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--git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 6e00aec87..b70c8c754 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 76fc0cf88..1b1f01adb 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": "2024-07-18T04:01:36.624070Z", - "iopub.status.busy": "2024-07-18T04:01:36.623720Z", - "iopub.status.idle": "2024-07-18T04:01:37.842464Z", - "shell.execute_reply": "2024-07-18T04:01:37.841899Z" + "iopub.execute_input": "2024-07-30T16:31:34.527671Z", + "iopub.status.busy": "2024-07-30T16:31:34.527492Z", + "iopub.status.idle": "2024-07-30T16:31:36.140632Z", + "shell.execute_reply": "2024-07-30T16:31:36.140024Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:01:37.845272Z", - "iopub.status.busy": "2024-07-18T04:01:37.844748Z", - "iopub.status.idle": "2024-07-18T04:01:37.863056Z", - "shell.execute_reply": "2024-07-18T04:01:37.862447Z" + "iopub.execute_input": "2024-07-30T16:31:36.143586Z", + "iopub.status.busy": "2024-07-30T16:31:36.143047Z", + "iopub.status.idle": "2024-07-30T16:31:36.178768Z", + "shell.execute_reply": "2024-07-30T16:31:36.178228Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:37.865460Z", - "iopub.status.busy": "2024-07-18T04:01:37.865067Z", - "iopub.status.idle": "2024-07-18T04:01:38.092310Z", - "shell.execute_reply": "2024-07-18T04:01:38.091732Z" + "iopub.execute_input": "2024-07-30T16:31:36.181589Z", + "iopub.status.busy": "2024-07-30T16:31:36.181045Z", + "iopub.status.idle": "2024-07-30T16:31:36.338074Z", + "shell.execute_reply": "2024-07-30T16:31:36.337466Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:38.121141Z", - "iopub.status.busy": "2024-07-18T04:01:38.120969Z", - "iopub.status.idle": "2024-07-18T04:01:38.124263Z", - "shell.execute_reply": "2024-07-18T04:01:38.123800Z" + "iopub.execute_input": "2024-07-30T16:31:36.372204Z", + "iopub.status.busy": "2024-07-30T16:31:36.371964Z", + "iopub.status.idle": "2024-07-30T16:31:36.377781Z", + "shell.execute_reply": "2024-07-30T16:31:36.377262Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:38.126350Z", - "iopub.status.busy": "2024-07-18T04:01:38.126009Z", - "iopub.status.idle": "2024-07-18T04:01:38.134504Z", - "shell.execute_reply": "2024-07-18T04:01:38.134029Z" + "iopub.execute_input": "2024-07-30T16:31:36.380079Z", + "iopub.status.busy": "2024-07-30T16:31:36.379702Z", + "iopub.status.idle": "2024-07-30T16:31:36.389163Z", + "shell.execute_reply": "2024-07-30T16:31:36.388645Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:38.136643Z", - "iopub.status.busy": "2024-07-18T04:01:38.136297Z", - "iopub.status.idle": "2024-07-18T04:01:38.138802Z", - "shell.execute_reply": "2024-07-18T04:01:38.138325Z" + "iopub.execute_input": "2024-07-30T16:31:36.391552Z", + "iopub.status.busy": "2024-07-30T16:31:36.391341Z", + "iopub.status.idle": "2024-07-30T16:31:36.394409Z", + "shell.execute_reply": "2024-07-30T16:31:36.393862Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:38.140938Z", - "iopub.status.busy": "2024-07-18T04:01:38.140608Z", - "iopub.status.idle": "2024-07-18T04:01:38.660247Z", - "shell.execute_reply": "2024-07-18T04:01:38.659701Z" + "iopub.execute_input": "2024-07-30T16:31:36.396451Z", + "iopub.status.busy": "2024-07-30T16:31:36.396262Z", + "iopub.status.idle": "2024-07-30T16:31:36.936436Z", + "shell.execute_reply": "2024-07-30T16:31:36.935844Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:38.662748Z", - "iopub.status.busy": "2024-07-18T04:01:38.662379Z", - "iopub.status.idle": "2024-07-18T04:01:40.571765Z", - "shell.execute_reply": "2024-07-18T04:01:40.571111Z" + "iopub.execute_input": "2024-07-30T16:31:36.939261Z", + "iopub.status.busy": "2024-07-30T16:31:36.938884Z", + "iopub.status.idle": "2024-07-30T16:31:39.269788Z", + "shell.execute_reply": "2024-07-30T16:31:39.269009Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.574577Z", - "iopub.status.busy": "2024-07-18T04:01:40.573829Z", - "iopub.status.idle": "2024-07-18T04:01:40.584210Z", - "shell.execute_reply": "2024-07-18T04:01:40.583746Z" + "iopub.execute_input": "2024-07-30T16:31:39.273002Z", + "iopub.status.busy": "2024-07-30T16:31:39.272142Z", + "iopub.status.idle": "2024-07-30T16:31:39.283199Z", + "shell.execute_reply": "2024-07-30T16:31:39.282635Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.586373Z", - "iopub.status.busy": "2024-07-18T04:01:40.586110Z", - "iopub.status.idle": "2024-07-18T04:01:40.590039Z", - "shell.execute_reply": "2024-07-18T04:01:40.589558Z" + "iopub.execute_input": "2024-07-30T16:31:39.285386Z", + "iopub.status.busy": "2024-07-30T16:31:39.285054Z", + "iopub.status.idle": "2024-07-30T16:31:39.289139Z", + "shell.execute_reply": "2024-07-30T16:31:39.288681Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.592168Z", - "iopub.status.busy": "2024-07-18T04:01:40.591831Z", - "iopub.status.idle": "2024-07-18T04:01:40.598929Z", - "shell.execute_reply": "2024-07-18T04:01:40.598494Z" + "iopub.execute_input": "2024-07-30T16:31:39.291246Z", + "iopub.status.busy": "2024-07-30T16:31:39.290919Z", + "iopub.status.idle": "2024-07-30T16:31:39.298453Z", + "shell.execute_reply": "2024-07-30T16:31:39.297891Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.600960Z", - "iopub.status.busy": "2024-07-18T04:01:40.600619Z", - "iopub.status.idle": "2024-07-18T04:01:40.712077Z", - "shell.execute_reply": "2024-07-18T04:01:40.711624Z" + "iopub.execute_input": "2024-07-30T16:31:39.301188Z", + "iopub.status.busy": "2024-07-30T16:31:39.300801Z", + "iopub.status.idle": "2024-07-30T16:31:39.419299Z", + "shell.execute_reply": "2024-07-30T16:31:39.418728Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.714154Z", - "iopub.status.busy": "2024-07-18T04:01:40.713728Z", - "iopub.status.idle": "2024-07-18T04:01:40.716643Z", - "shell.execute_reply": "2024-07-18T04:01:40.716066Z" + "iopub.execute_input": "2024-07-30T16:31:39.421608Z", + "iopub.status.busy": "2024-07-30T16:31:39.421234Z", + "iopub.status.idle": "2024-07-30T16:31:39.424361Z", + "shell.execute_reply": "2024-07-30T16:31:39.423765Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:40.718951Z", - "iopub.status.busy": "2024-07-18T04:01:40.718505Z", - "iopub.status.idle": "2024-07-18T04:01:42.835910Z", - "shell.execute_reply": "2024-07-18T04:01:42.835101Z" + "iopub.execute_input": "2024-07-30T16:31:39.426671Z", + "iopub.status.busy": "2024-07-30T16:31:39.426252Z", + "iopub.status.idle": "2024-07-30T16:31:41.720026Z", + "shell.execute_reply": "2024-07-30T16:31:41.719145Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:42.839563Z", - "iopub.status.busy": "2024-07-18T04:01:42.838450Z", - "iopub.status.idle": "2024-07-18T04:01:42.850192Z", - "shell.execute_reply": "2024-07-18T04:01:42.849635Z" + "iopub.execute_input": "2024-07-30T16:31:41.723999Z", + "iopub.status.busy": "2024-07-30T16:31:41.722968Z", + "iopub.status.idle": "2024-07-30T16:31:41.736024Z", + "shell.execute_reply": "2024-07-30T16:31:41.735553Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:42.852397Z", - "iopub.status.busy": "2024-07-18T04:01:42.851948Z", - "iopub.status.idle": "2024-07-18T04:01:42.971378Z", - "shell.execute_reply": "2024-07-18T04:01:42.970812Z" + "iopub.execute_input": "2024-07-30T16:31:41.738164Z", + "iopub.status.busy": "2024-07-30T16:31:41.737959Z", + "iopub.status.idle": "2024-07-30T16:31:41.800777Z", + "shell.execute_reply": "2024-07-30T16:31:41.800288Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index 6b885ed09..0ca024c1d 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": "2024-07-18T04:01:46.668712Z", - "iopub.status.busy": "2024-07-18T04:01:46.668554Z", - "iopub.status.idle": "2024-07-18T04:01:49.461835Z", - "shell.execute_reply": "2024-07-18T04:01:49.461271Z" + "iopub.execute_input": "2024-07-30T16:31:45.538656Z", + "iopub.status.busy": "2024-07-30T16:31:45.538493Z", + "iopub.status.idle": "2024-07-30T16:31:49.398554Z", + "shell.execute_reply": "2024-07-30T16:31:49.397834Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:01:49.464660Z", - "iopub.status.busy": "2024-07-18T04:01:49.464107Z", - "iopub.status.idle": "2024-07-18T04:01:49.467448Z", - "shell.execute_reply": "2024-07-18T04:01:49.466979Z" + "iopub.execute_input": "2024-07-30T16:31:49.401469Z", + "iopub.status.busy": "2024-07-30T16:31:49.401084Z", + "iopub.status.idle": "2024-07-30T16:31:49.404559Z", + "shell.execute_reply": "2024-07-30T16:31:49.404113Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.469527Z", - "iopub.status.busy": "2024-07-18T04:01:49.469126Z", - "iopub.status.idle": "2024-07-18T04:01:49.472275Z", - "shell.execute_reply": "2024-07-18T04:01:49.471805Z" + "iopub.execute_input": "2024-07-30T16:31:49.406866Z", + "iopub.status.busy": "2024-07-30T16:31:49.406454Z", + "iopub.status.idle": "2024-07-30T16:31:49.409954Z", + "shell.execute_reply": "2024-07-30T16:31:49.409295Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.474142Z", - "iopub.status.busy": "2024-07-18T04:01:49.473968Z", - "iopub.status.idle": "2024-07-18T04:01:49.588009Z", - "shell.execute_reply": "2024-07-18T04:01:49.587466Z" + "iopub.execute_input": "2024-07-30T16:31:49.412895Z", + "iopub.status.busy": "2024-07-30T16:31:49.412480Z", + "iopub.status.idle": "2024-07-30T16:31:49.471562Z", + "shell.execute_reply": "2024-07-30T16:31:49.470965Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.589968Z", - "iopub.status.busy": "2024-07-18T04:01:49.589787Z", - "iopub.status.idle": "2024-07-18T04:01:49.593376Z", - "shell.execute_reply": "2024-07-18T04:01:49.592931Z" + "iopub.execute_input": "2024-07-30T16:31:49.473824Z", + "iopub.status.busy": "2024-07-30T16:31:49.473632Z", + "iopub.status.idle": "2024-07-30T16:31:49.477513Z", + "shell.execute_reply": "2024-07-30T16:31:49.477050Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.595482Z", - "iopub.status.busy": "2024-07-18T04:01:49.595092Z", - "iopub.status.idle": "2024-07-18T04:01:49.598627Z", - "shell.execute_reply": "2024-07-18T04:01:49.598158Z" + "iopub.execute_input": "2024-07-30T16:31:49.479493Z", + "iopub.status.busy": "2024-07-30T16:31:49.479316Z", + "iopub.status.idle": "2024-07-30T16:31:49.482910Z", + "shell.execute_reply": "2024-07-30T16:31:49.482450Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'beneficiary_not_allowed', 'cancel_transfer', 'supported_cards_and_currencies', 'getting_spare_card', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'card_about_to_expire', 'lost_or_stolen_phone'}\n" + "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'visa_or_mastercard', 'cancel_transfer', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.600656Z", - "iopub.status.busy": "2024-07-18T04:01:49.600330Z", - "iopub.status.idle": "2024-07-18T04:01:49.603386Z", - "shell.execute_reply": "2024-07-18T04:01:49.602846Z" + "iopub.execute_input": "2024-07-30T16:31:49.484867Z", + "iopub.status.busy": "2024-07-30T16:31:49.484521Z", + "iopub.status.idle": "2024-07-30T16:31:49.487821Z", + "shell.execute_reply": "2024-07-30T16:31:49.487340Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.605480Z", - "iopub.status.busy": "2024-07-18T04:01:49.605149Z", - "iopub.status.idle": "2024-07-18T04:01:49.608580Z", - "shell.execute_reply": "2024-07-18T04:01:49.607995Z" + "iopub.execute_input": "2024-07-30T16:31:49.489683Z", + "iopub.status.busy": "2024-07-30T16:31:49.489500Z", + "iopub.status.idle": "2024-07-30T16:31:49.492910Z", + "shell.execute_reply": "2024-07-30T16:31:49.492341Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.610780Z", - "iopub.status.busy": "2024-07-18T04:01:49.610348Z", - "iopub.status.idle": "2024-07-18T04:01:54.796801Z", - "shell.execute_reply": "2024-07-18T04:01:54.796219Z" + "iopub.execute_input": "2024-07-30T16:31:49.495136Z", + "iopub.status.busy": "2024-07-30T16:31:49.494608Z", + "iopub.status.idle": "2024-07-30T16:31:53.944545Z", + "shell.execute_reply": "2024-07-30T16:31:53.943984Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83b2420f333343458a1e286e0240c1ae", + "model_id": "97c7a5a1b558446099d39e138e95bd3c", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b0962e591ec4487841dcc3bb135137b", + "model_id": "91c91931728e42119a5ed1a8e771a320", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "07d2941c07a44b5ba94397e076325ff4", + "model_id": "a58243eb6a194ea7957507b4c0fcd20c", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a4b6a256d1ca4f4eb87362bb9b27857a", + "model_id": "f47d961a8a6443fdb97fc093198a3a37", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "594bd335e58344c1a96d7e14714fe720", + "model_id": "cd86dc9f79bf4ea6b32ccf1b10684fdd", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27a9d77c69074a5d90239d0ec1bef651", + "model_id": "3f07a08b8ebf486cbecde146931b6ae3", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a916345ea2fb4a09a0a9731240dd711e", + "model_id": "d9438aa4de0a49669ef6cc3e242dea8a", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:54.799563Z", - "iopub.status.busy": "2024-07-18T04:01:54.799374Z", - "iopub.status.idle": "2024-07-18T04:01:54.802131Z", - "shell.execute_reply": "2024-07-18T04:01:54.801644Z" + "iopub.execute_input": "2024-07-30T16:31:53.947400Z", + "iopub.status.busy": "2024-07-30T16:31:53.946989Z", + "iopub.status.idle": "2024-07-30T16:31:53.949968Z", + "shell.execute_reply": "2024-07-30T16:31:53.949474Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:54.804143Z", - "iopub.status.busy": "2024-07-18T04:01:54.803966Z", - "iopub.status.idle": "2024-07-18T04:01:54.806508Z", - "shell.execute_reply": "2024-07-18T04:01:54.806059Z" + "iopub.execute_input": "2024-07-30T16:31:53.952023Z", + "iopub.status.busy": "2024-07-30T16:31:53.951691Z", + "iopub.status.idle": "2024-07-30T16:31:53.954244Z", + "shell.execute_reply": "2024-07-30T16:31:53.953779Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:54.808380Z", - "iopub.status.busy": "2024-07-18T04:01:54.808205Z", - "iopub.status.idle": "2024-07-18T04:01:57.530182Z", - "shell.execute_reply": "2024-07-18T04:01:57.529534Z" + "iopub.execute_input": "2024-07-30T16:31:53.956328Z", + "iopub.status.busy": "2024-07-30T16:31:53.955997Z", + "iopub.status.idle": "2024-07-30T16:31:56.844109Z", + "shell.execute_reply": "2024-07-30T16:31:56.843384Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:57.533334Z", - "iopub.status.busy": "2024-07-18T04:01:57.532532Z", - "iopub.status.idle": "2024-07-18T04:01:57.540347Z", - "shell.execute_reply": "2024-07-18T04:01:57.539824Z" + "iopub.execute_input": "2024-07-30T16:31:56.847565Z", + "iopub.status.busy": "2024-07-30T16:31:56.846661Z", + "iopub.status.idle": "2024-07-30T16:31:56.854826Z", + "shell.execute_reply": "2024-07-30T16:31:56.854335Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:57.542577Z", - "iopub.status.busy": "2024-07-18T04:01:57.542120Z", - "iopub.status.idle": "2024-07-18T04:01:57.546071Z", - "shell.execute_reply": "2024-07-18T04:01:57.545610Z" + "iopub.execute_input": "2024-07-30T16:31:56.856986Z", + "iopub.status.busy": "2024-07-30T16:31:56.856636Z", + "iopub.status.idle": "2024-07-30T16:31:56.860988Z", + "shell.execute_reply": "2024-07-30T16:31:56.860503Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:57.547927Z", - "iopub.status.busy": "2024-07-18T04:01:57.547755Z", - "iopub.status.idle": "2024-07-18T04:01:57.551227Z", - "shell.execute_reply": "2024-07-18T04:01:57.550746Z" + "iopub.execute_input": "2024-07-30T16:31:56.863129Z", + "iopub.status.busy": "2024-07-30T16:31:56.862787Z", + "iopub.status.idle": "2024-07-30T16:31:56.866220Z", + "shell.execute_reply": "2024-07-30T16:31:56.865721Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:57.553099Z", - "iopub.status.busy": "2024-07-18T04:01:57.552927Z", - "iopub.status.idle": "2024-07-18T04:01:57.555992Z", - 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"_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a3f98d4160d0404fb6f57ecf02ae64df", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c9824f35786644daa37d42bd953ade4a", + "layout": "IPY_MODEL_5a71b8cd70d84f7ba31770899fe09c9b", + "placeholder": "​", + "style": "IPY_MODEL_c394f7a4870a4f5f8de748644e8df357", "tabbable": null, "tooltip": null, - "value": 2211.0 + "value": "README.md: 100%" } }, - "fff28c4abb7743bcbc87068c18fc714d": { + "fe392344d7834620bada4587f2dbf065": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index c06368ad7..a2d329ad2 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": "2024-07-18T04:02:01.117687Z", - "iopub.status.busy": "2024-07-18T04:02:01.117526Z", - "iopub.status.idle": "2024-07-18T04:02:06.436581Z", - "shell.execute_reply": "2024-07-18T04:02:06.435951Z" + "iopub.execute_input": "2024-07-30T16:32:01.430387Z", + "iopub.status.busy": "2024-07-30T16:32:01.430194Z", + "iopub.status.idle": "2024-07-30T16:32:07.507356Z", + "shell.execute_reply": "2024-07-30T16:32:07.506775Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:06.439753Z", - "iopub.status.busy": "2024-07-18T04:02:06.438998Z", - "iopub.status.idle": "2024-07-18T04:02:06.442507Z", - "shell.execute_reply": "2024-07-18T04:02:06.442047Z" + "iopub.execute_input": "2024-07-30T16:32:07.510541Z", + "iopub.status.busy": "2024-07-30T16:32:07.509839Z", + "iopub.status.idle": "2024-07-30T16:32:07.513583Z", + "shell.execute_reply": "2024-07-30T16:32:07.513077Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:06.444488Z", - "iopub.status.busy": "2024-07-18T04:02:06.444150Z", - "iopub.status.idle": "2024-07-18T04:02:06.448808Z", - "shell.execute_reply": "2024-07-18T04:02:06.448377Z" + "iopub.execute_input": "2024-07-30T16:32:07.515817Z", + "iopub.status.busy": "2024-07-30T16:32:07.515454Z", + "iopub.status.idle": "2024-07-30T16:32:07.520703Z", + "shell.execute_reply": "2024-07-30T16:32:07.520274Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:06.450829Z", - "iopub.status.busy": "2024-07-18T04:02:06.450489Z", - "iopub.status.idle": "2024-07-18T04:02:08.286142Z", - "shell.execute_reply": "2024-07-18T04:02:08.285466Z" + "iopub.execute_input": "2024-07-30T16:32:07.522733Z", + "iopub.status.busy": "2024-07-30T16:32:07.522401Z", + "iopub.status.idle": "2024-07-30T16:32:09.284078Z", + "shell.execute_reply": "2024-07-30T16:32:09.283231Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.288955Z", - "iopub.status.busy": "2024-07-18T04:02:08.288547Z", - "iopub.status.idle": "2024-07-18T04:02:08.299806Z", - "shell.execute_reply": "2024-07-18T04:02:08.299296Z" + "iopub.execute_input": "2024-07-30T16:32:09.287053Z", + "iopub.status.busy": "2024-07-30T16:32:09.286654Z", + "iopub.status.idle": "2024-07-30T16:32:09.297621Z", + "shell.execute_reply": "2024-07-30T16:32:09.297169Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.302021Z", - "iopub.status.busy": "2024-07-18T04:02:08.301679Z", - "iopub.status.idle": "2024-07-18T04:02:08.307287Z", - "shell.execute_reply": "2024-07-18T04:02:08.306826Z" + "iopub.execute_input": "2024-07-30T16:32:09.299786Z", + "iopub.status.busy": "2024-07-30T16:32:09.299427Z", + "iopub.status.idle": "2024-07-30T16:32:09.304872Z", + "shell.execute_reply": "2024-07-30T16:32:09.304392Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.309302Z", - "iopub.status.busy": "2024-07-18T04:02:08.308959Z", - "iopub.status.idle": "2024-07-18T04:02:08.779123Z", - "shell.execute_reply": "2024-07-18T04:02:08.778615Z" + "iopub.execute_input": "2024-07-30T16:32:09.307010Z", + "iopub.status.busy": "2024-07-30T16:32:09.306676Z", + "iopub.status.idle": "2024-07-30T16:32:09.814179Z", + "shell.execute_reply": "2024-07-30T16:32:09.813575Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.781502Z", - "iopub.status.busy": "2024-07-18T04:02:08.780993Z", - "iopub.status.idle": "2024-07-18T04:02:09.831176Z", - "shell.execute_reply": "2024-07-18T04:02:09.830565Z" + "iopub.execute_input": "2024-07-30T16:32:09.816449Z", + "iopub.status.busy": "2024-07-30T16:32:09.816091Z", + "iopub.status.idle": "2024-07-30T16:32:11.566172Z", + "shell.execute_reply": "2024-07-30T16:32:11.565639Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.833594Z", - "iopub.status.busy": "2024-07-18T04:02:09.833376Z", - "iopub.status.idle": "2024-07-18T04:02:09.851912Z", - "shell.execute_reply": "2024-07-18T04:02:09.851466Z" + "iopub.execute_input": "2024-07-30T16:32:11.568615Z", + "iopub.status.busy": "2024-07-30T16:32:11.568320Z", + "iopub.status.idle": "2024-07-30T16:32:11.586724Z", + "shell.execute_reply": "2024-07-30T16:32:11.586277Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.853723Z", - "iopub.status.busy": "2024-07-18T04:02:09.853550Z", - "iopub.status.idle": "2024-07-18T04:02:09.856982Z", - "shell.execute_reply": "2024-07-18T04:02:09.856425Z" + "iopub.execute_input": "2024-07-30T16:32:11.588741Z", + "iopub.status.busy": "2024-07-30T16:32:11.588441Z", + "iopub.status.idle": "2024-07-30T16:32:11.591552Z", + "shell.execute_reply": "2024-07-30T16:32:11.591039Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.859010Z", - "iopub.status.busy": "2024-07-18T04:02:09.858672Z", - "iopub.status.idle": "2024-07-18T04:02:23.705662Z", - "shell.execute_reply": "2024-07-18T04:02:23.705044Z" + "iopub.execute_input": "2024-07-30T16:32:11.593585Z", + "iopub.status.busy": "2024-07-30T16:32:11.593193Z", + "iopub.status.idle": "2024-07-30T16:32:26.818572Z", + "shell.execute_reply": "2024-07-30T16:32:26.817879Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:23.708515Z", - "iopub.status.busy": "2024-07-18T04:02:23.708112Z", - "iopub.status.idle": "2024-07-18T04:02:23.711931Z", - "shell.execute_reply": "2024-07-18T04:02:23.711369Z" + "iopub.execute_input": "2024-07-30T16:32:26.821335Z", + "iopub.status.busy": "2024-07-30T16:32:26.821126Z", + "iopub.status.idle": "2024-07-30T16:32:26.825126Z", + "shell.execute_reply": "2024-07-30T16:32:26.824635Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:23.713930Z", - "iopub.status.busy": "2024-07-18T04:02:23.713624Z", - "iopub.status.idle": "2024-07-18T04:02:24.398596Z", - "shell.execute_reply": "2024-07-18T04:02:24.398020Z" + "iopub.execute_input": "2024-07-30T16:32:26.827091Z", + "iopub.status.busy": "2024-07-30T16:32:26.826917Z", + "iopub.status.idle": "2024-07-30T16:32:27.596925Z", + "shell.execute_reply": "2024-07-30T16:32:27.596317Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.401422Z", - "iopub.status.busy": "2024-07-18T04:02:24.401003Z", - "iopub.status.idle": "2024-07-18T04:02:24.406081Z", - "shell.execute_reply": "2024-07-18T04:02:24.405558Z" + "iopub.execute_input": "2024-07-30T16:32:27.600680Z", + "iopub.status.busy": "2024-07-30T16:32:27.599702Z", + "iopub.status.idle": "2024-07-30T16:32:27.606621Z", + "shell.execute_reply": "2024-07-30T16:32:27.606103Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.409353Z", - "iopub.status.busy": "2024-07-18T04:02:24.408424Z", - "iopub.status.idle": "2024-07-18T04:02:24.518831Z", - "shell.execute_reply": "2024-07-18T04:02:24.518201Z" + "iopub.execute_input": "2024-07-30T16:32:27.610268Z", + "iopub.status.busy": "2024-07-30T16:32:27.609309Z", + "iopub.status.idle": "2024-07-30T16:32:27.732351Z", + "shell.execute_reply": "2024-07-30T16:32:27.731717Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.521227Z", - "iopub.status.busy": "2024-07-18T04:02:24.521025Z", - "iopub.status.idle": "2024-07-18T04:02:24.533914Z", - "shell.execute_reply": "2024-07-18T04:02:24.533353Z" + "iopub.execute_input": "2024-07-30T16:32:27.734791Z", + "iopub.status.busy": "2024-07-30T16:32:27.734594Z", + "iopub.status.idle": "2024-07-30T16:32:27.747001Z", + "shell.execute_reply": "2024-07-30T16:32:27.746549Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.536409Z", - "iopub.status.busy": "2024-07-18T04:02:24.536011Z", - "iopub.status.idle": "2024-07-18T04:02:24.546219Z", - "shell.execute_reply": "2024-07-18T04:02:24.545646Z" + "iopub.execute_input": "2024-07-30T16:32:27.749097Z", + "iopub.status.busy": "2024-07-30T16:32:27.748752Z", + "iopub.status.idle": "2024-07-30T16:32:27.756559Z", + "shell.execute_reply": "2024-07-30T16:32:27.756099Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.548577Z", - "iopub.status.busy": "2024-07-18T04:02:24.548223Z", - "iopub.status.idle": "2024-07-18T04:02:24.552829Z", - "shell.execute_reply": "2024-07-18T04:02:24.552251Z" + "iopub.execute_input": "2024-07-30T16:32:27.758711Z", + "iopub.status.busy": "2024-07-30T16:32:27.758331Z", + "iopub.status.idle": "2024-07-30T16:32:27.762339Z", + "shell.execute_reply": "2024-07-30T16:32:27.761744Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.554997Z", - "iopub.status.busy": "2024-07-18T04:02:24.554598Z", - "iopub.status.idle": "2024-07-18T04:02:24.561408Z", - "shell.execute_reply": "2024-07-18T04:02:24.560893Z" + "iopub.execute_input": "2024-07-30T16:32:27.764292Z", + "iopub.status.busy": "2024-07-30T16:32:27.764112Z", + "iopub.status.idle": "2024-07-30T16:32:27.769903Z", + "shell.execute_reply": "2024-07-30T16:32:27.769437Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.563575Z", - "iopub.status.busy": "2024-07-18T04:02:24.563220Z", - "iopub.status.idle": "2024-07-18T04:02:24.675460Z", - "shell.execute_reply": "2024-07-18T04:02:24.674903Z" + "iopub.execute_input": "2024-07-30T16:32:27.772012Z", + "iopub.status.busy": "2024-07-30T16:32:27.771665Z", + "iopub.status.idle": "2024-07-30T16:32:27.883771Z", + "shell.execute_reply": "2024-07-30T16:32:27.883239Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": 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null, + "width": null + } + }, + "ffa1c84fb874460b957924837b893cd2": { + "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_2e035e4e57004636a79bb03dccd33238", + "placeholder": "​", + "style": "IPY_MODEL_d4103846b2bc4a4a8e12430befda4a7d", + "tabbable": null, + "tooltip": null, + "value": "classifier.ckpt: 100%" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 1d8d4bc13..899e02b72 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:29.204779Z", - "iopub.status.busy": "2024-07-18T04:02:29.204616Z", - "iopub.status.idle": "2024-07-18T04:02:30.379450Z", - "shell.execute_reply": "2024-07-18T04:02:30.378831Z" + "iopub.execute_input": "2024-07-30T16:32:32.656232Z", + "iopub.status.busy": "2024-07-30T16:32:32.656056Z", + "iopub.status.idle": "2024-07-30T16:32:34.118637Z", + "shell.execute_reply": "2024-07-30T16:32:34.117917Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:30.382052Z", - "iopub.status.busy": "2024-07-18T04:02:30.381624Z", - "iopub.status.idle": "2024-07-18T04:02:30.384679Z", - "shell.execute_reply": "2024-07-18T04:02:30.384157Z" + "iopub.execute_input": "2024-07-30T16:32:34.121608Z", + "iopub.status.busy": "2024-07-30T16:32:34.121097Z", + "iopub.status.idle": "2024-07-30T16:32:34.124191Z", + "shell.execute_reply": "2024-07-30T16:32:34.123735Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.386897Z", - "iopub.status.busy": "2024-07-18T04:02:30.386555Z", - "iopub.status.idle": "2024-07-18T04:02:30.395101Z", - "shell.execute_reply": "2024-07-18T04:02:30.394657Z" + "iopub.execute_input": "2024-07-30T16:32:34.126267Z", + "iopub.status.busy": "2024-07-30T16:32:34.126096Z", + "iopub.status.idle": "2024-07-30T16:32:34.134798Z", + "shell.execute_reply": "2024-07-30T16:32:34.134311Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.397072Z", - "iopub.status.busy": "2024-07-18T04:02:30.396747Z", - "iopub.status.idle": "2024-07-18T04:02:30.401452Z", - "shell.execute_reply": "2024-07-18T04:02:30.401038Z" + "iopub.execute_input": "2024-07-30T16:32:34.136971Z", + "iopub.status.busy": "2024-07-30T16:32:34.136634Z", + "iopub.status.idle": "2024-07-30T16:32:34.141247Z", + "shell.execute_reply": "2024-07-30T16:32:34.140806Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.403464Z", - "iopub.status.busy": "2024-07-18T04:02:30.403158Z", - "iopub.status.idle": "2024-07-18T04:02:30.584668Z", - "shell.execute_reply": "2024-07-18T04:02:30.584074Z" + "iopub.execute_input": "2024-07-30T16:32:34.143531Z", + "iopub.status.busy": "2024-07-30T16:32:34.143189Z", + "iopub.status.idle": "2024-07-30T16:32:34.151658Z", + "shell.execute_reply": "2024-07-30T16:32:34.151032Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.586832Z", - "iopub.status.busy": "2024-07-18T04:02:30.586642Z", - "iopub.status.idle": "2024-07-18T04:02:30.955459Z", - "shell.execute_reply": "2024-07-18T04:02:30.954890Z" + "iopub.execute_input": "2024-07-30T16:32:34.153882Z", + "iopub.status.busy": "2024-07-30T16:32:34.153554Z", + "iopub.status.idle": "2024-07-30T16:32:34.532146Z", + "shell.execute_reply": "2024-07-30T16:32:34.531569Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": 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a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index d88ccd2ad..dd27cd645 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": "2024-07-18T04:02:36.051413Z", - "iopub.status.busy": "2024-07-18T04:02:36.051244Z", - "iopub.status.idle": "2024-07-18T04:02:37.266874Z", - "shell.execute_reply": "2024-07-18T04:02:37.266234Z" + "iopub.execute_input": "2024-07-30T16:32:39.968392Z", + "iopub.status.busy": "2024-07-30T16:32:39.968219Z", + "iopub.status.idle": "2024-07-30T16:32:41.428731Z", + "shell.execute_reply": "2024-07-30T16:32:41.428142Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:37.269397Z", - "iopub.status.busy": "2024-07-18T04:02:37.269128Z", - "iopub.status.idle": "2024-07-18T04:02:37.272272Z", - "shell.execute_reply": "2024-07-18T04:02:37.271823Z" + "iopub.execute_input": "2024-07-30T16:32:41.431407Z", + "iopub.status.busy": "2024-07-30T16:32:41.430932Z", + "iopub.status.idle": "2024-07-30T16:32:41.433897Z", + "shell.execute_reply": "2024-07-30T16:32:41.433429Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.274522Z", - "iopub.status.busy": "2024-07-18T04:02:37.274188Z", - "iopub.status.idle": "2024-07-18T04:02:37.283099Z", - "shell.execute_reply": "2024-07-18T04:02:37.282643Z" + "iopub.execute_input": "2024-07-30T16:32:41.436056Z", + "iopub.status.busy": "2024-07-30T16:32:41.435693Z", + "iopub.status.idle": "2024-07-30T16:32:41.444683Z", + "shell.execute_reply": "2024-07-30T16:32:41.444228Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.285099Z", - "iopub.status.busy": "2024-07-18T04:02:37.284756Z", - "iopub.status.idle": "2024-07-18T04:02:37.289489Z", - "shell.execute_reply": "2024-07-18T04:02:37.289025Z" + "iopub.execute_input": "2024-07-30T16:32:41.446855Z", + "iopub.status.busy": "2024-07-30T16:32:41.446459Z", + "iopub.status.idle": "2024-07-30T16:32:41.451834Z", + "shell.execute_reply": "2024-07-30T16:32:41.451242Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.291764Z", - "iopub.status.busy": "2024-07-18T04:02:37.291422Z", - "iopub.status.idle": "2024-07-18T04:02:37.472524Z", - "shell.execute_reply": "2024-07-18T04:02:37.472008Z" + "iopub.execute_input": "2024-07-30T16:32:41.454128Z", + "iopub.status.busy": "2024-07-30T16:32:41.453789Z", + "iopub.status.idle": "2024-07-30T16:32:41.461841Z", + "shell.execute_reply": "2024-07-30T16:32:41.461254Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.474534Z", - "iopub.status.busy": "2024-07-18T04:02:37.474266Z", - "iopub.status.idle": "2024-07-18T04:02:37.844349Z", - "shell.execute_reply": "2024-07-18T04:02:37.843783Z" + "iopub.execute_input": "2024-07-30T16:32:41.463879Z", + "iopub.status.busy": "2024-07-30T16:32:41.463564Z", + "iopub.status.idle": "2024-07-30T16:32:41.841201Z", + "shell.execute_reply": "2024-07-30T16:32:41.840606Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.846655Z", - "iopub.status.busy": "2024-07-18T04:02:37.846287Z", - "iopub.status.idle": "2024-07-18T04:02:37.849124Z", - "shell.execute_reply": "2024-07-18T04:02:37.848661Z" + "iopub.execute_input": "2024-07-30T16:32:41.843720Z", + "iopub.status.busy": "2024-07-30T16:32:41.843358Z", + "iopub.status.idle": "2024-07-30T16:32:41.846353Z", + "shell.execute_reply": "2024-07-30T16:32:41.845761Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.851174Z", - "iopub.status.busy": "2024-07-18T04:02:37.850810Z", - "iopub.status.idle": "2024-07-18T04:02:37.884496Z", - "shell.execute_reply": "2024-07-18T04:02:37.884041Z" + "iopub.execute_input": "2024-07-30T16:32:41.848467Z", + "iopub.status.busy": "2024-07-30T16:32:41.848142Z", + "iopub.status.idle": "2024-07-30T16:32:41.882970Z", + "shell.execute_reply": "2024-07-30T16:32:41.882316Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.886502Z", - "iopub.status.busy": "2024-07-18T04:02:37.886186Z", - "iopub.status.idle": "2024-07-18T04:02:39.946867Z", - "shell.execute_reply": "2024-07-18T04:02:39.946264Z" + "iopub.execute_input": "2024-07-30T16:32:41.885593Z", + "iopub.status.busy": "2024-07-30T16:32:41.885224Z", + "iopub.status.idle": "2024-07-30T16:32:44.166781Z", + "shell.execute_reply": "2024-07-30T16:32:44.166160Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.949465Z", - "iopub.status.busy": "2024-07-18T04:02:39.948936Z", - "iopub.status.idle": "2024-07-18T04:02:39.967547Z", - "shell.execute_reply": "2024-07-18T04:02:39.967070Z" + "iopub.execute_input": "2024-07-30T16:32:44.169594Z", + "iopub.status.busy": "2024-07-30T16:32:44.168967Z", + "iopub.status.idle": "2024-07-30T16:32:44.189239Z", + "shell.execute_reply": "2024-07-30T16:32:44.188670Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.969591Z", - "iopub.status.busy": "2024-07-18T04:02:39.969258Z", - "iopub.status.idle": "2024-07-18T04:02:39.975832Z", - "shell.execute_reply": "2024-07-18T04:02:39.975379Z" + "iopub.execute_input": "2024-07-30T16:32:44.191720Z", + "iopub.status.busy": "2024-07-30T16:32:44.191313Z", + "iopub.status.idle": "2024-07-30T16:32:44.198409Z", + "shell.execute_reply": "2024-07-30T16:32:44.197866Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.977855Z", - "iopub.status.busy": "2024-07-18T04:02:39.977518Z", - "iopub.status.idle": "2024-07-18T04:02:39.983314Z", - "shell.execute_reply": "2024-07-18T04:02:39.982722Z" + "iopub.execute_input": "2024-07-30T16:32:44.200671Z", + "iopub.status.busy": "2024-07-30T16:32:44.200330Z", + "iopub.status.idle": "2024-07-30T16:32:44.206405Z", + "shell.execute_reply": "2024-07-30T16:32:44.205886Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.985271Z", - "iopub.status.busy": "2024-07-18T04:02:39.984975Z", - "iopub.status.idle": "2024-07-18T04:02:39.995497Z", - "shell.execute_reply": "2024-07-18T04:02:39.994935Z" + "iopub.execute_input": "2024-07-30T16:32:44.208540Z", + "iopub.status.busy": "2024-07-30T16:32:44.208187Z", + "iopub.status.idle": "2024-07-30T16:32:44.218769Z", + "shell.execute_reply": "2024-07-30T16:32:44.218292Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.997551Z", - "iopub.status.busy": "2024-07-18T04:02:39.997256Z", - "iopub.status.idle": "2024-07-18T04:02:40.006036Z", - "shell.execute_reply": "2024-07-18T04:02:40.005587Z" + "iopub.execute_input": "2024-07-30T16:32:44.221000Z", + "iopub.status.busy": "2024-07-30T16:32:44.220629Z", + "iopub.status.idle": "2024-07-30T16:32:44.230293Z", + "shell.execute_reply": "2024-07-30T16:32:44.229490Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.008141Z", - "iopub.status.busy": "2024-07-18T04:02:40.007823Z", - "iopub.status.idle": "2024-07-18T04:02:40.014540Z", - "shell.execute_reply": "2024-07-18T04:02:40.014100Z" + "iopub.execute_input": "2024-07-30T16:32:44.232925Z", + "iopub.status.busy": "2024-07-30T16:32:44.232545Z", + "iopub.status.idle": "2024-07-30T16:32:44.240606Z", + "shell.execute_reply": "2024-07-30T16:32:44.239954Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.016481Z", - "iopub.status.busy": "2024-07-18T04:02:40.016306Z", - "iopub.status.idle": "2024-07-18T04:02:40.025823Z", - "shell.execute_reply": "2024-07-18T04:02:40.025365Z" + "iopub.execute_input": "2024-07-30T16:32:44.242998Z", + "iopub.status.busy": "2024-07-30T16:32:44.242622Z", + "iopub.status.idle": "2024-07-30T16:32:44.252782Z", + "shell.execute_reply": "2024-07-30T16:32:44.252133Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.027917Z", - "iopub.status.busy": "2024-07-18T04:02:40.027593Z", - "iopub.status.idle": "2024-07-18T04:02:40.042624Z", - "shell.execute_reply": "2024-07-18T04:02:40.042164Z" + "iopub.execute_input": "2024-07-30T16:32:44.255163Z", + "iopub.status.busy": "2024-07-30T16:32:44.254809Z", + "iopub.status.idle": "2024-07-30T16:32:44.273057Z", + "shell.execute_reply": "2024-07-30T16:32:44.272419Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index ae9c3c784..4228b95b6 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": "2024-07-18T04:02:42.669719Z", - "iopub.status.busy": "2024-07-18T04:02:42.669546Z", - "iopub.status.idle": "2024-07-18T04:02:45.613067Z", - "shell.execute_reply": "2024-07-18T04:02:45.612422Z" + "iopub.execute_input": "2024-07-30T16:32:47.382198Z", + "iopub.status.busy": "2024-07-30T16:32:47.381761Z", + "iopub.status.idle": "2024-07-30T16:32:50.574056Z", + "shell.execute_reply": "2024-07-30T16:32:50.573420Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:45.615720Z", - "iopub.status.busy": "2024-07-18T04:02:45.615473Z", - "iopub.status.idle": "2024-07-18T04:02:45.619132Z", - "shell.execute_reply": "2024-07-18T04:02:45.618589Z" + "iopub.execute_input": "2024-07-30T16:32:50.576906Z", + "iopub.status.busy": "2024-07-30T16:32:50.576348Z", + "iopub.status.idle": "2024-07-30T16:32:50.580381Z", + "shell.execute_reply": "2024-07-30T16:32:50.579783Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:45.621224Z", - "iopub.status.busy": "2024-07-18T04:02:45.620918Z", - "iopub.status.idle": "2024-07-18T04:02:59.785458Z", - "shell.execute_reply": "2024-07-18T04:02:59.784890Z" + "iopub.execute_input": "2024-07-30T16:32:50.582599Z", + "iopub.status.busy": "2024-07-30T16:32:50.582229Z", + "iopub.status.idle": "2024-07-30T16:33:02.303436Z", + "shell.execute_reply": "2024-07-30T16:33:02.302939Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cfa127d2d15346cfaecddd1f501e10b5", + "model_id": "7cd770708ca5498492377d6a0fd76616", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84cc79fb8dfc4c8b9878470743351e05", + "model_id": "c4fa7fdeeb9446ddbf6516f8963fa52e", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff98a3cd12749eaa0f172837c5ee79f", + "model_id": "a6e2987ba28d48c28d884b33288562df", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5e8b53b8e7c4bbe8e49fb581f733d5b", + "model_id": "29ade62a53ac448198f24b5900001b05", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "777602f50c1c472883ca1e491224fbf8", + "model_id": "926846a8c6954c46acf37f4dd63e7eb9", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8253b9a8b625435f97de457cdd8b57a4", + "model_id": "6aaa8d39274f4cfea54a66eb8516a06f", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84b77d23a1794224b69e4e95c7b2f83d", + "model_id": "96697881a93440babad369ae2e2fd4b8", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d781af0165714c0d9c2a1ea7cc0d7fab", + "model_id": "efe64e5c44d94c6bb0bed3ad6e844c33", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:59.787598Z", - "iopub.status.busy": "2024-07-18T04:02:59.787373Z", - "iopub.status.idle": "2024-07-18T04:02:59.791159Z", - "shell.execute_reply": "2024-07-18T04:02:59.790668Z" + "iopub.execute_input": "2024-07-30T16:33:02.305705Z", + "iopub.status.busy": "2024-07-30T16:33:02.305351Z", + "iopub.status.idle": "2024-07-30T16:33:02.309197Z", + "shell.execute_reply": "2024-07-30T16:33:02.308695Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:59.793186Z", - "iopub.status.busy": "2024-07-18T04:02:59.792855Z", - "iopub.status.idle": "2024-07-18T04:03:11.249028Z", - "shell.execute_reply": "2024-07-18T04:03:11.248480Z" + "iopub.execute_input": "2024-07-30T16:33:02.311412Z", + "iopub.status.busy": "2024-07-30T16:33:02.311066Z", + "iopub.status.idle": "2024-07-30T16:33:14.158148Z", + "shell.execute_reply": "2024-07-30T16:33:14.157496Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe3ea1a98f724dee92ced94c01b8f215", + "model_id": "c48d73b75d8543b7900f7e3a24c14ff0", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:11.251514Z", - "iopub.status.busy": "2024-07-18T04:03:11.251273Z", - "iopub.status.idle": "2024-07-18T04:03:29.163263Z", - "shell.execute_reply": "2024-07-18T04:03:29.162608Z" + "iopub.execute_input": "2024-07-30T16:33:14.161054Z", + "iopub.status.busy": "2024-07-30T16:33:14.160637Z", + "iopub.status.idle": "2024-07-30T16:33:33.040556Z", + "shell.execute_reply": "2024-07-30T16:33:33.039889Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.166273Z", - "iopub.status.busy": "2024-07-18T04:03:29.165751Z", - "iopub.status.idle": "2024-07-18T04:03:29.171052Z", - "shell.execute_reply": "2024-07-18T04:03:29.170476Z" + "iopub.execute_input": "2024-07-30T16:33:33.043526Z", + "iopub.status.busy": "2024-07-30T16:33:33.043163Z", + "iopub.status.idle": "2024-07-30T16:33:33.048147Z", + "shell.execute_reply": "2024-07-30T16:33:33.047576Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.173245Z", - "iopub.status.busy": "2024-07-18T04:03:29.172808Z", - "iopub.status.idle": "2024-07-18T04:03:29.176965Z", - "shell.execute_reply": "2024-07-18T04:03:29.176548Z" + "iopub.execute_input": "2024-07-30T16:33:33.050340Z", + "iopub.status.busy": "2024-07-30T16:33:33.049814Z", + "iopub.status.idle": "2024-07-30T16:33:33.054210Z", + "shell.execute_reply": "2024-07-30T16:33:33.053653Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.179181Z", - "iopub.status.busy": "2024-07-18T04:03:29.178764Z", - "iopub.status.idle": "2024-07-18T04:03:29.187660Z", - "shell.execute_reply": "2024-07-18T04:03:29.187210Z" + "iopub.execute_input": "2024-07-30T16:33:33.056104Z", + "iopub.status.busy": "2024-07-30T16:33:33.055933Z", + "iopub.status.idle": "2024-07-30T16:33:33.065120Z", + "shell.execute_reply": "2024-07-30T16:33:33.064640Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.189749Z", - "iopub.status.busy": "2024-07-18T04:03:29.189427Z", - "iopub.status.idle": "2024-07-18T04:03:29.217121Z", - "shell.execute_reply": "2024-07-18T04:03:29.216709Z" + "iopub.execute_input": "2024-07-30T16:33:33.067246Z", + "iopub.status.busy": "2024-07-30T16:33:33.066927Z", + "iopub.status.idle": "2024-07-30T16:33:33.096315Z", + "shell.execute_reply": "2024-07-30T16:33:33.095690Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.219128Z", - "iopub.status.busy": "2024-07-18T04:03:29.218782Z", - "iopub.status.idle": "2024-07-18T04:04:02.310457Z", - "shell.execute_reply": "2024-07-18T04:04:02.309852Z" + "iopub.execute_input": "2024-07-30T16:33:33.098981Z", + "iopub.status.busy": "2024-07-30T16:33:33.098550Z", + "iopub.status.idle": "2024-07-30T16:34:08.613598Z", + "shell.execute_reply": "2024-07-30T16:34:08.612987Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.886\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.221\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.688\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f6a6c71bb6e4541a581f4f3310c0e87", + "model_id": "4800d17f20734ee3900349a11b2585dc", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8e7468aca074d3ea5c95769c1d98882", + "model_id": "21d92163462b4f67a981a814fcb48508", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.882\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.233\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.635\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7547caf87ef4a6e9cdaf34ecfe7a776", + "model_id": "8af1aec52aef434b81a22b708073556f", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf776da7e61c45d184a4ccbaec7843e3", + "model_id": "19a5e3a37b304b559df2c5101035122f", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.760\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.455\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.559\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce1365ddf37d4fd386cefbbc32a2b2a4", + "model_id": "a2738be416ce480c95fff046962f1137", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6cc5976d20884235b934ecfaa7687b01", + "model_id": "6c2f40cf42cc413e8b1040c82a085028", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.312910Z", - "iopub.status.busy": "2024-07-18T04:04:02.312660Z", - "iopub.status.idle": "2024-07-18T04:04:02.327049Z", - "shell.execute_reply": "2024-07-18T04:04:02.326569Z" + "iopub.execute_input": "2024-07-30T16:34:08.616395Z", + "iopub.status.busy": "2024-07-30T16:34:08.615872Z", + "iopub.status.idle": "2024-07-30T16:34:08.631241Z", + "shell.execute_reply": "2024-07-30T16:34:08.630690Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.329066Z", - "iopub.status.busy": "2024-07-18T04:04:02.328768Z", - "iopub.status.idle": "2024-07-18T04:04:02.796841Z", - "shell.execute_reply": "2024-07-18T04:04:02.796286Z" + "iopub.execute_input": "2024-07-30T16:34:08.633394Z", + "iopub.status.busy": "2024-07-30T16:34:08.633052Z", + "iopub.status.idle": "2024-07-30T16:34:09.125544Z", + "shell.execute_reply": "2024-07-30T16:34:09.124944Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.799526Z", - "iopub.status.busy": "2024-07-18T04:04:02.799081Z", - "iopub.status.idle": "2024-07-18T04:05:39.908807Z", - "shell.execute_reply": "2024-07-18T04:05:39.908161Z" + "iopub.execute_input": "2024-07-30T16:34:09.128240Z", + "iopub.status.busy": "2024-07-30T16:34:09.127855Z", + "iopub.status.idle": "2024-07-30T16:35:49.585066Z", + "shell.execute_reply": "2024-07-30T16:35:49.584319Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83f24aa593964f26bcdc4ca9d1acb2c1", + "model_id": "1e7ed9a8db3f47d499c32f8ab98695a3", "version_major": 2, "version_minor": 0 }, @@ -1120,7 +1120,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n", + "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Audit complete. 7714 issues found in the dataset.