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b/master/.doctrees/environment.pickle differ diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index c0cd39dc3..8aa24849b 100644 Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 813266fcf..729fbdf20 100644 Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index f3d888536..cea7ddcbf 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:19.683650Z", - "iopub.status.busy": "2024-06-25T23:13:19.683483Z", - "iopub.status.idle": "2024-06-25T23:13:20.876411Z", - "shell.execute_reply": "2024-06-25T23:13:20.875863Z" + "iopub.execute_input": "2024-06-27T15:39:08.585179Z", + "iopub.status.busy": "2024-06-27T15:39:08.584836Z", + "iopub.status.idle": "2024-06-27T15:39:09.813243Z", + "shell.execute_reply": "2024-06-27T15:39:09.812668Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:20.879016Z", - "iopub.status.busy": "2024-06-25T23:13:20.878582Z", - "iopub.status.idle": "2024-06-25T23:13:20.895831Z", - "shell.execute_reply": "2024-06-25T23:13:20.895402Z" + "iopub.execute_input": "2024-06-27T15:39:09.815976Z", + "iopub.status.busy": "2024-06-27T15:39:09.815484Z", + "iopub.status.idle": "2024-06-27T15:39:09.833810Z", + "shell.execute_reply": "2024-06-27T15:39:09.833360Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:20.897855Z", - "iopub.status.busy": "2024-06-25T23:13:20.897628Z", - "iopub.status.idle": "2024-06-25T23:13:21.010572Z", - "shell.execute_reply": "2024-06-25T23:13:21.009996Z" + "iopub.execute_input": "2024-06-27T15:39:09.836247Z", + "iopub.status.busy": "2024-06-27T15:39:09.835762Z", + "iopub.status.idle": "2024-06-27T15:39:10.211400Z", + "shell.execute_reply": "2024-06-27T15:39:10.210811Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.037181Z", - "iopub.status.busy": "2024-06-25T23:13:21.036568Z", - "iopub.status.idle": "2024-06-25T23:13:21.040405Z", - "shell.execute_reply": "2024-06-25T23:13:21.039967Z" + "iopub.execute_input": "2024-06-27T15:39:10.241419Z", + "iopub.status.busy": "2024-06-27T15:39:10.241204Z", + "iopub.status.idle": "2024-06-27T15:39:10.245054Z", + "shell.execute_reply": "2024-06-27T15:39:10.244590Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.042333Z", - "iopub.status.busy": "2024-06-25T23:13:21.042161Z", - "iopub.status.idle": "2024-06-25T23:13:21.050408Z", - "shell.execute_reply": "2024-06-25T23:13:21.049993Z" + "iopub.execute_input": "2024-06-27T15:39:10.247265Z", + "iopub.status.busy": "2024-06-27T15:39:10.246832Z", + "iopub.status.idle": "2024-06-27T15:39:10.255149Z", + "shell.execute_reply": "2024-06-27T15:39:10.254595Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.052411Z", - "iopub.status.busy": "2024-06-25T23:13:21.052111Z", - "iopub.status.idle": "2024-06-25T23:13:21.054810Z", - "shell.execute_reply": "2024-06-25T23:13:21.054263Z" + "iopub.execute_input": "2024-06-27T15:39:10.257365Z", + "iopub.status.busy": "2024-06-27T15:39:10.257091Z", + "iopub.status.idle": "2024-06-27T15:39:10.259655Z", + "shell.execute_reply": "2024-06-27T15:39:10.259220Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.056799Z", - "iopub.status.busy": "2024-06-25T23:13:21.056479Z", - "iopub.status.idle": "2024-06-25T23:13:21.584928Z", - "shell.execute_reply": "2024-06-25T23:13:21.584385Z" + "iopub.execute_input": "2024-06-27T15:39:10.261511Z", + "iopub.status.busy": "2024-06-27T15:39:10.261339Z", + "iopub.status.idle": "2024-06-27T15:39:10.790397Z", + "shell.execute_reply": "2024-06-27T15:39:10.789846Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.587427Z", - "iopub.status.busy": "2024-06-25T23:13:21.587080Z", - "iopub.status.idle": "2024-06-25T23:13:23.402116Z", - "shell.execute_reply": "2024-06-25T23:13:23.401472Z" + "iopub.execute_input": "2024-06-27T15:39:10.792795Z", + "iopub.status.busy": "2024-06-27T15:39:10.792563Z", + "iopub.status.idle": "2024-06-27T15:39:12.690033Z", + "shell.execute_reply": "2024-06-27T15:39:12.689420Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.404837Z", - "iopub.status.busy": "2024-06-25T23:13:23.404191Z", - "iopub.status.idle": "2024-06-25T23:13:23.414068Z", - "shell.execute_reply": "2024-06-25T23:13:23.413559Z" + "iopub.execute_input": "2024-06-27T15:39:12.692713Z", + "iopub.status.busy": "2024-06-27T15:39:12.692162Z", + "iopub.status.idle": "2024-06-27T15:39:12.702272Z", + "shell.execute_reply": "2024-06-27T15:39:12.701680Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.416257Z", - "iopub.status.busy": "2024-06-25T23:13:23.415941Z", - "iopub.status.idle": "2024-06-25T23:13:23.420056Z", - "shell.execute_reply": "2024-06-25T23:13:23.419521Z" + "iopub.execute_input": "2024-06-27T15:39:12.704223Z", + "iopub.status.busy": "2024-06-27T15:39:12.703971Z", + "iopub.status.idle": "2024-06-27T15:39:12.707870Z", + "shell.execute_reply": "2024-06-27T15:39:12.707432Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.422287Z", - "iopub.status.busy": "2024-06-25T23:13:23.421904Z", - "iopub.status.idle": "2024-06-25T23:13:23.429186Z", - "shell.execute_reply": "2024-06-25T23:13:23.428630Z" + "iopub.execute_input": "2024-06-27T15:39:12.709806Z", + "iopub.status.busy": "2024-06-27T15:39:12.709531Z", + "iopub.status.idle": "2024-06-27T15:39:12.716529Z", + "shell.execute_reply": "2024-06-27T15:39:12.716076Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.431342Z", - "iopub.status.busy": "2024-06-25T23:13:23.431023Z", - "iopub.status.idle": "2024-06-25T23:13:23.542534Z", - "shell.execute_reply": "2024-06-25T23:13:23.542044Z" + "iopub.execute_input": "2024-06-27T15:39:12.718621Z", + "iopub.status.busy": "2024-06-27T15:39:12.718222Z", + "iopub.status.idle": "2024-06-27T15:39:12.830714Z", + "shell.execute_reply": "2024-06-27T15:39:12.830147Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.544624Z", - "iopub.status.busy": "2024-06-25T23:13:23.544286Z", - "iopub.status.idle": "2024-06-25T23:13:23.546943Z", - "shell.execute_reply": "2024-06-25T23:13:23.546515Z" + "iopub.execute_input": "2024-06-27T15:39:12.832837Z", + "iopub.status.busy": "2024-06-27T15:39:12.832513Z", + "iopub.status.idle": "2024-06-27T15:39:12.835427Z", + "shell.execute_reply": "2024-06-27T15:39:12.834878Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.548943Z", - "iopub.status.busy": "2024-06-25T23:13:23.548635Z", - "iopub.status.idle": "2024-06-25T23:13:25.510005Z", - "shell.execute_reply": "2024-06-25T23:13:25.509395Z" + "iopub.execute_input": "2024-06-27T15:39:12.837277Z", + "iopub.status.busy": "2024-06-27T15:39:12.837102Z", + "iopub.status.idle": "2024-06-27T15:39:14.822404Z", + "shell.execute_reply": "2024-06-27T15:39:14.821784Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:25.513097Z", - "iopub.status.busy": "2024-06-25T23:13:25.512371Z", - "iopub.status.idle": "2024-06-25T23:13:25.523496Z", - "shell.execute_reply": "2024-06-25T23:13:25.522944Z" + "iopub.execute_input": "2024-06-27T15:39:14.825471Z", + "iopub.status.busy": "2024-06-27T15:39:14.824704Z", + "iopub.status.idle": "2024-06-27T15:39:14.836113Z", + "shell.execute_reply": "2024-06-27T15:39:14.835678Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:25.525641Z", - "iopub.status.busy": "2024-06-25T23:13:25.525323Z", - "iopub.status.idle": "2024-06-25T23:13:25.545176Z", - "shell.execute_reply": "2024-06-25T23:13:25.544739Z" + "iopub.execute_input": "2024-06-27T15:39:14.838261Z", + "iopub.status.busy": "2024-06-27T15:39:14.837937Z", + "iopub.status.idle": "2024-06-27T15:39:15.011888Z", + "shell.execute_reply": "2024-06-27T15:39:15.011390Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index 9af680b6f..acadfeca1 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:28.905676Z", - "iopub.status.busy": "2024-06-25T23:13:28.905503Z", - "iopub.status.idle": "2024-06-25T23:13:31.555296Z", - "shell.execute_reply": "2024-06-25T23:13:31.554730Z" + "iopub.execute_input": "2024-06-27T15:39:18.202742Z", + "iopub.status.busy": "2024-06-27T15:39:18.202571Z", + "iopub.status.idle": "2024-06-27T15:39:21.198042Z", + "shell.execute_reply": "2024-06-27T15:39:21.197385Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:31.557860Z", - "iopub.status.busy": "2024-06-25T23:13:31.557469Z", - "iopub.status.idle": "2024-06-25T23:13:31.560897Z", - "shell.execute_reply": "2024-06-25T23:13:31.560352Z" + "iopub.execute_input": "2024-06-27T15:39:21.200796Z", + "iopub.status.busy": "2024-06-27T15:39:21.200477Z", + "iopub.status.idle": "2024-06-27T15:39:21.203994Z", + "shell.execute_reply": "2024-06-27T15:39:21.203442Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.562942Z", - "iopub.status.busy": "2024-06-25T23:13:31.562629Z", - "iopub.status.idle": "2024-06-25T23:13:31.565542Z", - "shell.execute_reply": "2024-06-25T23:13:31.565096Z" + "iopub.execute_input": "2024-06-27T15:39:21.205955Z", + "iopub.status.busy": "2024-06-27T15:39:21.205635Z", + "iopub.status.idle": "2024-06-27T15:39:21.208797Z", + "shell.execute_reply": "2024-06-27T15:39:21.208260Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.567524Z", - "iopub.status.busy": "2024-06-25T23:13:31.567195Z", - "iopub.status.idle": "2024-06-25T23:13:31.589244Z", - "shell.execute_reply": "2024-06-25T23:13:31.588737Z" + "iopub.execute_input": "2024-06-27T15:39:21.210955Z", + "iopub.status.busy": "2024-06-27T15:39:21.210659Z", + "iopub.status.idle": "2024-06-27T15:39:21.370903Z", + "shell.execute_reply": "2024-06-27T15:39:21.370338Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.591105Z", - "iopub.status.busy": "2024-06-25T23:13:31.590840Z", - "iopub.status.idle": "2024-06-25T23:13:31.594215Z", - "shell.execute_reply": "2024-06-25T23:13:31.593789Z" + "iopub.execute_input": "2024-06-27T15:39:21.373180Z", + "iopub.status.busy": "2024-06-27T15:39:21.372853Z", + "iopub.status.idle": "2024-06-27T15:39:21.376455Z", + "shell.execute_reply": "2024-06-27T15:39:21.375919Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.596064Z", - "iopub.status.busy": "2024-06-25T23:13:31.595883Z", - "iopub.status.idle": "2024-06-25T23:13:31.599153Z", - "shell.execute_reply": "2024-06-25T23:13:31.598670Z" + "iopub.execute_input": "2024-06-27T15:39:21.378544Z", + "iopub.status.busy": "2024-06-27T15:39:21.378146Z", + "iopub.status.idle": "2024-06-27T15:39:21.381367Z", + "shell.execute_reply": "2024-06-27T15:39:21.380892Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'cancel_transfer'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.601175Z", - "iopub.status.busy": "2024-06-25T23:13:31.600751Z", - "iopub.status.idle": "2024-06-25T23:13:31.603901Z", - "shell.execute_reply": "2024-06-25T23:13:31.603365Z" + "iopub.execute_input": "2024-06-27T15:39:21.383438Z", + "iopub.status.busy": "2024-06-27T15:39:21.383129Z", + "iopub.status.idle": "2024-06-27T15:39:21.386120Z", + "shell.execute_reply": "2024-06-27T15:39:21.385587Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.606046Z", - "iopub.status.busy": "2024-06-25T23:13:31.605618Z", - "iopub.status.idle": "2024-06-25T23:13:31.608973Z", - "shell.execute_reply": "2024-06-25T23:13:31.608424Z" + "iopub.execute_input": "2024-06-27T15:39:21.388022Z", + "iopub.status.busy": "2024-06-27T15:39:21.387839Z", + "iopub.status.idle": "2024-06-27T15:39:21.391294Z", + "shell.execute_reply": "2024-06-27T15:39:21.390839Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.610942Z", - "iopub.status.busy": "2024-06-25T23:13:31.610641Z", - "iopub.status.idle": "2024-06-25T23:13:35.909329Z", - "shell.execute_reply": "2024-06-25T23:13:35.908695Z" + "iopub.execute_input": "2024-06-27T15:39:21.393210Z", + "iopub.status.busy": "2024-06-27T15:39:21.393036Z", + "iopub.status.idle": "2024-06-27T15:39:27.666464Z", + "shell.execute_reply": "2024-06-27T15:39:27.665783Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6477ae421e3e43aa814150445e014ac0", + "model_id": "45e38381b3044662a46d4f1951c0293c", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e62034a9e6c043cc997861592486168a", + "model_id": "227c52be562c41e894d3f47ecec18fc0", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e2c610098e54fea94839bb48d055f22", + "model_id": "1e809c0aacfd4c7e9856d270a4004a03", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b6e029c7f61484d880737df09cb3291", + "model_id": "f56dd1afd4f242f880025145f7e8d4ad", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e750160df873483c9096b064baeab112", + "model_id": "62d408a1fe9142d0bd15e24d5c3d91bf", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddf577727f0e42a1b074bcb455a4258a", + "model_id": "aa3957dd11454c9295aa863153bba9c9", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97155a33cf37454f8282b21b3806031d", + "model_id": "e43243d9257e4a88823097e7b7af5888", "version_major": 2, "version_minor": 0 }, @@ -564,14 +564,6 @@ "text": [ "No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n" - ] } ], "source": [ @@ -609,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:35.912144Z", - "iopub.status.busy": "2024-06-25T23:13:35.911799Z", - "iopub.status.idle": "2024-06-25T23:13:35.914630Z", - "shell.execute_reply": "2024-06-25T23:13:35.914095Z" + "iopub.execute_input": "2024-06-27T15:39:27.669349Z", + "iopub.status.busy": "2024-06-27T15:39:27.668878Z", + "iopub.status.idle": "2024-06-27T15:39:27.672003Z", + "shell.execute_reply": "2024-06-27T15:39:27.671571Z" } }, "outputs": [], @@ -634,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:35.916621Z", - "iopub.status.busy": "2024-06-25T23:13:35.916300Z", - "iopub.status.idle": "2024-06-25T23:13:35.918968Z", - "shell.execute_reply": "2024-06-25T23:13:35.918524Z" + "iopub.execute_input": "2024-06-27T15:39:27.673974Z", + "iopub.status.busy": "2024-06-27T15:39:27.673659Z", + "iopub.status.idle": "2024-06-27T15:39:27.676360Z", + "shell.execute_reply": "2024-06-27T15:39:27.675805Z" } }, "outputs": [], @@ -652,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:35.920827Z", - "iopub.status.busy": "2024-06-25T23:13:35.920512Z", - "iopub.status.idle": "2024-06-25T23:13:38.614446Z", - "shell.execute_reply": "2024-06-25T23:13:38.613776Z" + "iopub.execute_input": "2024-06-27T15:39:27.678361Z", + "iopub.status.busy": "2024-06-27T15:39:27.678034Z", + "iopub.status.idle": "2024-06-27T15:39:30.399774Z", + "shell.execute_reply": "2024-06-27T15:39:30.399140Z" }, "scrolled": true }, @@ -678,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:38.617773Z", - "iopub.status.busy": "2024-06-25T23:13:38.616881Z", - "iopub.status.idle": "2024-06-25T23:13:38.624576Z", - "shell.execute_reply": "2024-06-25T23:13:38.624128Z" + "iopub.execute_input": "2024-06-27T15:39:30.402953Z", + "iopub.status.busy": "2024-06-27T15:39:30.402101Z", + "iopub.status.idle": "2024-06-27T15:39:30.410001Z", + "shell.execute_reply": "2024-06-27T15:39:30.409330Z" } }, "outputs": [ @@ -782,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - 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"layout": "IPY_MODEL_2fe0de05b5e847e89ba80b8e4d398c4c", - "placeholder": "​", - "style": "IPY_MODEL_6f576e9c9374443588b496e63f781624", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index d40b1db54..93586bee5 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:42.048585Z", - "iopub.status.busy": "2024-06-25T23:13:42.048169Z", - "iopub.status.idle": "2024-06-25T23:13:47.015851Z", - "shell.execute_reply": "2024-06-25T23:13:47.015219Z" + "iopub.execute_input": "2024-06-27T15:39:35.362328Z", + "iopub.status.busy": "2024-06-27T15:39:35.361825Z", + "iopub.status.idle": "2024-06-27T15:39:40.570041Z", + "shell.execute_reply": "2024-06-27T15:39:40.569408Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:47.018616Z", - "iopub.status.busy": "2024-06-25T23:13:47.018295Z", - "iopub.status.idle": "2024-06-25T23:13:47.021447Z", - "shell.execute_reply": "2024-06-25T23:13:47.020989Z" + "iopub.execute_input": "2024-06-27T15:39:40.572766Z", + "iopub.status.busy": "2024-06-27T15:39:40.572228Z", + "iopub.status.idle": "2024-06-27T15:39:40.575350Z", + "shell.execute_reply": "2024-06-27T15:39:40.574921Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:47.023397Z", - "iopub.status.busy": "2024-06-25T23:13:47.023066Z", - "iopub.status.idle": "2024-06-25T23:13:47.027579Z", - "shell.execute_reply": "2024-06-25T23:13:47.027038Z" + "iopub.execute_input": "2024-06-27T15:39:40.577400Z", + "iopub.status.busy": "2024-06-27T15:39:40.577085Z", + "iopub.status.idle": "2024-06-27T15:39:40.581458Z", + "shell.execute_reply": "2024-06-27T15:39:40.581032Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T23:13:47.029706Z", - "iopub.status.busy": "2024-06-25T23:13:47.029408Z", - "iopub.status.idle": "2024-06-25T23:13:48.557949Z", - "shell.execute_reply": "2024-06-25T23:13:48.557324Z" + "iopub.execute_input": "2024-06-27T15:39:40.583428Z", + "iopub.status.busy": "2024-06-27T15:39:40.583105Z", + "iopub.status.idle": "2024-06-27T15:39:50.584027Z", + "shell.execute_reply": "2024-06-27T15:39:50.583280Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T23:13:48.560586Z", - "iopub.status.busy": "2024-06-25T23:13:48.560204Z", - "iopub.status.idle": "2024-06-25T23:13:48.570753Z", - "shell.execute_reply": "2024-06-25T23:13:48.570316Z" + "iopub.execute_input": "2024-06-27T15:39:50.586704Z", + "iopub.status.busy": "2024-06-27T15:39:50.586468Z", + "iopub.status.idle": "2024-06-27T15:39:50.596927Z", + "shell.execute_reply": "2024-06-27T15:39:50.596449Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:48.572948Z", - "iopub.status.busy": "2024-06-25T23:13:48.572614Z", - "iopub.status.idle": "2024-06-25T23:13:48.578335Z", - "shell.execute_reply": "2024-06-25T23:13:48.577906Z" + "iopub.execute_input": "2024-06-27T15:39:50.599153Z", + "iopub.status.busy": "2024-06-27T15:39:50.598811Z", + "iopub.status.idle": "2024-06-27T15:39:50.604158Z", + "shell.execute_reply": "2024-06-27T15:39:50.603720Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-25T23:13:48.580333Z", - "iopub.status.busy": "2024-06-25T23:13:48.580014Z", - "iopub.status.idle": "2024-06-25T23:13:49.044116Z", - "shell.execute_reply": "2024-06-25T23:13:49.043554Z" + "iopub.execute_input": "2024-06-27T15:39:50.606326Z", + "iopub.status.busy": "2024-06-27T15:39:50.605894Z", + "iopub.status.idle": "2024-06-27T15:39:51.061002Z", + "shell.execute_reply": "2024-06-27T15:39:51.060399Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:49.046459Z", - "iopub.status.busy": "2024-06-25T23:13:49.046048Z", - "iopub.status.idle": "2024-06-25T23:13:49.682286Z", - "shell.execute_reply": "2024-06-25T23:13:49.681791Z" + "iopub.execute_input": "2024-06-27T15:39:51.063242Z", + "iopub.status.busy": "2024-06-27T15:39:51.062823Z", + "iopub.status.idle": "2024-06-27T15:39:52.856305Z", + "shell.execute_reply": "2024-06-27T15:39:52.855828Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-06-25T23:13:49.685227Z", - "iopub.status.busy": "2024-06-25T23:13:49.684826Z", - "iopub.status.idle": "2024-06-25T23:13:49.703315Z", - "shell.execute_reply": "2024-06-25T23:13:49.702790Z" + "iopub.execute_input": "2024-06-27T15:39:52.858818Z", + "iopub.status.busy": "2024-06-27T15:39:52.858492Z", + "iopub.status.idle": "2024-06-27T15:39:52.876475Z", + "shell.execute_reply": "2024-06-27T15:39:52.875972Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:49.705384Z", - "iopub.status.busy": "2024-06-25T23:13:49.705205Z", - "iopub.status.idle": "2024-06-25T23:13:49.708482Z", - "shell.execute_reply": "2024-06-25T23:13:49.708013Z" + "iopub.execute_input": "2024-06-27T15:39:52.878507Z", + "iopub.status.busy": "2024-06-27T15:39:52.878190Z", + "iopub.status.idle": "2024-06-27T15:39:52.881147Z", + "shell.execute_reply": "2024-06-27T15:39:52.880732Z" }, "id": "I8JqhOZgi94g" }, @@ -582,24 +582,14 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:49.710490Z", - "iopub.status.busy": "2024-06-25T23:13:49.710159Z", - "iopub.status.idle": "2024-06-25T23:14:03.836426Z", - "shell.execute_reply": "2024-06-25T23:14:03.835865Z" + "iopub.execute_input": "2024-06-27T15:39:52.883147Z", + "iopub.status.busy": "2024-06-27T15:39:52.882820Z", + "iopub.status.idle": "2024-06-27T15:40:07.290357Z", + "shell.execute_reply": "2024-06-27T15:40:07.289818Z" }, "id": "2FSQ2GR9R_YA" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/functional.py:650: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n", - "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:863.)\n", - " return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n" - ] - } - ], + "outputs": [], "source": [ "# Extract audio embeddings\n", "embeddings_list = []\n", @@ -627,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T23:14:03.839037Z", - "iopub.status.busy": "2024-06-25T23:14:03.838661Z", - "iopub.status.idle": "2024-06-25T23:14:03.842744Z", - "shell.execute_reply": "2024-06-25T23:14:03.842282Z" + "iopub.execute_input": "2024-06-27T15:40:07.293008Z", + "iopub.status.busy": "2024-06-27T15:40:07.292676Z", + "iopub.status.idle": "2024-06-27T15:40:07.296534Z", + "shell.execute_reply": "2024-06-27T15:40:07.295978Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:03.844717Z", - "iopub.status.busy": "2024-06-25T23:14:03.844392Z", - "iopub.status.idle": "2024-06-25T23:14:04.554198Z", - "shell.execute_reply": "2024-06-25T23:14:04.553609Z" + "iopub.execute_input": "2024-06-27T15:40:07.298675Z", + "iopub.status.busy": "2024-06-27T15:40:07.298278Z", + "iopub.status.idle": "2024-06-27T15:40:08.006185Z", + "shell.execute_reply": "2024-06-27T15:40:08.005598Z" }, "id": "i_drkY9YOcw4" }, @@ -727,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-25T23:14:04.557144Z", - "iopub.status.busy": "2024-06-25T23:14:04.556722Z", - "iopub.status.idle": "2024-06-25T23:14:04.561566Z", - "shell.execute_reply": "2024-06-25T23:14:04.561058Z" + "iopub.execute_input": "2024-06-27T15:40:08.009254Z", + "iopub.status.busy": "2024-06-27T15:40:08.008928Z", + "iopub.status.idle": "2024-06-27T15:40:08.013451Z", + "shell.execute_reply": "2024-06-27T15:40:08.012979Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -777,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - 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"iopub.status.idle": "2024-06-25T23:14:10.319594Z", - "shell.execute_reply": "2024-06-25T23:14:10.319043Z" + "iopub.execute_input": "2024-06-27T15:40:11.815285Z", + "iopub.status.busy": "2024-06-27T15:40:11.815110Z", + "iopub.status.idle": "2024-06-27T15:40:12.989400Z", + "shell.execute_reply": "2024-06-27T15:40:12.988851Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:14:10.322187Z", - "iopub.status.busy": "2024-06-25T23:14:10.321743Z", - "iopub.status.idle": "2024-06-25T23:14:10.324748Z", - "shell.execute_reply": "2024-06-25T23:14:10.324303Z" + "iopub.execute_input": "2024-06-27T15:40:12.991789Z", + "iopub.status.busy": "2024-06-27T15:40:12.991513Z", + "iopub.status.idle": "2024-06-27T15:40:12.994521Z", + "shell.execute_reply": "2024-06-27T15:40:12.993986Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:10.326836Z", - "iopub.status.busy": "2024-06-25T23:14:10.326547Z", - "iopub.status.idle": "2024-06-25T23:14:10.335582Z", - "shell.execute_reply": "2024-06-25T23:14:10.335001Z" + "iopub.execute_input": "2024-06-27T15:40:12.996583Z", + "iopub.status.busy": "2024-06-27T15:40:12.996408Z", + "iopub.status.idle": "2024-06-27T15:40:13.004733Z", + "shell.execute_reply": "2024-06-27T15:40:13.004294Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - 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"layout": "IPY_MODEL_daa1004e74fa4197b88715988591c621", + "layout": "IPY_MODEL_63a5476a41dc4f07a300d89a3ff5240f", "max": 132.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_781b877c2bbc46b7969db8529c1eb5c3", + "style": "IPY_MODEL_c91d4d01810d4b2e9e81bb9595425ce7", "tabbable": null, "tooltip": null, "value": 132.0 diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 4fb163767..4b7588207 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:15.711188Z", - "iopub.status.busy": "2024-06-25T23:14:15.711012Z", - "iopub.status.idle": "2024-06-25T23:14:16.870873Z", - "shell.execute_reply": "2024-06-25T23:14:16.870268Z" + "iopub.execute_input": "2024-06-27T15:40:18.510907Z", + "iopub.status.busy": "2024-06-27T15:40:18.510569Z", + "iopub.status.idle": "2024-06-27T15:40:19.673840Z", + "shell.execute_reply": "2024-06-27T15:40:19.673269Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:14:16.873481Z", - "iopub.status.busy": "2024-06-25T23:14:16.873232Z", - "iopub.status.idle": "2024-06-25T23:14:16.876287Z", - "shell.execute_reply": "2024-06-25T23:14:16.875762Z" + "iopub.execute_input": "2024-06-27T15:40:19.676218Z", + "iopub.status.busy": "2024-06-27T15:40:19.675956Z", + "iopub.status.idle": "2024-06-27T15:40:19.678853Z", + "shell.execute_reply": "2024-06-27T15:40:19.678422Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:16.878379Z", - "iopub.status.busy": "2024-06-25T23:14:16.878075Z", - "iopub.status.idle": "2024-06-25T23:14:16.887427Z", - "shell.execute_reply": "2024-06-25T23:14:16.886901Z" + "iopub.execute_input": "2024-06-27T15:40:19.680895Z", + "iopub.status.busy": "2024-06-27T15:40:19.680636Z", + "iopub.status.idle": "2024-06-27T15:40:19.689587Z", + "shell.execute_reply": "2024-06-27T15:40:19.689155Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:16.889377Z", - "iopub.status.busy": "2024-06-25T23:14:16.889034Z", - "iopub.status.idle": "2024-06-25T23:14:16.893463Z", - "shell.execute_reply": "2024-06-25T23:14:16.893025Z" + "iopub.execute_input": "2024-06-27T15:40:19.691676Z", + "iopub.status.busy": "2024-06-27T15:40:19.691286Z", + "iopub.status.idle": "2024-06-27T15:40:19.696059Z", + "shell.execute_reply": "2024-06-27T15:40:19.695657Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:16.895454Z", - "iopub.status.busy": "2024-06-25T23:14:16.895124Z", - "iopub.status.idle": "2024-06-25T23:14:17.076668Z", - "shell.execute_reply": "2024-06-25T23:14:17.076135Z" + "iopub.execute_input": "2024-06-27T15:40:19.698308Z", + "iopub.status.busy": "2024-06-27T15:40:19.697913Z", + "iopub.status.idle": "2024-06-27T15:40:19.880985Z", + "shell.execute_reply": "2024-06-27T15:40:19.880426Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:17.079016Z", - "iopub.status.busy": "2024-06-25T23:14:17.078687Z", - "iopub.status.idle": "2024-06-25T23:14:17.444945Z", - "shell.execute_reply": "2024-06-25T23:14:17.444376Z" + "iopub.execute_input": "2024-06-27T15:40:19.883569Z", + "iopub.status.busy": "2024-06-27T15:40:19.883156Z", + "iopub.status.idle": "2024-06-27T15:40:20.200344Z", + "shell.execute_reply": "2024-06-27T15:40:20.199741Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:17.447239Z", - "iopub.status.busy": "2024-06-25T23:14:17.446903Z", - "iopub.status.idle": "2024-06-25T23:14:17.449525Z", - "shell.execute_reply": "2024-06-25T23:14:17.449111Z" + "iopub.execute_input": "2024-06-27T15:40:20.202400Z", + "iopub.status.busy": "2024-06-27T15:40:20.202212Z", + "iopub.status.idle": "2024-06-27T15:40:20.205007Z", + "shell.execute_reply": "2024-06-27T15:40:20.204573Z" } }, "outputs": [], @@ -602,22 +602,13 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:17.451586Z", - "iopub.status.busy": "2024-06-25T23:14:17.451263Z", - "iopub.status.idle": "2024-06-25T23:14:17.486312Z", - "shell.execute_reply": "2024-06-25T23:14:17.485793Z" + "iopub.execute_input": "2024-06-27T15:40:20.206898Z", + "iopub.status.busy": "2024-06-27T15:40:20.206724Z", + "iopub.status.idle": "2024-06-27T15:40:20.240804Z", + "shell.execute_reply": "2024-06-27T15:40:20.240385Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "model = LogisticRegression()\n", "pred_probs = cross_val_predict(\n", @@ -647,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:17.488380Z", - "iopub.status.busy": "2024-06-25T23:14:17.488048Z", - "iopub.status.idle": "2024-06-25T23:14:19.486184Z", - "shell.execute_reply": "2024-06-25T23:14:19.485493Z" + "iopub.execute_input": "2024-06-27T15:40:20.242677Z", + "iopub.status.busy": "2024-06-27T15:40:20.242500Z", + "iopub.status.idle": "2024-06-27T15:40:22.266097Z", + "shell.execute_reply": "2024-06-27T15:40:22.265435Z" } }, "outputs": [ @@ -662,14 +653,6 @@ "Finding label issues ...\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/filter.py:904: UserWarning: May not flag all label issues in class: 2, it has too few examples (see `min_examples_per_class` argument)\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -682,14 +665,6 @@ "\n", "Audit complete. 30 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -710,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.488814Z", - "iopub.status.busy": "2024-06-25T23:14:19.488306Z", - "iopub.status.idle": "2024-06-25T23:14:19.506666Z", - "shell.execute_reply": "2024-06-25T23:14:19.506238Z" + "iopub.execute_input": "2024-06-27T15:40:22.268793Z", + "iopub.status.busy": "2024-06-27T15:40:22.268136Z", + "iopub.status.idle": "2024-06-27T15:40:22.286425Z", + "shell.execute_reply": "2024-06-27T15:40:22.285883Z" } }, "outputs": [ @@ -846,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.508882Z", - "iopub.status.busy": "2024-06-25T23:14:19.508469Z", - "iopub.status.idle": "2024-06-25T23:14:19.514832Z", - "shell.execute_reply": "2024-06-25T23:14:19.514311Z" + "iopub.execute_input": "2024-06-27T15:40:22.288516Z", + "iopub.status.busy": "2024-06-27T15:40:22.288210Z", + "iopub.status.idle": "2024-06-27T15:40:22.294611Z", + "shell.execute_reply": "2024-06-27T15:40:22.294092Z" } }, "outputs": [ @@ -960,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.516791Z", - "iopub.status.busy": "2024-06-25T23:14:19.516482Z", - "iopub.status.idle": "2024-06-25T23:14:19.522090Z", - "shell.execute_reply": "2024-06-25T23:14:19.521611Z" + "iopub.execute_input": "2024-06-27T15:40:22.296776Z", + "iopub.status.busy": "2024-06-27T15:40:22.296476Z", + "iopub.status.idle": "2024-06-27T15:40:22.301964Z", + "shell.execute_reply": "2024-06-27T15:40:22.301404Z" } }, "outputs": [ @@ -1030,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.524150Z", - "iopub.status.busy": "2024-06-25T23:14:19.523753Z", - "iopub.status.idle": "2024-06-25T23:14:19.533902Z", - "shell.execute_reply": "2024-06-25T23:14:19.533440Z" + "iopub.execute_input": "2024-06-27T15:40:22.303895Z", + "iopub.status.busy": "2024-06-27T15:40:22.303724Z", + "iopub.status.idle": "2024-06-27T15:40:22.314345Z", + "shell.execute_reply": "2024-06-27T15:40:22.313828Z" } }, "outputs": [ @@ -1225,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.535870Z", - "iopub.status.busy": "2024-06-25T23:14:19.535545Z", - "iopub.status.idle": "2024-06-25T23:14:19.544125Z", - "shell.execute_reply": "2024-06-25T23:14:19.543654Z" + "iopub.execute_input": "2024-06-27T15:40:22.316236Z", + "iopub.status.busy": "2024-06-27T15:40:22.316064Z", + "iopub.status.idle": "2024-06-27T15:40:22.325038Z", + "shell.execute_reply": "2024-06-27T15:40:22.324559Z" } }, "outputs": [ @@ -1344,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.546177Z", - "iopub.status.busy": "2024-06-25T23:14:19.545853Z", - "iopub.status.idle": "2024-06-25T23:14:19.552700Z", - "shell.execute_reply": "2024-06-25T23:14:19.552255Z" + "iopub.execute_input": "2024-06-27T15:40:22.327062Z", + "iopub.status.busy": "2024-06-27T15:40:22.326737Z", + "iopub.status.idle": "2024-06-27T15:40:22.333539Z", + "shell.execute_reply": "2024-06-27T15:40:22.332994Z" }, "scrolled": true }, @@ -1472,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.554580Z", - "iopub.status.busy": "2024-06-25T23:14:19.554407Z", - "iopub.status.idle": "2024-06-25T23:14:19.563718Z", - "shell.execute_reply": "2024-06-25T23:14:19.563190Z" + "iopub.execute_input": "2024-06-27T15:40:22.335625Z", + "iopub.status.busy": "2024-06-27T15:40:22.335309Z", + "iopub.status.idle": "2024-06-27T15:40:22.344369Z", + "shell.execute_reply": "2024-06-27T15:40:22.343828Z" } }, "outputs": [ @@ -1578,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:19.565768Z", - "iopub.status.busy": "2024-06-25T23:14:19.565442Z", - "iopub.status.idle": "2024-06-25T23:14:19.576977Z", - "shell.execute_reply": "2024-06-25T23:14:19.576554Z" + "iopub.execute_input": "2024-06-27T15:40:22.346419Z", + "iopub.status.busy": "2024-06-27T15:40:22.346109Z", + "iopub.status.idle": "2024-06-27T15:40:22.357852Z", + "shell.execute_reply": "2024-06-27T15:40:22.357421Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index da3ecdeb8..38aca061f 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:22.349033Z", - "iopub.status.busy": "2024-06-25T23:14:22.348862Z", - "iopub.status.idle": "2024-06-25T23:14:25.155777Z", - "shell.execute_reply": "2024-06-25T23:14:25.155231Z" + "iopub.execute_input": "2024-06-27T15:40:25.001578Z", + "iopub.status.busy": "2024-06-27T15:40:25.001401Z", + "iopub.status.idle": "2024-06-27T15:40:27.866577Z", + "shell.execute_reply": "2024-06-27T15:40:27.866047Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:25.158288Z", - "iopub.status.busy": "2024-06-25T23:14:25.158017Z", - "iopub.status.idle": "2024-06-25T23:14:25.161499Z", - "shell.execute_reply": "2024-06-25T23:14:25.161043Z" + "iopub.execute_input": "2024-06-27T15:40:27.869664Z", + "iopub.status.busy": "2024-06-27T15:40:27.869112Z", + "iopub.status.idle": "2024-06-27T15:40:27.873387Z", + "shell.execute_reply": "2024-06-27T15:40:27.872879Z" } }, "outputs": [], @@ -152,27 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:25.163549Z", - "iopub.status.busy": "2024-06-25T23:14:25.163223Z", - "iopub.status.idle": "2024-06-25T23:14:35.757240Z", - "shell.execute_reply": "2024-06-25T23:14:35.756685Z" + "iopub.execute_input": "2024-06-27T15:40:27.875682Z", + "iopub.status.busy": "2024-06-27T15:40:27.875354Z", + "iopub.status.idle": "2024-06-27T15:40:42.283432Z", + "shell.execute_reply": "2024-06-27T15:40:42.282878Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/datasets/load.py:1486: FutureWarning: The repository for fashion_mnist contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/fashion_mnist\n", - "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", - "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", - " warnings.warn(\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99fb59566db2452bab382261d05e2879", + "model_id": "f6bff3421ffe4305b801978e8849eb8c", "version_major": 2, "version_minor": 0 }, @@ -186,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cacaca4358c34e93a46a3e2019d188d4", + "model_id": "32a70da0aa9b42359a5f17bf665b81ad", "version_major": 2, "version_minor": 0 }, @@ -200,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "46c5f1e4a9ca403d83a2aa33da63b600", + "model_id": "126a726b8a0445da91b339235e96dcad", "version_major": 2, "version_minor": 0 }, @@ -214,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc7010cd50844e48a3db713a6ea5f850", + "model_id": "5692b6450179456c853c8bda7c713903", "version_major": 2, "version_minor": 0 }, @@ -228,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e806f052f23419ba6ec80aa76644ed5", + "model_id": "570e337efa4b4edea75f724a0413e1eb", "version_major": 2, "version_minor": 0 }, @@ -242,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3590fcc9756749e0b9130b8809114216", + "model_id": "acd955b18cd54e8e9525c5e97b625466", "version_major": 2, "version_minor": 0 }, @@ -256,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "489746a2a7db4406b7ebfd5f2a155361", + "model_id": "40ca0cd146da417fb9d146f6a0460568", "version_major": 2, "version_minor": 0 }, @@ -270,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9c41de7ac0442aabfb15bbf3b5308c8", + "model_id": "9a62dbed8c0e414aba4d919f9a3b1266", "version_major": 2, "version_minor": 0 }, @@ -312,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:35.759372Z", - "iopub.status.busy": "2024-06-25T23:14:35.759148Z", - "iopub.status.idle": "2024-06-25T23:14:35.763037Z", - "shell.execute_reply": "2024-06-25T23:14:35.762503Z" + "iopub.execute_input": "2024-06-27T15:40:42.285586Z", + "iopub.status.busy": "2024-06-27T15:40:42.285388Z", + "iopub.status.idle": "2024-06-27T15:40:42.289192Z", + "shell.execute_reply": "2024-06-27T15:40:42.288724Z" } }, "outputs": [ @@ -340,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:35.765199Z", - "iopub.status.busy": "2024-06-25T23:14:35.764868Z", - "iopub.status.idle": "2024-06-25T23:14:46.667044Z", - "shell.execute_reply": "2024-06-25T23:14:46.666518Z" + "iopub.execute_input": "2024-06-27T15:40:42.291329Z", + "iopub.status.busy": "2024-06-27T15:40:42.290912Z", + "iopub.status.idle": "2024-06-27T15:40:53.511736Z", + "shell.execute_reply": "2024-06-27T15:40:53.511208Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e075f5bd416a447eb67433e0d225370f", + "model_id": "e616ea91cf69498784ed469d8d9c5d56", "version_major": 2, "version_minor": 0 }, @@ -388,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:46.669519Z", - "iopub.status.busy": "2024-06-25T23:14:46.669228Z", - "iopub.status.idle": "2024-06-25T23:15:05.072765Z", - "shell.execute_reply": "2024-06-25T23:15:05.072224Z" + "iopub.execute_input": "2024-06-27T15:40:53.514345Z", + "iopub.status.busy": "2024-06-27T15:40:53.514047Z", + "iopub.status.idle": "2024-06-27T15:41:11.376798Z", + "shell.execute_reply": "2024-06-27T15:41:11.376239Z" } }, "outputs": [], @@ -424,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.075378Z", - "iopub.status.busy": "2024-06-25T23:15:05.075000Z", - "iopub.status.idle": "2024-06-25T23:15:05.080668Z", - "shell.execute_reply": "2024-06-25T23:15:05.080229Z" + "iopub.execute_input": "2024-06-27T15:41:11.379517Z", + "iopub.status.busy": "2024-06-27T15:41:11.379127Z", + "iopub.status.idle": "2024-06-27T15:41:11.384918Z", + "shell.execute_reply": "2024-06-27T15:41:11.384491Z" } }, "outputs": [], @@ -465,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.082696Z", - "iopub.status.busy": "2024-06-25T23:15:05.082377Z", - "iopub.status.idle": "2024-06-25T23:15:05.086277Z", - "shell.execute_reply": "2024-06-25T23:15:05.085865Z" + "iopub.execute_input": "2024-06-27T15:41:11.386874Z", + "iopub.status.busy": "2024-06-27T15:41:11.386562Z", + "iopub.status.idle": "2024-06-27T15:41:11.390744Z", + "shell.execute_reply": "2024-06-27T15:41:11.390228Z" }, "nbsphinx": "hidden" }, @@ -605,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.088252Z", - "iopub.status.busy": "2024-06-25T23:15:05.087933Z", - "iopub.status.idle": "2024-06-25T23:15:05.096769Z", - "shell.execute_reply": "2024-06-25T23:15:05.096319Z" + "iopub.execute_input": "2024-06-27T15:41:11.392789Z", + "iopub.status.busy": "2024-06-27T15:41:11.392619Z", + "iopub.status.idle": "2024-06-27T15:41:11.401333Z", + "shell.execute_reply": "2024-06-27T15:41:11.400906Z" }, "nbsphinx": "hidden" }, @@ -733,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.098773Z", - "iopub.status.busy": "2024-06-25T23:15:05.098471Z", - "iopub.status.idle": "2024-06-25T23:15:05.125306Z", - "shell.execute_reply": "2024-06-25T23:15:05.124855Z" + "iopub.execute_input": "2024-06-27T15:41:11.403336Z", + "iopub.status.busy": "2024-06-27T15:41:11.403016Z", + "iopub.status.idle": "2024-06-27T15:41:11.431428Z", + "shell.execute_reply": "2024-06-27T15:41:11.430968Z" } }, "outputs": [], @@ -773,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.127482Z", - "iopub.status.busy": "2024-06-25T23:15:05.127151Z", - "iopub.status.idle": "2024-06-25T23:15:37.033092Z", - "shell.execute_reply": "2024-06-25T23:15:37.032511Z" + "iopub.execute_input": "2024-06-27T15:41:11.433579Z", + "iopub.status.busy": "2024-06-27T15:41:11.433263Z", + "iopub.status.idle": "2024-06-27T15:41:43.942353Z", + "shell.execute_reply": "2024-06-27T15:41:43.941537Z" } }, "outputs": [ @@ -792,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.649\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.871\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.594\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8835da69dbeb4826a96baa0561232a18", + "model_id": "68e3d0ba542f4dbeb8c6a0825b907d49", "version_major": 2, "version_minor": 0 }, @@ -827,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1141e88c1cd549c1ad36f5867b926978", + "model_id": "43d72b9736c74a1ca37c68dd80b5fda9", "version_major": 2, "version_minor": 0 }, @@ -850,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.663\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.845\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.415\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2a3f5349b34148209445198c9ae64559", + "model_id": "d204f6cd3d4946408d7f8da6c520e7d5", "version_major": 2, "version_minor": 0 }, @@ -885,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c1834764c78450699f4a69ba292fe8e", + "model_id": "5a7640261eb640c6adc4bf358876b135", "version_major": 2, "version_minor": 0 }, @@ -908,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.680\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.936\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.512\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02ef28fe5e5647e49f15e9889ac88c8f", + "model_id": "e3b81984ecf6429d82760070b58a79a2", "version_major": 2, "version_minor": 0 }, @@ -943,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebc081ac7cef42f58f0c46bdca672b27", + "model_id": "fa5b88ba2d5f4e2890a86a132006ee49", "version_major": 2, "version_minor": 0 }, @@ -1022,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.035751Z", - "iopub.status.busy": "2024-06-25T23:15:37.035236Z", - "iopub.status.idle": "2024-06-25T23:15:37.049525Z", - "shell.execute_reply": "2024-06-25T23:15:37.049035Z" + "iopub.execute_input": "2024-06-27T15:41:43.944871Z", + "iopub.status.busy": "2024-06-27T15:41:43.944476Z", + "iopub.status.idle": "2024-06-27T15:41:43.958369Z", + "shell.execute_reply": "2024-06-27T15:41:43.957946Z" } }, "outputs": [], @@ -1050,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.051460Z", - "iopub.status.busy": "2024-06-25T23:15:37.051284Z", - "iopub.status.idle": "2024-06-25T23:15:37.533678Z", - "shell.execute_reply": "2024-06-25T23:15:37.533181Z" + "iopub.execute_input": "2024-06-27T15:41:43.960372Z", + "iopub.status.busy": "2024-06-27T15:41:43.959983Z", + "iopub.status.idle": "2024-06-27T15:41:44.421845Z", + "shell.execute_reply": "2024-06-27T15:41:44.421194Z" } }, "outputs": [], @@ -1073,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.536010Z", - "iopub.status.busy": "2024-06-25T23:15:37.535826Z", - "iopub.status.idle": "2024-06-25T23:17:13.081610Z", - "shell.execute_reply": "2024-06-25T23:17:13.080989Z" + "iopub.execute_input": "2024-06-27T15:41:44.424353Z", + "iopub.status.busy": "2024-06-27T15:41:44.424167Z", + "iopub.status.idle": "2024-06-27T15:43:21.061556Z", + "shell.execute_reply": "2024-06-27T15:43:21.060943Z" } }, "outputs": [ @@ -1112,18 +1102,10 @@ "Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55c0a386d760485f92009bb75259396b", + "model_id": "bcbd871e719b4ad2aabe57836da305b9", "version_major": 2, "version_minor": 0 }, @@ -1162,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.084039Z", - "iopub.status.busy": "2024-06-25T23:17:13.083667Z", - "iopub.status.idle": "2024-06-25T23:17:13.530568Z", - "shell.execute_reply": "2024-06-25T23:17:13.530038Z" + "iopub.execute_input": "2024-06-27T15:43:21.064200Z", + "iopub.status.busy": "2024-06-27T15:43:21.063532Z", + "iopub.status.idle": "2024-06-27T15:43:21.524964Z", + "shell.execute_reply": "2024-06-27T15:43:21.524405Z" } }, "outputs": [ @@ -1311,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.532958Z", - "iopub.status.busy": "2024-06-25T23:17:13.532616Z", - "iopub.status.idle": "2024-06-25T23:17:13.595525Z", - "shell.execute_reply": "2024-06-25T23:17:13.594969Z" + "iopub.execute_input": "2024-06-27T15:43:21.527621Z", + "iopub.status.busy": "2024-06-27T15:43:21.527216Z", + "iopub.status.idle": "2024-06-27T15:43:21.589216Z", + "shell.execute_reply": "2024-06-27T15:43:21.588613Z" } }, "outputs": [ @@ -1418,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.597922Z", - "iopub.status.busy": "2024-06-25T23:17:13.597476Z", - "iopub.status.idle": "2024-06-25T23:17:13.606785Z", - "shell.execute_reply": "2024-06-25T23:17:13.606218Z" + "iopub.execute_input": "2024-06-27T15:43:21.591599Z", + "iopub.status.busy": "2024-06-27T15:43:21.591201Z", + "iopub.status.idle": "2024-06-27T15:43:21.600770Z", + "shell.execute_reply": "2024-06-27T15:43:21.600354Z" } }, "outputs": [ @@ -1551,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.609099Z", - "iopub.status.busy": "2024-06-25T23:17:13.608903Z", - "iopub.status.idle": "2024-06-25T23:17:13.613602Z", - "shell.execute_reply": "2024-06-25T23:17:13.613147Z" + "iopub.execute_input": "2024-06-27T15:43:21.602892Z", + "iopub.status.busy": "2024-06-27T15:43:21.602563Z", + "iopub.status.idle": "2024-06-27T15:43:21.607362Z", + "shell.execute_reply": "2024-06-27T15:43:21.606903Z" }, "nbsphinx": "hidden" }, @@ -1600,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.615408Z", - "iopub.status.busy": "2024-06-25T23:17:13.615233Z", - "iopub.status.idle": "2024-06-25T23:17:14.118370Z", - "shell.execute_reply": "2024-06-25T23:17:14.117787Z" + "iopub.execute_input": "2024-06-27T15:43:21.609426Z", + "iopub.status.busy": "2024-06-27T15:43:21.608985Z", + "iopub.status.idle": "2024-06-27T15:43:22.081442Z", + "shell.execute_reply": "2024-06-27T15:43:22.080884Z" } }, "outputs": [ @@ -1638,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.120491Z", - "iopub.status.busy": "2024-06-25T23:17:14.120304Z", - "iopub.status.idle": "2024-06-25T23:17:14.128885Z", - "shell.execute_reply": "2024-06-25T23:17:14.128442Z" + "iopub.execute_input": "2024-06-27T15:43:22.083743Z", + "iopub.status.busy": "2024-06-27T15:43:22.083562Z", + "iopub.status.idle": "2024-06-27T15:43:22.092122Z", + "shell.execute_reply": "2024-06-27T15:43:22.091588Z" } }, "outputs": [ @@ -1808,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.131053Z", - "iopub.status.busy": "2024-06-25T23:17:14.130639Z", - "iopub.status.idle": "2024-06-25T23:17:14.433754Z", - "shell.execute_reply": "2024-06-25T23:17:14.433138Z" + "iopub.execute_input": "2024-06-27T15:43:22.094350Z", + "iopub.status.busy": "2024-06-27T15:43:22.093931Z", + "iopub.status.idle": "2024-06-27T15:43:22.101089Z", + "shell.execute_reply": "2024-06-27T15:43:22.100516Z" }, "nbsphinx": "hidden" }, @@ -1887,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.437185Z", - "iopub.status.busy": "2024-06-25T23:17:14.436581Z", - "iopub.status.idle": "2024-06-25T23:17:14.910179Z", - "shell.execute_reply": "2024-06-25T23:17:14.909590Z" + "iopub.execute_input": "2024-06-27T15:43:22.103083Z", + "iopub.status.busy": "2024-06-27T15:43:22.102690Z", + "iopub.status.idle": "2024-06-27T15:43:22.862069Z", + "shell.execute_reply": "2024-06-27T15:43:22.861441Z" } }, "outputs": [ @@ -1927,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.912393Z", - "iopub.status.busy": "2024-06-25T23:17:14.912024Z", - "iopub.status.idle": "2024-06-25T23:17:14.927515Z", - "shell.execute_reply": "2024-06-25T23:17:14.926933Z" + "iopub.execute_input": "2024-06-27T15:43:22.864339Z", + "iopub.status.busy": "2024-06-27T15:43:22.864043Z", + "iopub.status.idle": "2024-06-27T15:43:22.879510Z", + "shell.execute_reply": "2024-06-27T15:43:22.879007Z" } }, "outputs": [ @@ -2087,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.929547Z", - "iopub.status.busy": "2024-06-25T23:17:14.929372Z", - "iopub.status.idle": "2024-06-25T23:17:14.935923Z", - "shell.execute_reply": "2024-06-25T23:17:14.935427Z" + "iopub.execute_input": "2024-06-27T15:43:22.881757Z", + "iopub.status.busy": "2024-06-27T15:43:22.881295Z", + "iopub.status.idle": "2024-06-27T15:43:22.886969Z", + "shell.execute_reply": "2024-06-27T15:43:22.886435Z" }, "nbsphinx": "hidden" }, @@ -2135,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.937944Z", - "iopub.status.busy": "2024-06-25T23:17:14.937612Z", - "iopub.status.idle": "2024-06-25T23:17:15.400691Z", - "shell.execute_reply": "2024-06-25T23:17:15.399712Z" + "iopub.execute_input": "2024-06-27T15:43:22.888928Z", + "iopub.status.busy": "2024-06-27T15:43:22.888635Z", + "iopub.status.idle": "2024-06-27T15:43:23.356645Z", + "shell.execute_reply": "2024-06-27T15:43:23.355700Z" } }, "outputs": [ @@ -2220,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.403380Z", - "iopub.status.busy": "2024-06-25T23:17:15.403170Z", - "iopub.status.idle": "2024-06-25T23:17:15.412375Z", - "shell.execute_reply": "2024-06-25T23:17:15.411801Z" + "iopub.execute_input": "2024-06-27T15:43:23.359134Z", + "iopub.status.busy": "2024-06-27T15:43:23.358923Z", + "iopub.status.idle": "2024-06-27T15:43:23.368538Z", + "shell.execute_reply": "2024-06-27T15:43:23.367969Z" } }, "outputs": [ @@ -2351,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.414938Z", - "iopub.status.busy": "2024-06-25T23:17:15.414744Z", - "iopub.status.idle": "2024-06-25T23:17:15.420451Z", - "shell.execute_reply": "2024-06-25T23:17:15.419881Z" + "iopub.execute_input": "2024-06-27T15:43:23.370975Z", + "iopub.status.busy": "2024-06-27T15:43:23.370626Z", + "iopub.status.idle": "2024-06-27T15:43:23.376432Z", + "shell.execute_reply": "2024-06-27T15:43:23.375942Z" }, "nbsphinx": "hidden" }, @@ -2391,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - 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" 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, @@ -2525,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.636681Z", - 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"_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_176f4fe537ab44709eed5f4771e5a748", - "placeholder": "​", - "style": "IPY_MODEL_b45281dbfb444428b286e76808d4a658", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "fb9b135b97bd45978b3759750aac7be4": { + "ff7d28ca3d77451c938e3d05c9d35bd5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 7f5df08d9..e10fb5ba3 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": "2024-06-25T23:17:19.488251Z", - "iopub.status.busy": "2024-06-25T23:17:19.488091Z", - "iopub.status.idle": "2024-06-25T23:17:20.586301Z", - "shell.execute_reply": "2024-06-25T23:17:20.585756Z" + "iopub.execute_input": "2024-06-27T15:43:27.478769Z", + "iopub.status.busy": "2024-06-27T15:43:27.478597Z", + "iopub.status.idle": "2024-06-27T15:43:28.615331Z", + "shell.execute_reply": "2024-06-27T15:43:28.614783Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:20.589007Z", - "iopub.status.busy": "2024-06-25T23:17:20.588566Z", - "iopub.status.idle": "2024-06-25T23:17:20.607142Z", - "shell.execute_reply": "2024-06-25T23:17:20.606704Z" + "iopub.execute_input": "2024-06-27T15:43:28.617939Z", + "iopub.status.busy": "2024-06-27T15:43:28.617477Z", + "iopub.status.idle": "2024-06-27T15:43:28.634988Z", + "shell.execute_reply": "2024-06-27T15:43:28.634564Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.609262Z", - "iopub.status.busy": "2024-06-25T23:17:20.608896Z", - "iopub.status.idle": "2024-06-25T23:17:20.630509Z", - "shell.execute_reply": "2024-06-25T23:17:20.630057Z" + "iopub.execute_input": "2024-06-27T15:43:28.637231Z", + "iopub.status.busy": "2024-06-27T15:43:28.636741Z", + "iopub.status.idle": "2024-06-27T15:43:28.683752Z", + "shell.execute_reply": "2024-06-27T15:43:28.683219Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.632342Z", - "iopub.status.busy": "2024-06-25T23:17:20.632168Z", - "iopub.status.idle": "2024-06-25T23:17:20.635695Z", - "shell.execute_reply": "2024-06-25T23:17:20.635234Z" + "iopub.execute_input": "2024-06-27T15:43:28.685832Z", + "iopub.status.busy": "2024-06-27T15:43:28.685492Z", + "iopub.status.idle": "2024-06-27T15:43:28.688933Z", + "shell.execute_reply": "2024-06-27T15:43:28.688419Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.637844Z", - "iopub.status.busy": "2024-06-25T23:17:20.637544Z", - "iopub.status.idle": "2024-06-25T23:17:20.644982Z", - "shell.execute_reply": "2024-06-25T23:17:20.644551Z" + "iopub.execute_input": "2024-06-27T15:43:28.691026Z", + "iopub.status.busy": "2024-06-27T15:43:28.690642Z", + "iopub.status.idle": "2024-06-27T15:43:28.698127Z", + "shell.execute_reply": "2024-06-27T15:43:28.697567Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.646840Z", - "iopub.status.busy": "2024-06-25T23:17:20.646673Z", - "iopub.status.idle": "2024-06-25T23:17:20.649384Z", - "shell.execute_reply": "2024-06-25T23:17:20.648911Z" + "iopub.execute_input": "2024-06-27T15:43:28.700172Z", + "iopub.status.busy": "2024-06-27T15:43:28.699860Z", + "iopub.status.idle": "2024-06-27T15:43:28.702299Z", + "shell.execute_reply": "2024-06-27T15:43:28.701866Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.651376Z", - "iopub.status.busy": "2024-06-25T23:17:20.651062Z", - "iopub.status.idle": "2024-06-25T23:17:23.603750Z", - "shell.execute_reply": "2024-06-25T23:17:23.603132Z" + "iopub.execute_input": "2024-06-27T15:43:28.704313Z", + "iopub.status.busy": "2024-06-27T15:43:28.703998Z", + "iopub.status.idle": "2024-06-27T15:43:31.609175Z", + "shell.execute_reply": "2024-06-27T15:43:31.608631Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:23.606640Z", - "iopub.status.busy": "2024-06-25T23:17:23.606173Z", - "iopub.status.idle": "2024-06-25T23:17:23.615532Z", - "shell.execute_reply": "2024-06-25T23:17:23.614991Z" + "iopub.execute_input": "2024-06-27T15:43:31.611891Z", + "iopub.status.busy": "2024-06-27T15:43:31.611669Z", + "iopub.status.idle": "2024-06-27T15:43:31.620906Z", + "shell.execute_reply": "2024-06-27T15:43:31.620481Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:23.617787Z", - "iopub.status.busy": "2024-06-25T23:17:23.617408Z", - "iopub.status.idle": "2024-06-25T23:17:25.503397Z", - "shell.execute_reply": "2024-06-25T23:17:25.502726Z" + "iopub.execute_input": "2024-06-27T15:43:31.622822Z", + "iopub.status.busy": "2024-06-27T15:43:31.622651Z", + "iopub.status.idle": "2024-06-27T15:43:33.535648Z", + "shell.execute_reply": "2024-06-27T15:43:33.535025Z" } }, "outputs": [ @@ -462,14 +462,6 @@ "\n", "Audit complete. 