150.236.99, 2400:52e0:1a00::1068:1
-Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.
+--2024-08-21 00:48:33--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 169.150.236.104, 2400:52e0:1a00::1070:1
+Connecting to data.deepai.org (data.deepai.org)|169.150.236.104|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  5.63MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
 
-2024-08-20 02:23:47 (5.63 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-08-21 00:48:33 (6.33 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -727,16 +727,16 @@ 
    1. Install required dependencies and download data

    diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 8177bb470..e3b73ccfd 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:47.189790Z", - "iopub.status.busy": "2024-08-20T02:23:47.189361Z", - "iopub.status.idle": "2024-08-20T02:23:48.403988Z", - "shell.execute_reply": "2024-08-20T02:23:48.403347Z" + "iopub.execute_input": "2024-08-21T00:48:33.131555Z", + "iopub.status.busy": "2024-08-21T00:48:33.131391Z", + "iopub.status.idle": "2024-08-21T00:48:34.477099Z", + "shell.execute_reply": "2024-08-21T00:48:34.476468Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:23:47-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-08-21 00:48:33-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.99, 2400:52e0:1a00::1068:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.\r\n" + "169.150.236.104, 2400:52e0:1a00::1070:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.104|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" ] }, { @@ -122,9 +129,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.63MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-08-20 02:23:47 (5.63 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-08-21 00:48:33 (6.33 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -136,24 +143,24 @@ "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 " + " inflating: data/train.txt " ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r\n" + "\r\n", + " inflating: data/valid.txt \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-08-20 02:23:47-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.69.145, 3.5.9.145, 3.5.25.176, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.69.145|:443... connected.\r\n", + "--2024-08-21 00:48:34-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.63.33, 52.217.95.209, 3.5.27.229, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.63.33|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,9 +181,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 103MB/s in 0.2s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.09s \r\n", "\r\n", - "2024-08-20 02:23:48 (103 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-08-21 00:48:34 (182 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -193,10 +200,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:48.406808Z", - "iopub.status.busy": "2024-08-20T02:23:48.406400Z", - "iopub.status.idle": "2024-08-20T02:23:50.001165Z", - "shell.execute_reply": "2024-08-20T02:23:50.000519Z" + "iopub.execute_input": "2024-08-21T00:48:34.479751Z", + "iopub.status.busy": "2024-08-21T00:48:34.479377Z", + "iopub.status.idle": "2024-08-21T00:48:35.780789Z", + "shell.execute_reply": "2024-08-21T00:48:35.780239Z" }, "nbsphinx": "hidden" }, @@ -207,7 +214,7 @@ "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@8bc7cc91f720f852a8b18530ef5dcbc166653031\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b9796a540c4b8fcfe0647dc7dafe447a3738499c\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -233,10 +240,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.004031Z", - "iopub.status.busy": "2024-08-20T02:23:50.003445Z", - "iopub.status.idle": "2024-08-20T02:23:50.007127Z", - "shell.execute_reply": "2024-08-20T02:23:50.006582Z" + "iopub.execute_input": "2024-08-21T00:48:35.783186Z", + "iopub.status.busy": "2024-08-21T00:48:35.782857Z", + "iopub.status.idle": "2024-08-21T00:48:35.786293Z", + "shell.execute_reply": "2024-08-21T00:48:35.785824Z" } }, "outputs": [], @@ -286,10 +293,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.009438Z", - "iopub.status.busy": "2024-08-20T02:23:50.009017Z", - "iopub.status.idle": "2024-08-20T02:23:50.012211Z", - "shell.execute_reply": "2024-08-20T02:23:50.011731Z" + "iopub.execute_input": "2024-08-21T00:48:35.788435Z", + "iopub.status.busy": "2024-08-21T00:48:35.788082Z", + "iopub.status.idle": "2024-08-21T00:48:35.791210Z", + "shell.execute_reply": "2024-08-21T00:48:35.790645Z" }, "nbsphinx": "hidden" }, @@ -307,10 +314,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:50.014371Z", - "iopub.status.busy": "2024-08-20T02:23:50.013919Z", - "iopub.status.idle": "2024-08-20T02:23:59.245717Z", - "shell.execute_reply": "2024-08-20T02:23:59.245080Z" + "iopub.execute_input": "2024-08-21T00:48:35.793110Z", + "iopub.status.busy": "2024-08-21T00:48:35.792936Z", + "iopub.status.idle": "2024-08-21T00:48:44.878008Z", + "shell.execute_reply": "2024-08-21T00:48:44.877430Z" } }, "outputs": [], @@ -384,10 +391,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.248529Z", - "iopub.status.busy": "2024-08-20T02:23:59.248146Z", - "iopub.status.idle": "2024-08-20T02:23:59.253721Z", - "shell.execute_reply": "2024-08-20T02:23:59.253260Z" + "iopub.execute_input": "2024-08-21T00:48:44.880606Z", + "iopub.status.busy": "2024-08-21T00:48:44.880228Z", + "iopub.status.idle": "2024-08-21T00:48:44.885938Z", + "shell.execute_reply": "2024-08-21T00:48:44.885464Z" }, "nbsphinx": "hidden" }, @@ -427,10 +434,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.255898Z", - "iopub.status.busy": "2024-08-20T02:23:59.255557Z", - "iopub.status.idle": "2024-08-20T02:23:59.636940Z", - "shell.execute_reply": "2024-08-20T02:23:59.636377Z" + "iopub.execute_input": "2024-08-21T00:48:44.887970Z", + "iopub.status.busy": "2024-08-21T00:48:44.887607Z", + "iopub.status.idle": "2024-08-21T00:48:45.236413Z", + "shell.execute_reply": "2024-08-21T00:48:45.235748Z" } }, "outputs": [], @@ -467,10 +474,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.639442Z", - "iopub.status.busy": "2024-08-20T02:23:59.639072Z", - "iopub.status.idle": "2024-08-20T02:23:59.643596Z", - "shell.execute_reply": "2024-08-20T02:23:59.643126Z" + "iopub.execute_input": "2024-08-21T00:48:45.238797Z", + "iopub.status.busy": "2024-08-21T00:48:45.238594Z", + "iopub.status.idle": "2024-08-21T00:48:45.242741Z", + "shell.execute_reply": "2024-08-21T00:48:45.242293Z" } }, "outputs": [ @@ -542,10 +549,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:23:59.645735Z", - "iopub.status.busy": "2024-08-20T02:23:59.645420Z", - "iopub.status.idle": "2024-08-20T02:24:02.450516Z", - "shell.execute_reply": "2024-08-20T02:24:02.449822Z" + "iopub.execute_input": "2024-08-21T00:48:45.244879Z", + "iopub.status.busy": "2024-08-21T00:48:45.244540Z", + "iopub.status.idle": "2024-08-21T00:48:47.886104Z", + "shell.execute_reply": "2024-08-21T00:48:47.885240Z" } }, "outputs": [], @@ -567,10 +574,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.453944Z", - "iopub.status.busy": "2024-08-20T02:24:02.453026Z", - "iopub.status.idle": "2024-08-20T02:24:02.457210Z", - "shell.execute_reply": "2024-08-20T02:24:02.456664Z" + "iopub.execute_input": "2024-08-21T00:48:47.889759Z", + "iopub.status.busy": "2024-08-21T00:48:47.888760Z", + "iopub.status.idle": "2024-08-21T00:48:47.893081Z", + "shell.execute_reply": "2024-08-21T00:48:47.892524Z" } }, "outputs": [ @@ -606,10 +613,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.459333Z", - "iopub.status.busy": "2024-08-20T02:24:02.458986Z", - "iopub.status.idle": "2024-08-20T02:24:02.464337Z", - "shell.execute_reply": "2024-08-20T02:24:02.463806Z" + "iopub.execute_input": "2024-08-21T00:48:47.895029Z", + "iopub.status.busy": "2024-08-21T00:48:47.894719Z", + "iopub.status.idle": "2024-08-21T00:48:47.900629Z", + "shell.execute_reply": "2024-08-21T00:48:47.900158Z" } }, "outputs": [ @@ -787,10 +794,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.466242Z", - "iopub.status.busy": "2024-08-20T02:24:02.466067Z", - "iopub.status.idle": "2024-08-20T02:24:02.492938Z", - "shell.execute_reply": "2024-08-20T02:24:02.492457Z" + "iopub.execute_input": "2024-08-21T00:48:47.902736Z", + "iopub.status.busy": "2024-08-21T00:48:47.902396Z", + "iopub.status.idle": "2024-08-21T00:48:47.928872Z", + "shell.execute_reply": "2024-08-21T00:48:47.928414Z" } }, "outputs": [ @@ -892,10 +899,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.495013Z", - "iopub.status.busy": "2024-08-20T02:24:02.494699Z", - "iopub.status.idle": "2024-08-20T02:24:02.499367Z", - "shell.execute_reply": "2024-08-20T02:24:02.498819Z" + "iopub.execute_input": "2024-08-21T00:48:47.931025Z", + "iopub.status.busy": "2024-08-21T00:48:47.930625Z", + "iopub.status.idle": "2024-08-21T00:48:47.935094Z", + "shell.execute_reply": "2024-08-21T00:48:47.934549Z" } }, "outputs": [ @@ -969,10 +976,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:02.501377Z", - "iopub.status.busy": "2024-08-20T02:24:02.501054Z", - "iopub.status.idle": "2024-08-20T02:24:03.978603Z", - "shell.execute_reply": "2024-08-20T02:24:03.978055Z" + "iopub.execute_input": "2024-08-21T00:48:47.937284Z", + "iopub.status.busy": "2024-08-21T00:48:47.936831Z", + "iopub.status.idle": "2024-08-21T00:48:49.344989Z", + "shell.execute_reply": "2024-08-21T00:48:49.344481Z" } }, "outputs": [ @@ -1144,10 +1151,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-08-20T02:24:03.980976Z", - "iopub.status.busy": "2024-08-20T02:24:03.980606Z", - "iopub.status.idle": "2024-08-20T02:24:03.984644Z", - "shell.execute_reply": "2024-08-20T02:24:03.984222Z" + "iopub.execute_input": "2024-08-21T00:48:49.347279Z", + "iopub.status.busy": "2024-08-21T00:48:49.346950Z", + "iopub.status.idle": "2024-08-21T00:48:49.351135Z", + "shell.execute_reply": "2024-08-21T00:48:49.350554Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 9daa3b840..352b1d7de 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "8bc7cc91f720f852a8b18530ef5dcbc166653031", + commit_hash: "b9796a540c4b8fcfe0647dc7dafe447a3738499c", }; \ No newline at end of file

    Types of issuesNon-IID Issue
  • Class Imbalance Issue
  • Image-specific Issues
    • +
  • Spurious Correlations between image-specific properties and labels
  • Underperforming Group Issue
  • Null Issue
  • Data Valuation Issue
    • diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html index 1aa61d2ab..34ecc5481 100644 --- a/master/cleanlab/datalab/guide/issue_type_description.html +++ b/master/cleanlab/datalab/guide/issue_type_description.html @@ -1135,6 +1135,60 @@

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