\n" ] @@ -1144,10 +1150,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:39.911277Z", - "iopub.status.busy": "2024-07-18T04:05:39.910826Z", - "iopub.status.idle": "2024-07-18T04:05:40.363119Z", - "shell.execute_reply": "2024-07-18T04:05:40.362545Z" + "iopub.execute_input": "2024-07-30T16:35:49.587886Z", + "iopub.status.busy": "2024-07-30T16:35:49.587314Z", + "iopub.status.idle": "2024-07-30T16:35:50.063963Z", + "shell.execute_reply": "2024-07-30T16:35:50.063372Z" } }, "outputs": [ @@ -1233,7 +1239,7 @@ "\n", "\n", "\n", - "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.5 \n", + "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1 \n", "------------------ low_information images ------------------\n", "\n", "Number of examples with this issue: 166\n", @@ -1293,10 +1299,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.365503Z", - "iopub.status.busy": "2024-07-18T04:05:40.365120Z", - "iopub.status.idle": "2024-07-18T04:05:40.427219Z", - "shell.execute_reply": "2024-07-18T04:05:40.426389Z" + "iopub.execute_input": "2024-07-30T16:35:50.066551Z", + "iopub.status.busy": "2024-07-30T16:35:50.065928Z", + "iopub.status.idle": "2024-07-30T16:35:50.128967Z", + "shell.execute_reply": "2024-07-30T16:35:50.128437Z" } }, "outputs": [ @@ -1400,10 +1406,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.429398Z", - "iopub.status.busy": "2024-07-18T04:05:40.428968Z", - "iopub.status.idle": "2024-07-18T04:05:40.437542Z", - "shell.execute_reply": "2024-07-18T04:05:40.437085Z" + "iopub.execute_input": "2024-07-30T16:35:50.131433Z", + "iopub.status.busy": "2024-07-30T16:35:50.130978Z", + "iopub.status.idle": "2024-07-30T16:35:50.141475Z", + "shell.execute_reply": "2024-07-30T16:35:50.140991Z" } }, "outputs": [ @@ -1533,10 +1539,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.439398Z", - "iopub.status.busy": "2024-07-18T04:05:40.439226Z", - "iopub.status.idle": "2024-07-18T04:05:40.443724Z", - "shell.execute_reply": "2024-07-18T04:05:40.443274Z" + "iopub.execute_input": "2024-07-30T16:35:50.143638Z", + "iopub.status.busy": "2024-07-30T16:35:50.143453Z", + "iopub.status.idle": "2024-07-30T16:35:50.148446Z", + "shell.execute_reply": "2024-07-30T16:35:50.147959Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1588,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.445775Z", - "iopub.status.busy": "2024-07-18T04:05:40.445449Z", - "iopub.status.idle": "2024-07-18T04:05:40.937446Z", - "shell.execute_reply": "2024-07-18T04:05:40.936903Z" + "iopub.execute_input": "2024-07-30T16:35:50.150589Z", + "iopub.status.busy": "2024-07-30T16:35:50.150254Z", + "iopub.status.idle": "2024-07-30T16:35:50.655702Z", + "shell.execute_reply": "2024-07-30T16:35:50.655117Z" } }, "outputs": [ @@ -1620,10 +1626,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.939591Z", - "iopub.status.busy": "2024-07-18T04:05:40.939250Z", - "iopub.status.idle": "2024-07-18T04:05:40.947145Z", - "shell.execute_reply": "2024-07-18T04:05:40.946558Z" + "iopub.execute_input": "2024-07-30T16:35:50.658180Z", + "iopub.status.busy": "2024-07-30T16:35:50.657804Z", + "iopub.status.idle": "2024-07-30T16:35:50.666671Z", + "shell.execute_reply": "2024-07-30T16:35:50.666179Z" } }, "outputs": [ @@ -1790,10 +1796,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.949355Z", - "iopub.status.busy": "2024-07-18T04:05:40.949021Z", - "iopub.status.idle": "2024-07-18T04:05:40.956013Z", - "shell.execute_reply": "2024-07-18T04:05:40.955573Z" + "iopub.execute_input": "2024-07-30T16:35:50.668871Z", + "iopub.status.busy": "2024-07-30T16:35:50.668531Z", + "iopub.status.idle": "2024-07-30T16:35:50.675953Z", + "shell.execute_reply": "2024-07-30T16:35:50.675476Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1875,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.957854Z", - "iopub.status.busy": "2024-07-18T04:05:40.957563Z", - "iopub.status.idle": "2024-07-18T04:05:41.700271Z", - "shell.execute_reply": "2024-07-18T04:05:41.699684Z" + "iopub.execute_input": "2024-07-30T16:35:50.677971Z", + "iopub.status.busy": "2024-07-30T16:35:50.677635Z", + "iopub.status.idle": "2024-07-30T16:35:51.461209Z", + "shell.execute_reply": "2024-07-30T16:35:51.460595Z" } }, "outputs": [ @@ -1909,10 +1915,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.702563Z", - "iopub.status.busy": "2024-07-18T04:05:41.702218Z", - "iopub.status.idle": "2024-07-18T04:05:41.717782Z", - "shell.execute_reply": "2024-07-18T04:05:41.717223Z" + "iopub.execute_input": "2024-07-30T16:35:51.463335Z", + "iopub.status.busy": "2024-07-30T16:35:51.463157Z", + "iopub.status.idle": "2024-07-30T16:35:51.478468Z", + "shell.execute_reply": "2024-07-30T16:35:51.477939Z" } }, "outputs": [ @@ -2069,10 +2075,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.720064Z", - "iopub.status.busy": "2024-07-18T04:05:41.719741Z", - "iopub.status.idle": "2024-07-18T04:05:41.725292Z", - "shell.execute_reply": "2024-07-18T04:05:41.724826Z" + "iopub.execute_input": "2024-07-30T16:35:51.480643Z", + "iopub.status.busy": "2024-07-30T16:35:51.480298Z", + "iopub.status.idle": "2024-07-30T16:35:51.486080Z", + "shell.execute_reply": "2024-07-30T16:35:51.485499Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2123,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.727255Z", - "iopub.status.busy": "2024-07-18T04:05:41.726947Z", - "iopub.status.idle": "2024-07-18T04:05:42.108095Z", - "shell.execute_reply": "2024-07-18T04:05:42.107541Z" + "iopub.execute_input": "2024-07-30T16:35:51.488104Z", + "iopub.status.busy": "2024-07-30T16:35:51.487778Z", + "iopub.status.idle": "2024-07-30T16:35:51.924919Z", + "shell.execute_reply": "2024-07-30T16:35:51.924107Z" } }, "outputs": [ @@ -2202,10 +2208,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.110467Z", - "iopub.status.busy": "2024-07-18T04:05:42.110297Z", - "iopub.status.idle": "2024-07-18T04:05:42.119483Z", - "shell.execute_reply": "2024-07-18T04:05:42.118929Z" + "iopub.execute_input": "2024-07-30T16:35:51.927416Z", + "iopub.status.busy": "2024-07-30T16:35:51.927225Z", + "iopub.status.idle": "2024-07-30T16:35:51.936102Z", + "shell.execute_reply": "2024-07-30T16:35:51.935657Z" } }, "outputs": [ @@ -2230,47 +2236,47 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 34848\n", - " True\n", " 0.203922\n", + " True\n", " \n", " \n", " 50270\n", - " True\n", " 0.204588\n", + " True\n", " \n", " \n", " 3936\n", - " True\n", " 0.213098\n", + " True\n", " \n", " \n", " 733\n", - " True\n", " 0.217686\n", + " True\n", " \n", " \n", " 8094\n", - " True\n", " 0.230118\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " is_dark_issue dark_score\n", - "34848 True 0.203922\n", - "50270 True 0.204588\n", - "3936 True 0.213098\n", - "733 True 0.217686\n", - "8094 True 0.230118" + " dark_score is_dark_issue\n", + "34848 0.203922 True\n", + "50270 0.204588 True\n", + "3936 0.213098 True\n", + "733 0.217686 True\n", + "8094 0.230118 True" ] }, "execution_count": 26, @@ -2333,10 +2339,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.121840Z", - "iopub.status.busy": "2024-07-18T04:05:42.121675Z", - "iopub.status.idle": "2024-07-18T04:05:42.127275Z", - "shell.execute_reply": "2024-07-18T04:05:42.126719Z" + "iopub.execute_input": "2024-07-30T16:35:51.938423Z", + "iopub.status.busy": "2024-07-30T16:35:51.938102Z", + "iopub.status.idle": "2024-07-30T16:35:51.942887Z", + "shell.execute_reply": "2024-07-30T16:35:51.942471Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2379,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.129465Z", - "iopub.status.busy": "2024-07-18T04:05:42.129300Z", - "iopub.status.idle": "2024-07-18T04:05:42.307685Z", - "shell.execute_reply": "2024-07-18T04:05:42.307222Z" + "iopub.execute_input": "2024-07-30T16:35:51.944959Z", + "iopub.status.busy": "2024-07-30T16:35:51.944785Z", + "iopub.status.idle": "2024-07-30T16:35:52.122597Z", + "shell.execute_reply": "2024-07-30T16:35:52.121954Z" } }, "outputs": [ @@ -2418,10 +2424,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.310041Z", - "iopub.status.busy": "2024-07-18T04:05:42.309876Z", - "iopub.status.idle": "2024-07-18T04:05:42.317464Z", - "shell.execute_reply": "2024-07-18T04:05:42.317044Z" + "iopub.execute_input": "2024-07-30T16:35:52.124950Z", + "iopub.status.busy": "2024-07-30T16:35:52.124756Z", + "iopub.status.idle": "2024-07-30T16:35:52.135215Z", + "shell.execute_reply": "2024-07-30T16:35:52.134594Z" } }, "outputs": [ @@ -2507,10 +2513,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.319520Z", - "iopub.status.busy": "2024-07-18T04:05:42.319216Z", - "iopub.status.idle": "2024-07-18T04:05:42.488104Z", - "shell.execute_reply": "2024-07-18T04:05:42.487720Z" + "iopub.execute_input": "2024-07-30T16:35:52.137816Z", + "iopub.status.busy": "2024-07-30T16:35:52.137602Z", + "iopub.status.idle": "2024-07-30T16:35:52.311705Z", + "shell.execute_reply": "2024-07-30T16:35:52.311080Z" } }, "outputs": [ @@ -2550,10 +2556,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.490200Z", - "iopub.status.busy": "2024-07-18T04:05:42.489883Z", - "iopub.status.idle": "2024-07-18T04:05:42.494879Z", - "shell.execute_reply": "2024-07-18T04:05:42.494374Z" + "iopub.execute_input": "2024-07-30T16:35:52.314392Z", + "iopub.status.busy": "2024-07-30T16:35:52.313905Z", + "iopub.status.idle": "2024-07-30T16:35:52.318429Z", + "shell.execute_reply": "2024-07-30T16:35:52.317890Z" }, "nbsphinx": "hidden" }, @@ -2590,7 +2596,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "025a1c24bb1742378b7e895247166823": { + "0013256c6393414d897e747fbb692b2e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2643,7 +2649,88 @@ "width": null } }, - "03829977c9ce4072b8e6be2abf9c81d1": { + "004fcea71628418685555fb760dec429": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6826340ac4a7479cb63a98919d60e1b5", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dcd9b7afd17f44798d2064cf5a3862de", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "0119a979303348feaf5374a2e7f3b418": { + "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", 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"horizontal", + "style": "IPY_MODEL_01972cf3b8f94fab866c986e21f7f91f", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 60000.0 } }, - "ff6c65a3e52748a9a9493f576321c597": { + "fd82af84285840158e600b4d0204c84e": { "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 4d38880f7..a6ed7ee61 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": "2024-07-18T04:05:46.059528Z", - "iopub.status.busy": "2024-07-18T04:05:46.059357Z", - "iopub.status.idle": "2024-07-18T04:05:47.184703Z", - "shell.execute_reply": "2024-07-18T04:05:47.184146Z" + "iopub.execute_input": "2024-07-30T16:35:56.051172Z", + "iopub.status.busy": "2024-07-30T16:35:56.050992Z", + "iopub.status.idle": "2024-07-30T16:35:57.510285Z", + "shell.execute_reply": "2024-07-30T16:35:57.509737Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:05:47.187068Z", - "iopub.status.busy": "2024-07-18T04:05:47.186797Z", - "iopub.status.idle": "2024-07-18T04:05:47.205431Z", - "shell.execute_reply": "2024-07-18T04:05:47.204882Z" + "iopub.execute_input": "2024-07-30T16:35:57.512868Z", + "iopub.status.busy": "2024-07-30T16:35:57.512386Z", + "iopub.status.idle": "2024-07-30T16:35:57.530631Z", + "shell.execute_reply": "2024-07-30T16:35:57.530189Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.207698Z", - "iopub.status.busy": "2024-07-18T04:05:47.207321Z", - "iopub.status.idle": "2024-07-18T04:05:47.233000Z", - "shell.execute_reply": "2024-07-18T04:05:47.232462Z" + "iopub.execute_input": "2024-07-30T16:35:57.532740Z", + "iopub.status.busy": "2024-07-30T16:35:57.532388Z", + "iopub.status.idle": "2024-07-30T16:35:57.570290Z", + "shell.execute_reply": "2024-07-30T16:35:57.569781Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.235099Z", - "iopub.status.busy": "2024-07-18T04:05:47.234749Z", - "iopub.status.idle": "2024-07-18T04:05:47.238601Z", - "shell.execute_reply": "2024-07-18T04:05:47.238176Z" + "iopub.execute_input": "2024-07-30T16:35:57.572398Z", + "iopub.status.busy": "2024-07-30T16:35:57.572060Z", + "iopub.status.idle": "2024-07-30T16:35:57.575328Z", + "shell.execute_reply": "2024-07-30T16:35:57.574903Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.240630Z", - "iopub.status.busy": "2024-07-18T04:05:47.240299Z", - "iopub.status.idle": "2024-07-18T04:05:47.247818Z", - "shell.execute_reply": "2024-07-18T04:05:47.247390Z" + "iopub.execute_input": "2024-07-30T16:35:57.577365Z", + "iopub.status.busy": "2024-07-30T16:35:57.576966Z", + "iopub.status.idle": "2024-07-30T16:35:57.584694Z", + "shell.execute_reply": "2024-07-30T16:35:57.584132Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.249866Z", - "iopub.status.busy": "2024-07-18T04:05:47.249525Z", - "iopub.status.idle": "2024-07-18T04:05:47.251977Z", - "shell.execute_reply": "2024-07-18T04:05:47.251524Z" + "iopub.execute_input": "2024-07-30T16:35:57.586954Z", + "iopub.status.busy": "2024-07-30T16:35:57.586638Z", + "iopub.status.idle": "2024-07-30T16:35:57.589280Z", + "shell.execute_reply": "2024-07-30T16:35:57.588805Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.254011Z", - "iopub.status.busy": "2024-07-18T04:05:47.253687Z", - "iopub.status.idle": "2024-07-18T04:05:50.331843Z", - "shell.execute_reply": "2024-07-18T04:05:50.331208Z" + "iopub.execute_input": "2024-07-30T16:35:57.591284Z", + "iopub.status.busy": "2024-07-30T16:35:57.590950Z", + "iopub.status.idle": "2024-07-30T16:36:00.688049Z", + "shell.execute_reply": "2024-07-30T16:36:00.687486Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:50.334588Z", - "iopub.status.busy": "2024-07-18T04:05:50.334171Z", - "iopub.status.idle": "2024-07-18T04:05:50.343958Z", - "shell.execute_reply": "2024-07-18T04:05:50.343394Z" + "iopub.execute_input": "2024-07-30T16:36:00.690868Z", + "iopub.status.busy": "2024-07-30T16:36:00.690451Z", + "iopub.status.idle": "2024-07-30T16:36:00.700262Z", + "shell.execute_reply": "2024-07-30T16:36:00.699795Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:50.346159Z", - "iopub.status.busy": "2024-07-18T04:05:50.345987Z", - "iopub.status.idle": "2024-07-18T04:05:52.286664Z", - "shell.execute_reply": "2024-07-18T04:05:52.286109Z" + "iopub.execute_input": "2024-07-30T16:36:00.702232Z", + "iopub.status.busy": "2024-07-30T16:36:00.702054Z", + "iopub.status.idle": "2024-07-30T16:36:02.934148Z", + "shell.execute_reply": "2024-07-30T16:36:02.933492Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.289279Z", - "iopub.status.busy": "2024-07-18T04:05:52.288785Z", - "iopub.status.idle": "2024-07-18T04:05:52.307212Z", - "shell.execute_reply": "2024-07-18T04:05:52.306758Z" + "iopub.execute_input": "2024-07-30T16:36:02.936729Z", + "iopub.status.busy": "2024-07-30T16:36:02.936205Z", + "iopub.status.idle": "2024-07-30T16:36:02.954952Z", + "shell.execute_reply": "2024-07-30T16:36:02.954378Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.309234Z", - "iopub.status.busy": "2024-07-18T04:05:52.308879Z", - "iopub.status.idle": "2024-07-18T04:05:52.316628Z", - "shell.execute_reply": "2024-07-18T04:05:52.316083Z" + "iopub.execute_input": "2024-07-30T16:36:02.957056Z", + "iopub.status.busy": "2024-07-30T16:36:02.956878Z", + "iopub.status.idle": "2024-07-30T16:36:02.964858Z", + "shell.execute_reply": "2024-07-30T16:36:02.964382Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.318738Z", - "iopub.status.busy": "2024-07-18T04:05:52.318420Z", - "iopub.status.idle": "2024-07-18T04:05:52.327148Z", - "shell.execute_reply": "2024-07-18T04:05:52.326685Z" + "iopub.execute_input": "2024-07-30T16:36:02.966898Z", + "iopub.status.busy": "2024-07-30T16:36:02.966576Z", + "iopub.status.idle": "2024-07-30T16:36:02.975334Z", + "shell.execute_reply": "2024-07-30T16:36:02.974877Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.329288Z", - "iopub.status.busy": "2024-07-18T04:05:52.328960Z", - "iopub.status.idle": "2024-07-18T04:05:52.336509Z", - "shell.execute_reply": "2024-07-18T04:05:52.336049Z" + "iopub.execute_input": "2024-07-30T16:36:02.977396Z", + "iopub.status.busy": "2024-07-30T16:36:02.977076Z", + "iopub.status.idle": "2024-07-30T16:36:02.984945Z", + "shell.execute_reply": "2024-07-30T16:36:02.984391Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.338513Z", - "iopub.status.busy": "2024-07-18T04:05:52.338182Z", - "iopub.status.idle": "2024-07-18T04:05:52.346902Z", - "shell.execute_reply": "2024-07-18T04:05:52.346464Z" + "iopub.execute_input": "2024-07-30T16:36:02.987002Z", + "iopub.status.busy": "2024-07-30T16:36:02.986692Z", + "iopub.status.idle": "2024-07-30T16:36:02.995395Z", + "shell.execute_reply": "2024-07-30T16:36:02.994843Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.349015Z", - "iopub.status.busy": "2024-07-18T04:05:52.348609Z", - "iopub.status.idle": "2024-07-18T04:05:52.356966Z", - "shell.execute_reply": "2024-07-18T04:05:52.356427Z" + "iopub.execute_input": "2024-07-30T16:36:02.997442Z", + "iopub.status.busy": "2024-07-30T16:36:02.997120Z", + "iopub.status.idle": "2024-07-30T16:36:03.004551Z", + "shell.execute_reply": "2024-07-30T16:36:03.004009Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.359411Z", - "iopub.status.busy": "2024-07-18T04:05:52.359043Z", - "iopub.status.idle": "2024-07-18T04:05:52.368156Z", - "shell.execute_reply": "2024-07-18T04:05:52.367618Z" + "iopub.execute_input": "2024-07-30T16:36:03.006659Z", + "iopub.status.busy": "2024-07-30T16:36:03.006343Z", + "iopub.status.idle": "2024-07-30T16:36:03.014117Z", + "shell.execute_reply": "2024-07-30T16:36:03.013629Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.370501Z", - "iopub.status.busy": "2024-07-18T04:05:52.370163Z", - "iopub.status.idle": "2024-07-18T04:05:52.378576Z", - "shell.execute_reply": "2024-07-18T04:05:52.378040Z" + "iopub.execute_input": "2024-07-30T16:36:03.016347Z", + "iopub.status.busy": "2024-07-30T16:36:03.016027Z", + "iopub.status.idle": "2024-07-30T16:36:03.024539Z", + "shell.execute_reply": "2024-07-30T16:36:03.023965Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 9ede7e8a3..b380e2cea 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": "2024-07-18T04:05:55.129783Z", - "iopub.status.busy": "2024-07-18T04:05:55.129618Z", - "iopub.status.idle": "2024-07-18T04:05:57.865032Z", - "shell.execute_reply": "2024-07-18T04:05:57.864464Z" + "iopub.execute_input": "2024-07-30T16:36:05.906897Z", + "iopub.status.busy": "2024-07-30T16:36:05.906716Z", + "iopub.status.idle": "2024-07-30T16:36:09.210694Z", + "shell.execute_reply": "2024-07-30T16:36:09.210137Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:05:57.867630Z", - "iopub.status.busy": "2024-07-18T04:05:57.867176Z", - "iopub.status.idle": "2024-07-18T04:05:57.870439Z", - "shell.execute_reply": "2024-07-18T04:05:57.869974Z" + "iopub.execute_input": "2024-07-30T16:36:09.213471Z", + "iopub.status.busy": "2024-07-30T16:36:09.212961Z", + "iopub.status.idle": "2024-07-30T16:36:09.216206Z", + "shell.execute_reply": "2024-07-30T16:36:09.215755Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.872389Z", - "iopub.status.busy": "2024-07-18T04:05:57.872088Z", - "iopub.status.idle": "2024-07-18T04:05:57.875228Z", - "shell.execute_reply": "2024-07-18T04:05:57.874636Z" + "iopub.execute_input": "2024-07-30T16:36:09.218344Z", + "iopub.status.busy": "2024-07-30T16:36:09.217971Z", + "iopub.status.idle": "2024-07-30T16:36:09.221010Z", + "shell.execute_reply": "2024-07-30T16:36:09.220555Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.877320Z", - "iopub.status.busy": "2024-07-18T04:05:57.876885Z", - "iopub.status.idle": "2024-07-18T04:05:57.898872Z", - "shell.execute_reply": "2024-07-18T04:05:57.898311Z" + "iopub.execute_input": "2024-07-30T16:36:09.223151Z", + "iopub.status.busy": "2024-07-30T16:36:09.222813Z", + "iopub.status.idle": "2024-07-30T16:36:09.264547Z", + "shell.execute_reply": "2024-07-30T16:36:09.263969Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.900978Z", - "iopub.status.busy": "2024-07-18T04:05:57.900574Z", - "iopub.status.idle": "2024-07-18T04:05:57.904377Z", - "shell.execute_reply": "2024-07-18T04:05:57.903827Z" + "iopub.execute_input": "2024-07-30T16:36:09.266828Z", + "iopub.status.busy": "2024-07-30T16:36:09.266456Z", + "iopub.status.idle": "2024-07-30T16:36:09.270175Z", + "shell.execute_reply": "2024-07-30T16:36:09.269659Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'card_about_to_expire', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'card_payment_fee_charged'}\n" + "Classes: {'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.906549Z", - "iopub.status.busy": "2024-07-18T04:05:57.906236Z", - "iopub.status.idle": "2024-07-18T04:05:57.909389Z", - "shell.execute_reply": "2024-07-18T04:05:57.908857Z" + "iopub.execute_input": "2024-07-30T16:36:09.272350Z", + "iopub.status.busy": "2024-07-30T16:36:09.271989Z", + "iopub.status.idle": "2024-07-30T16:36:09.275119Z", + "shell.execute_reply": "2024-07-30T16:36:09.274559Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.911634Z", - "iopub.status.busy": "2024-07-18T04:05:57.911199Z", - "iopub.status.idle": "2024-07-18T04:06:01.969964Z", - "shell.execute_reply": "2024-07-18T04:06:01.969310Z" + "iopub.execute_input": "2024-07-30T16:36:09.277262Z", + "iopub.status.busy": "2024-07-30T16:36:09.276911Z", + "iopub.status.idle": "2024-07-30T16:36:13.012240Z", + "shell.execute_reply": "2024-07-30T16:36:13.011588Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:01.972914Z", - "iopub.status.busy": "2024-07-18T04:06:01.972480Z", - "iopub.status.idle": "2024-07-18T04:06:02.905487Z", - "shell.execute_reply": "2024-07-18T04:06:02.904899Z" + "iopub.execute_input": "2024-07-30T16:36:13.015209Z", + "iopub.status.busy": "2024-07-30T16:36:13.014850Z", + "iopub.status.idle": "2024-07-30T16:36:13.913858Z", + "shell.execute_reply": "2024-07-30T16:36:13.913251Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:02.909182Z", - "iopub.status.busy": "2024-07-18T04:06:02.908234Z", - "iopub.status.idle": "2024-07-18T04:06:02.912302Z", - "shell.execute_reply": "2024-07-18T04:06:02.911802Z" + "iopub.execute_input": "2024-07-30T16:36:13.917763Z", + "iopub.status.busy": "2024-07-30T16:36:13.916780Z", + "iopub.status.idle": "2024-07-30T16:36:13.920912Z", + "shell.execute_reply": "2024-07-30T16:36:13.920410Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:02.915812Z", - "iopub.status.busy": "2024-07-18T04:06:02.914874Z", - "iopub.status.idle": "2024-07-18T04:06:04.898450Z", - "shell.execute_reply": "2024-07-18T04:06:04.897813Z" + "iopub.execute_input": "2024-07-30T16:36:13.924505Z", + "iopub.status.busy": "2024-07-30T16:36:13.923570Z", + "iopub.status.idle": "2024-07-30T16:36:16.057240Z", + "shell.execute_reply": "2024-07-30T16:36:16.056500Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.901548Z", - "iopub.status.busy": "2024-07-18T04:06:04.900932Z", - "iopub.status.idle": "2024-07-18T04:06:04.924390Z", - "shell.execute_reply": "2024-07-18T04:06:04.923883Z" + "iopub.execute_input": "2024-07-30T16:36:16.060459Z", + "iopub.status.busy": "2024-07-30T16:36:16.059879Z", + "iopub.status.idle": "2024-07-30T16:36:16.084329Z", + "shell.execute_reply": "2024-07-30T16:36:16.083774Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.926793Z", - "iopub.status.busy": "2024-07-18T04:06:04.926402Z", - "iopub.status.idle": "2024-07-18T04:06:04.935972Z", - "shell.execute_reply": "2024-07-18T04:06:04.935475Z" + "iopub.execute_input": "2024-07-30T16:36:16.087159Z", + "iopub.status.busy": "2024-07-30T16:36:16.086783Z", + "iopub.status.idle": "2024-07-30T16:36:16.096644Z", + "shell.execute_reply": "2024-07-30T16:36:16.096072Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.938170Z", - "iopub.status.busy": "2024-07-18T04:06:04.937989Z", - "iopub.status.idle": "2024-07-18T04:06:04.942216Z", - "shell.execute_reply": "2024-07-18T04:06:04.941644Z" + "iopub.execute_input": "2024-07-30T16:36:16.098946Z", + "iopub.status.busy": "2024-07-30T16:36:16.098549Z", + "iopub.status.idle": "2024-07-30T16:36:16.103008Z", + "shell.execute_reply": "2024-07-30T16:36:16.102445Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.944237Z", - "iopub.status.busy": "2024-07-18T04:06:04.943959Z", - "iopub.status.idle": "2024-07-18T04:06:04.950201Z", - "shell.execute_reply": "2024-07-18T04:06:04.949747Z" + "iopub.execute_input": "2024-07-30T16:36:16.105150Z", + "iopub.status.busy": "2024-07-30T16:36:16.104822Z", + "iopub.status.idle": "2024-07-30T16:36:16.111211Z", + "shell.execute_reply": "2024-07-30T16:36:16.110658Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.952147Z", - "iopub.status.busy": "2024-07-18T04:06:04.951973Z", - "iopub.status.idle": "2024-07-18T04:06:04.958732Z", - "shell.execute_reply": "2024-07-18T04:06:04.958268Z" + "iopub.execute_input": "2024-07-30T16:36:16.113187Z", + "iopub.status.busy": "2024-07-30T16:36:16.112885Z", + "iopub.status.idle": "2024-07-30T16:36:16.119267Z", + "shell.execute_reply": "2024-07-30T16:36:16.118719Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.960553Z", - "iopub.status.busy": "2024-07-18T04:06:04.960383Z", - "iopub.status.idle": "2024-07-18T04:06:04.966173Z", - "shell.execute_reply": "2024-07-18T04:06:04.965718Z" + "iopub.execute_input": "2024-07-30T16:36:16.121235Z", + "iopub.status.busy": "2024-07-30T16:36:16.120924Z", + "iopub.status.idle": "2024-07-30T16:36:16.126639Z", + "shell.execute_reply": "2024-07-30T16:36:16.126077Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.968159Z", - "iopub.status.busy": "2024-07-18T04:06:04.967881Z", - "iopub.status.idle": "2024-07-18T04:06:04.976603Z", - "shell.execute_reply": "2024-07-18T04:06:04.976039Z" + "iopub.execute_input": "2024-07-30T16:36:16.128700Z", + "iopub.status.busy": "2024-07-30T16:36:16.128385Z", + "iopub.status.idle": "2024-07-30T16:36:16.136815Z", + "shell.execute_reply": "2024-07-30T16:36:16.136243Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.978783Z", - "iopub.status.busy": "2024-07-18T04:06:04.978354Z", - "iopub.status.idle": "2024-07-18T04:06:04.983722Z", - "shell.execute_reply": "2024-07-18T04:06:04.983158Z" + "iopub.execute_input": "2024-07-30T16:36:16.138817Z", + "iopub.status.busy": "2024-07-30T16:36:16.138521Z", + "iopub.status.idle": "2024-07-30T16:36:16.143841Z", + "shell.execute_reply": "2024-07-30T16:36:16.143287Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.985767Z", - "iopub.status.busy": "2024-07-18T04:06:04.985339Z", - "iopub.status.idle": "2024-07-18T04:06:04.990755Z", - "shell.execute_reply": "2024-07-18T04:06:04.990193Z" + "iopub.execute_input": "2024-07-30T16:36:16.145732Z", + "iopub.status.busy": "2024-07-30T16:36:16.145554Z", + "iopub.status.idle": "2024-07-30T16:36:16.150879Z", + "shell.execute_reply": "2024-07-30T16:36:16.150344Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.992910Z", - "iopub.status.busy": "2024-07-18T04:06:04.992598Z", - "iopub.status.idle": "2024-07-18T04:06:04.996239Z", - "shell.execute_reply": "2024-07-18T04:06:04.995691Z" + "iopub.execute_input": "2024-07-30T16:36:16.152863Z", + "iopub.status.busy": "2024-07-30T16:36:16.152548Z", + "iopub.status.idle": "2024-07-30T16:36:16.156185Z", + "shell.execute_reply": "2024-07-30T16:36:16.155650Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.998334Z", - "iopub.status.busy": "2024-07-18T04:06:04.998026Z", - "iopub.status.idle": "2024-07-18T04:06:05.003252Z", - "shell.execute_reply": "2024-07-18T04:06:05.002678Z" + "iopub.execute_input": "2024-07-30T16:36:16.158400Z", + "iopub.status.busy": "2024-07-30T16:36:16.158078Z", + "iopub.status.idle": "2024-07-30T16:36:16.163394Z", + "shell.execute_reply": "2024-07-30T16:36:16.162837Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 8c4e0af06..d6d1d2769 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": "2024-07-18T04:06:09.315133Z", - "iopub.status.busy": "2024-07-18T04:06:09.314702Z", - "iopub.status.idle": "2024-07-18T04:06:09.737826Z", - "shell.execute_reply": "2024-07-18T04:06:09.737324Z" + "iopub.execute_input": "2024-07-30T16:36:19.928818Z", + "iopub.status.busy": "2024-07-30T16:36:19.928315Z", + "iopub.status.idle": "2024-07-30T16:36:20.362342Z", + "shell.execute_reply": "2024-07-30T16:36:20.361793Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.740480Z", - "iopub.status.busy": "2024-07-18T04:06:09.740061Z", - "iopub.status.idle": "2024-07-18T04:06:09.869002Z", - "shell.execute_reply": "2024-07-18T04:06:09.868461Z" + "iopub.execute_input": "2024-07-30T16:36:20.365042Z", + "iopub.status.busy": "2024-07-30T16:36:20.364603Z", + "iopub.status.idle": "2024-07-30T16:36:20.497373Z", + "shell.execute_reply": "2024-07-30T16:36:20.496781Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.871271Z", - "iopub.status.busy": "2024-07-18T04:06:09.870845Z", - "iopub.status.idle": "2024-07-18T04:06:09.893540Z", - "shell.execute_reply": "2024-07-18T04:06:09.892922Z" + "iopub.execute_input": "2024-07-30T16:36:20.499582Z", + "iopub.status.busy": "2024-07-30T16:36:20.499349Z", + "iopub.status.idle": "2024-07-30T16:36:20.524504Z", + "shell.execute_reply": "2024-07-30T16:36:20.523915Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.896111Z", - "iopub.status.busy": "2024-07-18T04:06:09.895870Z", - "iopub.status.idle": "2024-07-18T04:06:12.653881Z", - "shell.execute_reply": "2024-07-18T04:06:12.653225Z" + "iopub.execute_input": "2024-07-30T16:36:20.527274Z", + "iopub.status.busy": "2024-07-30T16:36:20.527018Z", + "iopub.status.idle": "2024-07-30T16:36:23.840701Z", + "shell.execute_reply": "2024-07-30T16:36:23.840107Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:12.656529Z", - "iopub.status.busy": "2024-07-18T04:06:12.656139Z", - "iopub.status.idle": "2024-07-18T04:06:31.030202Z", - "shell.execute_reply": "2024-07-18T04:06:31.029589Z" + "iopub.execute_input": "2024-07-30T16:36:23.843646Z", + "iopub.status.busy": "2024-07-30T16:36:23.843036Z", + "iopub.status.idle": "2024-07-30T16:36:32.528462Z", + "shell.execute_reply": "2024-07-30T16:36:32.527887Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:31.032605Z", - "iopub.status.busy": "2024-07-18T04:06:31.032254Z", - "iopub.status.idle": "2024-07-18T04:06:31.191251Z", - "shell.execute_reply": "2024-07-18T04:06:31.190718Z" + "iopub.execute_input": "2024-07-30T16:36:32.530759Z", + "iopub.status.busy": "2024-07-30T16:36:32.530398Z", + "iopub.status.idle": "2024-07-30T16:36:32.692452Z", + "shell.execute_reply": "2024-07-30T16:36:32.691890Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:31.193870Z", - "iopub.status.busy": "2024-07-18T04:06:31.193450Z", - "iopub.status.idle": "2024-07-18T04:06:32.501314Z", - "shell.execute_reply": "2024-07-18T04:06:32.500719Z" + "iopub.execute_input": "2024-07-30T16:36:32.695108Z", + "iopub.status.busy": "2024-07-30T16:36:32.694738Z", + "iopub.status.idle": "2024-07-30T16:36:34.079949Z", + "shell.execute_reply": "2024-07-30T16:36:34.079473Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.503635Z", - "iopub.status.busy": "2024-07-18T04:06:32.503261Z", - "iopub.status.idle": "2024-07-18T04:06:32.915426Z", - "shell.execute_reply": "2024-07-18T04:06:32.914838Z" + "iopub.execute_input": "2024-07-30T16:36:34.082291Z", + "iopub.status.busy": "2024-07-30T16:36:34.081898Z", + "iopub.status.idle": "2024-07-30T16:36:34.326676Z", + "shell.execute_reply": "2024-07-30T16:36:34.326094Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.917986Z", - "iopub.status.busy": "2024-07-18T04:06:32.917468Z", - "iopub.status.idle": "2024-07-18T04:06:32.930644Z", - "shell.execute_reply": "2024-07-18T04:06:32.930084Z" + "iopub.execute_input": "2024-07-30T16:36:34.329162Z", + "iopub.status.busy": "2024-07-30T16:36:34.328791Z", + "iopub.status.idle": "2024-07-30T16:36:34.342431Z", + "shell.execute_reply": "2024-07-30T16:36:34.341938Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.932608Z", - "iopub.status.busy": "2024-07-18T04:06:32.932309Z", - "iopub.status.idle": "2024-07-18T04:06:32.951966Z", - "shell.execute_reply": "2024-07-18T04:06:32.951527Z" + "iopub.execute_input": "2024-07-30T16:36:34.344554Z", + "iopub.status.busy": "2024-07-30T16:36:34.344214Z", + "iopub.status.idle": "2024-07-30T16:36:34.363020Z", + "shell.execute_reply": "2024-07-30T16:36:34.362540Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.953897Z", - "iopub.status.busy": "2024-07-18T04:06:32.953626Z", - "iopub.status.idle": "2024-07-18T04:06:33.209964Z", - "shell.execute_reply": "2024-07-18T04:06:33.209446Z" + "iopub.execute_input": "2024-07-30T16:36:34.365426Z", + "iopub.status.busy": "2024-07-30T16:36:34.365076Z", + "iopub.status.idle": "2024-07-30T16:36:34.596927Z", + "shell.execute_reply": "2024-07-30T16:36:34.596358Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.212305Z", - "iopub.status.busy": "2024-07-18T04:06:33.212116Z", - "iopub.status.idle": "2024-07-18T04:06:33.231185Z", - "shell.execute_reply": "2024-07-18T04:06:33.230616Z" + "iopub.execute_input": "2024-07-30T16:36:34.599630Z", + "iopub.status.busy": "2024-07-30T16:36:34.599295Z", + "iopub.status.idle": "2024-07-30T16:36:34.619495Z", + "shell.execute_reply": "2024-07-30T16:36:34.618989Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.233319Z", - "iopub.status.busy": "2024-07-18T04:06:33.232923Z", - "iopub.status.idle": "2024-07-18T04:06:33.400233Z", - "shell.execute_reply": "2024-07-18T04:06:33.399669Z" + "iopub.execute_input": "2024-07-30T16:36:34.621703Z", + "iopub.status.busy": "2024-07-30T16:36:34.621342Z", + "iopub.status.idle": "2024-07-30T16:36:34.761643Z", + "shell.execute_reply": "2024-07-30T16:36:34.761053Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.402401Z", - "iopub.status.busy": "2024-07-18T04:06:33.402059Z", - "iopub.status.idle": "2024-07-18T04:06:33.411545Z", - "shell.execute_reply": "2024-07-18T04:06:33.411006Z" + "iopub.execute_input": "2024-07-30T16:36:34.763998Z", + "iopub.status.busy": "2024-07-30T16:36:34.763799Z", + "iopub.status.idle": "2024-07-30T16:36:34.774206Z", + "shell.execute_reply": "2024-07-30T16:36:34.773713Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.413706Z", - "iopub.status.busy": "2024-07-18T04:06:33.413388Z", - "iopub.status.idle": "2024-07-18T04:06:33.422844Z", - "shell.execute_reply": "2024-07-18T04:06:33.422288Z" + "iopub.execute_input": "2024-07-30T16:36:34.776254Z", + "iopub.status.busy": "2024-07-30T16:36:34.776071Z", + "iopub.status.idle": "2024-07-30T16:36:34.785886Z", + "shell.execute_reply": "2024-07-30T16:36:34.785435Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.424928Z", - "iopub.status.busy": "2024-07-18T04:06:33.424591Z", - "iopub.status.idle": "2024-07-18T04:06:33.450139Z", - "shell.execute_reply": "2024-07-18T04:06:33.449701Z" + "iopub.execute_input": "2024-07-30T16:36:34.787902Z", + "iopub.status.busy": "2024-07-30T16:36:34.787724Z", + "iopub.status.idle": "2024-07-30T16:36:34.815725Z", + "shell.execute_reply": "2024-07-30T16:36:34.815298Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.452187Z", - "iopub.status.busy": "2024-07-18T04:06:33.451788Z", - "iopub.status.idle": "2024-07-18T04:06:33.454639Z", - "shell.execute_reply": "2024-07-18T04:06:33.454072Z" + "iopub.execute_input": "2024-07-30T16:36:34.817795Z", + "iopub.status.busy": "2024-07-30T16:36:34.817615Z", + "iopub.status.idle": "2024-07-30T16:36:34.820486Z", + "shell.execute_reply": "2024-07-30T16:36:34.820013Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.456808Z", - "iopub.status.busy": "2024-07-18T04:06:33.456509Z", - "iopub.status.idle": "2024-07-18T04:06:33.475334Z", - "shell.execute_reply": "2024-07-18T04:06:33.474761Z" + "iopub.execute_input": "2024-07-30T16:36:34.822391Z", + "iopub.status.busy": "2024-07-30T16:36:34.822219Z", + "iopub.status.idle": "2024-07-30T16:36:34.841825Z", + "shell.execute_reply": "2024-07-30T16:36:34.841320Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.477330Z", - "iopub.status.busy": "2024-07-18T04:06:33.477157Z", - "iopub.status.idle": "2024-07-18T04:06:33.481414Z", - "shell.execute_reply": "2024-07-18T04:06:33.480961Z" + "iopub.execute_input": "2024-07-30T16:36:34.843927Z", + "iopub.status.busy": "2024-07-30T16:36:34.843742Z", + "iopub.status.idle": "2024-07-30T16:36:34.848323Z", + "shell.execute_reply": "2024-07-30T16:36:34.847833Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.483270Z", - "iopub.status.busy": "2024-07-18T04:06:33.483101Z", - "iopub.status.idle": "2024-07-18T04:06:33.510551Z", - "shell.execute_reply": "2024-07-18T04:06:33.510000Z" + "iopub.execute_input": "2024-07-30T16:36:34.850257Z", + "iopub.status.busy": 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"iopub.execute_input": "2024-07-30T16:36:35.261459Z", + "iopub.status.busy": "2024-07-30T16:36:35.261275Z", + "iopub.status.idle": "2024-07-30T16:36:35.264720Z", + "shell.execute_reply": "2024-07-30T16:36:35.264247Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.887734Z", - "iopub.status.busy": "2024-07-18T04:06:33.887293Z", - "iopub.status.idle": "2024-07-18T04:06:33.899883Z", - "shell.execute_reply": "2024-07-18T04:06:33.899389Z" + "iopub.execute_input": "2024-07-30T16:36:35.266610Z", + "iopub.status.busy": "2024-07-30T16:36:35.266439Z", + "iopub.status.idle": "2024-07-30T16:36:35.280154Z", + "shell.execute_reply": "2024-07-30T16:36:35.279707Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.901737Z", - "iopub.status.busy": "2024-07-18T04:06:33.901564Z", - "iopub.status.idle": 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Load the Dataset\n", "\n", - "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n", - "\n", - "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n", - "- The correlation scores range from 0 to 1, where:\n", - " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n", - " - Scores close to 1 suggest little to no correlation between the property and class labels.\n", - "- By introducing this bias, we expect to see:\n", - " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n", - " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n", - "\n", - "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels." + "For this tutorial, we'll use a subset of the CIFAR-10 dataset with artificially introduced biases to illustrate how Datalab detects spurious correlations. We'll assume you have a directory of images organized into subdirectories by class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fetch the data for this tutorial, make sure you have `wget` and `zip` installed." ] }, { @@ -3760,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:34.241391Z", - "iopub.status.busy": "2024-07-18T04:06:34.241211Z", - "iopub.status.idle": "2024-07-18T04:06:43.025032Z", - "shell.execute_reply": "2024-07-18T04:06:43.024474Z" + "iopub.execute_input": "2024-07-30T16:36:35.596219Z", + "iopub.status.busy": "2024-07-30T16:36:35.596032Z", + "iopub.status.idle": "2024-07-30T16:36:36.032446Z", + "shell.execute_reply": "2024-07-30T16:36:36.031724Z" } }, "outputs": [ @@ -3771,409 +3767,40 @@ "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] 963.58K --.-KB/s in 0.03s \r\n", + "\r\n", + "2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "\r\n" ] } ], "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 image-related issues" + "# Download the dataset\n", + "!wget -nc https://s.cleanlab.ai/CIFAR-10-subset.zip\n", + "!unzip -q CIFAR-10-subset.zip" ] }, { @@ -4181,24 +3808,48 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.027804Z", - "iopub.status.busy": "2024-07-18T04:06:43.027169Z", - "iopub.status.idle": "2024-07-18T04:06:43.094948Z", - "shell.execute_reply": "2024-07-18T04:06:43.094485Z" + "iopub.execute_input": "2024-07-30T16:36:36.035427Z", + "iopub.status.busy": "2024-07-30T16:36:36.035017Z", + "iopub.status.idle": "2024-07-30T16:36:38.005904Z", + "shell.execute_reply": "2024-07-30T16:36:38.005342Z" } }, "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)" + "from datasets import Dataset\n", + "from torchvision.datasets import ImageFolder\n", + "\n", + "def load_image_dataset(data_dir: str):\n", + " \"\"\"\n", + " Load images from a directory structure and create a datasets.Dataset object.\n", + " \n", + " Parameters\n", + " ----------\n", + " data_dir : str\n", + " Path to the root directory containing class subdirectories.\n", + " \n", + " Returns\n", + " -------\n", + " datasets.Dataset\n", + " A Dataset object containing 'image' and 'label' columns.\n", + " \"\"\"\n", + " image_dataset = ImageFolder(data_dir)\n", + " images = [img for img, _ in image_dataset]\n", + " labels = [label for _, label in image_dataset]\n", + " return Dataset.from_dict({\"image\": images, \"label\": labels})\n", + "\n", + "# Load the dataset\n", + "data_dir = \"CIFAR-10-subset/darkened_images\"\n", + "dataset = load_image_dataset(data_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. (Optional) Creating a transformed dataset using `ImageEnhance` to induce darkness" + "### 2. Run Datalab Analysis\n", + "\n", + "Now that we have loaded our dataset, let's use `Datalab` to analyze it for potential spurious correlations." ] }, { @@ -4206,36 +3857,99 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.097002Z", - "iopub.status.busy": "2024-07-18T04:06:43.096662Z", - "iopub.status.idle": "2024-07-18T04:06:43.137694Z", - "shell.execute_reply": "2024-07-18T04:06:43.137236Z" + "iopub.execute_input": "2024-07-30T16:36:38.008499Z", + "iopub.status.busy": "2024-07-30T16:36:38.008189Z", + "iopub.status.idle": "2024-07-30T16:36:38.487271Z", + "shell.execute_reply": "2024-07-30T16:36:38.486659Z" } }, - "outputs": [], + "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": "2ed4efbeb1874db0a5e2316cc6fdcc53", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/200 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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)" + "from cleanlab import Datalab\n", + "\n", + "# Initialize Datalab with the dataset\n", + "lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n", + "\n", + "# Run the analysis\n", + "lab.find_issues()\n", + "\n", + "# Generate and display the report\n", + "lab.report()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. (Optional) Visualizing Images in the dataset" + "### 3. Interpret the Results\n", + "\n", + "While the `lab.report()` output is comprehensive, we can use more targeted methods to examine the results:" ] }, { @@ -4243,28 +3957,208 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.139729Z", - "iopub.status.busy": "2024-07-18T04:06:43.139403Z", - "iopub.status.idle": "2024-07-18T04:06:44.587790Z", - "shell.execute_reply": "2024-07-18T04:06:44.587181Z" + "iopub.execute_input": "2024-07-30T16:36:38.491380Z", + "iopub.status.busy": "2024-07-30T16:36:38.490234Z", + "iopub.status.idle": "2024-07-30T16:36:38.508457Z", + "shell.execute_reply": "2024-07-30T16:36:38.507923Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation scores for image properties:\n" + ] + }, { "data": { - "image/png": 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propertyscore
0dark_score0.000
1light_score0.180
2low_information_score0.015
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
6blurry_score0.015
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" + ], "text/plain": [ - "
" + " property score\n", + "0 dark_score 0.000\n", + "1 light_score 0.180\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" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Image-specific issues:\n" + ] + }, { "data": { - "image/png": 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" + " is_dark_issue dark_score\n", + "0 True 0.237196\n", + "1 True 0.197229\n", + "2 True 0.254188\n", + "3 True 0.229170\n", + "4 True 0.208907\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", + "\n", + "[200 rows x 2 columns]" ] }, "metadata": {}, @@ -4272,47 +4166,51 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "from IPython.display import display\n", + "\n", + "# Get the correlation scores for image properties\n", + "correlation_scores = lab._correlations_df\n", + "print(\"Correlation scores for image properties:\")\n", + "display(correlation_scores)\n", + "\n", + "# Get image-specific issues\n", + "issue_name = \"dark\"\n", + "image_issues = lab.get_issues(issue_name)\n", + "print(\"\\nImage-specific issues:\")\n", + "display(image_issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\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", + "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n", + "\n", + "Interpreting the results:\n", + "\n", + "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", + "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n", + "\n", + "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", "\n", - "plot_images(dataset_dict)\n", - "plot_images(transformed_dataset_dict)" + "For real-world datasets, pay attention to:\n", + "\n", + "- Properties with notably low scores in the correlation_scores DataFrame\n", + "- Prevalent issues in the image_issues DataFrame\n", + "\n", + "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n", + "\n", + "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Finding image-specific property scores" + "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n", + "\n", + "To understand the impact of spurious correlations, it can be helpful to compare our results with a dataset that doesn't have artificially introduced biases. In this case, we'll use the original CIFAR-10 subset." ] }, { @@ -4320,10 +4218,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:44.590173Z", - "iopub.status.busy": "2024-07-18T04:06:44.589828Z", - "iopub.status.idle": "2024-07-18T04:06:45.471688Z", - "shell.execute_reply": "2024-07-18T04:06:45.471060Z" + "iopub.execute_input": "2024-07-30T16:36:38.512133Z", + "iopub.status.busy": "2024-07-30T16:36:38.511200Z", + "iopub.status.idle": "2024-07-30T16:36:39.047457Z", + "shell.execute_reply": "2024-07-30T16:36:39.046797Z" } }, "outputs": [ @@ -4338,7 +4236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ba93e19df2346d8bc2249fbbe28c2da", + "model_id": "5704008e778b464799f617edec73de43", "version_major": 2, "version_minor": 0 }, @@ -4355,45 +4253,9 @@ "text": [ "\n", "Audit complete. 0 issues found in the dataset.\n", - "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": "d35057cc20b446b2915fc9ff955d81f2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -4423,7 +4285,7 @@ " \n", " 0\n", " dark_score\n", - " 0.295\n", + " 0.300\n", " \n", " \n", " 1\n", @@ -4453,7 +4315,7 @@ " \n", " 6\n", " blurry_score\n", - " 0.325\n", + " 0.335\n", " \n", " \n", "\n", @@ -4461,29 +4323,25 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.295\n", + "0 dark_score 0.300\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" + "6 blurry_score 0.335" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "text/markdown": [ - "### Image-specific property scores in the transformed dataset" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Image-specific issues in original dataset:\n" + ] }, { "data": { @@ -4506,59 +4364,86 @@ " \n", " \n", " \n", - " property\n", - " score\n", + " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " dark_score\n", - " 0.000\n", + " False\n", + " 0.797509\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.185\n", + " False\n", + " 0.663760\n", " \n", " \n", " 2\n", - " low_information_score\n", - " 0.015\n", + " False\n", + " 0.849826\n", " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " False\n", + " 0.773951\n", " \n", " \n", " 4\n", - " odd_size_score\n", - " 0.500\n", + " False\n", + " 0.699518\n", " \n", " \n", - " 5\n", - " grayscale_score\n", - " 0.500\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 6\n", - " blurry_score\n", - " 0.015\n", + " 195\n", + " False\n", + " 0.793840\n", + " \n", + " \n", + " 196\n", + " False\n", + " 1.000000\n", + " \n", + " \n", + " 197\n", + " False\n", + " 0.971560\n", + " \n", + " \n", + " 198\n", + " False\n", + " 0.862236\n", + " \n", + " \n", + " 199\n", + " False\n", + " 0.973533\n", " \n", " \n", "\n", + "