358 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -484,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.506132Z", - "iopub.status.busy": "2024-06-25T23:17:25.505476Z", - "iopub.status.idle": "2024-06-25T23:17:25.524117Z", - "shell.execute_reply": "2024-06-25T23:17:25.523676Z" + "iopub.execute_input": "2024-06-27T15:43:33.538337Z", + "iopub.status.busy": "2024-06-27T15:43:33.537758Z", + "iopub.status.idle": "2024-06-27T15:43:33.556718Z", + "shell.execute_reply": "2024-06-27T15:43:33.556253Z" }, "scrolled": true }, @@ -617,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.526096Z", - "iopub.status.busy": "2024-06-25T23:17:25.525830Z", - "iopub.status.idle": "2024-06-25T23:17:25.533770Z", - "shell.execute_reply": "2024-06-25T23:17:25.533230Z" + "iopub.execute_input": "2024-06-27T15:43:33.558849Z", + "iopub.status.busy": "2024-06-27T15:43:33.558552Z", + "iopub.status.idle": "2024-06-27T15:43:33.566516Z", + "shell.execute_reply": "2024-06-27T15:43:33.565968Z" } }, "outputs": [ @@ -724,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.535755Z", - "iopub.status.busy": "2024-06-25T23:17:25.535435Z", - "iopub.status.idle": "2024-06-25T23:17:25.544816Z", - "shell.execute_reply": "2024-06-25T23:17:25.544397Z" + "iopub.execute_input": "2024-06-27T15:43:33.568644Z", + "iopub.status.busy": "2024-06-27T15:43:33.568246Z", + "iopub.status.idle": "2024-06-27T15:43:33.577101Z", + "shell.execute_reply": "2024-06-27T15:43:33.576563Z" } }, "outputs": [ @@ -856,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.546828Z", - "iopub.status.busy": "2024-06-25T23:17:25.546524Z", - "iopub.status.idle": "2024-06-25T23:17:25.554523Z", - "shell.execute_reply": "2024-06-25T23:17:25.554077Z" + "iopub.execute_input": "2024-06-27T15:43:33.579284Z", + "iopub.status.busy": "2024-06-27T15:43:33.578978Z", + "iopub.status.idle": "2024-06-27T15:43:33.586679Z", + "shell.execute_reply": "2024-06-27T15:43:33.586154Z" } }, "outputs": [ @@ -973,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.556497Z", - "iopub.status.busy": "2024-06-25T23:17:25.556176Z", - "iopub.status.idle": "2024-06-25T23:17:25.564618Z", - "shell.execute_reply": "2024-06-25T23:17:25.564170Z" + "iopub.execute_input": "2024-06-27T15:43:33.588710Z", + "iopub.status.busy": "2024-06-27T15:43:33.588378Z", + "iopub.status.idle": "2024-06-27T15:43:33.596909Z", + "shell.execute_reply": "2024-06-27T15:43:33.596360Z" } }, "outputs": [ @@ -1087,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.566583Z", - "iopub.status.busy": "2024-06-25T23:17:25.566262Z", - "iopub.status.idle": "2024-06-25T23:17:25.573703Z", - "shell.execute_reply": "2024-06-25T23:17:25.573162Z" + "iopub.execute_input": "2024-06-27T15:43:33.598991Z", + "iopub.status.busy": "2024-06-27T15:43:33.598672Z", + "iopub.status.idle": "2024-06-27T15:43:33.606013Z", + "shell.execute_reply": "2024-06-27T15:43:33.605532Z" } }, "outputs": [ @@ -1205,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.575840Z", - "iopub.status.busy": "2024-06-25T23:17:25.575524Z", - "iopub.status.idle": "2024-06-25T23:17:25.582660Z", - "shell.execute_reply": "2024-06-25T23:17:25.582224Z" + "iopub.execute_input": "2024-06-27T15:43:33.608076Z", + "iopub.status.busy": "2024-06-27T15:43:33.607755Z", + "iopub.status.idle": "2024-06-27T15:43:33.614860Z", + "shell.execute_reply": "2024-06-27T15:43:33.614434Z" } }, "outputs": [ @@ -1308,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.584694Z", - "iopub.status.busy": "2024-06-25T23:17:25.584373Z", - "iopub.status.idle": "2024-06-25T23:17:25.592350Z", - "shell.execute_reply": "2024-06-25T23:17:25.591901Z" + "iopub.execute_input": "2024-06-27T15:43:33.616903Z", + "iopub.status.busy": "2024-06-27T15:43:33.616574Z", + "iopub.status.idle": "2024-06-27T15:43:33.624562Z", + "shell.execute_reply": "2024-06-27T15:43:33.624101Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 47d0847e3..858b70c6a 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": "2024-06-25T23:17:28.279893Z", - "iopub.status.busy": "2024-06-25T23:17:28.279723Z", - "iopub.status.idle": "2024-06-25T23:17:30.902204Z", - "shell.execute_reply": "2024-06-25T23:17:30.901649Z" + "iopub.execute_input": "2024-06-27T15:43:36.211196Z", + "iopub.status.busy": "2024-06-27T15:43:36.210616Z", + "iopub.status.idle": "2024-06-27T15:43:38.877876Z", + "shell.execute_reply": "2024-06-27T15:43:38.877226Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:30.904858Z", - "iopub.status.busy": "2024-06-25T23:17:30.904404Z", - "iopub.status.idle": "2024-06-25T23:17:30.907555Z", - "shell.execute_reply": "2024-06-25T23:17:30.907124Z" + "iopub.execute_input": "2024-06-27T15:43:38.880571Z", + "iopub.status.busy": "2024-06-27T15:43:38.880281Z", + "iopub.status.idle": "2024-06-27T15:43:38.883432Z", + "shell.execute_reply": "2024-06-27T15:43:38.882998Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:30.909531Z", - "iopub.status.busy": "2024-06-25T23:17:30.909235Z", - "iopub.status.idle": "2024-06-25T23:17:30.912305Z", - "shell.execute_reply": "2024-06-25T23:17:30.911777Z" + "iopub.execute_input": "2024-06-27T15:43:38.885401Z", + "iopub.status.busy": "2024-06-27T15:43:38.885086Z", + "iopub.status.idle": "2024-06-27T15:43:38.888163Z", + "shell.execute_reply": "2024-06-27T15:43:38.887637Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:30.914377Z", - "iopub.status.busy": "2024-06-25T23:17:30.913988Z", - "iopub.status.idle": "2024-06-25T23:17:30.934290Z", - "shell.execute_reply": "2024-06-25T23:17:30.933773Z" + "iopub.execute_input": "2024-06-27T15:43:38.890191Z", + "iopub.status.busy": "2024-06-27T15:43:38.889896Z", + "iopub.status.idle": "2024-06-27T15:43:38.941357Z", + "shell.execute_reply": "2024-06-27T15:43:38.940812Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:30.936266Z", - "iopub.status.busy": "2024-06-25T23:17:30.935961Z", - "iopub.status.idle": "2024-06-25T23:17:30.939627Z", - "shell.execute_reply": "2024-06-25T23:17:30.939095Z" + "iopub.execute_input": "2024-06-27T15:43:38.943383Z", + "iopub.status.busy": "2024-06-27T15:43:38.943067Z", + "iopub.status.idle": "2024-06-27T15:43:38.946734Z", + "shell.execute_reply": "2024-06-27T15:43:38.946211Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}\n" + "Classes: {'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'visa_or_mastercard'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:30.941560Z", - "iopub.status.busy": "2024-06-25T23:17:30.941250Z", - "iopub.status.idle": "2024-06-25T23:17:30.944331Z", - "shell.execute_reply": "2024-06-25T23:17:30.943818Z" + "iopub.execute_input": "2024-06-27T15:43:38.948819Z", + "iopub.status.busy": "2024-06-27T15:43:38.948384Z", + "iopub.status.idle": "2024-06-27T15:43:38.951555Z", + "shell.execute_reply": "2024-06-27T15:43:38.951036Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:30.946381Z", - "iopub.status.busy": "2024-06-25T23:17:30.946063Z", - "iopub.status.idle": "2024-06-25T23:17:34.606408Z", - "shell.execute_reply": "2024-06-25T23:17:34.605752Z" + "iopub.execute_input": "2024-06-27T15:43:38.953619Z", + "iopub.status.busy": "2024-06-27T15:43:38.953322Z", + "iopub.status.idle": "2024-06-27T15:43:45.236989Z", + "shell.execute_reply": "2024-06-27T15:43:45.236337Z" } }, "outputs": [ @@ -378,14 +378,6 @@ "text": [ "No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n" - ] } ], "source": [ @@ -424,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:34.609229Z", - "iopub.status.busy": "2024-06-25T23:17:34.608851Z", - "iopub.status.idle": "2024-06-25T23:17:35.466411Z", - "shell.execute_reply": "2024-06-25T23:17:35.465834Z" + "iopub.execute_input": "2024-06-27T15:43:45.239672Z", + "iopub.status.busy": "2024-06-27T15:43:45.239336Z", + "iopub.status.idle": "2024-06-27T15:43:46.123974Z", + "shell.execute_reply": "2024-06-27T15:43:46.123390Z" }, "scrolled": true }, @@ -459,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:35.469450Z", - "iopub.status.busy": "2024-06-25T23:17:35.469026Z", - "iopub.status.idle": "2024-06-25T23:17:35.471951Z", - "shell.execute_reply": "2024-06-25T23:17:35.471467Z" + "iopub.execute_input": "2024-06-27T15:43:46.127752Z", + "iopub.status.busy": "2024-06-27T15:43:46.126790Z", + "iopub.status.idle": "2024-06-27T15:43:46.130878Z", + "shell.execute_reply": "2024-06-27T15:43:46.130372Z" } }, "outputs": [], @@ -482,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:35.474346Z", - "iopub.status.busy": "2024-06-25T23:17:35.473954Z", - "iopub.status.idle": "2024-06-25T23:17:37.379211Z", - "shell.execute_reply": "2024-06-25T23:17:37.378561Z" + "iopub.execute_input": "2024-06-27T15:43:46.134460Z", + "iopub.status.busy": "2024-06-27T15:43:46.133513Z", + "iopub.status.idle": "2024-06-27T15:43:48.069108Z", + "shell.execute_reply": "2024-06-27T15:43:48.068128Z" }, "scrolled": true }, @@ -510,14 +502,6 @@ "\n", "Audit complete. 85 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -537,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.383383Z", - "iopub.status.busy": "2024-06-25T23:17:37.382233Z", - "iopub.status.idle": "2024-06-25T23:17:37.408704Z", - "shell.execute_reply": "2024-06-25T23:17:37.408212Z" + "iopub.execute_input": "2024-06-27T15:43:48.073009Z", + "iopub.status.busy": "2024-06-27T15:43:48.071851Z", + "iopub.status.idle": "2024-06-27T15:43:48.099092Z", + "shell.execute_reply": "2024-06-27T15:43:48.098582Z" }, "scrolled": true }, @@ -670,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.412193Z", - "iopub.status.busy": "2024-06-25T23:17:37.411277Z", - "iopub.status.idle": "2024-06-25T23:17:37.421651Z", - "shell.execute_reply": "2024-06-25T23:17:37.421256Z" + "iopub.execute_input": "2024-06-27T15:43:48.102662Z", + "iopub.status.busy": "2024-06-27T15:43:48.101747Z", + "iopub.status.idle": "2024-06-27T15:43:48.112519Z", + "shell.execute_reply": "2024-06-27T15:43:48.112121Z" }, "scrolled": true }, @@ -783,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.424437Z", - "iopub.status.busy": "2024-06-25T23:17:37.423704Z", - "iopub.status.idle": "2024-06-25T23:17:37.428917Z", - "shell.execute_reply": "2024-06-25T23:17:37.428520Z" + "iopub.execute_input": "2024-06-27T15:43:48.114945Z", + "iopub.status.busy": "2024-06-27T15:43:48.114571Z", + "iopub.status.idle": "2024-06-27T15:43:48.118624Z", + "shell.execute_reply": "2024-06-27T15:43:48.118139Z" } }, "outputs": [ @@ -824,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.430883Z", - "iopub.status.busy": "2024-06-25T23:17:37.430707Z", - "iopub.status.idle": "2024-06-25T23:17:37.438445Z", - "shell.execute_reply": "2024-06-25T23:17:37.437883Z" + "iopub.execute_input": "2024-06-27T15:43:48.120614Z", + "iopub.status.busy": "2024-06-27T15:43:48.120315Z", + "iopub.status.idle": "2024-06-27T15:43:48.126511Z", + "shell.execute_reply": "2024-06-27T15:43:48.125999Z" } }, "outputs": [ @@ -944,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.440387Z", - "iopub.status.busy": "2024-06-25T23:17:37.440214Z", - "iopub.status.idle": "2024-06-25T23:17:37.446599Z", - "shell.execute_reply": "2024-06-25T23:17:37.446157Z" + "iopub.execute_input": "2024-06-27T15:43:48.128507Z", + "iopub.status.busy": "2024-06-27T15:43:48.128148Z", + "iopub.status.idle": "2024-06-27T15:43:48.134473Z", + "shell.execute_reply": "2024-06-27T15:43:48.133964Z" } }, "outputs": [ @@ -1030,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.448520Z", - "iopub.status.busy": "2024-06-25T23:17:37.448196Z", - "iopub.status.idle": "2024-06-25T23:17:37.454046Z", - "shell.execute_reply": "2024-06-25T23:17:37.453485Z" + "iopub.execute_input": "2024-06-27T15:43:48.136403Z", + "iopub.status.busy": "2024-06-27T15:43:48.136103Z", + "iopub.status.idle": "2024-06-27T15:43:48.141827Z", + "shell.execute_reply": "2024-06-27T15:43:48.141387Z" } }, "outputs": [ @@ -1141,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.456157Z", - "iopub.status.busy": "2024-06-25T23:17:37.455839Z", - "iopub.status.idle": "2024-06-25T23:17:37.464219Z", - "shell.execute_reply": "2024-06-25T23:17:37.463796Z" + "iopub.execute_input": "2024-06-27T15:43:48.143837Z", + "iopub.status.busy": "2024-06-27T15:43:48.143538Z", + "iopub.status.idle": "2024-06-27T15:43:48.151732Z", + "shell.execute_reply": "2024-06-27T15:43:48.151300Z" } }, "outputs": [ @@ -1255,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.466195Z", - "iopub.status.busy": "2024-06-25T23:17:37.465883Z", - "iopub.status.idle": "2024-06-25T23:17:37.471233Z", - "shell.execute_reply": "2024-06-25T23:17:37.470679Z" + "iopub.execute_input": "2024-06-27T15:43:48.153724Z", + "iopub.status.busy": "2024-06-27T15:43:48.153415Z", + "iopub.status.idle": "2024-06-27T15:43:48.158681Z", + "shell.execute_reply": "2024-06-27T15:43:48.158138Z" } }, "outputs": [ @@ -1326,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.473304Z", - "iopub.status.busy": "2024-06-25T23:17:37.472970Z", - "iopub.status.idle": "2024-06-25T23:17:37.478474Z", - "shell.execute_reply": "2024-06-25T23:17:37.478028Z" + "iopub.execute_input": "2024-06-27T15:43:48.160608Z", + "iopub.status.busy": "2024-06-27T15:43:48.160431Z", + "iopub.status.idle": "2024-06-27T15:43:48.165627Z", + "shell.execute_reply": "2024-06-27T15:43:48.165151Z" } }, "outputs": [ @@ -1408,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.480531Z", - "iopub.status.busy": "2024-06-25T23:17:37.480222Z", - "iopub.status.idle": "2024-06-25T23:17:37.483860Z", - "shell.execute_reply": "2024-06-25T23:17:37.483411Z" + "iopub.execute_input": "2024-06-27T15:43:48.167686Z", + "iopub.status.busy": "2024-06-27T15:43:48.167383Z", + "iopub.status.idle": "2024-06-27T15:43:48.170918Z", + "shell.execute_reply": "2024-06-27T15:43:48.170385Z" } }, "outputs": [ @@ -1459,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:37.485748Z", - "iopub.status.busy": "2024-06-25T23:17:37.485580Z", - "iopub.status.idle": "2024-06-25T23:17:37.490849Z", - "shell.execute_reply": "2024-06-25T23:17:37.490382Z" + "iopub.execute_input": "2024-06-27T15:43:48.173029Z", + "iopub.status.busy": "2024-06-27T15:43:48.172853Z", + "iopub.status.idle": "2024-06-27T15:43:48.177856Z", + "shell.execute_reply": "2024-06-27T15:43:48.177424Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 05570c79a..271e14984 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": "2024-06-25T23:17:40.853361Z", - "iopub.status.busy": "2024-06-25T23:17:40.852930Z", - "iopub.status.idle": "2024-06-25T23:17:41.272322Z", - "shell.execute_reply": "2024-06-25T23:17:41.271713Z" + "iopub.execute_input": "2024-06-27T15:43:52.541747Z", + "iopub.status.busy": "2024-06-27T15:43:52.541554Z", + "iopub.status.idle": "2024-06-27T15:43:52.966739Z", + "shell.execute_reply": "2024-06-27T15:43:52.966247Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.275299Z", - "iopub.status.busy": "2024-06-25T23:17:41.274749Z", - "iopub.status.idle": "2024-06-25T23:17:41.403175Z", - "shell.execute_reply": "2024-06-25T23:17:41.402663Z" + "iopub.execute_input": "2024-06-27T15:43:52.969430Z", + "iopub.status.busy": "2024-06-27T15:43:52.968973Z", + "iopub.status.idle": "2024-06-27T15:43:53.099469Z", + "shell.execute_reply": "2024-06-27T15:43:53.098906Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.405438Z", - "iopub.status.busy": "2024-06-25T23:17:41.405028Z", - "iopub.status.idle": "2024-06-25T23:17:41.427834Z", - "shell.execute_reply": "2024-06-25T23:17:41.427281Z" + "iopub.execute_input": "2024-06-27T15:43:53.101731Z", + "iopub.status.busy": "2024-06-27T15:43:53.101470Z", + "iopub.status.idle": "2024-06-27T15:43:53.125646Z", + "shell.execute_reply": "2024-06-27T15:43:53.125036Z" } }, "outputs": [], @@ -210,21 +210,13 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.430652Z", - "iopub.status.busy": "2024-06-25T23:17:41.430206Z", - "iopub.status.idle": "2024-06-25T23:17:44.079438Z", - "shell.execute_reply": "2024-06-25T23:17:44.078785Z" + "iopub.execute_input": "2024-06-27T15:43:53.128358Z", + "iopub.status.busy": "2024-06-27T15:43:53.127895Z", + "iopub.status.idle": "2024-06-27T15:43:55.830328Z", + "shell.execute_reply": "2024-06-27T15:43:55.829757Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/issue_finder.py:116: UserWarning: Both `features` and `knn_graph` were provided. Most issue managers will likely prefer using `knn_graph` instead of `features` for efficiency.\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -243,15 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 523 issues found in the dataset.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" + "Audit complete. 524 issues found in the dataset.\n" ] }, { @@ -296,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356958\n", - " 362\n", + " 0.356925\n", + " 363\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619565\n", + " 0.619581\n", " 108\n", " \n", " \n", @@ -331,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356958 362\n", - "3 near_duplicate 0.619565 108\n", + "2 outlier 0.356925 363\n", + "3 near_duplicate 0.619581 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651929 0" @@ -716,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:44.082102Z", - "iopub.status.busy": "2024-06-25T23:17:44.081500Z", - "iopub.status.idle": "2024-06-25T23:17:51.711133Z", - "shell.execute_reply": "2024-06-25T23:17:51.710550Z" + "iopub.execute_input": "2024-06-27T15:43:55.832965Z", + "iopub.status.busy": "2024-06-27T15:43:55.832431Z", + "iopub.status.idle": "2024-06-27T15:44:04.492966Z", + "shell.execute_reply": "2024-06-27T15:44:04.492355Z" } }, "outputs": [ @@ -820,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:51.713313Z", - "iopub.status.busy": "2024-06-25T23:17:51.713127Z", - "iopub.status.idle": "2024-06-25T23:17:51.857400Z", - "shell.execute_reply": "2024-06-25T23:17:51.856753Z" + "iopub.execute_input": "2024-06-27T15:44:04.495312Z", + "iopub.status.busy": "2024-06-27T15:44:04.494968Z", + "iopub.status.idle": "2024-06-27T15:44:04.655997Z", + "shell.execute_reply": "2024-06-27T15:44:04.655499Z" } }, "outputs": [], @@ -854,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:51.860009Z", - "iopub.status.busy": "2024-06-25T23:17:51.859627Z", - "iopub.status.idle": "2024-06-25T23:17:53.181642Z", - "shell.execute_reply": "2024-06-25T23:17:53.181004Z" + "iopub.execute_input": "2024-06-27T15:44:04.658598Z", + "iopub.status.busy": "2024-06-27T15:44:04.658162Z", + "iopub.status.idle": "2024-06-27T15:44:06.008495Z", + "shell.execute_reply": "2024-06-27T15:44:06.008007Z" } }, "outputs": [ @@ -1016,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.183695Z", - "iopub.status.busy": "2024-06-25T23:17:53.183507Z", - "iopub.status.idle": "2024-06-25T23:17:53.614506Z", - "shell.execute_reply": "2024-06-25T23:17:53.613154Z" + "iopub.execute_input": "2024-06-27T15:44:06.010831Z", + "iopub.status.busy": "2024-06-27T15:44:06.010491Z", + "iopub.status.idle": "2024-06-27T15:44:06.454504Z", + "shell.execute_reply": "2024-06-27T15:44:06.453979Z" } }, "outputs": [ @@ -1098,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.617165Z", - "iopub.status.busy": "2024-06-25T23:17:53.616488Z", - "iopub.status.idle": "2024-06-25T23:17:53.625569Z", - "shell.execute_reply": "2024-06-25T23:17:53.625088Z" + "iopub.execute_input": "2024-06-27T15:44:06.457199Z", + "iopub.status.busy": "2024-06-27T15:44:06.456627Z", + "iopub.status.idle": "2024-06-27T15:44:06.468762Z", + "shell.execute_reply": "2024-06-27T15:44:06.468281Z" } }, "outputs": [], @@ -1131,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.627646Z", - "iopub.status.busy": "2024-06-25T23:17:53.627333Z", - "iopub.status.idle": "2024-06-25T23:17:53.647292Z", - "shell.execute_reply": "2024-06-25T23:17:53.646870Z" + "iopub.execute_input": "2024-06-27T15:44:06.470767Z", + "iopub.status.busy": "2024-06-27T15:44:06.470476Z", + "iopub.status.idle": "2024-06-27T15:44:06.488173Z", + "shell.execute_reply": "2024-06-27T15:44:06.487620Z" } }, "outputs": [], @@ -1162,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.649278Z", - "iopub.status.busy": "2024-06-25T23:17:53.648956Z", - "iopub.status.idle": "2024-06-25T23:17:53.876935Z", - "shell.execute_reply": "2024-06-25T23:17:53.876376Z" + "iopub.execute_input": "2024-06-27T15:44:06.490211Z", + "iopub.status.busy": "2024-06-27T15:44:06.489903Z", + "iopub.status.idle": "2024-06-27T15:44:06.712598Z", + "shell.execute_reply": "2024-06-27T15:44:06.711996Z" } }, "outputs": [], @@ -1205,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.879777Z", - "iopub.status.busy": "2024-06-25T23:17:53.879575Z", - "iopub.status.idle": "2024-06-25T23:17:53.898417Z", - "shell.execute_reply": "2024-06-25T23:17:53.897956Z" + "iopub.execute_input": "2024-06-27T15:44:06.715566Z", + "iopub.status.busy": "2024-06-27T15:44:06.715063Z", + "iopub.status.idle": "2024-06-27T15:44:06.733594Z", + "shell.execute_reply": "2024-06-27T15:44:06.733059Z" } }, "outputs": [ @@ -1406,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.900637Z", - "iopub.status.busy": "2024-06-25T23:17:53.900291Z", - "iopub.status.idle": "2024-06-25T23:17:54.067010Z", - "shell.execute_reply": "2024-06-25T23:17:54.066325Z" + "iopub.execute_input": "2024-06-27T15:44:06.735735Z", + "iopub.status.busy": "2024-06-27T15:44:06.735338Z", + "iopub.status.idle": "2024-06-27T15:44:06.904156Z", + "shell.execute_reply": "2024-06-27T15:44:06.903547Z" } }, "outputs": [ @@ -1476,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.069491Z", - "iopub.status.busy": "2024-06-25T23:17:54.069138Z", - "iopub.status.idle": "2024-06-25T23:17:54.080042Z", - "shell.execute_reply": "2024-06-25T23:17:54.079594Z" + "iopub.execute_input": "2024-06-27T15:44:06.906516Z", + "iopub.status.busy": "2024-06-27T15:44:06.906177Z", + "iopub.status.idle": "2024-06-27T15:44:06.916223Z", + "shell.execute_reply": "2024-06-27T15:44:06.915683Z" } }, "outputs": [ @@ -1745,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.083209Z", - "iopub.status.busy": "2024-06-25T23:17:54.082726Z", - "iopub.status.idle": "2024-06-25T23:17:54.092500Z", - "shell.execute_reply": "2024-06-25T23:17:54.092040Z" + "iopub.execute_input": "2024-06-27T15:44:06.918395Z", + "iopub.status.busy": "2024-06-27T15:44:06.918078Z", + "iopub.status.idle": "2024-06-27T15:44:06.927111Z", + "shell.execute_reply": "2024-06-27T15:44:06.926638Z" } }, "outputs": [ @@ -1935,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.094651Z", - "iopub.status.busy": "2024-06-25T23:17:54.094321Z", - "iopub.status.idle": "2024-06-25T23:17:54.125818Z", - "shell.execute_reply": "2024-06-25T23:17:54.122177Z" + "iopub.execute_input": "2024-06-27T15:44:06.929131Z", + "iopub.status.busy": "2024-06-27T15:44:06.928820Z", + "iopub.status.idle": "2024-06-27T15:44:06.957898Z", + "shell.execute_reply": "2024-06-27T15:44:06.957444Z" } }, "outputs": [], @@ -1972,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.128194Z", - "iopub.status.busy": "2024-06-25T23:17:54.127850Z", - "iopub.status.idle": "2024-06-25T23:17:54.130729Z", - "shell.execute_reply": "2024-06-25T23:17:54.130269Z" + "iopub.execute_input": "2024-06-27T15:44:06.960022Z", + "iopub.status.busy": "2024-06-27T15:44:06.959611Z", + "iopub.status.idle": "2024-06-27T15:44:06.962384Z", + "shell.execute_reply": "2024-06-27T15:44:06.961933Z" } }, "outputs": [], @@ -1997,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.132753Z", - "iopub.status.busy": "2024-06-25T23:17:54.132426Z", - "iopub.status.idle": "2024-06-25T23:17:54.151669Z", - "shell.execute_reply": "2024-06-25T23:17:54.151107Z" + "iopub.execute_input": "2024-06-27T15:44:06.964363Z", + "iopub.status.busy": "2024-06-27T15:44:06.964036Z", + "iopub.status.idle": "2024-06-27T15:44:06.983071Z", + "shell.execute_reply": "2024-06-27T15:44:06.982517Z" } }, "outputs": [ @@ -2158,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.153875Z", - "iopub.status.busy": "2024-06-25T23:17:54.153542Z", - "iopub.status.idle": "2024-06-25T23:17:54.157885Z", - "shell.execute_reply": "2024-06-25T23:17:54.157427Z" + "iopub.execute_input": "2024-06-27T15:44:06.985511Z", + "iopub.status.busy": "2024-06-27T15:44:06.985071Z", + "iopub.status.idle": "2024-06-27T15:44:06.989351Z", + "shell.execute_reply": "2024-06-27T15:44:06.988923Z" } }, "outputs": [], @@ -2194,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.159942Z", - "iopub.status.busy": "2024-06-25T23:17:54.159537Z", - "iopub.status.idle": "2024-06-25T23:17:54.187254Z", - "shell.execute_reply": "2024-06-25T23:17:54.186748Z" + "iopub.execute_input": "2024-06-27T15:44:06.991366Z", + "iopub.status.busy": "2024-06-27T15:44:06.991042Z", + "iopub.status.idle": "2024-06-27T15:44:07.018711Z", + "shell.execute_reply": "2024-06-27T15:44:07.018244Z" } }, "outputs": [ @@ -2343,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.189370Z", - "iopub.status.busy": "2024-06-25T23:17:54.189020Z", - "iopub.status.idle": "2024-06-25T23:17:54.563581Z", - "shell.execute_reply": "2024-06-25T23:17:54.563004Z" + "iopub.execute_input": "2024-06-27T15:44:07.020577Z", + "iopub.status.busy": "2024-06-27T15:44:07.020409Z", + "iopub.status.idle": "2024-06-27T15:44:07.337879Z", + "shell.execute_reply": "2024-06-27T15:44:07.337286Z" } }, "outputs": [ @@ -2413,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.566043Z", - "iopub.status.busy": "2024-06-25T23:17:54.565580Z", - "iopub.status.idle": "2024-06-25T23:17:54.568905Z", - "shell.execute_reply": "2024-06-25T23:17:54.568452Z" + "iopub.execute_input": "2024-06-27T15:44:07.340113Z", + "iopub.status.busy": "2024-06-27T15:44:07.339804Z", + "iopub.status.idle": "2024-06-27T15:44:07.343054Z", + "shell.execute_reply": "2024-06-27T15:44:07.342520Z" } }, "outputs": [ @@ -2467,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.570928Z", - "iopub.status.busy": "2024-06-25T23:17:54.570747Z", - "iopub.status.idle": "2024-06-25T23:17:54.584558Z", - "shell.execute_reply": "2024-06-25T23:17:54.584061Z" + "iopub.execute_input": "2024-06-27T15:44:07.345190Z", + "iopub.status.busy": "2024-06-27T15:44:07.344783Z", + "iopub.status.idle": "2024-06-27T15:44:07.357931Z", + "shell.execute_reply": "2024-06-27T15:44:07.357379Z" } }, "outputs": [ @@ -2749,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.586622Z", - "iopub.status.busy": "2024-06-25T23:17:54.586423Z", - "iopub.status.idle": "2024-06-25T23:17:54.600724Z", - "shell.execute_reply": "2024-06-25T23:17:54.600241Z" + "iopub.execute_input": "2024-06-27T15:44:07.360084Z", + "iopub.status.busy": "2024-06-27T15:44:07.359601Z", + "iopub.status.idle": "2024-06-27T15:44:07.372602Z", + "shell.execute_reply": "2024-06-27T15:44:07.372167Z" } }, "outputs": [ @@ -3019,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - 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1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
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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
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\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3567,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.686913Z", - "iopub.status.busy": "2024-06-25T23:17:54.686475Z", - "iopub.status.idle": "2024-06-25T23:17:54.692178Z", - "shell.execute_reply": "2024-06-25T23:17:54.691645Z" + "iopub.execute_input": "2024-06-27T15:44:07.454975Z", + "iopub.status.busy": "2024-06-27T15:44:07.454471Z", + "iopub.status.idle": "2024-06-27T15:44:07.460205Z", + "shell.execute_reply": "2024-06-27T15:44:07.459762Z" } }, "outputs": [], @@ -3609,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.694316Z", - "iopub.status.busy": "2024-06-25T23:17:54.693981Z", - "iopub.status.idle": "2024-06-25T23:17:54.705261Z", - "shell.execute_reply": "2024-06-25T23:17:54.704802Z" + "iopub.execute_input": "2024-06-27T15:44:07.462133Z", + "iopub.status.busy": "2024-06-27T15:44:07.461827Z", + "iopub.status.idle": "2024-06-27T15:44:07.472460Z", + "shell.execute_reply": "2024-06-27T15:44:07.471998Z" } }, "outputs": [ @@ -3648,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.707234Z", - "iopub.status.busy": "2024-06-25T23:17:54.707059Z", - "iopub.status.idle": "2024-06-25T23:17:54.923905Z", - "shell.execute_reply": "2024-06-25T23:17:54.923350Z" + "iopub.execute_input": "2024-06-27T15:44:07.474333Z", + "iopub.status.busy": "2024-06-27T15:44:07.474161Z", + "iopub.status.idle": "2024-06-27T15:44:07.692424Z", + "shell.execute_reply": "2024-06-27T15:44:07.691818Z" } }, "outputs": [ @@ -3703,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.926218Z", - "iopub.status.busy": "2024-06-25T23:17:54.925878Z", - "iopub.status.idle": "2024-06-25T23:17:54.933331Z", - "shell.execute_reply": "2024-06-25T23:17:54.932869Z" + "iopub.execute_input": "2024-06-27T15:44:07.694674Z", + "iopub.status.busy": "2024-06-27T15:44:07.694341Z", + "iopub.status.idle": "2024-06-27T15:44:07.701787Z", + "shell.execute_reply": "2024-06-27T15:44:07.701329Z" }, "nbsphinx": "hidden" }, @@ -3731,25 +3715,1586 @@ "assert all(class_imbalance_issues.query(\"is_class_imbalance_issue\")[\"class_imbalance_score\"] == 0.02), \"Class imbalance issue scores are not as expected\"\n", "assert all(class_imbalance_issues.query(\"not is_class_imbalance_issue\")[\"class_imbalance_score\"] == 1.0), \"Class imbalance issue scores are not as expected\"" ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Spurious Correlation between Vision Dataset features and class labels\n", + "\n", + "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n", + "\n", + "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load the dataset\n", + "\n", + "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n", + "\n", + "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T15:44:07.703783Z", + "iopub.status.busy": "2024-06-27T15:44:07.703532Z", + "iopub.status.idle": "2024-06-27T15:44:16.704543Z", + "shell.execute_reply": "2024-06-27T15:44:16.703978Z" + } }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 0%| | 0/170498071 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_images(dataset_dict):\n", + " \"\"\"Plots the first 15 images from the dataset dictionary.\"\"\"\n", + " images = dataset_dict['image']\n", + " labels = dataset_dict['label']\n", + " \n", + " # Define the number of images to plot\n", + " num_images_to_plot = 15\n", + " num_cols = 5 # Number of columns in the plot grid\n", + " num_rows = (num_images_to_plot + num_cols - 1) // num_cols # Calculate rows needed\n", + " \n", + " # Create a figure\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))\n", + " axes = axes.flatten()\n", + " \n", + " # Plot each image\n", + " for i in range(num_images_to_plot):\n", + " img = images[i]\n", + " label = labels[i]\n", + " axes[i].imshow(img)\n", + " axes[i].set_title(f'Label: {label}')\n", + " axes[i].axis('off')\n", + " \n", + " # Hide any remaining empty subplots\n", + " for i in range(num_images_to_plot, len(axes)):\n", + " axes[i].axis('off')\n", + " \n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_images(dataset_dict)\n", + "plot_images(transformed_dataset_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Finding image-specific property scores" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T15:44:18.292989Z", + "iopub.status.busy": "2024-06-27T15:44:18.292805Z", + "iopub.status.idle": "2024-06-27T15:44:19.075335Z", + "shell.execute_reply": "2024-06-27T15:44:19.074817Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding class_imbalance issues ...\n", + "Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "70462dbacd1f4317adbb0ee71a2daa15", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/200 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " property score\n", + "0 dark_score 0.295\n", + "1 light_score 0.415\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", + "6 blurry_score 0.325" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Vision-specific property scores in the transformed dataset" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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propertyscore
0dark_score0.000
1light_score0.185
2low_information_score0.015
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
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" + ], + "text/plain": [ + " property score\n", + "0 dark_score 0.000\n", + "1 light_score 0.185\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", + "6 blurry_score 0.015" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to find image-specific property scores given the dataset object\n", + "def get_property_scores(dataset):\n", + " lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n", + " lab.find_issues()\n", + " return lab._spurious_correlation()\n", + "\n", + "# Finds specific property score in the dataframe containing property scores \n", + "def get_specific_property_score(property_scores_df, property_name):\n", + " return property_scores_df[property_scores_df['property'] == property_name]['score'].iloc[0]\n", + "\n", + "# Finding scores in original and transformed dataset\n", + "standard_property_scores = get_property_scores(dataset)\n", + "transformed_property_scores = get_property_scores(transformed_dataset)\n", + "\n", + "# Displaying the scores dataframe\n", + "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n", + "display(standard_property_scores)\n", + "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n", + "display(transformed_property_scores)\n", + "\n", + "# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n", + "assert get_specific_property_score(standard_property_scores, 'dark_score') > get_specific_property_score(transformed_property_scores, 'dark_score')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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"_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0dab558fba14a74952f4a9b45f15158", + "placeholder": "​", + "style": "IPY_MODEL_07dbe0f79d8347918a74eb0fa73e4d15", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 827.64it/s]" + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index d462fdaea..69b3cd43c 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:58.501344Z", - "iopub.status.busy": "2024-06-25T23:17:58.500004Z", - "iopub.status.idle": "2024-06-25T23:17:59.801482Z", - "shell.execute_reply": "2024-06-25T23:17:59.800950Z" + "iopub.execute_input": "2024-06-27T15:44:23.089966Z", + "iopub.status.busy": "2024-06-27T15:44:23.089535Z", + "iopub.status.idle": "2024-06-27T15:44:24.255393Z", + "shell.execute_reply": "2024-06-27T15:44:24.254802Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:59.804004Z", - "iopub.status.busy": "2024-06-25T23:17:59.803708Z", - "iopub.status.idle": "2024-06-25T23:17:59.806736Z", - "shell.execute_reply": "2024-06-25T23:17:59.806281Z" + "iopub.execute_input": "2024-06-27T15:44:24.258234Z", + "iopub.status.busy": "2024-06-27T15:44:24.257585Z", + "iopub.status.idle": "2024-06-27T15:44:24.260732Z", + "shell.execute_reply": "2024-06-27T15:44:24.260197Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:59.808933Z", - "iopub.status.busy": "2024-06-25T23:17:59.808705Z", - "iopub.status.idle": "2024-06-25T23:17:59.821999Z", - "shell.execute_reply": "2024-06-25T23:17:59.821381Z" + "iopub.execute_input": "2024-06-27T15:44:24.263108Z", + "iopub.status.busy": "2024-06-27T15:44:24.262682Z", + "iopub.status.idle": "2024-06-27T15:44:24.274273Z", + "shell.execute_reply": "2024-06-27T15:44:24.273739Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:59.824481Z", - "iopub.status.busy": "2024-06-25T23:17:59.824047Z", - "iopub.status.idle": "2024-06-25T23:18:03.535596Z", - "shell.execute_reply": "2024-06-25T23:18:03.535061Z" + "iopub.execute_input": "2024-06-27T15:44:24.276467Z", + "iopub.status.busy": "2024-06-27T15:44:24.276029Z", + "iopub.status.idle": "2024-06-27T15:44:33.502020Z", + "shell.execute_reply": "2024-06-27T15:44:33.501414Z" }, "id": "dhTHOg8Pyv5G" }, @@ -694,7 +694,13 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n", @@ -2559,13 +2565,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 713861397..e60a48a49 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": "2024-06-25T23:18:05.926443Z", - "iopub.status.busy": "2024-06-25T23:18:05.926263Z", - "iopub.status.idle": "2024-06-25T23:18:07.103304Z", - "shell.execute_reply": "2024-06-25T23:18:07.102799Z" + "iopub.execute_input": "2024-06-27T15:44:35.812613Z", + "iopub.status.busy": "2024-06-27T15:44:35.812448Z", + "iopub.status.idle": "2024-06-27T15:44:36.959377Z", + "shell.execute_reply": "2024-06-27T15:44:36.958799Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:07.106148Z", - "iopub.status.busy": "2024-06-25T23:18:07.105603Z", - "iopub.status.idle": "2024-06-25T23:18:07.109155Z", - "shell.execute_reply": "2024-06-25T23:18:07.108679Z" + "iopub.execute_input": "2024-06-27T15:44:36.962118Z", + "iopub.status.busy": "2024-06-27T15:44:36.961823Z", + "iopub.status.idle": "2024-06-27T15:44:36.965274Z", + "shell.execute_reply": "2024-06-27T15:44:36.964816Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:07.111219Z", - "iopub.status.busy": "2024-06-25T23:18:07.110877Z", - "iopub.status.idle": "2024-06-25T23:18:10.366450Z", - "shell.execute_reply": "2024-06-25T23:18:10.365818Z" + "iopub.execute_input": "2024-06-27T15:44:36.967365Z", + "iopub.status.busy": "2024-06-27T15:44:36.967036Z", + "iopub.status.idle": "2024-06-27T15:44:40.212356Z", + "shell.execute_reply": "2024-06-27T15:44:40.211751Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.369846Z", - "iopub.status.busy": "2024-06-25T23:18:10.369009Z", - "iopub.status.idle": "2024-06-25T23:18:10.408435Z", - "shell.execute_reply": "2024-06-25T23:18:10.407723Z" + "iopub.execute_input": "2024-06-27T15:44:40.215518Z", + "iopub.status.busy": "2024-06-27T15:44:40.214659Z", + "iopub.status.idle": "2024-06-27T15:44:40.250385Z", + "shell.execute_reply": "2024-06-27T15:44:40.249775Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.411187Z", - "iopub.status.busy": "2024-06-25T23:18:10.410945Z", - "iopub.status.idle": "2024-06-25T23:18:10.447524Z", - "shell.execute_reply": "2024-06-25T23:18:10.446786Z" + "iopub.execute_input": "2024-06-27T15:44:40.253059Z", + "iopub.status.busy": "2024-06-27T15:44:40.252740Z", + "iopub.status.idle": "2024-06-27T15:44:40.289877Z", + "shell.execute_reply": "2024-06-27T15:44:40.289247Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.450344Z", - "iopub.status.busy": "2024-06-25T23:18:10.450101Z", - "iopub.status.idle": "2024-06-25T23:18:10.453289Z", - "shell.execute_reply": "2024-06-25T23:18:10.452762Z" + "iopub.execute_input": "2024-06-27T15:44:40.292569Z", + "iopub.status.busy": "2024-06-27T15:44:40.292302Z", + "iopub.status.idle": "2024-06-27T15:44:40.295458Z", + "shell.execute_reply": "2024-06-27T15:44:40.294917Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.455428Z", - "iopub.status.busy": "2024-06-25T23:18:10.455099Z", - "iopub.status.idle": "2024-06-25T23:18:10.457834Z", - "shell.execute_reply": "2024-06-25T23:18:10.457357Z" + "iopub.execute_input": "2024-06-27T15:44:40.297481Z", + "iopub.status.busy": "2024-06-27T15:44:40.297218Z", + "iopub.status.idle": "2024-06-27T15:44:40.300030Z", + "shell.execute_reply": "2024-06-27T15:44:40.299562Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.459894Z", - "iopub.status.busy": "2024-06-25T23:18:10.459627Z", - "iopub.status.idle": "2024-06-25T23:18:10.483748Z", - "shell.execute_reply": "2024-06-25T23:18:10.483202Z" + "iopub.execute_input": "2024-06-27T15:44:40.302271Z", + "iopub.status.busy": "2024-06-27T15:44:40.301972Z", + "iopub.status.idle": "2024-06-27T15:44:40.325394Z", + "shell.execute_reply": "2024-06-27T15:44:40.324857Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "558d7887a3b248ccbc78e41ae8f6a2ad", + "model_id": "b8fa97ab69bf43228c78ed3ed04acd70", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "633ecf7c235f443883ad78f8a1d748cd", + "model_id": "f5f6626fc81f4263b798ca0b721371b5", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.488896Z", - 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@@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.548998Z", - "iopub.status.busy": "2024-06-25T23:18:10.548767Z", - "iopub.status.idle": "2024-06-25T23:18:10.582483Z", - "shell.execute_reply": "2024-06-25T23:18:10.581909Z" + "iopub.execute_input": "2024-06-27T15:44:40.389816Z", + "iopub.status.busy": "2024-06-27T15:44:40.389562Z", + "iopub.status.idle": "2024-06-27T15:44:40.424566Z", + "shell.execute_reply": "2024-06-27T15:44:40.423985Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.585385Z", - "iopub.status.busy": "2024-06-25T23:18:10.584868Z", - "iopub.status.idle": "2024-06-25T23:18:10.710386Z", - "shell.execute_reply": "2024-06-25T23:18:10.709794Z" + "iopub.execute_input": "2024-06-27T15:44:40.427197Z", + "iopub.status.busy": "2024-06-27T15:44:40.426905Z", + "iopub.status.idle": "2024-06-27T15:44:40.551680Z", + 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"iopub.status.idle": "2024-06-27T15:44:43.651658Z", + "shell.execute_reply": "2024-06-27T15:44:43.651092Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.912512Z", - "iopub.status.busy": "2024-06-25T23:18:13.912056Z", - "iopub.status.idle": "2024-06-25T23:18:13.955394Z", - "shell.execute_reply": "2024-06-25T23:18:13.954784Z" + "iopub.execute_input": "2024-06-27T15:44:43.653801Z", + "iopub.status.busy": "2024-06-27T15:44:43.653552Z", + "iopub.status.idle": "2024-06-27T15:44:43.694296Z", + "shell.execute_reply": "2024-06-27T15:44:43.693693Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "411cb3b4", + "id": "40b044b4", "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": "c0fc51ac", + "id": "ebb7621c", "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": "31d0af7b", + "id": "c979fb67", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "ddefd054", + "id": "61925736", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.957642Z", - "iopub.status.busy": "2024-06-25T23:18:13.957445Z", - "iopub.status.idle": "2024-06-25T23:18:13.965853Z", - "shell.execute_reply": "2024-06-25T23:18:13.965258Z" + "iopub.execute_input": "2024-06-27T15:44:43.696599Z", + "iopub.status.busy": "2024-06-27T15:44:43.696241Z", + "iopub.status.idle": "2024-06-27T15:44:43.703974Z", + "shell.execute_reply": "2024-06-27T15:44:43.703400Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "96a1ec22", + "id": "7463b4fa", "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": "d478ad17", + "id": "6670b933", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.968394Z", - "iopub.status.busy": "2024-06-25T23:18:13.968108Z", - "iopub.status.idle": "2024-06-25T23:18:13.989832Z", - "shell.execute_reply": "2024-06-25T23:18:13.989245Z" + "iopub.execute_input": "2024-06-27T15:44:43.706022Z", + "iopub.status.busy": "2024-06-27T15:44:43.705663Z", + "iopub.status.idle": "2024-06-27T15:44:43.724461Z", + "shell.execute_reply": "2024-06-27T15:44:43.723993Z" } }, "outputs": [ @@ -1490,14 +1490,6 @@ "\n", "Audit complete. 3 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_7878/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" - ] } ], "source": [ @@ -1529,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "ff936017", + "id": "ad393460", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.992065Z", - "iopub.status.busy": "2024-06-25T23:18:13.991705Z", - "iopub.status.idle": "2024-06-25T23:18:13.994946Z", - "shell.execute_reply": "2024-06-25T23:18:13.994403Z" + "iopub.execute_input": "2024-06-27T15:44:43.726475Z", + "iopub.status.busy": "2024-06-27T15:44:43.726145Z", + "iopub.status.idle": "2024-06-27T15:44:43.729522Z", + "shell.execute_reply": "2024-06-27T15:44:43.729061Z" } }, "outputs": [ @@ -1630,43 +1622,46 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01de1302b1ac41a68c4d605171741bc4": { + "00d405cd874e4d93b1779853b89cd0f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - 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"iopub.execute_input": "2024-06-25T23:18:17.256748Z", - "iopub.status.busy": "2024-06-25T23:18:17.256569Z", - "iopub.status.idle": "2024-06-25T23:18:18.418999Z", - "shell.execute_reply": "2024-06-25T23:18:18.418397Z" + "iopub.execute_input": "2024-06-27T15:44:48.147366Z", + "iopub.status.busy": "2024-06-27T15:44:48.147196Z", + "iopub.status.idle": "2024-06-27T15:44:49.321368Z", + "shell.execute_reply": "2024-06-27T15:44:49.320829Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:18.421550Z", - "iopub.status.busy": "2024-06-25T23:18:18.421304Z", - "iopub.status.idle": "2024-06-25T23:18:18.599266Z", - "shell.execute_reply": "2024-06-25T23:18:18.598641Z" + "iopub.execute_input": "2024-06-27T15:44:49.324091Z", + "iopub.status.busy": "2024-06-27T15:44:49.323521Z", + "iopub.status.idle": "2024-06-27T15:44:49.506194Z", + "shell.execute_reply": "2024-06-27T15:44:49.505523Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:18.601825Z", - "iopub.status.busy": "2024-06-25T23:18:18.601625Z", - "iopub.status.idle": "2024-06-25T23:18:18.613136Z", - "shell.execute_reply": "2024-06-25T23:18:18.612703Z" + "iopub.execute_input": "2024-06-27T15:44:49.508864Z", + "iopub.status.busy": "2024-06-27T15:44:49.508610Z", + "iopub.status.idle": "2024-06-27T15:44:49.521078Z", + "shell.execute_reply": "2024-06-27T15:44:49.520490Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:18.615067Z", - "iopub.status.busy": "2024-06-25T23:18:18.614888Z", - "iopub.status.idle": "2024-06-25T23:18:18.849624Z", - "shell.execute_reply": "2024-06-25T23:18:18.849023Z" + "iopub.execute_input": "2024-06-27T15:44:49.523386Z", + "iopub.status.busy": "2024-06-27T15:44:49.522992Z", + "iopub.status.idle": "2024-06-27T15:44:49.761259Z", + "shell.execute_reply": "2024-06-27T15:44:49.760654Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:18.851953Z", - "iopub.status.busy": "2024-06-25T23:18:18.851541Z", - "iopub.status.idle": "2024-06-25T23:18:18.877468Z", - "shell.execute_reply": "2024-06-25T23:18:18.877017Z" + "iopub.execute_input": "2024-06-27T15:44:49.763628Z", + "iopub.status.busy": "2024-06-27T15:44:49.763381Z", + "iopub.status.idle": "2024-06-27T15:44:49.789884Z", + "shell.execute_reply": "2024-06-27T15:44:49.789414Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:18.879561Z", - "iopub.status.busy": "2024-06-25T23:18:18.879211Z", - "iopub.status.idle": "2024-06-25T23:18:20.899666Z", - "shell.execute_reply": "2024-06-25T23:18:20.898976Z" + "iopub.execute_input": "2024-06-27T15:44:49.791905Z", + "iopub.status.busy": "2024-06-27T15:44:49.791729Z", + "iopub.status.idle": "2024-06-27T15:44:51.886248Z", + "shell.execute_reply": "2024-06-27T15:44:51.885611Z" } }, "outputs": [ @@ -455,14 +455,6 @@ "\n", "Audit complete. 78 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -482,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:20.901960Z", - "iopub.status.busy": "2024-06-25T23:18:20.901648Z", - "iopub.status.idle": "2024-06-25T23:18:20.919398Z", - "shell.execute_reply": "2024-06-25T23:18:20.918920Z" + "iopub.execute_input": "2024-06-27T15:44:51.888650Z", + "iopub.status.busy": "2024-06-27T15:44:51.888335Z", + "iopub.status.idle": "2024-06-27T15:44:51.906193Z", + "shell.execute_reply": "2024-06-27T15:44:51.905621Z" }, "scrolled": true }, @@ -615,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:20.921440Z", - "iopub.status.busy": "2024-06-25T23:18:20.921079Z", - "iopub.status.idle": "2024-06-25T23:18:22.361092Z", - "shell.execute_reply": "2024-06-25T23:18:22.360462Z" + "iopub.execute_input": "2024-06-27T15:44:51.908417Z", + "iopub.status.busy": "2024-06-27T15:44:51.908017Z", + "iopub.status.idle": "2024-06-27T15:44:53.377092Z", + "shell.execute_reply": "2024-06-27T15:44:53.376553Z" }, "id": "AaHC5MRKjruT" }, @@ -737,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.363669Z", - "iopub.status.busy": "2024-06-25T23:18:22.363060Z", - "iopub.status.idle": "2024-06-25T23:18:22.376718Z", - "shell.execute_reply": "2024-06-25T23:18:22.376170Z" + "iopub.execute_input": "2024-06-27T15:44:53.380328Z", + "iopub.status.busy": "2024-06-27T15:44:53.379266Z", + "iopub.status.idle": "2024-06-27T15:44:53.392501Z", + "shell.execute_reply": "2024-06-27T15:44:53.392074Z" }, "id": "Wy27rvyhjruU" }, @@ -789,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.378792Z", - "iopub.status.busy": "2024-06-25T23:18:22.378469Z", - "iopub.status.idle": "2024-06-25T23:18:22.452119Z", - "shell.execute_reply": "2024-06-25T23:18:22.451527Z" + "iopub.execute_input": "2024-06-27T15:44:53.394717Z", + "iopub.status.busy": "2024-06-27T15:44:53.394312Z", + "iopub.status.idle": "2024-06-27T15:44:53.472241Z", + "shell.execute_reply": "2024-06-27T15:44:53.471703Z" }, "id": "Db8YHnyVjruU" }, @@ -899,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.454608Z", - "iopub.status.busy": "2024-06-25T23:18:22.454246Z", - "iopub.status.idle": "2024-06-25T23:18:22.662363Z", - "shell.execute_reply": "2024-06-25T23:18:22.661824Z" + "iopub.execute_input": "2024-06-27T15:44:53.474636Z", + "iopub.status.busy": "2024-06-27T15:44:53.474208Z", + "iopub.status.idle": "2024-06-27T15:44:53.682279Z", + "shell.execute_reply": "2024-06-27T15:44:53.681691Z" }, "id": "iJqAHuS2jruV" }, @@ -939,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.664593Z", - "iopub.status.busy": "2024-06-25T23:18:22.664244Z", - "iopub.status.idle": "2024-06-25T23:18:22.681198Z", - "shell.execute_reply": "2024-06-25T23:18:22.680722Z" + "iopub.execute_input": "2024-06-27T15:44:53.684408Z", + "iopub.status.busy": "2024-06-27T15:44:53.684211Z", + "iopub.status.idle": "2024-06-27T15:44:53.701275Z", + "shell.execute_reply": "2024-06-27T15:44:53.700807Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1408,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - 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"iopub.execute_input": "2024-06-25T23:18:22.778586Z", - "iopub.status.busy": "2024-06-25T23:18:22.778226Z", - "iopub.status.idle": "2024-06-25T23:18:22.894081Z", - "shell.execute_reply": "2024-06-25T23:18:22.893472Z" + "iopub.execute_input": "2024-06-27T15:44:53.801876Z", + "iopub.status.busy": "2024-06-27T15:44:53.801534Z", + "iopub.status.idle": "2024-06-27T15:44:53.928209Z", + "shell.execute_reply": "2024-06-27T15:44:53.927576Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1705,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.896294Z", - "iopub.status.busy": "2024-06-25T23:18:22.896067Z", - "iopub.status.idle": "2024-06-25T23:18:22.899817Z", - "shell.execute_reply": "2024-06-25T23:18:22.899290Z" + "iopub.execute_input": "2024-06-27T15:44:53.930682Z", + "iopub.status.busy": "2024-06-27T15:44:53.930336Z", + "iopub.status.idle": "2024-06-27T15:44:53.934290Z", + "shell.execute_reply": "2024-06-27T15:44:53.933731Z" }, "id": "0rXP3ZPWjruW" }, @@ -1746,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.901918Z", - "iopub.status.busy": "2024-06-25T23:18:22.901602Z", - "iopub.status.idle": "2024-06-25T23:18:22.905366Z", - "shell.execute_reply": "2024-06-25T23:18:22.904793Z" + "iopub.execute_input": "2024-06-27T15:44:53.936433Z", + "iopub.status.busy": "2024-06-27T15:44:53.936050Z", + "iopub.status.idle": "2024-06-27T15:44:53.939711Z", + "shell.execute_reply": "2024-06-27T15:44:53.939186Z" }, "id": "-iRPe8KXjruW" }, @@ -1804,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.907395Z", - "iopub.status.busy": "2024-06-25T23:18:22.907096Z", - "iopub.status.idle": "2024-06-25T23:18:22.943768Z", - "shell.execute_reply": "2024-06-25T23:18:22.943295Z" + "iopub.execute_input": "2024-06-27T15:44:53.941803Z", + "iopub.status.busy": "2024-06-27T15:44:53.941399Z", + "iopub.status.idle": "2024-06-27T15:44:53.978056Z", + "shell.execute_reply": "2024-06-27T15:44:53.977512Z" }, "id": "ZpipUliyjruW" }, @@ -1858,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.945705Z", - "iopub.status.busy": "2024-06-25T23:18:22.945390Z", - "iopub.status.idle": "2024-06-25T23:18:22.987000Z", - "shell.execute_reply": "2024-06-25T23:18:22.986556Z" + "iopub.execute_input": "2024-06-27T15:44:53.980221Z", + "iopub.status.busy": "2024-06-27T15:44:53.979917Z", + "iopub.status.idle": "2024-06-27T15:44:54.021764Z", + "shell.execute_reply": "2024-06-27T15:44:54.021192Z" }, "id": "SLq-3q4xjruX" }, @@ -1930,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:22.989099Z", - "iopub.status.busy": "2024-06-25T23:18:22.988778Z", - "iopub.status.idle": "2024-06-25T23:18:23.079367Z", - "shell.execute_reply": "2024-06-25T23:18:23.078808Z" + "iopub.execute_input": "2024-06-27T15:44:54.023732Z", + "iopub.status.busy": "2024-06-27T15:44:54.023409Z", + "iopub.status.idle": "2024-06-27T15:44:54.117648Z", + "shell.execute_reply": "2024-06-27T15:44:54.116975Z" }, "id": "g5LHhhuqFbXK" }, @@ -1965,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.081992Z", - "iopub.status.busy": "2024-06-25T23:18:23.081632Z", - "iopub.status.idle": "2024-06-25T23:18:23.163660Z", - "shell.execute_reply": "2024-06-25T23:18:23.163108Z" + "iopub.execute_input": "2024-06-27T15:44:54.120433Z", + "iopub.status.busy": "2024-06-27T15:44:54.120068Z", + "iopub.status.idle": "2024-06-27T15:44:54.202925Z", + "shell.execute_reply": "2024-06-27T15:44:54.202329Z" }, "id": "p7w8F8ezBcet" }, @@ -2025,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.166170Z", - "iopub.status.busy": "2024-06-25T23:18:23.165696Z", - "iopub.status.idle": "2024-06-25T23:18:23.373652Z", - "shell.execute_reply": "2024-06-25T23:18:23.373076Z" + "iopub.execute_input": "2024-06-27T15:44:54.205153Z", + "iopub.status.busy": "2024-06-27T15:44:54.204919Z", + "iopub.status.idle": "2024-06-27T15:44:54.415014Z", + "shell.execute_reply": "2024-06-27T15:44:54.414492Z" }, "id": "WETRL74tE_sU" }, @@ -2063,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.375920Z", - "iopub.status.busy": "2024-06-25T23:18:23.375563Z", - "iopub.status.idle": "2024-06-25T23:18:23.542133Z", - "shell.execute_reply": "2024-06-25T23:18:23.541601Z" + "iopub.execute_input": "2024-06-27T15:44:54.417178Z", + "iopub.status.busy": "2024-06-27T15:44:54.416852Z", + "iopub.status.idle": "2024-06-27T15:44:54.599512Z", + "shell.execute_reply": "2024-06-27T15:44:54.598904Z" }, "id": "kCfdx2gOLmXS" }, @@ -2228,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.544310Z", - "iopub.status.busy": "2024-06-25T23:18:23.544080Z", - "iopub.status.idle": "2024-06-25T23:18:23.550244Z", - "shell.execute_reply": "2024-06-25T23:18:23.549696Z" + "iopub.execute_input": "2024-06-27T15:44:54.601799Z", + "iopub.status.busy": "2024-06-27T15:44:54.601584Z", + "iopub.status.idle": "2024-06-27T15:44:54.607587Z", + "shell.execute_reply": "2024-06-27T15:44:54.607130Z" }, "id": "-uogYRWFYnuu" }, @@ -2285,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.552552Z", - "iopub.status.busy": "2024-06-25T23:18:23.552102Z", - "iopub.status.idle": "2024-06-25T23:18:23.765551Z", - "shell.execute_reply": "2024-06-25T23:18:23.764971Z" + "iopub.execute_input": "2024-06-27T15:44:54.609450Z", + "iopub.status.busy": "2024-06-27T15:44:54.609279Z", + "iopub.status.idle": "2024-06-27T15:44:54.823297Z", + "shell.execute_reply": "2024-06-27T15:44:54.822719Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2335,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:23.767794Z", - "iopub.status.busy": "2024-06-25T23:18:23.767426Z", - "iopub.status.idle": "2024-06-25T23:18:24.838654Z", - "shell.execute_reply": "2024-06-25T23:18:24.838036Z" + "iopub.execute_input": "2024-06-27T15:44:54.825510Z", + "iopub.status.busy": "2024-06-27T15:44:54.825318Z", + "iopub.status.idle": "2024-06-27T15:44:55.887198Z", + "shell.execute_reply": "2024-06-27T15:44:55.886648Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index b4c4a33f9..e955474a9 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": "2024-06-25T23:18:28.410867Z", - "iopub.status.busy": "2024-06-25T23:18:28.410704Z", - "iopub.status.idle": "2024-06-25T23:18:29.523341Z", - "shell.execute_reply": "2024-06-25T23:18:29.522804Z" + "iopub.execute_input": "2024-06-27T15:44:59.468627Z", + "iopub.status.busy": "2024-06-27T15:44:59.468466Z", + "iopub.status.idle": "2024-06-27T15:45:00.591032Z", + "shell.execute_reply": "2024-06-27T15:45:00.590468Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:29.525967Z", - "iopub.status.busy": "2024-06-25T23:18:29.525510Z", - "iopub.status.idle": "2024-06-25T23:18:29.528645Z", - "shell.execute_reply": "2024-06-25T23:18:29.528187Z" + "iopub.execute_input": "2024-06-27T15:45:00.593760Z", + "iopub.status.busy": "2024-06-27T15:45:00.593202Z", + "iopub.status.idle": "2024-06-27T15:45:00.596242Z", + "shell.execute_reply": "2024-06-27T15:45:00.595817Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.530912Z", - "iopub.status.busy": "2024-06-25T23:18:29.530502Z", - "iopub.status.idle": "2024-06-25T23:18:29.538778Z", - "shell.execute_reply": "2024-06-25T23:18:29.538338Z" + "iopub.execute_input": "2024-06-27T15:45:00.598385Z", + "iopub.status.busy": "2024-06-27T15:45:00.598077Z", + "iopub.status.idle": "2024-06-27T15:45:00.605729Z", + "shell.execute_reply": "2024-06-27T15:45:00.605174Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.540895Z", - "iopub.status.busy": "2024-06-25T23:18:29.540489Z", - "iopub.status.idle": "2024-06-25T23:18:29.587259Z", - "shell.execute_reply": "2024-06-25T23:18:29.586733Z" + "iopub.execute_input": "2024-06-27T15:45:00.607687Z", + "iopub.status.busy": "2024-06-27T15:45:00.607416Z", + "iopub.status.idle": "2024-06-27T15:45:00.653808Z", + "shell.execute_reply": "2024-06-27T15:45:00.653208Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.589466Z", - "iopub.status.busy": "2024-06-25T23:18:29.589277Z", - "iopub.status.idle": "2024-06-25T23:18:29.606524Z", - "shell.execute_reply": "2024-06-25T23:18:29.606095Z" + "iopub.execute_input": "2024-06-27T15:45:00.656075Z", + "iopub.status.busy": "2024-06-27T15:45:00.655764Z", + "iopub.status.idle": "2024-06-27T15:45:00.672779Z", + "shell.execute_reply": "2024-06-27T15:45:00.672331Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.608443Z", - "iopub.status.busy": "2024-06-25T23:18:29.608267Z", - "iopub.status.idle": "2024-06-25T23:18:29.612218Z", - "shell.execute_reply": "2024-06-25T23:18:29.611771Z" + "iopub.execute_input": "2024-06-27T15:45:00.674888Z", + "iopub.status.busy": "2024-06-27T15:45:00.674484Z", + "iopub.status.idle": "2024-06-27T15:45:00.678316Z", + "shell.execute_reply": "2024-06-27T15:45:00.677783Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.614226Z", - "iopub.status.busy": "2024-06-25T23:18:29.614054Z", - "iopub.status.idle": "2024-06-25T23:18:29.631367Z", - "shell.execute_reply": "2024-06-25T23:18:29.630956Z" + "iopub.execute_input": "2024-06-27T15:45:00.680411Z", + "iopub.status.busy": "2024-06-27T15:45:00.680099Z", + "iopub.status.idle": "2024-06-27T15:45:00.693774Z", + "shell.execute_reply": "2024-06-27T15:45:00.693335Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.633306Z", - "iopub.status.busy": "2024-06-25T23:18:29.632964Z", - "iopub.status.idle": "2024-06-25T23:18:29.658440Z", - "shell.execute_reply": "2024-06-25T23:18:29.658012Z" + "iopub.execute_input": "2024-06-27T15:45:00.695788Z", + "iopub.status.busy": "2024-06-27T15:45:00.695460Z", + "iopub.status.idle": "2024-06-27T15:45:00.721230Z", + "shell.execute_reply": "2024-06-27T15:45:00.720808Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:29.660435Z", - "iopub.status.busy": "2024-06-25T23:18:29.660092Z", - "iopub.status.idle": "2024-06-25T23:18:31.561212Z", - "shell.execute_reply": "2024-06-25T23:18:31.560640Z" + "iopub.execute_input": "2024-06-27T15:45:00.723435Z", + "iopub.status.busy": "2024-06-27T15:45:00.723118Z", + "iopub.status.idle": "2024-06-27T15:45:02.652946Z", + "shell.execute_reply": "2024-06-27T15:45:02.652251Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.563955Z", - "iopub.status.busy": "2024-06-25T23:18:31.563327Z", - "iopub.status.idle": "2024-06-25T23:18:31.570324Z", - "shell.execute_reply": "2024-06-25T23:18:31.569880Z" + "iopub.execute_input": "2024-06-27T15:45:02.655941Z", + "iopub.status.busy": "2024-06-27T15:45:02.655387Z", + "iopub.status.idle": "2024-06-27T15:45:02.662756Z", + "shell.execute_reply": "2024-06-27T15:45:02.662202Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.572276Z", - "iopub.status.busy": "2024-06-25T23:18:31.571950Z", - "iopub.status.idle": "2024-06-25T23:18:31.584255Z", - "shell.execute_reply": "2024-06-25T23:18:31.583817Z" + "iopub.execute_input": "2024-06-27T15:45:02.665017Z", + "iopub.status.busy": "2024-06-27T15:45:02.664581Z", + "iopub.status.idle": "2024-06-27T15:45:02.677290Z", + "shell.execute_reply": "2024-06-27T15:45:02.676854Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.586203Z", - "iopub.status.busy": "2024-06-25T23:18:31.585878Z", - "iopub.status.idle": "2024-06-25T23:18:31.591999Z", - "shell.execute_reply": "2024-06-25T23:18:31.591576Z" + "iopub.execute_input": "2024-06-27T15:45:02.679365Z", + "iopub.status.busy": "2024-06-27T15:45:02.679031Z", + "iopub.status.idle": "2024-06-27T15:45:02.685486Z", + "shell.execute_reply": "2024-06-27T15:45:02.684929Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.594128Z", - "iopub.status.busy": "2024-06-25T23:18:31.593809Z", - "iopub.status.idle": "2024-06-25T23:18:31.596328Z", - "shell.execute_reply": "2024-06-25T23:18:31.595895Z" + "iopub.execute_input": "2024-06-27T15:45:02.687634Z", + "iopub.status.busy": "2024-06-27T15:45:02.687246Z", + "iopub.status.idle": "2024-06-27T15:45:02.690043Z", + "shell.execute_reply": "2024-06-27T15:45:02.689488Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.598281Z", - "iopub.status.busy": "2024-06-25T23:18:31.597974Z", - "iopub.status.idle": "2024-06-25T23:18:31.601541Z", - "shell.execute_reply": "2024-06-25T23:18:31.600983Z" + "iopub.execute_input": "2024-06-27T15:45:02.692044Z", + "iopub.status.busy": "2024-06-27T15:45:02.691745Z", + "iopub.status.idle": "2024-06-27T15:45:02.695116Z", + "shell.execute_reply": "2024-06-27T15:45:02.694642Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.603595Z", - "iopub.status.busy": "2024-06-25T23:18:31.603264Z", - "iopub.status.idle": "2024-06-25T23:18:31.605889Z", - "shell.execute_reply": "2024-06-25T23:18:31.605456Z" + "iopub.execute_input": "2024-06-27T15:45:02.697161Z", + "iopub.status.busy": "2024-06-27T15:45:02.696845Z", + "iopub.status.idle": "2024-06-27T15:45:02.699375Z", + "shell.execute_reply": "2024-06-27T15:45:02.698946Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.607856Z", - "iopub.status.busy": "2024-06-25T23:18:31.607558Z", - "iopub.status.idle": "2024-06-25T23:18:31.611501Z", - "shell.execute_reply": "2024-06-25T23:18:31.611048Z" + "iopub.execute_input": "2024-06-27T15:45:02.701167Z", + "iopub.status.busy": "2024-06-27T15:45:02.700997Z", + "iopub.status.idle": "2024-06-27T15:45:02.705189Z", + "shell.execute_reply": "2024-06-27T15:45:02.704724Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.613408Z", - "iopub.status.busy": "2024-06-25T23:18:31.613238Z", - "iopub.status.idle": "2024-06-25T23:18:31.641822Z", - "shell.execute_reply": "2024-06-25T23:18:31.641266Z" + "iopub.execute_input": "2024-06-27T15:45:02.707181Z", + "iopub.status.busy": "2024-06-27T15:45:02.707011Z", + "iopub.status.idle": "2024-06-27T15:45:02.735513Z", + "shell.execute_reply": "2024-06-27T15:45:02.735059Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:31.644000Z", - "iopub.status.busy": "2024-06-25T23:18:31.643674Z", - "iopub.status.idle": "2024-06-25T23:18:31.648272Z", - "shell.execute_reply": "2024-06-25T23:18:31.647708Z" + "iopub.execute_input": "2024-06-27T15:45:02.737575Z", + "iopub.status.busy": "2024-06-27T15:45:02.737383Z", + "iopub.status.idle": "2024-06-27T15:45:02.742352Z", + "shell.execute_reply": "2024-06-27T15:45:02.741808Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 9593cdb90..2ef3b5160 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": "2024-06-25T23:18:34.388005Z", - "iopub.status.busy": "2024-06-25T23:18:34.387509Z", - "iopub.status.idle": "2024-06-25T23:18:35.555688Z", - "shell.execute_reply": "2024-06-25T23:18:35.555141Z" + "iopub.execute_input": "2024-06-27T15:45:05.699973Z", + "iopub.status.busy": "2024-06-27T15:45:05.699801Z", + "iopub.status.idle": "2024-06-27T15:45:06.857177Z", + "shell.execute_reply": "2024-06-27T15:45:06.856603Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:35.558285Z", - "iopub.status.busy": "2024-06-25T23:18:35.557842Z", - "iopub.status.idle": "2024-06-25T23:18:35.751397Z", - "shell.execute_reply": "2024-06-25T23:18:35.750860Z" + "iopub.execute_input": "2024-06-27T15:45:06.859790Z", + "iopub.status.busy": "2024-06-27T15:45:06.859298Z", + "iopub.status.idle": "2024-06-27T15:45:07.049527Z", + "shell.execute_reply": "2024-06-27T15:45:07.048975Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:35.754209Z", - "iopub.status.busy": "2024-06-25T23:18:35.753733Z", - "iopub.status.idle": "2024-06-25T23:18:35.767096Z", - "shell.execute_reply": "2024-06-25T23:18:35.766635Z" + "iopub.execute_input": "2024-06-27T15:45:07.052251Z", + "iopub.status.busy": "2024-06-27T15:45:07.051728Z", + "iopub.status.idle": "2024-06-27T15:45:07.064966Z", + "shell.execute_reply": "2024-06-27T15:45:07.064409Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:35.769292Z", - "iopub.status.busy": "2024-06-25T23:18:35.768939Z", - "iopub.status.idle": "2024-06-25T23:18:38.460798Z", - "shell.execute_reply": "2024-06-25T23:18:38.460293Z" + "iopub.execute_input": "2024-06-27T15:45:07.067098Z", + "iopub.status.busy": "2024-06-27T15:45:07.066806Z", + "iopub.status.idle": "2024-06-27T15:45:09.757625Z", + "shell.execute_reply": "2024-06-27T15:45:09.757094Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:38.463138Z", - "iopub.status.busy": "2024-06-25T23:18:38.462688Z", - "iopub.status.idle": "2024-06-25T23:18:39.817391Z", - "shell.execute_reply": "2024-06-25T23:18:39.816843Z" + "iopub.execute_input": "2024-06-27T15:45:09.759992Z", + "iopub.status.busy": "2024-06-27T15:45:09.759608Z", + "iopub.status.idle": "2024-06-27T15:45:11.112703Z", + "shell.execute_reply": "2024-06-27T15:45:11.112185Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:39.819916Z", - "iopub.status.busy": "2024-06-25T23:18:39.819475Z", - "iopub.status.idle": "2024-06-25T23:18:39.823477Z", - "shell.execute_reply": "2024-06-25T23:18:39.822931Z" + "iopub.execute_input": "2024-06-27T15:45:11.115163Z", + "iopub.status.busy": "2024-06-27T15:45:11.114818Z", + "iopub.status.idle": "2024-06-27T15:45:11.118894Z", + "shell.execute_reply": "2024-06-27T15:45:11.118425Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:39.825523Z", - "iopub.status.busy": "2024-06-25T23:18:39.825189Z", - "iopub.status.idle": "2024-06-25T23:18:41.816360Z", - "shell.execute_reply": "2024-06-25T23:18:41.815747Z" + "iopub.execute_input": "2024-06-27T15:45:11.120805Z", + "iopub.status.busy": "2024-06-27T15:45:11.120500Z", + "iopub.status.idle": "2024-06-27T15:45:13.138189Z", + "shell.execute_reply": "2024-06-27T15:45:13.137525Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:41.818930Z", - "iopub.status.busy": "2024-06-25T23:18:41.818429Z", - "iopub.status.idle": "2024-06-25T23:18:41.826321Z", - "shell.execute_reply": "2024-06-25T23:18:41.825851Z" + "iopub.execute_input": "2024-06-27T15:45:13.140803Z", + "iopub.status.busy": "2024-06-27T15:45:13.140321Z", + "iopub.status.idle": "2024-06-27T15:45:13.147889Z", + "shell.execute_reply": "2024-06-27T15:45:13.147408Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:41.828406Z", - "iopub.status.busy": "2024-06-25T23:18:41.828097Z", - "iopub.status.idle": "2024-06-25T23:18:44.431218Z", - "shell.execute_reply": "2024-06-25T23:18:44.430687Z" + "iopub.execute_input": "2024-06-27T15:45:13.149938Z", + "iopub.status.busy": "2024-06-27T15:45:13.149581Z", + "iopub.status.idle": "2024-06-27T15:45:15.762528Z", + "shell.execute_reply": "2024-06-27T15:45:15.761940Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:44.433395Z", - "iopub.status.busy": "2024-06-25T23:18:44.433032Z", - "iopub.status.idle": "2024-06-25T23:18:44.436462Z", - "shell.execute_reply": "2024-06-25T23:18:44.435934Z" + "iopub.execute_input": "2024-06-27T15:45:15.764619Z", + "iopub.status.busy": "2024-06-27T15:45:15.764424Z", + "iopub.status.idle": "2024-06-27T15:45:15.768184Z", + "shell.execute_reply": "2024-06-27T15:45:15.767636Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:44.438430Z", - "iopub.status.busy": "2024-06-25T23:18:44.438123Z", - "iopub.status.idle": "2024-06-25T23:18:44.441587Z", - "shell.execute_reply": "2024-06-25T23:18:44.441125Z" + "iopub.execute_input": "2024-06-27T15:45:15.770204Z", + "iopub.status.busy": "2024-06-27T15:45:15.769898Z", + "iopub.status.idle": "2024-06-27T15:45:15.773509Z", + "shell.execute_reply": "2024-06-27T15:45:15.772957Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:44.443586Z", - "iopub.status.busy": "2024-06-25T23:18:44.443248Z", - "iopub.status.idle": "2024-06-25T23:18:44.446272Z", - "shell.execute_reply": "2024-06-25T23:18:44.445845Z" + "iopub.execute_input": "2024-06-27T15:45:15.775576Z", + "iopub.status.busy": "2024-06-27T15:45:15.775249Z", + "iopub.status.idle": "2024-06-27T15:45:15.778400Z", + "shell.execute_reply": "2024-06-27T15:45:15.777870Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index ceb7220d6..ff2404cec 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": "2024-06-25T23:18:46.821534Z", - "iopub.status.busy": "2024-06-25T23:18:46.821356Z", - "iopub.status.idle": "2024-06-25T23:18:47.991566Z", - "shell.execute_reply": "2024-06-25T23:18:47.991020Z" + "iopub.execute_input": "2024-06-27T15:45:18.463792Z", + "iopub.status.busy": "2024-06-27T15:45:18.463378Z", + "iopub.status.idle": "2024-06-27T15:45:19.617248Z", + "shell.execute_reply": "2024-06-27T15:45:19.616753Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:47.994044Z", - "iopub.status.busy": "2024-06-25T23:18:47.993746Z", - "iopub.status.idle": "2024-06-25T23:18:49.077383Z", - "shell.execute_reply": "2024-06-25T23:18:49.076740Z" + "iopub.execute_input": "2024-06-27T15:45:19.619702Z", + "iopub.status.busy": "2024-06-27T15:45:19.619248Z", + "iopub.status.idle": "2024-06-27T15:45:22.409416Z", + "shell.execute_reply": "2024-06-27T15:45:22.408754Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.079931Z", - "iopub.status.busy": "2024-06-25T23:18:49.079715Z", - "iopub.status.idle": "2024-06-25T23:18:49.083128Z", - "shell.execute_reply": "2024-06-25T23:18:49.082576Z" + "iopub.execute_input": "2024-06-27T15:45:22.412130Z", + "iopub.status.busy": "2024-06-27T15:45:22.411640Z", + "iopub.status.idle": "2024-06-27T15:45:22.415047Z", + "shell.execute_reply": "2024-06-27T15:45:22.414491Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.085315Z", - "iopub.status.busy": "2024-06-25T23:18:49.084875Z", - "iopub.status.idle": "2024-06-25T23:18:49.090995Z", - "shell.execute_reply": "2024-06-25T23:18:49.090565Z" + "iopub.execute_input": "2024-06-27T15:45:22.417375Z", + "iopub.status.busy": "2024-06-27T15:45:22.417044Z", + "iopub.status.idle": "2024-06-27T15:45:22.423110Z", + "shell.execute_reply": "2024-06-27T15:45:22.422685Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.092987Z", - "iopub.status.busy": "2024-06-25T23:18:49.092664Z", - "iopub.status.idle": "2024-06-25T23:18:49.578049Z", - "shell.execute_reply": "2024-06-25T23:18:49.577480Z" + "iopub.execute_input": "2024-06-27T15:45:22.425113Z", + "iopub.status.busy": "2024-06-27T15:45:22.424847Z", + "iopub.status.idle": "2024-06-27T15:45:22.910812Z", + "shell.execute_reply": "2024-06-27T15:45:22.910225Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.581141Z", - "iopub.status.busy": "2024-06-25T23:18:49.580804Z", - "iopub.status.idle": "2024-06-25T23:18:49.586187Z", - "shell.execute_reply": "2024-06-25T23:18:49.585728Z" + "iopub.execute_input": "2024-06-27T15:45:22.913649Z", + "iopub.status.busy": "2024-06-27T15:45:22.913317Z", + "iopub.status.idle": "2024-06-27T15:45:22.918911Z", + "shell.execute_reply": "2024-06-27T15:45:22.918469Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.588207Z", - "iopub.status.busy": "2024-06-25T23:18:49.587912Z", - "iopub.status.idle": "2024-06-25T23:18:49.592364Z", - "shell.execute_reply": "2024-06-25T23:18:49.591919Z" + "iopub.execute_input": "2024-06-27T15:45:22.920816Z", + "iopub.status.busy": "2024-06-27T15:45:22.920511Z", + "iopub.status.idle": "2024-06-27T15:45:22.924388Z", + "shell.execute_reply": "2024-06-27T15:45:22.923851Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:49.594287Z", - "iopub.status.busy": "2024-06-25T23:18:49.594112Z", - "iopub.status.idle": "2024-06-25T23:18:50.586165Z", - "shell.execute_reply": "2024-06-25T23:18:50.585507Z" + "iopub.execute_input": "2024-06-27T15:45:22.926540Z", + "iopub.status.busy": "2024-06-27T15:45:22.926241Z", + "iopub.status.idle": "2024-06-27T15:45:23.849633Z", + "shell.execute_reply": "2024-06-27T15:45:23.849043Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:50.588520Z", - "iopub.status.busy": "2024-06-25T23:18:50.588324Z", - "iopub.status.idle": "2024-06-25T23:18:50.808698Z", - "shell.execute_reply": "2024-06-25T23:18:50.808228Z" + "iopub.execute_input": "2024-06-27T15:45:23.851970Z", + "iopub.status.busy": "2024-06-27T15:45:23.851776Z", + "iopub.status.idle": "2024-06-27T15:45:24.083809Z", + "shell.execute_reply": "2024-06-27T15:45:24.083290Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:50.810921Z", - "iopub.status.busy": "2024-06-25T23:18:50.810585Z", - "iopub.status.idle": "2024-06-25T23:18:50.815013Z", - "shell.execute_reply": "2024-06-25T23:18:50.814577Z" + "iopub.execute_input": "2024-06-27T15:45:24.085835Z", + "iopub.status.busy": "2024-06-27T15:45:24.085633Z", + "iopub.status.idle": "2024-06-27T15:45:24.090084Z", + "shell.execute_reply": "2024-06-27T15:45:24.089637Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:50.816841Z", - "iopub.status.busy": "2024-06-25T23:18:50.816666Z", - "iopub.status.idle": "2024-06-25T23:18:51.264514Z", - "shell.execute_reply": "2024-06-25T23:18:51.263937Z" + "iopub.execute_input": "2024-06-27T15:45:24.092087Z", + "iopub.status.busy": "2024-06-27T15:45:24.091759Z", + "iopub.status.idle": "2024-06-27T15:45:24.545615Z", + "shell.execute_reply": "2024-06-27T15:45:24.545021Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:51.267205Z", - "iopub.status.busy": "2024-06-25T23:18:51.266984Z", - "iopub.status.idle": "2024-06-25T23:18:51.597569Z", - "shell.execute_reply": "2024-06-25T23:18:51.596965Z" + "iopub.execute_input": "2024-06-27T15:45:24.548711Z", + "iopub.status.busy": "2024-06-27T15:45:24.548340Z", + "iopub.status.idle": "2024-06-27T15:45:24.880563Z", + "shell.execute_reply": "2024-06-27T15:45:24.880008Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:51.599806Z", - "iopub.status.busy": "2024-06-25T23:18:51.599595Z", - "iopub.status.idle": "2024-06-25T23:18:51.933374Z", - "shell.execute_reply": "2024-06-25T23:18:51.932766Z" + "iopub.execute_input": "2024-06-27T15:45:24.883240Z", + "iopub.status.busy": "2024-06-27T15:45:24.883058Z", + "iopub.status.idle": "2024-06-27T15:45:25.247794Z", + "shell.execute_reply": "2024-06-27T15:45:25.247180Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:51.936579Z", - "iopub.status.busy": "2024-06-25T23:18:51.936094Z", - "iopub.status.idle": "2024-06-25T23:18:52.348181Z", - "shell.execute_reply": "2024-06-25T23:18:52.347588Z" + "iopub.execute_input": "2024-06-27T15:45:25.250664Z", + "iopub.status.busy": "2024-06-27T15:45:25.250300Z", + "iopub.status.idle": "2024-06-27T15:45:25.690141Z", + "shell.execute_reply": "2024-06-27T15:45:25.689553Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:52.352428Z", - "iopub.status.busy": "2024-06-25T23:18:52.351994Z", - "iopub.status.idle": "2024-06-25T23:18:52.773521Z", - "shell.execute_reply": "2024-06-25T23:18:52.772929Z" + "iopub.execute_input": "2024-06-27T15:45:25.694772Z", + "iopub.status.busy": "2024-06-27T15:45:25.694396Z", + "iopub.status.idle": "2024-06-27T15:45:26.140688Z", + "shell.execute_reply": "2024-06-27T15:45:26.140107Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:52.776870Z", - "iopub.status.busy": "2024-06-25T23:18:52.776447Z", - "iopub.status.idle": "2024-06-25T23:18:52.965633Z", - "shell.execute_reply": "2024-06-25T23:18:52.965014Z" + "iopub.execute_input": "2024-06-27T15:45:26.143603Z", + "iopub.status.busy": "2024-06-27T15:45:26.143156Z", + "iopub.status.idle": "2024-06-27T15:45:26.332015Z", + "shell.execute_reply": "2024-06-27T15:45:26.331369Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:52.968518Z", - "iopub.status.busy": "2024-06-25T23:18:52.968035Z", - "iopub.status.idle": "2024-06-25T23:18:53.169696Z", - "shell.execute_reply": "2024-06-25T23:18:53.169139Z" + "iopub.execute_input": "2024-06-27T15:45:26.334333Z", + "iopub.status.busy": "2024-06-27T15:45:26.334028Z", + "iopub.status.idle": "2024-06-27T15:45:26.517564Z", + "shell.execute_reply": "2024-06-27T15:45:26.516954Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:53.171908Z", - "iopub.status.busy": "2024-06-25T23:18:53.171701Z", - "iopub.status.idle": "2024-06-25T23:18:53.174679Z", - "shell.execute_reply": "2024-06-25T23:18:53.174135Z" + "iopub.execute_input": "2024-06-27T15:45:26.519990Z", + "iopub.status.busy": "2024-06-27T15:45:26.519546Z", + "iopub.status.idle": "2024-06-27T15:45:26.522634Z", + "shell.execute_reply": "2024-06-27T15:45:26.522203Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:53.176658Z", - "iopub.status.busy": "2024-06-25T23:18:53.176332Z", - "iopub.status.idle": "2024-06-25T23:18:54.151841Z", - "shell.execute_reply": "2024-06-25T23:18:54.151257Z" + "iopub.execute_input": "2024-06-27T15:45:26.524690Z", + "iopub.status.busy": "2024-06-27T15:45:26.524304Z", + "iopub.status.idle": "2024-06-27T15:45:27.479354Z", + "shell.execute_reply": "2024-06-27T15:45:27.478760Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:54.153970Z", - "iopub.status.busy": "2024-06-25T23:18:54.153788Z", - "iopub.status.idle": "2024-06-25T23:18:54.367334Z", - "shell.execute_reply": "2024-06-25T23:18:54.366782Z" + "iopub.execute_input": "2024-06-27T15:45:27.481677Z", + "iopub.status.busy": "2024-06-27T15:45:27.481269Z", + "iopub.status.idle": "2024-06-27T15:45:27.662233Z", + "shell.execute_reply": "2024-06-27T15:45:27.661738Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:54.369532Z", - "iopub.status.busy": "2024-06-25T23:18:54.369222Z", - "iopub.status.idle": "2024-06-25T23:18:54.583472Z", - "shell.execute_reply": "2024-06-25T23:18:54.582875Z" + "iopub.execute_input": "2024-06-27T15:45:27.664486Z", + "iopub.status.busy": "2024-06-27T15:45:27.664165Z", + "iopub.status.idle": "2024-06-27T15:45:27.839845Z", + "shell.execute_reply": "2024-06-27T15:45:27.839265Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:54.585760Z", - "iopub.status.busy": "2024-06-25T23:18:54.585359Z", - "iopub.status.idle": "2024-06-25T23:18:55.323353Z", - "shell.execute_reply": "2024-06-25T23:18:55.322814Z" + "iopub.execute_input": "2024-06-27T15:45:27.841913Z", + "iopub.status.busy": "2024-06-27T15:45:27.841731Z", + "iopub.status.idle": "2024-06-27T15:45:28.508692Z", + "shell.execute_reply": "2024-06-27T15:45:28.508151Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:55.325548Z", - "iopub.status.busy": "2024-06-25T23:18:55.325207Z", - "iopub.status.idle": "2024-06-25T23:18:55.329284Z", - "shell.execute_reply": "2024-06-25T23:18:55.328852Z" + "iopub.execute_input": "2024-06-27T15:45:28.511345Z", + "iopub.status.busy": "2024-06-27T15:45:28.510832Z", + "iopub.status.idle": "2024-06-27T15:45:28.514788Z", + "shell.execute_reply": "2024-06-27T15:45:28.514332Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 4aeee095a..dce990b87 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": "2024-06-25T23:18:57.455185Z", - "iopub.status.busy": "2024-06-25T23:18:57.455007Z", - "iopub.status.idle": "2024-06-25T23:19:00.140522Z", - "shell.execute_reply": "2024-06-25T23:19:00.139964Z" + "iopub.execute_input": "2024-06-27T15:45:30.811235Z", + "iopub.status.busy": "2024-06-27T15:45:30.811074Z", + "iopub.status.idle": "2024-06-27T15:45:33.603967Z", + "shell.execute_reply": "2024-06-27T15:45:33.603351Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:19:00.143299Z", - "iopub.status.busy": "2024-06-25T23:19:00.142777Z", - "iopub.status.idle": "2024-06-25T23:19:00.459330Z", - "shell.execute_reply": "2024-06-25T23:19:00.458710Z" + "iopub.execute_input": "2024-06-27T15:45:33.606565Z", + "iopub.status.busy": "2024-06-27T15:45:33.606249Z", + "iopub.status.idle": "2024-06-27T15:45:33.934741Z", + "shell.execute_reply": "2024-06-27T15:45:33.934122Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:00.461903Z", - "iopub.status.busy": "2024-06-25T23:19:00.461603Z", - "iopub.status.idle": "2024-06-25T23:19:00.465997Z", - "shell.execute_reply": "2024-06-25T23:19:00.465462Z" + "iopub.execute_input": "2024-06-27T15:45:33.937444Z", + "iopub.status.busy": "2024-06-27T15:45:33.937123Z", + "iopub.status.idle": "2024-06-27T15:45:33.941418Z", + "shell.execute_reply": "2024-06-27T15:45:33.940892Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:00.468073Z", - "iopub.status.busy": "2024-06-25T23:19:00.467652Z", - "iopub.status.idle": "2024-06-25T23:19:04.713802Z", - "shell.execute_reply": "2024-06-25T23:19:04.713212Z" + "iopub.execute_input": "2024-06-27T15:45:33.943554Z", + "iopub.status.busy": "2024-06-27T15:45:33.943246Z", + "iopub.status.idle": "2024-06-27T15:45:40.969059Z", + "shell.execute_reply": "2024-06-27T15:45:40.968467Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1867776/170498071 [00:00<00:09, 18674661.14it/s]" + " 0%| | 32768/170498071 [00:00<10:29, 270980.66it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13533184/170498071 [00:00<00:02, 76238255.65it/s]" + " 0%| | 229376/170498071 [00:00<02:41, 1053920.96it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25133056/170498071 [00:00<00:01, 94330786.80it/s]" + " 1%| | 884736/170498071 [00:00<00:53, 3192666.86it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36732928/170498071 [00:00<00:01, 102749472.78it/s]" + " 2%|▏ | 3506176/170498071 [00:00<00:15, 10604458.14it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 48431104/170498071 [00:00<00:01, 107856210.55it/s]" + " 5%|▍ | 8519680/170498071 [00:00<00:06, 23321397.03it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 59244544/170498071 [00:00<00:01, 104969542.54it/s]" + " 8%|▊ | 12877824/170498071 [00:00<00:05, 29461386.54it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70385664/170498071 [00:00<00:00, 106967167.13it/s]" + " 11%|█ | 18513920/170498071 [00:00<00:04, 37736470.82it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82051072/170498071 [00:00<00:00, 109967427.14it/s]" + " 13%|█▎ | 23003136/170498071 [00:00<00:03, 38396688.94it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 93618176/170498071 [00:00<00:00, 111587704.91it/s]" + " 17%|█▋ | 28278784/170498071 [00:00<00:03, 42619760.41it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 105381888/170498071 [00:01<00:00, 113341455.44it/s]" + " 19%|█▉ | 32636928/170498071 [00:01<00:03, 42167836.16it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 116850688/170498071 [00:01<00:00, 113640126.52it/s]" + " 23%|██▎ | 38436864/170498071 [00:01<00:02, 45347567.59it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 128221184/170498071 [00:01<00:00, 112577089.48it/s]" + " 26%|██▌ | 43581440/170498071 [00:01<00:02, 47004803.84it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-06-25T23:19:04.715884Z", - "iopub.status.busy": "2024-06-25T23:19:04.715703Z", - "iopub.status.idle": "2024-06-25T23:19:04.720331Z", - "shell.execute_reply": "2024-06-25T23:19:04.719901Z" + "iopub.execute_input": "2024-06-27T15:45:40.971400Z", + "iopub.status.busy": "2024-06-27T15:45:40.971201Z", + "iopub.status.idle": "2024-06-27T15:45:40.975993Z", + "shell.execute_reply": "2024-06-27T15:45:40.975557Z" }, "nbsphinx": "hidden" }, @@ -544,10 +720,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:04.722383Z", - "iopub.status.busy": "2024-06-25T23:19:04.722067Z", - "iopub.status.idle": "2024-06-25T23:19:05.264665Z", - "shell.execute_reply": "2024-06-25T23:19:05.264156Z" + "iopub.execute_input": "2024-06-27T15:45:40.977819Z", + "iopub.status.busy": "2024-06-27T15:45:40.977599Z", + "iopub.status.idle": "2024-06-27T15:45:41.527781Z", + "shell.execute_reply": "2024-06-27T15:45:41.527141Z" } }, "outputs": [ @@ -580,10 +756,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.266862Z", - "iopub.status.busy": "2024-06-25T23:19:05.266533Z", - "iopub.status.idle": "2024-06-25T23:19:05.782683Z", - "shell.execute_reply": "2024-06-25T23:19:05.782187Z" + "iopub.execute_input": "2024-06-27T15:45:41.530331Z", + "iopub.status.busy": "2024-06-27T15:45:41.529870Z", + "iopub.status.idle": "2024-06-27T15:45:42.057214Z", + "shell.execute_reply": "2024-06-27T15:45:42.056595Z" } }, "outputs": [ @@ -621,10 +797,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.784979Z", - "iopub.status.busy": "2024-06-25T23:19:05.784496Z", - "iopub.status.idle": "2024-06-25T23:19:05.788141Z", - "shell.execute_reply": "2024-06-25T23:19:05.787582Z" + "iopub.execute_input": "2024-06-27T15:45:42.059555Z", + "iopub.status.busy": "2024-06-27T15:45:42.059223Z", + "iopub.status.idle": "2024-06-27T15:45:42.062832Z", + "shell.execute_reply": "2024-06-27T15:45:42.062275Z" } }, "outputs": [], @@ -647,17 +823,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.790197Z", - "iopub.status.busy": "2024-06-25T23:19:05.789889Z", - "iopub.status.idle": "2024-06-25T23:19:18.262418Z", - "shell.execute_reply": "2024-06-25T23:19:18.261753Z" + "iopub.execute_input": "2024-06-27T15:45:42.064935Z", + "iopub.status.busy": "2024-06-27T15:45:42.064505Z", + "iopub.status.idle": "2024-06-27T15:45:55.556440Z", + "shell.execute_reply": "2024-06-27T15:45:55.555646Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c487c0c381e74a26a4f49ff121b50dc9", + "model_id": "a6c22c3e77c84cc8bd08a52e1cf9b902", "version_major": 2, "version_minor": 0 }, @@ -716,10 +892,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:18.264580Z", - "iopub.status.busy": "2024-06-25T23:19:18.264398Z", - "iopub.status.idle": "2024-06-25T23:19:20.351765Z", - "shell.execute_reply": "2024-06-25T23:19:20.351216Z" + "iopub.execute_input": "2024-06-27T15:45:55.558966Z", + "iopub.status.busy": "2024-06-27T15:45:55.558582Z", + "iopub.status.idle": "2024-06-27T15:45:57.651196Z", + "shell.execute_reply": "2024-06-27T15:45:57.650529Z" } }, "outputs": [ @@ -763,10 +939,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:20.354001Z", - "iopub.status.busy": "2024-06-25T23:19:20.353823Z", - "iopub.status.idle": "2024-06-25T23:19:20.594457Z", - "shell.execute_reply": "2024-06-25T23:19:20.593862Z" + "iopub.execute_input": "2024-06-27T15:45:57.654042Z", + "iopub.status.busy": "2024-06-27T15:45:57.653576Z", + "iopub.status.idle": "2024-06-27T15:45:57.907027Z", + "shell.execute_reply": "2024-06-27T15:45:57.906457Z" } }, "outputs": [ @@ -802,10 +978,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:20.597502Z", - "iopub.status.busy": "2024-06-25T23:19:20.596997Z", - "iopub.status.idle": "2024-06-25T23:19:21.266452Z", - "shell.execute_reply": "2024-06-25T23:19:21.265865Z" + "iopub.execute_input": "2024-06-27T15:45:57.909700Z", + "iopub.status.busy": "2024-06-27T15:45:57.909398Z", + "iopub.status.idle": "2024-06-27T15:45:58.574563Z", + "shell.execute_reply": "2024-06-27T15:45:58.574010Z" } }, "outputs": [ @@ -855,10 +1031,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.269364Z", - "iopub.status.busy": "2024-06-25T23:19:21.269161Z", - "iopub.status.idle": "2024-06-25T23:19:21.610132Z", - "shell.execute_reply": "2024-06-25T23:19:21.609580Z" + "iopub.execute_input": "2024-06-27T15:45:58.577526Z", + "iopub.status.busy": "2024-06-27T15:45:58.577074Z", + "iopub.status.idle": "2024-06-27T15:45:58.918893Z", + "shell.execute_reply": "2024-06-27T15:45:58.918198Z" } }, "outputs": [ @@ -906,10 +1082,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.612438Z", - "iopub.status.busy": "2024-06-25T23:19:21.612030Z", - "iopub.status.idle": "2024-06-25T23:19:21.854392Z", - "shell.execute_reply": "2024-06-25T23:19:21.853886Z" + "iopub.execute_input": "2024-06-27T15:45:58.921501Z", + "iopub.status.busy": "2024-06-27T15:45:58.921042Z", + "iopub.status.idle": "2024-06-27T15:45:59.189588Z", + "shell.execute_reply": "2024-06-27T15:45:59.188711Z" } }, "outputs": [ @@ -965,10 +1141,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.857100Z", - "iopub.status.busy": "2024-06-25T23:19:21.856718Z", - "iopub.status.idle": "2024-06-25T23:19:21.949451Z", - "shell.execute_reply": "2024-06-25T23:19:21.948923Z" + "iopub.execute_input": "2024-06-27T15:45:59.192574Z", + "iopub.status.busy": "2024-06-27T15:45:59.192290Z", + "iopub.status.idle": "2024-06-27T15:45:59.276757Z", + "shell.execute_reply": "2024-06-27T15:45:59.276135Z" } }, "outputs": [], @@ -989,10 +1165,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.951971Z", - "iopub.status.busy": "2024-06-25T23:19:21.951792Z", - "iopub.status.idle": "2024-06-25T23:19:32.299281Z", - "shell.execute_reply": "2024-06-25T23:19:32.298639Z" + "iopub.execute_input": "2024-06-27T15:45:59.279342Z", + "iopub.status.busy": "2024-06-27T15:45:59.279159Z", + "iopub.status.idle": "2024-06-27T15:46:09.531418Z", + "shell.execute_reply": "2024-06-27T15:46:09.530750Z" } }, "outputs": [ @@ -1029,10 +1205,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:32.301554Z", - "iopub.status.busy": "2024-06-25T23:19:32.301299Z", - "iopub.status.idle": "2024-06-25T23:19:34.462906Z", - "shell.execute_reply": "2024-06-25T23:19:34.462279Z" + "iopub.execute_input": "2024-06-27T15:46:09.534232Z", + "iopub.status.busy": "2024-06-27T15:46:09.533762Z", + "iopub.status.idle": "2024-06-27T15:46:11.746017Z", + "shell.execute_reply": "2024-06-27T15:46:11.745431Z" } }, "outputs": [ @@ -1063,10 +1239,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.465814Z", - "iopub.status.busy": "2024-06-25T23:19:34.465269Z", - "iopub.status.idle": "2024-06-25T23:19:34.668011Z", - "shell.execute_reply": "2024-06-25T23:19:34.667512Z" + "iopub.execute_input": "2024-06-27T15:46:11.748794Z", + "iopub.status.busy": "2024-06-27T15:46:11.748237Z", + "iopub.status.idle": "2024-06-27T15:46:11.948800Z", + "shell.execute_reply": "2024-06-27T15:46:11.948305Z" } }, "outputs": [], @@ -1080,10 +1256,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.670373Z", - "iopub.status.busy": "2024-06-25T23:19:34.670183Z", - "iopub.status.idle": "2024-06-25T23:19:34.673433Z", - "shell.execute_reply": "2024-06-25T23:19:34.672977Z" + "iopub.execute_input": "2024-06-27T15:46:11.951095Z", + "iopub.status.busy": "2024-06-27T15:46:11.950910Z", + "iopub.status.idle": "2024-06-27T15:46:11.954096Z", + "shell.execute_reply": "2024-06-27T15:46:11.953666Z" } }, "outputs": [], @@ -1105,10 +1281,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.675236Z", - "iopub.status.busy": "2024-06-25T23:19:34.675068Z", - "iopub.status.idle": "2024-06-25T23:19:34.683440Z", - "shell.execute_reply": "2024-06-25T23:19:34.682879Z" + "iopub.execute_input": "2024-06-27T15:46:11.956074Z", + "iopub.status.busy": "2024-06-27T15:46:11.955782Z", + "iopub.status.idle": "2024-06-27T15:46:11.963897Z", + "shell.execute_reply": "2024-06-27T15:46:11.963383Z" }, "nbsphinx": "hidden" }, @@ -1153,23 +1329,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "2a6c0688ba264a97aee90eb6e37e9dc2": { - "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": "" - } - }, - "2ed8f22a488e446d9dd09c9e760c1fb2": { + "2867482e9d3444df873ee05b569a66cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1187,7 +1347,7 @@ "text_color": null } }, - "3a433760fb114684b1ce550e725410f3": { + "2d9ce638a81541a69c6b0f5430fcf5f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1203,17 +1363,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - 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"_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b8d3a0d6f70d45e596f645995e60a137": { + "4d6c4b8b33aa493e93329931c7f4f318": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1360,31 +1502,7 @@ "width": null } }, - "c487c0c381e74a26a4f49ff121b50dc9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7b2c005a31c74359a9e6040ef88246ae", - "IPY_MODEL_3a433760fb114684b1ce550e725410f3", - "IPY_MODEL_d732fe2e90af4c978675ca6771e1718c" - ], - "layout": "IPY_MODEL_d9309721bf15440c9369f140e0453a0f", - "tabbable": null, - "tooltip": null - } - }, - "cac77136c4f843fea7898f1735deb973": { + "5dfc1d62bb0e4dc58b2161ae9c1bc8d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1437,30 +1555,41 @@ "width": null } }, - "d732fe2e90af4c978675ca6771e1718c": { + "74a0e7dc77404b66aa096c6ac1086de1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b8d3a0d6f70d45e596f645995e60a137", - "placeholder": "​", - "style": "IPY_MODEL_acf4b18dbb8e42a391b0dfa7d26e3612", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 218MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7629b1d5428148fe8288ca24f99967f8": { + "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": "" } }, - "d9309721bf15440c9369f140e0453a0f": { + "a0cc2013504e4b7886e198d1c391e884": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1512,6 +1641,53 @@ "visibility": null, "width": null } + }, + "a6c22c3e77c84cc8bd08a52e1cf9b902": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a9415174d636420a9369ae15096b0f3b", + "IPY_MODEL_2d9ce638a81541a69c6b0f5430fcf5f7", + "IPY_MODEL_46dfbd06512a46a6b805e755503dec3d" + ], + "layout": "IPY_MODEL_5dfc1d62bb0e4dc58b2161ae9c1bc8d0", + "tabbable": null, + "tooltip": null + } + }, + "a9415174d636420a9369ae15096b0f3b": { + "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_4d6c4b8b33aa493e93329931c7f4f318", + "placeholder": "​", + "style": "IPY_MODEL_74a0e7dc77404b66aa096c6ac1086de1", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 4dccd9a0a..a6fce8c94 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": "2024-06-25T23:19:38.796252Z", - "iopub.status.busy": "2024-06-25T23:19:38.796082Z", - "iopub.status.idle": "2024-06-25T23:19:39.953258Z", - "shell.execute_reply": "2024-06-25T23:19:39.952691Z" + "iopub.execute_input": "2024-06-27T15:46:16.118840Z", + "iopub.status.busy": "2024-06-27T15:46:16.118673Z", + "iopub.status.idle": "2024-06-27T15:46:17.349519Z", + "shell.execute_reply": "2024-06-27T15:46:17.349002Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:19:39.955862Z", - "iopub.status.busy": "2024-06-25T23:19:39.955512Z", - "iopub.status.idle": "2024-06-25T23:19:39.972881Z", - "shell.execute_reply": "2024-06-25T23:19:39.972463Z" + "iopub.execute_input": "2024-06-27T15:46:17.352024Z", + "iopub.status.busy": "2024-06-27T15:46:17.351735Z", + "iopub.status.idle": "2024-06-27T15:46:17.369618Z", + "shell.execute_reply": "2024-06-27T15:46:17.369156Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:39.975108Z", - "iopub.status.busy": "2024-06-25T23:19:39.974726Z", - "iopub.status.idle": "2024-06-25T23:19:39.977547Z", - "shell.execute_reply": "2024-06-25T23:19:39.977124Z" + "iopub.execute_input": "2024-06-27T15:46:17.371774Z", + "iopub.status.busy": "2024-06-27T15:46:17.371486Z", + "iopub.status.idle": "2024-06-27T15:46:17.374581Z", + "shell.execute_reply": "2024-06-27T15:46:17.374129Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:39.979571Z", - "iopub.status.busy": "2024-06-25T23:19:39.979249Z", - "iopub.status.idle": "2024-06-25T23:19:40.010006Z", - "shell.execute_reply": "2024-06-25T23:19:40.009548Z" + "iopub.execute_input": "2024-06-27T15:46:17.376456Z", + "iopub.status.busy": "2024-06-27T15:46:17.376280Z", + "iopub.status.idle": "2024-06-27T15:46:17.774991Z", + "shell.execute_reply": "2024-06-27T15:46:17.774436Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.012066Z", - "iopub.status.busy": "2024-06-25T23:19:40.011740Z", - "iopub.status.idle": "2024-06-25T23:19:40.191233Z", - "shell.execute_reply": "2024-06-25T23:19:40.190672Z" + "iopub.execute_input": "2024-06-27T15:46:17.777150Z", + "iopub.status.busy": "2024-06-27T15:46:17.776935Z", + "iopub.status.idle": "2024-06-27T15:46:17.961475Z", + "shell.execute_reply": "2024-06-27T15:46:17.960994Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.193662Z", - "iopub.status.busy": "2024-06-25T23:19:40.193313Z", - "iopub.status.idle": "2024-06-25T23:19:40.401417Z", - "shell.execute_reply": "2024-06-25T23:19:40.400809Z" + "iopub.execute_input": "2024-06-27T15:46:17.963889Z", + "iopub.status.busy": "2024-06-27T15:46:17.963694Z", + "iopub.status.idle": "2024-06-27T15:46:18.173983Z", + "shell.execute_reply": "2024-06-27T15:46:18.173345Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.403764Z", - "iopub.status.busy": "2024-06-25T23:19:40.403425Z", - "iopub.status.idle": "2024-06-25T23:19:40.407638Z", - "shell.execute_reply": "2024-06-25T23:19:40.407217Z" + "iopub.execute_input": "2024-06-27T15:46:18.176210Z", + "iopub.status.busy": "2024-06-27T15:46:18.176008Z", + "iopub.status.idle": "2024-06-27T15:46:18.180677Z", + "shell.execute_reply": "2024-06-27T15:46:18.180114Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.409677Z", - "iopub.status.busy": "2024-06-25T23:19:40.409360Z", - "iopub.status.idle": "2024-06-25T23:19:40.415770Z", - "shell.execute_reply": "2024-06-25T23:19:40.415356Z" + "iopub.execute_input": "2024-06-27T15:46:18.182807Z", + "iopub.status.busy": "2024-06-27T15:46:18.182476Z", + "iopub.status.idle": "2024-06-27T15:46:18.188147Z", + "shell.execute_reply": "2024-06-27T15:46:18.187711Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.417772Z", - "iopub.status.busy": "2024-06-25T23:19:40.417455Z", - "iopub.status.idle": "2024-06-25T23:19:40.420042Z", - "shell.execute_reply": "2024-06-25T23:19:40.419591Z" + "iopub.execute_input": "2024-06-27T15:46:18.190157Z", + "iopub.status.busy": "2024-06-27T15:46:18.189845Z", + "iopub.status.idle": "2024-06-27T15:46:18.192555Z", + "shell.execute_reply": "2024-06-27T15:46:18.191997Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.421960Z", - "iopub.status.busy": "2024-06-25T23:19:40.421649Z", - "iopub.status.idle": "2024-06-25T23:19:48.997759Z", - "shell.execute_reply": "2024-06-25T23:19:48.997063Z" + "iopub.execute_input": "2024-06-27T15:46:18.194646Z", + "iopub.status.busy": "2024-06-27T15:46:18.194322Z", + "iopub.status.idle": "2024-06-27T15:46:26.858189Z", + "shell.execute_reply": "2024-06-27T15:46:26.857616Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.000433Z", - "iopub.status.busy": "2024-06-25T23:19:49.000048Z", - "iopub.status.idle": "2024-06-25T23:19:49.007281Z", - "shell.execute_reply": "2024-06-25T23:19:49.006704Z" + "iopub.execute_input": "2024-06-27T15:46:26.861110Z", + "iopub.status.busy": "2024-06-27T15:46:26.860469Z", + "iopub.status.idle": "2024-06-27T15:46:26.867521Z", + "shell.execute_reply": "2024-06-27T15:46:26.866967Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.009612Z", - "iopub.status.busy": "2024-06-25T23:19:49.009171Z", - "iopub.status.idle": "2024-06-25T23:19:49.013898Z", - "shell.execute_reply": "2024-06-25T23:19:49.013343Z" + "iopub.execute_input": "2024-06-27T15:46:26.869584Z", + "iopub.status.busy": "2024-06-27T15:46:26.869247Z", + "iopub.status.idle": "2024-06-27T15:46:26.872878Z", + "shell.execute_reply": "2024-06-27T15:46:26.872434Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.016095Z", - "iopub.status.busy": "2024-06-25T23:19:49.015919Z", - "iopub.status.idle": "2024-06-25T23:19:49.019068Z", - "shell.execute_reply": "2024-06-25T23:19:49.018547Z" + "iopub.execute_input": "2024-06-27T15:46:26.874782Z", + "iopub.status.busy": "2024-06-27T15:46:26.874481Z", + "iopub.status.idle": "2024-06-27T15:46:26.877465Z", + "shell.execute_reply": "2024-06-27T15:46:26.876953Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.020914Z", - "iopub.status.busy": "2024-06-25T23:19:49.020745Z", - "iopub.status.idle": "2024-06-25T23:19:49.023808Z", - "shell.execute_reply": "2024-06-25T23:19:49.023350Z" + "iopub.execute_input": "2024-06-27T15:46:26.879407Z", + "iopub.status.busy": "2024-06-27T15:46:26.879233Z", + "iopub.status.idle": "2024-06-27T15:46:26.882138Z", + "shell.execute_reply": "2024-06-27T15:46:26.881703Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.025803Z", - "iopub.status.busy": "2024-06-25T23:19:49.025488Z", - "iopub.status.idle": "2024-06-25T23:19:49.033564Z", - "shell.execute_reply": "2024-06-25T23:19:49.033138Z" + "iopub.execute_input": "2024-06-27T15:46:26.884148Z", + "iopub.status.busy": "2024-06-27T15:46:26.883831Z", + "iopub.status.idle": "2024-06-27T15:46:26.891896Z", + "shell.execute_reply": "2024-06-27T15:46:26.891360Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.035573Z", - "iopub.status.busy": "2024-06-25T23:19:49.035256Z", - "iopub.status.idle": "2024-06-25T23:19:49.037707Z", - "shell.execute_reply": "2024-06-25T23:19:49.037270Z" + "iopub.execute_input": "2024-06-27T15:46:26.893931Z", + "iopub.status.busy": "2024-06-27T15:46:26.893513Z", + "iopub.status.idle": "2024-06-27T15:46:26.896207Z", + "shell.execute_reply": "2024-06-27T15:46:26.895688Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.039639Z", - "iopub.status.busy": "2024-06-25T23:19:49.039383Z", - "iopub.status.idle": "2024-06-25T23:19:49.162747Z", - "shell.execute_reply": "2024-06-25T23:19:49.162268Z" + "iopub.execute_input": "2024-06-27T15:46:26.898155Z", + "iopub.status.busy": "2024-06-27T15:46:26.897980Z", + "iopub.status.idle": "2024-06-27T15:46:27.016942Z", + "shell.execute_reply": "2024-06-27T15:46:27.016432Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.164799Z", - "iopub.status.busy": "2024-06-25T23:19:49.164444Z", - "iopub.status.idle": "2024-06-25T23:19:49.269361Z", - "shell.execute_reply": "2024-06-25T23:19:49.268836Z" + "iopub.execute_input": "2024-06-27T15:46:27.019091Z", + "iopub.status.busy": "2024-06-27T15:46:27.018918Z", + "iopub.status.idle": "2024-06-27T15:46:27.120975Z", + "shell.execute_reply": "2024-06-27T15:46:27.120481Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.271927Z", - "iopub.status.busy": "2024-06-25T23:19:49.271573Z", - "iopub.status.idle": "2024-06-25T23:19:49.761626Z", - "shell.execute_reply": "2024-06-25T23:19:49.760979Z" + "iopub.execute_input": "2024-06-27T15:46:27.123143Z", + "iopub.status.busy": "2024-06-27T15:46:27.122879Z", + "iopub.status.idle": "2024-06-27T15:46:27.608490Z", + "shell.execute_reply": "2024-06-27T15:46:27.607880Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.764216Z", - "iopub.status.busy": "2024-06-25T23:19:49.763834Z", - "iopub.status.idle": "2024-06-25T23:19:49.843367Z", - "shell.execute_reply": "2024-06-25T23:19:49.842743Z" + "iopub.execute_input": "2024-06-27T15:46:27.611193Z", + "iopub.status.busy": "2024-06-27T15:46:27.610715Z", + "iopub.status.idle": "2024-06-27T15:46:27.686929Z", + "shell.execute_reply": "2024-06-27T15:46:27.686299Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.845464Z", - "iopub.status.busy": "2024-06-25T23:19:49.845235Z", - "iopub.status.idle": "2024-06-25T23:19:49.853788Z", - "shell.execute_reply": "2024-06-25T23:19:49.853340Z" + "iopub.execute_input": "2024-06-27T15:46:27.689393Z", + "iopub.status.busy": "2024-06-27T15:46:27.688927Z", + "iopub.status.idle": "2024-06-27T15:46:27.697939Z", + "shell.execute_reply": "2024-06-27T15:46:27.697502Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.855684Z", - "iopub.status.busy": "2024-06-25T23:19:49.855513Z", - "iopub.status.idle": "2024-06-25T23:19:49.858498Z", - "shell.execute_reply": "2024-06-25T23:19:49.857932Z" + "iopub.execute_input": "2024-06-27T15:46:27.700034Z", + "iopub.status.busy": "2024-06-27T15:46:27.699717Z", + "iopub.status.idle": "2024-06-27T15:46:27.702476Z", + "shell.execute_reply": "2024-06-27T15:46:27.701901Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.860435Z", - "iopub.status.busy": "2024-06-25T23:19:49.860125Z", - "iopub.status.idle": "2024-06-25T23:19:55.315583Z", - "shell.execute_reply": "2024-06-25T23:19:55.315005Z" + "iopub.execute_input": "2024-06-27T15:46:27.704489Z", + "iopub.status.busy": "2024-06-27T15:46:27.704077Z", + "iopub.status.idle": "2024-06-27T15:46:33.069416Z", + "shell.execute_reply": "2024-06-27T15:46:33.068814Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:55.317789Z", - "iopub.status.busy": "2024-06-25T23:19:55.317611Z", - "iopub.status.idle": "2024-06-25T23:19:55.326379Z", - "shell.execute_reply": "2024-06-25T23:19:55.325834Z" + "iopub.execute_input": "2024-06-27T15:46:33.071900Z", + "iopub.status.busy": "2024-06-27T15:46:33.071501Z", + "iopub.status.idle": "2024-06-27T15:46:33.080090Z", + "shell.execute_reply": "2024-06-27T15:46:33.079649Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:55.328528Z", - "iopub.status.busy": "2024-06-25T23:19:55.328125Z", - "iopub.status.idle": "2024-06-25T23:19:55.396039Z", - "shell.execute_reply": "2024-06-25T23:19:55.395563Z" + "iopub.execute_input": "2024-06-27T15:46:33.082101Z", + "iopub.status.busy": "2024-06-27T15:46:33.081924Z", + "iopub.status.idle": "2024-06-27T15:46:33.150392Z", + "shell.execute_reply": "2024-06-27T15:46:33.149925Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index d70cfaf4e..cae98b96a 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": "2024-06-25T23:19:58.399516Z", - "iopub.status.busy": "2024-06-25T23:19:58.399339Z", - "iopub.status.idle": "2024-06-25T23:19:59.729255Z", - "shell.execute_reply": "2024-06-25T23:19:59.728521Z" + "iopub.execute_input": "2024-06-27T15:46:36.239038Z", + "iopub.status.busy": "2024-06-27T15:46:36.238550Z", + "iopub.status.idle": "2024-06-27T15:46:55.062132Z", + "shell.execute_reply": "2024-06-27T15:46:55.061471Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:59.731986Z", - "iopub.status.busy": "2024-06-25T23:19:59.731605Z", - "iopub.status.idle": "2024-06-25T23:20:48.710370Z", - "shell.execute_reply": "2024-06-25T23:20:48.709721Z" + "iopub.execute_input": "2024-06-27T15:46:55.064797Z", + "iopub.status.busy": "2024-06-27T15:46:55.064588Z", + "iopub.status.idle": "2024-06-27T15:47:51.045666Z", + "shell.execute_reply": "2024-06-27T15:47:51.045022Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:48.712844Z", - "iopub.status.busy": "2024-06-25T23:20:48.712648Z", - "iopub.status.idle": "2024-06-25T23:20:49.819754Z", - "shell.execute_reply": "2024-06-25T23:20:49.819207Z" + "iopub.execute_input": "2024-06-27T15:47:51.048381Z", + "iopub.status.busy": "2024-06-27T15:47:51.048003Z", + "iopub.status.idle": "2024-06-27T15:47:52.172865Z", + "shell.execute_reply": "2024-06-27T15:47:52.172316Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:20:49.822551Z", - "iopub.status.busy": "2024-06-25T23:20:49.821983Z", - "iopub.status.idle": "2024-06-25T23:20:49.825360Z", - "shell.execute_reply": "2024-06-25T23:20:49.824898Z" + "iopub.execute_input": "2024-06-27T15:47:52.175429Z", + "iopub.status.busy": "2024-06-27T15:47:52.175045Z", + "iopub.status.idle": "2024-06-27T15:47:52.178148Z", + "shell.execute_reply": "2024-06-27T15:47:52.177722Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.827409Z", - "iopub.status.busy": "2024-06-25T23:20:49.827081Z", - "iopub.status.idle": "2024-06-25T23:20:49.830739Z", - "shell.execute_reply": "2024-06-25T23:20:49.830321Z" + "iopub.execute_input": "2024-06-27T15:47:52.180268Z", + "iopub.status.busy": "2024-06-27T15:47:52.179988Z", + "iopub.status.idle": "2024-06-27T15:47:52.183739Z", + "shell.execute_reply": "2024-06-27T15:47:52.183313Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.832814Z", - "iopub.status.busy": "2024-06-25T23:20:49.832481Z", - "iopub.status.idle": "2024-06-25T23:20:49.835986Z", - "shell.execute_reply": "2024-06-25T23:20:49.835546Z" + "iopub.execute_input": "2024-06-27T15:47:52.185671Z", + "iopub.status.busy": "2024-06-27T15:47:52.185493Z", + "iopub.status.idle": "2024-06-27T15:47:52.189156Z", + "shell.execute_reply": "2024-06-27T15:47:52.188640Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.837816Z", - "iopub.status.busy": "2024-06-25T23:20:49.837650Z", - "iopub.status.idle": "2024-06-25T23:20:49.841360Z", - "shell.execute_reply": "2024-06-25T23:20:49.840870Z" + "iopub.execute_input": "2024-06-27T15:47:52.191280Z", + "iopub.status.busy": "2024-06-27T15:47:52.190962Z", + "iopub.status.idle": "2024-06-27T15:47:52.193884Z", + "shell.execute_reply": "2024-06-27T15:47:52.193311Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.843348Z", - "iopub.status.busy": "2024-06-25T23:20:49.843045Z", - "iopub.status.idle": "2024-06-25T23:21:23.312097Z", - "shell.execute_reply": "2024-06-25T23:21:23.311395Z" + "iopub.execute_input": "2024-06-27T15:47:52.195916Z", + "iopub.status.busy": "2024-06-27T15:47:52.195507Z", + "iopub.status.idle": "2024-06-27T15:48:25.584743Z", + "shell.execute_reply": "2024-06-27T15:48:25.584154Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "198f978c68c04b42bb7f505400e75581", + "model_id": "7f71a5822c7846d0b034ab5b1a0b1c3f", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b186141820047419c3ae004111754f6", + "model_id": "fe3d775f10d64239907ab79615645233", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:21:23.314740Z", - "iopub.status.busy": "2024-06-25T23:21:23.314519Z", - "iopub.status.idle": "2024-06-25T23:21:23.985247Z", - "shell.execute_reply": "2024-06-25T23:21:23.984646Z" + "iopub.execute_input": "2024-06-27T15:48:25.587438Z", + "iopub.status.busy": "2024-06-27T15:48:25.587187Z", + "iopub.status.idle": "2024-06-27T15:48:26.263424Z", + "shell.execute_reply": "2024-06-27T15:48:26.262811Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:21:23.987564Z", - "iopub.status.busy": "2024-06-25T23:21:23.987136Z", - "iopub.status.idle": "2024-06-25T23:21:26.705173Z", - "shell.execute_reply": "2024-06-25T23:21:26.704585Z" + "iopub.execute_input": "2024-06-27T15:48:26.265887Z", + "iopub.status.busy": "2024-06-27T15:48:26.265299Z", + "iopub.status.idle": "2024-06-27T15:48:29.122069Z", + "shell.execute_reply": "2024-06-27T15:48:29.121432Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:21:26.707473Z", - 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"iopub.execute_input": "2024-06-25T23:22:28.297877Z", - "iopub.status.busy": "2024-06-25T23:22:28.297692Z", - "iopub.status.idle": "2024-06-25T23:22:29.566144Z", - "shell.execute_reply": "2024-06-25T23:22:29.565466Z" + "iopub.execute_input": "2024-06-27T15:49:29.640132Z", + "iopub.status.busy": "2024-06-27T15:49:29.639946Z", + "iopub.status.idle": "2024-06-27T15:49:31.619306Z", + "shell.execute_reply": "2024-06-27T15:49:31.618696Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 23:22:28-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-06-27 15:49:29-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,15 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.250, 2400:52e0:1a00::1068:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n", + "143.244.49.180, 2400:52e0:1a01::998:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.49.180|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -123,9 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.06s \r\n", "\r\n", - "2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-06-27 15:49:30 (16.8 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +138,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 23:22:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.25.196, 54.231.139.49, 52.216.48.57, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.25.196|:443... connected.\r\n", + "--2024-06-27 15:49:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52.217.80.44, 3.5.27.43, 3.5.11.148, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.80.44|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,7 +181,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 58%[==========> ] 9.47M 47.3MB/s " + "pred_probs.npz 1%[ ] 312.11K 1.26MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 31%[=====> ] 5.18M 10.7MB/s " ] }, { @@ -176,9 +197,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 55.6MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 23.9MB/s in 0.7s \r\n", "\r\n", - "2024-06-25 23:22:29 (55.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-06-27 15:49:31 (23.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -195,10 +216,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:29.568875Z", - "iopub.status.busy": "2024-06-25T23:22:29.568431Z", - "iopub.status.idle": "2024-06-25T23:22:30.789853Z", - "shell.execute_reply": "2024-06-25T23:22:30.789338Z" + "iopub.execute_input": "2024-06-27T15:49:31.621702Z", + "iopub.status.busy": "2024-06-27T15:49:31.621505Z", + "iopub.status.idle": "2024-06-27T15:49:32.918088Z", + "shell.execute_reply": "2024-06-27T15:49:32.917440Z" }, "nbsphinx": "hidden" }, @@ -209,7 +230,7 @@ "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -235,10 +256,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.792349Z", - "iopub.status.busy": "2024-06-25T23:22:30.792077Z", - "iopub.status.idle": "2024-06-25T23:22:30.795305Z", - "shell.execute_reply": "2024-06-25T23:22:30.794873Z" + "iopub.execute_input": "2024-06-27T15:49:32.920910Z", + "iopub.status.busy": "2024-06-27T15:49:32.920272Z", + "iopub.status.idle": "2024-06-27T15:49:32.923938Z", + "shell.execute_reply": "2024-06-27T15:49:32.923418Z" } }, "outputs": [], @@ -288,10 +309,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.797547Z", - "iopub.status.busy": "2024-06-25T23:22:30.797222Z", - "iopub.status.idle": "2024-06-25T23:22:30.800066Z", - "shell.execute_reply": "2024-06-25T23:22:30.799649Z" + "iopub.execute_input": "2024-06-27T15:49:32.925895Z", + "iopub.status.busy": "2024-06-27T15:49:32.925700Z", + "iopub.status.idle": "2024-06-27T15:49:32.928588Z", + "shell.execute_reply": "2024-06-27T15:49:32.928176Z" }, "nbsphinx": "hidden" }, @@ -309,10 +330,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.801968Z", - "iopub.status.busy": "2024-06-25T23:22:30.801793Z", - "iopub.status.idle": "2024-06-25T23:22:39.539487Z", - "shell.execute_reply": "2024-06-25T23:22:39.538935Z" + "iopub.execute_input": "2024-06-27T15:49:32.930400Z", + "iopub.status.busy": "2024-06-27T15:49:32.930225Z", + "iopub.status.idle": "2024-06-27T15:49:41.990953Z", + "shell.execute_reply": "2024-06-27T15:49:41.990399Z" } }, "outputs": [], @@ -386,10 +407,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.542320Z", - "iopub.status.busy": "2024-06-25T23:22:39.541861Z", - "iopub.status.idle": "2024-06-25T23:22:39.547429Z", - "shell.execute_reply": "2024-06-25T23:22:39.546974Z" + "iopub.execute_input": "2024-06-27T15:49:41.993525Z", + "iopub.status.busy": "2024-06-27T15:49:41.993154Z", + "iopub.status.idle": "2024-06-27T15:49:41.998594Z", + "shell.execute_reply": "2024-06-27T15:49:41.998138Z" }, "nbsphinx": "hidden" }, @@ -429,10 +450,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.549434Z", - "iopub.status.busy": "2024-06-25T23:22:39.549088Z", - "iopub.status.idle": "2024-06-25T23:22:39.886323Z", - "shell.execute_reply": "2024-06-25T23:22:39.885773Z" + "iopub.execute_input": "2024-06-27T15:49:42.000606Z", + "iopub.status.busy": "2024-06-27T15:49:42.000287Z", + "iopub.status.idle": "2024-06-27T15:49:42.352443Z", + "shell.execute_reply": "2024-06-27T15:49:42.351803Z" } }, "outputs": [], @@ -469,10 +490,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.888760Z", - "iopub.status.busy": "2024-06-25T23:22:39.888567Z", - "iopub.status.idle": "2024-06-25T23:22:39.892822Z", - "shell.execute_reply": "2024-06-25T23:22:39.892289Z" + "iopub.execute_input": "2024-06-27T15:49:42.354868Z", + "iopub.status.busy": "2024-06-27T15:49:42.354676Z", + "iopub.status.idle": "2024-06-27T15:49:42.358986Z", + "shell.execute_reply": "2024-06-27T15:49:42.358462Z" } }, "outputs": [ @@ -544,10 +565,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.894754Z", - "iopub.status.busy": "2024-06-25T23:22:39.894582Z", - "iopub.status.idle": "2024-06-25T23:22:42.439150Z", - "shell.execute_reply": "2024-06-25T23:22:42.438377Z" + "iopub.execute_input": "2024-06-27T15:49:42.360910Z", + "iopub.status.busy": "2024-06-27T15:49:42.360734Z", + "iopub.status.idle": "2024-06-27T15:49:44.947322Z", + "shell.execute_reply": "2024-06-27T15:49:44.946542Z" } }, "outputs": [], @@ -569,10 +590,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.442203Z", - "iopub.status.busy": "2024-06-25T23:22:42.441641Z", - "iopub.status.idle": "2024-06-25T23:22:42.445478Z", - "shell.execute_reply": "2024-06-25T23:22:42.444915Z" + "iopub.execute_input": "2024-06-27T15:49:44.950509Z", + "iopub.status.busy": "2024-06-27T15:49:44.949955Z", + "iopub.status.idle": "2024-06-27T15:49:44.954069Z", + "shell.execute_reply": "2024-06-27T15:49:44.953529Z" } }, "outputs": [ @@ -608,10 +629,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.447472Z", - "iopub.status.busy": "2024-06-25T23:22:42.447297Z", - "iopub.status.idle": "2024-06-25T23:22:42.452716Z", - "shell.execute_reply": "2024-06-25T23:22:42.452215Z" + "iopub.execute_input": "2024-06-27T15:49:44.956163Z", + "iopub.status.busy": "2024-06-27T15:49:44.955856Z", + "iopub.status.idle": "2024-06-27T15:49:44.961496Z", + "shell.execute_reply": "2024-06-27T15:49:44.960948Z" } }, "outputs": [ @@ -789,10 +810,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.454685Z", - "iopub.status.busy": "2024-06-25T23:22:42.454421Z", - "iopub.status.idle": "2024-06-25T23:22:42.480225Z", - "shell.execute_reply": "2024-06-25T23:22:42.479796Z" + "iopub.execute_input": "2024-06-27T15:49:44.963667Z", + "iopub.status.busy": "2024-06-27T15:49:44.963243Z", + "iopub.status.idle": "2024-06-27T15:49:44.989856Z", + "shell.execute_reply": "2024-06-27T15:49:44.989282Z" } }, "outputs": [ @@ -894,10 +915,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.482279Z", - "iopub.status.busy": "2024-06-25T23:22:42.481978Z", - "iopub.status.idle": "2024-06-25T23:22:42.486286Z", - "shell.execute_reply": "2024-06-25T23:22:42.485735Z" + "iopub.execute_input": "2024-06-27T15:49:44.991934Z", + "iopub.status.busy": "2024-06-27T15:49:44.991544Z", + "iopub.status.idle": "2024-06-27T15:49:44.995987Z", + "shell.execute_reply": "2024-06-27T15:49:44.995465Z" } }, "outputs": [ @@ -971,10 +992,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.488404Z", - "iopub.status.busy": "2024-06-25T23:22:42.487905Z", - "iopub.status.idle": "2024-06-25T23:22:43.900411Z", - "shell.execute_reply": "2024-06-25T23:22:43.899904Z" + "iopub.execute_input": "2024-06-27T15:49:44.997969Z", + "iopub.status.busy": "2024-06-27T15:49:44.997628Z", + "iopub.status.idle": "2024-06-27T15:49:46.406945Z", + "shell.execute_reply": "2024-06-27T15:49:46.406319Z" } }, "outputs": [ @@ -1146,10 +1167,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:43.902625Z", - "iopub.status.busy": "2024-06-25T23:22:43.902291Z", - "iopub.status.idle": "2024-06-25T23:22:43.906202Z", - "shell.execute_reply": "2024-06-25T23:22:43.905768Z" + "iopub.execute_input": "2024-06-27T15:49:46.409049Z", + "iopub.status.busy": "2024-06-27T15:49:46.408845Z", + "iopub.status.idle": "2024-06-27T15:49:46.413172Z", + "shell.execute_reply": "2024-06-27T15:49:46.412698Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials_datalab_workflows_84_0.png b/master/.doctrees/nbsphinx/tutorials_datalab_workflows_84_0.png new file mode 100644 index 000000000..a5c34f10e Binary files /dev/null and b/master/.doctrees/nbsphinx/tutorials_datalab_workflows_84_0.png differ diff --git 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a/master/_images/tutorials_datalab_workflows_84_0.png b/master/_images/tutorials_datalab_workflows_84_0.png new file mode 100644 index 000000000..a5c34f10e Binary files /dev/null and b/master/_images/tutorials_datalab_workflows_84_0.png differ diff --git a/master/_images/tutorials_datalab_workflows_84_1.png b/master/_images/tutorials_datalab_workflows_84_1.png new file mode 100644 index 000000000..1e508d945 Binary files /dev/null and b/master/_images/tutorials_datalab_workflows_84_1.png differ diff --git a/master/_modules/cleanlab/datalab/datalab.html b/master/_modules/cleanlab/datalab/datalab.html index 83f0aa9aa..cb7ad7f14 100644 --- a/master/_modules/cleanlab/datalab/datalab.html +++ b/master/_modules/cleanlab/datalab/datalab.html @@ -657,6 +657,7 @@