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\n", "" ], "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" + " is_dark_issue dark_score\n", + "0 False 0.797509\n", + "1 False 0.663760\n", + "2 False 0.849826\n", + "3 False 0.773951\n", + "4 False 0.699518\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", + "\n", + "[200 rows x 2 columns]" ] }, "metadata": {}, @@ -4566,28 +4451,35 @@ } ], "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(\"### Image-specific property scores in the original dataset\"))\n", - "display(standard_property_scores)\n", - "display(Markdown(\"### Image-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')" + "# Load the original dataset\n", + "original_data_dir = \"CIFAR-10-subset/original_images\"\n", + "original_dataset = load_image_dataset(original_data_dir)\n", + "\n", + "# Create a new Datalab instance and run analysis\n", + "original_lab = Datalab(data=original_dataset, label_name=\"label\", image_key=\"image\")\n", + "original_lab.find_issues()\n", + "\n", + "# Compare correlation scores\n", + "original_scores = original_lab._correlations_df\n", + "print(\"Correlation scores for original dataset:\")\n", + "display(original_scores)\n", + "\n", + "# Compare image-specific issues\n", + "original_issues = original_lab.get_issues(\"dark\")\n", + "print(\"\\nImage-specific issues in original dataset:\")\n", + "display(original_issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When comparing the results:\n", + "\n", + "1. Look for differences in the correlation scores, especially for the 'dark' property.\n", + "2. Compare the number and types of image-specific issues detected.\n", + "\n", + "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. 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"8c33c90c192140fcb39f8e0fb7da534c": { + "a8c94e907c12444e8d0364601f96e159": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "93a20e1de6f0471daf520d49388c326d": { + "b7913d94eed4408cb306a3e2b47761cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5020,88 +5026,7 @@ "width": null } }, - "b5a2e16c332146b09a0dd2087d5d539c": { - "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_5ba7f559a9da406493cf8689d8496a25", - "placeholder": "​", - "style": "IPY_MODEL_4239c58acfb54ff6adf6796d156be031", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 681.35it/s]" - } - }, - "bf6016b0169f460290ed5938134db880": { - "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 - } - }, - "c8838ce4540a48b487ecdf51e26a4c5f": { - "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": "" - } - }, - "d35057cc20b446b2915fc9ff955d81f2": { - "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_dff6d70ab4ed4762bc4586d001f53274", - "IPY_MODEL_082f92a4818742ecbc4d1f4857988df5", - "IPY_MODEL_ff9de7e97a1047acabe0749ea6273a26" - ], - "layout": "IPY_MODEL_fb7e314d793f46dcbea0cef7c9a2f60f", - "tabbable": null, - "tooltip": null - } - }, - "df3530bc714a437eb8417ba37251ba85": { + "bd875ac3a62042068a132488eecbb1c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5154,7 +5079,7 @@ "width": null } }, - "dff6d70ab4ed4762bc4586d001f53274": { + "d4d36e246db746c4acbc8f4787f82381": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5169,15 +5094,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e2a6647ccce04986a5bc483752fbbb50", + "layout": "IPY_MODEL_9ab790ab579f415bbc804c1f992660a0", "placeholder": "​", - "style": "IPY_MODEL_756f2e8b41ae4894b44c354d6b8cc1f3", + "style": "IPY_MODEL_72b98c9996864aa9abad1934ce4b27c3", "tabbable": null, "tooltip": null, "value": "100%" } }, - "e2a6647ccce04986a5bc483752fbbb50": { + "df924bb590a44f27a4f68916d0e77c53": { + "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": "" + } + }, + "f2cb72313c294a56bc0221871a2d5717": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5230,7 +5171,7 @@ "width": null } }, - "fb7e314d793f46dcbea0cef7c9a2f60f": { + "f59fcaa8e8d04931980396c1bdc42425": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5282,55 +5223,6 @@ "visibility": null, "width": null } - }, - "fdefd9edead8430e994cc0b608b54228": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_93a20e1de6f0471daf520d49388c326d", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c8838ce4540a48b487ecdf51e26a4c5f", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "ff9de7e97a1047acabe0749ea6273a26": { - "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_19e7b8a023924ce1bdaa09961f57d5eb", - "placeholder": "​", - "style": "IPY_MODEL_bf6016b0169f460290ed5938134db880", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 672.89it/s]" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 35668b032..b41d28ba4 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": "2024-07-18T04:06:49.364909Z", - "iopub.status.busy": "2024-07-18T04:06:49.364426Z", - "iopub.status.idle": "2024-07-18T04:06:50.490342Z", - "shell.execute_reply": "2024-07-18T04:06:50.489718Z" + "iopub.execute_input": "2024-07-30T16:36:43.263935Z", + "iopub.status.busy": "2024-07-30T16:36:43.263754Z", + "iopub.status.idle": "2024-07-30T16:36:44.677036Z", + "shell.execute_reply": "2024-07-30T16:36:44.676454Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:06:50.492975Z", - "iopub.status.busy": "2024-07-18T04:06:50.492531Z", - "iopub.status.idle": "2024-07-18T04:06:50.495387Z", - "shell.execute_reply": "2024-07-18T04:06:50.494931Z" + "iopub.execute_input": "2024-07-30T16:36:44.679704Z", + "iopub.status.busy": "2024-07-30T16:36:44.679219Z", + "iopub.status.idle": "2024-07-30T16:36:44.681960Z", + "shell.execute_reply": "2024-07-30T16:36:44.681516Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:50.497498Z", - "iopub.status.busy": "2024-07-18T04:06:50.497161Z", - "iopub.status.idle": "2024-07-18T04:06:50.508789Z", - "shell.execute_reply": "2024-07-18T04:06:50.508333Z" + "iopub.execute_input": "2024-07-30T16:36:44.684134Z", + "iopub.status.busy": "2024-07-30T16:36:44.683779Z", + "iopub.status.idle": "2024-07-30T16:36:44.695519Z", + "shell.execute_reply": "2024-07-30T16:36:44.695059Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:50.510953Z", - "iopub.status.busy": "2024-07-18T04:06:50.510604Z", - "iopub.status.idle": "2024-07-18T04:06:55.636999Z", - "shell.execute_reply": "2024-07-18T04:06:55.636495Z" + "iopub.execute_input": "2024-07-30T16:36:44.697494Z", + "iopub.status.busy": "2024-07-30T16:36:44.697321Z", + "iopub.status.idle": "2024-07-30T16:36:50.818481Z", + "shell.execute_reply": "2024-07-30T16:36:50.817920Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 908e32eaa..0e282dd07 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": "2024-07-18T04:06:57.905922Z", - "iopub.status.busy": "2024-07-18T04:06:57.905505Z", - "iopub.status.idle": "2024-07-18T04:06:59.031674Z", - "shell.execute_reply": "2024-07-18T04:06:59.031132Z" + "iopub.execute_input": "2024-07-30T16:36:53.364898Z", + "iopub.status.busy": "2024-07-30T16:36:53.364365Z", + "iopub.status.idle": "2024-07-30T16:36:54.816084Z", + "shell.execute_reply": "2024-07-30T16:36:54.815502Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:59.034547Z", - "iopub.status.busy": "2024-07-18T04:06:59.034090Z", - "iopub.status.idle": "2024-07-18T04:06:59.037514Z", - "shell.execute_reply": "2024-07-18T04:06:59.037039Z" + "iopub.execute_input": "2024-07-30T16:36:54.819086Z", + "iopub.status.busy": "2024-07-30T16:36:54.818586Z", + "iopub.status.idle": "2024-07-30T16:36:54.821882Z", + "shell.execute_reply": "2024-07-30T16:36:54.821439Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:59.039633Z", - "iopub.status.busy": "2024-07-18T04:06:59.039294Z", - "iopub.status.idle": "2024-07-18T04:07:02.365487Z", - "shell.execute_reply": "2024-07-18T04:07:02.364710Z" + "iopub.execute_input": "2024-07-30T16:36:54.824015Z", + "iopub.status.busy": "2024-07-30T16:36:54.823672Z", + "iopub.status.idle": "2024-07-30T16:36:58.536010Z", + "shell.execute_reply": "2024-07-30T16:36:58.535180Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.368915Z", - "iopub.status.busy": "2024-07-18T04:07:02.368093Z", - "iopub.status.idle": "2024-07-18T04:07:02.410734Z", - "shell.execute_reply": "2024-07-18T04:07:02.410117Z" + "iopub.execute_input": "2024-07-30T16:36:58.539755Z", + "iopub.status.busy": "2024-07-30T16:36:58.538755Z", + "iopub.status.idle": "2024-07-30T16:36:58.591095Z", + "shell.execute_reply": "2024-07-30T16:36:58.590433Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.413396Z", - "iopub.status.busy": "2024-07-18T04:07:02.413137Z", - "iopub.status.idle": "2024-07-18T04:07:02.451147Z", - "shell.execute_reply": "2024-07-18T04:07:02.450498Z" + "iopub.execute_input": "2024-07-30T16:36:58.593884Z", + "iopub.status.busy": "2024-07-30T16:36:58.593478Z", + "iopub.status.idle": "2024-07-30T16:36:58.639623Z", + "shell.execute_reply": "2024-07-30T16:36:58.638845Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.453755Z", - "iopub.status.busy": "2024-07-18T04:07:02.453375Z", - "iopub.status.idle": "2024-07-18T04:07:02.456615Z", - "shell.execute_reply": "2024-07-18T04:07:02.456146Z" + "iopub.execute_input": "2024-07-30T16:36:58.642513Z", + "iopub.status.busy": "2024-07-30T16:36:58.642101Z", + "iopub.status.idle": "2024-07-30T16:36:58.645752Z", + "shell.execute_reply": "2024-07-30T16:36:58.645291Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.458726Z", - "iopub.status.busy": "2024-07-18T04:07:02.458390Z", - "iopub.status.idle": "2024-07-18T04:07:02.460984Z", - "shell.execute_reply": "2024-07-18T04:07:02.460533Z" + "iopub.execute_input": "2024-07-30T16:36:58.647868Z", + "iopub.status.busy": "2024-07-30T16:36:58.647530Z", + "iopub.status.idle": "2024-07-30T16:36:58.650324Z", + "shell.execute_reply": "2024-07-30T16:36:58.649625Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.463310Z", - "iopub.status.busy": "2024-07-18T04:07:02.462737Z", - "iopub.status.idle": "2024-07-18T04:07:02.489598Z", - "shell.execute_reply": "2024-07-18T04:07:02.489036Z" + "iopub.execute_input": "2024-07-30T16:36:58.652675Z", + "iopub.status.busy": "2024-07-30T16:36:58.652185Z", + "iopub.status.idle": "2024-07-30T16:36:58.676038Z", + "shell.execute_reply": "2024-07-30T16:36:58.675495Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7a01475effc42c7a0d0df5831be2afd", + "model_id": "57581a07cda143f5ae3947a8ceb2effa", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2060b8b81e144c6a49a7e7fa3958df1", + "model_id": "8036196b7e194ee38336f33c15df9344", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.494143Z", - "iopub.status.busy": "2024-07-18T04:07:02.493831Z", - "iopub.status.idle": "2024-07-18T04:07:02.500460Z", - "shell.execute_reply": "2024-07-18T04:07:02.499905Z" + "iopub.execute_input": "2024-07-30T16:36:58.681445Z", + "iopub.status.busy": "2024-07-30T16:36:58.681234Z", + "iopub.status.idle": "2024-07-30T16:36:58.688163Z", + "shell.execute_reply": "2024-07-30T16:36:58.687730Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.502621Z", - 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"id": "a846fe33", + "id": "984213fa", "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": "abe989bf", + "id": "2618e545", "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": "682e16e3", + "id": "1e0becd2", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "82d68237", + "id": "cba58da6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.843106Z", - "iopub.status.busy": "2024-07-18T04:07:05.842779Z", - "iopub.status.idle": "2024-07-18T04:07:05.850387Z", - "shell.execute_reply": "2024-07-18T04:07:05.849822Z" + "iopub.execute_input": "2024-07-30T16:37:02.136245Z", + "iopub.status.busy": "2024-07-30T16:37:02.136064Z", + "iopub.status.idle": "2024-07-30T16:37:02.143652Z", + "shell.execute_reply": "2024-07-30T16:37:02.143210Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "e698fd46", + "id": "fea318fb", "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": "e6b8075c", + "id": "6afc3734", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.852549Z", - "iopub.status.busy": "2024-07-18T04:07:05.852221Z", - "iopub.status.idle": "2024-07-18T04:07:05.870747Z", - "shell.execute_reply": "2024-07-18T04:07:05.870171Z" + "iopub.execute_input": "2024-07-30T16:37:02.145731Z", + "iopub.status.busy": "2024-07-30T16:37:02.145388Z", + "iopub.status.idle": "2024-07-30T16:37:02.165432Z", + "shell.execute_reply": "2024-07-30T16:37:02.164935Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "307c5ea3", + "id": "b8513ca9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.873062Z", - "iopub.status.busy": "2024-07-18T04:07:05.872717Z", - "iopub.status.idle": "2024-07-18T04:07:05.876179Z", - "shell.execute_reply": "2024-07-18T04:07:05.875603Z" + "iopub.execute_input": "2024-07-30T16:37:02.167476Z", + "iopub.status.busy": "2024-07-30T16:37:02.167285Z", + "iopub.status.idle": "2024-07-30T16:37:02.170854Z", + "shell.execute_reply": "2024-07-30T16:37:02.170369Z" } }, "outputs": [ @@ -1622,99 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"iopub.execute_input": "2024-07-18T04:07:10.188653Z", - "iopub.status.busy": "2024-07-18T04:07:10.188169Z", - "iopub.status.idle": "2024-07-18T04:07:11.330300Z", - "shell.execute_reply": "2024-07-18T04:07:11.329753Z" + "iopub.execute_input": "2024-07-30T16:37:06.847486Z", + "iopub.status.busy": "2024-07-30T16:37:06.846996Z", + "iopub.status.idle": "2024-07-30T16:37:08.300373Z", + "shell.execute_reply": "2024-07-30T16:37:08.299802Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - 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"iopub.execute_input": "2024-07-18T04:07:12.016335Z", - "iopub.status.busy": "2024-07-18T04:07:12.015426Z", - "iopub.status.idle": "2024-07-18T04:07:12.021208Z", - "shell.execute_reply": "2024-07-18T04:07:12.020724Z" + "iopub.execute_input": "2024-07-30T16:37:08.707757Z", + "iopub.status.busy": "2024-07-30T16:37:08.707363Z", + "iopub.status.idle": "2024-07-30T16:37:08.711693Z", + "shell.execute_reply": "2024-07-30T16:37:08.711182Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.024634Z", - "iopub.status.busy": "2024-07-18T04:07:12.023730Z", - "iopub.status.idle": "2024-07-18T04:07:12.029722Z", - "shell.execute_reply": "2024-07-18T04:07:12.029237Z" + "iopub.execute_input": "2024-07-30T16:37:08.714035Z", + "iopub.status.busy": "2024-07-30T16:37:08.713629Z", + "iopub.status.idle": "2024-07-30T16:37:08.718166Z", + "shell.execute_reply": "2024-07-30T16:37:08.717631Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.032990Z", - "iopub.status.busy": "2024-07-18T04:07:12.032270Z", - "iopub.status.idle": "2024-07-18T04:07:12.041800Z", - "shell.execute_reply": "2024-07-18T04:07:12.041134Z" + "iopub.execute_input": "2024-07-30T16:37:08.720507Z", + "iopub.status.busy": "2024-07-30T16:37:08.720111Z", + "iopub.status.idle": "2024-07-30T16:37:08.731439Z", + "shell.execute_reply": "2024-07-30T16:37:08.730910Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.044012Z", - "iopub.status.busy": "2024-07-18T04:07:12.043657Z", - "iopub.status.idle": "2024-07-18T04:07:12.048264Z", - "shell.execute_reply": "2024-07-18T04:07:12.047845Z" + "iopub.execute_input": "2024-07-30T16:37:08.733378Z", + "iopub.status.busy": "2024-07-30T16:37:08.733061Z", + "iopub.status.idle": "2024-07-30T16:37:08.737822Z", + "shell.execute_reply": "2024-07-30T16:37:08.737271Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.050252Z", - "iopub.status.busy": "2024-07-18T04:07:12.049932Z", - "iopub.status.idle": "2024-07-18T04:07:12.161492Z", - "shell.execute_reply": "2024-07-18T04:07:12.160998Z" + "iopub.execute_input": "2024-07-30T16:37:08.739993Z", + "iopub.status.busy": "2024-07-30T16:37:08.739678Z", + "iopub.status.idle": "2024-07-30T16:37:08.850454Z", + "shell.execute_reply": "2024-07-30T16:37:08.849916Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.163832Z", - "iopub.status.busy": "2024-07-18T04:07:12.163548Z", - "iopub.status.idle": "2024-07-18T04:07:12.169337Z", - "shell.execute_reply": "2024-07-18T04:07:12.168771Z" + "iopub.execute_input": "2024-07-30T16:37:08.852557Z", + "iopub.status.busy": "2024-07-30T16:37:08.852223Z", + "iopub.status.idle": "2024-07-30T16:37:08.858285Z", + "shell.execute_reply": "2024-07-30T16:37:08.857774Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.171714Z", - "iopub.status.busy": "2024-07-18T04:07:12.171220Z", - "iopub.status.idle": "2024-07-18T04:07:14.132704Z", - "shell.execute_reply": "2024-07-18T04:07:14.132092Z" + "iopub.execute_input": "2024-07-30T16:37:08.860639Z", + "iopub.status.busy": "2024-07-30T16:37:08.860116Z", + "iopub.status.idle": "2024-07-30T16:37:11.081523Z", + "shell.execute_reply": "2024-07-30T16:37:11.080894Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.136911Z", - "iopub.status.busy": "2024-07-18T04:07:14.135833Z", - "iopub.status.idle": "2024-07-18T04:07:14.150521Z", - "shell.execute_reply": "2024-07-18T04:07:14.150012Z" + "iopub.execute_input": "2024-07-30T16:37:11.085782Z", + "iopub.status.busy": "2024-07-30T16:37:11.084683Z", + "iopub.status.idle": "2024-07-30T16:37:11.100012Z", + "shell.execute_reply": "2024-07-30T16:37:11.099506Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.154039Z", - "iopub.status.busy": "2024-07-18T04:07:14.153107Z", - "iopub.status.idle": "2024-07-18T04:07:14.157115Z", - "shell.execute_reply": "2024-07-18T04:07:14.156603Z" + "iopub.execute_input": "2024-07-30T16:37:11.103570Z", + "iopub.status.busy": "2024-07-30T16:37:11.102644Z", + "iopub.status.idle": "2024-07-30T16:37:11.106644Z", + "shell.execute_reply": "2024-07-30T16:37:11.106149Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.160572Z", - "iopub.status.busy": "2024-07-18T04:07:14.159645Z", - "iopub.status.idle": "2024-07-18T04:07:14.165136Z", - "shell.execute_reply": "2024-07-18T04:07:14.164638Z" + "iopub.execute_input": "2024-07-30T16:37:11.110093Z", + "iopub.status.busy": "2024-07-30T16:37:11.109154Z", + "iopub.status.idle": "2024-07-30T16:37:11.114773Z", + "shell.execute_reply": "2024-07-30T16:37:11.114272Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.168650Z", - "iopub.status.busy": "2024-07-18T04:07:14.167719Z", - "iopub.status.idle": "2024-07-18T04:07:14.201487Z", - "shell.execute_reply": "2024-07-18T04:07:14.200991Z" + "iopub.execute_input": "2024-07-30T16:37:11.118266Z", + "iopub.status.busy": "2024-07-30T16:37:11.117324Z", + "iopub.status.idle": "2024-07-30T16:37:11.149228Z", + "shell.execute_reply": "2024-07-30T16:37:11.148699Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.204946Z", - "iopub.status.busy": "2024-07-18T04:07:14.204060Z", - "iopub.status.idle": "2024-07-18T04:07:14.715733Z", - "shell.execute_reply": "2024-07-18T04:07:14.715232Z" + "iopub.execute_input": "2024-07-30T16:37:11.152348Z", + "iopub.status.busy": "2024-07-30T16:37:11.151900Z", + "iopub.status.idle": "2024-07-30T16:37:11.662729Z", + "shell.execute_reply": "2024-07-30T16:37:11.662153Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.719208Z", - "iopub.status.busy": "2024-07-18T04:07:14.718293Z", - "iopub.status.idle": "2024-07-18T04:07:14.851216Z", - "shell.execute_reply": "2024-07-18T04:07:14.850601Z" + "iopub.execute_input": "2024-07-30T16:37:11.665547Z", + "iopub.status.busy": "2024-07-30T16:37:11.665125Z", + "iopub.status.idle": "2024-07-30T16:37:11.811588Z", + "shell.execute_reply": "2024-07-30T16:37:11.810893Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.854177Z", - "iopub.status.busy": "2024-07-18T04:07:14.853837Z", - "iopub.status.idle": "2024-07-18T04:07:14.860224Z", - "shell.execute_reply": "2024-07-18T04:07:14.859741Z" + "iopub.execute_input": "2024-07-30T16:37:11.814740Z", + "iopub.status.busy": "2024-07-30T16:37:11.814355Z", + "iopub.status.idle": "2024-07-30T16:37:11.821641Z", + "shell.execute_reply": "2024-07-30T16:37:11.821112Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.862683Z", - "iopub.status.busy": "2024-07-18T04:07:14.862372Z", - "iopub.status.idle": "2024-07-18T04:07:14.868198Z", - "shell.execute_reply": "2024-07-18T04:07:14.867715Z" + "iopub.execute_input": "2024-07-30T16:37:11.825192Z", + "iopub.status.busy": "2024-07-30T16:37:11.824261Z", + "iopub.status.idle": "2024-07-30T16:37:11.832276Z", + "shell.execute_reply": "2024-07-30T16:37:11.831780Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.870508Z", - "iopub.status.busy": "2024-07-18T04:07:14.870130Z", - "iopub.status.idle": "2024-07-18T04:07:14.875378Z", - "shell.execute_reply": "2024-07-18T04:07:14.874887Z" + "iopub.execute_input": "2024-07-30T16:37:11.835774Z", + "iopub.status.busy": "2024-07-30T16:37:11.834852Z", + "iopub.status.idle": "2024-07-30T16:37:11.842134Z", + "shell.execute_reply": "2024-07-30T16:37:11.841617Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.877652Z", - "iopub.status.busy": "2024-07-18T04:07:14.877284Z", - "iopub.status.idle": "2024-07-18T04:07:14.881329Z", - "shell.execute_reply": "2024-07-18T04:07:14.880859Z" + "iopub.execute_input": "2024-07-30T16:37:11.845557Z", + "iopub.status.busy": "2024-07-30T16:37:11.844646Z", + "iopub.status.idle": "2024-07-30T16:37:11.849988Z", + "shell.execute_reply": "2024-07-30T16:37:11.849571Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.883619Z", - "iopub.status.busy": "2024-07-18T04:07:14.883248Z", - "iopub.status.idle": "2024-07-18T04:07:14.887859Z", - "shell.execute_reply": "2024-07-18T04:07:14.887364Z" + "iopub.execute_input": "2024-07-30T16:37:11.852085Z", + "iopub.status.busy": "2024-07-30T16:37:11.851735Z", + "iopub.status.idle": "2024-07-30T16:37:11.856242Z", + "shell.execute_reply": "2024-07-30T16:37:11.855836Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.890172Z", - "iopub.status.busy": "2024-07-18T04:07:14.889802Z", - "iopub.status.idle": "2024-07-18T04:07:14.967278Z", - "shell.execute_reply": "2024-07-18T04:07:14.966734Z" + "iopub.execute_input": "2024-07-30T16:37:11.858461Z", + "iopub.status.busy": "2024-07-30T16:37:11.858025Z", + "iopub.status.idle": "2024-07-30T16:37:11.938221Z", + "shell.execute_reply": "2024-07-30T16:37:11.937709Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.969554Z", - "iopub.status.busy": "2024-07-18T04:07:14.969394Z", - "iopub.status.idle": "2024-07-18T04:07:14.980191Z", - "shell.execute_reply": "2024-07-18T04:07:14.979708Z" + "iopub.execute_input": "2024-07-30T16:37:11.940497Z", + "iopub.status.busy": "2024-07-30T16:37:11.940305Z", + "iopub.status.idle": "2024-07-30T16:37:11.950235Z", + "shell.execute_reply": "2024-07-30T16:37:11.949598Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.983421Z", - "iopub.status.busy": "2024-07-18T04:07:14.982704Z", - "iopub.status.idle": "2024-07-18T04:07:14.986309Z", - "shell.execute_reply": "2024-07-18T04:07:14.985259Z" + "iopub.execute_input": "2024-07-30T16:37:11.952707Z", + "iopub.status.busy": "2024-07-30T16:37:11.952413Z", + "iopub.status.idle": "2024-07-30T16:37:11.955525Z", + "shell.execute_reply": "2024-07-30T16:37:11.954939Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.988371Z", - "iopub.status.busy": "2024-07-18T04:07:14.988062Z", - "iopub.status.idle": "2024-07-18T04:07:14.997162Z", - "shell.execute_reply": "2024-07-18T04:07:14.996732Z" + "iopub.execute_input": "2024-07-30T16:37:11.957491Z", + "iopub.status.busy": "2024-07-30T16:37:11.957322Z", + "iopub.status.idle": "2024-07-30T16:37:11.968955Z", + "shell.execute_reply": "2024-07-30T16:37:11.968450Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.999345Z", - "iopub.status.busy": "2024-07-18T04:07:14.999017Z", - "iopub.status.idle": "2024-07-18T04:07:15.005252Z", - "shell.execute_reply": "2024-07-18T04:07:15.004791Z" + "iopub.execute_input": "2024-07-30T16:37:11.971083Z", + "iopub.status.busy": "2024-07-30T16:37:11.970902Z", + "iopub.status.idle": "2024-07-30T16:37:11.977806Z", + "shell.execute_reply": "2024-07-30T16:37:11.977330Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:15.007199Z", - "iopub.status.busy": "2024-07-18T04:07:15.006868Z", - "iopub.status.idle": "2024-07-18T04:07:15.009987Z", - "shell.execute_reply": "2024-07-18T04:07:15.009541Z" + "iopub.execute_input": "2024-07-30T16:37:11.979672Z", + "iopub.status.busy": "2024-07-30T16:37:11.979501Z", + "iopub.status.idle": "2024-07-30T16:37:11.982695Z", + "shell.execute_reply": "2024-07-30T16:37:11.982238Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:15.011951Z", - "iopub.status.busy": "2024-07-18T04:07:15.011621Z", - "iopub.status.idle": "2024-07-18T04:07:19.005757Z", - "shell.execute_reply": "2024-07-18T04:07:19.005204Z" + "iopub.execute_input": "2024-07-30T16:37:11.984563Z", + "iopub.status.busy": "2024-07-30T16:37:11.984385Z", + "iopub.status.idle": "2024-07-30T16:37:16.038898Z", + "shell.execute_reply": "2024-07-30T16:37:16.038334Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:19.009017Z", - "iopub.status.busy": "2024-07-18T04:07:19.008110Z", - "iopub.status.idle": "2024-07-18T04:07:19.011999Z", - "shell.execute_reply": "2024-07-18T04:07:19.011599Z" + "iopub.execute_input": "2024-07-30T16:37:16.041419Z", + "iopub.status.busy": "2024-07-30T16:37:16.041044Z", + "iopub.status.idle": "2024-07-30T16:37:16.044136Z", + "shell.execute_reply": "2024-07-30T16:37:16.043742Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:19.013973Z", - "iopub.status.busy": "2024-07-18T04:07:19.013518Z", - "iopub.status.idle": "2024-07-18T04:07:19.016164Z", - "shell.execute_reply": "2024-07-18T04:07:19.015768Z" + "iopub.execute_input": "2024-07-30T16:37:16.046136Z", + "iopub.status.busy": "2024-07-30T16:37:16.045835Z", + "iopub.status.idle": "2024-07-30T16:37:16.048832Z", + "shell.execute_reply": "2024-07-30T16:37:16.048207Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 773aea810..63d074d15 100644 --- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:22.076336Z", - "iopub.status.busy": "2024-07-18T04:07:22.075827Z", - "iopub.status.idle": "2024-07-18T04:07:23.267351Z", - "shell.execute_reply": "2024-07-18T04:07:23.266808Z" + "iopub.execute_input": "2024-07-30T16:37:19.514665Z", + "iopub.status.busy": "2024-07-30T16:37:19.514193Z", + "iopub.status.idle": "2024-07-30T16:37:20.970203Z", + "shell.execute_reply": "2024-07-30T16:37:20.969599Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:23.269779Z", - "iopub.status.busy": "2024-07-18T04:07:23.269491Z", - "iopub.status.idle": "2024-07-18T04:07:23.449090Z", - "shell.execute_reply": "2024-07-18T04:07:23.448580Z" + "iopub.execute_input": "2024-07-30T16:37:20.972868Z", + "iopub.status.busy": "2024-07-30T16:37:20.972378Z", + "iopub.status.idle": "2024-07-30T16:37:20.975839Z", + "shell.execute_reply": "2024-07-30T16:37:20.975373Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.451531Z", - "iopub.status.busy": "2024-07-18T04:07:23.451185Z", - "iopub.status.idle": "2024-07-18T04:07:23.462992Z", - "shell.execute_reply": "2024-07-18T04:07:23.462568Z" + "iopub.execute_input": "2024-07-30T16:37:20.977983Z", + "iopub.status.busy": "2024-07-30T16:37:20.977647Z", + "iopub.status.idle": "2024-07-30T16:37:20.988855Z", + "shell.execute_reply": "2024-07-30T16:37:20.988422Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.465102Z", - "iopub.status.busy": "2024-07-18T04:07:23.464771Z", - "iopub.status.idle": "2024-07-18T04:07:23.699321Z", - "shell.execute_reply": "2024-07-18T04:07:23.698710Z" + "iopub.execute_input": "2024-07-30T16:37:20.990750Z", + "iopub.status.busy": "2024-07-30T16:37:20.990413Z", + "iopub.status.idle": "2024-07-30T16:37:21.236239Z", + "shell.execute_reply": "2024-07-30T16:37:21.235736Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.701468Z", - "iopub.status.busy": "2024-07-18T04:07:23.701288Z", - "iopub.status.idle": "2024-07-18T04:07:23.726925Z", - "shell.execute_reply": "2024-07-18T04:07:23.726344Z" + "iopub.execute_input": "2024-07-30T16:37:21.238707Z", + "iopub.status.busy": "2024-07-30T16:37:21.238345Z", + "iopub.status.idle": "2024-07-30T16:37:21.264617Z", + "shell.execute_reply": "2024-07-30T16:37:21.264131Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.729027Z", - "iopub.status.busy": "2024-07-18T04:07:23.728854Z", - "iopub.status.idle": "2024-07-18T04:07:25.797394Z", - "shell.execute_reply": "2024-07-18T04:07:25.796767Z" + "iopub.execute_input": "2024-07-30T16:37:21.266767Z", + "iopub.status.busy": "2024-07-30T16:37:21.266578Z", + "iopub.status.idle": "2024-07-30T16:37:23.611867Z", + "shell.execute_reply": "2024-07-30T16:37:23.611160Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:25.799776Z", - "iopub.status.busy": "2024-07-18T04:07:25.799466Z", - "iopub.status.idle": "2024-07-18T04:07:25.818394Z", - "shell.execute_reply": "2024-07-18T04:07:25.817813Z" + "iopub.execute_input": "2024-07-30T16:37:23.614599Z", + "iopub.status.busy": "2024-07-30T16:37:23.614210Z", + "iopub.status.idle": "2024-07-30T16:37:23.634028Z", + "shell.execute_reply": "2024-07-30T16:37:23.633465Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:25.820464Z", - "iopub.status.busy": "2024-07-18T04:07:25.820179Z", - "iopub.status.idle": "2024-07-18T04:07:27.394088Z", - "shell.execute_reply": "2024-07-18T04:07:27.393509Z" + "iopub.execute_input": "2024-07-30T16:37:23.636438Z", + "iopub.status.busy": "2024-07-30T16:37:23.635973Z", + "iopub.status.idle": "2024-07-30T16:37:25.305599Z", + "shell.execute_reply": "2024-07-30T16:37:25.304862Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.396818Z", - "iopub.status.busy": "2024-07-18T04:07:27.396136Z", - "iopub.status.idle": "2024-07-18T04:07:27.409817Z", - "shell.execute_reply": "2024-07-18T04:07:27.409253Z" + "iopub.execute_input": "2024-07-30T16:37:25.308828Z", + "iopub.status.busy": "2024-07-30T16:37:25.307885Z", + "iopub.status.idle": "2024-07-30T16:37:25.322222Z", + "shell.execute_reply": "2024-07-30T16:37:25.321727Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.411938Z", - "iopub.status.busy": "2024-07-18T04:07:27.411554Z", - "iopub.status.idle": "2024-07-18T04:07:27.487589Z", - "shell.execute_reply": "2024-07-18T04:07:27.486978Z" + "iopub.execute_input": "2024-07-30T16:37:25.324721Z", + "iopub.status.busy": "2024-07-30T16:37:25.324152Z", + "iopub.status.idle": "2024-07-30T16:37:25.419049Z", + "shell.execute_reply": "2024-07-30T16:37:25.418364Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.490046Z", - "iopub.status.busy": "2024-07-18T04:07:27.489661Z", - "iopub.status.idle": "2024-07-18T04:07:27.700426Z", - "shell.execute_reply": "2024-07-18T04:07:27.699861Z" + "iopub.execute_input": "2024-07-30T16:37:25.421430Z", + "iopub.status.busy": "2024-07-30T16:37:25.421173Z", + "iopub.status.idle": "2024-07-30T16:37:25.644270Z", + "shell.execute_reply": "2024-07-30T16:37:25.643645Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.702570Z", - "iopub.status.busy": "2024-07-18T04:07:27.702242Z", - "iopub.status.idle": "2024-07-18T04:07:27.719037Z", - "shell.execute_reply": "2024-07-18T04:07:27.718497Z" + "iopub.execute_input": "2024-07-30T16:37:25.646795Z", + "iopub.status.busy": "2024-07-30T16:37:25.646428Z", + "iopub.status.idle": "2024-07-30T16:37:25.665764Z", + "shell.execute_reply": "2024-07-30T16:37:25.665270Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.721046Z", - "iopub.status.busy": "2024-07-18T04:07:27.720735Z", - "iopub.status.idle": "2024-07-18T04:07:27.731534Z", - "shell.execute_reply": "2024-07-18T04:07:27.730958Z" + "iopub.execute_input": "2024-07-30T16:37:25.667885Z", + "iopub.status.busy": "2024-07-30T16:37:25.667692Z", + "iopub.status.idle": "2024-07-30T16:37:25.678270Z", + "shell.execute_reply": "2024-07-30T16:37:25.677775Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.733768Z", - "iopub.status.busy": "2024-07-18T04:07:27.733373Z", - "iopub.status.idle": "2024-07-18T04:07:27.825217Z", - "shell.execute_reply": "2024-07-18T04:07:27.824625Z" + "iopub.execute_input": "2024-07-30T16:37:25.680557Z", + "iopub.status.busy": "2024-07-30T16:37:25.680215Z", + "iopub.status.idle": "2024-07-30T16:37:25.783566Z", + "shell.execute_reply": "2024-07-30T16:37:25.782891Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.827744Z", - "iopub.status.busy": "2024-07-18T04:07:27.827350Z", - "iopub.status.idle": "2024-07-18T04:07:27.961216Z", - "shell.execute_reply": "2024-07-18T04:07:27.960600Z" + "iopub.execute_input": "2024-07-30T16:37:25.786394Z", + "iopub.status.busy": "2024-07-30T16:37:25.785963Z", + "iopub.status.idle": "2024-07-30T16:37:25.944890Z", + "shell.execute_reply": "2024-07-30T16:37:25.944224Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.963793Z", - "iopub.status.busy": "2024-07-18T04:07:27.963318Z", - "iopub.status.idle": "2024-07-18T04:07:27.967349Z", - "shell.execute_reply": "2024-07-18T04:07:27.966769Z" + "iopub.execute_input": "2024-07-30T16:37:25.947223Z", + "iopub.status.busy": "2024-07-30T16:37:25.947014Z", + "iopub.status.idle": "2024-07-30T16:37:25.951228Z", + "shell.execute_reply": "2024-07-30T16:37:25.950663Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.969456Z", - "iopub.status.busy": "2024-07-18T04:07:27.969185Z", - "iopub.status.idle": "2024-07-18T04:07:27.972925Z", - "shell.execute_reply": "2024-07-18T04:07:27.972378Z" + "iopub.execute_input": "2024-07-30T16:37:25.953418Z", + "iopub.status.busy": "2024-07-30T16:37:25.953075Z", + "iopub.status.idle": "2024-07-30T16:37:25.957102Z", + "shell.execute_reply": "2024-07-30T16:37:25.956520Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.974780Z", - "iopub.status.busy": "2024-07-18T04:07:27.974604Z", - "iopub.status.idle": "2024-07-18T04:07:28.011316Z", - "shell.execute_reply": "2024-07-18T04:07:28.010835Z" + "iopub.execute_input": "2024-07-30T16:37:25.959055Z", + "iopub.status.busy": "2024-07-30T16:37:25.958872Z", + "iopub.status.idle": "2024-07-30T16:37:25.996394Z", + "shell.execute_reply": "2024-07-30T16:37:25.995898Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.013167Z", - "iopub.status.busy": "2024-07-18T04:07:28.012994Z", - "iopub.status.idle": "2024-07-18T04:07:28.054350Z", - "shell.execute_reply": "2024-07-18T04:07:28.053903Z" + "iopub.execute_input": "2024-07-30T16:37:25.998305Z", + "iopub.status.busy": "2024-07-30T16:37:25.998128Z", + "iopub.status.idle": "2024-07-30T16:37:26.039427Z", + "shell.execute_reply": "2024-07-30T16:37:26.038868Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.056361Z", - "iopub.status.busy": "2024-07-18T04:07:28.056029Z", - "iopub.status.idle": "2024-07-18T04:07:28.151883Z", - "shell.execute_reply": "2024-07-18T04:07:28.151289Z" + "iopub.execute_input": "2024-07-30T16:37:26.041607Z", + "iopub.status.busy": "2024-07-30T16:37:26.041417Z", + "iopub.status.idle": "2024-07-30T16:37:26.162225Z", + "shell.execute_reply": "2024-07-30T16:37:26.161548Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.154465Z", - "iopub.status.busy": "2024-07-18T04:07:28.154094Z", - "iopub.status.idle": "2024-07-18T04:07:28.255502Z", - "shell.execute_reply": "2024-07-18T04:07:28.254823Z" + "iopub.execute_input": "2024-07-30T16:37:26.165084Z", + "iopub.status.busy": "2024-07-30T16:37:26.164618Z", + "iopub.status.idle": "2024-07-30T16:37:26.285845Z", + "shell.execute_reply": "2024-07-30T16:37:26.285184Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.258165Z", - "iopub.status.busy": "2024-07-18T04:07:28.257804Z", - "iopub.status.idle": "2024-07-18T04:07:28.470060Z", - "shell.execute_reply": "2024-07-18T04:07:28.469555Z" + "iopub.execute_input": "2024-07-30T16:37:26.288464Z", + "iopub.status.busy": "2024-07-30T16:37:26.288093Z", + "iopub.status.idle": "2024-07-30T16:37:26.502063Z", + "shell.execute_reply": "2024-07-30T16:37:26.501416Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.472345Z", - "iopub.status.busy": "2024-07-18T04:07:28.471991Z", - "iopub.status.idle": "2024-07-18T04:07:28.679158Z", - "shell.execute_reply": "2024-07-18T04:07:28.678514Z" + "iopub.execute_input": "2024-07-30T16:37:26.504441Z", + "iopub.status.busy": "2024-07-30T16:37:26.503981Z", + "iopub.status.idle": "2024-07-30T16:37:26.744760Z", + "shell.execute_reply": "2024-07-30T16:37:26.744174Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.681512Z", - "iopub.status.busy": "2024-07-18T04:07:28.681268Z", - "iopub.status.idle": "2024-07-18T04:07:28.687496Z", - "shell.execute_reply": "2024-07-18T04:07:28.686962Z" + "iopub.execute_input": "2024-07-30T16:37:26.747291Z", + "iopub.status.busy": "2024-07-30T16:37:26.746891Z", + "iopub.status.idle": "2024-07-30T16:37:26.752870Z", + "shell.execute_reply": "2024-07-30T16:37:26.752415Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.689401Z", - "iopub.status.busy": "2024-07-18T04:07:28.689228Z", - "iopub.status.idle": "2024-07-18T04:07:28.907545Z", - "shell.execute_reply": "2024-07-18T04:07:28.907027Z" + "iopub.execute_input": "2024-07-30T16:37:26.754943Z", + "iopub.status.busy": "2024-07-30T16:37:26.754598Z", + "iopub.status.idle": "2024-07-30T16:37:26.972039Z", + "shell.execute_reply": "2024-07-30T16:37:26.971400Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.909667Z", - "iopub.status.busy": "2024-07-18T04:07:28.909321Z", - "iopub.status.idle": "2024-07-18T04:07:29.967572Z", - "shell.execute_reply": "2024-07-18T04:07:29.967037Z" + "iopub.execute_input": "2024-07-30T16:37:26.974261Z", + "iopub.status.busy": "2024-07-30T16:37:26.974064Z", + "iopub.status.idle": "2024-07-30T16:37:28.066231Z", + "shell.execute_reply": "2024-07-30T16:37:28.065649Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 