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
@@ -1248,7 +1249,60 @@ 
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
+ 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 diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index 02da15562..44e8ac144 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 1d4643a4c..f6e30eaf3 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 e7aadf6ca..679092720 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 7eaeed6b0..d7ef7612a 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 c7ddd7477..301a03425 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 16a8a36cf..f364ab1d4 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 b28ce2a19..03a4b164f 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 9abe7458d..9fbabadc7 100644 --- a/master/_sources/tutorials/datalab/workflows.ipynb +++ b/master/_sources/tutorials/datalab/workflows.ipynb @@ -1326,6 +1326,211 @@ "assert all(class_imbalance_issues.query(\"is_class_imbalance_issue\")[\"class_imbalance_score\"] == 0.02), \"Class imbalance issue scores are not as expected\"\n", "assert all(class_imbalance_issues.query(\"not is_class_imbalance_issue\")[\"class_imbalance_score\"] == 1.0), \"Class imbalance issue scores are not as expected\"" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Spurious Correlation between Vision Dataset features and class labels\n", + "\n", + "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n", + "\n", + "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load the dataset\n", + "\n", + "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n", + "\n", + "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cleanlab import Datalab\n", + "from torchvision.datasets import CIFAR10\n", + "from datasets import Dataset\n", + "import io\n", + "from PIL import Image, ImageEnhance\n", + "import random\n", + "import numpy as np\n", + "from IPython.display import display, Markdown\n", + "\n", + "# Download the CIFAR-10 test dataset\n", + "data = CIFAR10(root='./data', train=False, download=True)\n", + "\n", + "# Set seed for reproducibility\n", + "np.random.seed(0)\n", + "random.seed(0)\n", + "\n", + "# Randomly select two classes\n", + "classes = list(range(len(data.classes)))\n", + "selected_classes = random.sample(classes, 2)\n", + "\n", + "# Function to convert PIL object to PNG image to be passed to the Datalab object\n", + "def convert_to_png_image(image):\n", + " buffer = io.BytesIO()\n", + " image.save(buffer, format='PNG')\n", + " buffer.seek(0)\n", + " return Image.open(buffer)\n", + "\n", + "# Generating 100 ('max_num_images') images from each of the two chosen classes\n", + "max_num_images = 100\n", + "list_images, list_labels = [], []\n", + "num_images = {selected_classes[0]: 0, selected_classes[1]: 0}\n", + "\n", + "for img, label in data:\n", + " if num_images[selected_classes[0]] == max_num_images and num_images[selected_classes[1]] == max_num_images:\n", + " break\n", + " if label in selected_classes:\n", + " if num_images[label] == max_num_images:\n", + " continue\n", + " list_images.append(convert_to_png_image(img))\n", + " list_labels.append(label)\n", + " num_images[label] += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find vision-related issues" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a datasets.Dataset object from list of images and their corresponding labels\n", + "dataset_dict = {'image': list_images, 'label': list_labels}\n", + "dataset = Dataset.from_dict(dataset_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. (Optional) Creating a transformed dataset using `ImageEnhance` to induce darkness" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to reduce brightness to 30%\n", + "def apply_dark(image):\n", + " \"\"\"Decreases brightness of the image.\"\"\"\n", + " enhancer = ImageEnhance.Brightness(image)\n", + " return enhancer.enhance(0.3)\n", + "\n", + "# Applying the darkness filter to one of the classes\n", + "transformed_list_images = [\n", + " apply_dark(img) if label == selected_classes[0] else img\n", + " for label, img in zip(list_labels, list_images)\n", + "]\n", + "\n", + "# Creating datasets.Dataset object from the transformed dataset\n", + "transformed_dataset_dict = {'image': transformed_list_images, 'label': list_labels}\n", + "transformed_dataset = Dataset.from_dict(transformed_dataset_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. (Optional) Visualizing Images in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_images(dataset_dict):\n", + " \"\"\"Plots the first 15 images from the dataset dictionary.\"\"\"\n", + " images = dataset_dict['image']\n", + " labels = dataset_dict['label']\n", + " \n", + " # Define the number of images to plot\n", + " num_images_to_plot = 15\n", + " num_cols = 5 # Number of columns in the plot grid\n", + " num_rows = (num_images_to_plot + num_cols - 1) // num_cols # Calculate rows needed\n", + " \n", + " # Create a figure\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))\n", + " axes = axes.flatten()\n", + " \n", + " # Plot each image\n", + " for i in range(num_images_to_plot):\n", + " img = images[i]\n", + " label = labels[i]\n", + " axes[i].imshow(img)\n", + " axes[i].set_title(f'Label: {label}')\n", + " axes[i].axis('off')\n", + " \n", + " # Hide any remaining empty subplots\n", + " for i in range(num_images_to_plot, len(axes)):\n", + " axes[i].axis('off')\n", + " \n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_images(dataset_dict)\n", + "plot_images(transformed_dataset_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Finding image-specific property scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to find image-specific property scores given the dataset object\n", + "def get_property_scores(dataset):\n", + " lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n", + " lab.find_issues()\n", + " return lab._spurious_correlation()\n", + "\n", + "# Finds specific property score in the dataframe containing property scores \n", + "def get_specific_property_score(property_scores_df, property_name):\n", + " return property_scores_df[property_scores_df['property'] == property_name]['score'].iloc[0]\n", + "\n", + "# Finding scores in original and transformed dataset\n", + "standard_property_scores = get_property_scores(dataset)\n", + "transformed_property_scores = get_property_scores(transformed_dataset)\n", + "\n", + "# Displaying the scores dataframe\n", + "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n", + "display(standard_property_scores)\n", + "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n", + "display(transformed_property_scores)\n", + "\n", + "# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n", + "assert get_specific_property_score(standard_property_scores, 'dark_score') > get_specific_property_score(transformed_property_scores, 'dark_score')" + ] } ], "metadata": { diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index e14cdce07..a8258d936 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 aa12460c1..fe46ee941 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 2a6fe63a9..5e76a814e 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 1ea329f55..836d2a8b5 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 b95a571a7..28042a60e 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 010ae4316..c68c7095e 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 04c5f0872..3d4b7275f 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 1333c9749..09d35cc12 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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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 745c17b57..44a722d4c 100644 --- a/master/_sources/tutorials/token_classification.ipynb +++ b/master/_sources/tutorials/token_classification.ipynb @@ -95,7 +95,7 @@ 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Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. 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Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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"module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index f3d888536..cea7ddcbf 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": "2024-06-25T23:13:19.683650Z", - "iopub.status.busy": "2024-06-25T23:13:19.683483Z", - "iopub.status.idle": "2024-06-25T23:13:20.876411Z", - "shell.execute_reply": "2024-06-25T23:13:20.875863Z" + "iopub.execute_input": "2024-06-27T15:39:08.585179Z", + "iopub.status.busy": "2024-06-27T15:39:08.584836Z", + "iopub.status.idle": "2024-06-27T15:39:09.813243Z", + "shell.execute_reply": "2024-06-27T15:39:09.812668Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:20.879016Z", - "iopub.status.busy": "2024-06-25T23:13:20.878582Z", - "iopub.status.idle": "2024-06-25T23:13:20.895831Z", - "shell.execute_reply": "2024-06-25T23:13:20.895402Z" + "iopub.execute_input": "2024-06-27T15:39:09.815976Z", + "iopub.status.busy": "2024-06-27T15:39:09.815484Z", + "iopub.status.idle": "2024-06-27T15:39:09.833810Z", + "shell.execute_reply": "2024-06-27T15:39:09.833360Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:20.897855Z", - "iopub.status.busy": "2024-06-25T23:13:20.897628Z", - "iopub.status.idle": "2024-06-25T23:13:21.010572Z", - "shell.execute_reply": "2024-06-25T23:13:21.009996Z" + "iopub.execute_input": "2024-06-27T15:39:09.836247Z", + "iopub.status.busy": "2024-06-27T15:39:09.835762Z", + "iopub.status.idle": "2024-06-27T15:39:10.211400Z", + "shell.execute_reply": "2024-06-27T15:39:10.210811Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.037181Z", - "iopub.status.busy": "2024-06-25T23:13:21.036568Z", - "iopub.status.idle": "2024-06-25T23:13:21.040405Z", - "shell.execute_reply": "2024-06-25T23:13:21.039967Z" + "iopub.execute_input": "2024-06-27T15:39:10.241419Z", + "iopub.status.busy": "2024-06-27T15:39:10.241204Z", + "iopub.status.idle": "2024-06-27T15:39:10.245054Z", + "shell.execute_reply": "2024-06-27T15:39:10.244590Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.042333Z", - "iopub.status.busy": "2024-06-25T23:13:21.042161Z", - "iopub.status.idle": "2024-06-25T23:13:21.050408Z", - "shell.execute_reply": "2024-06-25T23:13:21.049993Z" + "iopub.execute_input": "2024-06-27T15:39:10.247265Z", + "iopub.status.busy": "2024-06-27T15:39:10.246832Z", + "iopub.status.idle": "2024-06-27T15:39:10.255149Z", + "shell.execute_reply": "2024-06-27T15:39:10.254595Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.052411Z", - "iopub.status.busy": "2024-06-25T23:13:21.052111Z", - "iopub.status.idle": "2024-06-25T23:13:21.054810Z", - "shell.execute_reply": "2024-06-25T23:13:21.054263Z" + "iopub.execute_input": "2024-06-27T15:39:10.257365Z", + "iopub.status.busy": "2024-06-27T15:39:10.257091Z", + "iopub.status.idle": "2024-06-27T15:39:10.259655Z", + "shell.execute_reply": "2024-06-27T15:39:10.259220Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.056799Z", - "iopub.status.busy": "2024-06-25T23:13:21.056479Z", - "iopub.status.idle": "2024-06-25T23:13:21.584928Z", - "shell.execute_reply": "2024-06-25T23:13:21.584385Z" + "iopub.execute_input": "2024-06-27T15:39:10.261511Z", + "iopub.status.busy": "2024-06-27T15:39:10.261339Z", + "iopub.status.idle": "2024-06-27T15:39:10.790397Z", + "shell.execute_reply": "2024-06-27T15:39:10.789846Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:21.587427Z", - "iopub.status.busy": "2024-06-25T23:13:21.587080Z", - "iopub.status.idle": "2024-06-25T23:13:23.402116Z", - "shell.execute_reply": "2024-06-25T23:13:23.401472Z" + "iopub.execute_input": "2024-06-27T15:39:10.792795Z", + "iopub.status.busy": "2024-06-27T15:39:10.792563Z", + "iopub.status.idle": "2024-06-27T15:39:12.690033Z", + "shell.execute_reply": "2024-06-27T15:39:12.689420Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.404837Z", - "iopub.status.busy": "2024-06-25T23:13:23.404191Z", - "iopub.status.idle": "2024-06-25T23:13:23.414068Z", - "shell.execute_reply": "2024-06-25T23:13:23.413559Z" + "iopub.execute_input": "2024-06-27T15:39:12.692713Z", + "iopub.status.busy": "2024-06-27T15:39:12.692162Z", + "iopub.status.idle": "2024-06-27T15:39:12.702272Z", + "shell.execute_reply": "2024-06-27T15:39:12.701680Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.416257Z", - "iopub.status.busy": "2024-06-25T23:13:23.415941Z", - "iopub.status.idle": "2024-06-25T23:13:23.420056Z", - "shell.execute_reply": "2024-06-25T23:13:23.419521Z" + "iopub.execute_input": "2024-06-27T15:39:12.704223Z", + "iopub.status.busy": "2024-06-27T15:39:12.703971Z", + "iopub.status.idle": "2024-06-27T15:39:12.707870Z", + "shell.execute_reply": "2024-06-27T15:39:12.707432Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:23.422287Z", - "iopub.status.busy": "2024-06-25T23:13:23.421904Z", - "iopub.status.idle": "2024-06-25T23:13:23.429186Z", - 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--git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index bab4e39b3..e29404df2 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_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}
+Classes: {'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'cancel_transfer'}