04b78b7b1..fd2c70404 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": "2024-07-18T04:07:34.296538Z", - "iopub.status.busy": "2024-07-18T04:07:34.296363Z", - "iopub.status.idle": "2024-07-18T04:07:35.422497Z", - "shell.execute_reply": "2024-07-18T04:07:35.421862Z" + "iopub.execute_input": "2024-07-30T16:37:32.718320Z", + "iopub.status.busy": "2024-07-30T16:37:32.718143Z", + "iopub.status.idle": "2024-07-30T16:37:34.160547Z", + "shell.execute_reply": "2024-07-30T16:37:34.159900Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:35.425676Z", - "iopub.status.busy": "2024-07-18T04:07:35.425102Z", - "iopub.status.idle": "2024-07-18T04:07:35.428437Z", - "shell.execute_reply": "2024-07-18T04:07:35.427876Z" + "iopub.execute_input": "2024-07-30T16:37:34.163333Z", + "iopub.status.busy": "2024-07-30T16:37:34.163023Z", + "iopub.status.idle": "2024-07-30T16:37:34.166128Z", + "shell.execute_reply": "2024-07-30T16:37:34.165659Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.430702Z", - "iopub.status.busy": "2024-07-18T04:07:35.430517Z", - "iopub.status.idle": "2024-07-18T04:07:35.438434Z", - "shell.execute_reply": "2024-07-18T04:07:35.437884Z" + "iopub.execute_input": "2024-07-30T16:37:34.168192Z", + "iopub.status.busy": "2024-07-30T16:37:34.168013Z", + "iopub.status.idle": "2024-07-30T16:37:34.175994Z", + "shell.execute_reply": "2024-07-30T16:37:34.175519Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.440572Z", - "iopub.status.busy": "2024-07-18T04:07:35.440110Z", - "iopub.status.idle": "2024-07-18T04:07:35.486384Z", - "shell.execute_reply": "2024-07-18T04:07:35.485828Z" + "iopub.execute_input": "2024-07-30T16:37:34.178122Z", + "iopub.status.busy": "2024-07-30T16:37:34.177688Z", + "iopub.status.idle": "2024-07-30T16:37:34.225806Z", + "shell.execute_reply": "2024-07-30T16:37:34.225161Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.488599Z", - "iopub.status.busy": "2024-07-18T04:07:35.488257Z", - "iopub.status.idle": "2024-07-18T04:07:35.504681Z", - "shell.execute_reply": "2024-07-18T04:07:35.504225Z" + "iopub.execute_input": "2024-07-30T16:37:34.228546Z", + "iopub.status.busy": "2024-07-30T16:37:34.228175Z", + "iopub.status.idle": "2024-07-30T16:37:34.246251Z", + "shell.execute_reply": "2024-07-30T16:37:34.245703Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.506559Z", - "iopub.status.busy": "2024-07-18T04:07:35.506378Z", - "iopub.status.idle": "2024-07-18T04:07:35.510269Z", - "shell.execute_reply": "2024-07-18T04:07:35.509744Z" + "iopub.execute_input": "2024-07-30T16:37:34.248429Z", + "iopub.status.busy": "2024-07-30T16:37:34.248067Z", + "iopub.status.idle": "2024-07-30T16:37:34.251958Z", + "shell.execute_reply": "2024-07-30T16:37:34.251523Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.512387Z", - "iopub.status.busy": "2024-07-18T04:07:35.511994Z", - "iopub.status.idle": "2024-07-18T04:07:35.528137Z", - "shell.execute_reply": "2024-07-18T04:07:35.527604Z" + "iopub.execute_input": "2024-07-30T16:37:34.254230Z", + "iopub.status.busy": "2024-07-30T16:37:34.253755Z", + "iopub.status.idle": "2024-07-30T16:37:34.270486Z", + "shell.execute_reply": "2024-07-30T16:37:34.269879Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.530221Z", - "iopub.status.busy": "2024-07-18T04:07:35.529800Z", - "iopub.status.idle": "2024-07-18T04:07:35.555407Z", - "shell.execute_reply": "2024-07-18T04:07:35.554844Z" + "iopub.execute_input": "2024-07-30T16:37:34.272682Z", + "iopub.status.busy": "2024-07-30T16:37:34.272503Z", + "iopub.status.idle": "2024-07-30T16:37:34.299362Z", + "shell.execute_reply": "2024-07-30T16:37:34.298706Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.557521Z", - "iopub.status.busy": "2024-07-18T04:07:35.557213Z", - "iopub.status.idle": "2024-07-18T04:07:37.509880Z", - "shell.execute_reply": "2024-07-18T04:07:37.509298Z" + "iopub.execute_input": "2024-07-30T16:37:34.302325Z", + "iopub.status.busy": "2024-07-30T16:37:34.301951Z", + "iopub.status.idle": "2024-07-30T16:37:36.536542Z", + "shell.execute_reply": "2024-07-30T16:37:36.535928Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.512502Z", - "iopub.status.busy": "2024-07-18T04:07:37.511961Z", - "iopub.status.idle": "2024-07-18T04:07:37.518638Z", - "shell.execute_reply": "2024-07-18T04:07:37.518085Z" + "iopub.execute_input": "2024-07-30T16:37:36.540387Z", + "iopub.status.busy": "2024-07-30T16:37:36.538845Z", + "iopub.status.idle": "2024-07-30T16:37:36.547424Z", + "shell.execute_reply": "2024-07-30T16:37:36.546819Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.520639Z", - "iopub.status.busy": "2024-07-18T04:07:37.520331Z", - "iopub.status.idle": "2024-07-18T04:07:37.532904Z", - "shell.execute_reply": "2024-07-18T04:07:37.532451Z" + "iopub.execute_input": "2024-07-30T16:37:36.549619Z", + "iopub.status.busy": "2024-07-30T16:37:36.549270Z", + "iopub.status.idle": "2024-07-30T16:37:36.562222Z", + "shell.execute_reply": "2024-07-30T16:37:36.561697Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.534769Z", - "iopub.status.busy": "2024-07-18T04:07:37.534595Z", - "iopub.status.idle": "2024-07-18T04:07:37.541067Z", - "shell.execute_reply": "2024-07-18T04:07:37.540607Z" + "iopub.execute_input": "2024-07-30T16:37:36.564431Z", + "iopub.status.busy": "2024-07-30T16:37:36.564072Z", + "iopub.status.idle": "2024-07-30T16:37:36.570665Z", + "shell.execute_reply": "2024-07-30T16:37:36.570168Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.543185Z", - "iopub.status.busy": "2024-07-18T04:07:37.542911Z", - "iopub.status.idle": "2024-07-18T04:07:37.545708Z", - "shell.execute_reply": "2024-07-18T04:07:37.545133Z" + "iopub.execute_input": "2024-07-30T16:37:36.572817Z", + "iopub.status.busy": "2024-07-30T16:37:36.572406Z", + "iopub.status.idle": "2024-07-30T16:37:36.575372Z", + "shell.execute_reply": "2024-07-30T16:37:36.574796Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.547886Z", - "iopub.status.busy": "2024-07-18T04:07:37.547445Z", - "iopub.status.idle": "2024-07-18T04:07:37.550823Z", - "shell.execute_reply": "2024-07-18T04:07:37.550384Z" + "iopub.execute_input": "2024-07-30T16:37:36.577427Z", + "iopub.status.busy": "2024-07-30T16:37:36.577104Z", + "iopub.status.idle": "2024-07-30T16:37:36.580747Z", + "shell.execute_reply": "2024-07-30T16:37:36.580200Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.552820Z", - "iopub.status.busy": "2024-07-18T04:07:37.552645Z", - "iopub.status.idle": "2024-07-18T04:07:37.555115Z", - "shell.execute_reply": "2024-07-18T04:07:37.554670Z" + "iopub.execute_input": "2024-07-30T16:37:36.582932Z", + "iopub.status.busy": "2024-07-30T16:37:36.582604Z", + "iopub.status.idle": "2024-07-30T16:37:36.585678Z", + "shell.execute_reply": "2024-07-30T16:37:36.585251Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.557056Z", - "iopub.status.busy": "2024-07-18T04:07:37.556883Z", - "iopub.status.idle": "2024-07-18T04:07:37.561041Z", - "shell.execute_reply": "2024-07-18T04:07:37.560482Z" + "iopub.execute_input": "2024-07-30T16:37:36.587663Z", + "iopub.status.busy": "2024-07-30T16:37:36.587336Z", + "iopub.status.idle": "2024-07-30T16:37:36.591506Z", + "shell.execute_reply": "2024-07-30T16:37:36.590945Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.563256Z", - "iopub.status.busy": "2024-07-18T04:07:37.562843Z", - "iopub.status.idle": "2024-07-18T04:07:37.591846Z", - "shell.execute_reply": "2024-07-18T04:07:37.591369Z" + "iopub.execute_input": "2024-07-30T16:37:36.593587Z", + "iopub.status.busy": "2024-07-30T16:37:36.593411Z", + "iopub.status.idle": "2024-07-30T16:37:36.622081Z", + "shell.execute_reply": "2024-07-30T16:37:36.621614Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.593815Z", - "iopub.status.busy": "2024-07-18T04:07:37.593607Z", - "iopub.status.idle": "2024-07-18T04:07:37.598269Z", - "shell.execute_reply": "2024-07-18T04:07:37.597815Z" + "iopub.execute_input": "2024-07-30T16:37:36.624325Z", + "iopub.status.busy": "2024-07-30T16:37:36.623993Z", + "iopub.status.idle": "2024-07-30T16:37:36.628891Z", + "shell.execute_reply": "2024-07-30T16:37:36.628307Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 2d0d39b6e..85921d220 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": "2024-07-18T04:07:40.549664Z", - "iopub.status.busy": "2024-07-18T04:07:40.549497Z", - "iopub.status.idle": "2024-07-18T04:07:41.730292Z", - "shell.execute_reply": "2024-07-18T04:07:41.729739Z" + "iopub.execute_input": "2024-07-30T16:37:39.759530Z", + "iopub.status.busy": "2024-07-30T16:37:39.759170Z", + "iopub.status.idle": "2024-07-30T16:37:41.225938Z", + "shell.execute_reply": "2024-07-30T16:37:41.225361Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:41.732831Z", - "iopub.status.busy": "2024-07-18T04:07:41.732430Z", - "iopub.status.idle": "2024-07-18T04:07:41.924995Z", - "shell.execute_reply": "2024-07-18T04:07:41.924470Z" + "iopub.execute_input": "2024-07-30T16:37:41.228688Z", + "iopub.status.busy": "2024-07-30T16:37:41.228204Z", + "iopub.status.idle": "2024-07-30T16:37:41.249656Z", + "shell.execute_reply": "2024-07-30T16:37:41.249163Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:41.927419Z", - "iopub.status.busy": "2024-07-18T04:07:41.927009Z", - "iopub.status.idle": "2024-07-18T04:07:41.940256Z", - "shell.execute_reply": "2024-07-18T04:07:41.939813Z" + "iopub.execute_input": "2024-07-30T16:37:41.252375Z", + "iopub.status.busy": "2024-07-30T16:37:41.251838Z", + "iopub.status.idle": "2024-07-30T16:37:41.265158Z", + "shell.execute_reply": "2024-07-30T16:37:41.264726Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:41.942329Z", - "iopub.status.busy": "2024-07-18T04:07:41.942007Z", - "iopub.status.idle": "2024-07-18T04:07:44.554754Z", - "shell.execute_reply": "2024-07-18T04:07:44.554282Z" + "iopub.execute_input": "2024-07-30T16:37:41.267369Z", + "iopub.status.busy": "2024-07-30T16:37:41.266961Z", + "iopub.status.idle": "2024-07-30T16:37:43.948010Z", + "shell.execute_reply": "2024-07-30T16:37:43.947423Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:44.557154Z", - "iopub.status.busy": "2024-07-18T04:07:44.556790Z", - "iopub.status.idle": "2024-07-18T04:07:45.887262Z", - "shell.execute_reply": "2024-07-18T04:07:45.886610Z" + "iopub.execute_input": "2024-07-30T16:37:43.950421Z", + "iopub.status.busy": "2024-07-30T16:37:43.950035Z", + "iopub.status.idle": "2024-07-30T16:37:45.317858Z", + "shell.execute_reply": "2024-07-30T16:37:45.317234Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:45.889687Z", - "iopub.status.busy": "2024-07-18T04:07:45.889483Z", - "iopub.status.idle": "2024-07-18T04:07:45.893592Z", - "shell.execute_reply": "2024-07-18T04:07:45.893123Z" + "iopub.execute_input": "2024-07-30T16:37:45.320689Z", + "iopub.status.busy": "2024-07-30T16:37:45.320261Z", + "iopub.status.idle": "2024-07-30T16:37:45.325116Z", + "shell.execute_reply": "2024-07-30T16:37:45.324609Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:45.895685Z", - "iopub.status.busy": "2024-07-18T04:07:45.895358Z", - "iopub.status.idle": "2024-07-18T04:07:47.938953Z", - "shell.execute_reply": "2024-07-18T04:07:47.938261Z" + "iopub.execute_input": "2024-07-30T16:37:45.327504Z", + "iopub.status.busy": "2024-07-30T16:37:45.327099Z", + "iopub.status.idle": "2024-07-30T16:37:47.549771Z", + "shell.execute_reply": "2024-07-30T16:37:47.549091Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:47.941361Z", - "iopub.status.busy": "2024-07-18T04:07:47.941011Z", - "iopub.status.idle": "2024-07-18T04:07:47.949262Z", - "shell.execute_reply": "2024-07-18T04:07:47.948769Z" + "iopub.execute_input": "2024-07-30T16:37:47.552479Z", + "iopub.status.busy": "2024-07-30T16:37:47.551972Z", + "iopub.status.idle": "2024-07-30T16:37:47.560612Z", + "shell.execute_reply": "2024-07-30T16:37:47.560120Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:47.951205Z", - "iopub.status.busy": "2024-07-18T04:07:47.950934Z", - "iopub.status.idle": "2024-07-18T04:07:50.493764Z", - "shell.execute_reply": "2024-07-18T04:07:50.493249Z" + "iopub.execute_input": "2024-07-30T16:37:47.562648Z", + "iopub.status.busy": "2024-07-30T16:37:47.562368Z", + "iopub.status.idle": "2024-07-30T16:37:50.183671Z", + "shell.execute_reply": "2024-07-30T16:37:50.183000Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.495904Z", - "iopub.status.busy": "2024-07-18T04:07:50.495718Z", - "iopub.status.idle": "2024-07-18T04:07:50.499462Z", - "shell.execute_reply": "2024-07-18T04:07:50.498995Z" + "iopub.execute_input": "2024-07-30T16:37:50.186114Z", + "iopub.status.busy": "2024-07-30T16:37:50.185699Z", + "iopub.status.idle": "2024-07-30T16:37:50.189636Z", + "shell.execute_reply": "2024-07-30T16:37:50.189140Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.501330Z", - "iopub.status.busy": "2024-07-18T04:07:50.501162Z", - "iopub.status.idle": "2024-07-18T04:07:50.504431Z", - "shell.execute_reply": "2024-07-18T04:07:50.503994Z" + "iopub.execute_input": "2024-07-30T16:37:50.191867Z", + "iopub.status.busy": "2024-07-30T16:37:50.191497Z", + "iopub.status.idle": "2024-07-30T16:37:50.195269Z", + "shell.execute_reply": "2024-07-30T16:37:50.194765Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.506301Z", - "iopub.status.busy": "2024-07-18T04:07:50.506126Z", - "iopub.status.idle": "2024-07-18T04:07:50.509100Z", - "shell.execute_reply": "2024-07-18T04:07:50.508661Z" + "iopub.execute_input": "2024-07-30T16:37:50.197502Z", + "iopub.status.busy": "2024-07-30T16:37:50.197113Z", + "iopub.status.idle": "2024-07-30T16:37:50.200387Z", + "shell.execute_reply": "2024-07-30T16:37:50.199867Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index c73a5e42a..b908d214b 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": "2024-07-18T04:07:53.027507Z", - "iopub.status.busy": "2024-07-18T04:07:53.027336Z", - "iopub.status.idle": "2024-07-18T04:07:54.203836Z", - "shell.execute_reply": "2024-07-18T04:07:54.203291Z" + "iopub.execute_input": "2024-07-30T16:37:52.967876Z", + "iopub.status.busy": "2024-07-30T16:37:52.967697Z", + "iopub.status.idle": "2024-07-30T16:37:54.423334Z", + "shell.execute_reply": "2024-07-30T16:37:54.422716Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:54.206499Z", - "iopub.status.busy": "2024-07-18T04:07:54.205989Z", - "iopub.status.idle": "2024-07-18T04:07:56.950673Z", - "shell.execute_reply": "2024-07-18T04:07:56.949957Z" + "iopub.execute_input": "2024-07-30T16:37:54.426120Z", + "iopub.status.busy": "2024-07-30T16:37:54.425563Z", + "iopub.status.idle": "2024-07-30T16:37:55.812519Z", + "shell.execute_reply": "2024-07-30T16:37:55.811700Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.953257Z", - "iopub.status.busy": "2024-07-18T04:07:56.953039Z", - "iopub.status.idle": "2024-07-18T04:07:56.956605Z", - "shell.execute_reply": "2024-07-18T04:07:56.956021Z" + "iopub.execute_input": "2024-07-30T16:37:55.815457Z", + "iopub.status.busy": "2024-07-30T16:37:55.815048Z", + "iopub.status.idle": "2024-07-30T16:37:55.818533Z", + "shell.execute_reply": "2024-07-30T16:37:55.817973Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.958812Z", - "iopub.status.busy": "2024-07-18T04:07:56.958471Z", - "iopub.status.idle": "2024-07-18T04:07:56.965097Z", - "shell.execute_reply": "2024-07-18T04:07:56.964666Z" + "iopub.execute_input": "2024-07-30T16:37:55.820681Z", + "iopub.status.busy": "2024-07-30T16:37:55.820334Z", + "iopub.status.idle": "2024-07-30T16:37:55.827147Z", + "shell.execute_reply": "2024-07-30T16:37:55.826691Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.967310Z", - "iopub.status.busy": "2024-07-18T04:07:56.966946Z", - "iopub.status.idle": "2024-07-18T04:07:57.458211Z", - "shell.execute_reply": "2024-07-18T04:07:57.457607Z" + "iopub.execute_input": "2024-07-30T16:37:55.829268Z", + "iopub.status.busy": "2024-07-30T16:37:55.828920Z", + "iopub.status.idle": "2024-07-30T16:37:56.150888Z", + "shell.execute_reply": "2024-07-30T16:37:56.150240Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.460514Z", - "iopub.status.busy": "2024-07-18T04:07:57.460329Z", - "iopub.status.idle": "2024-07-18T04:07:57.465713Z", - "shell.execute_reply": "2024-07-18T04:07:57.465150Z" + "iopub.execute_input": "2024-07-30T16:37:56.154023Z", + "iopub.status.busy": "2024-07-30T16:37:56.153563Z", + "iopub.status.idle": "2024-07-30T16:37:56.159171Z", + "shell.execute_reply": "2024-07-30T16:37:56.158713Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.467728Z", - "iopub.status.busy": "2024-07-18T04:07:57.467432Z", - "iopub.status.idle": "2024-07-18T04:07:57.471282Z", - "shell.execute_reply": "2024-07-18T04:07:57.470724Z" + "iopub.execute_input": "2024-07-30T16:37:56.161294Z", + "iopub.status.busy": "2024-07-30T16:37:56.160941Z", + "iopub.status.idle": "2024-07-30T16:37:56.164954Z", + "shell.execute_reply": "2024-07-30T16:37:56.164403Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.473417Z", - "iopub.status.busy": "2024-07-18T04:07:57.473021Z", - "iopub.status.idle": "2024-07-18T04:07:58.320437Z", - "shell.execute_reply": "2024-07-18T04:07:58.319767Z" + "iopub.execute_input": "2024-07-30T16:37:56.166946Z", + "iopub.status.busy": "2024-07-30T16:37:56.166762Z", + "iopub.status.idle": "2024-07-30T16:37:57.061837Z", + "shell.execute_reply": "2024-07-30T16:37:57.061214Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.322734Z", - "iopub.status.busy": "2024-07-18T04:07:58.322537Z", - "iopub.status.idle": "2024-07-18T04:07:58.596192Z", - "shell.execute_reply": "2024-07-18T04:07:58.595728Z" + "iopub.execute_input": "2024-07-30T16:37:57.064231Z", + "iopub.status.busy": "2024-07-30T16:37:57.064020Z", + "iopub.status.idle": "2024-07-30T16:37:57.269680Z", + "shell.execute_reply": "2024-07-30T16:37:57.269071Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.598116Z", - "iopub.status.busy": "2024-07-18T04:07:58.597937Z", - "iopub.status.idle": "2024-07-18T04:07:58.602256Z", - "shell.execute_reply": "2024-07-18T04:07:58.601801Z" + "iopub.execute_input": "2024-07-30T16:37:57.271779Z", + "iopub.status.busy": "2024-07-30T16:37:57.271589Z", + "iopub.status.idle": "2024-07-30T16:37:57.276069Z", + "shell.execute_reply": "2024-07-30T16:37:57.275620Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.604102Z", - "iopub.status.busy": "2024-07-18T04:07:58.603930Z", - "iopub.status.idle": "2024-07-18T04:07:59.051714Z", - "shell.execute_reply": "2024-07-18T04:07:59.051138Z" + "iopub.execute_input": "2024-07-30T16:37:57.277956Z", + "iopub.status.busy": "2024-07-30T16:37:57.277779Z", + "iopub.status.idle": "2024-07-30T16:37:57.741717Z", + "shell.execute_reply": "2024-07-30T16:37:57.741080Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.054893Z", - "iopub.status.busy": "2024-07-18T04:07:59.054683Z", - "iopub.status.idle": "2024-07-18T04:07:59.362985Z", - "shell.execute_reply": "2024-07-18T04:07:59.362380Z" + "iopub.execute_input": "2024-07-30T16:37:57.744943Z", + "iopub.status.busy": "2024-07-30T16:37:57.744706Z", + "iopub.status.idle": "2024-07-30T16:37:58.080727Z", + "shell.execute_reply": "2024-07-30T16:37:58.080133Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.365227Z", - "iopub.status.busy": "2024-07-18T04:07:59.364819Z", - "iopub.status.idle": "2024-07-18T04:07:59.721287Z", - "shell.execute_reply": "2024-07-18T04:07:59.720689Z" + "iopub.execute_input": "2024-07-30T16:37:58.083717Z", + "iopub.status.busy": "2024-07-30T16:37:58.083475Z", + "iopub.status.idle": "2024-07-30T16:37:58.448789Z", + "shell.execute_reply": "2024-07-30T16:37:58.448130Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.723711Z", - "iopub.status.busy": "2024-07-18T04:07:59.723524Z", - "iopub.status.idle": "2024-07-18T04:08:00.129832Z", - "shell.execute_reply": "2024-07-18T04:08:00.129234Z" + "iopub.execute_input": "2024-07-30T16:37:58.451979Z", + "iopub.status.busy": "2024-07-30T16:37:58.451737Z", + "iopub.status.idle": "2024-07-30T16:37:58.897902Z", + "shell.execute_reply": "2024-07-30T16:37:58.897266Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.134389Z", - "iopub.status.busy": "2024-07-18T04:08:00.134195Z", - "iopub.status.idle": "2024-07-18T04:08:00.555058Z", - "shell.execute_reply": "2024-07-18T04:08:00.554472Z" + "iopub.execute_input": "2024-07-30T16:37:58.902565Z", + "iopub.status.busy": "2024-07-30T16:37:58.902206Z", + "iopub.status.idle": "2024-07-30T16:37:59.331261Z", + "shell.execute_reply": "2024-07-30T16:37:59.330642Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.557791Z", - "iopub.status.busy": "2024-07-18T04:08:00.557601Z", - "iopub.status.idle": "2024-07-18T04:08:00.745423Z", - "shell.execute_reply": "2024-07-18T04:08:00.744861Z" + "iopub.execute_input": "2024-07-30T16:37:59.334413Z", + "iopub.status.busy": "2024-07-30T16:37:59.334051Z", + "iopub.status.idle": "2024-07-30T16:37:59.529024Z", + "shell.execute_reply": "2024-07-30T16:37:59.528352Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.747619Z", - "iopub.status.busy": "2024-07-18T04:08:00.747438Z", - "iopub.status.idle": "2024-07-18T04:08:00.951881Z", - "shell.execute_reply": "2024-07-18T04:08:00.951263Z" + "iopub.execute_input": "2024-07-30T16:37:59.531621Z", + "iopub.status.busy": "2024-07-30T16:37:59.531147Z", + "iopub.status.idle": "2024-07-30T16:37:59.713867Z", + "shell.execute_reply": "2024-07-30T16:37:59.713268Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.954083Z", - "iopub.status.busy": "2024-07-18T04:08:00.953900Z", - "iopub.status.idle": "2024-07-18T04:08:00.956913Z", - "shell.execute_reply": "2024-07-18T04:08:00.956455Z" + "iopub.execute_input": "2024-07-30T16:37:59.716604Z", + "iopub.status.busy": "2024-07-30T16:37:59.716372Z", + "iopub.status.idle": "2024-07-30T16:37:59.719701Z", + "shell.execute_reply": "2024-07-30T16:37:59.719240Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.958720Z", - "iopub.status.busy": "2024-07-18T04:08:00.958549Z", - "iopub.status.idle": "2024-07-18T04:08:01.900993Z", - "shell.execute_reply": "2024-07-18T04:08:01.900441Z" + "iopub.execute_input": "2024-07-30T16:37:59.721469Z", + "iopub.status.busy": "2024-07-30T16:37:59.721297Z", + "iopub.status.idle": "2024-07-30T16:38:00.653952Z", + "shell.execute_reply": "2024-07-30T16:38:00.653313Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:01.903897Z", - "iopub.status.busy": "2024-07-18T04:08:01.903502Z", - "iopub.status.idle": "2024-07-18T04:08:02.024595Z", - "shell.execute_reply": "2024-07-18T04:08:02.024142Z" + "iopub.execute_input": "2024-07-30T16:38:00.656659Z", + "iopub.status.busy": "2024-07-30T16:38:00.656200Z", + "iopub.status.idle": "2024-07-30T16:38:00.806657Z", + "shell.execute_reply": "2024-07-30T16:38:00.806013Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.026835Z", - "iopub.status.busy": "2024-07-18T04:08:02.026498Z", - "iopub.status.idle": "2024-07-18T04:08:02.149086Z", - "shell.execute_reply": "2024-07-18T04:08:02.148602Z" + "iopub.execute_input": "2024-07-30T16:38:00.809105Z", + "iopub.status.busy": "2024-07-30T16:38:00.808873Z", + "iopub.status.idle": "2024-07-30T16:38:01.017879Z", + "shell.execute_reply": "2024-07-30T16:38:01.017223Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.151149Z", - "iopub.status.busy": "2024-07-18T04:08:02.150799Z", - "iopub.status.idle": "2024-07-18T04:08:02.892035Z", - "shell.execute_reply": "2024-07-18T04:08:02.891450Z" + "iopub.execute_input": "2024-07-30T16:38:01.020087Z", + "iopub.status.busy": "2024-07-30T16:38:01.019752Z", + "iopub.status.idle": "2024-07-30T16:38:01.734744Z", + "shell.execute_reply": "2024-07-30T16:38:01.734246Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.894338Z", - "iopub.status.busy": "2024-07-18T04:08:02.894143Z", - "iopub.status.idle": "2024-07-18T04:08:02.898103Z", - "shell.execute_reply": "2024-07-18T04:08:02.897554Z" + "iopub.execute_input": "2024-07-30T16:38:01.737160Z", + "iopub.status.busy": "2024-07-30T16:38:01.736731Z", + "iopub.status.idle": "2024-07-30T16:38:01.740592Z", + "shell.execute_reply": "2024-07-30T16:38:01.740039Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index db08980fc..3df92007c 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": "2024-07-18T04:08:05.194442Z", - "iopub.status.busy": "2024-07-18T04:08:05.194273Z", - "iopub.status.idle": "2024-07-18T04:08:07.990167Z", - "shell.execute_reply": "2024-07-18T04:08:07.989536Z" + "iopub.execute_input": "2024-07-30T16:38:03.978289Z", + "iopub.status.busy": "2024-07-30T16:38:03.977787Z", + "iopub.status.idle": "2024-07-30T16:38:07.296478Z", + "shell.execute_reply": "2024-07-30T16:38:07.295899Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:08:07.993089Z", - "iopub.status.busy": "2024-07-18T04:08:07.992487Z", - "iopub.status.idle": "2024-07-18T04:08:08.306676Z", - "shell.execute_reply": "2024-07-18T04:08:08.306053Z" + "iopub.execute_input": "2024-07-30T16:38:07.299136Z", + "iopub.status.busy": "2024-07-30T16:38:07.298701Z", + "iopub.status.idle": "2024-07-30T16:38:07.318355Z", + "shell.execute_reply": "2024-07-30T16:38:07.317750Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:08.309275Z", - "iopub.status.busy": "2024-07-18T04:08:08.308985Z", - "iopub.status.idle": "2024-07-18T04:08:08.313469Z", - "shell.execute_reply": "2024-07-18T04:08:08.312921Z" + "iopub.execute_input": "2024-07-30T16:38:07.320466Z", + "iopub.status.busy": "2024-07-30T16:38:07.320062Z", + "iopub.status.idle": "2024-07-30T16:38:07.324323Z", + "shell.execute_reply": "2024-07-30T16:38:07.323777Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:08.315749Z", - "iopub.status.busy": "2024-07-18T04:08:08.315344Z", - "iopub.status.idle": "2024-07-18T04:08:15.710803Z", - "shell.execute_reply": "2024-07-18T04:08:15.710242Z" + "iopub.execute_input": "2024-07-30T16:38:07.326455Z", + "iopub.status.busy": "2024-07-30T16:38:07.325959Z", + "iopub.status.idle": "2024-07-30T16:38:11.831429Z", + "shell.execute_reply": "2024-07-30T16:38:11.830839Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 65536/170498071 [00:00<05:54, 480174.66it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8226376.49it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-07-18T04:08:15.713264Z", - "iopub.status.busy": "2024-07-18T04:08:15.712838Z", - "iopub.status.idle": "2024-07-18T04:08:15.717532Z", - "shell.execute_reply": "2024-07-18T04:08:15.717089Z" + "iopub.execute_input": "2024-07-30T16:38:11.833918Z", + "iopub.status.busy": "2024-07-30T16:38:11.833462Z", + "iopub.status.idle": "2024-07-30T16:38:11.838434Z", + "shell.execute_reply": "2024-07-30T16:38:11.837866Z" }, "nbsphinx": "hidden" }, @@ -728,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:15.719428Z", - "iopub.status.busy": "2024-07-18T04:08:15.719259Z", - "iopub.status.idle": "2024-07-18T04:08:16.258630Z", - "shell.execute_reply": "2024-07-18T04:08:16.258056Z" + "iopub.execute_input": "2024-07-30T16:38:11.840562Z", + "iopub.status.busy": "2024-07-30T16:38:11.840251Z", + "iopub.status.idle": "2024-07-30T16:38:12.371586Z", + "shell.execute_reply": "2024-07-30T16:38:12.371033Z" } }, "outputs": [ @@ -764,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.260848Z", - "iopub.status.busy": "2024-07-18T04:08:16.260520Z", - "iopub.status.idle": "2024-07-18T04:08:16.767687Z", - "shell.execute_reply": "2024-07-18T04:08:16.767215Z" + "iopub.execute_input": "2024-07-30T16:38:12.373894Z", + "iopub.status.busy": "2024-07-30T16:38:12.373541Z", + "iopub.status.idle": "2024-07-30T16:38:12.887091Z", + "shell.execute_reply": "2024-07-30T16:38:12.886524Z" } }, "outputs": [ @@ -805,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.769863Z", - "iopub.status.busy": "2024-07-18T04:08:16.769508Z", - "iopub.status.idle": "2024-07-18T04:08:16.772822Z", - "shell.execute_reply": "2024-07-18T04:08:16.772376Z" + "iopub.execute_input": "2024-07-30T16:38:12.889299Z", + "iopub.status.busy": "2024-07-30T16:38:12.888937Z", + "iopub.status.idle": "2024-07-30T16:38:12.892536Z", + "shell.execute_reply": "2024-07-30T16:38:12.892076Z" } }, "outputs": [], @@ -831,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.774860Z", - "iopub.status.busy": "2024-07-18T04:08:16.774532Z", - "iopub.status.idle": "2024-07-18T04:08:29.229998Z", - "shell.execute_reply": "2024-07-18T04:08:29.229404Z" + "iopub.execute_input": "2024-07-30T16:38:12.894534Z", + "iopub.status.busy": "2024-07-30T16:38:12.894200Z", + "iopub.status.idle": "2024-07-30T16:38:25.488449Z", + "shell.execute_reply": "2024-07-30T16:38:25.487794Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "424409b5a3c248919a596aef89b959d3", + "model_id": "23a512869c5e4f05a2356b8f464b1bcc", "version_major": 2, "version_minor": 0 }, @@ -900,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:29.232469Z", - "iopub.status.busy": "2024-07-18T04:08:29.232033Z", - "iopub.status.idle": "2024-07-18T04:08:31.261531Z", - "shell.execute_reply": "2024-07-18T04:08:31.260905Z" + "iopub.execute_input": "2024-07-30T16:38:25.490754Z", + "iopub.status.busy": "2024-07-30T16:38:25.490545Z", + "iopub.status.idle": "2024-07-30T16:38:27.681301Z", + "shell.execute_reply": "2024-07-30T16:38:27.680552Z" } }, "outputs": [ @@ -947,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:31.264147Z", - "iopub.status.busy": "2024-07-18T04:08:31.263800Z", - "iopub.status.idle": "2024-07-18T04:08:31.486008Z", - "shell.execute_reply": "2024-07-18T04:08:31.485290Z" + "iopub.execute_input": "2024-07-30T16:38:27.684463Z", + "iopub.status.busy": "2024-07-30T16:38:27.683946Z", + "iopub.status.idle": "2024-07-30T16:38:27.951193Z", + "shell.execute_reply": "2024-07-30T16:38:27.950604Z" } }, "outputs": [ @@ -986,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:31.488468Z", - "iopub.status.busy": "2024-07-18T04:08:31.488018Z", - "iopub.status.idle": "2024-07-18T04:08:32.142236Z", - "shell.execute_reply": "2024-07-18T04:08:32.141607Z" + "iopub.execute_input": "2024-07-30T16:38:27.953780Z", + "iopub.status.busy": "2024-07-30T16:38:27.953567Z", + "iopub.status.idle": "2024-07-30T16:38:28.631392Z", + "shell.execute_reply": "2024-07-30T16:38:28.630768Z" } }, "outputs": [ @@ -1039,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.144771Z", - "iopub.status.busy": "2024-07-18T04:08:32.144585Z", - "iopub.status.idle": "2024-07-18T04:08:32.436148Z", - "shell.execute_reply": "2024-07-18T04:08:32.435672Z" + "iopub.execute_input": "2024-07-30T16:38:28.634456Z", + "iopub.status.busy": "2024-07-30T16:38:28.633952Z", + "iopub.status.idle": "2024-07-30T16:38:28.975662Z", + "shell.execute_reply": "2024-07-30T16:38:28.975098Z" } }, "outputs": [ @@ -1090,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.438256Z", - "iopub.status.busy": "2024-07-18T04:08:32.437904Z", - "iopub.status.idle": "2024-07-18T04:08:32.675912Z", - "shell.execute_reply": "2024-07-18T04:08:32.675301Z" + "iopub.execute_input": "2024-07-30T16:38:28.978011Z", + "iopub.status.busy": "2024-07-30T16:38:28.977574Z", + "iopub.status.idle": "2024-07-30T16:38:29.207618Z", + "shell.execute_reply": "2024-07-30T16:38:29.206996Z" } }, "outputs": [ @@ -1149,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.678608Z", - "iopub.status.busy": "2024-07-18T04:08:32.678123Z", - "iopub.status.idle": "2024-07-18T04:08:32.775298Z", - "shell.execute_reply": "2024-07-18T04:08:32.774751Z" + "iopub.execute_input": "2024-07-30T16:38:29.209892Z", + "iopub.status.busy": "2024-07-30T16:38:29.209709Z", + "iopub.status.idle": "2024-07-30T16:38:29.298647Z", + "shell.execute_reply": "2024-07-30T16:38:29.297971Z" } }, "outputs": [], @@ -1173,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.778213Z", - "iopub.status.busy": "2024-07-18T04:08:32.777804Z", - "iopub.status.idle": "2024-07-18T04:08:43.316731Z", - "shell.execute_reply": "2024-07-18T04:08:43.316056Z" + "iopub.execute_input": "2024-07-30T16:38:29.301052Z", + "iopub.status.busy": "2024-07-30T16:38:29.300869Z", + "iopub.status.idle": "2024-07-30T16:38:39.931040Z", + "shell.execute_reply": "2024-07-30T16:38:39.930336Z" } }, "outputs": [ @@ -1213,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:43.319383Z", - "iopub.status.busy": "2024-07-18T04:08:43.319007Z", - "iopub.status.idle": "2024-07-18T04:08:45.511352Z", - "shell.execute_reply": "2024-07-18T04:08:45.510818Z" + "iopub.execute_input": "2024-07-30T16:38:39.933469Z", + "iopub.status.busy": "2024-07-30T16:38:39.933256Z", + "iopub.status.idle": "2024-07-30T16:38:42.292073Z", + "shell.execute_reply": "2024-07-30T16:38:42.291503Z" } }, "outputs": [ @@ -1247,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:45.513955Z", - "iopub.status.busy": "2024-07-18T04:08:45.513429Z", - "iopub.status.idle": "2024-07-18T04:08:45.711540Z", - "shell.execute_reply": "2024-07-18T04:08:45.711028Z" + "iopub.execute_input": "2024-07-30T16:38:42.295015Z", + "iopub.status.busy": "2024-07-30T16:38:42.294328Z", + "iopub.status.idle": "2024-07-30T16:38:42.501084Z", + "shell.execute_reply": "2024-07-30T16:38:42.500563Z" } }, "outputs": [], @@ -1264,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - 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"layout": "IPY_MODEL_5955528c2a2f4be686f4bb6106813bff", + "layout": "IPY_MODEL_f1fa8803defc478f8f1a9688f96d5a79", "tabbable": null, "tooltip": null } }, - "5955528c2a2f4be686f4bb6106813bff": { + "3094116b83c34a98b8ed5ce27da55168": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1466,7 +1230,71 @@ "width": null } }, - "62d287d26948474db68cc5ea75df7b81": { + "313c673f5ae140548d908be43be34294": { + "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_afe5f4fb71e54d6b97c4b44ecea40c54", + "placeholder": "​", + "style": "IPY_MODEL_59856c986feb4d3abc586fed584de5c0", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 274MB/s]" + } + }, + "58618ff0d8be415dbaa56326b7b1db8c": { + "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_3094116b83c34a98b8ed5ce27da55168", + "placeholder": "​", + "style": "IPY_MODEL_7105d5b497e34139b8cba14426fdd044", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "59856c986feb4d3abc586fed584de5c0": { + "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 + } + }, + "633d743f49c44f93be1bfe7c09cb76e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1482,17 +1310,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7d11920d1e3a4b0f923cc702ffaaadcd", + "layout": "IPY_MODEL_6eb5b857715449aaa4e92a9a9560a833", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_1ea943e162c94baba190703922291391", + "style": "IPY_MODEL_dae8ad4cd0df4444bf5ff766e8012dc4", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "7d11920d1e3a4b0f923cc702ffaaadcd": { + "6eb5b857715449aaa4e92a9a9560a833": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,7 +1373,25 @@ "width": null } }, - "83d24ca021e7439990012fc1cfae5ebc": { + "7105d5b497e34139b8cba14426fdd044": { + "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 + } + }, + "afe5f4fb71e54d6b97c4b44ecea40c54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1598,53 +1444,23 @@ "width": null } }, - "e35529740133431397715ff5e470a55e": { + "dae8ad4cd0df4444bf5ff766e8012dc4": { "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_f169574227104d7b94f6e1a96b5ff27b", - "placeholder": "​", - "style": "IPY_MODEL_13b75646e4ae45bb8738a8342199ed8d", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 305MB/s]" - } - }, - "eb3b2b87a32446c892767b6727cf7c96": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_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_83d24ca021e7439990012fc1cfae5ebc", - "placeholder": "​", - "style": "IPY_MODEL_26b0e201445143b6ad5363d6f61e02c4", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f169574227104d7b94f6e1a96b5ff27b": { + "f1fa8803defc478f8f1a9688f96d5a79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 28ed3a546..7c5c07d39 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": "2024-07-18T04:08:49.857100Z", - "iopub.status.busy": "2024-07-18T04:08:49.856925Z", - "iopub.status.idle": "2024-07-18T04:08:51.033463Z", - "shell.execute_reply": "2024-07-18T04:08:51.032826Z" + "iopub.execute_input": "2024-07-30T16:38:46.925237Z", + "iopub.status.busy": "2024-07-30T16:38:46.925067Z", + "iopub.status.idle": "2024-07-30T16:38:48.345531Z", + "shell.execute_reply": "2024-07-30T16:38:48.344960Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:08:51.036042Z", - "iopub.status.busy": "2024-07-18T04:08:51.035764Z", - "iopub.status.idle": "2024-07-18T04:08:51.053474Z", - "shell.execute_reply": "2024-07-18T04:08:51.052908Z" + "iopub.execute_input": "2024-07-30T16:38:48.348157Z", + "iopub.status.busy": "2024-07-30T16:38:48.347674Z", + "iopub.status.idle": "2024-07-30T16:38:48.365919Z", + "shell.execute_reply": "2024-07-30T16:38:48.365467Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.055718Z", - "iopub.status.busy": "2024-07-18T04:08:51.055333Z", - "iopub.status.idle": "2024-07-18T04:08:51.058387Z", - "shell.execute_reply": "2024-07-18T04:08:51.057846Z" + "iopub.execute_input": "2024-07-30T16:38:48.368246Z", + "iopub.status.busy": "2024-07-30T16:38:48.367803Z", + "iopub.status.idle": "2024-07-30T16:38:48.370780Z", + "shell.execute_reply": "2024-07-30T16:38:48.370332Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.060619Z", - "iopub.status.busy": "2024-07-18T04:08:51.060155Z", - "iopub.status.idle": "2024-07-18T04:08:51.265981Z", - "shell.execute_reply": "2024-07-18T04:08:51.265535Z" + "iopub.execute_input": "2024-07-30T16:38:48.372766Z", + "iopub.status.busy": "2024-07-30T16:38:48.372450Z", + "iopub.status.idle": "2024-07-30T16:38:48.468454Z", + "shell.execute_reply": "2024-07-30T16:38:48.467839Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.268127Z", - "iopub.status.busy": "2024-07-18T04:08:51.267788Z", - "iopub.status.idle": "2024-07-18T04:08:51.448821Z", - "shell.execute_reply": "2024-07-18T04:08:51.448311Z" + "iopub.execute_input": "2024-07-30T16:38:48.471122Z", + "iopub.status.busy": "2024-07-30T16:38:48.470653Z", + "iopub.status.idle": "2024-07-30T16:38:48.475521Z", + "shell.execute_reply": "2024-07-30T16:38:48.475049Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.450979Z", - "iopub.status.busy": "2024-07-18T04:08:51.450781Z", - "iopub.status.idle": "2024-07-18T04:08:51.660929Z", - "shell.execute_reply": "2024-07-18T04:08:51.660315Z" + "iopub.execute_input": "2024-07-30T16:38:48.477468Z", + "iopub.status.busy": "2024-07-30T16:38:48.477131Z", + "iopub.status.idle": "2024-07-30T16:38:48.720327Z", + "shell.execute_reply": "2024-07-30T16:38:48.719696Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.663179Z", - "iopub.status.busy": "2024-07-18T04:08:51.662774Z", - "iopub.status.idle": "2024-07-18T04:08:51.667262Z", - "shell.execute_reply": "2024-07-18T04:08:51.666686Z" + "iopub.execute_input": "2024-07-30T16:38:48.722633Z", + "iopub.status.busy": "2024-07-30T16:38:48.722278Z", + "iopub.status.idle": "2024-07-30T16:38:48.726622Z", + "shell.execute_reply": "2024-07-30T16:38:48.726163Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.669382Z", - "iopub.status.busy": "2024-07-18T04:08:51.669036Z", - "iopub.status.idle": "2024-07-18T04:08:51.674698Z", - "shell.execute_reply": "2024-07-18T04:08:51.674244Z" + "iopub.execute_input": "2024-07-30T16:38:48.728701Z", + "iopub.status.busy": "2024-07-30T16:38:48.728352Z", + "iopub.status.idle": "2024-07-30T16:38:48.734485Z", + "shell.execute_reply": "2024-07-30T16:38:48.734046Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.676720Z", - "iopub.status.busy": "2024-07-18T04:08:51.676391Z", - "iopub.status.idle": "2024-07-18T04:08:51.678866Z", - "shell.execute_reply": "2024-07-18T04:08:51.678427Z" + "iopub.execute_input": "2024-07-30T16:38:48.736597Z", + "iopub.status.busy": "2024-07-30T16:38:48.736263Z", + "iopub.status.idle": "2024-07-30T16:38:48.738985Z", + "shell.execute_reply": "2024-07-30T16:38:48.738429Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.680800Z", - "iopub.status.busy": "2024-07-18T04:08:51.680488Z", - "iopub.status.idle": "2024-07-18T04:09:00.539921Z", - "shell.execute_reply": "2024-07-18T04:09:00.539355Z" + "iopub.execute_input": "2024-07-30T16:38:48.741068Z", + "iopub.status.busy": "2024-07-30T16:38:48.740746Z", + "iopub.status.idle": "2024-07-30T16:38:57.890643Z", + "shell.execute_reply": "2024-07-30T16:38:57.890064Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.542544Z", - "iopub.status.busy": "2024-07-18T04:09:00.542172Z", - "iopub.status.idle": "2024-07-18T04:09:00.549395Z", - "shell.execute_reply": "2024-07-18T04:09:00.548943Z" + "iopub.execute_input": "2024-07-30T16:38:57.893640Z", + "iopub.status.busy": "2024-07-30T16:38:57.893011Z", + "iopub.status.idle": "2024-07-30T16:38:57.900759Z", + "shell.execute_reply": "2024-07-30T16:38:57.900288Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.551482Z", - "iopub.status.busy": "2024-07-18T04:09:00.551165Z", - "iopub.status.idle": "2024-07-18T04:09:00.554829Z", - "shell.execute_reply": "2024-07-18T04:09:00.554351Z" + "iopub.execute_input": "2024-07-30T16:38:57.903196Z", + "iopub.status.busy": "2024-07-30T16:38:57.902725Z", + "iopub.status.idle": "2024-07-30T16:38:57.906622Z", + "shell.execute_reply": "2024-07-30T16:38:57.906179Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.556730Z", - "iopub.status.busy": "2024-07-18T04:09:00.556561Z", - "iopub.status.idle": "2024-07-18T04:09:00.559924Z", - "shell.execute_reply": "2024-07-18T04:09:00.559463Z" + "iopub.execute_input": "2024-07-30T16:38:57.908608Z", + "iopub.status.busy": "2024-07-30T16:38:57.908262Z", + "iopub.status.idle": "2024-07-30T16:38:57.911716Z", + "shell.execute_reply": "2024-07-30T16:38:57.911253Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.561758Z", - "iopub.status.busy": "2024-07-18T04:09:00.561590Z", - "iopub.status.idle": "2024-07-18T04:09:00.564614Z", - "shell.execute_reply": "2024-07-18T04:09:00.564159Z" + "iopub.execute_input": "2024-07-30T16:38:57.913595Z", + "iopub.status.busy": "2024-07-30T16:38:57.913317Z", + "iopub.status.idle": "2024-07-30T16:38:57.916287Z", + "shell.execute_reply": "2024-07-30T16:38:57.915835Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.566608Z", - "iopub.status.busy": "2024-07-18T04:09:00.566208Z", - "iopub.status.idle": "2024-07-18T04:09:00.574230Z", - "shell.execute_reply": "2024-07-18T04:09:00.573776Z" + "iopub.execute_input": "2024-07-30T16:38:57.918353Z", + "iopub.status.busy": "2024-07-30T16:38:57.918022Z", + "iopub.status.idle": "2024-07-30T16:38:57.925798Z", + "shell.execute_reply": "2024-07-30T16:38:57.925354Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.576300Z", - "iopub.status.busy": "2024-07-18T04:09:00.575910Z", - "iopub.status.idle": "2024-07-18T04:09:00.578480Z", - "shell.execute_reply": "2024-07-18T04:09:00.578034Z" + "iopub.execute_input": "2024-07-30T16:38:57.927921Z", + "iopub.status.busy": "2024-07-30T16:38:57.927572Z", + "iopub.status.idle": "2024-07-30T16:38:57.930349Z", + "shell.execute_reply": "2024-07-30T16:38:57.929874Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.580633Z", - "iopub.status.busy": "2024-07-18T04:09:00.580237Z", - "iopub.status.idle": "2024-07-18T04:09:00.703841Z", - "shell.execute_reply": "2024-07-18T04:09:00.703327Z" + "iopub.execute_input": "2024-07-30T16:38:57.932400Z", + "iopub.status.busy": "2024-07-30T16:38:57.932062Z", + "iopub.status.idle": "2024-07-30T16:38:58.059365Z", + "shell.execute_reply": "2024-07-30T16:38:58.058741Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.705943Z", - "iopub.status.busy": "2024-07-18T04:09:00.705589Z", - "iopub.status.idle": "2024-07-18T04:09:00.829071Z", - "shell.execute_reply": "2024-07-18T04:09:00.828455Z" + "iopub.execute_input": "2024-07-30T16:38:58.061937Z", + "iopub.status.busy": "2024-07-30T16:38:58.061368Z", + "iopub.status.idle": "2024-07-30T16:38:58.173574Z", + "shell.execute_reply": "2024-07-30T16:38:58.172969Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.831504Z", - "iopub.status.busy": "2024-07-18T04:09:00.831176Z", - "iopub.status.idle": "2024-07-18T04:09:01.327951Z", - "shell.execute_reply": "2024-07-18T04:09:01.327366Z" + "iopub.execute_input": "2024-07-30T16:38:58.176096Z", + "iopub.status.busy": "2024-07-30T16:38:58.175756Z", + "iopub.status.idle": "2024-07-30T16:38:58.686374Z", + "shell.execute_reply": "2024-07-30T16:38:58.685762Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:01.330201Z", - "iopub.status.busy": "2024-07-18T04:09:01.329830Z", - "iopub.status.idle": "2024-07-18T04:09:01.439967Z", - "shell.execute_reply": "2024-07-18T04:09:01.439456Z" + "iopub.execute_input": "2024-07-30T16:38:58.689154Z", + "iopub.status.busy": "2024-07-30T16:38:58.688791Z", + "iopub.status.idle": "2024-07-30T16:38:58.788057Z", + "shell.execute_reply": "2024-07-30T16:38:58.787414Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:01.442336Z", - "iopub.status.busy": "2024-07-18T04:09:01.441869Z", - "iopub.status.idle": "2024-07-18T04:09:01.450335Z", - "shell.execute_reply": "2024-07-18T04:09:01.449911Z" + "iopub.execute_input": "2024-07-30T16:38:58.790444Z", + "iopub.status.busy": "2024-07-30T16:38:58.790105Z", + "iopub.status.idle": "2024-07-30T16:38:58.799165Z", + "shell.execute_reply": "2024-07-30T16:38:58.798679Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:01.452412Z", - "iopub.status.busy": "2024-07-18T04:09:01.452106Z", - "iopub.status.idle": "2024-07-18T04:09:01.454865Z", - "shell.execute_reply": "2024-07-18T04:09:01.454378Z" + "iopub.execute_input": "2024-07-30T16:38:58.801513Z", + "iopub.status.busy": "2024-07-30T16:38:58.801103Z", + "iopub.status.idle": "2024-07-30T16:38:58.804084Z", + "shell.execute_reply": "2024-07-30T16:38:58.803524Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:01.456775Z", - "iopub.status.busy": "2024-07-18T04:09:01.456602Z", - "iopub.status.idle": "2024-07-18T04:09:07.281893Z", - "shell.execute_reply": "2024-07-18T04:09:07.281280Z" + "iopub.execute_input": "2024-07-30T16:38:58.806513Z", + "iopub.status.busy": "2024-07-30T16:38:58.805981Z", + "iopub.status.idle": "2024-07-30T16:39:04.543731Z", + "shell.execute_reply": "2024-07-30T16:39:04.543118Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:07.284419Z", - "iopub.status.busy": "2024-07-18T04:09:07.284045Z", - "iopub.status.idle": "2024-07-18T04:09:07.292412Z", - "shell.execute_reply": "2024-07-18T04:09:07.291957Z" + "iopub.execute_input": "2024-07-30T16:39:04.546340Z", + "iopub.status.busy": "2024-07-30T16:39:04.545828Z", + "iopub.status.idle": "2024-07-30T16:39:04.554540Z", + "shell.execute_reply": "2024-07-30T16:39:04.553952Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:07.294524Z", - "iopub.status.busy": "2024-07-18T04:09:07.294199Z", - "iopub.status.idle": "2024-07-18T04:09:07.357921Z", - "shell.execute_reply": "2024-07-18T04:09:07.357469Z" + "iopub.execute_input": "2024-07-30T16:39:04.556793Z", + "iopub.status.busy": "2024-07-30T16:39:04.556293Z", + "iopub.status.idle": "2024-07-30T16:39:04.620999Z", + "shell.execute_reply": "2024-07-30T16:39:04.620353Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 907281bf0..ddf315699 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": "2024-07-18T04:09:10.560124Z", - "iopub.status.busy": "2024-07-18T04:09:10.559958Z", - "iopub.status.idle": "2024-07-18T04:09:13.104127Z", - "shell.execute_reply": "2024-07-18T04:09:13.103356Z" + "iopub.execute_input": "2024-07-30T16:39:08.949260Z", + "iopub.status.busy": "2024-07-30T16:39:08.949088Z", + "iopub.status.idle": "2024-07-30T16:39:10.916447Z", + "shell.execute_reply": "2024-07-30T16:39:10.915748Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:13.106805Z", - "iopub.status.busy": "2024-07-18T04:09:13.106609Z", - "iopub.status.idle": "2024-07-18T04:10:28.268458Z", - "shell.execute_reply": "2024-07-18T04:10:28.267678Z" + "iopub.execute_input": "2024-07-30T16:39:10.918962Z", + "iopub.status.busy": "2024-07-30T16:39:10.918773Z", + "iopub.status.idle": "2024-07-30T16:40:31.011988Z", + "shell.execute_reply": "2024-07-30T16:40:31.011219Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:28.271144Z", - "iopub.status.busy": "2024-07-18T04:10:28.270903Z", - "iopub.status.idle": "2024-07-18T04:10:29.405571Z", - "shell.execute_reply": "2024-07-18T04:10:29.405039Z" + "iopub.execute_input": "2024-07-30T16:40:31.014861Z", + "iopub.status.busy": "2024-07-30T16:40:31.014482Z", + "iopub.status.idle": "2024-07-30T16:40:32.523099Z", + "shell.execute_reply": "2024-07-30T16:40:32.522526Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:10:29.408244Z", - "iopub.status.busy": "2024-07-18T04:10:29.407724Z", - "iopub.status.idle": "2024-07-18T04:10:29.411085Z", - "shell.execute_reply": "2024-07-18T04:10:29.410512Z" + "iopub.execute_input": "2024-07-30T16:40:32.525566Z", + "iopub.status.busy": "2024-07-30T16:40:32.525262Z", + "iopub.status.idle": "2024-07-30T16:40:32.528712Z", + "shell.execute_reply": "2024-07-30T16:40:32.528246Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.413314Z", - "iopub.status.busy": "2024-07-18T04:10:29.413010Z", - "iopub.status.idle": "2024-07-18T04:10:29.416803Z", - "shell.execute_reply": "2024-07-18T04:10:29.416373Z" + "iopub.execute_input": "2024-07-30T16:40:32.530916Z", + "iopub.status.busy": "2024-07-30T16:40:32.530497Z", + "iopub.status.idle": "2024-07-30T16:40:32.534386Z", + "shell.execute_reply": "2024-07-30T16:40:32.533915Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.418928Z", - "iopub.status.busy": "2024-07-18T04:10:29.418571Z", - "iopub.status.idle": "2024-07-18T04:10:29.422140Z", - "shell.execute_reply": "2024-07-18T04:10:29.421708Z" + "iopub.execute_input": "2024-07-30T16:40:32.536602Z", + "iopub.status.busy": "2024-07-30T16:40:32.536175Z", + "iopub.status.idle": "2024-07-30T16:40:32.539968Z", + "shell.execute_reply": "2024-07-30T16:40:32.539531Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.424233Z", - "iopub.status.busy": "2024-07-18T04:10:29.423908Z", - "iopub.status.idle": "2024-07-18T04:10:29.426602Z", - "shell.execute_reply": "2024-07-18T04:10:29.426181Z" + "iopub.execute_input": "2024-07-30T16:40:32.542052Z", + "iopub.status.busy": "2024-07-30T16:40:32.541706Z", + "iopub.status.idle": "2024-07-30T16:40:32.544446Z", + "shell.execute_reply": "2024-07-30T16:40:32.544021Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.428591Z", - "iopub.status.busy": "2024-07-18T04:10:29.428256Z", - "iopub.status.idle": "2024-07-18T04:11:06.947288Z", - "shell.execute_reply": "2024-07-18T04:11:06.946575Z" + "iopub.execute_input": "2024-07-30T16:40:32.546293Z", + "iopub.status.busy": "2024-07-30T16:40:32.546119Z", + "iopub.status.idle": "2024-07-30T16:41:10.690446Z", + "shell.execute_reply": "2024-07-30T16:41:10.689776Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b72b2c1cac4c46cf8fdcb9a698c41e2d", + "model_id": "3c621015e28040a280bd1034a80975dc", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59e1bc568b054e7ba3e6e8735e0b46ca", + "model_id": "0f880a204f2942c89dcc00391ef9c5e7", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:11:06.950015Z", - "iopub.status.busy": "2024-07-18T04:11:06.949795Z", - "iopub.status.idle": "2024-07-18T04:11:07.618088Z", - "shell.execute_reply": "2024-07-18T04:11:07.617601Z" + "iopub.execute_input": "2024-07-30T16:41:10.693189Z", + "iopub.status.busy": "2024-07-30T16:41:10.692781Z", + "iopub.status.idle": "2024-07-30T16:41:11.146443Z", + "shell.execute_reply": "2024-07-30T16:41:11.145848Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:11:07.620378Z", - "iopub.status.busy": "2024-07-18T04:11:07.619929Z", - "iopub.status.idle": "2024-07-18T04:11:10.617575Z", - "shell.execute_reply": "2024-07-18T04:11:10.616973Z" + "iopub.execute_input": "2024-07-30T16:41:11.148909Z", + "iopub.status.busy": "2024-07-30T16:41:11.148535Z", + "iopub.status.idle": "2024-07-30T16:41:14.025111Z", + "shell.execute_reply": "2024-07-30T16:41:14.024529Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:11:10.619936Z", - "iopub.status.busy": "2024-07-18T04:11:10.619515Z", - "iopub.status.idle": "2024-07-18T04:11:43.703098Z", - "shell.execute_reply": "2024-07-18T04:11:43.702508Z" + "iopub.execute_input": "2024-07-30T16:41:14.027425Z", + "iopub.status.busy": "2024-07-30T16:41:14.027043Z", + "iopub.status.idle": "2024-07-30T16:41:46.636156Z", + "shell.execute_reply": "2024-07-30T16:41:46.635613Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abf636d0171541789af2388c3416e5d8", + "model_id": "8aadf42791a644d7aa84ea5ea93db52a", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:11:43.705345Z", - "iopub.status.busy": "2024-07-18T04:11:43.704988Z", - "iopub.status.idle": "2024-07-18T04:11:59.439667Z", - "shell.execute_reply": "2024-07-18T04:11:59.439016Z" + "iopub.execute_input": "2024-07-30T16:41:46.638629Z", + "iopub.status.busy": "2024-07-30T16:41:46.638229Z", + "iopub.status.idle": "2024-07-30T16:42:01.915473Z", + "shell.execute_reply": "2024-07-30T16:42:01.914892Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:11:59.442385Z", - "iopub.status.busy": "2024-07-18T04:11:59.441988Z", - "iopub.status.idle": "2024-07-18T04:12:03.320177Z", - "shell.execute_reply": "2024-07-18T04:12:03.319687Z" + "iopub.execute_input": "2024-07-30T16:42:01.918239Z", + "iopub.status.busy": "2024-07-30T16:42:01.917804Z", + "iopub.status.idle": "2024-07-30T16:42:05.845501Z", + "shell.execute_reply": "2024-07-30T16:42:05.844878Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:03.322503Z", - "iopub.status.busy": "2024-07-18T04:12:03.322159Z", - "iopub.status.idle": "2024-07-18T04:12:04.826127Z", - "shell.execute_reply": "2024-07-18T04:12:04.825565Z" + "iopub.execute_input": "2024-07-30T16:42:05.847761Z", + "iopub.status.busy": "2024-07-30T16:42:05.847553Z", + "iopub.status.idle": "2024-07-30T16:42:07.336104Z", + "shell.execute_reply": "2024-07-30T16:42:07.335452Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"iopub.execute_input": "2024-07-18T04:12:13.563318Z", - "iopub.status.busy": "2024-07-18T04:12:13.563162Z", - "iopub.status.idle": "2024-07-18T04:12:15.665934Z", - "shell.execute_reply": "2024-07-18T04:12:15.665276Z" + "iopub.execute_input": "2024-07-30T16:42:16.108435Z", + "iopub.status.busy": "2024-07-30T16:42:16.108277Z", + "iopub.status.idle": "2024-07-30T16:42:17.473595Z", + "shell.execute_reply": "2024-07-30T16:42:17.472949Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-18 04:12:13-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-30 16:42:16-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "143.244.49.183, 2400:52e0:1a01::1001:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|143.244.49.183|:443... connected.\r\n", + "185.93.1.250, 2400:52e0:1a00::1070:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -116,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-18 04:12:13 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-30 16:42:16 (6.62 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -138,22 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-18 04:12:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.140.161, 52.217.200.145, 16.182.74.81, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.140.161|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-07-30 16:42:16-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.138.89, 52.217.134.249, 52.216.41.17, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.138.89|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,34 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 0%[ ] 160.53K 750KB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 8%[> ] 1.42M 3.31MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 49%[========> ] 7.97M 12.4MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 99%[==================> ] 16.12M 18.8MB/s \r", - "pred_probs.npz 100%[===================>] 16.26M 19.0MB/s in 0.9s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-18 04:12:15 (19.0 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-30 16:42:17 (125 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -218,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:15.668596Z", - "iopub.status.busy": "2024-07-18T04:12:15.668398Z", - "iopub.status.idle": "2024-07-18T04:12:16.911759Z", - "shell.execute_reply": "2024-07-18T04:12:16.911145Z" + "iopub.execute_input": "2024-07-30T16:42:17.476282Z", + "iopub.status.busy": "2024-07-30T16:42:17.475905Z", + "iopub.status.idle": "2024-07-30T16:42:18.926532Z", + "shell.execute_reply": "2024-07-30T16:42:18.925850Z" }, "nbsphinx": "hidden" }, @@ -232,7 +200,7 @@ "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -258,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.914442Z", - "iopub.status.busy": "2024-07-18T04:12:16.914008Z", - "iopub.status.idle": "2024-07-18T04:12:16.917267Z", - "shell.execute_reply": "2024-07-18T04:12:16.916832Z" + "iopub.execute_input": "2024-07-30T16:42:18.929007Z", + "iopub.status.busy": "2024-07-30T16:42:18.928712Z", + "iopub.status.idle": "2024-07-30T16:42:18.932103Z", + "shell.execute_reply": "2024-07-30T16:42:18.931658Z" } }, "outputs": [], @@ -311,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.919339Z", - "iopub.status.busy": "2024-07-18T04:12:16.919000Z", - "iopub.status.idle": "2024-07-18T04:12:16.922078Z", - "shell.execute_reply": "2024-07-18T04:12:16.921530Z" + "iopub.execute_input": "2024-07-30T16:42:18.934243Z", + "iopub.status.busy": "2024-07-30T16:42:18.933903Z", + "iopub.status.idle": "2024-07-30T16:42:18.937344Z", + "shell.execute_reply": "2024-07-30T16:42:18.936919Z" }, "nbsphinx": "hidden" }, @@ -332,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.924213Z", - "iopub.status.busy": "2024-07-18T04:12:16.923864Z", - "iopub.status.idle": "2024-07-18T04:12:26.037149Z", - "shell.execute_reply": "2024-07-18T04:12:26.036588Z" + "iopub.execute_input": "2024-07-30T16:42:18.939414Z", + "iopub.status.busy": "2024-07-30T16:42:18.939071Z", + "iopub.status.idle": "2024-07-30T16:42:28.307360Z", + "shell.execute_reply": "2024-07-30T16:42:28.306819Z" } }, "outputs": [], @@ -409,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.039560Z", - "iopub.status.busy": "2024-07-18T04:12:26.039344Z", - "iopub.status.idle": "2024-07-18T04:12:26.044846Z", - "shell.execute_reply": "2024-07-18T04:12:26.044413Z" + "iopub.execute_input": "2024-07-30T16:42:28.310072Z", + "iopub.status.busy": "2024-07-30T16:42:28.309609Z", + "iopub.status.idle": "2024-07-30T16:42:28.315308Z", + "shell.execute_reply": "2024-07-30T16:42:28.314851Z" }, "nbsphinx": "hidden" }, @@ -452,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.046633Z", - "iopub.status.busy": "2024-07-18T04:12:26.046462Z", - "iopub.status.idle": "2024-07-18T04:12:26.386018Z", - "shell.execute_reply": "2024-07-18T04:12:26.385389Z" + "iopub.execute_input": "2024-07-30T16:42:28.317438Z", + "iopub.status.busy": "2024-07-30T16:42:28.317037Z", + "iopub.status.idle": "2024-07-30T16:42:28.691191Z", + "shell.execute_reply": "2024-07-30T16:42:28.690527Z" } }, "outputs": [], @@ -492,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.388817Z", - "iopub.status.busy": "2024-07-18T04:12:26.388359Z", - "iopub.status.idle": "2024-07-18T04:12:26.392513Z", - "shell.execute_reply": "2024-07-18T04:12:26.392066Z" + "iopub.execute_input": "2024-07-30T16:42:28.693775Z", + "iopub.status.busy": "2024-07-30T16:42:28.693565Z", + "iopub.status.idle": "2024-07-30T16:42:28.698316Z", + "shell.execute_reply": "2024-07-30T16:42:28.697696Z" } }, "outputs": [ @@ -567,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.394754Z", - "iopub.status.busy": "2024-07-18T04:12:26.394386Z", - "iopub.status.idle": "2024-07-18T04:12:29.007823Z", - "shell.execute_reply": "2024-07-18T04:12:29.007030Z" + "iopub.execute_input": "2024-07-30T16:42:28.700484Z", + "iopub.status.busy": "2024-07-30T16:42:28.700147Z", + "iopub.status.idle": "2024-07-30T16:42:31.466890Z", + "shell.execute_reply": "2024-07-30T16:42:31.466180Z" } }, "outputs": [], @@ -592,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.011403Z", - "iopub.status.busy": "2024-07-18T04:12:29.010454Z", - "iopub.status.idle": "2024-07-18T04:12:29.015308Z", - "shell.execute_reply": "2024-07-18T04:12:29.014770Z" + "iopub.execute_input": "2024-07-30T16:42:31.470029Z", + "iopub.status.busy": "2024-07-30T16:42:31.469337Z", + "iopub.status.idle": "2024-07-30T16:42:31.473767Z", + "shell.execute_reply": "2024-07-30T16:42:31.473222Z" } }, "outputs": [ @@ -631,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.017563Z", - "iopub.status.busy": "2024-07-18T04:12:29.017204Z", - "iopub.status.idle": "2024-07-18T04:12:29.023529Z", - "shell.execute_reply": "2024-07-18T04:12:29.022965Z" + "iopub.execute_input": "2024-07-30T16:42:31.476028Z", + "iopub.status.busy": "2024-07-30T16:42:31.475684Z", + "iopub.status.idle": "2024-07-30T16:42:31.481390Z", + "shell.execute_reply": "2024-07-30T16:42:31.480918Z" } }, "outputs": [ @@ -812,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.025886Z", - "iopub.status.busy": "2024-07-18T04:12:29.025543Z", - "iopub.status.idle": "2024-07-18T04:12:29.053000Z", - "shell.execute_reply": "2024-07-18T04:12:29.052460Z" + "iopub.execute_input": "2024-07-30T16:42:31.483594Z", + "iopub.status.busy": "2024-07-30T16:42:31.483253Z", + "iopub.status.idle": "2024-07-30T16:42:31.509722Z", + "shell.execute_reply": "2024-07-30T16:42:31.509269Z" } }, "outputs": [ @@ -917,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.055238Z", - "iopub.status.busy": "2024-07-18T04:12:29.054781Z", - "iopub.status.idle": "2024-07-18T04:12:29.059137Z", - "shell.execute_reply": "2024-07-18T04:12:29.058579Z" + "iopub.execute_input": "2024-07-30T16:42:31.511897Z", + "iopub.status.busy": "2024-07-30T16:42:31.511537Z", + "iopub.status.idle": "2024-07-30T16:42:31.516114Z", + "shell.execute_reply": "2024-07-30T16:42:31.515643Z" } }, "outputs": [ @@ -994,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.061173Z", - "iopub.status.busy": "2024-07-18T04:12:29.061001Z", - "iopub.status.idle": "2024-07-18T04:12:30.455263Z", - "shell.execute_reply": "2024-07-18T04:12:30.454724Z" + "iopub.execute_input": "2024-07-30T16:42:31.518017Z", + "iopub.status.busy": "2024-07-30T16:42:31.517821Z", + "iopub.status.idle": "2024-07-30T16:42:33.009420Z", + "shell.execute_reply": "2024-07-30T16:42:33.008849Z" } }, "outputs": [ @@ -1169,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:30.457343Z", - "iopub.status.busy": "2024-07-18T04:12:30.457166Z", - "iopub.status.idle": "2024-07-18T04:12:30.461345Z", - "shell.execute_reply": "2024-07-18T04:12:30.460894Z" + "iopub.execute_input": "2024-07-30T16:42:33.011845Z", + "iopub.status.busy": "2024-07-30T16:42:33.011502Z", + "iopub.status.idle": "2024-07-30T16:42:33.015539Z", + "shell.execute_reply": "2024-07-30T16:42:33.015098Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials_datalab_workflows_82_3.png b/master/.doctrees/nbsphinx/tutorials_datalab_workflows_82_3.png new file mode 100644 index 000000000..e605bd643 Binary files /dev/null and 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diff --git a/master/_images/tutorials_datalab_workflows_82_3.png b/master/_images/tutorials_datalab_workflows_82_3.png new file mode 100644 index 000000000..e605bd643 Binary files /dev/null and b/master/_images/tutorials_datalab_workflows_82_3.png differ diff --git a/master/_modules/cleanlab/datalab/datalab.html b/master/_modules/cleanlab/datalab/datalab.html index f859ca058..6c56bc5a7 100644 --- a/master/_modules/cleanlab/datalab/datalab.html +++ b/master/_modules/cleanlab/datalab/datalab.html @@ -746,6 +746,7 @@