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

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

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -924,8 +924,6 @@

2. Load and format the text dataset 
 No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  return self.fget.__get__(instance, owner)()

Our subsequent ML model will directly operate on elements of train_texts and test_texts in order to classify the customer service requests.

@@ -1215,7 +1213,7 @@

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"model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_557a0462e666492da24bb670d1cdb9e6", "IPY_MODEL_4dd634f0f25a4a8c88b73572fb638979", "IPY_MODEL_484ba1921a7a4b5ab97a65274b5e03f3"], "layout": "IPY_MODEL_52c0d4d2ca364b15a22f5aa4b5badec9", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index 9af680b6f..acadfeca1 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": "2024-06-25T23:13:28.905676Z", - "iopub.status.busy": "2024-06-25T23:13:28.905503Z", - "iopub.status.idle": "2024-06-25T23:13:31.555296Z", - "shell.execute_reply": "2024-06-25T23:13:31.554730Z" + "iopub.execute_input": "2024-06-27T15:39:18.202742Z", + "iopub.status.busy": "2024-06-27T15:39:18.202571Z", + "iopub.status.idle": "2024-06-27T15:39:21.198042Z", + "shell.execute_reply": "2024-06-27T15:39:21.197385Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:31.557860Z", - "iopub.status.busy": "2024-06-25T23:13:31.557469Z", - "iopub.status.idle": "2024-06-25T23:13:31.560897Z", - "shell.execute_reply": "2024-06-25T23:13:31.560352Z" + "iopub.execute_input": "2024-06-27T15:39:21.200796Z", + "iopub.status.busy": "2024-06-27T15:39:21.200477Z", + "iopub.status.idle": "2024-06-27T15:39:21.203994Z", + "shell.execute_reply": "2024-06-27T15:39:21.203442Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.562942Z", - "iopub.status.busy": "2024-06-25T23:13:31.562629Z", - "iopub.status.idle": "2024-06-25T23:13:31.565542Z", - "shell.execute_reply": "2024-06-25T23:13:31.565096Z" + "iopub.execute_input": "2024-06-27T15:39:21.205955Z", + "iopub.status.busy": "2024-06-27T15:39:21.205635Z", + "iopub.status.idle": "2024-06-27T15:39:21.208797Z", + "shell.execute_reply": "2024-06-27T15:39:21.208260Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.567524Z", - "iopub.status.busy": "2024-06-25T23:13:31.567195Z", - "iopub.status.idle": "2024-06-25T23:13:31.589244Z", - "shell.execute_reply": "2024-06-25T23:13:31.588737Z" + "iopub.execute_input": "2024-06-27T15:39:21.210955Z", + "iopub.status.busy": "2024-06-27T15:39:21.210659Z", + "iopub.status.idle": "2024-06-27T15:39:21.370903Z", + "shell.execute_reply": "2024-06-27T15:39:21.370338Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.591105Z", - "iopub.status.busy": "2024-06-25T23:13:31.590840Z", - "iopub.status.idle": "2024-06-25T23:13:31.594215Z", - "shell.execute_reply": "2024-06-25T23:13:31.593789Z" + "iopub.execute_input": "2024-06-27T15:39:21.373180Z", + "iopub.status.busy": "2024-06-27T15:39:21.372853Z", + "iopub.status.idle": "2024-06-27T15:39:21.376455Z", + "shell.execute_reply": "2024-06-27T15:39:21.375919Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.596064Z", - "iopub.status.busy": "2024-06-25T23:13:31.595883Z", - "iopub.status.idle": "2024-06-25T23:13:31.599153Z", - "shell.execute_reply": "2024-06-25T23:13:31.598670Z" + "iopub.execute_input": "2024-06-27T15:39:21.378544Z", + "iopub.status.busy": "2024-06-27T15:39:21.378146Z", + "iopub.status.idle": "2024-06-27T15:39:21.381367Z", + "shell.execute_reply": "2024-06-27T15:39:21.380892Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'cancel_transfer'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.601175Z", - "iopub.status.busy": "2024-06-25T23:13:31.600751Z", - "iopub.status.idle": "2024-06-25T23:13:31.603901Z", - "shell.execute_reply": "2024-06-25T23:13:31.603365Z" + "iopub.execute_input": "2024-06-27T15:39:21.383438Z", + "iopub.status.busy": "2024-06-27T15:39:21.383129Z", + "iopub.status.idle": "2024-06-27T15:39:21.386120Z", + "shell.execute_reply": "2024-06-27T15:39:21.385587Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:31.606046Z", - "iopub.status.busy": "2024-06-25T23:13:31.605618Z", - "iopub.status.idle": "2024-06-25T23:13:31.608973Z", - 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Creating a new one with MEAN pooling.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n" - ] } ], "source": [ @@ -609,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:35.912144Z", - "iopub.status.busy": "2024-06-25T23:13:35.911799Z", - "iopub.status.idle": "2024-06-25T23:13:35.914630Z", - "shell.execute_reply": "2024-06-25T23:13:35.914095Z" + "iopub.execute_input": "2024-06-27T15:39:27.669349Z", + "iopub.status.busy": "2024-06-27T15:39:27.668878Z", + "iopub.status.idle": "2024-06-27T15:39:27.672003Z", + "shell.execute_reply": "2024-06-27T15:39:27.671571Z" } }, "outputs": [], @@ -634,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:13:35.916621Z", - "iopub.status.busy": "2024-06-25T23:13:35.916300Z", - "iopub.status.idle": "2024-06-25T23:13:35.918968Z", - "shell.execute_reply": "2024-06-25T23:13:35.918524Z" + "iopub.execute_input": "2024-06-27T15:39:27.673974Z", + "iopub.status.busy": "2024-06-27T15:39:27.673659Z", + "iopub.status.idle": "2024-06-27T15:39:27.676360Z", + "shell.execute_reply": "2024-06-27T15:39:27.675805Z" } }, "outputs": [], @@ -652,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - 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3. Use pre-trained SpeechBrain model to featurize audio -
+
[11]:
@@ -915,16 +915,6 @@

3. Use pre-trained SpeechBrain model to featurize audio

-
-
-
-
-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/functional.py:650: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
-Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:863.)
-  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
-
-

Now we have our features in a 2D numpy array. Each row in the array corresponds to an audio clip. We’re now able to represent each audio clip as a 512-dimensional feature vector!

[12]:
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5. Use cleanlab to find label issues
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diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb
index d40b1db54..93586bee5 100644
--- a/master/tutorials/datalab/audio.ipynb
+++ b/master/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:42.048585Z",
-     "iopub.status.busy": "2024-06-25T23:13:42.048169Z",
-     "iopub.status.idle": "2024-06-25T23:13:47.015851Z",
-     "shell.execute_reply": "2024-06-25T23:13:47.015219Z"
+     "iopub.execute_input": "2024-06-27T15:39:35.362328Z",
+     "iopub.status.busy": "2024-06-27T15:39:35.361825Z",
+     "iopub.status.idle": "2024-06-27T15:39:40.570041Z",
+     "shell.execute_reply": "2024-06-27T15:39:40.569408Z"
     },
     "nbsphinx": "hidden"
    },
@@ -97,7 +97,7 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:13:47.018616Z",
-     "iopub.status.busy": "2024-06-25T23:13:47.018295Z",
-     "iopub.status.idle": "2024-06-25T23:13:47.021447Z",
-     "shell.execute_reply": "2024-06-25T23:13:47.020989Z"
+     "iopub.execute_input": "2024-06-27T15:39:40.572766Z",
+     "iopub.status.busy": "2024-06-27T15:39:40.572228Z",
+     "iopub.status.idle": "2024-06-27T15:39:40.575350Z",
+     "shell.execute_reply": "2024-06-27T15:39:40.574921Z"
     },
     "id": "LaEiwXUiVHCS"
    },
@@ -157,10 +157,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:47.023397Z",
-     "iopub.status.busy": "2024-06-25T23:13:47.023066Z",
-     "iopub.status.idle": "2024-06-25T23:13:47.027579Z",
-     "shell.execute_reply": "2024-06-25T23:13:47.027038Z"
+     "iopub.execute_input": "2024-06-27T15:39:40.577400Z",
+     "iopub.status.busy": "2024-06-27T15:39:40.577085Z",
+     "iopub.status.idle": "2024-06-27T15:39:40.581458Z",
+     "shell.execute_reply": "2024-06-27T15:39:40.581032Z"
     },
     "nbsphinx": "hidden"
    },
@@ -208,10 +208,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:47.029706Z",
-     "iopub.status.busy": "2024-06-25T23:13:47.029408Z",
-     "iopub.status.idle": "2024-06-25T23:13:48.557949Z",
-     "shell.execute_reply": "2024-06-25T23:13:48.557324Z"
+     "iopub.execute_input": "2024-06-27T15:39:40.583428Z",
+     "iopub.status.busy": "2024-06-27T15:39:40.583105Z",
+     "iopub.status.idle": "2024-06-27T15:39:50.584027Z",
+     "shell.execute_reply": "2024-06-27T15:39:50.583280Z"
     },
     "id": "GRDPEg7-VOQe",
     "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:48.560586Z",
-     "iopub.status.busy": "2024-06-25T23:13:48.560204Z",
-     "iopub.status.idle": "2024-06-25T23:13:48.570753Z",
-     "shell.execute_reply": "2024-06-25T23:13:48.570316Z"
+     "iopub.execute_input": "2024-06-27T15:39:50.586704Z",
+     "iopub.status.busy": "2024-06-27T15:39:50.586468Z",
+     "iopub.status.idle": "2024-06-27T15:39:50.596927Z",
+     "shell.execute_reply": "2024-06-27T15:39:50.596449Z"
     },
     "id": "FDA5sGZwUSur",
     "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:48.572948Z",
-     "iopub.status.busy": "2024-06-25T23:13:48.572614Z",
-     "iopub.status.idle": "2024-06-25T23:13:48.578335Z",
-     "shell.execute_reply": "2024-06-25T23:13:48.577906Z"
+     "iopub.execute_input": "2024-06-27T15:39:50.599153Z",
+     "iopub.status.busy": "2024-06-27T15:39:50.598811Z",
+     "iopub.status.idle": "2024-06-27T15:39:50.604158Z",
+     "shell.execute_reply": "2024-06-27T15:39:50.603720Z"
     },
     "nbsphinx": "hidden"
    },
@@ -380,10 +380,10 @@
      "height": 92
     },
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:48.580333Z",
-     "iopub.status.busy": "2024-06-25T23:13:48.580014Z",
-     "iopub.status.idle": "2024-06-25T23:13:49.044116Z",
-     "shell.execute_reply": "2024-06-25T23:13:49.043554Z"
+     "iopub.execute_input": "2024-06-27T15:39:50.606326Z",
+     "iopub.status.busy": "2024-06-27T15:39:50.605894Z",
+     "iopub.status.idle": "2024-06-27T15:39:51.061002Z",
+     "shell.execute_reply": "2024-06-27T15:39:51.060399Z"
     },
     "id": "dLBvUZLlII5w",
     "outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:49.046459Z",
-     "iopub.status.busy": "2024-06-25T23:13:49.046048Z",
-     "iopub.status.idle": "2024-06-25T23:13:49.682286Z",
-     "shell.execute_reply": "2024-06-25T23:13:49.681791Z"
+     "iopub.execute_input": "2024-06-27T15:39:51.063242Z",
+     "iopub.status.busy": "2024-06-27T15:39:51.062823Z",
+     "iopub.status.idle": "2024-06-27T15:39:52.856305Z",
+     "shell.execute_reply": "2024-06-27T15:39:52.855828Z"
     },
     "id": "vL9lkiKsHvKr"
    },
@@ -474,10 +474,10 @@
      "height": 143
     },
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:49.685227Z",
-     "iopub.status.busy": "2024-06-25T23:13:49.684826Z",
-     "iopub.status.idle": "2024-06-25T23:13:49.703315Z",
-     "shell.execute_reply": "2024-06-25T23:13:49.702790Z"
+     "iopub.execute_input": "2024-06-27T15:39:52.858818Z",
+     "iopub.status.busy": "2024-06-27T15:39:52.858492Z",
+     "iopub.status.idle": "2024-06-27T15:39:52.876475Z",
+     "shell.execute_reply": "2024-06-27T15:39:52.875972Z"
     },
     "id": "obQYDKdLiUU6",
     "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:49.705384Z",
-     "iopub.status.busy": "2024-06-25T23:13:49.705205Z",
-     "iopub.status.idle": "2024-06-25T23:13:49.708482Z",
-     "shell.execute_reply": "2024-06-25T23:13:49.708013Z"
+     "iopub.execute_input": "2024-06-27T15:39:52.878507Z",
+     "iopub.status.busy": "2024-06-27T15:39:52.878190Z",
+     "iopub.status.idle": "2024-06-27T15:39:52.881147Z",
+     "shell.execute_reply": "2024-06-27T15:39:52.880732Z"
     },
     "id": "I8JqhOZgi94g"
    },
@@ -582,24 +582,14 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:13:49.710490Z",
-     "iopub.status.busy": "2024-06-25T23:13:49.710159Z",
-     "iopub.status.idle": "2024-06-25T23:14:03.836426Z",
-     "shell.execute_reply": "2024-06-25T23:14:03.835865Z"
+     "iopub.execute_input": "2024-06-27T15:39:52.883147Z",
+     "iopub.status.busy": "2024-06-27T15:39:52.882820Z",
+     "iopub.status.idle": "2024-06-27T15:40:07.290357Z",
+     "shell.execute_reply": "2024-06-27T15:40:07.289818Z"
     },
     "id": "2FSQ2GR9R_YA"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/functional.py:650: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
-      "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:863.)\n",
-      "  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Extract audio embeddings\n",
     "embeddings_list = []\n",
@@ -627,10 +617,10 @@
      "base_uri": "https://localhost:8080/"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:03.839037Z",
-     "iopub.status.busy": "2024-06-25T23:14:03.838661Z",
-     "iopub.status.idle": "2024-06-25T23:14:03.842744Z",
-     "shell.execute_reply": "2024-06-25T23:14:03.842282Z"
+     "iopub.execute_input": "2024-06-27T15:40:07.293008Z",
+     "iopub.status.busy": "2024-06-27T15:40:07.292676Z",
+     "iopub.status.idle": "2024-06-27T15:40:07.296534Z",
+     "shell.execute_reply": "2024-06-27T15:40:07.295978Z"
     },
     "id": "kAkY31IVXyr8",
     "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -690,10 +680,10 @@
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    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-25T23:14:03.844392Z",
-     "iopub.status.idle": "2024-06-25T23:14:04.554198Z",
-     "shell.execute_reply": "2024-06-25T23:14:04.553609Z"
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index 54ecba124..e69ba2419 100644
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Functionality 2: Specifying nondefault arguments
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You can also increase the verbosity of the report to see additional information about the data issues and control how many top-ranked examples are shown for each issue.

 

 
[12]:
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Functionality 3: Save and load Datalab objects
 

 

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diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb
index 17bb19429..8c230d5d3 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": "2024-06-25T23:14:09.178246Z",
-     "iopub.status.busy": "2024-06-25T23:14:09.177763Z",
-     "iopub.status.idle": "2024-06-25T23:14:10.319594Z",
-     "shell.execute_reply": "2024-06-25T23:14:10.319043Z"
+     "iopub.execute_input": "2024-06-27T15:40:11.815285Z",
+     "iopub.status.busy": "2024-06-27T15:40:11.815110Z",
+     "iopub.status.idle": "2024-06-27T15:40:12.989400Z",
+     "shell.execute_reply": "2024-06-27T15:40:12.988851Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:14:10.322187Z",
-     "iopub.status.busy": "2024-06-25T23:14:10.321743Z",
-     "iopub.status.idle": "2024-06-25T23:14:10.324748Z",
-     "shell.execute_reply": "2024-06-25T23:14:10.324303Z"
+     "iopub.execute_input": "2024-06-27T15:40:12.991789Z",
+     "iopub.status.busy": "2024-06-27T15:40:12.991513Z",
+     "iopub.status.idle": "2024-06-27T15:40:12.994521Z",
+     "shell.execute_reply": "2024-06-27T15:40:12.993986Z"
     }
    },
    "outputs": [],
@@ -252,10 +252,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:10.326836Z",
-     "iopub.status.busy": "2024-06-25T23:14:10.326547Z",
-     "iopub.status.idle": "2024-06-25T23:14:10.335582Z",
-     "shell.execute_reply": "2024-06-25T23:14:10.335001Z"
+     "iopub.execute_input": "2024-06-27T15:40:12.996583Z",
+     "iopub.status.busy": "2024-06-27T15:40:12.996408Z",
+     "iopub.status.idle": "2024-06-27T15:40:13.004733Z",
+     "shell.execute_reply": "2024-06-27T15:40:13.004294Z"
     },
     "nbsphinx": "hidden"
    },
@@ -353,10 +353,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:10.337605Z",
-     "iopub.status.busy": "2024-06-25T23:14:10.337298Z",
-     "iopub.status.idle": "2024-06-25T23:14:10.342294Z",
-     "shell.execute_reply": "2024-06-25T23:14:10.341736Z"
+     "iopub.execute_input": "2024-06-27T15:40:13.006788Z",
+     "iopub.status.busy": "2024-06-27T15:40:13.006454Z",
+     "iopub.status.idle": "2024-06-27T15:40:13.011412Z",
+     "shell.execute_reply": "2024-06-27T15:40:13.010879Z"
     }
    },
    "outputs": [],
@@ -445,10 +445,10 @@
    "execution_count": 5,
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        "description_allow_html": false,
-       "layout": "IPY_MODEL_daa1004e74fa4197b88715988591c621",
+       "layout": "IPY_MODEL_63a5476a41dc4f07a300d89a3ff5240f",
        "max": 132.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_781b877c2bbc46b7969db8529c1eb5c3",
+       "style": "IPY_MODEL_c91d4d01810d4b2e9e81bb9595425ce7",
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diff --git a/master/tutorials/datalab/datalab_quickstart.html b/master/tutorials/datalab/datalab_quickstart.html
index bd8387625..bbfc15651 100644
--- a/master/tutorials/datalab/datalab_quickstart.html
+++ b/master/tutorials/datalab/datalab_quickstart.html
@@ -899,7 +899,7 @@ 
3. Get out-of-sample predicted probabilities from a classifierDatalab relies on predicted class probabilities from a trained model. Ideally, the prediction for each example should be out-of-sample (to avoid overfitting), coming from a copy of the model that was not trained on this example.

 
This tutorial uses a simple logistic regression model and the cross_val_predict() function from scikit-learn to generate out-of-sample predicted class probabilities for every example in the training set. You can replace this with any other classifier model and train it with cross-validation to get out-of-sample predictions. Make sure that the columns of your pred_probs are properly ordered with respect to the ordering of classes, which for Datalab is: lexicographically sorted by class
 name.

-

+

 
[8]:
 

 

@@ -910,15 +910,6 @@ 
3. Get out-of-sample predicted probabilities from a classifier
-

-

-

-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.
-  warnings.warn(
-

-

 
 

 
4. Use Datalab to find issues in the dataset#

@@ -934,29 +925,13 @@ 
4. Use Datalab to find issues in the dataset
+

 

 

 

 
 Finding null issues ...
 Finding label issues ...
-

-

-

-

-

-

-
-/home/runner/work/cleanlab/cleanlab/cleanlab/filter.py:904: UserWarning: May not flag all label issues in class: 2, it has too few examples (see `min_examples_per_class` argument)
-  warnings.warn(
-

-

-

-

-

-

-
 Finding outlier issues ...
 Finding near_duplicate issues ...
 Finding non_iid issues ...
@@ -966,15 +941,6 @@ 
4. Use Datalab to find issues in the dataset
-

-

-

-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
-

-

 
Now let’s review the results of this audit using report(). This provides a high-level summary of each type of issue found in the dataset.

 

 
[10]:
diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb
index 4fb163767..4b7588207 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": "2024-06-25T23:14:15.711188Z",
-     "iopub.status.busy": "2024-06-25T23:14:15.711012Z",
-     "iopub.status.idle": "2024-06-25T23:14:16.870873Z",
-     "shell.execute_reply": "2024-06-25T23:14:16.870268Z"
+     "iopub.execute_input": "2024-06-27T15:40:18.510907Z",
+     "iopub.status.busy": "2024-06-27T15:40:18.510569Z",
+     "iopub.status.idle": "2024-06-27T15:40:19.673840Z",
+     "shell.execute_reply": "2024-06-27T15:40:19.673269Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:14:16.873481Z",
-     "iopub.status.busy": "2024-06-25T23:14:16.873232Z",
-     "iopub.status.idle": "2024-06-25T23:14:16.876287Z",
-     "shell.execute_reply": "2024-06-25T23:14:16.875762Z"
+     "iopub.execute_input": "2024-06-27T15:40:19.676218Z",
+     "iopub.status.busy": "2024-06-27T15:40:19.675956Z",
+     "iopub.status.idle": "2024-06-27T15:40:19.678853Z",
+     "shell.execute_reply": "2024-06-27T15:40:19.678422Z"
     }
    },
    "outputs": [],
@@ -250,10 +250,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:16.878379Z",
-     "iopub.status.busy": "2024-06-25T23:14:16.878075Z",
-     "iopub.status.idle": "2024-06-25T23:14:16.887427Z",
-     "shell.execute_reply": "2024-06-25T23:14:16.886901Z"
+     "iopub.execute_input": "2024-06-27T15:40:19.680895Z",
+     "iopub.status.busy": "2024-06-27T15:40:19.680636Z",
+     "iopub.status.idle": "2024-06-27T15:40:19.689587Z",
+     "shell.execute_reply": "2024-06-27T15:40:19.689155Z"
     },
     "nbsphinx": "hidden"
    },
@@ -356,10 +356,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:16.889377Z",
-     "iopub.status.busy": "2024-06-25T23:14:16.889034Z",
-     "iopub.status.idle": "2024-06-25T23:14:16.893463Z",
-     "shell.execute_reply": "2024-06-25T23:14:16.893025Z"
+     "iopub.execute_input": "2024-06-27T15:40:19.691676Z",
+     "iopub.status.busy": "2024-06-27T15:40:19.691286Z",
+     "iopub.status.idle": "2024-06-27T15:40:19.696059Z",
+     "shell.execute_reply": "2024-06-27T15:40:19.695657Z"
     }
    },
    "outputs": [],
@@ -448,10 +448,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:16.895454Z",
-     "iopub.status.busy": "2024-06-25T23:14:16.895124Z",
-     "iopub.status.idle": "2024-06-25T23:14:17.076668Z",
-     "shell.execute_reply": "2024-06-25T23:14:17.076135Z"
+     "iopub.execute_input": "2024-06-27T15:40:19.698308Z",
+     "iopub.status.busy": "2024-06-27T15:40:19.697913Z",
+     "iopub.status.idle": "2024-06-27T15:40:19.880985Z",
+     "shell.execute_reply": "2024-06-27T15:40:19.880426Z"
     },
     "nbsphinx": "hidden"
    },
@@ -520,10 +520,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:17.079016Z",
-     "iopub.status.busy": "2024-06-25T23:14:17.078687Z",
-     "iopub.status.idle": "2024-06-25T23:14:17.444945Z",
-     "shell.execute_reply": "2024-06-25T23:14:17.444376Z"
+     "iopub.execute_input": "2024-06-27T15:40:19.883569Z",
+     "iopub.status.busy": "2024-06-27T15:40:19.883156Z",
+     "iopub.status.idle": "2024-06-27T15:40:20.200344Z",
+     "shell.execute_reply": "2024-06-27T15:40:20.199741Z"
     }
    },
    "outputs": [
@@ -559,10 +559,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:17.447239Z",
-     "iopub.status.busy": "2024-06-25T23:14:17.446903Z",
-     "iopub.status.idle": "2024-06-25T23:14:17.449525Z",
-     "shell.execute_reply": "2024-06-25T23:14:17.449111Z"
+     "iopub.execute_input": "2024-06-27T15:40:20.202400Z",
+     "iopub.status.busy": "2024-06-27T15:40:20.202212Z",
+     "iopub.status.idle": "2024-06-27T15:40:20.205007Z",
+     "shell.execute_reply": "2024-06-27T15:40:20.204573Z"
     }
    },
    "outputs": [],
@@ -602,22 +602,13 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:17.451586Z",
-     "iopub.status.busy": "2024-06-25T23:14:17.451263Z",
-     "iopub.status.idle": "2024-06-25T23:14:17.486312Z",
-     "shell.execute_reply": "2024-06-25T23:14:17.485793Z"
+     "iopub.execute_input": "2024-06-27T15:40:20.206898Z",
+     "iopub.status.busy": "2024-06-27T15:40:20.206724Z",
+     "iopub.status.idle": "2024-06-27T15:40:20.240804Z",
+     "shell.execute_reply": "2024-06-27T15:40:20.240385Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LogisticRegression()\n",
     "pred_probs = cross_val_predict(\n",
@@ -647,10 +638,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:17.488380Z",
-     "iopub.status.busy": "2024-06-25T23:14:17.488048Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.486184Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.485493Z"
+     "iopub.execute_input": "2024-06-27T15:40:20.242677Z",
+     "iopub.status.busy": "2024-06-27T15:40:20.242500Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.266097Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.265435Z"
     }
    },
    "outputs": [
@@ -662,14 +653,6 @@
       "Finding label issues ...\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/runner/work/cleanlab/cleanlab/cleanlab/filter.py:904: UserWarning: May not flag all label issues in class: 2, it has too few examples (see `min_examples_per_class` argument)\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -682,14 +665,6 @@
       "\n",
       "Audit complete. 30 issues found in the dataset.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n",
-      "  warnings.warn(\n"
-     ]
     }
    ],
    "source": [
@@ -710,10 +685,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.488814Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.488306Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.506666Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.506238Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.268793Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.268136Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.286425Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.285883Z"
     }
    },
    "outputs": [
@@ -846,10 +821,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.508882Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.508469Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.514832Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.514311Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.288516Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.288210Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.294611Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.294092Z"
     }
    },
    "outputs": [
@@ -960,10 +935,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.516791Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.516482Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.522090Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.521611Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.296776Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.296476Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.301964Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.301404Z"
     }
    },
    "outputs": [
@@ -1030,10 +1005,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.524150Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.523753Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.533902Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.533440Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.303895Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.303724Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.314345Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.313828Z"
     }
    },
    "outputs": [
@@ -1225,10 +1200,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.535870Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.535545Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.544125Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.543654Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.316236Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.316064Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.325038Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.324559Z"
     }
    },
    "outputs": [
@@ -1344,10 +1319,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.546177Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.545853Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.552700Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.552255Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.327062Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.326737Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.333539Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.332994Z"
     },
     "scrolled": true
    },
@@ -1472,10 +1447,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.554580Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.554407Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.563718Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.563190Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.335625Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.335309Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.344369Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.343828Z"
     }
    },
    "outputs": [
@@ -1578,10 +1553,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:14:19.565768Z",
-     "iopub.status.busy": "2024-06-25T23:14:19.565442Z",
-     "iopub.status.idle": "2024-06-25T23:14:19.576977Z",
-     "shell.execute_reply": "2024-06-25T23:14:19.576554Z"
+     "iopub.execute_input": "2024-06-27T15:40:22.346419Z",
+     "iopub.status.busy": "2024-06-27T15:40:22.346109Z",
+     "iopub.status.idle": "2024-06-27T15:40:22.357852Z",
+     "shell.execute_reply": "2024-06-27T15:40:22.357421Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html
index 1c428fccc..178684275 100644
--- a/master/tutorials/datalab/image.html
+++ b/master/tutorials/datalab/image.html
@@ -726,61 +726,50 @@ 
2. Fetch and normalize the Fashion-MNIST dataset
 

 

-

-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/datasets/load.py:1486: FutureWarning: The repository for fashion_mnist contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/fashion_mnist
-You can avoid this message in future by passing the argument `trust_remote_code=True`.
-Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-  warnings.warn(
-

-

-

-

-

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 

 
[3]:
@@ -835,7 +824,7 @@ 
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.

 

@@ -1084,8 +1073,8 @@ 
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: 4.649
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.871
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.594
 Computing feature embeddings ...
@@ -1093,7 +1082,7 @@

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

-
+
@@ -1107,7 +1096,7 @@

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

-
+
@@ -1116,8 +1105,8 @@

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: 4.663
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.845
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.415
 Computing feature embeddings ...

@@ -1125,7 +1114,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1139,7 +1128,7 @@

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

-
+
@@ -1148,8 +1137,8 @@

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: 4.680
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.936
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.512
 Computing feature embeddings ...

@@ -1157,7 +1146,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1171,7 +1160,7 @@

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

-
+
@@ -1232,17 +1221,8 @@

7. Use cleanlab to find issues
-
-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
-
-

-
-
-
-
+
@@ -2079,35 +2059,35 @@

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 @@ -2135,7 +2115,7 @@

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 da3ecdeb8..38aca061f 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": "2024-06-25T23:14:22.349033Z", - "iopub.status.busy": "2024-06-25T23:14:22.348862Z", - "iopub.status.idle": "2024-06-25T23:14:25.155777Z", - "shell.execute_reply": "2024-06-25T23:14:25.155231Z" + "iopub.execute_input": "2024-06-27T15:40:25.001578Z", + "iopub.status.busy": "2024-06-27T15:40:25.001401Z", + "iopub.status.idle": "2024-06-27T15:40:27.866577Z", + "shell.execute_reply": "2024-06-27T15:40:27.866047Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:25.158288Z", - "iopub.status.busy": "2024-06-25T23:14:25.158017Z", - "iopub.status.idle": "2024-06-25T23:14:25.161499Z", - "shell.execute_reply": "2024-06-25T23:14:25.161043Z" + "iopub.execute_input": "2024-06-27T15:40:27.869664Z", + "iopub.status.busy": "2024-06-27T15:40:27.869112Z", + "iopub.status.idle": "2024-06-27T15:40:27.873387Z", + "shell.execute_reply": "2024-06-27T15:40:27.872879Z" } }, "outputs": [], @@ -152,27 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:25.163549Z", - "iopub.status.busy": "2024-06-25T23:14:25.163223Z", - "iopub.status.idle": "2024-06-25T23:14:35.757240Z", - "shell.execute_reply": "2024-06-25T23:14:35.756685Z" + "iopub.execute_input": "2024-06-27T15:40:27.875682Z", + "iopub.status.busy": "2024-06-27T15:40:27.875354Z", + "iopub.status.idle": "2024-06-27T15:40:42.283432Z", + "shell.execute_reply": "2024-06-27T15:40:42.282878Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/datasets/load.py:1486: FutureWarning: The repository for fashion_mnist contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/fashion_mnist\n", - "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", - "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", - " warnings.warn(\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99fb59566db2452bab382261d05e2879", + "model_id": "f6bff3421ffe4305b801978e8849eb8c", "version_major": 2, "version_minor": 0 }, @@ -186,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cacaca4358c34e93a46a3e2019d188d4", + "model_id": "32a70da0aa9b42359a5f17bf665b81ad", "version_major": 2, "version_minor": 0 }, @@ -200,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "46c5f1e4a9ca403d83a2aa33da63b600", + "model_id": "126a726b8a0445da91b339235e96dcad", "version_major": 2, "version_minor": 0 }, @@ -214,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc7010cd50844e48a3db713a6ea5f850", + "model_id": "5692b6450179456c853c8bda7c713903", "version_major": 2, "version_minor": 0 }, @@ -228,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e806f052f23419ba6ec80aa76644ed5", + "model_id": "570e337efa4b4edea75f724a0413e1eb", "version_major": 2, "version_minor": 0 }, @@ -242,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3590fcc9756749e0b9130b8809114216", + "model_id": "acd955b18cd54e8e9525c5e97b625466", "version_major": 2, "version_minor": 0 }, @@ -256,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "489746a2a7db4406b7ebfd5f2a155361", + "model_id": "40ca0cd146da417fb9d146f6a0460568", "version_major": 2, "version_minor": 0 }, @@ -270,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9c41de7ac0442aabfb15bbf3b5308c8", + "model_id": "9a62dbed8c0e414aba4d919f9a3b1266", "version_major": 2, "version_minor": 0 }, @@ -312,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:35.759372Z", - "iopub.status.busy": "2024-06-25T23:14:35.759148Z", - "iopub.status.idle": "2024-06-25T23:14:35.763037Z", - "shell.execute_reply": "2024-06-25T23:14:35.762503Z" + "iopub.execute_input": "2024-06-27T15:40:42.285586Z", + "iopub.status.busy": "2024-06-27T15:40:42.285388Z", + "iopub.status.idle": "2024-06-27T15:40:42.289192Z", + "shell.execute_reply": "2024-06-27T15:40:42.288724Z" } }, "outputs": [ @@ -340,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:35.765199Z", - "iopub.status.busy": "2024-06-25T23:14:35.764868Z", - "iopub.status.idle": "2024-06-25T23:14:46.667044Z", - "shell.execute_reply": "2024-06-25T23:14:46.666518Z" + "iopub.execute_input": "2024-06-27T15:40:42.291329Z", + "iopub.status.busy": "2024-06-27T15:40:42.290912Z", + "iopub.status.idle": "2024-06-27T15:40:53.511736Z", + "shell.execute_reply": "2024-06-27T15:40:53.511208Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e075f5bd416a447eb67433e0d225370f", + "model_id": "e616ea91cf69498784ed469d8d9c5d56", "version_major": 2, "version_minor": 0 }, @@ -388,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:14:46.669519Z", - "iopub.status.busy": "2024-06-25T23:14:46.669228Z", - "iopub.status.idle": "2024-06-25T23:15:05.072765Z", - "shell.execute_reply": "2024-06-25T23:15:05.072224Z" + "iopub.execute_input": "2024-06-27T15:40:53.514345Z", + "iopub.status.busy": "2024-06-27T15:40:53.514047Z", + "iopub.status.idle": "2024-06-27T15:41:11.376798Z", + "shell.execute_reply": "2024-06-27T15:41:11.376239Z" } }, "outputs": [], @@ -424,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.075378Z", - "iopub.status.busy": "2024-06-25T23:15:05.075000Z", - "iopub.status.idle": "2024-06-25T23:15:05.080668Z", - "shell.execute_reply": "2024-06-25T23:15:05.080229Z" + "iopub.execute_input": "2024-06-27T15:41:11.379517Z", + "iopub.status.busy": "2024-06-27T15:41:11.379127Z", + "iopub.status.idle": "2024-06-27T15:41:11.384918Z", + "shell.execute_reply": "2024-06-27T15:41:11.384491Z" } }, "outputs": [], @@ -465,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.082696Z", - "iopub.status.busy": "2024-06-25T23:15:05.082377Z", - "iopub.status.idle": "2024-06-25T23:15:05.086277Z", - "shell.execute_reply": "2024-06-25T23:15:05.085865Z" + "iopub.execute_input": "2024-06-27T15:41:11.386874Z", + "iopub.status.busy": "2024-06-27T15:41:11.386562Z", + "iopub.status.idle": "2024-06-27T15:41:11.390744Z", + "shell.execute_reply": "2024-06-27T15:41:11.390228Z" }, "nbsphinx": "hidden" }, @@ -605,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.088252Z", - "iopub.status.busy": "2024-06-25T23:15:05.087933Z", - "iopub.status.idle": "2024-06-25T23:15:05.096769Z", - "shell.execute_reply": "2024-06-25T23:15:05.096319Z" + "iopub.execute_input": "2024-06-27T15:41:11.392789Z", + "iopub.status.busy": "2024-06-27T15:41:11.392619Z", + "iopub.status.idle": "2024-06-27T15:41:11.401333Z", + "shell.execute_reply": "2024-06-27T15:41:11.400906Z" }, "nbsphinx": "hidden" }, @@ -733,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.098773Z", - "iopub.status.busy": "2024-06-25T23:15:05.098471Z", - "iopub.status.idle": "2024-06-25T23:15:05.125306Z", - "shell.execute_reply": "2024-06-25T23:15:05.124855Z" + "iopub.execute_input": "2024-06-27T15:41:11.403336Z", + "iopub.status.busy": "2024-06-27T15:41:11.403016Z", + "iopub.status.idle": "2024-06-27T15:41:11.431428Z", + "shell.execute_reply": "2024-06-27T15:41:11.430968Z" } }, "outputs": [], @@ -773,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:05.127482Z", - "iopub.status.busy": "2024-06-25T23:15:05.127151Z", - "iopub.status.idle": "2024-06-25T23:15:37.033092Z", - "shell.execute_reply": "2024-06-25T23:15:37.032511Z" + "iopub.execute_input": "2024-06-27T15:41:11.433579Z", + "iopub.status.busy": "2024-06-27T15:41:11.433263Z", + "iopub.status.idle": "2024-06-27T15:41:43.942353Z", + "shell.execute_reply": "2024-06-27T15:41:43.941537Z" } }, "outputs": [ @@ -792,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.649\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.871\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.481\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.594\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8835da69dbeb4826a96baa0561232a18", + "model_id": "68e3d0ba542f4dbeb8c6a0825b907d49", "version_major": 2, "version_minor": 0 }, @@ -827,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1141e88c1cd549c1ad36f5867b926978", + "model_id": "43d72b9736c74a1ca37c68dd80b5fda9", "version_major": 2, "version_minor": 0 }, @@ -850,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.663\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.845\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.663\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.415\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2a3f5349b34148209445198c9ae64559", + "model_id": "d204f6cd3d4946408d7f8da6c520e7d5", "version_major": 2, "version_minor": 0 }, @@ -885,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c1834764c78450699f4a69ba292fe8e", + "model_id": "5a7640261eb640c6adc4bf358876b135", "version_major": 2, "version_minor": 0 }, @@ -908,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.680\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.936\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.450\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.512\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02ef28fe5e5647e49f15e9889ac88c8f", + "model_id": "e3b81984ecf6429d82760070b58a79a2", "version_major": 2, "version_minor": 0 }, @@ -943,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebc081ac7cef42f58f0c46bdca672b27", + "model_id": "fa5b88ba2d5f4e2890a86a132006ee49", "version_major": 2, "version_minor": 0 }, @@ -1022,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.035751Z", - "iopub.status.busy": "2024-06-25T23:15:37.035236Z", - "iopub.status.idle": "2024-06-25T23:15:37.049525Z", - "shell.execute_reply": "2024-06-25T23:15:37.049035Z" + "iopub.execute_input": "2024-06-27T15:41:43.944871Z", + "iopub.status.busy": "2024-06-27T15:41:43.944476Z", + "iopub.status.idle": "2024-06-27T15:41:43.958369Z", + "shell.execute_reply": "2024-06-27T15:41:43.957946Z" } }, "outputs": [], @@ -1050,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.051460Z", - "iopub.status.busy": "2024-06-25T23:15:37.051284Z", - "iopub.status.idle": "2024-06-25T23:15:37.533678Z", - "shell.execute_reply": "2024-06-25T23:15:37.533181Z" + "iopub.execute_input": "2024-06-27T15:41:43.960372Z", + "iopub.status.busy": "2024-06-27T15:41:43.959983Z", + "iopub.status.idle": "2024-06-27T15:41:44.421845Z", + "shell.execute_reply": "2024-06-27T15:41:44.421194Z" } }, "outputs": [], @@ -1073,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:15:37.536010Z", - "iopub.status.busy": "2024-06-25T23:15:37.535826Z", - "iopub.status.idle": "2024-06-25T23:17:13.081610Z", - "shell.execute_reply": "2024-06-25T23:17:13.080989Z" + "iopub.execute_input": "2024-06-27T15:41:44.424353Z", + "iopub.status.busy": "2024-06-27T15:41:44.424167Z", + "iopub.status.idle": "2024-06-27T15:43:21.061556Z", + "shell.execute_reply": "2024-06-27T15:43:21.060943Z" } }, "outputs": [ @@ -1112,18 +1102,10 @@ "Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55c0a386d760485f92009bb75259396b", + "model_id": "bcbd871e719b4ad2aabe57836da305b9", "version_major": 2, "version_minor": 0 }, @@ -1162,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.084039Z", - "iopub.status.busy": "2024-06-25T23:17:13.083667Z", - "iopub.status.idle": "2024-06-25T23:17:13.530568Z", - "shell.execute_reply": "2024-06-25T23:17:13.530038Z" + "iopub.execute_input": "2024-06-27T15:43:21.064200Z", + "iopub.status.busy": "2024-06-27T15:43:21.063532Z", + "iopub.status.idle": "2024-06-27T15:43:21.524964Z", + "shell.execute_reply": "2024-06-27T15:43:21.524405Z" } }, "outputs": [ @@ -1311,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.532958Z", - "iopub.status.busy": "2024-06-25T23:17:13.532616Z", - "iopub.status.idle": "2024-06-25T23:17:13.595525Z", - "shell.execute_reply": "2024-06-25T23:17:13.594969Z" + "iopub.execute_input": "2024-06-27T15:43:21.527621Z", + "iopub.status.busy": "2024-06-27T15:43:21.527216Z", + "iopub.status.idle": "2024-06-27T15:43:21.589216Z", + "shell.execute_reply": "2024-06-27T15:43:21.588613Z" } }, "outputs": [ @@ -1418,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.597922Z", - "iopub.status.busy": "2024-06-25T23:17:13.597476Z", - "iopub.status.idle": "2024-06-25T23:17:13.606785Z", - "shell.execute_reply": "2024-06-25T23:17:13.606218Z" + "iopub.execute_input": "2024-06-27T15:43:21.591599Z", + "iopub.status.busy": "2024-06-27T15:43:21.591201Z", + "iopub.status.idle": "2024-06-27T15:43:21.600770Z", + "shell.execute_reply": "2024-06-27T15:43:21.600354Z" } }, "outputs": [ @@ -1551,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.609099Z", - "iopub.status.busy": "2024-06-25T23:17:13.608903Z", - "iopub.status.idle": "2024-06-25T23:17:13.613602Z", - "shell.execute_reply": "2024-06-25T23:17:13.613147Z" + "iopub.execute_input": "2024-06-27T15:43:21.602892Z", + "iopub.status.busy": "2024-06-27T15:43:21.602563Z", + "iopub.status.idle": "2024-06-27T15:43:21.607362Z", + "shell.execute_reply": "2024-06-27T15:43:21.606903Z" }, "nbsphinx": "hidden" }, @@ -1600,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:13.615408Z", - "iopub.status.busy": "2024-06-25T23:17:13.615233Z", - "iopub.status.idle": "2024-06-25T23:17:14.118370Z", - "shell.execute_reply": "2024-06-25T23:17:14.117787Z" + "iopub.execute_input": "2024-06-27T15:43:21.609426Z", + "iopub.status.busy": "2024-06-27T15:43:21.608985Z", + "iopub.status.idle": "2024-06-27T15:43:22.081442Z", + "shell.execute_reply": "2024-06-27T15:43:22.080884Z" } }, "outputs": [ @@ -1638,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.120491Z", - "iopub.status.busy": "2024-06-25T23:17:14.120304Z", - "iopub.status.idle": "2024-06-25T23:17:14.128885Z", - "shell.execute_reply": "2024-06-25T23:17:14.128442Z" + "iopub.execute_input": "2024-06-27T15:43:22.083743Z", + "iopub.status.busy": "2024-06-27T15:43:22.083562Z", + "iopub.status.idle": "2024-06-27T15:43:22.092122Z", + "shell.execute_reply": "2024-06-27T15:43:22.091588Z" } }, "outputs": [ @@ -1808,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.131053Z", - "iopub.status.busy": "2024-06-25T23:17:14.130639Z", - "iopub.status.idle": "2024-06-25T23:17:14.433754Z", - "shell.execute_reply": "2024-06-25T23:17:14.433138Z" + "iopub.execute_input": "2024-06-27T15:43:22.094350Z", + "iopub.status.busy": "2024-06-27T15:43:22.093931Z", + "iopub.status.idle": "2024-06-27T15:43:22.101089Z", + "shell.execute_reply": "2024-06-27T15:43:22.100516Z" }, "nbsphinx": "hidden" }, @@ -1887,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.437185Z", - "iopub.status.busy": "2024-06-25T23:17:14.436581Z", - "iopub.status.idle": "2024-06-25T23:17:14.910179Z", - "shell.execute_reply": "2024-06-25T23:17:14.909590Z" + "iopub.execute_input": "2024-06-27T15:43:22.103083Z", + "iopub.status.busy": "2024-06-27T15:43:22.102690Z", + "iopub.status.idle": "2024-06-27T15:43:22.862069Z", + "shell.execute_reply": "2024-06-27T15:43:22.861441Z" } }, "outputs": [ @@ -1927,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.912393Z", - "iopub.status.busy": "2024-06-25T23:17:14.912024Z", - "iopub.status.idle": "2024-06-25T23:17:14.927515Z", - "shell.execute_reply": "2024-06-25T23:17:14.926933Z" + "iopub.execute_input": "2024-06-27T15:43:22.864339Z", + "iopub.status.busy": "2024-06-27T15:43:22.864043Z", + "iopub.status.idle": "2024-06-27T15:43:22.879510Z", + "shell.execute_reply": "2024-06-27T15:43:22.879007Z" } }, "outputs": [ @@ -2087,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.929547Z", - "iopub.status.busy": "2024-06-25T23:17:14.929372Z", - "iopub.status.idle": "2024-06-25T23:17:14.935923Z", - "shell.execute_reply": "2024-06-25T23:17:14.935427Z" + "iopub.execute_input": "2024-06-27T15:43:22.881757Z", + "iopub.status.busy": "2024-06-27T15:43:22.881295Z", + "iopub.status.idle": "2024-06-27T15:43:22.886969Z", + "shell.execute_reply": "2024-06-27T15:43:22.886435Z" }, "nbsphinx": "hidden" }, @@ -2135,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:14.937944Z", - "iopub.status.busy": "2024-06-25T23:17:14.937612Z", - "iopub.status.idle": "2024-06-25T23:17:15.400691Z", - "shell.execute_reply": "2024-06-25T23:17:15.399712Z" + "iopub.execute_input": "2024-06-27T15:43:22.888928Z", + "iopub.status.busy": "2024-06-27T15:43:22.888635Z", + "iopub.status.idle": "2024-06-27T15:43:23.356645Z", + "shell.execute_reply": "2024-06-27T15:43:23.355700Z" } }, "outputs": [ @@ -2220,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.403380Z", - "iopub.status.busy": "2024-06-25T23:17:15.403170Z", - "iopub.status.idle": "2024-06-25T23:17:15.412375Z", - "shell.execute_reply": "2024-06-25T23:17:15.411801Z" + "iopub.execute_input": "2024-06-27T15:43:23.359134Z", + "iopub.status.busy": "2024-06-27T15:43:23.358923Z", + "iopub.status.idle": "2024-06-27T15:43:23.368538Z", + "shell.execute_reply": "2024-06-27T15:43:23.367969Z" } }, "outputs": [ @@ -2351,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.414938Z", - "iopub.status.busy": "2024-06-25T23:17:15.414744Z", - "iopub.status.idle": "2024-06-25T23:17:15.420451Z", - "shell.execute_reply": "2024-06-25T23:17:15.419881Z" + "iopub.execute_input": "2024-06-27T15:43:23.370975Z", + "iopub.status.busy": "2024-06-27T15:43:23.370626Z", + "iopub.status.idle": "2024-06-27T15:43:23.376432Z", + "shell.execute_reply": "2024-06-27T15:43:23.375942Z" }, "nbsphinx": "hidden" }, @@ -2391,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.422902Z", - "iopub.status.busy": "2024-06-25T23:17:15.422710Z", - "iopub.status.idle": "2024-06-25T23:17:15.624779Z", - "shell.execute_reply": "2024-06-25T23:17:15.624291Z" + "iopub.execute_input": "2024-06-27T15:43:23.378787Z", + "iopub.status.busy": "2024-06-27T15:43:23.378307Z", + "iopub.status.idle": "2024-06-27T15:43:23.579346Z", + "shell.execute_reply": "2024-06-27T15:43:23.578743Z" } }, "outputs": [ @@ -2436,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.627227Z", - "iopub.status.busy": "2024-06-25T23:17:15.626865Z", - "iopub.status.idle": "2024-06-25T23:17:15.634605Z", - "shell.execute_reply": "2024-06-25T23:17:15.634166Z" + "iopub.execute_input": "2024-06-27T15:43:23.581679Z", + "iopub.status.busy": "2024-06-27T15:43:23.581319Z", + "iopub.status.idle": "2024-06-27T15:43:23.589513Z", + "shell.execute_reply": "2024-06-27T15:43:23.589053Z" } }, "outputs": [ @@ -2464,47 +2446,47 @@ " \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, @@ -2525,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:15.636681Z", - "iopub.status.busy": "2024-06-25T23:17:15.636363Z", - "iopub.status.idle": "2024-06-25T23:17:15.834587Z", - "shell.execute_reply": "2024-06-25T23:17:15.834003Z" + "iopub.execute_input": "2024-06-27T15:43:23.591459Z", + "iopub.status.busy": "2024-06-27T15:43:23.591165Z", + "iopub.status.idle": "2024-06-27T15:43:23.764728Z", + "shell.execute_reply": "2024-06-27T15:43:23.764122Z" } }, "outputs": [ @@ -2568,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - 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5. Use cleanlab to find label issues +
@@ -906,15 +906,6 @@

5. Use cleanlab to find label issues -
-
-
-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
-
-

[10]:
diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 7f5df08d9..e10fb5ba3 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:19.488251Z", - "iopub.status.busy": "2024-06-25T23:17:19.488091Z", - "iopub.status.idle": "2024-06-25T23:17:20.586301Z", - "shell.execute_reply": "2024-06-25T23:17:20.585756Z" + "iopub.execute_input": "2024-06-27T15:43:27.478769Z", + "iopub.status.busy": "2024-06-27T15:43:27.478597Z", + "iopub.status.idle": "2024-06-27T15:43:28.615331Z", + "shell.execute_reply": "2024-06-27T15:43:28.614783Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:20.589007Z", - "iopub.status.busy": "2024-06-25T23:17:20.588566Z", - "iopub.status.idle": "2024-06-25T23:17:20.607142Z", - "shell.execute_reply": "2024-06-25T23:17:20.606704Z" + "iopub.execute_input": "2024-06-27T15:43:28.617939Z", + "iopub.status.busy": "2024-06-27T15:43:28.617477Z", + "iopub.status.idle": "2024-06-27T15:43:28.634988Z", + "shell.execute_reply": "2024-06-27T15:43:28.634564Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.609262Z", - "iopub.status.busy": "2024-06-25T23:17:20.608896Z", - "iopub.status.idle": "2024-06-25T23:17:20.630509Z", - "shell.execute_reply": "2024-06-25T23:17:20.630057Z" + "iopub.execute_input": "2024-06-27T15:43:28.637231Z", + "iopub.status.busy": "2024-06-27T15:43:28.636741Z", + "iopub.status.idle": "2024-06-27T15:43:28.683752Z", + "shell.execute_reply": "2024-06-27T15:43:28.683219Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.632342Z", - "iopub.status.busy": "2024-06-25T23:17:20.632168Z", - "iopub.status.idle": "2024-06-25T23:17:20.635695Z", - "shell.execute_reply": "2024-06-25T23:17:20.635234Z" + "iopub.execute_input": "2024-06-27T15:43:28.685832Z", + "iopub.status.busy": "2024-06-27T15:43:28.685492Z", + "iopub.status.idle": "2024-06-27T15:43:28.688933Z", + "shell.execute_reply": "2024-06-27T15:43:28.688419Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.637844Z", - "iopub.status.busy": "2024-06-25T23:17:20.637544Z", - "iopub.status.idle": "2024-06-25T23:17:20.644982Z", - "shell.execute_reply": "2024-06-25T23:17:20.644551Z" + "iopub.execute_input": "2024-06-27T15:43:28.691026Z", + "iopub.status.busy": "2024-06-27T15:43:28.690642Z", + "iopub.status.idle": "2024-06-27T15:43:28.698127Z", + "shell.execute_reply": "2024-06-27T15:43:28.697567Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.646840Z", - "iopub.status.busy": "2024-06-25T23:17:20.646673Z", - "iopub.status.idle": "2024-06-25T23:17:20.649384Z", - "shell.execute_reply": "2024-06-25T23:17:20.648911Z" + "iopub.execute_input": "2024-06-27T15:43:28.700172Z", + "iopub.status.busy": "2024-06-27T15:43:28.699860Z", + "iopub.status.idle": "2024-06-27T15:43:28.702299Z", + "shell.execute_reply": "2024-06-27T15:43:28.701866Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:20.651376Z", - "iopub.status.busy": "2024-06-25T23:17:20.651062Z", - "iopub.status.idle": "2024-06-25T23:17:23.603750Z", - "shell.execute_reply": "2024-06-25T23:17:23.603132Z" + "iopub.execute_input": "2024-06-27T15:43:28.704313Z", + "iopub.status.busy": "2024-06-27T15:43:28.703998Z", + "iopub.status.idle": "2024-06-27T15:43:31.609175Z", + "shell.execute_reply": "2024-06-27T15:43:31.608631Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:23.606640Z", - "iopub.status.busy": "2024-06-25T23:17:23.606173Z", - "iopub.status.idle": "2024-06-25T23:17:23.615532Z", - "shell.execute_reply": "2024-06-25T23:17:23.614991Z" + "iopub.execute_input": "2024-06-27T15:43:31.611891Z", + "iopub.status.busy": "2024-06-27T15:43:31.611669Z", + "iopub.status.idle": "2024-06-27T15:43:31.620906Z", + "shell.execute_reply": "2024-06-27T15:43:31.620481Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:23.617787Z", - "iopub.status.busy": "2024-06-25T23:17:23.617408Z", - "iopub.status.idle": "2024-06-25T23:17:25.503397Z", - "shell.execute_reply": "2024-06-25T23:17:25.502726Z" + "iopub.execute_input": "2024-06-27T15:43:31.622822Z", + "iopub.status.busy": "2024-06-27T15:43:31.622651Z", + "iopub.status.idle": "2024-06-27T15:43:33.535648Z", + "shell.execute_reply": "2024-06-27T15:43:33.535025Z" } }, "outputs": [ @@ -462,14 +462,6 @@ "\n", "Audit complete. 358 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -484,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.506132Z", - "iopub.status.busy": "2024-06-25T23:17:25.505476Z", - "iopub.status.idle": "2024-06-25T23:17:25.524117Z", - "shell.execute_reply": "2024-06-25T23:17:25.523676Z" + "iopub.execute_input": "2024-06-27T15:43:33.538337Z", + "iopub.status.busy": "2024-06-27T15:43:33.537758Z", + "iopub.status.idle": "2024-06-27T15:43:33.556718Z", + "shell.execute_reply": "2024-06-27T15:43:33.556253Z" }, "scrolled": true }, @@ -617,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.526096Z", - "iopub.status.busy": "2024-06-25T23:17:25.525830Z", - "iopub.status.idle": "2024-06-25T23:17:25.533770Z", - "shell.execute_reply": "2024-06-25T23:17:25.533230Z" + "iopub.execute_input": "2024-06-27T15:43:33.558849Z", + "iopub.status.busy": "2024-06-27T15:43:33.558552Z", + "iopub.status.idle": "2024-06-27T15:43:33.566516Z", + "shell.execute_reply": "2024-06-27T15:43:33.565968Z" } }, "outputs": [ @@ -724,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.535755Z", - "iopub.status.busy": "2024-06-25T23:17:25.535435Z", - "iopub.status.idle": "2024-06-25T23:17:25.544816Z", - "shell.execute_reply": "2024-06-25T23:17:25.544397Z" + "iopub.execute_input": "2024-06-27T15:43:33.568644Z", + "iopub.status.busy": "2024-06-27T15:43:33.568246Z", + "iopub.status.idle": "2024-06-27T15:43:33.577101Z", + "shell.execute_reply": "2024-06-27T15:43:33.576563Z" } }, "outputs": [ @@ -856,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.546828Z", - "iopub.status.busy": "2024-06-25T23:17:25.546524Z", - "iopub.status.idle": "2024-06-25T23:17:25.554523Z", - "shell.execute_reply": "2024-06-25T23:17:25.554077Z" + "iopub.execute_input": "2024-06-27T15:43:33.579284Z", + "iopub.status.busy": "2024-06-27T15:43:33.578978Z", + "iopub.status.idle": "2024-06-27T15:43:33.586679Z", + "shell.execute_reply": "2024-06-27T15:43:33.586154Z" } }, "outputs": [ @@ -973,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.556497Z", - "iopub.status.busy": "2024-06-25T23:17:25.556176Z", - "iopub.status.idle": "2024-06-25T23:17:25.564618Z", - "shell.execute_reply": "2024-06-25T23:17:25.564170Z" + "iopub.execute_input": "2024-06-27T15:43:33.588710Z", + "iopub.status.busy": "2024-06-27T15:43:33.588378Z", + "iopub.status.idle": "2024-06-27T15:43:33.596909Z", + "shell.execute_reply": "2024-06-27T15:43:33.596360Z" } }, "outputs": [ @@ -1087,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.566583Z", - "iopub.status.busy": "2024-06-25T23:17:25.566262Z", - "iopub.status.idle": "2024-06-25T23:17:25.573703Z", - "shell.execute_reply": "2024-06-25T23:17:25.573162Z" + "iopub.execute_input": "2024-06-27T15:43:33.598991Z", + "iopub.status.busy": "2024-06-27T15:43:33.598672Z", + "iopub.status.idle": "2024-06-27T15:43:33.606013Z", + "shell.execute_reply": "2024-06-27T15:43:33.605532Z" } }, "outputs": [ @@ -1205,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.575840Z", - "iopub.status.busy": "2024-06-25T23:17:25.575524Z", - "iopub.status.idle": "2024-06-25T23:17:25.582660Z", - "shell.execute_reply": "2024-06-25T23:17:25.582224Z" + "iopub.execute_input": "2024-06-27T15:43:33.608076Z", + "iopub.status.busy": "2024-06-27T15:43:33.607755Z", + "iopub.status.idle": "2024-06-27T15:43:33.614860Z", + "shell.execute_reply": "2024-06-27T15:43:33.614434Z" } }, "outputs": [ @@ -1308,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:25.584694Z", - "iopub.status.busy": "2024-06-25T23:17:25.584373Z", - "iopub.status.idle": "2024-06-25T23:17:25.592350Z", - "shell.execute_reply": "2024-06-25T23:17:25.591901Z" + "iopub.execute_input": "2024-06-27T15:43:33.616903Z", + "iopub.status.busy": "2024-06-27T15:43:33.616574Z", + "iopub.status.idle": "2024-06-27T15:43:33.624562Z", + "shell.execute_reply": "2024-06-27T15:43:33.624101Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 1b14ea34d..cfa27517b 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: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}
+Classes: {'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'visa_or_mastercard'}

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

@@ -840,8 +840,6 @@

2. Load and format the text dataset 
 No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  return self.fget.__get__(instance, owner)()

Our subsequent ML model will directly operate on elements of text_embeddings in order to classify the customer service requests.

@@ -884,7 +882,7 @@

4. Use cleanlab to find issues in your dataset +
@@ -900,15 +898,6 @@

4. Use cleanlab to find issues in your dataset -
-
-
-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
-
-

After the audit is complete, review the findings using the report method:

[11]:
diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb
index 47d0847e3..858b70c6a 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": "2024-06-25T23:17:28.279893Z",
-     "iopub.status.busy": "2024-06-25T23:17:28.279723Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.902204Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.901649Z"
+     "iopub.execute_input": "2024-06-27T15:43:36.211196Z",
+     "iopub.status.busy": "2024-06-27T15:43:36.210616Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.877876Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.877226Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:30.904858Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.904404Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.907555Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.907124Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.880571Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.880281Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.883432Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.882998Z"
     }
    },
    "outputs": [],
@@ -145,10 +145,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:30.909531Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.909235Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.912305Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.911777Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.885401Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.885086Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.888163Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.887637Z"
     },
     "nbsphinx": "hidden"
    },
@@ -178,10 +178,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:30.914377Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.913988Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.934290Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.933773Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.890191Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.889896Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.941357Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.940812Z"
     }
    },
    "outputs": [
@@ -271,10 +271,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:30.936266Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.935961Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.939627Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.939095Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.943383Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.943067Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.946734Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.946211Z"
     }
    },
    "outputs": [
@@ -283,7 +283,7 @@
      "output_type": "stream",
      "text": [
       "This dataset has 10 classes.\n",
-      "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_about_to_expire', 'getting_spare_card', 'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer'}\n"
+      "Classes: {'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'visa_or_mastercard'}\n"
      ]
     }
    ],
@@ -307,10 +307,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:30.941560Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.941250Z",
-     "iopub.status.idle": "2024-06-25T23:17:30.944331Z",
-     "shell.execute_reply": "2024-06-25T23:17:30.943818Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.948819Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.948384Z",
+     "iopub.status.idle": "2024-06-27T15:43:38.951555Z",
+     "shell.execute_reply": "2024-06-27T15:43:38.951036Z"
     }
    },
    "outputs": [
@@ -365,10 +365,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:30.946381Z",
-     "iopub.status.busy": "2024-06-25T23:17:30.946063Z",
-     "iopub.status.idle": "2024-06-25T23:17:34.606408Z",
-     "shell.execute_reply": "2024-06-25T23:17:34.605752Z"
+     "iopub.execute_input": "2024-06-27T15:43:38.953619Z",
+     "iopub.status.busy": "2024-06-27T15:43:38.953322Z",
+     "iopub.status.idle": "2024-06-27T15:43:45.236989Z",
+     "shell.execute_reply": "2024-06-27T15:43:45.236337Z"
     }
    },
    "outputs": [
@@ -378,14 +378,6 @@
      "text": [
       "No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return self.fget.__get__(instance, owner)()\n"
-     ]
     }
    ],
    "source": [
@@ -424,10 +416,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:34.609229Z",
-     "iopub.status.busy": "2024-06-25T23:17:34.608851Z",
-     "iopub.status.idle": "2024-06-25T23:17:35.466411Z",
-     "shell.execute_reply": "2024-06-25T23:17:35.465834Z"
+     "iopub.execute_input": "2024-06-27T15:43:45.239672Z",
+     "iopub.status.busy": "2024-06-27T15:43:45.239336Z",
+     "iopub.status.idle": "2024-06-27T15:43:46.123974Z",
+     "shell.execute_reply": "2024-06-27T15:43:46.123390Z"
     },
     "scrolled": true
    },
@@ -459,10 +451,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:35.469450Z",
-     "iopub.status.busy": "2024-06-25T23:17:35.469026Z",
-     "iopub.status.idle": "2024-06-25T23:17:35.471951Z",
-     "shell.execute_reply": "2024-06-25T23:17:35.471467Z"
+     "iopub.execute_input": "2024-06-27T15:43:46.127752Z",
+     "iopub.status.busy": "2024-06-27T15:43:46.126790Z",
+     "iopub.status.idle": "2024-06-27T15:43:46.130878Z",
+     "shell.execute_reply": "2024-06-27T15:43:46.130372Z"
     }
    },
    "outputs": [],
@@ -482,10 +474,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:35.474346Z",
-     "iopub.status.busy": "2024-06-25T23:17:35.473954Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.379211Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.378561Z"
+     "iopub.execute_input": "2024-06-27T15:43:46.134460Z",
+     "iopub.status.busy": "2024-06-27T15:43:46.133513Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.069108Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.068128Z"
     },
     "scrolled": true
    },
@@ -510,14 +502,6 @@
       "\n",
       "Audit complete. 85 issues found in the dataset.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n",
-      "  warnings.warn(\n"
-     ]
     }
    ],
    "source": [
@@ -537,10 +521,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.383383Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.382233Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.408704Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.408212Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.073009Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.071851Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.099092Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.098582Z"
     },
     "scrolled": true
    },
@@ -670,10 +654,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.412193Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.411277Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.421651Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.421256Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.102662Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.101747Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.112519Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.112121Z"
     },
     "scrolled": true
    },
@@ -783,10 +767,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.424437Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.423704Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.428917Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.428520Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.114945Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.114571Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.118624Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.118139Z"
     }
    },
    "outputs": [
@@ -824,10 +808,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.430883Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.430707Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.438445Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.437883Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.120614Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.120315Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.126511Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.125999Z"
     }
    },
    "outputs": [
@@ -944,10 +928,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.440387Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.440214Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.446599Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.446157Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.128507Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.128148Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.134473Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.133964Z"
     }
    },
    "outputs": [
@@ -1030,10 +1014,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.448520Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.448196Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.454046Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.453485Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.136403Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.136103Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.141827Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.141387Z"
     }
    },
    "outputs": [
@@ -1141,10 +1125,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.456157Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.455839Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.464219Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.463796Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.143837Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.143538Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.151732Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.151300Z"
     }
    },
    "outputs": [
@@ -1255,10 +1239,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.466195Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.465883Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.471233Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.470679Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.153724Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.153415Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.158681Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.158138Z"
     }
    },
    "outputs": [
@@ -1326,10 +1310,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.473304Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.472970Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.478474Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.478028Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.160608Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.160431Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.165627Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.165151Z"
     }
    },
    "outputs": [
@@ -1408,10 +1392,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.480531Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.480222Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.483860Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.483411Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.167686Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.167383Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.170918Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.170385Z"
     }
    },
    "outputs": [
@@ -1459,10 +1443,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:17:37.485748Z",
-     "iopub.status.busy": "2024-06-25T23:17:37.485580Z",
-     "iopub.status.idle": "2024-06-25T23:17:37.490849Z",
-     "shell.execute_reply": "2024-06-25T23:17:37.490382Z"
+     "iopub.execute_input": "2024-06-27T15:43:48.173029Z",
+     "iopub.status.busy": "2024-06-27T15:43:48.172853Z",
+     "iopub.status.idle": "2024-06-27T15:43:48.177856Z",
+     "shell.execute_reply": "2024-06-27T15:43:48.177424Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html
index 965f5ea9c..66e8a3155 100644
--- a/master/tutorials/datalab/workflows.html
+++ b/master/tutorials/datalab/workflows.html
@@ -823,15 +823,6 @@ 
4. Identify Data Issues Using Datalab
 

 

-

-
-/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/issue_finder.py:116: UserWarning: Both `features` and `knn_graph` were provided. Most issue managers will likely prefer using `knn_graph` instead of `features` for efficiency.
-  warnings.warn(
-

-
-
-
-
 Finding null issues ...
@@ -842,16 +833,7 @@ 
4. Identify Data Issues Using Datalab
-

-

-

-
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
+Audit complete. 524 issues found in the dataset.
@@ -897,13 +879,13 @@

4. Identify Data Issues Using Datalab - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 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

@@ -3491,6 +3473,410 @@

3. (Optional) Visualize class imbalance issues +

Find Spurious Correlation between Vision Dataset features and class labels#

+

In this section, we demonstrate how to identify spurious correlations in a vision dataset using the cleanlab library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.

+

We will utilize the Datalab class from cleanlab with the image_key attribute to pinpoint vision-specific issues such as dark_score, blurry_score, odd_aspect_ratio_score, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models.

+
+

1. Load the dataset#

+

We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the dark_score. This demonstrates the strong correlation detection of darkness within the dataset.

+

Similarly, we can observe significant reductions in blurry_score and odd_aspect_ratio_score when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height.

+
+
[33]:
+
+
+
from cleanlab import Datalab
+from torchvision.datasets import CIFAR10
+from datasets import Dataset
+import io
+from PIL import Image, ImageEnhance
+import random
+import numpy as np
+from IPython.display import display, Markdown
+
+# Download the CIFAR-10 test dataset
+data = CIFAR10(root='./data', train=False, download=True)
+
+# Set seed for reproducibility
+np.random.seed(0)
+random.seed(0)
+
+# Randomly select two classes
+classes = list(range(len(data.classes)))
+selected_classes = random.sample(classes, 2)
+
+# Function to convert PIL object to PNG image to be passed to the Datalab object
+def convert_to_png_image(image):
+    buffer = io.BytesIO()
+    image.save(buffer, format='PNG')
+    buffer.seek(0)
+    return Image.open(buffer)
+
+# Generating 100 ('max_num_images') images from each of the two chosen classes
+max_num_images = 100
+list_images, list_labels = [], []
+num_images = {selected_classes[0]: 0, selected_classes[1]: 0}
+
+for img, label in data:
+    if num_images[selected_classes[0]] == max_num_images and num_images[selected_classes[1]] == max_num_images:
+        break
+    if label in selected_classes:
+        if num_images[label] == max_num_images:
+            continue
+        list_images.append(convert_to_png_image(img))
+        list_labels.append(label)
+        num_images[label] += 1
+
+
+
+
+
+
+
+
+Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
+
+
+
+
+
+
+
+100%|██████████| 170498071/170498071 [00:04<00:00, 42480288.68it/s]
+
+
+
+
+
+
+
+Extracting ./data/cifar-10-python.tar.gz to ./data
+
+
+
+ +
+

3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness#

+
+
[35]:
+
+
+
# Function to reduce brightness to 30%
+def apply_dark(image):
+    """Decreases brightness of the image."""
+    enhancer = ImageEnhance.Brightness(image)
+    return enhancer.enhance(0.3)
+
+# Applying the darkness filter to one of the classes
+transformed_list_images = [
+    apply_dark(img) if label == selected_classes[0] else img
+    for label, img in zip(list_labels, list_images)
+]
+
+# Creating datasets.Dataset object from the transformed dataset
+transformed_dataset_dict = {'image': transformed_list_images, 'label': list_labels}
+transformed_dataset = Dataset.from_dict(transformed_dataset_dict)
+
+
+
+
+
+

4. (Optional) Visualizing Images in the dataset#

+
+
[36]:
+
+
+
import matplotlib.pyplot as plt
+
+def plot_images(dataset_dict):
+    """Plots the first 15 images from the dataset dictionary."""
+    images = dataset_dict['image']
+    labels = dataset_dict['label']
+
+    # Define the number of images to plot
+    num_images_to_plot = 15
+    num_cols = 5  # Number of columns in the plot grid
+    num_rows = (num_images_to_plot + num_cols - 1) // num_cols  # Calculate rows needed
+
+    # Create a figure
+    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))
+    axes = axes.flatten()
+
+    # Plot each image
+    for i in range(num_images_to_plot):
+        img = images[i]
+        label = labels[i]
+        axes[i].imshow(img)
+        axes[i].set_title(f'Label: {label}')
+        axes[i].axis('off')
+
+    # Hide any remaining empty subplots
+    for i in range(num_images_to_plot, len(axes)):
+        axes[i].axis('off')
+
+    # Show the plot
+    plt.tight_layout()
+    plt.show()
+
+plot_images(dataset_dict)
+plot_images(transformed_dataset_dict)
+
+
+
+
+
+
+
+../../_images/tutorials_datalab_workflows_84_0.png +
+
+
+
+
+
+../../_images/tutorials_datalab_workflows_84_1.png +
+
+
+
+

5. Finding image-specific property scores#

+
+
[37]:
+
+
+
# Function to find image-specific property scores given the dataset object
+def get_property_scores(dataset):
+    lab = Datalab(data=dataset, label_name="label", image_key="image")
+    lab.find_issues()
+    return lab._spurious_correlation()
+
+# Finds specific property score in the dataframe containing property scores
+def get_specific_property_score(property_scores_df, property_name):
+    return property_scores_df[property_scores_df['property'] == property_name]['score'].iloc[0]
+
+# Finding scores in original and transformed dataset
+standard_property_scores = get_property_scores(dataset)
+transformed_property_scores = get_property_scores(transformed_dataset)
+
+# Displaying the scores dataframe
+display(Markdown("### Vision-specific property scores in the original dataset"))
+display(standard_property_scores)
+display(Markdown("### Vision-specific property scores in the transformed dataset"))
+display(transformed_property_scores)
+
+# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset
+assert get_specific_property_score(standard_property_scores, 'dark_score') > get_specific_property_score(transformed_property_scores, 'dark_score')
+
+
+
+
+
+
+
+
+Finding class_imbalance issues ...
+Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Audit complete. 0 issues found in the dataset.
+Finding class_imbalance issues ...
+Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...
+
+
+
+
+
+
+
+
+
+
+
+
+
+Removing dark, blurry from potential issues in the dataset as it exceeds max_prevalence=0.1
+
+Audit complete. 0 issues found in the dataset.
+
+
+
+
+
+
+
+

Vision-specific property scores in the original dataset#

+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
propertyscore
0dark_score0.295
1light_score0.415
2low_information_score0.325
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
6blurry_score0.325
+
+
+
+
+
+
+
+

Vision-specific property scores in the transformed dataset#

+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
propertyscore
0dark_score0.000
1light_score0.185
2low_information_score0.015
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
6blurry_score0.015
+
+
+
+ @@ -3622,6 +4008,14 @@