Source code for cleanlab.datalab.datalab

         self.cleanlab_version = cleanlab.version.__version__
         self.verbosity = verbosity
         self._imagelab = create_imagelab(dataset=self.data, image_key=image_key)
+        self._correlations_df = pd.DataFrame(columns=["property", "score"])
 
         # Create the builder for DataIssues
         builder = _DataIssuesBuilder(self._data)
@@ -1033,6 +1034,7 @@ 
Source code for cleanlab.datalab.datalab
             show_summary_score=show_summary_score,
             show_all_issues=show_all_issues,
             imagelab=self._imagelab,
+            correlations_df=self._correlations_df,
         )
         reporter.report(num_examples=num_examples)

 
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 45ca43268..28232a59a 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 c14fac2ae..fcc9d1478 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 5e7efeb22..39ad45277 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 5e7b3aaac..aab33d6e3 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 421f154a5..64e8b75c3 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 7837c0c27..9132d3724 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 49f95ce4d..84937486a 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 0a17e353b..ccd7d003e 100644
--- a/master/_sources/tutorials/datalab/workflows.ipynb
+++ b/master/_sources/tutorials/datalab/workflows.ipynb
@@ -1336,15 +1336,14 @@
     "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n",
     "These correlations could lead to unreliable model predictions and poor generalization.\n",
     "\n",
-    "\n",
-    "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n",
+    "`Datalab` automatically analyzes image-specific attributes such as:\n",
     "\n",
     "- Darkness\n",
     "- Blurriness\n",
     "- Aspect ratio anomalies\n",
     "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n",
     "\n",
-    "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n"
+    "This analysis helps identify unintended biases in datasets and guides steps to enhance the robustness of machine learning models.\n"
    ]
   },
   {
@@ -1353,73 +1352,25 @@
    "source": [
     "### 1. Load the Dataset\n",
     "\n",
-    "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n",
-    "\n",
-    "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n",
-    "- The correlation scores range from 0 to 1, where:\n",
-    "  - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n",
-    "  - Scores close to 1 suggest little to no correlation between the property and class labels.\n",
-    "- By introducing this bias, we expect to see:\n",
-    "  - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n",
-    "  - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n",
-    "\n",
-    "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels."
+    "For this tutorial, we'll use a subset of the CIFAR-10 dataset with artificially introduced biases to illustrate how Datalab detects spurious correlations. We'll assume you have a directory of images organized into subdirectories by class."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "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"
+    "To fetch the data for this tutorial, make sure you have `wget` and `zip` installed."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find image-related issues"
+    "# Download the dataset\n",
+    "!wget -nc https://s.cleanlab.ai/CIFAR-10-subset.zip\n",
+    "!unzip -q CIFAR-10-subset.zip"
    ]
   },
   {
@@ -1428,16 +1379,40 @@
    "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)"
+    "from datasets import Dataset\n",
+    "from torchvision.datasets import ImageFolder\n",
+    "\n",
+    "def load_image_dataset(data_dir: str):\n",
+    "    \"\"\"\n",
+    "    Load images from a directory structure and create a datasets.Dataset object.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    data_dir : str\n",
+    "        Path to the root directory containing class subdirectories.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    datasets.Dataset\n",
+    "        A Dataset object containing 'image' and 'label' columns.\n",
+    "    \"\"\"\n",
+    "    image_dataset = ImageFolder(data_dir)\n",
+    "    images = [img for img, _ in image_dataset]\n",
+    "    labels = [label for _, label in image_dataset]\n",
+    "    return Dataset.from_dict({\"image\": images, \"label\": labels})\n",
+    "\n",
+    "# Load the dataset\n",
+    "data_dir = \"CIFAR-10-subset/darkened_images\"\n",
+    "dataset = load_image_dataset(data_dir)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. (Optional) Creating a transformed dataset using `ImageEnhance` to induce darkness"
+    "### 2. Run Datalab Analysis\n",
+    "\n",
+    "Now that we have loaded our dataset, let's use `Datalab` to analyze it for potential spurious correlations."
    ]
   },
   {
@@ -1446,28 +1421,25 @@
    "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",
+    "from cleanlab import Datalab\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",
+    "# Initialize Datalab with the dataset\n",
+    "lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n",
+    "\n",
+    "# Run the analysis\n",
+    "lab.find_issues()\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)"
+    "# Generate and display the report\n",
+    "lab.report()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 4. (Optional) Visualizing Images in the dataset"
+    "### 3. Interpret the Results\n",
+    "\n",
+    "While the `lab.report()` output is comprehensive, we can use more targeted methods to examine the results:"
    ]
   },
   {
@@ -1476,47 +1448,51 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display\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",
+    "# Get the correlation scores for image properties\n",
+    "correlation_scores = lab._correlations_df\n",
+    "print(\"Correlation scores for image properties:\")\n",
+    "display(correlation_scores)\n",
     "\n",
-    "plot_images(dataset_dict)\n",
-    "plot_images(transformed_dataset_dict)"
+    "# Get image-specific issues\n",
+    "issue_name = \"dark\"\n",
+    "image_issues = lab.get_issues(issue_name)\n",
+    "print(\"\\nImage-specific issues:\")\n",
+    "display(image_issues)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5. Finding image-specific property scores"
+    "\n",
+    "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n",
+    "\n",
+    "Interpreting the results:\n",
+    "\n",
+    "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n",
+    "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n",
+    "\n",
+    "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n",
+    "\n",
+    "For real-world datasets, pay attention to:\n",
+    "\n",
+    "- Properties with notably low scores in the correlation_scores DataFrame\n",
+    "- Prevalent issues in the image_issues DataFrame\n",
+    "\n",
+    "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n",
+    "\n",
+    "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n",
+    "\n",
+    "To understand the impact of spurious correlations, it can be helpful to compare our results with a dataset that doesn't have artificially introduced biases. In this case, we'll use the original CIFAR-10 subset."
    ]
   },
   {
@@ -1525,28 +1501,35 @@
    "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",
+    "# Load the original dataset\n",
+    "original_data_dir = \"CIFAR-10-subset/original_images\"\n",
+    "original_dataset = load_image_dataset(original_data_dir)\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",
+    "# Create a new Datalab instance and run analysis\n",
+    "original_lab = Datalab(data=original_dataset, label_name=\"label\", image_key=\"image\")\n",
+    "original_lab.find_issues()\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",
+    "# Compare correlation scores\n",
+    "original_scores = original_lab._correlations_df\n",
+    "print(\"Correlation scores for original dataset:\")\n",
+    "display(original_scores)\n",
+    "\n",
+    "# Compare image-specific issues\n",
+    "original_issues = original_lab.get_issues(\"dark\")\n",
+    "print(\"\\nImage-specific issues in original dataset:\")\n",
+    "display(original_issues)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When comparing the results:\n",
     "\n",
-    "# Displaying the scores dataframe\n",
-    "display(Markdown(\"### Image-specific property scores in the original dataset\"))\n",
-    "display(standard_property_scores)\n",
-    "display(Markdown(\"### Image-specific property scores in the transformed dataset\"))\n",
-    "display(transformed_property_scores)\n",
+    "1. Look for differences in the correlation scores, especially for the 'dark' property.\n",
+    "2. Compare the number and types of image-specific issues detected.\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')"
+    "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. This comparison highlights how spurious correlations can be detected by `Datalab`."
    ]
   }
  ],
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 8c71ca66b..1d2cfdc12 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb
index e79be378b..438ca9320 100644
--- a/master/_sources/tutorials/improving_ml_performance.ipynb
+++ b/master/_sources/tutorials/improving_ml_performance.ipynb
@@ -67,7 +67,7 @@
     "dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 18d4cf7fb..feb419f3f 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 2f099ee7b..cda239e55 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 a897a974b..bb30f1dc1 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 51f78152c..bcff2400f 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 9545eb9e4..a2fa23c15 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 02ded3480..29bd5bde2 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 6c1096146..64d2a9d9e 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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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 ae68fbb16..16a5dc3ee 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/objects.inv b/master/objects.inv
index 0b91c3a02..5d4ef7f2d 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/searchindex.js b/master/searchindex.js
index fcec6e052..a8dde8682 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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"module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [91, "Easy-Mode"], [93, "Easy-Mode"], [94, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[95, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[95, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[95, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[95, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[95, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[95, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. 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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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34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 61, 63, 65, 66, 68, 69, 70, 71, 73, 75, 77, 78, 82], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 61, 62, 63, 65, 66, 82, 87, 88, 90, 91, 93, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 61, 63, 65, 68, 69, 70, 71, 73, 74, 79, 81, 82, 83, 88, 91, 93, 94, 95, 96, 97, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 73, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 64, 70, 89, 90, 91, 93, 94, 98, 99], "yield": [1, 38, 42, 98], 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49, 50, 52, 56, 57, 60, 61, 62, 63, 64, 65, 68, 69, 70, 71, 73, 75, 77, 78, 79, 81, 82, 83, 84, 86, 87, 88, 89, 91, 93, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 89, 95], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 60, 61, 62, 64, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 61, 65, 69, 73, 78, 82, 83, 86, 87, 88, 90, 94, 97, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 59, 60, 63, 70, 71, 73, 75, 83, 84, 88, 95, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 60, 61, 62, 63, 65, 68, 69, 70, 71, 73, 77, 78, 79, 81, 87, 90, 91, 94, 95, 96, 97, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 60, 63, 65, 66, 68, 70, 71, 73, 74, 86, 87, 89, 90, 91, 93, 94, 95, 96, 98, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 60, 61, 62, 63, 66, 68, 69, 70, 71, 73, 75, 78, 79, 81, 83, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 88, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 60, 61, 63, 66, 68, 69, 70, 71, 73, 75, 77, 78, 86, 87, 89, 90, 93, 94, 95, 97, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 60, 61, 66, 68, 69, 70, 71, 73, 74, 78, 84, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 59, 60, 61, 63, 66, 68, 69, 70, 71, 73, 74, 75, 78, 79, 82, 83, 84, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 97, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 68, 71, 73, 86, 87, 89, 90, 91, 95, 96, 98, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 66, 68, 69, 70, 71, 73, 75, 78, 82, 84, 87, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 60, 61, 70, 73, 78, 81, 82, 83, 86, 87, 88, 89, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 61, 83, 89, 90, 94, 95, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 89, 91, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 89, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 60, 61, 63, 65, 69, 70, 71, 73, 74, 77, 79, 81, 84, 86, 87, 88, 91, 93, 94, 95, 97, 98, 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"is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [70, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [72, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [62, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [63, "module-cleanlab.multilabel_classification.filter"], [66, "filter"], [75, "filter"], [79, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [91, "Easy-Mode"], [93, "Easy-Mode"], [94, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "4. (Optional) Compare with a Dataset Without Spurious Correlations": [[95, "4.-(Optional)-Compare-with-a-Dataset-Without-Spurious-Correlations"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module 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": [[59, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[60, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[61, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[62, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[63, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[64, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[65, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[66, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[66, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[67, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[68, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[72, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[73, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[73, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[73, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[74, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[74, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[75, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[75, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[76, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[77, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[78, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 76fc0cf88..1b1f01adb 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": "2024-07-18T04:01:36.624070Z",
-     "iopub.status.busy": "2024-07-18T04:01:36.623720Z",
-     "iopub.status.idle": "2024-07-18T04:01:37.842464Z",
-     "shell.execute_reply": "2024-07-18T04:01:37.841899Z"
+     "iopub.execute_input": "2024-07-30T16:31:34.527671Z",
+     "iopub.status.busy": "2024-07-30T16:31:34.527492Z",
+     "iopub.status.idle": "2024-07-30T16:31:36.140632Z",
+     "shell.execute_reply": "2024-07-30T16:31:36.140024Z"
     },
     "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:01:37.845272Z",
-     "iopub.status.busy": "2024-07-18T04:01:37.844748Z",
-     "iopub.status.idle": "2024-07-18T04:01:37.863056Z",
-     "shell.execute_reply": "2024-07-18T04:01:37.862447Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.143586Z",
+     "iopub.status.busy": "2024-07-30T16:31:36.143047Z",
+     "iopub.status.idle": "2024-07-30T16:31:36.178768Z",
+     "shell.execute_reply": "2024-07-30T16:31:36.178228Z"
     }
    },
    "outputs": [],
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:37.865460Z",
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-     "iopub.status.idle": "2024-07-18T04:01:38.092310Z",
-     "shell.execute_reply": "2024-07-18T04:01:38.091732Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.181589Z",
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+     "iopub.status.idle": "2024-07-30T16:31:36.338074Z",
+     "shell.execute_reply": "2024-07-30T16:31:36.337466Z"
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@@ -305,10 +305,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:38.121141Z",
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-     "iopub.status.idle": "2024-07-18T04:01:38.124263Z",
-     "shell.execute_reply": "2024-07-18T04:01:38.123800Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.372204Z",
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+     "iopub.status.idle": "2024-07-30T16:31:36.377781Z",
+     "shell.execute_reply": "2024-07-30T16:31:36.377262Z"
     }
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    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:38.126350Z",
-     "iopub.status.busy": "2024-07-18T04:01:38.126009Z",
-     "iopub.status.idle": "2024-07-18T04:01:38.134504Z",
-     "shell.execute_reply": "2024-07-18T04:01:38.134029Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.380079Z",
+     "iopub.status.busy": "2024-07-30T16:31:36.379702Z",
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+     "shell.execute_reply": "2024-07-30T16:31:36.388645Z"
     }
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@@ -384,10 +384,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:38.136643Z",
-     "iopub.status.busy": "2024-07-18T04:01:38.136297Z",
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-     "shell.execute_reply": "2024-07-18T04:01:38.138325Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.391552Z",
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+     "shell.execute_reply": "2024-07-30T16:31:36.393862Z"
     }
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@@ -409,10 +409,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2024-07-18T04:01:38.659701Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.396451Z",
+     "iopub.status.busy": "2024-07-30T16:31:36.396262Z",
+     "iopub.status.idle": "2024-07-30T16:31:36.936436Z",
+     "shell.execute_reply": "2024-07-30T16:31:36.935844Z"
     }
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@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:38.662748Z",
-     "iopub.status.busy": "2024-07-18T04:01:38.662379Z",
-     "iopub.status.idle": "2024-07-18T04:01:40.571765Z",
-     "shell.execute_reply": "2024-07-18T04:01:40.571111Z"
+     "iopub.execute_input": "2024-07-30T16:31:36.939261Z",
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+     "shell.execute_reply": "2024-07-30T16:31:39.269009Z"
     }
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@@ -481,10 +481,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:40.574577Z",
-     "iopub.status.busy": "2024-07-18T04:01:40.573829Z",
-     "iopub.status.idle": "2024-07-18T04:01:40.584210Z",
-     "shell.execute_reply": "2024-07-18T04:01:40.583746Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.273002Z",
+     "iopub.status.busy": "2024-07-30T16:31:39.272142Z",
+     "iopub.status.idle": "2024-07-30T16:31:39.283199Z",
+     "shell.execute_reply": "2024-07-30T16:31:39.282635Z"
     }
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    "outputs": [
@@ -605,10 +605,10 @@
    "execution_count": 10,
    "metadata": {
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-     "iopub.execute_input": "2024-07-18T04:01:40.586373Z",
-     "iopub.status.busy": "2024-07-18T04:01:40.586110Z",
-     "iopub.status.idle": "2024-07-18T04:01:40.590039Z",
-     "shell.execute_reply": "2024-07-18T04:01:40.589558Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.285386Z",
+     "iopub.status.busy": "2024-07-30T16:31:39.285054Z",
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+     "shell.execute_reply": "2024-07-30T16:31:39.288681Z"
     }
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@@ -633,10 +633,10 @@
    "execution_count": 11,
    "metadata": {
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-     "iopub.execute_input": "2024-07-18T04:01:40.592168Z",
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-     "shell.execute_reply": "2024-07-18T04:01:40.598494Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.291246Z",
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     }
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@@ -658,10 +658,10 @@
    "execution_count": 12,
    "metadata": {
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-     "shell.execute_reply": "2024-07-18T04:01:40.711624Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.301188Z",
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     }
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    "outputs": [
@@ -691,10 +691,10 @@
    "execution_count": 13,
    "metadata": {
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-     "iopub.execute_input": "2024-07-18T04:01:40.714154Z",
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-     "shell.execute_reply": "2024-07-18T04:01:40.716066Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.421608Z",
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     }
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    "outputs": [],
@@ -715,10 +715,10 @@
    "execution_count": 14,
    "metadata": {
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-     "iopub.execute_input": "2024-07-18T04:01:40.718951Z",
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-     "shell.execute_reply": "2024-07-18T04:01:42.835101Z"
+     "iopub.execute_input": "2024-07-30T16:31:39.426671Z",
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+     "shell.execute_reply": "2024-07-30T16:31:41.719145Z"
     }
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    "outputs": [],
@@ -738,10 +738,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:42.839563Z",
-     "iopub.status.busy": "2024-07-18T04:01:42.838450Z",
-     "iopub.status.idle": "2024-07-18T04:01:42.850192Z",
-     "shell.execute_reply": "2024-07-18T04:01:42.849635Z"
+     "iopub.execute_input": "2024-07-30T16:31:41.723999Z",
+     "iopub.status.busy": "2024-07-30T16:31:41.722968Z",
+     "iopub.status.idle": "2024-07-30T16:31:41.736024Z",
+     "shell.execute_reply": "2024-07-30T16:31:41.735553Z"
     }
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    "outputs": [
@@ -771,10 +771,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-18T04:01:42.852397Z",
-     "iopub.status.busy": "2024-07-18T04:01:42.851948Z",
-     "iopub.status.idle": "2024-07-18T04:01:42.971378Z",
-     "shell.execute_reply": "2024-07-18T04:01:42.970812Z"
+     "iopub.execute_input": "2024-07-30T16:31:41.738164Z",
+     "iopub.status.busy": "2024-07-30T16:31:41.737959Z",
+     "iopub.status.idle": "2024-07-30T16:31:41.800777Z",
+     "shell.execute_reply": "2024-07-30T16:31:41.800288Z"
     },
     "nbsphinx": "hidden"
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diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 973404da4..5f3a2c5ca 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: {'visa_or_mastercard', 'beneficiary_not_allowed', 'cancel_transfer', 'supported_cards_and_currencies', 'getting_spare_card', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'card_about_to_expire', 'lost_or_stolen_phone'}
+Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'visa_or_mastercard', 'cancel_transfer', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'change_pin'}

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

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

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1213,7 +1213,7 @@

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"2024-07-18T04:01:46.668554Z", - "iopub.status.idle": "2024-07-18T04:01:49.461835Z", - "shell.execute_reply": "2024-07-18T04:01:49.461271Z" + "iopub.execute_input": "2024-07-30T16:31:45.538656Z", + "iopub.status.busy": "2024-07-30T16:31:45.538493Z", + "iopub.status.idle": "2024-07-30T16:31:49.398554Z", + "shell.execute_reply": "2024-07-30T16:31:49.397834Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:01:49.464660Z", - "iopub.status.busy": "2024-07-18T04:01:49.464107Z", - "iopub.status.idle": "2024-07-18T04:01:49.467448Z", - "shell.execute_reply": "2024-07-18T04:01:49.466979Z" + "iopub.execute_input": "2024-07-30T16:31:49.401469Z", + "iopub.status.busy": "2024-07-30T16:31:49.401084Z", + "iopub.status.idle": "2024-07-30T16:31:49.404559Z", + "shell.execute_reply": "2024-07-30T16:31:49.404113Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.469527Z", - "iopub.status.busy": "2024-07-18T04:01:49.469126Z", - "iopub.status.idle": "2024-07-18T04:01:49.472275Z", - "shell.execute_reply": "2024-07-18T04:01:49.471805Z" + "iopub.execute_input": "2024-07-30T16:31:49.406866Z", + "iopub.status.busy": "2024-07-30T16:31:49.406454Z", + "iopub.status.idle": "2024-07-30T16:31:49.409954Z", + "shell.execute_reply": "2024-07-30T16:31:49.409295Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.474142Z", - "iopub.status.busy": "2024-07-18T04:01:49.473968Z", - "iopub.status.idle": "2024-07-18T04:01:49.588009Z", - "shell.execute_reply": "2024-07-18T04:01:49.587466Z" + "iopub.execute_input": "2024-07-30T16:31:49.412895Z", + "iopub.status.busy": "2024-07-30T16:31:49.412480Z", + "iopub.status.idle": "2024-07-30T16:31:49.471562Z", + "shell.execute_reply": "2024-07-30T16:31:49.470965Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.589968Z", - "iopub.status.busy": "2024-07-18T04:01:49.589787Z", - "iopub.status.idle": "2024-07-18T04:01:49.593376Z", - "shell.execute_reply": "2024-07-18T04:01:49.592931Z" + "iopub.execute_input": "2024-07-30T16:31:49.473824Z", + "iopub.status.busy": "2024-07-30T16:31:49.473632Z", + "iopub.status.idle": "2024-07-30T16:31:49.477513Z", + "shell.execute_reply": "2024-07-30T16:31:49.477050Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.595482Z", - "iopub.status.busy": "2024-07-18T04:01:49.595092Z", - "iopub.status.idle": "2024-07-18T04:01:49.598627Z", - "shell.execute_reply": "2024-07-18T04:01:49.598158Z" + "iopub.execute_input": "2024-07-30T16:31:49.479493Z", + "iopub.status.busy": "2024-07-30T16:31:49.479316Z", + "iopub.status.idle": "2024-07-30T16:31:49.482910Z", + "shell.execute_reply": "2024-07-30T16:31:49.482450Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'beneficiary_not_allowed', 'cancel_transfer', 'supported_cards_and_currencies', 'getting_spare_card', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'card_about_to_expire', 'lost_or_stolen_phone'}\n" + "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'visa_or_mastercard', 'cancel_transfer', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.600656Z", - "iopub.status.busy": "2024-07-18T04:01:49.600330Z", - "iopub.status.idle": "2024-07-18T04:01:49.603386Z", - "shell.execute_reply": "2024-07-18T04:01:49.602846Z" + "iopub.execute_input": "2024-07-30T16:31:49.484867Z", + "iopub.status.busy": "2024-07-30T16:31:49.484521Z", + "iopub.status.idle": "2024-07-30T16:31:49.487821Z", + "shell.execute_reply": "2024-07-30T16:31:49.487340Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:01:49.605480Z", - "iopub.status.busy": "2024-07-18T04:01:49.605149Z", - "iopub.status.idle": "2024-07-18T04:01:49.608580Z", - 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["IPY_MODEL_a6e2619a05cf4c6892ae13e2c851eb0e", "IPY_MODEL_ef4ce1e627f64413bff7de5ef03c1544", "IPY_MODEL_dce5f0e1a3174c649eb53b9cab013b58"], "layout": "IPY_MODEL_9dc82a7b78864ce18d1e3cff48063e14", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index c06368ad7..a2d329ad2 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": "2024-07-18T04:02:01.117687Z", - "iopub.status.busy": "2024-07-18T04:02:01.117526Z", - "iopub.status.idle": "2024-07-18T04:02:06.436581Z", - "shell.execute_reply": "2024-07-18T04:02:06.435951Z" + "iopub.execute_input": "2024-07-30T16:32:01.430387Z", + "iopub.status.busy": "2024-07-30T16:32:01.430194Z", + "iopub.status.idle": "2024-07-30T16:32:07.507356Z", + "shell.execute_reply": "2024-07-30T16:32:07.506775Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:06.439753Z", - "iopub.status.busy": "2024-07-18T04:02:06.438998Z", - "iopub.status.idle": "2024-07-18T04:02:06.442507Z", - "shell.execute_reply": "2024-07-18T04:02:06.442047Z" + "iopub.execute_input": "2024-07-30T16:32:07.510541Z", + "iopub.status.busy": "2024-07-30T16:32:07.509839Z", + "iopub.status.idle": "2024-07-30T16:32:07.513583Z", + "shell.execute_reply": "2024-07-30T16:32:07.513077Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:06.444488Z", - "iopub.status.busy": "2024-07-18T04:02:06.444150Z", - "iopub.status.idle": "2024-07-18T04:02:06.448808Z", - "shell.execute_reply": "2024-07-18T04:02:06.448377Z" + "iopub.execute_input": "2024-07-30T16:32:07.515817Z", + "iopub.status.busy": "2024-07-30T16:32:07.515454Z", + "iopub.status.idle": "2024-07-30T16:32:07.520703Z", + "shell.execute_reply": "2024-07-30T16:32:07.520274Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:06.450829Z", - "iopub.status.busy": "2024-07-18T04:02:06.450489Z", - "iopub.status.idle": "2024-07-18T04:02:08.286142Z", - "shell.execute_reply": "2024-07-18T04:02:08.285466Z" + "iopub.execute_input": "2024-07-30T16:32:07.522733Z", + "iopub.status.busy": "2024-07-30T16:32:07.522401Z", + "iopub.status.idle": "2024-07-30T16:32:09.284078Z", + "shell.execute_reply": "2024-07-30T16:32:09.283231Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.288955Z", - "iopub.status.busy": "2024-07-18T04:02:08.288547Z", - "iopub.status.idle": "2024-07-18T04:02:08.299806Z", - "shell.execute_reply": "2024-07-18T04:02:08.299296Z" + "iopub.execute_input": "2024-07-30T16:32:09.287053Z", + "iopub.status.busy": "2024-07-30T16:32:09.286654Z", + "iopub.status.idle": "2024-07-30T16:32:09.297621Z", + "shell.execute_reply": "2024-07-30T16:32:09.297169Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.302021Z", - "iopub.status.busy": "2024-07-18T04:02:08.301679Z", - "iopub.status.idle": "2024-07-18T04:02:08.307287Z", - "shell.execute_reply": "2024-07-18T04:02:08.306826Z" + "iopub.execute_input": "2024-07-30T16:32:09.299786Z", + "iopub.status.busy": "2024-07-30T16:32:09.299427Z", + "iopub.status.idle": "2024-07-30T16:32:09.304872Z", + "shell.execute_reply": "2024-07-30T16:32:09.304392Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.309302Z", - "iopub.status.busy": "2024-07-18T04:02:08.308959Z", - "iopub.status.idle": "2024-07-18T04:02:08.779123Z", - "shell.execute_reply": "2024-07-18T04:02:08.778615Z" + "iopub.execute_input": "2024-07-30T16:32:09.307010Z", + "iopub.status.busy": "2024-07-30T16:32:09.306676Z", + "iopub.status.idle": "2024-07-30T16:32:09.814179Z", + "shell.execute_reply": "2024-07-30T16:32:09.813575Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:08.781502Z", - "iopub.status.busy": "2024-07-18T04:02:08.780993Z", - "iopub.status.idle": "2024-07-18T04:02:09.831176Z", - "shell.execute_reply": "2024-07-18T04:02:09.830565Z" + "iopub.execute_input": "2024-07-30T16:32:09.816449Z", + "iopub.status.busy": "2024-07-30T16:32:09.816091Z", + "iopub.status.idle": "2024-07-30T16:32:11.566172Z", + "shell.execute_reply": "2024-07-30T16:32:11.565639Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.833594Z", - "iopub.status.busy": "2024-07-18T04:02:09.833376Z", - "iopub.status.idle": "2024-07-18T04:02:09.851912Z", - "shell.execute_reply": "2024-07-18T04:02:09.851466Z" + "iopub.execute_input": "2024-07-30T16:32:11.568615Z", + "iopub.status.busy": "2024-07-30T16:32:11.568320Z", + "iopub.status.idle": "2024-07-30T16:32:11.586724Z", + "shell.execute_reply": "2024-07-30T16:32:11.586277Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.853723Z", - "iopub.status.busy": "2024-07-18T04:02:09.853550Z", - "iopub.status.idle": "2024-07-18T04:02:09.856982Z", - "shell.execute_reply": "2024-07-18T04:02:09.856425Z" + "iopub.execute_input": "2024-07-30T16:32:11.588741Z", + "iopub.status.busy": "2024-07-30T16:32:11.588441Z", + "iopub.status.idle": "2024-07-30T16:32:11.591552Z", + "shell.execute_reply": "2024-07-30T16:32:11.591039Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:09.859010Z", - "iopub.status.busy": "2024-07-18T04:02:09.858672Z", - "iopub.status.idle": "2024-07-18T04:02:23.705662Z", - "shell.execute_reply": "2024-07-18T04:02:23.705044Z" + "iopub.execute_input": "2024-07-30T16:32:11.593585Z", + "iopub.status.busy": "2024-07-30T16:32:11.593193Z", + "iopub.status.idle": "2024-07-30T16:32:26.818572Z", + "shell.execute_reply": "2024-07-30T16:32:26.817879Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:23.708515Z", - "iopub.status.busy": "2024-07-18T04:02:23.708112Z", - "iopub.status.idle": "2024-07-18T04:02:23.711931Z", - "shell.execute_reply": "2024-07-18T04:02:23.711369Z" + "iopub.execute_input": "2024-07-30T16:32:26.821335Z", + "iopub.status.busy": "2024-07-30T16:32:26.821126Z", + "iopub.status.idle": "2024-07-30T16:32:26.825126Z", + "shell.execute_reply": "2024-07-30T16:32:26.824635Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:23.713930Z", - "iopub.status.busy": "2024-07-18T04:02:23.713624Z", - "iopub.status.idle": "2024-07-18T04:02:24.398596Z", - "shell.execute_reply": "2024-07-18T04:02:24.398020Z" + "iopub.execute_input": "2024-07-30T16:32:26.827091Z", + "iopub.status.busy": "2024-07-30T16:32:26.826917Z", + "iopub.status.idle": "2024-07-30T16:32:27.596925Z", + "shell.execute_reply": "2024-07-30T16:32:27.596317Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.401422Z", - "iopub.status.busy": "2024-07-18T04:02:24.401003Z", - "iopub.status.idle": "2024-07-18T04:02:24.406081Z", - "shell.execute_reply": "2024-07-18T04:02:24.405558Z" + "iopub.execute_input": "2024-07-30T16:32:27.600680Z", + "iopub.status.busy": "2024-07-30T16:32:27.599702Z", + "iopub.status.idle": "2024-07-30T16:32:27.606621Z", + "shell.execute_reply": "2024-07-30T16:32:27.606103Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.409353Z", - "iopub.status.busy": "2024-07-18T04:02:24.408424Z", - "iopub.status.idle": "2024-07-18T04:02:24.518831Z", - "shell.execute_reply": "2024-07-18T04:02:24.518201Z" + "iopub.execute_input": "2024-07-30T16:32:27.610268Z", + "iopub.status.busy": "2024-07-30T16:32:27.609309Z", + "iopub.status.idle": "2024-07-30T16:32:27.732351Z", + "shell.execute_reply": "2024-07-30T16:32:27.731717Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:24.521227Z", - "iopub.status.busy": "2024-07-18T04:02:24.521025Z", - "iopub.status.idle": "2024-07-18T04:02:24.533914Z", - "shell.execute_reply": "2024-07-18T04:02:24.533353Z" + "iopub.execute_input": "2024-07-30T16:32:27.734791Z", + "iopub.status.busy": "2024-07-30T16:32:27.734594Z", + "iopub.status.idle": "2024-07-30T16:32:27.747001Z", + "shell.execute_reply": "2024-07-30T16:32:27.746549Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { 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"version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 1d8d4bc13..899e02b72 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": "2024-07-18T04:02:29.204779Z", - "iopub.status.busy": "2024-07-18T04:02:29.204616Z", - "iopub.status.idle": "2024-07-18T04:02:30.379450Z", - "shell.execute_reply": "2024-07-18T04:02:30.378831Z" + "iopub.execute_input": "2024-07-30T16:32:32.656232Z", + "iopub.status.busy": "2024-07-30T16:32:32.656056Z", + "iopub.status.idle": "2024-07-30T16:32:34.118637Z", + "shell.execute_reply": "2024-07-30T16:32:34.117917Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:30.382052Z", - "iopub.status.busy": "2024-07-18T04:02:30.381624Z", - "iopub.status.idle": "2024-07-18T04:02:30.384679Z", - "shell.execute_reply": "2024-07-18T04:02:30.384157Z" + "iopub.execute_input": "2024-07-30T16:32:34.121608Z", + "iopub.status.busy": "2024-07-30T16:32:34.121097Z", + "iopub.status.idle": "2024-07-30T16:32:34.124191Z", + "shell.execute_reply": "2024-07-30T16:32:34.123735Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.386897Z", - "iopub.status.busy": "2024-07-18T04:02:30.386555Z", - "iopub.status.idle": "2024-07-18T04:02:30.395101Z", - "shell.execute_reply": "2024-07-18T04:02:30.394657Z" + "iopub.execute_input": "2024-07-30T16:32:34.126267Z", + "iopub.status.busy": "2024-07-30T16:32:34.126096Z", + "iopub.status.idle": "2024-07-30T16:32:34.134798Z", + "shell.execute_reply": "2024-07-30T16:32:34.134311Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.397072Z", - "iopub.status.busy": "2024-07-18T04:02:30.396747Z", - "iopub.status.idle": "2024-07-18T04:02:30.401452Z", - "shell.execute_reply": "2024-07-18T04:02:30.401038Z" + "iopub.execute_input": "2024-07-30T16:32:34.136971Z", + "iopub.status.busy": "2024-07-30T16:32:34.136634Z", + "iopub.status.idle": "2024-07-30T16:32:34.141247Z", + "shell.execute_reply": "2024-07-30T16:32:34.140806Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.403464Z", - "iopub.status.busy": "2024-07-18T04:02:30.403158Z", - "iopub.status.idle": "2024-07-18T04:02:30.584668Z", - "shell.execute_reply": "2024-07-18T04:02:30.584074Z" + "iopub.execute_input": "2024-07-30T16:32:34.143531Z", + "iopub.status.busy": "2024-07-30T16:32:34.143189Z", + "iopub.status.idle": "2024-07-30T16:32:34.151658Z", + "shell.execute_reply": "2024-07-30T16:32:34.151032Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.586832Z", - "iopub.status.busy": "2024-07-18T04:02:30.586642Z", - "iopub.status.idle": "2024-07-18T04:02:30.955459Z", - "shell.execute_reply": "2024-07-18T04:02:30.954890Z" + "iopub.execute_input": "2024-07-30T16:32:34.153882Z", + "iopub.status.busy": "2024-07-30T16:32:34.153554Z", + "iopub.status.idle": "2024-07-30T16:32:34.532146Z", + "shell.execute_reply": 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"iopub.status.idle": "2024-07-30T16:32:34.574011Z", + "shell.execute_reply": "2024-07-30T16:32:34.573551Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:30.995299Z", - "iopub.status.busy": "2024-07-18T04:02:30.994966Z", - "iopub.status.idle": "2024-07-18T04:02:33.034709Z", - "shell.execute_reply": "2024-07-18T04:02:33.034140Z" + "iopub.execute_input": "2024-07-30T16:32:34.576252Z", + "iopub.status.busy": "2024-07-30T16:32:34.575906Z", + "iopub.status.idle": "2024-07-30T16:32:36.755631Z", + "shell.execute_reply": "2024-07-30T16:32:36.755030Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:33.037102Z", - "iopub.status.busy": "2024-07-18T04:02:33.036820Z", - "iopub.status.idle": "2024-07-18T04:02:33.057501Z", - "shell.execute_reply": "2024-07-18T04:02:33.057031Z" + "iopub.execute_input": 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"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": "" + } + }, + "f9c800ac49fd453fbbdcc88f12d35194": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1705,7 +1728,33 @@ "text_color": null } }, - "960116dab3344fc8a824d39c6d7c5b37": { + "fa38f1de6f7849ee9f7cd7ad7a504555": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a8759625ec944bbbac04db9cec87a317", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee9027d19159444e8d46c66083251836", + "tabbable": null, + "tooltip": null, + "value": 132.0 + } + }, + "fe623ece577749be8a8ce449c28b2074": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1757,55 +1806,6 @@ "visibility": null, "width": null } - }, - "a2f48d81a080433fa84848011182083e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": 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"IPY_MODEL_839aa6fe30a94d79a49415b12a228e66", - "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index d88ccd2ad..dd27cd645 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": "2024-07-18T04:02:36.051413Z", - "iopub.status.busy": "2024-07-18T04:02:36.051244Z", - "iopub.status.idle": "2024-07-18T04:02:37.266874Z", - "shell.execute_reply": "2024-07-18T04:02:37.266234Z" + "iopub.execute_input": "2024-07-30T16:32:39.968392Z", + "iopub.status.busy": "2024-07-30T16:32:39.968219Z", + "iopub.status.idle": "2024-07-30T16:32:41.428731Z", + "shell.execute_reply": "2024-07-30T16:32:41.428142Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:02:37.269397Z", - "iopub.status.busy": "2024-07-18T04:02:37.269128Z", - "iopub.status.idle": "2024-07-18T04:02:37.272272Z", - "shell.execute_reply": "2024-07-18T04:02:37.271823Z" + "iopub.execute_input": "2024-07-30T16:32:41.431407Z", + "iopub.status.busy": "2024-07-30T16:32:41.430932Z", + "iopub.status.idle": "2024-07-30T16:32:41.433897Z", + "shell.execute_reply": "2024-07-30T16:32:41.433429Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.274522Z", - "iopub.status.busy": "2024-07-18T04:02:37.274188Z", - "iopub.status.idle": "2024-07-18T04:02:37.283099Z", - "shell.execute_reply": "2024-07-18T04:02:37.282643Z" + "iopub.execute_input": "2024-07-30T16:32:41.436056Z", + "iopub.status.busy": "2024-07-30T16:32:41.435693Z", + "iopub.status.idle": "2024-07-30T16:32:41.444683Z", + "shell.execute_reply": "2024-07-30T16:32:41.444228Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.285099Z", - "iopub.status.busy": "2024-07-18T04:02:37.284756Z", - "iopub.status.idle": "2024-07-18T04:02:37.289489Z", - "shell.execute_reply": "2024-07-18T04:02:37.289025Z" + "iopub.execute_input": "2024-07-30T16:32:41.446855Z", + "iopub.status.busy": "2024-07-30T16:32:41.446459Z", + "iopub.status.idle": "2024-07-30T16:32:41.451834Z", + "shell.execute_reply": "2024-07-30T16:32:41.451242Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.291764Z", - "iopub.status.busy": "2024-07-18T04:02:37.291422Z", - "iopub.status.idle": "2024-07-18T04:02:37.472524Z", - "shell.execute_reply": "2024-07-18T04:02:37.472008Z" + "iopub.execute_input": "2024-07-30T16:32:41.454128Z", + "iopub.status.busy": "2024-07-30T16:32:41.453789Z", + "iopub.status.idle": "2024-07-30T16:32:41.461841Z", + "shell.execute_reply": "2024-07-30T16:32:41.461254Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.474534Z", - "iopub.status.busy": "2024-07-18T04:02:37.474266Z", - "iopub.status.idle": "2024-07-18T04:02:37.844349Z", - "shell.execute_reply": "2024-07-18T04:02:37.843783Z" + "iopub.execute_input": "2024-07-30T16:32:41.463879Z", + "iopub.status.busy": "2024-07-30T16:32:41.463564Z", + "iopub.status.idle": "2024-07-30T16:32:41.841201Z", + "shell.execute_reply": "2024-07-30T16:32:41.840606Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.846655Z", - "iopub.status.busy": "2024-07-18T04:02:37.846287Z", - "iopub.status.idle": "2024-07-18T04:02:37.849124Z", - "shell.execute_reply": "2024-07-18T04:02:37.848661Z" + "iopub.execute_input": "2024-07-30T16:32:41.843720Z", + "iopub.status.busy": "2024-07-30T16:32:41.843358Z", + "iopub.status.idle": "2024-07-30T16:32:41.846353Z", + "shell.execute_reply": "2024-07-30T16:32:41.845761Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.851174Z", - "iopub.status.busy": "2024-07-18T04:02:37.850810Z", - "iopub.status.idle": "2024-07-18T04:02:37.884496Z", - "shell.execute_reply": "2024-07-18T04:02:37.884041Z" + "iopub.execute_input": "2024-07-30T16:32:41.848467Z", + "iopub.status.busy": "2024-07-30T16:32:41.848142Z", + "iopub.status.idle": "2024-07-30T16:32:41.882970Z", + "shell.execute_reply": "2024-07-30T16:32:41.882316Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:37.886502Z", - "iopub.status.busy": "2024-07-18T04:02:37.886186Z", - "iopub.status.idle": "2024-07-18T04:02:39.946867Z", - "shell.execute_reply": "2024-07-18T04:02:39.946264Z" + "iopub.execute_input": "2024-07-30T16:32:41.885593Z", + "iopub.status.busy": "2024-07-30T16:32:41.885224Z", + "iopub.status.idle": "2024-07-30T16:32:44.166781Z", + "shell.execute_reply": "2024-07-30T16:32:44.166160Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.949465Z", - "iopub.status.busy": "2024-07-18T04:02:39.948936Z", - "iopub.status.idle": "2024-07-18T04:02:39.967547Z", - "shell.execute_reply": "2024-07-18T04:02:39.967070Z" + "iopub.execute_input": "2024-07-30T16:32:44.169594Z", + "iopub.status.busy": "2024-07-30T16:32:44.168967Z", + "iopub.status.idle": "2024-07-30T16:32:44.189239Z", + "shell.execute_reply": "2024-07-30T16:32:44.188670Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.969591Z", - "iopub.status.busy": "2024-07-18T04:02:39.969258Z", - "iopub.status.idle": "2024-07-18T04:02:39.975832Z", - "shell.execute_reply": "2024-07-18T04:02:39.975379Z" + "iopub.execute_input": "2024-07-30T16:32:44.191720Z", + "iopub.status.busy": "2024-07-30T16:32:44.191313Z", + "iopub.status.idle": "2024-07-30T16:32:44.198409Z", + "shell.execute_reply": "2024-07-30T16:32:44.197866Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.977855Z", - "iopub.status.busy": "2024-07-18T04:02:39.977518Z", - "iopub.status.idle": "2024-07-18T04:02:39.983314Z", - "shell.execute_reply": "2024-07-18T04:02:39.982722Z" + "iopub.execute_input": "2024-07-30T16:32:44.200671Z", + "iopub.status.busy": "2024-07-30T16:32:44.200330Z", + "iopub.status.idle": "2024-07-30T16:32:44.206405Z", + "shell.execute_reply": "2024-07-30T16:32:44.205886Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.985271Z", - "iopub.status.busy": "2024-07-18T04:02:39.984975Z", - "iopub.status.idle": "2024-07-18T04:02:39.995497Z", - "shell.execute_reply": "2024-07-18T04:02:39.994935Z" + "iopub.execute_input": "2024-07-30T16:32:44.208540Z", + "iopub.status.busy": "2024-07-30T16:32:44.208187Z", + "iopub.status.idle": "2024-07-30T16:32:44.218769Z", + "shell.execute_reply": "2024-07-30T16:32:44.218292Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:39.997551Z", - "iopub.status.busy": "2024-07-18T04:02:39.997256Z", - "iopub.status.idle": "2024-07-18T04:02:40.006036Z", - "shell.execute_reply": "2024-07-18T04:02:40.005587Z" + "iopub.execute_input": "2024-07-30T16:32:44.221000Z", + "iopub.status.busy": "2024-07-30T16:32:44.220629Z", + "iopub.status.idle": "2024-07-30T16:32:44.230293Z", + "shell.execute_reply": "2024-07-30T16:32:44.229490Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.008141Z", - "iopub.status.busy": "2024-07-18T04:02:40.007823Z", - "iopub.status.idle": "2024-07-18T04:02:40.014540Z", - "shell.execute_reply": "2024-07-18T04:02:40.014100Z" + "iopub.execute_input": "2024-07-30T16:32:44.232925Z", + "iopub.status.busy": "2024-07-30T16:32:44.232545Z", + "iopub.status.idle": "2024-07-30T16:32:44.240606Z", + "shell.execute_reply": "2024-07-30T16:32:44.239954Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.016481Z", - "iopub.status.busy": "2024-07-18T04:02:40.016306Z", - "iopub.status.idle": "2024-07-18T04:02:40.025823Z", - "shell.execute_reply": "2024-07-18T04:02:40.025365Z" + "iopub.execute_input": "2024-07-30T16:32:44.242998Z", + "iopub.status.busy": "2024-07-30T16:32:44.242622Z", + "iopub.status.idle": "2024-07-30T16:32:44.252782Z", + "shell.execute_reply": "2024-07-30T16:32:44.252133Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:40.027917Z", - "iopub.status.busy": "2024-07-18T04:02:40.027593Z", - "iopub.status.idle": "2024-07-18T04:02:40.042624Z", - "shell.execute_reply": "2024-07-18T04:02:40.042164Z" + "iopub.execute_input": "2024-07-30T16:32:44.255163Z", + "iopub.status.busy": "2024-07-30T16:32:44.254809Z", + "iopub.status.idle": "2024-07-30T16:32:44.273057Z", + "shell.execute_reply": "2024-07-30T16:32:44.272419Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 050e10ef0..c0c41a388 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -727,49 +727,49 @@

2. Fetch and normalize the Fashion-MNIST dataset

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

@@ -1073,8 +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: 4.886
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.688
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.221
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922
 Computing feature embeddings ...

@@ -1082,7 +1082,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
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@@ -1096,7 +1096,7 @@

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

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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: 4.882
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.635
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.233
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913
 Computing feature embeddings ...

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

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

-
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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: 4.760
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.559
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.455
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031
 Computing feature embeddings ...

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

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

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7. Use cleanlab to find issues