3. (Optional) Visualize class imbalance issues3. (Optional) Visualize class imbalance issues +
  • Find Spurious Correlation between Vision Dataset features and class labels +
    • diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 05570c79a..271e14984 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": "2024-06-25T23:17:40.853361Z", - "iopub.status.busy": "2024-06-25T23:17:40.852930Z", - "iopub.status.idle": "2024-06-25T23:17:41.272322Z", - "shell.execute_reply": "2024-06-25T23:17:41.271713Z" + "iopub.execute_input": "2024-06-27T15:43:52.541747Z", + "iopub.status.busy": "2024-06-27T15:43:52.541554Z", + "iopub.status.idle": "2024-06-27T15:43:52.966739Z", + "shell.execute_reply": "2024-06-27T15:43:52.966247Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.275299Z", - "iopub.status.busy": "2024-06-25T23:17:41.274749Z", - "iopub.status.idle": "2024-06-25T23:17:41.403175Z", - "shell.execute_reply": "2024-06-25T23:17:41.402663Z" + "iopub.execute_input": "2024-06-27T15:43:52.969430Z", + "iopub.status.busy": "2024-06-27T15:43:52.968973Z", + "iopub.status.idle": "2024-06-27T15:43:53.099469Z", + "shell.execute_reply": "2024-06-27T15:43:53.098906Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.405438Z", - "iopub.status.busy": "2024-06-25T23:17:41.405028Z", - "iopub.status.idle": "2024-06-25T23:17:41.427834Z", - "shell.execute_reply": "2024-06-25T23:17:41.427281Z" + "iopub.execute_input": "2024-06-27T15:43:53.101731Z", + "iopub.status.busy": "2024-06-27T15:43:53.101470Z", + "iopub.status.idle": "2024-06-27T15:43:53.125646Z", + "shell.execute_reply": "2024-06-27T15:43:53.125036Z" } }, "outputs": [], @@ -210,21 +210,13 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:41.430652Z", - "iopub.status.busy": "2024-06-25T23:17:41.430206Z", - "iopub.status.idle": "2024-06-25T23:17:44.079438Z", - "shell.execute_reply": "2024-06-25T23:17:44.078785Z" + "iopub.execute_input": "2024-06-27T15:43:53.128358Z", + "iopub.status.busy": "2024-06-27T15:43:53.127895Z", + "iopub.status.idle": "2024-06-27T15:43:55.830328Z", + "shell.execute_reply": "2024-06-27T15:43:55.829757Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/issue_finder.py:116: UserWarning: Both `features` and `knn_graph` were provided. Most issue managers will likely prefer using `knn_graph` instead of `features` for efficiency.\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -243,15 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 523 issues found in the dataset.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n", - " warnings.warn(\n" + "Audit complete. 524 issues found in the dataset.\n" ] }, { @@ -296,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356958\n", - " 362\n", + " 0.356925\n", + " 363\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619565\n", + " 0.619581\n", " 108\n", " \n", " \n", @@ -331,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356958 362\n", - "3 near_duplicate 0.619565 108\n", + "2 outlier 0.356925 363\n", + "3 near_duplicate 0.619581 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651929 0" @@ -716,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:44.082102Z", - "iopub.status.busy": "2024-06-25T23:17:44.081500Z", - "iopub.status.idle": "2024-06-25T23:17:51.711133Z", - "shell.execute_reply": "2024-06-25T23:17:51.710550Z" + "iopub.execute_input": "2024-06-27T15:43:55.832965Z", + "iopub.status.busy": "2024-06-27T15:43:55.832431Z", + "iopub.status.idle": "2024-06-27T15:44:04.492966Z", + "shell.execute_reply": "2024-06-27T15:44:04.492355Z" } }, "outputs": [ @@ -820,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:51.713313Z", - "iopub.status.busy": "2024-06-25T23:17:51.713127Z", - "iopub.status.idle": "2024-06-25T23:17:51.857400Z", - "shell.execute_reply": "2024-06-25T23:17:51.856753Z" + "iopub.execute_input": "2024-06-27T15:44:04.495312Z", + "iopub.status.busy": "2024-06-27T15:44:04.494968Z", + "iopub.status.idle": "2024-06-27T15:44:04.655997Z", + "shell.execute_reply": "2024-06-27T15:44:04.655499Z" } }, "outputs": [], @@ -854,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:51.860009Z", - "iopub.status.busy": "2024-06-25T23:17:51.859627Z", - "iopub.status.idle": "2024-06-25T23:17:53.181642Z", - "shell.execute_reply": "2024-06-25T23:17:53.181004Z" + "iopub.execute_input": "2024-06-27T15:44:04.658598Z", + "iopub.status.busy": "2024-06-27T15:44:04.658162Z", + "iopub.status.idle": "2024-06-27T15:44:06.008495Z", + "shell.execute_reply": "2024-06-27T15:44:06.008007Z" } }, "outputs": [ @@ -1016,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.183695Z", - "iopub.status.busy": "2024-06-25T23:17:53.183507Z", - "iopub.status.idle": "2024-06-25T23:17:53.614506Z", - "shell.execute_reply": "2024-06-25T23:17:53.613154Z" + "iopub.execute_input": "2024-06-27T15:44:06.010831Z", + "iopub.status.busy": "2024-06-27T15:44:06.010491Z", + "iopub.status.idle": "2024-06-27T15:44:06.454504Z", + "shell.execute_reply": "2024-06-27T15:44:06.453979Z" } }, "outputs": [ @@ -1098,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.617165Z", - "iopub.status.busy": "2024-06-25T23:17:53.616488Z", - "iopub.status.idle": "2024-06-25T23:17:53.625569Z", - "shell.execute_reply": "2024-06-25T23:17:53.625088Z" + "iopub.execute_input": "2024-06-27T15:44:06.457199Z", + "iopub.status.busy": "2024-06-27T15:44:06.456627Z", + "iopub.status.idle": "2024-06-27T15:44:06.468762Z", + "shell.execute_reply": "2024-06-27T15:44:06.468281Z" } }, "outputs": [], @@ -1131,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.627646Z", - "iopub.status.busy": "2024-06-25T23:17:53.627333Z", - "iopub.status.idle": "2024-06-25T23:17:53.647292Z", - "shell.execute_reply": "2024-06-25T23:17:53.646870Z" + "iopub.execute_input": "2024-06-27T15:44:06.470767Z", + "iopub.status.busy": "2024-06-27T15:44:06.470476Z", + "iopub.status.idle": "2024-06-27T15:44:06.488173Z", + "shell.execute_reply": "2024-06-27T15:44:06.487620Z" } }, "outputs": [], @@ -1162,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.649278Z", - "iopub.status.busy": "2024-06-25T23:17:53.648956Z", - "iopub.status.idle": "2024-06-25T23:17:53.876935Z", - "shell.execute_reply": "2024-06-25T23:17:53.876376Z" + "iopub.execute_input": "2024-06-27T15:44:06.490211Z", + "iopub.status.busy": "2024-06-27T15:44:06.489903Z", + "iopub.status.idle": "2024-06-27T15:44:06.712598Z", + "shell.execute_reply": "2024-06-27T15:44:06.711996Z" } }, "outputs": [], @@ -1205,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.879777Z", - "iopub.status.busy": "2024-06-25T23:17:53.879575Z", - "iopub.status.idle": "2024-06-25T23:17:53.898417Z", - "shell.execute_reply": "2024-06-25T23:17:53.897956Z" + "iopub.execute_input": "2024-06-27T15:44:06.715566Z", + "iopub.status.busy": "2024-06-27T15:44:06.715063Z", + "iopub.status.idle": "2024-06-27T15:44:06.733594Z", + "shell.execute_reply": "2024-06-27T15:44:06.733059Z" } }, "outputs": [ @@ -1406,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:53.900637Z", - "iopub.status.busy": "2024-06-25T23:17:53.900291Z", - "iopub.status.idle": "2024-06-25T23:17:54.067010Z", - "shell.execute_reply": "2024-06-25T23:17:54.066325Z" + "iopub.execute_input": "2024-06-27T15:44:06.735735Z", + "iopub.status.busy": "2024-06-27T15:44:06.735338Z", + "iopub.status.idle": "2024-06-27T15:44:06.904156Z", + "shell.execute_reply": "2024-06-27T15:44:06.903547Z" } }, "outputs": [ @@ -1476,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.069491Z", - "iopub.status.busy": "2024-06-25T23:17:54.069138Z", - "iopub.status.idle": "2024-06-25T23:17:54.080042Z", - "shell.execute_reply": "2024-06-25T23:17:54.079594Z" + "iopub.execute_input": "2024-06-27T15:44:06.906516Z", + "iopub.status.busy": "2024-06-27T15:44:06.906177Z", + "iopub.status.idle": "2024-06-27T15:44:06.916223Z", + "shell.execute_reply": "2024-06-27T15:44:06.915683Z" } }, "outputs": [ @@ -1745,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.083209Z", - "iopub.status.busy": "2024-06-25T23:17:54.082726Z", - "iopub.status.idle": "2024-06-25T23:17:54.092500Z", - "shell.execute_reply": "2024-06-25T23:17:54.092040Z" + "iopub.execute_input": "2024-06-27T15:44:06.918395Z", + "iopub.status.busy": "2024-06-27T15:44:06.918078Z", + "iopub.status.idle": "2024-06-27T15:44:06.927111Z", + "shell.execute_reply": "2024-06-27T15:44:06.926638Z" } }, "outputs": [ @@ -1935,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.094651Z", - "iopub.status.busy": "2024-06-25T23:17:54.094321Z", - "iopub.status.idle": "2024-06-25T23:17:54.125818Z", - "shell.execute_reply": "2024-06-25T23:17:54.122177Z" + "iopub.execute_input": "2024-06-27T15:44:06.929131Z", + "iopub.status.busy": "2024-06-27T15:44:06.928820Z", + "iopub.status.idle": "2024-06-27T15:44:06.957898Z", + "shell.execute_reply": "2024-06-27T15:44:06.957444Z" } }, "outputs": [], @@ -1972,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.128194Z", - "iopub.status.busy": "2024-06-25T23:17:54.127850Z", - "iopub.status.idle": "2024-06-25T23:17:54.130729Z", - "shell.execute_reply": "2024-06-25T23:17:54.130269Z" + "iopub.execute_input": "2024-06-27T15:44:06.960022Z", + "iopub.status.busy": "2024-06-27T15:44:06.959611Z", + "iopub.status.idle": "2024-06-27T15:44:06.962384Z", + "shell.execute_reply": "2024-06-27T15:44:06.961933Z" } }, "outputs": [], @@ -1997,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.132753Z", - "iopub.status.busy": "2024-06-25T23:17:54.132426Z", - "iopub.status.idle": "2024-06-25T23:17:54.151669Z", - "shell.execute_reply": "2024-06-25T23:17:54.151107Z" + "iopub.execute_input": "2024-06-27T15:44:06.964363Z", + "iopub.status.busy": "2024-06-27T15:44:06.964036Z", + "iopub.status.idle": "2024-06-27T15:44:06.983071Z", + "shell.execute_reply": "2024-06-27T15:44:06.982517Z" } }, "outputs": [ @@ -2158,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.153875Z", - "iopub.status.busy": "2024-06-25T23:17:54.153542Z", - "iopub.status.idle": "2024-06-25T23:17:54.157885Z", - "shell.execute_reply": "2024-06-25T23:17:54.157427Z" + "iopub.execute_input": "2024-06-27T15:44:06.985511Z", + "iopub.status.busy": "2024-06-27T15:44:06.985071Z", + "iopub.status.idle": "2024-06-27T15:44:06.989351Z", + "shell.execute_reply": "2024-06-27T15:44:06.988923Z" } }, "outputs": [], @@ -2194,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.159942Z", - "iopub.status.busy": "2024-06-25T23:17:54.159537Z", - "iopub.status.idle": "2024-06-25T23:17:54.187254Z", - "shell.execute_reply": "2024-06-25T23:17:54.186748Z" + "iopub.execute_input": "2024-06-27T15:44:06.991366Z", + "iopub.status.busy": "2024-06-27T15:44:06.991042Z", + "iopub.status.idle": "2024-06-27T15:44:07.018711Z", + "shell.execute_reply": "2024-06-27T15:44:07.018244Z" } }, "outputs": [ @@ -2343,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.189370Z", - "iopub.status.busy": "2024-06-25T23:17:54.189020Z", - "iopub.status.idle": "2024-06-25T23:17:54.563581Z", - "shell.execute_reply": "2024-06-25T23:17:54.563004Z" + "iopub.execute_input": "2024-06-27T15:44:07.020577Z", + "iopub.status.busy": "2024-06-27T15:44:07.020409Z", + "iopub.status.idle": "2024-06-27T15:44:07.337879Z", + "shell.execute_reply": "2024-06-27T15:44:07.337286Z" } }, "outputs": [ @@ -2413,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.566043Z", - "iopub.status.busy": "2024-06-25T23:17:54.565580Z", - "iopub.status.idle": "2024-06-25T23:17:54.568905Z", - "shell.execute_reply": "2024-06-25T23:17:54.568452Z" + "iopub.execute_input": "2024-06-27T15:44:07.340113Z", + "iopub.status.busy": "2024-06-27T15:44:07.339804Z", + "iopub.status.idle": "2024-06-27T15:44:07.343054Z", + "shell.execute_reply": "2024-06-27T15:44:07.342520Z" } }, "outputs": [ @@ -2467,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.570928Z", - "iopub.status.busy": "2024-06-25T23:17:54.570747Z", - "iopub.status.idle": "2024-06-25T23:17:54.584558Z", - "shell.execute_reply": "2024-06-25T23:17:54.584061Z" + "iopub.execute_input": "2024-06-27T15:44:07.345190Z", + "iopub.status.busy": "2024-06-27T15:44:07.344783Z", + "iopub.status.idle": "2024-06-27T15:44:07.357931Z", + "shell.execute_reply": "2024-06-27T15:44:07.357379Z" } }, "outputs": [ @@ -2749,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.586622Z", - "iopub.status.busy": "2024-06-25T23:17:54.586423Z", - "iopub.status.idle": "2024-06-25T23:17:54.600724Z", - "shell.execute_reply": "2024-06-25T23:17:54.600241Z" + "iopub.execute_input": "2024-06-27T15:44:07.360084Z", + "iopub.status.busy": "2024-06-27T15:44:07.359601Z", + "iopub.status.idle": "2024-06-27T15:44:07.372602Z", + "shell.execute_reply": "2024-06-27T15:44:07.372167Z" } }, "outputs": [ @@ -3019,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - 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    \n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3567,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.686913Z", - "iopub.status.busy": "2024-06-25T23:17:54.686475Z", - "iopub.status.idle": "2024-06-25T23:17:54.692178Z", - "shell.execute_reply": "2024-06-25T23:17:54.691645Z" + "iopub.execute_input": "2024-06-27T15:44:07.454975Z", + "iopub.status.busy": "2024-06-27T15:44:07.454471Z", + "iopub.status.idle": "2024-06-27T15:44:07.460205Z", + "shell.execute_reply": "2024-06-27T15:44:07.459762Z" } }, "outputs": [], @@ -3609,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.694316Z", - "iopub.status.busy": "2024-06-25T23:17:54.693981Z", - "iopub.status.idle": "2024-06-25T23:17:54.705261Z", - "shell.execute_reply": "2024-06-25T23:17:54.704802Z" + "iopub.execute_input": "2024-06-27T15:44:07.462133Z", + "iopub.status.busy": "2024-06-27T15:44:07.461827Z", + "iopub.status.idle": "2024-06-27T15:44:07.472460Z", + "shell.execute_reply": "2024-06-27T15:44:07.471998Z" } }, "outputs": [ @@ -3648,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.707234Z", - "iopub.status.busy": "2024-06-25T23:17:54.707059Z", - "iopub.status.idle": "2024-06-25T23:17:54.923905Z", - "shell.execute_reply": "2024-06-25T23:17:54.923350Z" + "iopub.execute_input": "2024-06-27T15:44:07.474333Z", + "iopub.status.busy": "2024-06-27T15:44:07.474161Z", + "iopub.status.idle": "2024-06-27T15:44:07.692424Z", + "shell.execute_reply": "2024-06-27T15:44:07.691818Z" } }, "outputs": [ @@ -3703,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:54.926218Z", - "iopub.status.busy": "2024-06-25T23:17:54.925878Z", - "iopub.status.idle": "2024-06-25T23:17:54.933331Z", - "shell.execute_reply": "2024-06-25T23:17:54.932869Z" + "iopub.execute_input": "2024-06-27T15:44:07.694674Z", + "iopub.status.busy": "2024-06-27T15:44:07.694341Z", + "iopub.status.idle": "2024-06-27T15:44:07.701787Z", + "shell.execute_reply": "2024-06-27T15:44:07.701329Z" }, "nbsphinx": "hidden" }, @@ -3731,25 +3715,1586 @@ "assert all(class_imbalance_issues.query(\"is_class_imbalance_issue\")[\"class_imbalance_score\"] == 0.02), \"Class imbalance issue scores are not as expected\"\n", "assert all(class_imbalance_issues.query(\"not is_class_imbalance_issue\")[\"class_imbalance_score\"] == 1.0), \"Class imbalance issue scores are not as expected\"" ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Spurious Correlation between Vision Dataset features and class labels\n", + "\n", + "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n", + "\n", + "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Load the dataset\n", + "\n", + "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n", + "\n", + "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T15:44:07.703783Z", + "iopub.status.busy": "2024-06-27T15:44:07.703532Z", + "iopub.status.idle": "2024-06-27T15:44:16.704543Z", + "shell.execute_reply": "2024-06-27T15:44:16.703978Z" + } }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 0%| | 0/170498071 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_images(dataset_dict):\n", + " \"\"\"Plots the first 15 images from the dataset dictionary.\"\"\"\n", + " images = dataset_dict['image']\n", + " labels = dataset_dict['label']\n", + " \n", + " # Define the number of images to plot\n", + " num_images_to_plot = 15\n", + " num_cols = 5 # Number of columns in the plot grid\n", + " num_rows = (num_images_to_plot + num_cols - 1) // num_cols # Calculate rows needed\n", + " \n", + " # Create a figure\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))\n", + " axes = axes.flatten()\n", + " \n", + " # Plot each image\n", + " for i in range(num_images_to_plot):\n", + " img = images[i]\n", + " label = labels[i]\n", + " axes[i].imshow(img)\n", + " axes[i].set_title(f'Label: {label}')\n", + " axes[i].axis('off')\n", + " \n", + " # Hide any remaining empty subplots\n", + " for i in range(num_images_to_plot, len(axes)):\n", + " axes[i].axis('off')\n", + " \n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_images(dataset_dict)\n", + "plot_images(transformed_dataset_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Finding image-specific property scores" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T15:44:18.292989Z", + "iopub.status.busy": "2024-06-27T15:44:18.292805Z", + "iopub.status.idle": "2024-06-27T15:44:19.075335Z", + "shell.execute_reply": "2024-06-27T15:44:19.074817Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding class_imbalance issues ...\n", + "Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "70462dbacd1f4317adbb0ee71a2daa15", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/200 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " property score\n", + "0 dark_score 0.295\n", + "1 light_score 0.415\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", + "6 blurry_score 0.325" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Vision-specific property scores in the transformed dataset" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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    propertyscore
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    " + ], + "text/plain": [ + " property score\n", + "0 dark_score 0.000\n", + "1 light_score 0.185\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", + "6 blurry_score 0.015" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to find image-specific property scores given the dataset object\n", + "def get_property_scores(dataset):\n", + " lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n", + " lab.find_issues()\n", + " return lab._spurious_correlation()\n", + "\n", + "# Finds specific property score in the dataframe containing property scores \n", + "def get_specific_property_score(property_scores_df, property_name):\n", + " return property_scores_df[property_scores_df['property'] == property_name]['score'].iloc[0]\n", + "\n", + "# Finding scores in original and transformed dataset\n", + "standard_property_scores = get_property_scores(dataset)\n", + "transformed_property_scores = get_property_scores(transformed_dataset)\n", + "\n", + "# Displaying the scores dataframe\n", + "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n", + "display(standard_property_scores)\n", + "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n", + "display(transformed_property_scores)\n", + "\n", + "# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n", + "assert get_specific_property_score(standard_property_scores, 'dark_score') > get_specific_property_score(transformed_property_scores, 'dark_score')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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"_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0dab558fba14a74952f4a9b45f15158", + "placeholder": "​", + "style": "IPY_MODEL_07dbe0f79d8347918a74eb0fa73e4d15", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 827.64it/s]" + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index d462fdaea..69b3cd43c 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:58.501344Z", - "iopub.status.busy": "2024-06-25T23:17:58.500004Z", - "iopub.status.idle": "2024-06-25T23:17:59.801482Z", - "shell.execute_reply": "2024-06-25T23:17:59.800950Z" + "iopub.execute_input": "2024-06-27T15:44:23.089966Z", + "iopub.status.busy": "2024-06-27T15:44:23.089535Z", + "iopub.status.idle": "2024-06-27T15:44:24.255393Z", + "shell.execute_reply": "2024-06-27T15:44:24.254802Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:17:59.804004Z", - "iopub.status.busy": "2024-06-25T23:17:59.803708Z", - "iopub.status.idle": "2024-06-25T23:17:59.806736Z", - "shell.execute_reply": "2024-06-25T23:17:59.806281Z" + "iopub.execute_input": "2024-06-27T15:44:24.258234Z", + "iopub.status.busy": "2024-06-27T15:44:24.257585Z", + "iopub.status.idle": "2024-06-27T15:44:24.260732Z", + "shell.execute_reply": "2024-06-27T15:44:24.260197Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:59.808933Z", - "iopub.status.busy": "2024-06-25T23:17:59.808705Z", - "iopub.status.idle": "2024-06-25T23:17:59.821999Z", - "shell.execute_reply": "2024-06-25T23:17:59.821381Z" + "iopub.execute_input": "2024-06-27T15:44:24.263108Z", + "iopub.status.busy": "2024-06-27T15:44:24.262682Z", + "iopub.status.idle": "2024-06-27T15:44:24.274273Z", + "shell.execute_reply": "2024-06-27T15:44:24.273739Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:17:59.824481Z", - "iopub.status.busy": "2024-06-25T23:17:59.824047Z", - "iopub.status.idle": "2024-06-25T23:18:03.535596Z", - "shell.execute_reply": "2024-06-25T23:18:03.535061Z" + "iopub.execute_input": "2024-06-27T15:44:24.276467Z", + "iopub.status.busy": "2024-06-27T15:44:24.276029Z", + "iopub.status.idle": "2024-06-27T15:44:33.502020Z", + "shell.execute_reply": "2024-06-27T15:44:33.501414Z" }, "id": "dhTHOg8Pyv5G" }, @@ -694,7 +694,13 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n", @@ -2559,13 +2565,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index acbd3933c..c1c9c7b18 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?

    -
    +
    -
    +
    @@ -1654,7 +1654,7 @@

    How to handle near-duplicate data identified by Datalab? +
    @@ -1664,15 +1664,6 @@

    How to handle near-duplicate data identified by Datalab? -
    -
    -
    -
    -/tmp/ipykernel_7878/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
-  to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values
-
    -

    [20]:
    @@ -1711,7 +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 713861397..e60a48a49 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:05.926443Z", - "iopub.status.busy": "2024-06-25T23:18:05.926263Z", - "iopub.status.idle": "2024-06-25T23:18:07.103304Z", - "shell.execute_reply": "2024-06-25T23:18:07.102799Z" + "iopub.execute_input": "2024-06-27T15:44:35.812613Z", + "iopub.status.busy": "2024-06-27T15:44:35.812448Z", + "iopub.status.idle": "2024-06-27T15:44:36.959377Z", + "shell.execute_reply": "2024-06-27T15:44:36.958799Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:07.106148Z", - "iopub.status.busy": "2024-06-25T23:18:07.105603Z", - "iopub.status.idle": "2024-06-25T23:18:07.109155Z", - "shell.execute_reply": "2024-06-25T23:18:07.108679Z" + "iopub.execute_input": "2024-06-27T15:44:36.962118Z", + "iopub.status.busy": "2024-06-27T15:44:36.961823Z", + "iopub.status.idle": "2024-06-27T15:44:36.965274Z", + "shell.execute_reply": "2024-06-27T15:44:36.964816Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:07.111219Z", - "iopub.status.busy": "2024-06-25T23:18:07.110877Z", - "iopub.status.idle": "2024-06-25T23:18:10.366450Z", - "shell.execute_reply": "2024-06-25T23:18:10.365818Z" + "iopub.execute_input": "2024-06-27T15:44:36.967365Z", + "iopub.status.busy": "2024-06-27T15:44:36.967036Z", + "iopub.status.idle": "2024-06-27T15:44:40.212356Z", + "shell.execute_reply": "2024-06-27T15:44:40.211751Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.369846Z", - "iopub.status.busy": "2024-06-25T23:18:10.369009Z", - "iopub.status.idle": "2024-06-25T23:18:10.408435Z", - "shell.execute_reply": "2024-06-25T23:18:10.407723Z" + "iopub.execute_input": "2024-06-27T15:44:40.215518Z", + "iopub.status.busy": "2024-06-27T15:44:40.214659Z", + "iopub.status.idle": "2024-06-27T15:44:40.250385Z", + "shell.execute_reply": "2024-06-27T15:44:40.249775Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.411187Z", - "iopub.status.busy": "2024-06-25T23:18:10.410945Z", - "iopub.status.idle": "2024-06-25T23:18:10.447524Z", - "shell.execute_reply": "2024-06-25T23:18:10.446786Z" + "iopub.execute_input": "2024-06-27T15:44:40.253059Z", + "iopub.status.busy": "2024-06-27T15:44:40.252740Z", + "iopub.status.idle": "2024-06-27T15:44:40.289877Z", + "shell.execute_reply": "2024-06-27T15:44:40.289247Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.450344Z", - "iopub.status.busy": "2024-06-25T23:18:10.450101Z", - "iopub.status.idle": "2024-06-25T23:18:10.453289Z", - "shell.execute_reply": "2024-06-25T23:18:10.452762Z" + "iopub.execute_input": "2024-06-27T15:44:40.292569Z", + "iopub.status.busy": "2024-06-27T15:44:40.292302Z", + "iopub.status.idle": "2024-06-27T15:44:40.295458Z", + "shell.execute_reply": "2024-06-27T15:44:40.294917Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.455428Z", - "iopub.status.busy": "2024-06-25T23:18:10.455099Z", - "iopub.status.idle": "2024-06-25T23:18:10.457834Z", - "shell.execute_reply": "2024-06-25T23:18:10.457357Z" + "iopub.execute_input": "2024-06-27T15:44:40.297481Z", + "iopub.status.busy": "2024-06-27T15:44:40.297218Z", + "iopub.status.idle": "2024-06-27T15:44:40.300030Z", + "shell.execute_reply": "2024-06-27T15:44:40.299562Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.459894Z", - "iopub.status.busy": "2024-06-25T23:18:10.459627Z", - "iopub.status.idle": "2024-06-25T23:18:10.483748Z", - "shell.execute_reply": "2024-06-25T23:18:10.483202Z" + "iopub.execute_input": "2024-06-27T15:44:40.302271Z", + "iopub.status.busy": "2024-06-27T15:44:40.301972Z", + "iopub.status.idle": "2024-06-27T15:44:40.325394Z", + "shell.execute_reply": "2024-06-27T15:44:40.324857Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "558d7887a3b248ccbc78e41ae8f6a2ad", + "model_id": "b8fa97ab69bf43228c78ed3ed04acd70", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "633ecf7c235f443883ad78f8a1d748cd", + "model_id": "f5f6626fc81f4263b798ca0b721371b5", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.488896Z", - "iopub.status.busy": "2024-06-25T23:18:10.488605Z", - "iopub.status.idle": "2024-06-25T23:18:10.495342Z", - "shell.execute_reply": "2024-06-25T23:18:10.494804Z" + "iopub.execute_input": "2024-06-27T15:44:40.330533Z", + "iopub.status.busy": "2024-06-27T15:44:40.330080Z", + "iopub.status.idle": "2024-06-27T15:44:40.336361Z", + "shell.execute_reply": "2024-06-27T15:44:40.335930Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:10.497491Z", - 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"id": "411cb3b4", + "id": "40b044b4", "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": "c0fc51ac", + "id": "ebb7621c", "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": "31d0af7b", + "id": "c979fb67", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "ddefd054", + "id": "61925736", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.957642Z", - "iopub.status.busy": "2024-06-25T23:18:13.957445Z", - "iopub.status.idle": "2024-06-25T23:18:13.965853Z", - "shell.execute_reply": "2024-06-25T23:18:13.965258Z" + "iopub.execute_input": "2024-06-27T15:44:43.696599Z", + "iopub.status.busy": "2024-06-27T15:44:43.696241Z", + "iopub.status.idle": "2024-06-27T15:44:43.703974Z", + "shell.execute_reply": "2024-06-27T15:44:43.703400Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "96a1ec22", + "id": "7463b4fa", "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": "d478ad17", + "id": "6670b933", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.968394Z", - "iopub.status.busy": "2024-06-25T23:18:13.968108Z", - "iopub.status.idle": "2024-06-25T23:18:13.989832Z", - "shell.execute_reply": "2024-06-25T23:18:13.989245Z" + "iopub.execute_input": "2024-06-27T15:44:43.706022Z", + "iopub.status.busy": "2024-06-27T15:44:43.705663Z", + "iopub.status.idle": "2024-06-27T15:44:43.724461Z", + "shell.execute_reply": "2024-06-27T15:44:43.723993Z" } }, "outputs": [ @@ -1490,14 +1490,6 @@ "\n", "Audit complete. 3 issues found in the dataset.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_7878/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" - ] } ], "source": [ @@ -1529,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "ff936017", + "id": "ad393460", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:13.992065Z", - "iopub.status.busy": "2024-06-25T23:18:13.991705Z", - "iopub.status.idle": "2024-06-25T23:18:13.994946Z", - "shell.execute_reply": "2024-06-25T23:18:13.994403Z" + "iopub.execute_input": "2024-06-27T15:44:43.726475Z", + "iopub.status.busy": "2024-06-27T15:44:43.726145Z", + "iopub.status.idle": "2024-06-27T15:44:43.729522Z", + "shell.execute_reply": "2024-06-27T15:44:43.729061Z" } }, "outputs": [ @@ -1630,43 +1622,46 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01de1302b1ac41a68c4d605171741bc4": { + "00d405cd874e4d93b1779853b89cd0f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - 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"placeholder": "​", - "style": "IPY_MODEL_01de1302b1ac41a68c4d605171741bc4", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1cc2f905137a468e8835d65cec38038b", + "IPY_MODEL_350d9d23aa68460eb3705e7d9f43c50d", + "IPY_MODEL_0f4e70486c0943199e248019574dfa45" + ], + "layout": "IPY_MODEL_ec33686c3f7944c1810d5fcb8f494d91", "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " + "tooltip": null } }, - "b5ef166a707247e98779b17c09955c28": { + "c7732a3a74644a5196a3616939028df2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2132,17 +2184,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c4f061a7d9044c3cbc2d5dc4162c65ef", + "layout": "IPY_MODEL_e7ae634431c44d099f615a40e61b9fa4", "max": 50.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_0f44c68a58214c4e8e72391024cc96e8", + "style": "IPY_MODEL_00d405cd874e4d93b1779853b89cd0f7", "tabbable": null, "tooltip": null, "value": 50.0 } }, - "c4f061a7d9044c3cbc2d5dc4162c65ef": { + "d71a2298c0664cabb50d3e7cd8d76a12": { + "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 + } + }, + "e7ae634431c44d099f615a40e61b9fa4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2195,7 +2265,7 @@ "width": null } }, - "c84021d7d3264151a5fb14b29eaf1cee": { + "ec33686c3f7944c1810d5fcb8f494d91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2248,106 +2318,28 @@ "width": null } }, - "db994fd696e94ac29224215d7f867ee2": { + "f5f6626fc81f4263b798ca0b721371b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - 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"_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_80ffedccfbbc46d0bd395580acfaf87f", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0e5d155060264c219bd191119ba7e533", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_81482564dab54ea4bc615bb395105d98", + "IPY_MODEL_c7732a3a74644a5196a3616939028df2", + "IPY_MODEL_b46b36118bba4d82aed9017dca726dfb" + ], + "layout": "IPY_MODEL_21317cfda48d4538b212082d0f2cc4fb", "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "efd5cc9a580b4925b2b71b81f5bd69d9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - 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    Workflow 1: Use Datalab to detect many types of issues

    -
    +
    @@ -859,15 +859,6 @@

    Workflow 1: Use Datalab to detect many types of issues

    -
    -
    -
    -
    -
    -/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
-  warnings.warn(
-
    -

    After the audit is complete, review the findings using the report method:

    [7]:
diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb
index 902b836cf..652551c5d 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": "2024-06-25T23:18:17.256748Z",
-     "iopub.status.busy": "2024-06-25T23:18:17.256569Z",
-     "iopub.status.idle": "2024-06-25T23:18:18.418999Z",
-     "shell.execute_reply": "2024-06-25T23:18:18.418397Z"
+     "iopub.execute_input": "2024-06-27T15:44:48.147366Z",
+     "iopub.status.busy": "2024-06-27T15:44:48.147196Z",
+     "iopub.status.idle": "2024-06-27T15:44:49.321368Z",
+     "shell.execute_reply": "2024-06-27T15:44:49.320829Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:18.421550Z",
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-     "iopub.status.idle": "2024-06-25T23:18:18.599266Z",
-     "shell.execute_reply": "2024-06-25T23:18:18.598641Z"
+     "iopub.execute_input": "2024-06-27T15:44:49.324091Z",
+     "iopub.status.busy": "2024-06-27T15:44:49.323521Z",
+     "iopub.status.idle": "2024-06-27T15:44:49.506194Z",
+     "shell.execute_reply": "2024-06-27T15:44:49.505523Z"
     },
     "id": "avXlHJcXjruP"
    },
@@ -234,10 +234,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:18.601825Z",
-     "iopub.status.busy": "2024-06-25T23:18:18.601625Z",
-     "iopub.status.idle": "2024-06-25T23:18:18.613136Z",
-     "shell.execute_reply": "2024-06-25T23:18:18.612703Z"
+     "iopub.execute_input": "2024-06-27T15:44:49.508864Z",
+     "iopub.status.busy": "2024-06-27T15:44:49.508610Z",
+     "iopub.status.idle": "2024-06-27T15:44:49.521078Z",
+     "shell.execute_reply": "2024-06-27T15:44:49.520490Z"
     },
     "nbsphinx": "hidden"
    },
@@ -340,10 +340,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:18.615067Z",
-     "iopub.status.busy": "2024-06-25T23:18:18.614888Z",
-     "iopub.status.idle": "2024-06-25T23:18:18.849624Z",
-     "shell.execute_reply": "2024-06-25T23:18:18.849023Z"
+     "iopub.execute_input": "2024-06-27T15:44:49.523386Z",
+     "iopub.status.busy": "2024-06-27T15:44:49.522992Z",
+     "iopub.status.idle": "2024-06-27T15:44:49.761259Z",
+     "shell.execute_reply": "2024-06-27T15:44:49.760654Z"
     }
    },
    "outputs": [
@@ -393,10 +393,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:18.851953Z",
-     "iopub.status.busy": "2024-06-25T23:18:18.851541Z",
-     "iopub.status.idle": "2024-06-25T23:18:18.877468Z",
-     "shell.execute_reply": "2024-06-25T23:18:18.877017Z"
+     "iopub.execute_input": "2024-06-27T15:44:49.763628Z",
+     "iopub.status.busy": "2024-06-27T15:44:49.763381Z",
+     "iopub.status.idle": "2024-06-27T15:44:49.789884Z",
+     "shell.execute_reply": "2024-06-27T15:44:49.789414Z"
     }
    },
    "outputs": [],
@@ -428,10 +428,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:18.879561Z",
-     "iopub.status.busy": "2024-06-25T23:18:18.879211Z",
-     "iopub.status.idle": "2024-06-25T23:18:20.899666Z",
-     "shell.execute_reply": "2024-06-25T23:18:20.898976Z"
+     "iopub.execute_input": "2024-06-27T15:44:49.791905Z",
+     "iopub.status.busy": "2024-06-27T15:44:49.791729Z",
+     "iopub.status.idle": "2024-06-27T15:44:51.886248Z",
+     "shell.execute_reply": "2024-06-27T15:44:51.885611Z"
     }
    },
    "outputs": [
@@ -455,14 +455,6 @@
       "\n",
       "Audit complete. 78 issues found in the dataset.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/sklearn/neighbors/_base.py:246: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.\n",
-      "  warnings.warn(\n"
-     ]
     }
    ],
    "source": [
@@ -482,10 +474,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:20.901960Z",
-     "iopub.status.busy": "2024-06-25T23:18:20.901648Z",
-     "iopub.status.idle": "2024-06-25T23:18:20.919398Z",
-     "shell.execute_reply": "2024-06-25T23:18:20.918920Z"
+     "iopub.execute_input": "2024-06-27T15:44:51.888650Z",
+     "iopub.status.busy": "2024-06-27T15:44:51.888335Z",
+     "iopub.status.idle": "2024-06-27T15:44:51.906193Z",
+     "shell.execute_reply": "2024-06-27T15:44:51.905621Z"
     },
     "scrolled": true
    },
@@ -615,10 +607,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:20.921440Z",
-     "iopub.status.busy": "2024-06-25T23:18:20.921079Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.361092Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.360462Z"
+     "iopub.execute_input": "2024-06-27T15:44:51.908417Z",
+     "iopub.status.busy": "2024-06-27T15:44:51.908017Z",
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     },
     "id": "AaHC5MRKjruT"
    },
@@ -737,10 +729,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.363669Z",
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-     "shell.execute_reply": "2024-06-25T23:18:22.376170Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.380328Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.379266Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.392501Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.392074Z"
     },
     "id": "Wy27rvyhjruU"
    },
@@ -789,10 +781,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.378792Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.378469Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.452119Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.451527Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.394717Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.394312Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.472241Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.471703Z"
     },
     "id": "Db8YHnyVjruU"
    },
@@ -899,10 +891,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.454608Z",
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-     "iopub.status.idle": "2024-06-25T23:18:22.662363Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.661824Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.474636Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.474208Z",
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+     "shell.execute_reply": "2024-06-27T15:44:53.681691Z"
     },
     "id": "iJqAHuS2jruV"
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@@ -939,10 +931,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2024-06-25T23:18:22.680722Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.684408Z",
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+     "shell.execute_reply": "2024-06-27T15:44:53.700807Z"
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    },
@@ -1408,10 +1400,10 @@
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    "metadata": {
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     },
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@@ -1558,10 +1550,10 @@
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-     "shell.execute_reply": "2024-06-25T23:18:22.775639Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.715593Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.715092Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.799524Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.798982Z"
     },
     "id": "MfqTCa3kjruV"
    },
@@ -1642,10 +1634,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.778586Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.778226Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.894081Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.893472Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.801876Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.801534Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.928209Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.927576Z"
     },
     "id": "9ZtWAYXqMAPL"
    },
@@ -1705,10 +1697,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.896294Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.896067Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.899817Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.899290Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.930682Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.930336Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.934290Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.933731Z"
     },
     "id": "0rXP3ZPWjruW"
    },
@@ -1746,10 +1738,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.901918Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.901602Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.905366Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.904793Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.936433Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.936050Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.939711Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.939186Z"
     },
     "id": "-iRPe8KXjruW"
    },
@@ -1804,10 +1796,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.907395Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.907096Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.943768Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.943295Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.941803Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.941399Z",
+     "iopub.status.idle": "2024-06-27T15:44:53.978056Z",
+     "shell.execute_reply": "2024-06-27T15:44:53.977512Z"
     },
     "id": "ZpipUliyjruW"
    },
@@ -1858,10 +1850,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.945705Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.945390Z",
-     "iopub.status.idle": "2024-06-25T23:18:22.987000Z",
-     "shell.execute_reply": "2024-06-25T23:18:22.986556Z"
+     "iopub.execute_input": "2024-06-27T15:44:53.980221Z",
+     "iopub.status.busy": "2024-06-27T15:44:53.979917Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.021764Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.021192Z"
     },
     "id": "SLq-3q4xjruX"
    },
@@ -1930,10 +1922,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:22.989099Z",
-     "iopub.status.busy": "2024-06-25T23:18:22.988778Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.079367Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.078808Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.023732Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.023409Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.117648Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.116975Z"
     },
     "id": "g5LHhhuqFbXK"
    },
@@ -1965,10 +1957,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.081992Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.081632Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.163660Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.163108Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.120433Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.120068Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.202925Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.202329Z"
     },
     "id": "p7w8F8ezBcet"
    },
@@ -2025,10 +2017,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.166170Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.165696Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.373652Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.373076Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.205153Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.204919Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.415014Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.414492Z"
     },
     "id": "WETRL74tE_sU"
    },
@@ -2063,10 +2055,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.375920Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.375563Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.542133Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.541601Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.417178Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.416852Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.599512Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.598904Z"
     },
     "id": "kCfdx2gOLmXS"
    },
@@ -2228,10 +2220,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.544310Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.544080Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.550244Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.549696Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.601799Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.601584Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.607587Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.607130Z"
     },
     "id": "-uogYRWFYnuu"
    },
@@ -2285,10 +2277,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.552552Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.552102Z",
-     "iopub.status.idle": "2024-06-25T23:18:23.765551Z",
-     "shell.execute_reply": "2024-06-25T23:18:23.764971Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.609450Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.609279Z",
+     "iopub.status.idle": "2024-06-27T15:44:54.823297Z",
+     "shell.execute_reply": "2024-06-27T15:44:54.822719Z"
     },
     "id": "pG-ljrmcYp9Q"
    },
@@ -2335,10 +2327,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:23.767794Z",
-     "iopub.status.busy": "2024-06-25T23:18:23.767426Z",
-     "iopub.status.idle": "2024-06-25T23:18:24.838654Z",
-     "shell.execute_reply": "2024-06-25T23:18:24.838036Z"
+     "iopub.execute_input": "2024-06-27T15:44:54.825510Z",
+     "iopub.status.busy": "2024-06-27T15:44:54.825318Z",
+     "iopub.status.idle": "2024-06-27T15:44:55.887198Z",
+     "shell.execute_reply": "2024-06-27T15:44:55.886648Z"
     },
     "id": "wL3ngCnuLEWd"
    },
diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb
index b4c4a33f9..e955474a9 100644
--- a/master/tutorials/multiannotator.ipynb
+++ b/master/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
    "id": "a3ddc95f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:28.410867Z",
-     "iopub.status.busy": "2024-06-25T23:18:28.410704Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.523341Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.522804Z"
+     "iopub.execute_input": "2024-06-27T15:44:59.468627Z",
+     "iopub.status.busy": "2024-06-27T15:44:59.468466Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.591032Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.590468Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:29.525967Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.525510Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.528645Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.528187Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.593760Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.593202Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.596242Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.595817Z"
     }
    },
    "outputs": [],
@@ -263,10 +263,10 @@
    "id": "c37c0a69",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.530912Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.530502Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.538778Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.538338Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.598385Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.598077Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.605729Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.605174Z"
     },
     "nbsphinx": "hidden"
    },
@@ -350,10 +350,10 @@
    "id": "99f69523",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.540895Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.540489Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.587259Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.586733Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.607687Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.607416Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.653808Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.653208Z"
     }
    },
    "outputs": [],
@@ -379,10 +379,10 @@
    "id": "8f241c16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.589466Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.589277Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.606524Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.606095Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.656075Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.655764Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.672779Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.672331Z"
     }
    },
    "outputs": [
@@ -597,10 +597,10 @@
    "id": "4f0819ba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.608443Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.608267Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.612218Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.611771Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.674888Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.674484Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.678316Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.677783Z"
     }
    },
    "outputs": [
@@ -671,10 +671,10 @@
    "id": "d009f347",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.614226Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.614054Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.631367Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.630956Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.680411Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.680099Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.693774Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.693335Z"
     }
    },
    "outputs": [],
@@ -698,10 +698,10 @@
    "id": "cbd1e415",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.633306Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.632964Z",
-     "iopub.status.idle": "2024-06-25T23:18:29.658440Z",
-     "shell.execute_reply": "2024-06-25T23:18:29.658012Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.695788Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.695460Z",
+     "iopub.status.idle": "2024-06-27T15:45:00.721230Z",
+     "shell.execute_reply": "2024-06-27T15:45:00.720808Z"
     }
    },
    "outputs": [],
@@ -738,10 +738,10 @@
    "id": "6ca92617",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:29.660435Z",
-     "iopub.status.busy": "2024-06-25T23:18:29.660092Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.561212Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.560640Z"
+     "iopub.execute_input": "2024-06-27T15:45:00.723435Z",
+     "iopub.status.busy": "2024-06-27T15:45:00.723118Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.652946Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.652251Z"
     }
    },
    "outputs": [],
@@ -771,10 +771,10 @@
    "id": "bf945113",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.563955Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.563327Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.570324Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.569880Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.655941Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.655387Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.662756Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.662202Z"
     },
     "scrolled": true
    },
@@ -885,10 +885,10 @@
    "id": "14251ee0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.572276Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.571950Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.584255Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.583817Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.665017Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.664581Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.677290Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.676854Z"
     }
    },
    "outputs": [
@@ -1138,10 +1138,10 @@
    "id": "efe16638",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.586203Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.585878Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.591999Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.591576Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.679365Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.679031Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.685486Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.684929Z"
     },
     "scrolled": true
    },
@@ -1315,10 +1315,10 @@
    "id": "abd0fb0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.594128Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.593809Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.596328Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.595895Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.687634Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.687246Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.690043Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.689488Z"
     }
    },
    "outputs": [],
@@ -1340,10 +1340,10 @@
    "id": "cdf061df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.598281Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.597974Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.601541Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.600983Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.692044Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.691745Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.695116Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.694642Z"
     },
     "scrolled": true
    },
@@ -1395,10 +1395,10 @@
    "id": "08949890",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.603595Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.603264Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.605889Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.605456Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.697161Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.696845Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.699375Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.698946Z"
     }
    },
    "outputs": [],
@@ -1422,10 +1422,10 @@
    "id": "6948b073",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.607856Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.607558Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.611501Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.611048Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.701167Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.700997Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.705189Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.704724Z"
     }
    },
    "outputs": [
@@ -1480,10 +1480,10 @@
    "id": "6f8e6914",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.613408Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.613238Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.641822Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.641266Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.707181Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.707011Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.735513Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.735059Z"
     }
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    "outputs": [],
@@ -1526,10 +1526,10 @@
    "id": "b806d2ea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:31.644000Z",
-     "iopub.status.busy": "2024-06-25T23:18:31.643674Z",
-     "iopub.status.idle": "2024-06-25T23:18:31.648272Z",
-     "shell.execute_reply": "2024-06-25T23:18:31.647708Z"
+     "iopub.execute_input": "2024-06-27T15:45:02.737575Z",
+     "iopub.status.busy": "2024-06-27T15:45:02.737383Z",
+     "iopub.status.idle": "2024-06-27T15:45:02.742352Z",
+     "shell.execute_reply": "2024-06-27T15:45:02.741808Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb
index 9593cdb90..2ef3b5160 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": "2024-06-25T23:18:34.388005Z",
-     "iopub.status.busy": "2024-06-25T23:18:34.387509Z",
-     "iopub.status.idle": "2024-06-25T23:18:35.555688Z",
-     "shell.execute_reply": "2024-06-25T23:18:35.555141Z"
+     "iopub.execute_input": "2024-06-27T15:45:05.699973Z",
+     "iopub.status.busy": "2024-06-27T15:45:05.699801Z",
+     "iopub.status.idle": "2024-06-27T15:45:06.857177Z",
+     "shell.execute_reply": "2024-06-27T15:45:06.856603Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:35.558285Z",
-     "iopub.status.busy": "2024-06-25T23:18:35.557842Z",
-     "iopub.status.idle": "2024-06-25T23:18:35.751397Z",
-     "shell.execute_reply": "2024-06-25T23:18:35.750860Z"
+     "iopub.execute_input": "2024-06-27T15:45:06.859790Z",
+     "iopub.status.busy": "2024-06-27T15:45:06.859298Z",
+     "iopub.status.idle": "2024-06-27T15:45:07.049527Z",
+     "shell.execute_reply": "2024-06-27T15:45:07.048975Z"
     }
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    "outputs": [],
@@ -268,10 +268,10 @@
    "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:35.754209Z",
-     "iopub.status.busy": "2024-06-25T23:18:35.753733Z",
-     "iopub.status.idle": "2024-06-25T23:18:35.767096Z",
-     "shell.execute_reply": "2024-06-25T23:18:35.766635Z"
+     "iopub.execute_input": "2024-06-27T15:45:07.052251Z",
+     "iopub.status.busy": "2024-06-27T15:45:07.051728Z",
+     "iopub.status.idle": "2024-06-27T15:45:07.064966Z",
+     "shell.execute_reply": "2024-06-27T15:45:07.064409Z"
     },
     "nbsphinx": "hidden"
    },
@@ -407,10 +407,10 @@
    "id": "dac65d3b-51e8-4682-b829-beab610b56d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:35.769292Z",
-     "iopub.status.busy": "2024-06-25T23:18:35.768939Z",
-     "iopub.status.idle": "2024-06-25T23:18:38.460798Z",
-     "shell.execute_reply": "2024-06-25T23:18:38.460293Z"
+     "iopub.execute_input": "2024-06-27T15:45:07.067098Z",
+     "iopub.status.busy": "2024-06-27T15:45:07.066806Z",
+     "iopub.status.idle": "2024-06-27T15:45:09.757625Z",
+     "shell.execute_reply": "2024-06-27T15:45:09.757094Z"
     }
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    "outputs": [
@@ -454,10 +454,10 @@
    "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:38.463138Z",
-     "iopub.status.busy": "2024-06-25T23:18:38.462688Z",
-     "iopub.status.idle": "2024-06-25T23:18:39.817391Z",
-     "shell.execute_reply": "2024-06-25T23:18:39.816843Z"
+     "iopub.execute_input": "2024-06-27T15:45:09.759992Z",
+     "iopub.status.busy": "2024-06-27T15:45:09.759608Z",
+     "iopub.status.idle": "2024-06-27T15:45:11.112703Z",
+     "shell.execute_reply": "2024-06-27T15:45:11.112185Z"
     }
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    "outputs": [],
@@ -499,10 +499,10 @@
    "id": "ac1a60df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:39.819916Z",
-     "iopub.status.busy": "2024-06-25T23:18:39.819475Z",
-     "iopub.status.idle": "2024-06-25T23:18:39.823477Z",
-     "shell.execute_reply": "2024-06-25T23:18:39.822931Z"
+     "iopub.execute_input": "2024-06-27T15:45:11.115163Z",
+     "iopub.status.busy": "2024-06-27T15:45:11.114818Z",
+     "iopub.status.idle": "2024-06-27T15:45:11.118894Z",
+     "shell.execute_reply": "2024-06-27T15:45:11.118425Z"
     }
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    "outputs": [
@@ -544,10 +544,10 @@
    "id": "d09115b6-ad44-474f-9c8a-85a459586439",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:39.825523Z",
-     "iopub.status.busy": "2024-06-25T23:18:39.825189Z",
-     "iopub.status.idle": "2024-06-25T23:18:41.816360Z",
-     "shell.execute_reply": "2024-06-25T23:18:41.815747Z"
+     "iopub.execute_input": "2024-06-27T15:45:11.120805Z",
+     "iopub.status.busy": "2024-06-27T15:45:11.120500Z",
+     "iopub.status.idle": "2024-06-27T15:45:13.138189Z",
+     "shell.execute_reply": "2024-06-27T15:45:13.137525Z"
     }
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    "outputs": [
@@ -594,10 +594,10 @@
    "id": "c18dd83b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:41.818930Z",
-     "iopub.status.busy": "2024-06-25T23:18:41.818429Z",
-     "iopub.status.idle": "2024-06-25T23:18:41.826321Z",
-     "shell.execute_reply": "2024-06-25T23:18:41.825851Z"
+     "iopub.execute_input": "2024-06-27T15:45:13.140803Z",
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+     "shell.execute_reply": "2024-06-27T15:45:13.147408Z"
     }
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    "outputs": [
@@ -633,10 +633,10 @@
    "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:41.828406Z",
-     "iopub.status.busy": "2024-06-25T23:18:41.828097Z",
-     "iopub.status.idle": "2024-06-25T23:18:44.431218Z",
-     "shell.execute_reply": "2024-06-25T23:18:44.430687Z"
+     "iopub.execute_input": "2024-06-27T15:45:13.149938Z",
+     "iopub.status.busy": "2024-06-27T15:45:13.149581Z",
+     "iopub.status.idle": "2024-06-27T15:45:15.762528Z",
+     "shell.execute_reply": "2024-06-27T15:45:15.761940Z"
     }
    },
    "outputs": [
@@ -671,10 +671,10 @@
    "id": "c1198575",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:44.433395Z",
-     "iopub.status.busy": "2024-06-25T23:18:44.433032Z",
-     "iopub.status.idle": "2024-06-25T23:18:44.436462Z",
-     "shell.execute_reply": "2024-06-25T23:18:44.435934Z"
+     "iopub.execute_input": "2024-06-27T15:45:15.764619Z",
+     "iopub.status.busy": "2024-06-27T15:45:15.764424Z",
+     "iopub.status.idle": "2024-06-27T15:45:15.768184Z",
+     "shell.execute_reply": "2024-06-27T15:45:15.767636Z"
     }
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    "outputs": [
@@ -721,10 +721,10 @@
    "id": "49161b19-7625-4fb7-add9-607d91a7eca1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:44.438430Z",
-     "iopub.status.busy": "2024-06-25T23:18:44.438123Z",
-     "iopub.status.idle": "2024-06-25T23:18:44.441587Z",
-     "shell.execute_reply": "2024-06-25T23:18:44.441125Z"
+     "iopub.execute_input": "2024-06-27T15:45:15.770204Z",
+     "iopub.status.busy": "2024-06-27T15:45:15.769898Z",
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+     "shell.execute_reply": "2024-06-27T15:45:15.772957Z"
     }
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    "outputs": [],
@@ -752,10 +752,10 @@
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-     "iopub.execute_input": "2024-06-25T23:18:44.443586Z",
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-     "shell.execute_reply": "2024-06-25T23:18:44.445845Z"
+     "iopub.execute_input": "2024-06-27T15:45:15.775576Z",
+     "iopub.status.busy": "2024-06-27T15:45:15.775249Z",
+     "iopub.status.idle": "2024-06-27T15:45:15.778400Z",
+     "shell.execute_reply": "2024-06-27T15:45:15.777870Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb
index ceb7220d6..ff2404cec 100644
--- a/master/tutorials/object_detection.ipynb
+++ b/master/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
    "id": "0ba0dc70",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:46.821534Z",
-     "iopub.status.busy": "2024-06-25T23:18:46.821356Z",
-     "iopub.status.idle": "2024-06-25T23:18:47.991566Z",
-     "shell.execute_reply": "2024-06-25T23:18:47.991020Z"
+     "iopub.execute_input": "2024-06-27T15:45:18.463792Z",
+     "iopub.status.busy": "2024-06-27T15:45:18.463378Z",
+     "iopub.status.idle": "2024-06-27T15:45:19.617248Z",
+     "shell.execute_reply": "2024-06-27T15:45:19.616753Z"
     },
     "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:18:47.994044Z",
-     "iopub.status.busy": "2024-06-25T23:18:47.993746Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.077383Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.076740Z"
+     "iopub.execute_input": "2024-06-27T15:45:19.619702Z",
+     "iopub.status.busy": "2024-06-27T15:45:19.619248Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.409416Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.408754Z"
     }
    },
    "outputs": [],
@@ -130,10 +130,10 @@
    "id": "df8be4c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.079931Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.079715Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.083128Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.082576Z"
+     "iopub.execute_input": "2024-06-27T15:45:22.412130Z",
+     "iopub.status.busy": "2024-06-27T15:45:22.411640Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.415047Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.414491Z"
     }
    },
    "outputs": [],
@@ -169,10 +169,10 @@
    "id": "2e9ffd6f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.085315Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.084875Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.090995Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.090565Z"
+     "iopub.execute_input": "2024-06-27T15:45:22.417375Z",
+     "iopub.status.busy": "2024-06-27T15:45:22.417044Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.423110Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.422685Z"
     }
    },
    "outputs": [],
@@ -198,10 +198,10 @@
    "id": "56705562",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.092987Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.092664Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.578049Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.577480Z"
+     "iopub.execute_input": "2024-06-27T15:45:22.425113Z",
+     "iopub.status.busy": "2024-06-27T15:45:22.424847Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.910812Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.910225Z"
     },
     "scrolled": true
    },
@@ -242,10 +242,10 @@
    "id": "b08144d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.581141Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.580804Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.586187Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.585728Z"
+     "iopub.execute_input": "2024-06-27T15:45:22.913649Z",
+     "iopub.status.busy": "2024-06-27T15:45:22.913317Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.918911Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.918469Z"
     }
    },
    "outputs": [
@@ -497,10 +497,10 @@
    "id": "3d70bec6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.588207Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.587912Z",
-     "iopub.status.idle": "2024-06-25T23:18:49.592364Z",
-     "shell.execute_reply": "2024-06-25T23:18:49.591919Z"
+     "iopub.execute_input": "2024-06-27T15:45:22.920816Z",
+     "iopub.status.busy": "2024-06-27T15:45:22.920511Z",
+     "iopub.status.idle": "2024-06-27T15:45:22.924388Z",
+     "shell.execute_reply": "2024-06-27T15:45:22.923851Z"
     }
    },
    "outputs": [
@@ -557,10 +557,10 @@
    "id": "4caa635d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:49.594287Z",
-     "iopub.status.busy": "2024-06-25T23:18:49.594112Z",
-     "iopub.status.idle": "2024-06-25T23:18:50.586165Z",
-     "shell.execute_reply": "2024-06-25T23:18:50.585507Z"
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+     "shell.execute_reply": "2024-06-27T15:45:23.849043Z"
     }
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    "outputs": [
@@ -616,10 +616,10 @@
    "id": "a9b4c590",
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-     "iopub.execute_input": "2024-06-25T23:18:50.588520Z",
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    "outputs": [
@@ -660,10 +660,10 @@
    "id": "ffd9ebcc",
    "metadata": {
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-     "shell.execute_reply": "2024-06-25T23:18:50.814577Z"
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    "outputs": [
@@ -700,10 +700,10 @@
    "id": "4dd46d67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:50.816841Z",
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-     "shell.execute_reply": "2024-06-25T23:18:51.263937Z"
+     "iopub.execute_input": "2024-06-27T15:45:24.092087Z",
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+     "shell.execute_reply": "2024-06-27T15:45:24.545021Z"
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    "outputs": [
@@ -762,10 +762,10 @@
    "id": "ceec2394",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:51.267205Z",
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-     "shell.execute_reply": "2024-06-25T23:18:51.596965Z"
+     "iopub.execute_input": "2024-06-27T15:45:24.548711Z",
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+     "shell.execute_reply": "2024-06-27T15:45:24.880008Z"
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    "outputs": [
@@ -812,10 +812,10 @@
    "id": "94f82b0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:51.599806Z",
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-     "shell.execute_reply": "2024-06-25T23:18:51.932766Z"
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    "outputs": [
@@ -862,10 +862,10 @@
    "id": "1ea18c5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:51.936579Z",
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-     "shell.execute_reply": "2024-06-25T23:18:52.347588Z"
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    "outputs": [
@@ -925,10 +925,10 @@
    "id": "7e770d23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:52.352428Z",
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-     "shell.execute_reply": "2024-06-25T23:18:52.772929Z"
+     "iopub.execute_input": "2024-06-27T15:45:25.694772Z",
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+     "shell.execute_reply": "2024-06-27T15:45:26.140107Z"
     }
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    "outputs": [
@@ -971,10 +971,10 @@
    "id": "57e84a27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:52.776870Z",
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-     "shell.execute_reply": "2024-06-25T23:18:52.965014Z"
+     "iopub.execute_input": "2024-06-27T15:45:26.143603Z",
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+     "shell.execute_reply": "2024-06-27T15:45:26.331369Z"
     }
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    "outputs": [
@@ -1017,10 +1017,10 @@
    "id": "0302818a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:52.968518Z",
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-     "shell.execute_reply": "2024-06-25T23:18:53.169139Z"
+     "iopub.execute_input": "2024-06-27T15:45:26.334333Z",
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+     "shell.execute_reply": "2024-06-27T15:45:26.516954Z"
     }
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    "outputs": [
@@ -1067,10 +1067,10 @@
    "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:53.171908Z",
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-     "shell.execute_reply": "2024-06-25T23:18:53.174135Z"
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+     "shell.execute_reply": "2024-06-27T15:45:26.522203Z"
     }
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    "outputs": [],
@@ -1090,10 +1090,10 @@
    "id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:53.176658Z",
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-     "shell.execute_reply": "2024-06-25T23:18:54.151257Z"
+     "iopub.execute_input": "2024-06-27T15:45:26.524690Z",
+     "iopub.status.busy": "2024-06-27T15:45:26.524304Z",
+     "iopub.status.idle": "2024-06-27T15:45:27.479354Z",
+     "shell.execute_reply": "2024-06-27T15:45:27.478760Z"
     }
    },
    "outputs": [
@@ -1172,10 +1172,10 @@
    "id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:54.153970Z",
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-     "shell.execute_reply": "2024-06-25T23:18:54.366782Z"
+     "iopub.execute_input": "2024-06-27T15:45:27.481677Z",
+     "iopub.status.busy": "2024-06-27T15:45:27.481269Z",
+     "iopub.status.idle": "2024-06-27T15:45:27.662233Z",
+     "shell.execute_reply": "2024-06-27T15:45:27.661738Z"
     }
    },
    "outputs": [
@@ -1214,10 +1214,10 @@
    "id": "59d7ee39-3785-434b-8680-9133014851cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:54.369532Z",
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-     "shell.execute_reply": "2024-06-25T23:18:54.582875Z"
+     "iopub.execute_input": "2024-06-27T15:45:27.664486Z",
+     "iopub.status.busy": "2024-06-27T15:45:27.664165Z",
+     "iopub.status.idle": "2024-06-27T15:45:27.839845Z",
+     "shell.execute_reply": "2024-06-27T15:45:27.839265Z"
     }
    },
    "outputs": [],
@@ -1266,10 +1266,10 @@
    "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:54.585760Z",
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-     "shell.execute_reply": "2024-06-25T23:18:55.322814Z"
+     "iopub.execute_input": "2024-06-27T15:45:27.841913Z",
+     "iopub.status.busy": "2024-06-27T15:45:27.841731Z",
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+     "shell.execute_reply": "2024-06-27T15:45:28.508151Z"
     }
    },
    "outputs": [
@@ -1351,10 +1351,10 @@
    "id": "8ce74938",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-25T23:18:55.325548Z",
-     "iopub.status.busy": "2024-06-25T23:18:55.325207Z",
-     "iopub.status.idle": "2024-06-25T23:18:55.329284Z",
-     "shell.execute_reply": "2024-06-25T23:18:55.328852Z"
+     "iopub.execute_input": "2024-06-27T15:45:28.511345Z",
+     "iopub.status.busy": "2024-06-27T15:45:28.510832Z",
+     "iopub.status.idle": "2024-06-27T15:45:28.514788Z",
+     "shell.execute_reply": "2024-06-27T15:45:28.514332Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html
index c663c5ea2..dd13b1260 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, 107997102.42it/s]
+100%|██████████| 170498071/170498071 [00:03<00:00, 42685946.86it/s]
    @@ -910,7 +910,7 @@

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

    -
    +

    @@ -1124,7 +1124,7 @@

    4. Use cleanlab and here.

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 4aeee095a..dce990b87 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:18:57.455185Z", - "iopub.status.busy": "2024-06-25T23:18:57.455007Z", - "iopub.status.idle": "2024-06-25T23:19:00.140522Z", - "shell.execute_reply": "2024-06-25T23:19:00.139964Z" + "iopub.execute_input": "2024-06-27T15:45:30.811235Z", + "iopub.status.busy": "2024-06-27T15:45:30.811074Z", + "iopub.status.idle": "2024-06-27T15:45:33.603967Z", + "shell.execute_reply": "2024-06-27T15:45:33.603351Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:19:00.143299Z", - "iopub.status.busy": "2024-06-25T23:19:00.142777Z", - "iopub.status.idle": "2024-06-25T23:19:00.459330Z", - "shell.execute_reply": "2024-06-25T23:19:00.458710Z" + "iopub.execute_input": "2024-06-27T15:45:33.606565Z", + "iopub.status.busy": "2024-06-27T15:45:33.606249Z", + "iopub.status.idle": "2024-06-27T15:45:33.934741Z", + "shell.execute_reply": "2024-06-27T15:45:33.934122Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:00.461903Z", - "iopub.status.busy": "2024-06-25T23:19:00.461603Z", - "iopub.status.idle": "2024-06-25T23:19:00.465997Z", - "shell.execute_reply": "2024-06-25T23:19:00.465462Z" + "iopub.execute_input": "2024-06-27T15:45:33.937444Z", + "iopub.status.busy": "2024-06-27T15:45:33.937123Z", + "iopub.status.idle": "2024-06-27T15:45:33.941418Z", + "shell.execute_reply": "2024-06-27T15:45:33.940892Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:00.468073Z", - "iopub.status.busy": "2024-06-25T23:19:00.467652Z", - "iopub.status.idle": "2024-06-25T23:19:04.713802Z", - "shell.execute_reply": "2024-06-25T23:19:04.713212Z" + "iopub.execute_input": "2024-06-27T15:45:33.943554Z", + "iopub.status.busy": "2024-06-27T15:45:33.943246Z", + "iopub.status.idle": "2024-06-27T15:45:40.969059Z", + "shell.execute_reply": "2024-06-27T15:45:40.968467Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1867776/170498071 [00:00<00:09, 18674661.14it/s]" + " 0%| | 32768/170498071 [00:00<10:29, 270980.66it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13533184/170498071 [00:00<00:02, 76238255.65it/s]" + " 0%| | 229376/170498071 [00:00<02:41, 1053920.96it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25133056/170498071 [00:00<00:01, 94330786.80it/s]" + " 1%| | 884736/170498071 [00:00<00:53, 3192666.86it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 36732928/170498071 [00:00<00:01, 102749472.78it/s]" + " 2%|▏ | 3506176/170498071 [00:00<00:15, 10604458.14it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 48431104/170498071 [00:00<00:01, 107856210.55it/s]" + " 5%|▍ | 8519680/170498071 [00:00<00:06, 23321397.03it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 59244544/170498071 [00:00<00:01, 104969542.54it/s]" + " 8%|▊ | 12877824/170498071 [00:00<00:05, 29461386.54it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70385664/170498071 [00:00<00:00, 106967167.13it/s]" + " 11%|█ | 18513920/170498071 [00:00<00:04, 37736470.82it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 82051072/170498071 [00:00<00:00, 109967427.14it/s]" + " 13%|█▎ | 23003136/170498071 [00:00<00:03, 38396688.94it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 93618176/170498071 [00:00<00:00, 111587704.91it/s]" + " 17%|█▋ | 28278784/170498071 [00:00<00:03, 42619760.41it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 105381888/170498071 [00:01<00:00, 113341455.44it/s]" + " 19%|█▉ | 32636928/170498071 [00:01<00:03, 42167836.16it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 116850688/170498071 [00:01<00:00, 113640126.52it/s]" + " 23%|██▎ | 38436864/170498071 [00:01<00:02, 45347567.59it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 128221184/170498071 [00:01<00:00, 112577089.48it/s]" + " 26%|██▌ | 43581440/170498071 [00:01<00:02, 47004803.84it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 139886592/170498071 [00:01<00:00, 113733636.99it/s]" + " 28%|██▊ | 48332800/170498071 [00:01<00:02, 45932449.35it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 151355392/170498071 [00:01<00:00, 113996738.39it/s]" + " 31%|███ | 53149696/170498071 [00:01<00:02, 46308281.48it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-06-25T23:19:04.715884Z", - "iopub.status.busy": "2024-06-25T23:19:04.715703Z", - "iopub.status.idle": "2024-06-25T23:19:04.720331Z", - "shell.execute_reply": "2024-06-25T23:19:04.719901Z" + "iopub.execute_input": "2024-06-27T15:45:40.971400Z", + "iopub.status.busy": "2024-06-27T15:45:40.971201Z", + "iopub.status.idle": "2024-06-27T15:45:40.975993Z", + "shell.execute_reply": "2024-06-27T15:45:40.975557Z" }, "nbsphinx": "hidden" }, @@ -544,10 +720,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:04.722383Z", - "iopub.status.busy": "2024-06-25T23:19:04.722067Z", - "iopub.status.idle": "2024-06-25T23:19:05.264665Z", - "shell.execute_reply": "2024-06-25T23:19:05.264156Z" + "iopub.execute_input": "2024-06-27T15:45:40.977819Z", + "iopub.status.busy": "2024-06-27T15:45:40.977599Z", + "iopub.status.idle": "2024-06-27T15:45:41.527781Z", + "shell.execute_reply": "2024-06-27T15:45:41.527141Z" } }, "outputs": [ @@ -580,10 +756,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.266862Z", - "iopub.status.busy": "2024-06-25T23:19:05.266533Z", - "iopub.status.idle": "2024-06-25T23:19:05.782683Z", - "shell.execute_reply": "2024-06-25T23:19:05.782187Z" + "iopub.execute_input": "2024-06-27T15:45:41.530331Z", + "iopub.status.busy": "2024-06-27T15:45:41.529870Z", + "iopub.status.idle": "2024-06-27T15:45:42.057214Z", + "shell.execute_reply": "2024-06-27T15:45:42.056595Z" } }, "outputs": [ @@ -621,10 +797,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.784979Z", - "iopub.status.busy": "2024-06-25T23:19:05.784496Z", - "iopub.status.idle": "2024-06-25T23:19:05.788141Z", - "shell.execute_reply": "2024-06-25T23:19:05.787582Z" + "iopub.execute_input": "2024-06-27T15:45:42.059555Z", + "iopub.status.busy": "2024-06-27T15:45:42.059223Z", + "iopub.status.idle": "2024-06-27T15:45:42.062832Z", + "shell.execute_reply": "2024-06-27T15:45:42.062275Z" } }, "outputs": [], @@ -647,17 +823,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:05.790197Z", - "iopub.status.busy": "2024-06-25T23:19:05.789889Z", - "iopub.status.idle": "2024-06-25T23:19:18.262418Z", - "shell.execute_reply": "2024-06-25T23:19:18.261753Z" + "iopub.execute_input": "2024-06-27T15:45:42.064935Z", + "iopub.status.busy": "2024-06-27T15:45:42.064505Z", + "iopub.status.idle": "2024-06-27T15:45:55.556440Z", + "shell.execute_reply": "2024-06-27T15:45:55.555646Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c487c0c381e74a26a4f49ff121b50dc9", + "model_id": "a6c22c3e77c84cc8bd08a52e1cf9b902", "version_major": 2, "version_minor": 0 }, @@ -716,10 +892,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:18.264580Z", - "iopub.status.busy": "2024-06-25T23:19:18.264398Z", - "iopub.status.idle": "2024-06-25T23:19:20.351765Z", - "shell.execute_reply": "2024-06-25T23:19:20.351216Z" + "iopub.execute_input": "2024-06-27T15:45:55.558966Z", + "iopub.status.busy": "2024-06-27T15:45:55.558582Z", + "iopub.status.idle": "2024-06-27T15:45:57.651196Z", + "shell.execute_reply": "2024-06-27T15:45:57.650529Z" } }, "outputs": [ @@ -763,10 +939,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:20.354001Z", - "iopub.status.busy": "2024-06-25T23:19:20.353823Z", - "iopub.status.idle": "2024-06-25T23:19:20.594457Z", - "shell.execute_reply": "2024-06-25T23:19:20.593862Z" + "iopub.execute_input": "2024-06-27T15:45:57.654042Z", + "iopub.status.busy": "2024-06-27T15:45:57.653576Z", + "iopub.status.idle": "2024-06-27T15:45:57.907027Z", + "shell.execute_reply": "2024-06-27T15:45:57.906457Z" } }, "outputs": [ @@ -802,10 +978,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:20.597502Z", - "iopub.status.busy": "2024-06-25T23:19:20.596997Z", - "iopub.status.idle": "2024-06-25T23:19:21.266452Z", - "shell.execute_reply": "2024-06-25T23:19:21.265865Z" + "iopub.execute_input": "2024-06-27T15:45:57.909700Z", + "iopub.status.busy": "2024-06-27T15:45:57.909398Z", + "iopub.status.idle": "2024-06-27T15:45:58.574563Z", + "shell.execute_reply": "2024-06-27T15:45:58.574010Z" } }, "outputs": [ @@ -855,10 +1031,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.269364Z", - "iopub.status.busy": "2024-06-25T23:19:21.269161Z", - "iopub.status.idle": "2024-06-25T23:19:21.610132Z", - "shell.execute_reply": "2024-06-25T23:19:21.609580Z" + "iopub.execute_input": "2024-06-27T15:45:58.577526Z", + "iopub.status.busy": "2024-06-27T15:45:58.577074Z", + "iopub.status.idle": "2024-06-27T15:45:58.918893Z", + "shell.execute_reply": "2024-06-27T15:45:58.918198Z" } }, "outputs": [ @@ -906,10 +1082,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.612438Z", - "iopub.status.busy": "2024-06-25T23:19:21.612030Z", - "iopub.status.idle": "2024-06-25T23:19:21.854392Z", - "shell.execute_reply": "2024-06-25T23:19:21.853886Z" + "iopub.execute_input": "2024-06-27T15:45:58.921501Z", + "iopub.status.busy": "2024-06-27T15:45:58.921042Z", + "iopub.status.idle": "2024-06-27T15:45:59.189588Z", + "shell.execute_reply": "2024-06-27T15:45:59.188711Z" } }, "outputs": [ @@ -965,10 +1141,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.857100Z", - "iopub.status.busy": "2024-06-25T23:19:21.856718Z", - "iopub.status.idle": "2024-06-25T23:19:21.949451Z", - "shell.execute_reply": "2024-06-25T23:19:21.948923Z" + "iopub.execute_input": "2024-06-27T15:45:59.192574Z", + "iopub.status.busy": "2024-06-27T15:45:59.192290Z", + "iopub.status.idle": "2024-06-27T15:45:59.276757Z", + "shell.execute_reply": "2024-06-27T15:45:59.276135Z" } }, "outputs": [], @@ -989,10 +1165,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:21.951971Z", - "iopub.status.busy": "2024-06-25T23:19:21.951792Z", - "iopub.status.idle": "2024-06-25T23:19:32.299281Z", - "shell.execute_reply": "2024-06-25T23:19:32.298639Z" + "iopub.execute_input": "2024-06-27T15:45:59.279342Z", + "iopub.status.busy": "2024-06-27T15:45:59.279159Z", + "iopub.status.idle": "2024-06-27T15:46:09.531418Z", + "shell.execute_reply": "2024-06-27T15:46:09.530750Z" } }, "outputs": [ @@ -1029,10 +1205,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:32.301554Z", - "iopub.status.busy": "2024-06-25T23:19:32.301299Z", - "iopub.status.idle": "2024-06-25T23:19:34.462906Z", - "shell.execute_reply": "2024-06-25T23:19:34.462279Z" + "iopub.execute_input": "2024-06-27T15:46:09.534232Z", + "iopub.status.busy": "2024-06-27T15:46:09.533762Z", + "iopub.status.idle": "2024-06-27T15:46:11.746017Z", + "shell.execute_reply": "2024-06-27T15:46:11.745431Z" } }, "outputs": [ @@ -1063,10 +1239,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.465814Z", - "iopub.status.busy": "2024-06-25T23:19:34.465269Z", - "iopub.status.idle": "2024-06-25T23:19:34.668011Z", - "shell.execute_reply": "2024-06-25T23:19:34.667512Z" + "iopub.execute_input": "2024-06-27T15:46:11.748794Z", + "iopub.status.busy": "2024-06-27T15:46:11.748237Z", + "iopub.status.idle": "2024-06-27T15:46:11.948800Z", + "shell.execute_reply": "2024-06-27T15:46:11.948305Z" } }, "outputs": [], @@ -1080,10 +1256,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.670373Z", - "iopub.status.busy": "2024-06-25T23:19:34.670183Z", - "iopub.status.idle": "2024-06-25T23:19:34.673433Z", - "shell.execute_reply": "2024-06-25T23:19:34.672977Z" + "iopub.execute_input": "2024-06-27T15:46:11.951095Z", + "iopub.status.busy": "2024-06-27T15:46:11.950910Z", + "iopub.status.idle": "2024-06-27T15:46:11.954096Z", + "shell.execute_reply": "2024-06-27T15:46:11.953666Z" } }, "outputs": [], @@ -1105,10 +1281,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:34.675236Z", - "iopub.status.busy": "2024-06-25T23:19:34.675068Z", - "iopub.status.idle": "2024-06-25T23:19:34.683440Z", - "shell.execute_reply": "2024-06-25T23:19:34.682879Z" + "iopub.execute_input": "2024-06-27T15:46:11.956074Z", + "iopub.status.busy": "2024-06-27T15:46:11.955782Z", + "iopub.status.idle": "2024-06-27T15:46:11.963897Z", + "shell.execute_reply": "2024-06-27T15:46:11.963383Z" }, "nbsphinx": "hidden" }, @@ -1153,23 +1329,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"layout": "IPY_MODEL_cac77136c4f843fea7898f1735deb973", + "layout": "IPY_MODEL_472e6894066f4beb9d6bb48527daa24c", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2a6c0688ba264a97aee90eb6e37e9dc2", + "style": "IPY_MODEL_7629b1d5428148fe8288ca24f99967f8", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "55e4c9a3e7c7475b9b79a0916ea74432": { + "46dfbd06512a46a6b805e755503dec3d": { + "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_a0cc2013504e4b7886e198d1c391e884", + "placeholder": "​", + "style": "IPY_MODEL_2867482e9d3444df873ee05b569a66cc", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 257MB/s]" + } + }, + "472e6894066f4beb9d6bb48527daa24c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1266,48 +1449,7 @@ "width": null } }, - 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"_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b8d3a0d6f70d45e596f645995e60a137": { + "4d6c4b8b33aa493e93329931c7f4f318": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1360,31 +1502,7 @@ "width": null } }, - "c487c0c381e74a26a4f49ff121b50dc9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7b2c005a31c74359a9e6040ef88246ae", - "IPY_MODEL_3a433760fb114684b1ce550e725410f3", - "IPY_MODEL_d732fe2e90af4c978675ca6771e1718c" - ], - "layout": "IPY_MODEL_d9309721bf15440c9369f140e0453a0f", - "tabbable": null, - "tooltip": null - } - }, - "cac77136c4f843fea7898f1735deb973": { + "5dfc1d62bb0e4dc58b2161ae9c1bc8d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1437,30 +1555,41 @@ "width": null } }, - "d732fe2e90af4c978675ca6771e1718c": { + "74a0e7dc77404b66aa096c6ac1086de1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b8d3a0d6f70d45e596f645995e60a137", - "placeholder": "​", - "style": "IPY_MODEL_acf4b18dbb8e42a391b0dfa7d26e3612", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 218MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7629b1d5428148fe8288ca24f99967f8": { + "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": "" } }, - "d9309721bf15440c9369f140e0453a0f": { + "a0cc2013504e4b7886e198d1c391e884": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1512,6 +1641,53 @@ "visibility": null, "width": null } + }, + "a6c22c3e77c84cc8bd08a52e1cf9b902": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a9415174d636420a9369ae15096b0f3b", + "IPY_MODEL_2d9ce638a81541a69c6b0f5430fcf5f7", + "IPY_MODEL_46dfbd06512a46a6b805e755503dec3d" + ], + "layout": "IPY_MODEL_5dfc1d62bb0e4dc58b2161ae9c1bc8d0", + "tabbable": null, + "tooltip": null + } + }, + "a9415174d636420a9369ae15096b0f3b": { + "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_4d6c4b8b33aa493e93329931c7f4f318", + "placeholder": "​", + "style": "IPY_MODEL_74a0e7dc77404b66aa096c6ac1086de1", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } } }, "version_major": 2, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 4dccd9a0a..a6fce8c94 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:38.796252Z", - "iopub.status.busy": "2024-06-25T23:19:38.796082Z", - "iopub.status.idle": "2024-06-25T23:19:39.953258Z", - "shell.execute_reply": "2024-06-25T23:19:39.952691Z" + "iopub.execute_input": "2024-06-27T15:46:16.118840Z", + "iopub.status.busy": "2024-06-27T15:46:16.118673Z", + "iopub.status.idle": "2024-06-27T15:46:17.349519Z", + "shell.execute_reply": "2024-06-27T15:46:17.349002Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:19:39.955862Z", - "iopub.status.busy": "2024-06-25T23:19:39.955512Z", - "iopub.status.idle": "2024-06-25T23:19:39.972881Z", - "shell.execute_reply": "2024-06-25T23:19:39.972463Z" + "iopub.execute_input": "2024-06-27T15:46:17.352024Z", + "iopub.status.busy": "2024-06-27T15:46:17.351735Z", + "iopub.status.idle": "2024-06-27T15:46:17.369618Z", + "shell.execute_reply": "2024-06-27T15:46:17.369156Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:39.975108Z", - "iopub.status.busy": "2024-06-25T23:19:39.974726Z", - "iopub.status.idle": "2024-06-25T23:19:39.977547Z", - "shell.execute_reply": "2024-06-25T23:19:39.977124Z" + "iopub.execute_input": "2024-06-27T15:46:17.371774Z", + "iopub.status.busy": "2024-06-27T15:46:17.371486Z", + "iopub.status.idle": "2024-06-27T15:46:17.374581Z", + "shell.execute_reply": "2024-06-27T15:46:17.374129Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:39.979571Z", - "iopub.status.busy": "2024-06-25T23:19:39.979249Z", - "iopub.status.idle": "2024-06-25T23:19:40.010006Z", - "shell.execute_reply": "2024-06-25T23:19:40.009548Z" + "iopub.execute_input": "2024-06-27T15:46:17.376456Z", + "iopub.status.busy": "2024-06-27T15:46:17.376280Z", + "iopub.status.idle": "2024-06-27T15:46:17.774991Z", + "shell.execute_reply": "2024-06-27T15:46:17.774436Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.012066Z", - "iopub.status.busy": "2024-06-25T23:19:40.011740Z", - "iopub.status.idle": "2024-06-25T23:19:40.191233Z", - "shell.execute_reply": "2024-06-25T23:19:40.190672Z" + "iopub.execute_input": "2024-06-27T15:46:17.777150Z", + "iopub.status.busy": "2024-06-27T15:46:17.776935Z", + "iopub.status.idle": "2024-06-27T15:46:17.961475Z", + "shell.execute_reply": "2024-06-27T15:46:17.960994Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.193662Z", - "iopub.status.busy": "2024-06-25T23:19:40.193313Z", - "iopub.status.idle": "2024-06-25T23:19:40.401417Z", - "shell.execute_reply": "2024-06-25T23:19:40.400809Z" + "iopub.execute_input": "2024-06-27T15:46:17.963889Z", + "iopub.status.busy": "2024-06-27T15:46:17.963694Z", + "iopub.status.idle": "2024-06-27T15:46:18.173983Z", + "shell.execute_reply": "2024-06-27T15:46:18.173345Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.403764Z", - "iopub.status.busy": "2024-06-25T23:19:40.403425Z", - "iopub.status.idle": "2024-06-25T23:19:40.407638Z", - "shell.execute_reply": "2024-06-25T23:19:40.407217Z" + "iopub.execute_input": "2024-06-27T15:46:18.176210Z", + "iopub.status.busy": "2024-06-27T15:46:18.176008Z", + "iopub.status.idle": "2024-06-27T15:46:18.180677Z", + "shell.execute_reply": "2024-06-27T15:46:18.180114Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.409677Z", - "iopub.status.busy": "2024-06-25T23:19:40.409360Z", - "iopub.status.idle": "2024-06-25T23:19:40.415770Z", - "shell.execute_reply": "2024-06-25T23:19:40.415356Z" + "iopub.execute_input": "2024-06-27T15:46:18.182807Z", + "iopub.status.busy": "2024-06-27T15:46:18.182476Z", + "iopub.status.idle": "2024-06-27T15:46:18.188147Z", + "shell.execute_reply": "2024-06-27T15:46:18.187711Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.417772Z", - "iopub.status.busy": "2024-06-25T23:19:40.417455Z", - "iopub.status.idle": "2024-06-25T23:19:40.420042Z", - "shell.execute_reply": "2024-06-25T23:19:40.419591Z" + "iopub.execute_input": "2024-06-27T15:46:18.190157Z", + "iopub.status.busy": "2024-06-27T15:46:18.189845Z", + "iopub.status.idle": "2024-06-27T15:46:18.192555Z", + "shell.execute_reply": "2024-06-27T15:46:18.191997Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:40.421960Z", - "iopub.status.busy": "2024-06-25T23:19:40.421649Z", - "iopub.status.idle": "2024-06-25T23:19:48.997759Z", - "shell.execute_reply": "2024-06-25T23:19:48.997063Z" + "iopub.execute_input": "2024-06-27T15:46:18.194646Z", + "iopub.status.busy": "2024-06-27T15:46:18.194322Z", + "iopub.status.idle": "2024-06-27T15:46:26.858189Z", + "shell.execute_reply": "2024-06-27T15:46:26.857616Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.000433Z", - "iopub.status.busy": "2024-06-25T23:19:49.000048Z", - "iopub.status.idle": "2024-06-25T23:19:49.007281Z", - "shell.execute_reply": "2024-06-25T23:19:49.006704Z" + "iopub.execute_input": "2024-06-27T15:46:26.861110Z", + "iopub.status.busy": "2024-06-27T15:46:26.860469Z", + "iopub.status.idle": "2024-06-27T15:46:26.867521Z", + "shell.execute_reply": "2024-06-27T15:46:26.866967Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.009612Z", - "iopub.status.busy": "2024-06-25T23:19:49.009171Z", - "iopub.status.idle": "2024-06-25T23:19:49.013898Z", - "shell.execute_reply": "2024-06-25T23:19:49.013343Z" + "iopub.execute_input": "2024-06-27T15:46:26.869584Z", + "iopub.status.busy": "2024-06-27T15:46:26.869247Z", + "iopub.status.idle": "2024-06-27T15:46:26.872878Z", + "shell.execute_reply": "2024-06-27T15:46:26.872434Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:49.016095Z", - "iopub.status.busy": "2024-06-25T23:19:49.015919Z", - "iopub.status.idle": "2024-06-25T23:19:49.019068Z", - "shell.execute_reply": "2024-06-25T23:19:49.018547Z" + "iopub.execute_input": "2024-06-27T15:46:26.874782Z", + "iopub.status.busy": "2024-06-27T15:46:26.874481Z", + "iopub.status.idle": "2024-06-27T15:46:26.877465Z", + "shell.execute_reply": "2024-06-27T15:46:26.876953Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "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

    -
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    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().

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"2024-06-27T15:46:36.239038Z", + "iopub.status.busy": "2024-06-27T15:46:36.238550Z", + "iopub.status.idle": "2024-06-27T15:46:55.062132Z", + "shell.execute_reply": "2024-06-27T15:46:55.061471Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:19:59.731986Z", - "iopub.status.busy": "2024-06-25T23:19:59.731605Z", - "iopub.status.idle": "2024-06-25T23:20:48.710370Z", - "shell.execute_reply": "2024-06-25T23:20:48.709721Z" + "iopub.execute_input": "2024-06-27T15:46:55.064797Z", + "iopub.status.busy": "2024-06-27T15:46:55.064588Z", + "iopub.status.idle": "2024-06-27T15:47:51.045666Z", + "shell.execute_reply": "2024-06-27T15:47:51.045022Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:48.712844Z", - "iopub.status.busy": "2024-06-25T23:20:48.712648Z", - "iopub.status.idle": "2024-06-25T23:20:49.819754Z", - "shell.execute_reply": "2024-06-25T23:20:49.819207Z" + "iopub.execute_input": "2024-06-27T15:47:51.048381Z", + "iopub.status.busy": "2024-06-27T15:47:51.048003Z", + "iopub.status.idle": "2024-06-27T15:47:52.172865Z", + "shell.execute_reply": "2024-06-27T15:47:52.172316Z" }, "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\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": "2024-06-25T23:20:49.822551Z", - "iopub.status.busy": "2024-06-25T23:20:49.821983Z", - "iopub.status.idle": "2024-06-25T23:20:49.825360Z", - "shell.execute_reply": "2024-06-25T23:20:49.824898Z" + "iopub.execute_input": "2024-06-27T15:47:52.175429Z", + "iopub.status.busy": "2024-06-27T15:47:52.175045Z", + "iopub.status.idle": "2024-06-27T15:47:52.178148Z", + "shell.execute_reply": "2024-06-27T15:47:52.177722Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.827409Z", - "iopub.status.busy": "2024-06-25T23:20:49.827081Z", - "iopub.status.idle": "2024-06-25T23:20:49.830739Z", - "shell.execute_reply": "2024-06-25T23:20:49.830321Z" + "iopub.execute_input": "2024-06-27T15:47:52.180268Z", + "iopub.status.busy": "2024-06-27T15:47:52.179988Z", + "iopub.status.idle": "2024-06-27T15:47:52.183739Z", + "shell.execute_reply": "2024-06-27T15:47:52.183313Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.832814Z", - "iopub.status.busy": "2024-06-25T23:20:49.832481Z", - "iopub.status.idle": "2024-06-25T23:20:49.835986Z", - "shell.execute_reply": "2024-06-25T23:20:49.835546Z" + "iopub.execute_input": "2024-06-27T15:47:52.185671Z", + "iopub.status.busy": "2024-06-27T15:47:52.185493Z", + "iopub.status.idle": "2024-06-27T15:47:52.189156Z", + "shell.execute_reply": "2024-06-27T15:47:52.188640Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.837816Z", - "iopub.status.busy": "2024-06-25T23:20:49.837650Z", - "iopub.status.idle": "2024-06-25T23:20:49.841360Z", - "shell.execute_reply": "2024-06-25T23:20:49.840870Z" + "iopub.execute_input": "2024-06-27T15:47:52.191280Z", + "iopub.status.busy": "2024-06-27T15:47:52.190962Z", + "iopub.status.idle": "2024-06-27T15:47:52.193884Z", + "shell.execute_reply": "2024-06-27T15:47:52.193311Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:20:49.843348Z", - 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    1. Install required dependencies and download data 
    ---2024-06-25 23:22:28--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 185.93.1.250, 2400:52e0:1a00::1068:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.
+--2024-06-27 15:49:29--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 143.244.49.180, 2400:52e0:1a01::998:1
+Connecting to data.deepai.org (data.deepai.org)|143.244.49.180|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.06s
 
-2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-06-27 15:49:30 (16.8 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 28feac438..aae3879ef 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:28.297877Z", - "iopub.status.busy": "2024-06-25T23:22:28.297692Z", - "iopub.status.idle": "2024-06-25T23:22:29.566144Z", - "shell.execute_reply": "2024-06-25T23:22:29.565466Z" + "iopub.execute_input": "2024-06-27T15:49:29.640132Z", + "iopub.status.busy": "2024-06-27T15:49:29.639946Z", + "iopub.status.idle": "2024-06-27T15:49:31.619306Z", + "shell.execute_reply": "2024-06-27T15:49:31.618696Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 23:22:28-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-06-27 15:49:29-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,15 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.250, 2400:52e0:1a00::1068:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n", + "143.244.49.180, 2400:52e0:1a01::998:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.49.180|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -123,9 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.06s \r\n", "\r\n", - "2024-06-25 23:22:28 (6.31 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-06-27 15:49:30 (16.8 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +138,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-25 23:22:29-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.25.196, 54.231.139.49, 52.216.48.57, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.25.196|:443... connected.\r\n", + "--2024-06-27 15:49:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52.217.80.44, 3.5.27.43, 3.5.11.148, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.80.44|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,7 +181,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 58%[==========> ] 9.47M 47.3MB/s " + "pred_probs.npz 1%[ ] 312.11K 1.26MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 31%[=====> ] 5.18M 10.7MB/s " ] }, { @@ -176,9 +197,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 55.6MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 23.9MB/s in 0.7s \r\n", "\r\n", - "2024-06-25 23:22:29 (55.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-06-27 15:49:31 (23.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -195,10 +216,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:29.568875Z", - "iopub.status.busy": "2024-06-25T23:22:29.568431Z", - "iopub.status.idle": "2024-06-25T23:22:30.789853Z", - "shell.execute_reply": "2024-06-25T23:22:30.789338Z" + "iopub.execute_input": "2024-06-27T15:49:31.621702Z", + "iopub.status.busy": "2024-06-27T15:49:31.621505Z", + "iopub.status.idle": "2024-06-27T15:49:32.918088Z", + "shell.execute_reply": "2024-06-27T15:49:32.917440Z" }, "nbsphinx": "hidden" }, @@ -209,7 +230,7 @@ "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@bd550980fa8b7af85d37f375e0cc0e3ff9ced23e\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@d3fd6280f438718567230bde1dfb2db271e3c0c5\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -235,10 +256,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.792349Z", - "iopub.status.busy": "2024-06-25T23:22:30.792077Z", - "iopub.status.idle": "2024-06-25T23:22:30.795305Z", - "shell.execute_reply": "2024-06-25T23:22:30.794873Z" + "iopub.execute_input": "2024-06-27T15:49:32.920910Z", + "iopub.status.busy": "2024-06-27T15:49:32.920272Z", + "iopub.status.idle": "2024-06-27T15:49:32.923938Z", + "shell.execute_reply": "2024-06-27T15:49:32.923418Z" } }, "outputs": [], @@ -288,10 +309,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.797547Z", - "iopub.status.busy": "2024-06-25T23:22:30.797222Z", - "iopub.status.idle": "2024-06-25T23:22:30.800066Z", - "shell.execute_reply": "2024-06-25T23:22:30.799649Z" + "iopub.execute_input": "2024-06-27T15:49:32.925895Z", + "iopub.status.busy": "2024-06-27T15:49:32.925700Z", + "iopub.status.idle": "2024-06-27T15:49:32.928588Z", + "shell.execute_reply": "2024-06-27T15:49:32.928176Z" }, "nbsphinx": "hidden" }, @@ -309,10 +330,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:30.801968Z", - "iopub.status.busy": "2024-06-25T23:22:30.801793Z", - "iopub.status.idle": "2024-06-25T23:22:39.539487Z", - "shell.execute_reply": "2024-06-25T23:22:39.538935Z" + "iopub.execute_input": "2024-06-27T15:49:32.930400Z", + "iopub.status.busy": "2024-06-27T15:49:32.930225Z", + "iopub.status.idle": "2024-06-27T15:49:41.990953Z", + "shell.execute_reply": "2024-06-27T15:49:41.990399Z" } }, "outputs": [], @@ -386,10 +407,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.542320Z", - "iopub.status.busy": "2024-06-25T23:22:39.541861Z", - "iopub.status.idle": "2024-06-25T23:22:39.547429Z", - "shell.execute_reply": "2024-06-25T23:22:39.546974Z" + "iopub.execute_input": "2024-06-27T15:49:41.993525Z", + "iopub.status.busy": "2024-06-27T15:49:41.993154Z", + "iopub.status.idle": "2024-06-27T15:49:41.998594Z", + "shell.execute_reply": "2024-06-27T15:49:41.998138Z" }, "nbsphinx": "hidden" }, @@ -429,10 +450,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.549434Z", - "iopub.status.busy": "2024-06-25T23:22:39.549088Z", - "iopub.status.idle": "2024-06-25T23:22:39.886323Z", - "shell.execute_reply": "2024-06-25T23:22:39.885773Z" + "iopub.execute_input": "2024-06-27T15:49:42.000606Z", + "iopub.status.busy": "2024-06-27T15:49:42.000287Z", + "iopub.status.idle": "2024-06-27T15:49:42.352443Z", + "shell.execute_reply": "2024-06-27T15:49:42.351803Z" } }, "outputs": [], @@ -469,10 +490,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.888760Z", - "iopub.status.busy": "2024-06-25T23:22:39.888567Z", - "iopub.status.idle": "2024-06-25T23:22:39.892822Z", - "shell.execute_reply": "2024-06-25T23:22:39.892289Z" + "iopub.execute_input": "2024-06-27T15:49:42.354868Z", + "iopub.status.busy": "2024-06-27T15:49:42.354676Z", + "iopub.status.idle": "2024-06-27T15:49:42.358986Z", + "shell.execute_reply": "2024-06-27T15:49:42.358462Z" } }, "outputs": [ @@ -544,10 +565,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:39.894754Z", - "iopub.status.busy": "2024-06-25T23:22:39.894582Z", - "iopub.status.idle": "2024-06-25T23:22:42.439150Z", - "shell.execute_reply": "2024-06-25T23:22:42.438377Z" + "iopub.execute_input": "2024-06-27T15:49:42.360910Z", + "iopub.status.busy": "2024-06-27T15:49:42.360734Z", + "iopub.status.idle": "2024-06-27T15:49:44.947322Z", + "shell.execute_reply": "2024-06-27T15:49:44.946542Z" } }, "outputs": [], @@ -569,10 +590,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.442203Z", - "iopub.status.busy": "2024-06-25T23:22:42.441641Z", - "iopub.status.idle": "2024-06-25T23:22:42.445478Z", - "shell.execute_reply": "2024-06-25T23:22:42.444915Z" + "iopub.execute_input": "2024-06-27T15:49:44.950509Z", + "iopub.status.busy": "2024-06-27T15:49:44.949955Z", + "iopub.status.idle": "2024-06-27T15:49:44.954069Z", + "shell.execute_reply": "2024-06-27T15:49:44.953529Z" } }, "outputs": [ @@ -608,10 +629,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.447472Z", - "iopub.status.busy": "2024-06-25T23:22:42.447297Z", - "iopub.status.idle": "2024-06-25T23:22:42.452716Z", - "shell.execute_reply": "2024-06-25T23:22:42.452215Z" + "iopub.execute_input": "2024-06-27T15:49:44.956163Z", + "iopub.status.busy": "2024-06-27T15:49:44.955856Z", + "iopub.status.idle": "2024-06-27T15:49:44.961496Z", + "shell.execute_reply": "2024-06-27T15:49:44.960948Z" } }, "outputs": [ @@ -789,10 +810,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.454685Z", - "iopub.status.busy": "2024-06-25T23:22:42.454421Z", - "iopub.status.idle": "2024-06-25T23:22:42.480225Z", - "shell.execute_reply": "2024-06-25T23:22:42.479796Z" + "iopub.execute_input": "2024-06-27T15:49:44.963667Z", + "iopub.status.busy": "2024-06-27T15:49:44.963243Z", + "iopub.status.idle": "2024-06-27T15:49:44.989856Z", + "shell.execute_reply": "2024-06-27T15:49:44.989282Z" } }, "outputs": [ @@ -894,10 +915,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.482279Z", - "iopub.status.busy": "2024-06-25T23:22:42.481978Z", - "iopub.status.idle": "2024-06-25T23:22:42.486286Z", - "shell.execute_reply": "2024-06-25T23:22:42.485735Z" + "iopub.execute_input": "2024-06-27T15:49:44.991934Z", + "iopub.status.busy": "2024-06-27T15:49:44.991544Z", + "iopub.status.idle": "2024-06-27T15:49:44.995987Z", + "shell.execute_reply": "2024-06-27T15:49:44.995465Z" } }, "outputs": [ @@ -971,10 +992,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:42.488404Z", - "iopub.status.busy": "2024-06-25T23:22:42.487905Z", - "iopub.status.idle": "2024-06-25T23:22:43.900411Z", - "shell.execute_reply": "2024-06-25T23:22:43.899904Z" + "iopub.execute_input": "2024-06-27T15:49:44.997969Z", + "iopub.status.busy": "2024-06-27T15:49:44.997628Z", + "iopub.status.idle": "2024-06-27T15:49:46.406945Z", + "shell.execute_reply": "2024-06-27T15:49:46.406319Z" } }, "outputs": [ @@ -1146,10 +1167,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-25T23:22:43.902625Z", - "iopub.status.busy": "2024-06-25T23:22:43.902291Z", - "iopub.status.idle": "2024-06-25T23:22:43.906202Z", - "shell.execute_reply": "2024-06-25T23:22:43.905768Z" + "iopub.execute_input": "2024-06-27T15:49:46.409049Z", + "iopub.status.busy": "2024-06-27T15:49:46.408845Z", + "iopub.status.idle": "2024-06-27T15:49:46.413172Z", + "shell.execute_reply": "2024-06-27T15:49:46.412698Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index f2c68c3f2..d8cde4318 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "bd550980fa8b7af85d37f375e0cc0e3ff9ced23e", + commit_hash: "d3fd6280f438718567230bde1dfb2db271e3c0c5", }; \ No newline at end of file