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+
@@ -1328,7 +1328,7 @@

View report - is_dark_issue dark_score + is_dark_issue 34848 - True 0.203922 + True 50270 - True 0.204588 + True 3936 - True 0.213098 + True 733 - True 0.217686 + True 8094 - True 0.230118 + True @@ -2115,7 +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 ae9c3c784..4228b95b6 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": "2024-07-18T04:02:42.669719Z", - "iopub.status.busy": "2024-07-18T04:02:42.669546Z", - "iopub.status.idle": "2024-07-18T04:02:45.613067Z", - "shell.execute_reply": "2024-07-18T04:02:45.612422Z" + "iopub.execute_input": "2024-07-30T16:32:47.382198Z", + "iopub.status.busy": "2024-07-30T16:32:47.381761Z", + "iopub.status.idle": "2024-07-30T16:32:50.574056Z", + "shell.execute_reply": "2024-07-30T16:32:50.573420Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:45.615720Z", - "iopub.status.busy": "2024-07-18T04:02:45.615473Z", - "iopub.status.idle": "2024-07-18T04:02:45.619132Z", - "shell.execute_reply": "2024-07-18T04:02:45.618589Z" + "iopub.execute_input": "2024-07-30T16:32:50.576906Z", + "iopub.status.busy": "2024-07-30T16:32:50.576348Z", + "iopub.status.idle": "2024-07-30T16:32:50.580381Z", + "shell.execute_reply": "2024-07-30T16:32:50.579783Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:45.621224Z", - "iopub.status.busy": "2024-07-18T04:02:45.620918Z", - "iopub.status.idle": "2024-07-18T04:02:59.785458Z", - "shell.execute_reply": "2024-07-18T04:02:59.784890Z" + "iopub.execute_input": "2024-07-30T16:32:50.582599Z", + "iopub.status.busy": "2024-07-30T16:32:50.582229Z", + "iopub.status.idle": "2024-07-30T16:33:02.303436Z", + "shell.execute_reply": "2024-07-30T16:33:02.302939Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cfa127d2d15346cfaecddd1f501e10b5", + "model_id": "7cd770708ca5498492377d6a0fd76616", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84cc79fb8dfc4c8b9878470743351e05", + "model_id": "c4fa7fdeeb9446ddbf6516f8963fa52e", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff98a3cd12749eaa0f172837c5ee79f", + "model_id": "a6e2987ba28d48c28d884b33288562df", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5e8b53b8e7c4bbe8e49fb581f733d5b", + "model_id": "29ade62a53ac448198f24b5900001b05", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "777602f50c1c472883ca1e491224fbf8", + "model_id": "926846a8c6954c46acf37f4dd63e7eb9", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8253b9a8b625435f97de457cdd8b57a4", + "model_id": "6aaa8d39274f4cfea54a66eb8516a06f", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84b77d23a1794224b69e4e95c7b2f83d", + "model_id": "96697881a93440babad369ae2e2fd4b8", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d781af0165714c0d9c2a1ea7cc0d7fab", + "model_id": "efe64e5c44d94c6bb0bed3ad6e844c33", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:59.787598Z", - "iopub.status.busy": "2024-07-18T04:02:59.787373Z", - "iopub.status.idle": "2024-07-18T04:02:59.791159Z", - "shell.execute_reply": "2024-07-18T04:02:59.790668Z" + "iopub.execute_input": "2024-07-30T16:33:02.305705Z", + "iopub.status.busy": "2024-07-30T16:33:02.305351Z", + "iopub.status.idle": "2024-07-30T16:33:02.309197Z", + "shell.execute_reply": "2024-07-30T16:33:02.308695Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:02:59.793186Z", - "iopub.status.busy": "2024-07-18T04:02:59.792855Z", - "iopub.status.idle": "2024-07-18T04:03:11.249028Z", - "shell.execute_reply": "2024-07-18T04:03:11.248480Z" + "iopub.execute_input": "2024-07-30T16:33:02.311412Z", + "iopub.status.busy": "2024-07-30T16:33:02.311066Z", + "iopub.status.idle": "2024-07-30T16:33:14.158148Z", + "shell.execute_reply": "2024-07-30T16:33:14.157496Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe3ea1a98f724dee92ced94c01b8f215", + "model_id": "c48d73b75d8543b7900f7e3a24c14ff0", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:11.251514Z", - "iopub.status.busy": "2024-07-18T04:03:11.251273Z", - "iopub.status.idle": "2024-07-18T04:03:29.163263Z", - "shell.execute_reply": "2024-07-18T04:03:29.162608Z" + "iopub.execute_input": "2024-07-30T16:33:14.161054Z", + "iopub.status.busy": "2024-07-30T16:33:14.160637Z", + "iopub.status.idle": "2024-07-30T16:33:33.040556Z", + "shell.execute_reply": "2024-07-30T16:33:33.039889Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.166273Z", - "iopub.status.busy": "2024-07-18T04:03:29.165751Z", - "iopub.status.idle": "2024-07-18T04:03:29.171052Z", - "shell.execute_reply": "2024-07-18T04:03:29.170476Z" + "iopub.execute_input": "2024-07-30T16:33:33.043526Z", + "iopub.status.busy": "2024-07-30T16:33:33.043163Z", + "iopub.status.idle": "2024-07-30T16:33:33.048147Z", + "shell.execute_reply": "2024-07-30T16:33:33.047576Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.173245Z", - "iopub.status.busy": "2024-07-18T04:03:29.172808Z", - "iopub.status.idle": "2024-07-18T04:03:29.176965Z", - "shell.execute_reply": "2024-07-18T04:03:29.176548Z" + "iopub.execute_input": "2024-07-30T16:33:33.050340Z", + "iopub.status.busy": "2024-07-30T16:33:33.049814Z", + "iopub.status.idle": "2024-07-30T16:33:33.054210Z", + "shell.execute_reply": "2024-07-30T16:33:33.053653Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.179181Z", - "iopub.status.busy": "2024-07-18T04:03:29.178764Z", - "iopub.status.idle": "2024-07-18T04:03:29.187660Z", - "shell.execute_reply": "2024-07-18T04:03:29.187210Z" + "iopub.execute_input": "2024-07-30T16:33:33.056104Z", + "iopub.status.busy": "2024-07-30T16:33:33.055933Z", + "iopub.status.idle": "2024-07-30T16:33:33.065120Z", + "shell.execute_reply": "2024-07-30T16:33:33.064640Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.189749Z", - "iopub.status.busy": "2024-07-18T04:03:29.189427Z", - "iopub.status.idle": "2024-07-18T04:03:29.217121Z", - "shell.execute_reply": "2024-07-18T04:03:29.216709Z" + "iopub.execute_input": "2024-07-30T16:33:33.067246Z", + "iopub.status.busy": "2024-07-30T16:33:33.066927Z", + "iopub.status.idle": "2024-07-30T16:33:33.096315Z", + "shell.execute_reply": "2024-07-30T16:33:33.095690Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:03:29.219128Z", - "iopub.status.busy": "2024-07-18T04:03:29.218782Z", - "iopub.status.idle": "2024-07-18T04:04:02.310457Z", - "shell.execute_reply": "2024-07-18T04:04:02.309852Z" + "iopub.execute_input": "2024-07-30T16:33:33.098981Z", + "iopub.status.busy": "2024-07-30T16:33:33.098550Z", + "iopub.status.idle": "2024-07-30T16:34:08.613598Z", + "shell.execute_reply": "2024-07-30T16:34:08.612987Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.886\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.221\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.688\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f6a6c71bb6e4541a581f4f3310c0e87", + "model_id": "4800d17f20734ee3900349a11b2585dc", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8e7468aca074d3ea5c95769c1d98882", + "model_id": "21d92163462b4f67a981a814fcb48508", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.882\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.233\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.635\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7547caf87ef4a6e9cdaf34ecfe7a776", + "model_id": "8af1aec52aef434b81a22b708073556f", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf776da7e61c45d184a4ccbaec7843e3", + "model_id": "19a5e3a37b304b559df2c5101035122f", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.760\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.455\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.559\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce1365ddf37d4fd386cefbbc32a2b2a4", + "model_id": "a2738be416ce480c95fff046962f1137", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6cc5976d20884235b934ecfaa7687b01", + "model_id": "6c2f40cf42cc413e8b1040c82a085028", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.312910Z", - "iopub.status.busy": "2024-07-18T04:04:02.312660Z", - "iopub.status.idle": "2024-07-18T04:04:02.327049Z", - "shell.execute_reply": "2024-07-18T04:04:02.326569Z" + "iopub.execute_input": "2024-07-30T16:34:08.616395Z", + "iopub.status.busy": "2024-07-30T16:34:08.615872Z", + "iopub.status.idle": "2024-07-30T16:34:08.631241Z", + "shell.execute_reply": "2024-07-30T16:34:08.630690Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.329066Z", - "iopub.status.busy": "2024-07-18T04:04:02.328768Z", - "iopub.status.idle": "2024-07-18T04:04:02.796841Z", - "shell.execute_reply": "2024-07-18T04:04:02.796286Z" + "iopub.execute_input": "2024-07-30T16:34:08.633394Z", + "iopub.status.busy": "2024-07-30T16:34:08.633052Z", + "iopub.status.idle": "2024-07-30T16:34:09.125544Z", + "shell.execute_reply": "2024-07-30T16:34:09.124944Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:04:02.799526Z", - "iopub.status.busy": "2024-07-18T04:04:02.799081Z", - "iopub.status.idle": "2024-07-18T04:05:39.908807Z", - "shell.execute_reply": "2024-07-18T04:05:39.908161Z" + "iopub.execute_input": "2024-07-30T16:34:09.128240Z", + "iopub.status.busy": "2024-07-30T16:34:09.127855Z", + "iopub.status.idle": "2024-07-30T16:35:49.585066Z", + "shell.execute_reply": "2024-07-30T16:35:49.584319Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83f24aa593964f26bcdc4ca9d1acb2c1", + "model_id": "1e7ed9a8db3f47d499c32f8ab98695a3", "version_major": 2, "version_minor": 0 }, @@ -1120,7 +1120,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n", + "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Audit complete. 7714 issues found in the dataset.\n" ] @@ -1144,10 +1150,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:39.911277Z", - "iopub.status.busy": "2024-07-18T04:05:39.910826Z", - "iopub.status.idle": "2024-07-18T04:05:40.363119Z", - "shell.execute_reply": "2024-07-18T04:05:40.362545Z" + "iopub.execute_input": "2024-07-30T16:35:49.587886Z", + "iopub.status.busy": "2024-07-30T16:35:49.587314Z", + "iopub.status.idle": "2024-07-30T16:35:50.063963Z", + "shell.execute_reply": "2024-07-30T16:35:50.063372Z" } }, "outputs": [ @@ -1233,7 +1239,7 @@ "\n", "\n", "\n", - "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.5 \n", + "Removing grayscale from potential issues in the dataset as it exceeds max_prevalence=0.1 \n", "------------------ low_information images ------------------\n", "\n", "Number of examples with this issue: 166\n", @@ -1293,10 +1299,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.365503Z", - "iopub.status.busy": "2024-07-18T04:05:40.365120Z", - "iopub.status.idle": "2024-07-18T04:05:40.427219Z", - "shell.execute_reply": "2024-07-18T04:05:40.426389Z" + "iopub.execute_input": "2024-07-30T16:35:50.066551Z", + "iopub.status.busy": "2024-07-30T16:35:50.065928Z", + "iopub.status.idle": "2024-07-30T16:35:50.128967Z", + "shell.execute_reply": "2024-07-30T16:35:50.128437Z" } }, "outputs": [ @@ -1400,10 +1406,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.429398Z", - "iopub.status.busy": "2024-07-18T04:05:40.428968Z", - "iopub.status.idle": "2024-07-18T04:05:40.437542Z", - "shell.execute_reply": "2024-07-18T04:05:40.437085Z" + "iopub.execute_input": "2024-07-30T16:35:50.131433Z", + "iopub.status.busy": "2024-07-30T16:35:50.130978Z", + "iopub.status.idle": "2024-07-30T16:35:50.141475Z", + "shell.execute_reply": "2024-07-30T16:35:50.140991Z" } }, "outputs": [ @@ -1533,10 +1539,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.439398Z", - "iopub.status.busy": "2024-07-18T04:05:40.439226Z", - "iopub.status.idle": "2024-07-18T04:05:40.443724Z", - "shell.execute_reply": "2024-07-18T04:05:40.443274Z" + "iopub.execute_input": "2024-07-30T16:35:50.143638Z", + "iopub.status.busy": "2024-07-30T16:35:50.143453Z", + "iopub.status.idle": "2024-07-30T16:35:50.148446Z", + "shell.execute_reply": "2024-07-30T16:35:50.147959Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1588,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.445775Z", - "iopub.status.busy": "2024-07-18T04:05:40.445449Z", - "iopub.status.idle": "2024-07-18T04:05:40.937446Z", - "shell.execute_reply": "2024-07-18T04:05:40.936903Z" + "iopub.execute_input": "2024-07-30T16:35:50.150589Z", + "iopub.status.busy": "2024-07-30T16:35:50.150254Z", + "iopub.status.idle": "2024-07-30T16:35:50.655702Z", + "shell.execute_reply": "2024-07-30T16:35:50.655117Z" } }, "outputs": [ @@ -1620,10 +1626,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.939591Z", - "iopub.status.busy": "2024-07-18T04:05:40.939250Z", - "iopub.status.idle": "2024-07-18T04:05:40.947145Z", - "shell.execute_reply": "2024-07-18T04:05:40.946558Z" + "iopub.execute_input": "2024-07-30T16:35:50.658180Z", + "iopub.status.busy": "2024-07-30T16:35:50.657804Z", + "iopub.status.idle": "2024-07-30T16:35:50.666671Z", + "shell.execute_reply": "2024-07-30T16:35:50.666179Z" } }, "outputs": [ @@ -1790,10 +1796,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.949355Z", - "iopub.status.busy": "2024-07-18T04:05:40.949021Z", - "iopub.status.idle": "2024-07-18T04:05:40.956013Z", - "shell.execute_reply": "2024-07-18T04:05:40.955573Z" + "iopub.execute_input": "2024-07-30T16:35:50.668871Z", + "iopub.status.busy": "2024-07-30T16:35:50.668531Z", + "iopub.status.idle": "2024-07-30T16:35:50.675953Z", + "shell.execute_reply": "2024-07-30T16:35:50.675476Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1875,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:40.957854Z", - "iopub.status.busy": "2024-07-18T04:05:40.957563Z", - "iopub.status.idle": "2024-07-18T04:05:41.700271Z", - "shell.execute_reply": "2024-07-18T04:05:41.699684Z" + "iopub.execute_input": "2024-07-30T16:35:50.677971Z", + "iopub.status.busy": "2024-07-30T16:35:50.677635Z", + "iopub.status.idle": "2024-07-30T16:35:51.461209Z", + "shell.execute_reply": "2024-07-30T16:35:51.460595Z" } }, "outputs": [ @@ -1909,10 +1915,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.702563Z", - "iopub.status.busy": "2024-07-18T04:05:41.702218Z", - "iopub.status.idle": "2024-07-18T04:05:41.717782Z", - "shell.execute_reply": "2024-07-18T04:05:41.717223Z" + "iopub.execute_input": "2024-07-30T16:35:51.463335Z", + "iopub.status.busy": "2024-07-30T16:35:51.463157Z", + "iopub.status.idle": "2024-07-30T16:35:51.478468Z", + "shell.execute_reply": "2024-07-30T16:35:51.477939Z" } }, "outputs": [ @@ -2069,10 +2075,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.720064Z", - "iopub.status.busy": "2024-07-18T04:05:41.719741Z", - "iopub.status.idle": "2024-07-18T04:05:41.725292Z", - "shell.execute_reply": "2024-07-18T04:05:41.724826Z" + "iopub.execute_input": "2024-07-30T16:35:51.480643Z", + "iopub.status.busy": "2024-07-30T16:35:51.480298Z", + "iopub.status.idle": "2024-07-30T16:35:51.486080Z", + "shell.execute_reply": "2024-07-30T16:35:51.485499Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2123,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:41.727255Z", - "iopub.status.busy": "2024-07-18T04:05:41.726947Z", - "iopub.status.idle": "2024-07-18T04:05:42.108095Z", - "shell.execute_reply": "2024-07-18T04:05:42.107541Z" + "iopub.execute_input": "2024-07-30T16:35:51.488104Z", + "iopub.status.busy": "2024-07-30T16:35:51.487778Z", + "iopub.status.idle": "2024-07-30T16:35:51.924919Z", + "shell.execute_reply": "2024-07-30T16:35:51.924107Z" } }, "outputs": [ @@ -2202,10 +2208,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:42.110467Z", - "iopub.status.busy": "2024-07-18T04:05:42.110297Z", - "iopub.status.idle": "2024-07-18T04:05:42.119483Z", - "shell.execute_reply": "2024-07-18T04:05:42.118929Z" + "iopub.execute_input": "2024-07-30T16:35:51.927416Z", + "iopub.status.busy": "2024-07-30T16:35:51.927225Z", + "iopub.status.idle": "2024-07-30T16:35:51.936102Z", + "shell.execute_reply": "2024-07-30T16:35:51.935657Z" } }, "outputs": [ @@ -2230,47 +2236,47 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 34848\n", - " True\n", " 0.203922\n", + " True\n", " \n", " \n", " 50270\n", - " True\n", " 0.204588\n", + " True\n", " \n", " \n", " 3936\n", - " True\n", " 0.213098\n", + " True\n", " \n", " \n", " 733\n", - " True\n", " 0.217686\n", + " True\n", " \n", " \n", " 8094\n", - " True\n", " 0.230118\n", + " True\n", " \n", " \n", "\n", "

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"IPY_MODEL_57f0147d0b40481487ead645778ad6f0", + "tabbable": null, + "tooltip": null, + "value": 5148.0 + } + }, + "f6229a7aab1d42ca8805f534935617d9": { + "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_a13807ee4fd84d609c078799a117b6c5", + "placeholder": "​", + "style": "IPY_MODEL_c15afcf9ca54456fadfde7d364d41e12", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "f7d9dcc168074809a9f461109ae607c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8610,54 +8619,51 @@ "width": null } }, - "fdd378c59c5640b6b2e1303e4ed8ed50": { + "faaac1dac8fa4eccb56fbb10b45393c3": { "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_918570fe365c40839c1c0e55002a0ff6", - "placeholder": "​", - "style": "IPY_MODEL_1581d2e68d7847108100e4a2b1f305b1", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "fe3ea1a98f724dee92ced94c01b8f215": { + "fabe89de1f3f41f493f2490c661f6d02": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_06e5b497708943179a656ffcb7a430e9", - "IPY_MODEL_3a7f6b08a8134701aaa1be5a8ca7d7b0", - "IPY_MODEL_e85de978a90644fc9de2f534a52cb11e" - ], - "layout": "IPY_MODEL_30b0eb3dc72a4f1b9697396466fae05a", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c72eded270c04594b9cd5ab865ec18ee", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_01972cf3b8f94fab866c986e21f7f91f", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 60000.0 } }, - "ff6c65a3e52748a9a9493f576321c597": { + "fd82af84285840158e600b4d0204c84e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 4d38880f7..a6ed7ee61 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": "2024-07-18T04:05:46.059528Z", - "iopub.status.busy": "2024-07-18T04:05:46.059357Z", - "iopub.status.idle": "2024-07-18T04:05:47.184703Z", - "shell.execute_reply": "2024-07-18T04:05:47.184146Z" + "iopub.execute_input": "2024-07-30T16:35:56.051172Z", + "iopub.status.busy": "2024-07-30T16:35:56.050992Z", + "iopub.status.idle": "2024-07-30T16:35:57.510285Z", + "shell.execute_reply": "2024-07-30T16:35:57.509737Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:05:47.187068Z", - "iopub.status.busy": "2024-07-18T04:05:47.186797Z", - "iopub.status.idle": "2024-07-18T04:05:47.205431Z", - "shell.execute_reply": "2024-07-18T04:05:47.204882Z" + "iopub.execute_input": "2024-07-30T16:35:57.512868Z", + "iopub.status.busy": "2024-07-30T16:35:57.512386Z", + "iopub.status.idle": "2024-07-30T16:35:57.530631Z", + "shell.execute_reply": "2024-07-30T16:35:57.530189Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.207698Z", - "iopub.status.busy": "2024-07-18T04:05:47.207321Z", - "iopub.status.idle": "2024-07-18T04:05:47.233000Z", - "shell.execute_reply": "2024-07-18T04:05:47.232462Z" + "iopub.execute_input": "2024-07-30T16:35:57.532740Z", + "iopub.status.busy": "2024-07-30T16:35:57.532388Z", + "iopub.status.idle": "2024-07-30T16:35:57.570290Z", + "shell.execute_reply": "2024-07-30T16:35:57.569781Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.235099Z", - "iopub.status.busy": "2024-07-18T04:05:47.234749Z", - "iopub.status.idle": "2024-07-18T04:05:47.238601Z", - "shell.execute_reply": "2024-07-18T04:05:47.238176Z" + "iopub.execute_input": "2024-07-30T16:35:57.572398Z", + "iopub.status.busy": "2024-07-30T16:35:57.572060Z", + "iopub.status.idle": "2024-07-30T16:35:57.575328Z", + "shell.execute_reply": "2024-07-30T16:35:57.574903Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.240630Z", - "iopub.status.busy": "2024-07-18T04:05:47.240299Z", - "iopub.status.idle": "2024-07-18T04:05:47.247818Z", - "shell.execute_reply": "2024-07-18T04:05:47.247390Z" + "iopub.execute_input": "2024-07-30T16:35:57.577365Z", + "iopub.status.busy": "2024-07-30T16:35:57.576966Z", + "iopub.status.idle": "2024-07-30T16:35:57.584694Z", + "shell.execute_reply": "2024-07-30T16:35:57.584132Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.249866Z", - "iopub.status.busy": "2024-07-18T04:05:47.249525Z", - "iopub.status.idle": "2024-07-18T04:05:47.251977Z", - "shell.execute_reply": "2024-07-18T04:05:47.251524Z" + "iopub.execute_input": "2024-07-30T16:35:57.586954Z", + "iopub.status.busy": "2024-07-30T16:35:57.586638Z", + "iopub.status.idle": "2024-07-30T16:35:57.589280Z", + "shell.execute_reply": "2024-07-30T16:35:57.588805Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:47.254011Z", - "iopub.status.busy": "2024-07-18T04:05:47.253687Z", - "iopub.status.idle": "2024-07-18T04:05:50.331843Z", - "shell.execute_reply": "2024-07-18T04:05:50.331208Z" + "iopub.execute_input": "2024-07-30T16:35:57.591284Z", + "iopub.status.busy": "2024-07-30T16:35:57.590950Z", + "iopub.status.idle": "2024-07-30T16:36:00.688049Z", + "shell.execute_reply": "2024-07-30T16:36:00.687486Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:50.334588Z", - "iopub.status.busy": "2024-07-18T04:05:50.334171Z", - "iopub.status.idle": "2024-07-18T04:05:50.343958Z", - "shell.execute_reply": "2024-07-18T04:05:50.343394Z" + "iopub.execute_input": "2024-07-30T16:36:00.690868Z", + "iopub.status.busy": "2024-07-30T16:36:00.690451Z", + "iopub.status.idle": "2024-07-30T16:36:00.700262Z", + "shell.execute_reply": "2024-07-30T16:36:00.699795Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:50.346159Z", - "iopub.status.busy": "2024-07-18T04:05:50.345987Z", - "iopub.status.idle": "2024-07-18T04:05:52.286664Z", - "shell.execute_reply": "2024-07-18T04:05:52.286109Z" + "iopub.execute_input": "2024-07-30T16:36:00.702232Z", + "iopub.status.busy": "2024-07-30T16:36:00.702054Z", + "iopub.status.idle": "2024-07-30T16:36:02.934148Z", + "shell.execute_reply": "2024-07-30T16:36:02.933492Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.289279Z", - "iopub.status.busy": "2024-07-18T04:05:52.288785Z", - "iopub.status.idle": "2024-07-18T04:05:52.307212Z", - "shell.execute_reply": "2024-07-18T04:05:52.306758Z" + "iopub.execute_input": "2024-07-30T16:36:02.936729Z", + "iopub.status.busy": "2024-07-30T16:36:02.936205Z", + "iopub.status.idle": "2024-07-30T16:36:02.954952Z", + "shell.execute_reply": "2024-07-30T16:36:02.954378Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.309234Z", - "iopub.status.busy": "2024-07-18T04:05:52.308879Z", - "iopub.status.idle": "2024-07-18T04:05:52.316628Z", - "shell.execute_reply": "2024-07-18T04:05:52.316083Z" + "iopub.execute_input": "2024-07-30T16:36:02.957056Z", + "iopub.status.busy": "2024-07-30T16:36:02.956878Z", + "iopub.status.idle": "2024-07-30T16:36:02.964858Z", + "shell.execute_reply": "2024-07-30T16:36:02.964382Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.318738Z", - "iopub.status.busy": "2024-07-18T04:05:52.318420Z", - "iopub.status.idle": "2024-07-18T04:05:52.327148Z", - "shell.execute_reply": "2024-07-18T04:05:52.326685Z" + "iopub.execute_input": "2024-07-30T16:36:02.966898Z", + "iopub.status.busy": "2024-07-30T16:36:02.966576Z", + "iopub.status.idle": "2024-07-30T16:36:02.975334Z", + "shell.execute_reply": "2024-07-30T16:36:02.974877Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.329288Z", - "iopub.status.busy": "2024-07-18T04:05:52.328960Z", - "iopub.status.idle": "2024-07-18T04:05:52.336509Z", - "shell.execute_reply": "2024-07-18T04:05:52.336049Z" + "iopub.execute_input": "2024-07-30T16:36:02.977396Z", + "iopub.status.busy": "2024-07-30T16:36:02.977076Z", + "iopub.status.idle": "2024-07-30T16:36:02.984945Z", + "shell.execute_reply": "2024-07-30T16:36:02.984391Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.338513Z", - "iopub.status.busy": "2024-07-18T04:05:52.338182Z", - "iopub.status.idle": "2024-07-18T04:05:52.346902Z", - "shell.execute_reply": "2024-07-18T04:05:52.346464Z" + "iopub.execute_input": "2024-07-30T16:36:02.987002Z", + "iopub.status.busy": "2024-07-30T16:36:02.986692Z", + "iopub.status.idle": "2024-07-30T16:36:02.995395Z", + "shell.execute_reply": "2024-07-30T16:36:02.994843Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.349015Z", - "iopub.status.busy": "2024-07-18T04:05:52.348609Z", - "iopub.status.idle": "2024-07-18T04:05:52.356966Z", - "shell.execute_reply": "2024-07-18T04:05:52.356427Z" + "iopub.execute_input": "2024-07-30T16:36:02.997442Z", + "iopub.status.busy": "2024-07-30T16:36:02.997120Z", + "iopub.status.idle": "2024-07-30T16:36:03.004551Z", + "shell.execute_reply": "2024-07-30T16:36:03.004009Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.359411Z", - "iopub.status.busy": "2024-07-18T04:05:52.359043Z", - "iopub.status.idle": "2024-07-18T04:05:52.368156Z", - "shell.execute_reply": "2024-07-18T04:05:52.367618Z" + "iopub.execute_input": "2024-07-30T16:36:03.006659Z", + "iopub.status.busy": "2024-07-30T16:36:03.006343Z", + "iopub.status.idle": "2024-07-30T16:36:03.014117Z", + "shell.execute_reply": "2024-07-30T16:36:03.013629Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:52.370501Z", - "iopub.status.busy": "2024-07-18T04:05:52.370163Z", - "iopub.status.idle": "2024-07-18T04:05:52.378576Z", - "shell.execute_reply": "2024-07-18T04:05:52.378040Z" + "iopub.execute_input": "2024-07-30T16:36:03.016347Z", + "iopub.status.busy": "2024-07-30T16:36:03.016027Z", + "iopub.status.idle": "2024-07-30T16:36:03.024539Z", + "shell.execute_reply": "2024-07-30T16:36:03.023965Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index b0048df12..ae13fe500 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: {'visa_or_mastercard', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'card_about_to_expire', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'card_payment_fee_charged'}
+Classes: {'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin'}

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

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 9ede7e8a3..b380e2cea 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": "2024-07-18T04:05:55.129783Z", - "iopub.status.busy": "2024-07-18T04:05:55.129618Z", - "iopub.status.idle": "2024-07-18T04:05:57.865032Z", - "shell.execute_reply": "2024-07-18T04:05:57.864464Z" + "iopub.execute_input": "2024-07-30T16:36:05.906897Z", + "iopub.status.busy": "2024-07-30T16:36:05.906716Z", + "iopub.status.idle": "2024-07-30T16:36:09.210694Z", + "shell.execute_reply": "2024-07-30T16:36:09.210137Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:05:57.867630Z", - "iopub.status.busy": "2024-07-18T04:05:57.867176Z", - "iopub.status.idle": "2024-07-18T04:05:57.870439Z", - "shell.execute_reply": "2024-07-18T04:05:57.869974Z" + "iopub.execute_input": "2024-07-30T16:36:09.213471Z", + "iopub.status.busy": "2024-07-30T16:36:09.212961Z", + "iopub.status.idle": "2024-07-30T16:36:09.216206Z", + "shell.execute_reply": "2024-07-30T16:36:09.215755Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.872389Z", - "iopub.status.busy": "2024-07-18T04:05:57.872088Z", - "iopub.status.idle": "2024-07-18T04:05:57.875228Z", - "shell.execute_reply": "2024-07-18T04:05:57.874636Z" + "iopub.execute_input": "2024-07-30T16:36:09.218344Z", + "iopub.status.busy": "2024-07-30T16:36:09.217971Z", + "iopub.status.idle": "2024-07-30T16:36:09.221010Z", + "shell.execute_reply": "2024-07-30T16:36:09.220555Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.877320Z", - "iopub.status.busy": "2024-07-18T04:05:57.876885Z", - "iopub.status.idle": "2024-07-18T04:05:57.898872Z", - "shell.execute_reply": "2024-07-18T04:05:57.898311Z" + "iopub.execute_input": "2024-07-30T16:36:09.223151Z", + "iopub.status.busy": "2024-07-30T16:36:09.222813Z", + "iopub.status.idle": "2024-07-30T16:36:09.264547Z", + "shell.execute_reply": "2024-07-30T16:36:09.263969Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.900978Z", - "iopub.status.busy": "2024-07-18T04:05:57.900574Z", - "iopub.status.idle": "2024-07-18T04:05:57.904377Z", - "shell.execute_reply": "2024-07-18T04:05:57.903827Z" + "iopub.execute_input": "2024-07-30T16:36:09.266828Z", + "iopub.status.busy": "2024-07-30T16:36:09.266456Z", + "iopub.status.idle": "2024-07-30T16:36:09.270175Z", + "shell.execute_reply": "2024-07-30T16:36:09.269659Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'card_about_to_expire', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'change_pin', 'beneficiary_not_allowed', 'card_payment_fee_charged'}\n" + "Classes: {'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.906549Z", - "iopub.status.busy": "2024-07-18T04:05:57.906236Z", - "iopub.status.idle": "2024-07-18T04:05:57.909389Z", - "shell.execute_reply": "2024-07-18T04:05:57.908857Z" + "iopub.execute_input": "2024-07-30T16:36:09.272350Z", + "iopub.status.busy": "2024-07-30T16:36:09.271989Z", + "iopub.status.idle": "2024-07-30T16:36:09.275119Z", + "shell.execute_reply": "2024-07-30T16:36:09.274559Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:05:57.911634Z", - "iopub.status.busy": "2024-07-18T04:05:57.911199Z", - "iopub.status.idle": "2024-07-18T04:06:01.969964Z", - "shell.execute_reply": "2024-07-18T04:06:01.969310Z" + "iopub.execute_input": "2024-07-30T16:36:09.277262Z", + "iopub.status.busy": "2024-07-30T16:36:09.276911Z", + "iopub.status.idle": "2024-07-30T16:36:13.012240Z", + "shell.execute_reply": "2024-07-30T16:36:13.011588Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:01.972914Z", - "iopub.status.busy": "2024-07-18T04:06:01.972480Z", - "iopub.status.idle": "2024-07-18T04:06:02.905487Z", - "shell.execute_reply": "2024-07-18T04:06:02.904899Z" + "iopub.execute_input": "2024-07-30T16:36:13.015209Z", + "iopub.status.busy": "2024-07-30T16:36:13.014850Z", + "iopub.status.idle": "2024-07-30T16:36:13.913858Z", + "shell.execute_reply": "2024-07-30T16:36:13.913251Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:02.909182Z", - "iopub.status.busy": "2024-07-18T04:06:02.908234Z", - "iopub.status.idle": "2024-07-18T04:06:02.912302Z", - "shell.execute_reply": "2024-07-18T04:06:02.911802Z" + "iopub.execute_input": "2024-07-30T16:36:13.917763Z", + "iopub.status.busy": "2024-07-30T16:36:13.916780Z", + "iopub.status.idle": "2024-07-30T16:36:13.920912Z", + "shell.execute_reply": "2024-07-30T16:36:13.920410Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:02.915812Z", - "iopub.status.busy": "2024-07-18T04:06:02.914874Z", - "iopub.status.idle": "2024-07-18T04:06:04.898450Z", - "shell.execute_reply": "2024-07-18T04:06:04.897813Z" + "iopub.execute_input": "2024-07-30T16:36:13.924505Z", + "iopub.status.busy": "2024-07-30T16:36:13.923570Z", + "iopub.status.idle": "2024-07-30T16:36:16.057240Z", + "shell.execute_reply": "2024-07-30T16:36:16.056500Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.901548Z", - "iopub.status.busy": "2024-07-18T04:06:04.900932Z", - "iopub.status.idle": "2024-07-18T04:06:04.924390Z", - "shell.execute_reply": "2024-07-18T04:06:04.923883Z" + "iopub.execute_input": "2024-07-30T16:36:16.060459Z", + "iopub.status.busy": "2024-07-30T16:36:16.059879Z", + "iopub.status.idle": "2024-07-30T16:36:16.084329Z", + "shell.execute_reply": "2024-07-30T16:36:16.083774Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.926793Z", - "iopub.status.busy": "2024-07-18T04:06:04.926402Z", - "iopub.status.idle": "2024-07-18T04:06:04.935972Z", - "shell.execute_reply": "2024-07-18T04:06:04.935475Z" + "iopub.execute_input": "2024-07-30T16:36:16.087159Z", + "iopub.status.busy": "2024-07-30T16:36:16.086783Z", + "iopub.status.idle": "2024-07-30T16:36:16.096644Z", + "shell.execute_reply": "2024-07-30T16:36:16.096072Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.938170Z", - "iopub.status.busy": "2024-07-18T04:06:04.937989Z", - "iopub.status.idle": "2024-07-18T04:06:04.942216Z", - "shell.execute_reply": "2024-07-18T04:06:04.941644Z" + "iopub.execute_input": "2024-07-30T16:36:16.098946Z", + "iopub.status.busy": "2024-07-30T16:36:16.098549Z", + "iopub.status.idle": "2024-07-30T16:36:16.103008Z", + "shell.execute_reply": "2024-07-30T16:36:16.102445Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.944237Z", - "iopub.status.busy": "2024-07-18T04:06:04.943959Z", - "iopub.status.idle": "2024-07-18T04:06:04.950201Z", - "shell.execute_reply": "2024-07-18T04:06:04.949747Z" + "iopub.execute_input": "2024-07-30T16:36:16.105150Z", + "iopub.status.busy": "2024-07-30T16:36:16.104822Z", + "iopub.status.idle": "2024-07-30T16:36:16.111211Z", + "shell.execute_reply": "2024-07-30T16:36:16.110658Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.952147Z", - "iopub.status.busy": "2024-07-18T04:06:04.951973Z", - "iopub.status.idle": "2024-07-18T04:06:04.958732Z", - "shell.execute_reply": "2024-07-18T04:06:04.958268Z" + "iopub.execute_input": "2024-07-30T16:36:16.113187Z", + "iopub.status.busy": "2024-07-30T16:36:16.112885Z", + "iopub.status.idle": "2024-07-30T16:36:16.119267Z", + "shell.execute_reply": "2024-07-30T16:36:16.118719Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.960553Z", - "iopub.status.busy": "2024-07-18T04:06:04.960383Z", - "iopub.status.idle": "2024-07-18T04:06:04.966173Z", - "shell.execute_reply": "2024-07-18T04:06:04.965718Z" + "iopub.execute_input": "2024-07-30T16:36:16.121235Z", + "iopub.status.busy": "2024-07-30T16:36:16.120924Z", + "iopub.status.idle": "2024-07-30T16:36:16.126639Z", + "shell.execute_reply": "2024-07-30T16:36:16.126077Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.968159Z", - "iopub.status.busy": "2024-07-18T04:06:04.967881Z", - "iopub.status.idle": "2024-07-18T04:06:04.976603Z", - "shell.execute_reply": "2024-07-18T04:06:04.976039Z" + "iopub.execute_input": "2024-07-30T16:36:16.128700Z", + "iopub.status.busy": "2024-07-30T16:36:16.128385Z", + "iopub.status.idle": "2024-07-30T16:36:16.136815Z", + "shell.execute_reply": "2024-07-30T16:36:16.136243Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.978783Z", - "iopub.status.busy": "2024-07-18T04:06:04.978354Z", - "iopub.status.idle": "2024-07-18T04:06:04.983722Z", - "shell.execute_reply": "2024-07-18T04:06:04.983158Z" + "iopub.execute_input": "2024-07-30T16:36:16.138817Z", + "iopub.status.busy": "2024-07-30T16:36:16.138521Z", + "iopub.status.idle": "2024-07-30T16:36:16.143841Z", + "shell.execute_reply": "2024-07-30T16:36:16.143287Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.985767Z", - "iopub.status.busy": "2024-07-18T04:06:04.985339Z", - "iopub.status.idle": "2024-07-18T04:06:04.990755Z", - "shell.execute_reply": "2024-07-18T04:06:04.990193Z" + "iopub.execute_input": "2024-07-30T16:36:16.145732Z", + "iopub.status.busy": "2024-07-30T16:36:16.145554Z", + "iopub.status.idle": "2024-07-30T16:36:16.150879Z", + "shell.execute_reply": "2024-07-30T16:36:16.150344Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.992910Z", - "iopub.status.busy": "2024-07-18T04:06:04.992598Z", - "iopub.status.idle": "2024-07-18T04:06:04.996239Z", - "shell.execute_reply": "2024-07-18T04:06:04.995691Z" + "iopub.execute_input": "2024-07-30T16:36:16.152863Z", + "iopub.status.busy": "2024-07-30T16:36:16.152548Z", + "iopub.status.idle": "2024-07-30T16:36:16.156185Z", + "shell.execute_reply": "2024-07-30T16:36:16.155650Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:04.998334Z", - "iopub.status.busy": "2024-07-18T04:06:04.998026Z", - "iopub.status.idle": "2024-07-18T04:06:05.003252Z", - "shell.execute_reply": "2024-07-18T04:06:05.002678Z" + "iopub.execute_input": "2024-07-30T16:36:16.158400Z", + "iopub.status.busy": "2024-07-30T16:36:16.158078Z", + "iopub.status.idle": "2024-07-30T16:36:16.163394Z", + "shell.execute_reply": "2024-07-30T16:36:16.162837Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index 54c4c621f..ab5136da2 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -3140,224 +3140,224 @@

6. (Optional) Visualize the Results - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
@@ -3476,184 +3476,96 @@

3. (Optional) Visualize class imbalance issues

Identify Spurious Correlations in Image Datasets#

This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels. These correlations could lead to unreliable model predictions and poor generalization.

-

By providing an image_key argument, we can analyze image-specific attributes such as:

+

Datalab automatically analyzes image-specific attributes such as:

  • Darkness

  • Blurriness

  • Aspect ratio anomalies

  • More image-specific features from CleanVision

-

This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.

+

This analysis helps identify unintended biases in datasets and guides steps to enhance the robustness of machine learning models.

1. Load the Dataset#

-

We’ll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:

-
    -

  • We’ll artificially introduce a bias by altering all images of one class (e.g., darkening them).

    • -

  • The correlation scores range from 0 to 1, where:

    -
      -

    • Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.

      • -

    • Scores close to 1 suggest little to no correlation between the property and class labels.

      • -
    -
    • -

  • By introducing this bias, we expect to see:

    -
      -

    • A decrease in the dark_score for the darkened class, indicating an increased correlation between darkness and that class label.

      • -

    • Similar effects can be observed with blurry_score or odd_aspect_ratio_score by introducing corresponding characteristics to one class.

      • -
    -
    • -
-

This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels.

+

For this tutorial, we’ll use a subset of the CIFAR-10 dataset with artificially introduced biases to illustrate how Datalab detects spurious correlations. We’ll assume you have a directory of images organized into subdirectories by class.

+

To fetch the data for this tutorial, make sure you have wget and zip installed.

[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
+
# Download the dataset
+!wget -nc https://s.cleanlab.ai/CIFAR-10-subset.zip
+!unzip -q CIFAR-10-subset.zip
-
-
-
-
-
-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, 40217729.65it/s]
-
-
-Extracting ./data/cifar-10-python.tar.gz to ./data
+--2024-07-30 16:36:35--  https://s.cleanlab.ai/CIFAR-10-subset.zip
+Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.108.153, 185.199.110.153, ...
+Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 986707 (964K) [application/zip]
+Saving to: ‘CIFAR-10-subset.zip’
+
+CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.03s
+
+2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
+
-
- -
-

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)
+
from datasets import Dataset
+from torchvision.datasets import ImageFolder
 
-# 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)
-]
+def load_image_dataset(data_dir: str):
+    """
+    Load images from a directory structure and create a datasets.Dataset object.
+
+    Parameters
+    ----------
+    data_dir : str
+        Path to the root directory containing class subdirectories.
 
-# 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)
+    Returns
+    -------
+    datasets.Dataset
+        A Dataset object containing 'image' and 'label' columns.
+    """
+    image_dataset = ImageFolder(data_dir)
+    images = [img for img, _ in image_dataset]
+    labels = [label for _, label in image_dataset]
+    return Dataset.from_dict({"image": images, "label": labels})
+
+# Load the dataset
+data_dir = "CIFAR-10-subset/darkened_images"
+dataset = load_image_dataset(data_dir)
-
-

4. (Optional) Visualizing Images in the dataset#

+
+

2. Run Datalab Analysis#

+

Now that we have loaded our dataset, let’s use Datalab to analyze it for potential spurious correlations.

-
[36]:
+
[35]:
 

 

-
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')
+
from cleanlab import Datalab
 
-    # Hide any remaining empty subplots
-    for i in range(num_images_to_plot, len(axes)):
-        axes[i].axis('off')
+# Initialize Datalab with the dataset
+lab = Datalab(data=dataset, label_name="label", image_key="image")
 
-    # Show the plot
-    plt.tight_layout()
-    plt.show()
+# Run the analysis
+lab.find_issues()
 
-plot_images(dataset_dict)
-plot_images(transformed_dataset_dict)
+# Generate and display the report
+lab.report()
 

 

 

@@ -3661,45 +3573,78 @@ 
4. (Optional) Visualizing Images in the dataset
 

 

-../../_images/tutorials_datalab_workflows_84_0.png
+
+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.
+No issues found in the data. Good job!
+
+Try re-running Datalab.report() with `show_summary_score = True` and `show_all_issues = True`.
+
+
+
+Removing dark from potential issues in the dataset as it exceeds max_prevalence=0.1
+Removing blurry from potential issues in the dataset as it exceeds max_prevalence=0.1
+
+
+
+Here is a summary of spurious correlations between image features like 'dark_score', 'blurry_score', etc., and class labels detected in the data.
+
+A lower score for each property implies a higher correlation of that property with the class labels.
+
+
+property  score
+    dark    0.0
+
+Here are the images corresponding to the extreme (minimum and maximum) individual scores for each of the detected correlated properties:
+
+
+Images with minimum and maximum individual scores for dark issue:
+
+
-../../_images/tutorials_datalab_workflows_84_1.png +../../_images/tutorials_datalab_workflows_82_3.png
-
-

5. Finding image-specific property scores#

+
+

3. Interpret the Results#

+

While the lab.report() output is comprehensive, we can use more targeted methods to examine the results:

-
[37]:
+
[36]:
 

 

-
# 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]
+
from IPython.display import display
 
-# Finding scores in original and transformed dataset
-standard_property_scores = get_property_scores(dataset)
-transformed_property_scores = get_property_scores(transformed_dataset)
+# Get the correlation scores for image properties
+correlation_scores = lab._correlations_df
+print("Correlation scores for image properties:")
+display(correlation_scores)
 
-# Displaying the scores dataframe
-display(Markdown("### Image-specific property scores in the original dataset"))
-display(standard_property_scores)
-display(Markdown("### Image-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')
+# Get image-specific issues
+issue_name = "dark"
+image_issues = lab.get_issues(issue_name)
+print("\nImage-specific issues:")
+display(image_issues)
 

 

 

@@ -3708,15 +3653,74 @@ 
5. Finding image-specific property scores
 
-Finding class_imbalance issues ...
-Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images ...
+Correlation scores for image properties:
-
-
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
propertyscore
0dark_score0.000
1light_score0.180
2low_information_score0.015
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
6blurry_score0.015
+
@@ -3724,35 +3728,166 @@

5. Finding image-specific property scores 
 
-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 ...
+Image-specific issues:

-
+
-
-
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
is_dark_issuedark_score
0True0.237196
1True0.197229
2True0.254188
3True0.229170
4True0.208907
.........
195False0.793840
196False1.000000
197False0.971560
198False0.862236
199False0.973533
+

200 rows × 2 columns

+
+
+
+

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

+
+

Interpreting the results:

+
    +

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

    2. +

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

    3. +
+

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

+

For real-world datasets, pay attention to:

+
    +

  • Properties with notably low scores in the correlation_scores DataFrame

    • +

  • Prevalent issues in the image_issues DataFrame

    • +
+

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

+
+

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

+
+
+
+

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

+

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

+
+
[37]:
+
+
+
# Load the original dataset
+original_data_dir = "CIFAR-10-subset/original_images"
+original_dataset = load_image_dataset(original_data_dir)
+
+# Create a new Datalab instance and run analysis
+original_lab = Datalab(data=original_dataset, label_name="label", image_key="image")
+original_lab.find_issues()
+
+# Compare correlation scores
+original_scores = original_lab._correlations_df
+print("Correlation scores for original dataset:")
+display(original_scores)
+
+# Compare image-specific issues
+original_issues = original_lab.get_issues("dark")
+print("\nImage-specific issues in original dataset:")
+display(original_issues)
+
+
-Removing dark, blurry from potential issues in the dataset as it exceeds max_prevalence=0.1
-
-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 ...
-
-

Image-specific property scores in the original dataset#

-
+
+
+
+
+
+
+
+Audit complete. 0 issues found in the dataset.
+Correlation scores for original dataset:
+
@@ -3784,7 +3919,7 @@

Image-specific property scores in the original dataset 0 dark_score - 0.295 + 0.300 1 @@ -3814,7 +3949,7 @@

Image-specific property scores in the original dataset 6 blurry_score - 0.325 + 0.335 @@ -3824,10 +3959,10 @@

Image-specific property scores in the original dataset

-
-

Image-specific property scores in the transformed dataset#

-
-
+
+
+Image-specific issues in original dataset:
+
@@ -3851,52 +3986,79 @@

Image-specific property scores in the transformed dataset - property - score + is_dark_issue + dark_score 0 - dark_score - 0.000 + False + 0.797509 1 - light_score - 0.185 + False + 0.663760 2 - low_information_score - 0.015 + False + 0.849826 3 - odd_aspect_ratio_score - 0.500 + False + 0.773951 4 - odd_size_score - 0.500 + False + 0.699518 - 5 - grayscale_score - 0.500 + ... + ... + ... - 6 - blurry_score - 0.015 + 195 + False + 0.793840 + + + 196 + False + 1.000000 + + + 197 + False + 0.971560 + + + 198 + False + 0.862236 + + + 199 + False + 0.973533 +

200 rows × 2 columns

+

When comparing the results:

+
    +

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

    2. +

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

    3. +
+

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

@@ -4032,10 +4194,9 @@

Image-specific property scores in the transformed datasetIdentify Spurious Correlations in Image Datasets diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 8c4e0af06..d6d1d2769 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": "2024-07-18T04:06:09.315133Z", - "iopub.status.busy": "2024-07-18T04:06:09.314702Z", - "iopub.status.idle": "2024-07-18T04:06:09.737826Z", - "shell.execute_reply": "2024-07-18T04:06:09.737324Z" + "iopub.execute_input": "2024-07-30T16:36:19.928818Z", + "iopub.status.busy": "2024-07-30T16:36:19.928315Z", + "iopub.status.idle": "2024-07-30T16:36:20.362342Z", + "shell.execute_reply": "2024-07-30T16:36:20.361793Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.740480Z", - "iopub.status.busy": "2024-07-18T04:06:09.740061Z", - "iopub.status.idle": "2024-07-18T04:06:09.869002Z", - "shell.execute_reply": "2024-07-18T04:06:09.868461Z" + "iopub.execute_input": "2024-07-30T16:36:20.365042Z", + "iopub.status.busy": "2024-07-30T16:36:20.364603Z", + "iopub.status.idle": "2024-07-30T16:36:20.497373Z", + "shell.execute_reply": "2024-07-30T16:36:20.496781Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.871271Z", - "iopub.status.busy": "2024-07-18T04:06:09.870845Z", - "iopub.status.idle": "2024-07-18T04:06:09.893540Z", - "shell.execute_reply": "2024-07-18T04:06:09.892922Z" + "iopub.execute_input": "2024-07-30T16:36:20.499582Z", + "iopub.status.busy": "2024-07-30T16:36:20.499349Z", + "iopub.status.idle": "2024-07-30T16:36:20.524504Z", + "shell.execute_reply": "2024-07-30T16:36:20.523915Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:09.896111Z", - "iopub.status.busy": "2024-07-18T04:06:09.895870Z", - "iopub.status.idle": "2024-07-18T04:06:12.653881Z", - "shell.execute_reply": "2024-07-18T04:06:12.653225Z" + "iopub.execute_input": "2024-07-30T16:36:20.527274Z", + "iopub.status.busy": "2024-07-30T16:36:20.527018Z", + "iopub.status.idle": "2024-07-30T16:36:23.840701Z", + "shell.execute_reply": "2024-07-30T16:36:23.840107Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:12.656529Z", - "iopub.status.busy": "2024-07-18T04:06:12.656139Z", - "iopub.status.idle": "2024-07-18T04:06:31.030202Z", - "shell.execute_reply": "2024-07-18T04:06:31.029589Z" + "iopub.execute_input": "2024-07-30T16:36:23.843646Z", + "iopub.status.busy": "2024-07-30T16:36:23.843036Z", + "iopub.status.idle": "2024-07-30T16:36:32.528462Z", + "shell.execute_reply": "2024-07-30T16:36:32.527887Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:31.032605Z", - "iopub.status.busy": "2024-07-18T04:06:31.032254Z", - "iopub.status.idle": "2024-07-18T04:06:31.191251Z", - "shell.execute_reply": "2024-07-18T04:06:31.190718Z" + "iopub.execute_input": "2024-07-30T16:36:32.530759Z", + "iopub.status.busy": "2024-07-30T16:36:32.530398Z", + "iopub.status.idle": "2024-07-30T16:36:32.692452Z", + "shell.execute_reply": "2024-07-30T16:36:32.691890Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:31.193870Z", - "iopub.status.busy": "2024-07-18T04:06:31.193450Z", - "iopub.status.idle": "2024-07-18T04:06:32.501314Z", - "shell.execute_reply": "2024-07-18T04:06:32.500719Z" + "iopub.execute_input": "2024-07-30T16:36:32.695108Z", + "iopub.status.busy": "2024-07-30T16:36:32.694738Z", + "iopub.status.idle": "2024-07-30T16:36:34.079949Z", + "shell.execute_reply": "2024-07-30T16:36:34.079473Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.503635Z", - "iopub.status.busy": "2024-07-18T04:06:32.503261Z", - "iopub.status.idle": "2024-07-18T04:06:32.915426Z", - "shell.execute_reply": "2024-07-18T04:06:32.914838Z" + "iopub.execute_input": "2024-07-30T16:36:34.082291Z", + "iopub.status.busy": "2024-07-30T16:36:34.081898Z", + "iopub.status.idle": "2024-07-30T16:36:34.326676Z", + "shell.execute_reply": "2024-07-30T16:36:34.326094Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.917986Z", - "iopub.status.busy": "2024-07-18T04:06:32.917468Z", - "iopub.status.idle": "2024-07-18T04:06:32.930644Z", - "shell.execute_reply": "2024-07-18T04:06:32.930084Z" + "iopub.execute_input": "2024-07-30T16:36:34.329162Z", + "iopub.status.busy": "2024-07-30T16:36:34.328791Z", + "iopub.status.idle": "2024-07-30T16:36:34.342431Z", + "shell.execute_reply": "2024-07-30T16:36:34.341938Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.932608Z", - "iopub.status.busy": "2024-07-18T04:06:32.932309Z", - "iopub.status.idle": "2024-07-18T04:06:32.951966Z", - "shell.execute_reply": "2024-07-18T04:06:32.951527Z" + "iopub.execute_input": "2024-07-30T16:36:34.344554Z", + "iopub.status.busy": "2024-07-30T16:36:34.344214Z", + "iopub.status.idle": "2024-07-30T16:36:34.363020Z", + "shell.execute_reply": "2024-07-30T16:36:34.362540Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:32.953897Z", - "iopub.status.busy": "2024-07-18T04:06:32.953626Z", - "iopub.status.idle": "2024-07-18T04:06:33.209964Z", - "shell.execute_reply": "2024-07-18T04:06:33.209446Z" + "iopub.execute_input": "2024-07-30T16:36:34.365426Z", + "iopub.status.busy": "2024-07-30T16:36:34.365076Z", + "iopub.status.idle": "2024-07-30T16:36:34.596927Z", + "shell.execute_reply": "2024-07-30T16:36:34.596358Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.212305Z", - "iopub.status.busy": "2024-07-18T04:06:33.212116Z", - "iopub.status.idle": "2024-07-18T04:06:33.231185Z", - "shell.execute_reply": "2024-07-18T04:06:33.230616Z" + "iopub.execute_input": "2024-07-30T16:36:34.599630Z", + "iopub.status.busy": "2024-07-30T16:36:34.599295Z", + "iopub.status.idle": "2024-07-30T16:36:34.619495Z", + "shell.execute_reply": "2024-07-30T16:36:34.618989Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.233319Z", - "iopub.status.busy": "2024-07-18T04:06:33.232923Z", - "iopub.status.idle": "2024-07-18T04:06:33.400233Z", - "shell.execute_reply": "2024-07-18T04:06:33.399669Z" + "iopub.execute_input": "2024-07-30T16:36:34.621703Z", + "iopub.status.busy": "2024-07-30T16:36:34.621342Z", + "iopub.status.idle": "2024-07-30T16:36:34.761643Z", + "shell.execute_reply": "2024-07-30T16:36:34.761053Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.402401Z", - "iopub.status.busy": "2024-07-18T04:06:33.402059Z", - "iopub.status.idle": "2024-07-18T04:06:33.411545Z", - "shell.execute_reply": "2024-07-18T04:06:33.411006Z" + "iopub.execute_input": "2024-07-30T16:36:34.763998Z", + "iopub.status.busy": "2024-07-30T16:36:34.763799Z", + "iopub.status.idle": "2024-07-30T16:36:34.774206Z", + "shell.execute_reply": "2024-07-30T16:36:34.773713Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.413706Z", - "iopub.status.busy": "2024-07-18T04:06:33.413388Z", - "iopub.status.idle": "2024-07-18T04:06:33.422844Z", - "shell.execute_reply": "2024-07-18T04:06:33.422288Z" + "iopub.execute_input": "2024-07-30T16:36:34.776254Z", + "iopub.status.busy": "2024-07-30T16:36:34.776071Z", + "iopub.status.idle": "2024-07-30T16:36:34.785886Z", + "shell.execute_reply": "2024-07-30T16:36:34.785435Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.424928Z", - "iopub.status.busy": "2024-07-18T04:06:33.424591Z", - "iopub.status.idle": "2024-07-18T04:06:33.450139Z", - "shell.execute_reply": "2024-07-18T04:06:33.449701Z" + "iopub.execute_input": "2024-07-30T16:36:34.787902Z", + "iopub.status.busy": "2024-07-30T16:36:34.787724Z", + "iopub.status.idle": "2024-07-30T16:36:34.815725Z", + "shell.execute_reply": "2024-07-30T16:36:34.815298Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.452187Z", - "iopub.status.busy": "2024-07-18T04:06:33.451788Z", - "iopub.status.idle": "2024-07-18T04:06:33.454639Z", - "shell.execute_reply": "2024-07-18T04:06:33.454072Z" + "iopub.execute_input": "2024-07-30T16:36:34.817795Z", + "iopub.status.busy": "2024-07-30T16:36:34.817615Z", + "iopub.status.idle": "2024-07-30T16:36:34.820486Z", + "shell.execute_reply": "2024-07-30T16:36:34.820013Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.456808Z", - "iopub.status.busy": "2024-07-18T04:06:33.456509Z", - "iopub.status.idle": "2024-07-18T04:06:33.475334Z", - "shell.execute_reply": "2024-07-18T04:06:33.474761Z" + "iopub.execute_input": "2024-07-30T16:36:34.822391Z", + "iopub.status.busy": "2024-07-30T16:36:34.822219Z", + "iopub.status.idle": "2024-07-30T16:36:34.841825Z", + "shell.execute_reply": "2024-07-30T16:36:34.841320Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.477330Z", - "iopub.status.busy": "2024-07-18T04:06:33.477157Z", - "iopub.status.idle": "2024-07-18T04:06:33.481414Z", - "shell.execute_reply": "2024-07-18T04:06:33.480961Z" + "iopub.execute_input": "2024-07-30T16:36:34.843927Z", + "iopub.status.busy": "2024-07-30T16:36:34.843742Z", + "iopub.status.idle": "2024-07-30T16:36:34.848323Z", + "shell.execute_reply": "2024-07-30T16:36:34.847833Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.483270Z", - "iopub.status.busy": "2024-07-18T04:06:33.483101Z", - "iopub.status.idle": "2024-07-18T04:06:33.510551Z", - "shell.execute_reply": "2024-07-18T04:06:33.510000Z" + "iopub.execute_input": "2024-07-30T16:36:34.850257Z", + "iopub.status.busy": "2024-07-30T16:36:34.850081Z", + "iopub.status.idle": "2024-07-30T16:36:34.879946Z", + "shell.execute_reply": "2024-07-30T16:36:34.879477Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.512640Z", - "iopub.status.busy": "2024-07-18T04:06:33.512331Z", - "iopub.status.idle": "2024-07-18T04:06:33.880625Z", - "shell.execute_reply": "2024-07-18T04:06:33.880120Z" + "iopub.execute_input": "2024-07-30T16:36:34.881917Z", + "iopub.status.busy": "2024-07-30T16:36:34.881737Z", + "iopub.status.idle": "2024-07-30T16:36:35.259340Z", + "shell.execute_reply": "2024-07-30T16:36:35.258831Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.882759Z", - "iopub.status.busy": "2024-07-18T04:06:33.882433Z", - "iopub.status.idle": "2024-07-18T04:06:33.885582Z", - "shell.execute_reply": "2024-07-18T04:06:33.885032Z" + "iopub.execute_input": "2024-07-30T16:36:35.261459Z", + "iopub.status.busy": "2024-07-30T16:36:35.261275Z", + "iopub.status.idle": "2024-07-30T16:36:35.264720Z", + "shell.execute_reply": "2024-07-30T16:36:35.264247Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.887734Z", - "iopub.status.busy": "2024-07-18T04:06:33.887293Z", - "iopub.status.idle": "2024-07-18T04:06:33.899883Z", - "shell.execute_reply": "2024-07-18T04:06:33.899389Z" + "iopub.execute_input": "2024-07-30T16:36:35.266610Z", + "iopub.status.busy": "2024-07-30T16:36:35.266439Z", + "iopub.status.idle": "2024-07-30T16:36:35.280154Z", + "shell.execute_reply": "2024-07-30T16:36:35.279707Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.901737Z", - "iopub.status.busy": "2024-07-18T04:06:33.901564Z", - "iopub.status.idle": "2024-07-18T04:06:33.915004Z", - "shell.execute_reply": "2024-07-18T04:06:33.914529Z" + "iopub.execute_input": "2024-07-30T16:36:35.282287Z", + "iopub.status.busy": "2024-07-30T16:36:35.281841Z", + "iopub.status.idle": "2024-07-30T16:36:35.296091Z", + "shell.execute_reply": "2024-07-30T16:36:35.295529Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.916796Z", - "iopub.status.busy": "2024-07-18T04:06:33.916623Z", - "iopub.status.idle": "2024-07-18T04:06:33.926482Z", - "shell.execute_reply": "2024-07-18T04:06:33.926021Z" + "iopub.execute_input": "2024-07-30T16:36:35.298365Z", + "iopub.status.busy": "2024-07-30T16:36:35.297955Z", + "iopub.status.idle": "2024-07-30T16:36:35.308443Z", + "shell.execute_reply": "2024-07-30T16:36:35.307980Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - 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8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:33.998159Z", - "iopub.status.busy": "2024-07-18T04:06:33.997771Z", - "iopub.status.idle": "2024-07-18T04:06:34.003397Z", - "shell.execute_reply": "2024-07-18T04:06:34.002868Z" + "iopub.execute_input": "2024-07-30T16:36:35.382058Z", + "iopub.status.busy": "2024-07-30T16:36:35.381569Z", + "iopub.status.idle": "2024-07-30T16:36:35.388152Z", + "shell.execute_reply": "2024-07-30T16:36:35.387714Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:34.005349Z", - "iopub.status.busy": "2024-07-18T04:06:34.005044Z", - "iopub.status.idle": "2024-07-18T04:06:34.015924Z", - "shell.execute_reply": "2024-07-18T04:06:34.015375Z" + "iopub.execute_input": "2024-07-30T16:36:35.390322Z", + "iopub.status.busy": "2024-07-30T16:36:35.389885Z", + "iopub.status.idle": "2024-07-30T16:36:35.401052Z", + "shell.execute_reply": "2024-07-30T16:36:35.400566Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:34.018073Z", - "iopub.status.busy": "2024-07-18T04:06:34.017731Z", - "iopub.status.idle": "2024-07-18T04:06:34.230278Z", - "shell.execute_reply": "2024-07-18T04:06:34.229718Z" + "iopub.execute_input": "2024-07-30T16:36:35.402986Z", + "iopub.status.busy": "2024-07-30T16:36:35.402810Z", + "iopub.status.idle": "2024-07-30T16:36:35.583541Z", + "shell.execute_reply": "2024-07-30T16:36:35.582923Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:34.232505Z", - "iopub.status.busy": "2024-07-18T04:06:34.232146Z", - "iopub.status.idle": "2024-07-18T04:06:34.239448Z", - "shell.execute_reply": "2024-07-18T04:06:34.238981Z" + "iopub.execute_input": "2024-07-30T16:36:35.586049Z", + "iopub.status.busy": "2024-07-30T16:36:35.585823Z", + "iopub.status.idle": "2024-07-30T16:36:35.594126Z", + "shell.execute_reply": "2024-07-30T16:36:35.593611Z" }, "nbsphinx": "hidden" }, @@ -3725,15 +3725,14 @@ "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n", "These correlations could lead to unreliable model predictions and poor generalization.\n", "\n", - "\n", - "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n", + "`Datalab` automatically analyzes image-specific attributes such as:\n", "\n", "- Darkness\n", "- Blurriness\n", "- Aspect ratio anomalies\n", "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n", "\n", - "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n" + "This analysis helps identify unintended biases in datasets and guides steps to enhance the robustness of machine learning models.\n" ] }, { @@ -3742,17 +3741,14 @@ "source": [ "### 1. Load the Dataset\n", "\n", - "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n", - "\n", - "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n", - "- The correlation scores range from 0 to 1, where:\n", - " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n", - " - Scores close to 1 suggest little to no correlation between the property and class labels.\n", - "- By introducing this bias, we expect to see:\n", - " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n", - " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n", - "\n", - "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels." + "For this tutorial, we'll use a subset of the CIFAR-10 dataset with artificially introduced biases to illustrate how Datalab detects spurious correlations. We'll assume you have a directory of images organized into subdirectories by class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fetch the data for this tutorial, make sure you have `wget` and `zip` installed." ] }, { @@ -3760,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:34.241391Z", - "iopub.status.busy": "2024-07-18T04:06:34.241211Z", - "iopub.status.idle": "2024-07-18T04:06:43.025032Z", - "shell.execute_reply": "2024-07-18T04:06:43.024474Z" + "iopub.execute_input": "2024-07-30T16:36:35.596219Z", + "iopub.status.busy": "2024-07-30T16:36:35.596032Z", + "iopub.status.idle": "2024-07-30T16:36:36.032446Z", + "shell.execute_reply": "2024-07-30T16:36:36.031724Z" } }, "outputs": [ @@ -3771,409 +3767,40 @@ "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] 963.58K --.-KB/s in 0.03s \r\n", + "\r\n", + "2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "\r\n" ] } ], "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 image-related issues" + "# Download the dataset\n", + "!wget -nc https://s.cleanlab.ai/CIFAR-10-subset.zip\n", + "!unzip -q CIFAR-10-subset.zip" ] }, { @@ -4181,24 +3808,48 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.027804Z", - "iopub.status.busy": "2024-07-18T04:06:43.027169Z", - "iopub.status.idle": "2024-07-18T04:06:43.094948Z", - "shell.execute_reply": "2024-07-18T04:06:43.094485Z" + "iopub.execute_input": "2024-07-30T16:36:36.035427Z", + "iopub.status.busy": "2024-07-30T16:36:36.035017Z", + "iopub.status.idle": "2024-07-30T16:36:38.005904Z", + "shell.execute_reply": "2024-07-30T16:36:38.005342Z" } }, "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)" + "from datasets import Dataset\n", + "from torchvision.datasets import ImageFolder\n", + "\n", + "def load_image_dataset(data_dir: str):\n", + " \"\"\"\n", + " Load images from a directory structure and create a datasets.Dataset object.\n", + " \n", + " Parameters\n", + " ----------\n", + " data_dir : str\n", + " Path to the root directory containing class subdirectories.\n", + " \n", + " Returns\n", + " -------\n", + " datasets.Dataset\n", + " A Dataset object containing 'image' and 'label' columns.\n", + " \"\"\"\n", + " image_dataset = ImageFolder(data_dir)\n", + " images = [img for img, _ in image_dataset]\n", + " labels = [label for _, label in image_dataset]\n", + " return Dataset.from_dict({\"image\": images, \"label\": labels})\n", + "\n", + "# Load the dataset\n", + "data_dir = \"CIFAR-10-subset/darkened_images\"\n", + "dataset = load_image_dataset(data_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. (Optional) Creating a transformed dataset using `ImageEnhance` to induce darkness" + "### 2. Run Datalab Analysis\n", + "\n", + "Now that we have loaded our dataset, let's use `Datalab` to analyze it for potential spurious correlations." ] }, { @@ -4206,36 +3857,99 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.097002Z", - "iopub.status.busy": "2024-07-18T04:06:43.096662Z", - "iopub.status.idle": "2024-07-18T04:06:43.137694Z", - "shell.execute_reply": "2024-07-18T04:06:43.137236Z" + "iopub.execute_input": "2024-07-30T16:36:38.008499Z", + "iopub.status.busy": "2024-07-30T16:36:38.008189Z", + "iopub.status.idle": "2024-07-30T16:36:38.487271Z", + "shell.execute_reply": "2024-07-30T16:36:38.486659Z" } }, - "outputs": [], + "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": "2ed4efbeb1874db0a5e2316cc6fdcc53", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/200 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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)" + "from cleanlab import Datalab\n", + "\n", + "# Initialize Datalab with the dataset\n", + "lab = Datalab(data=dataset, label_name=\"label\", image_key=\"image\")\n", + "\n", + "# Run the analysis\n", + "lab.find_issues()\n", + "\n", + "# Generate and display the report\n", + "lab.report()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. (Optional) Visualizing Images in the dataset" + "### 3. Interpret the Results\n", + "\n", + "While the `lab.report()` output is comprehensive, we can use more targeted methods to examine the results:" ] }, { @@ -4243,28 +3957,208 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:43.139729Z", - "iopub.status.busy": "2024-07-18T04:06:43.139403Z", - "iopub.status.idle": "2024-07-18T04:06:44.587790Z", - "shell.execute_reply": "2024-07-18T04:06:44.587181Z" + "iopub.execute_input": "2024-07-30T16:36:38.491380Z", + "iopub.status.busy": "2024-07-30T16:36:38.490234Z", + "iopub.status.idle": "2024-07-30T16:36:38.508457Z", + "shell.execute_reply": "2024-07-30T16:36:38.507923Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation scores for image properties:\n" + ] + }, { "data": { - "image/png": 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propertyscore
0dark_score0.000
1light_score0.180
2low_information_score0.015
3odd_aspect_ratio_score0.500
4odd_size_score0.500
5grayscale_score0.500
6blurry_score0.015
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" + ], "text/plain": [ - "
" + " property score\n", + "0 dark_score 0.000\n", + "1 light_score 0.180\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" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Image-specific issues:\n" + ] + }, { "data": { - "image/png": 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" + " is_dark_issue dark_score\n", + "0 True 0.237196\n", + "1 True 0.197229\n", + "2 True 0.254188\n", + "3 True 0.229170\n", + "4 True 0.208907\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", + "\n", + "[200 rows x 2 columns]" ] }, "metadata": {}, @@ -4272,47 +4166,51 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "from IPython.display import display\n", + "\n", + "# Get the correlation scores for image properties\n", + "correlation_scores = lab._correlations_df\n", + "print(\"Correlation scores for image properties:\")\n", + "display(correlation_scores)\n", + "\n", + "# Get image-specific issues\n", + "issue_name = \"dark\"\n", + "image_issues = lab.get_issues(issue_name)\n", + "print(\"\\nImage-specific issues:\")\n", + "display(image_issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\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", + "> **Important Note**: The `_correlations_df` attribute is an internal implementation detail of Datalab. It may change or be removed in future versions without notice. For production use or if you need stable interfaces, consider using the public methods and attributes provided by Datalab.\n", + "\n", + "Interpreting the results:\n", + "\n", + "1. **Correlation Scores**: The `correlation_scores` DataFrame shows scores for various image properties. Lower scores (closer to 0) indicate stronger correlations with class labels, suggesting potential spurious correlations.\n", + "2. **Image-Specific Issues**: The `image_issues` DataFrame provides details on detected image-specific problems, including the issue type and affected samples.\n", + "\n", + "In our CIFAR-10 subset example, you should see that the 'dark' property has a low score in the correlation_scores, indicating a strong correlation with one of the classes (likely the 'frog' class). This is due to our artificial darkening of these images to demonstrate the concept.\n", "\n", - "plot_images(dataset_dict)\n", - "plot_images(transformed_dataset_dict)" + "For real-world datasets, pay attention to:\n", + "\n", + "- Properties with notably low scores in the correlation_scores DataFrame\n", + "- Prevalent issues in the image_issues DataFrame\n", + "\n", + "These may represent unintended biases in your data collection or preprocessing steps and warrant further investigation.\n", + "\n", + "> **Note**: Using these methods provides a more programmatic and focused way to analyze the results compared to the verbose output of `lab.report()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Finding image-specific property scores" + "### 4. (Optional) Compare with a Dataset Without Spurious Correlations\n", + "\n", + "To understand the impact of spurious correlations, it can be helpful to compare our results with a dataset that doesn't have artificially introduced biases. In this case, we'll use the original CIFAR-10 subset." ] }, { @@ -4320,10 +4218,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:44.590173Z", - "iopub.status.busy": "2024-07-18T04:06:44.589828Z", - "iopub.status.idle": "2024-07-18T04:06:45.471688Z", - "shell.execute_reply": "2024-07-18T04:06:45.471060Z" + "iopub.execute_input": "2024-07-30T16:36:38.512133Z", + "iopub.status.busy": "2024-07-30T16:36:38.511200Z", + "iopub.status.idle": "2024-07-30T16:36:39.047457Z", + "shell.execute_reply": "2024-07-30T16:36:39.046797Z" } }, "outputs": [ @@ -4338,7 +4236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ba93e19df2346d8bc2249fbbe28c2da", + "model_id": "5704008e778b464799f617edec73de43", "version_major": 2, "version_minor": 0 }, @@ -4355,45 +4253,9 @@ "text": [ "\n", "Audit complete. 0 issues found in the dataset.\n", - "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": "d35057cc20b446b2915fc9ff955d81f2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/200 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -4423,7 +4285,7 @@ " \n", " 0\n", " dark_score\n", - " 0.295\n", + " 0.300\n", " \n", " \n", " 1\n", @@ -4453,7 +4315,7 @@ " \n", " 6\n", " blurry_score\n", - " 0.325\n", + " 0.335\n", " \n", " \n", "\n", @@ -4461,29 +4323,25 @@ ], "text/plain": [ " property score\n", - "0 dark_score 0.295\n", + "0 dark_score 0.300\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" + "6 blurry_score 0.335" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "text/markdown": [ - "### Image-specific property scores in the transformed dataset" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Image-specific issues in original dataset:\n" + ] }, { "data": { @@ -4506,59 +4364,86 @@ " \n", " \n", " \n", - " property\n", - " score\n", + " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 0\n", - " dark_score\n", - " 0.000\n", + " False\n", + " 0.797509\n", " \n", " \n", " 1\n", - " light_score\n", - " 0.185\n", + " False\n", + " 0.663760\n", " \n", " \n", " 2\n", - " low_information_score\n", - " 0.015\n", + " False\n", + " 0.849826\n", " \n", " \n", " 3\n", - " odd_aspect_ratio_score\n", - " 0.500\n", + " False\n", + " 0.773951\n", " \n", " \n", " 4\n", - " odd_size_score\n", - " 0.500\n", + " False\n", + " 0.699518\n", " \n", " \n", - " 5\n", - " grayscale_score\n", - " 0.500\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 6\n", - " blurry_score\n", - " 0.015\n", + " 195\n", + " False\n", + " 0.793840\n", + " \n", + " \n", + " 196\n", + " False\n", + " 1.000000\n", + " \n", + " \n", + " 197\n", + " False\n", + " 0.971560\n", + " \n", + " \n", + " 198\n", + " False\n", + " 0.862236\n", + " \n", + " \n", + " 199\n", + " False\n", + " 0.973533\n", " \n", " \n", "\n", + "

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\n", "" ], "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" + " is_dark_issue dark_score\n", + "0 False 0.797509\n", + "1 False 0.663760\n", + "2 False 0.849826\n", + "3 False 0.773951\n", + "4 False 0.699518\n", + ".. ... ...\n", + "195 False 0.793840\n", + "196 False 1.000000\n", + "197 False 0.971560\n", + "198 False 0.862236\n", + "199 False 0.973533\n", + "\n", + "[200 rows x 2 columns]" ] }, "metadata": {}, @@ -4566,28 +4451,35 @@ } ], "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(\"### Image-specific property scores in the original dataset\"))\n", - "display(standard_property_scores)\n", - "display(Markdown(\"### Image-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')" + "# Load the original dataset\n", + "original_data_dir = \"CIFAR-10-subset/original_images\"\n", + "original_dataset = load_image_dataset(original_data_dir)\n", + "\n", + "# Create a new Datalab instance and run analysis\n", + "original_lab = Datalab(data=original_dataset, label_name=\"label\", image_key=\"image\")\n", + "original_lab.find_issues()\n", + "\n", + "# Compare correlation scores\n", + "original_scores = original_lab._correlations_df\n", + "print(\"Correlation scores for original dataset:\")\n", + "display(original_scores)\n", + "\n", + "# Compare image-specific issues\n", + "original_issues = original_lab.get_issues(\"dark\")\n", + "print(\"\\nImage-specific issues in original dataset:\")\n", + "display(original_issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When comparing the results:\n", + "\n", + "1. Look for differences in the correlation scores, especially for the 'dark' property.\n", + "2. Compare the number and types of image-specific issues detected.\n", + "\n", + "You should notice that the original dataset has more balanced correlation scores and fewer (or no) issues related to darkness. 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"8c33c90c192140fcb39f8e0fb7da534c": { + "a8c94e907c12444e8d0364601f96e159": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "93a20e1de6f0471daf520d49388c326d": { + "b7913d94eed4408cb306a3e2b47761cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5020,88 +5026,7 @@ "width": null } }, - "b5a2e16c332146b09a0dd2087d5d539c": { - "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_5ba7f559a9da406493cf8689d8496a25", - "placeholder": "​", - "style": "IPY_MODEL_4239c58acfb54ff6adf6796d156be031", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 681.35it/s]" - } - }, - "bf6016b0169f460290ed5938134db880": { - "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 - } - }, - "c8838ce4540a48b487ecdf51e26a4c5f": { - "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": "" - } - }, - "d35057cc20b446b2915fc9ff955d81f2": { - "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_dff6d70ab4ed4762bc4586d001f53274", - "IPY_MODEL_082f92a4818742ecbc4d1f4857988df5", - "IPY_MODEL_ff9de7e97a1047acabe0749ea6273a26" - ], - "layout": "IPY_MODEL_fb7e314d793f46dcbea0cef7c9a2f60f", - "tabbable": null, - "tooltip": null - } - }, - "df3530bc714a437eb8417ba37251ba85": { + "bd875ac3a62042068a132488eecbb1c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5154,7 +5079,7 @@ "width": null } }, - "dff6d70ab4ed4762bc4586d001f53274": { + "d4d36e246db746c4acbc8f4787f82381": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5169,15 +5094,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e2a6647ccce04986a5bc483752fbbb50", + "layout": "IPY_MODEL_9ab790ab579f415bbc804c1f992660a0", "placeholder": "​", - "style": "IPY_MODEL_756f2e8b41ae4894b44c354d6b8cc1f3", + "style": "IPY_MODEL_72b98c9996864aa9abad1934ce4b27c3", "tabbable": null, "tooltip": null, "value": "100%" } }, - "e2a6647ccce04986a5bc483752fbbb50": { + "df924bb590a44f27a4f68916d0e77c53": { + "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": "" + } + }, + "f2cb72313c294a56bc0221871a2d5717": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5230,7 +5171,7 @@ "width": null } }, - "fb7e314d793f46dcbea0cef7c9a2f60f": { + "f59fcaa8e8d04931980396c1bdc42425": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5282,55 +5223,6 @@ "visibility": null, "width": null } - }, - "fdefd9edead8430e994cc0b608b54228": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_93a20e1de6f0471daf520d49388c326d", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c8838ce4540a48b487ecdf51e26a4c5f", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "ff9de7e97a1047acabe0749ea6273a26": { - "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_19e7b8a023924ce1bdaa09961f57d5eb", - "placeholder": "​", - "style": "IPY_MODEL_bf6016b0169f460290ed5938134db880", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 672.89it/s]" - } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 35668b032..b41d28ba4 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": "2024-07-18T04:06:49.364909Z", - "iopub.status.busy": "2024-07-18T04:06:49.364426Z", - "iopub.status.idle": "2024-07-18T04:06:50.490342Z", - "shell.execute_reply": "2024-07-18T04:06:50.489718Z" + "iopub.execute_input": "2024-07-30T16:36:43.263935Z", + "iopub.status.busy": "2024-07-30T16:36:43.263754Z", + "iopub.status.idle": "2024-07-30T16:36:44.677036Z", + "shell.execute_reply": "2024-07-30T16:36:44.676454Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:06:50.492975Z", - "iopub.status.busy": "2024-07-18T04:06:50.492531Z", - "iopub.status.idle": "2024-07-18T04:06:50.495387Z", - "shell.execute_reply": "2024-07-18T04:06:50.494931Z" + "iopub.execute_input": "2024-07-30T16:36:44.679704Z", + "iopub.status.busy": "2024-07-30T16:36:44.679219Z", + "iopub.status.idle": "2024-07-30T16:36:44.681960Z", + "shell.execute_reply": "2024-07-30T16:36:44.681516Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:50.497498Z", - "iopub.status.busy": "2024-07-18T04:06:50.497161Z", - "iopub.status.idle": "2024-07-18T04:06:50.508789Z", - "shell.execute_reply": "2024-07-18T04:06:50.508333Z" + "iopub.execute_input": "2024-07-30T16:36:44.684134Z", + "iopub.status.busy": "2024-07-30T16:36:44.683779Z", + "iopub.status.idle": "2024-07-30T16:36:44.695519Z", + "shell.execute_reply": "2024-07-30T16:36:44.695059Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:50.510953Z", - "iopub.status.busy": "2024-07-18T04:06:50.510604Z", - "iopub.status.idle": "2024-07-18T04:06:55.636999Z", - "shell.execute_reply": "2024-07-18T04:06:55.636495Z" + "iopub.execute_input": "2024-07-30T16:36:44.697494Z", + "iopub.status.busy": "2024-07-30T16:36:44.697321Z", + "iopub.status.idle": "2024-07-30T16:36:50.818481Z", + "shell.execute_reply": "2024-07-30T16:36:50.817920Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index c3e4aed8f..3a4b6f1a2 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -831,13 +831,13 @@

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

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

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

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 908e32eaa..0e282dd07 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:57.905922Z", - "iopub.status.busy": "2024-07-18T04:06:57.905505Z", - "iopub.status.idle": "2024-07-18T04:06:59.031674Z", - "shell.execute_reply": "2024-07-18T04:06:59.031132Z" + "iopub.execute_input": "2024-07-30T16:36:53.364898Z", + "iopub.status.busy": "2024-07-30T16:36:53.364365Z", + "iopub.status.idle": "2024-07-30T16:36:54.816084Z", + "shell.execute_reply": "2024-07-30T16:36:54.815502Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:59.034547Z", - "iopub.status.busy": "2024-07-18T04:06:59.034090Z", - "iopub.status.idle": "2024-07-18T04:06:59.037514Z", - "shell.execute_reply": "2024-07-18T04:06:59.037039Z" + "iopub.execute_input": "2024-07-30T16:36:54.819086Z", + "iopub.status.busy": "2024-07-30T16:36:54.818586Z", + "iopub.status.idle": "2024-07-30T16:36:54.821882Z", + "shell.execute_reply": "2024-07-30T16:36:54.821439Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:06:59.039633Z", - "iopub.status.busy": "2024-07-18T04:06:59.039294Z", - "iopub.status.idle": "2024-07-18T04:07:02.365487Z", - "shell.execute_reply": "2024-07-18T04:07:02.364710Z" + "iopub.execute_input": "2024-07-30T16:36:54.824015Z", + "iopub.status.busy": "2024-07-30T16:36:54.823672Z", + "iopub.status.idle": "2024-07-30T16:36:58.536010Z", + "shell.execute_reply": "2024-07-30T16:36:58.535180Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.368915Z", - "iopub.status.busy": "2024-07-18T04:07:02.368093Z", - "iopub.status.idle": "2024-07-18T04:07:02.410734Z", - "shell.execute_reply": "2024-07-18T04:07:02.410117Z" + "iopub.execute_input": "2024-07-30T16:36:58.539755Z", + "iopub.status.busy": "2024-07-30T16:36:58.538755Z", + "iopub.status.idle": "2024-07-30T16:36:58.591095Z", + "shell.execute_reply": "2024-07-30T16:36:58.590433Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.413396Z", - "iopub.status.busy": "2024-07-18T04:07:02.413137Z", - "iopub.status.idle": "2024-07-18T04:07:02.451147Z", - "shell.execute_reply": "2024-07-18T04:07:02.450498Z" + "iopub.execute_input": "2024-07-30T16:36:58.593884Z", + "iopub.status.busy": "2024-07-30T16:36:58.593478Z", + "iopub.status.idle": "2024-07-30T16:36:58.639623Z", + "shell.execute_reply": "2024-07-30T16:36:58.638845Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.453755Z", - "iopub.status.busy": "2024-07-18T04:07:02.453375Z", - "iopub.status.idle": "2024-07-18T04:07:02.456615Z", - "shell.execute_reply": "2024-07-18T04:07:02.456146Z" + "iopub.execute_input": "2024-07-30T16:36:58.642513Z", + "iopub.status.busy": "2024-07-30T16:36:58.642101Z", + "iopub.status.idle": "2024-07-30T16:36:58.645752Z", + "shell.execute_reply": "2024-07-30T16:36:58.645291Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.458726Z", - "iopub.status.busy": "2024-07-18T04:07:02.458390Z", - "iopub.status.idle": "2024-07-18T04:07:02.460984Z", - "shell.execute_reply": "2024-07-18T04:07:02.460533Z" + "iopub.execute_input": "2024-07-30T16:36:58.647868Z", + "iopub.status.busy": "2024-07-30T16:36:58.647530Z", + "iopub.status.idle": "2024-07-30T16:36:58.650324Z", + "shell.execute_reply": "2024-07-30T16:36:58.649625Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.463310Z", - "iopub.status.busy": "2024-07-18T04:07:02.462737Z", - "iopub.status.idle": "2024-07-18T04:07:02.489598Z", - "shell.execute_reply": "2024-07-18T04:07:02.489036Z" + "iopub.execute_input": "2024-07-30T16:36:58.652675Z", + "iopub.status.busy": "2024-07-30T16:36:58.652185Z", + "iopub.status.idle": "2024-07-30T16:36:58.676038Z", + "shell.execute_reply": "2024-07-30T16:36:58.675495Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7a01475effc42c7a0d0df5831be2afd", + "model_id": "57581a07cda143f5ae3947a8ceb2effa", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2060b8b81e144c6a49a7e7fa3958df1", + "model_id": "8036196b7e194ee38336f33c15df9344", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.494143Z", - "iopub.status.busy": "2024-07-18T04:07:02.493831Z", - "iopub.status.idle": "2024-07-18T04:07:02.500460Z", - "shell.execute_reply": "2024-07-18T04:07:02.499905Z" + "iopub.execute_input": "2024-07-30T16:36:58.681445Z", + "iopub.status.busy": "2024-07-30T16:36:58.681234Z", + "iopub.status.idle": "2024-07-30T16:36:58.688163Z", + "shell.execute_reply": "2024-07-30T16:36:58.687730Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:02.502621Z", - 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"id": "a846fe33", + "id": "984213fa", "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": "abe989bf", + "id": "2618e545", "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": "682e16e3", + "id": "1e0becd2", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "82d68237", + "id": "cba58da6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.843106Z", - "iopub.status.busy": "2024-07-18T04:07:05.842779Z", - "iopub.status.idle": "2024-07-18T04:07:05.850387Z", - "shell.execute_reply": "2024-07-18T04:07:05.849822Z" + "iopub.execute_input": "2024-07-30T16:37:02.136245Z", + "iopub.status.busy": "2024-07-30T16:37:02.136064Z", + "iopub.status.idle": "2024-07-30T16:37:02.143652Z", + "shell.execute_reply": "2024-07-30T16:37:02.143210Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "e698fd46", + "id": "fea318fb", "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": "e6b8075c", + "id": "6afc3734", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.852549Z", - "iopub.status.busy": "2024-07-18T04:07:05.852221Z", - "iopub.status.idle": "2024-07-18T04:07:05.870747Z", - "shell.execute_reply": "2024-07-18T04:07:05.870171Z" + "iopub.execute_input": "2024-07-30T16:37:02.145731Z", + "iopub.status.busy": "2024-07-30T16:37:02.145388Z", + "iopub.status.idle": "2024-07-30T16:37:02.165432Z", + "shell.execute_reply": "2024-07-30T16:37:02.164935Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "307c5ea3", + "id": "b8513ca9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:05.873062Z", - "iopub.status.busy": "2024-07-18T04:07:05.872717Z", - "iopub.status.idle": "2024-07-18T04:07:05.876179Z", - "shell.execute_reply": "2024-07-18T04:07:05.875603Z" + "iopub.execute_input": "2024-07-30T16:37:02.167476Z", + "iopub.status.busy": "2024-07-30T16:37:02.167285Z", + "iopub.status.idle": "2024-07-30T16:37:02.170854Z", + "shell.execute_reply": "2024-07-30T16:37:02.170369Z" } }, "outputs": [ @@ -1622,99 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"iopub.execute_input": "2024-07-18T04:07:10.188653Z", - "iopub.status.busy": "2024-07-18T04:07:10.188169Z", - "iopub.status.idle": "2024-07-18T04:07:11.330300Z", - "shell.execute_reply": "2024-07-18T04:07:11.329753Z" + "iopub.execute_input": "2024-07-30T16:37:06.847486Z", + "iopub.status.busy": "2024-07-30T16:37:06.846996Z", + "iopub.status.idle": "2024-07-30T16:37:08.300373Z", + "shell.execute_reply": "2024-07-30T16:37:08.299802Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - 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"iopub.execute_input": "2024-07-18T04:07:12.016335Z", - "iopub.status.busy": "2024-07-18T04:07:12.015426Z", - "iopub.status.idle": "2024-07-18T04:07:12.021208Z", - "shell.execute_reply": "2024-07-18T04:07:12.020724Z" + "iopub.execute_input": "2024-07-30T16:37:08.707757Z", + "iopub.status.busy": "2024-07-30T16:37:08.707363Z", + "iopub.status.idle": "2024-07-30T16:37:08.711693Z", + "shell.execute_reply": "2024-07-30T16:37:08.711182Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.024634Z", - "iopub.status.busy": "2024-07-18T04:07:12.023730Z", - "iopub.status.idle": "2024-07-18T04:07:12.029722Z", - "shell.execute_reply": "2024-07-18T04:07:12.029237Z" + "iopub.execute_input": "2024-07-30T16:37:08.714035Z", + "iopub.status.busy": "2024-07-30T16:37:08.713629Z", + "iopub.status.idle": "2024-07-30T16:37:08.718166Z", + "shell.execute_reply": "2024-07-30T16:37:08.717631Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.032990Z", - "iopub.status.busy": "2024-07-18T04:07:12.032270Z", - "iopub.status.idle": "2024-07-18T04:07:12.041800Z", - "shell.execute_reply": "2024-07-18T04:07:12.041134Z" + "iopub.execute_input": "2024-07-30T16:37:08.720507Z", + "iopub.status.busy": "2024-07-30T16:37:08.720111Z", + "iopub.status.idle": "2024-07-30T16:37:08.731439Z", + "shell.execute_reply": "2024-07-30T16:37:08.730910Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.044012Z", - "iopub.status.busy": "2024-07-18T04:07:12.043657Z", - "iopub.status.idle": "2024-07-18T04:07:12.048264Z", - "shell.execute_reply": "2024-07-18T04:07:12.047845Z" + "iopub.execute_input": "2024-07-30T16:37:08.733378Z", + "iopub.status.busy": "2024-07-30T16:37:08.733061Z", + "iopub.status.idle": "2024-07-30T16:37:08.737822Z", + "shell.execute_reply": "2024-07-30T16:37:08.737271Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.050252Z", - "iopub.status.busy": "2024-07-18T04:07:12.049932Z", - "iopub.status.idle": "2024-07-18T04:07:12.161492Z", - "shell.execute_reply": "2024-07-18T04:07:12.160998Z" + "iopub.execute_input": "2024-07-30T16:37:08.739993Z", + "iopub.status.busy": "2024-07-30T16:37:08.739678Z", + "iopub.status.idle": "2024-07-30T16:37:08.850454Z", + "shell.execute_reply": "2024-07-30T16:37:08.849916Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.163832Z", - "iopub.status.busy": "2024-07-18T04:07:12.163548Z", - "iopub.status.idle": "2024-07-18T04:07:12.169337Z", - "shell.execute_reply": "2024-07-18T04:07:12.168771Z" + "iopub.execute_input": "2024-07-30T16:37:08.852557Z", + "iopub.status.busy": "2024-07-30T16:37:08.852223Z", + "iopub.status.idle": "2024-07-30T16:37:08.858285Z", + "shell.execute_reply": "2024-07-30T16:37:08.857774Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:12.171714Z", - "iopub.status.busy": "2024-07-18T04:07:12.171220Z", - "iopub.status.idle": "2024-07-18T04:07:14.132704Z", - "shell.execute_reply": "2024-07-18T04:07:14.132092Z" + "iopub.execute_input": "2024-07-30T16:37:08.860639Z", + "iopub.status.busy": "2024-07-30T16:37:08.860116Z", + "iopub.status.idle": "2024-07-30T16:37:11.081523Z", + "shell.execute_reply": "2024-07-30T16:37:11.080894Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.136911Z", - "iopub.status.busy": "2024-07-18T04:07:14.135833Z", - "iopub.status.idle": "2024-07-18T04:07:14.150521Z", - "shell.execute_reply": "2024-07-18T04:07:14.150012Z" + "iopub.execute_input": "2024-07-30T16:37:11.085782Z", + "iopub.status.busy": "2024-07-30T16:37:11.084683Z", + "iopub.status.idle": "2024-07-30T16:37:11.100012Z", + "shell.execute_reply": "2024-07-30T16:37:11.099506Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.154039Z", - "iopub.status.busy": "2024-07-18T04:07:14.153107Z", - "iopub.status.idle": "2024-07-18T04:07:14.157115Z", - "shell.execute_reply": "2024-07-18T04:07:14.156603Z" + "iopub.execute_input": "2024-07-30T16:37:11.103570Z", + "iopub.status.busy": "2024-07-30T16:37:11.102644Z", + "iopub.status.idle": "2024-07-30T16:37:11.106644Z", + "shell.execute_reply": "2024-07-30T16:37:11.106149Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.160572Z", - "iopub.status.busy": "2024-07-18T04:07:14.159645Z", - "iopub.status.idle": "2024-07-18T04:07:14.165136Z", - "shell.execute_reply": "2024-07-18T04:07:14.164638Z" + "iopub.execute_input": "2024-07-30T16:37:11.110093Z", + "iopub.status.busy": "2024-07-30T16:37:11.109154Z", + "iopub.status.idle": "2024-07-30T16:37:11.114773Z", + "shell.execute_reply": "2024-07-30T16:37:11.114272Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.168650Z", - "iopub.status.busy": "2024-07-18T04:07:14.167719Z", - "iopub.status.idle": "2024-07-18T04:07:14.201487Z", - "shell.execute_reply": "2024-07-18T04:07:14.200991Z" + "iopub.execute_input": "2024-07-30T16:37:11.118266Z", + "iopub.status.busy": "2024-07-30T16:37:11.117324Z", + "iopub.status.idle": "2024-07-30T16:37:11.149228Z", + "shell.execute_reply": "2024-07-30T16:37:11.148699Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.204946Z", - "iopub.status.busy": "2024-07-18T04:07:14.204060Z", - "iopub.status.idle": "2024-07-18T04:07:14.715733Z", - "shell.execute_reply": "2024-07-18T04:07:14.715232Z" + "iopub.execute_input": "2024-07-30T16:37:11.152348Z", + "iopub.status.busy": "2024-07-30T16:37:11.151900Z", + "iopub.status.idle": "2024-07-30T16:37:11.662729Z", + "shell.execute_reply": "2024-07-30T16:37:11.662153Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.719208Z", - "iopub.status.busy": "2024-07-18T04:07:14.718293Z", - "iopub.status.idle": "2024-07-18T04:07:14.851216Z", - "shell.execute_reply": "2024-07-18T04:07:14.850601Z" + "iopub.execute_input": "2024-07-30T16:37:11.665547Z", + "iopub.status.busy": "2024-07-30T16:37:11.665125Z", + "iopub.status.idle": "2024-07-30T16:37:11.811588Z", + "shell.execute_reply": "2024-07-30T16:37:11.810893Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.854177Z", - "iopub.status.busy": "2024-07-18T04:07:14.853837Z", - "iopub.status.idle": "2024-07-18T04:07:14.860224Z", - "shell.execute_reply": "2024-07-18T04:07:14.859741Z" + "iopub.execute_input": "2024-07-30T16:37:11.814740Z", + "iopub.status.busy": "2024-07-30T16:37:11.814355Z", + "iopub.status.idle": "2024-07-30T16:37:11.821641Z", + "shell.execute_reply": "2024-07-30T16:37:11.821112Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.862683Z", - "iopub.status.busy": "2024-07-18T04:07:14.862372Z", - "iopub.status.idle": "2024-07-18T04:07:14.868198Z", - "shell.execute_reply": "2024-07-18T04:07:14.867715Z" + "iopub.execute_input": "2024-07-30T16:37:11.825192Z", + "iopub.status.busy": "2024-07-30T16:37:11.824261Z", + "iopub.status.idle": "2024-07-30T16:37:11.832276Z", + "shell.execute_reply": "2024-07-30T16:37:11.831780Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.870508Z", - "iopub.status.busy": "2024-07-18T04:07:14.870130Z", - "iopub.status.idle": "2024-07-18T04:07:14.875378Z", - "shell.execute_reply": "2024-07-18T04:07:14.874887Z" + "iopub.execute_input": "2024-07-30T16:37:11.835774Z", + "iopub.status.busy": "2024-07-30T16:37:11.834852Z", + "iopub.status.idle": "2024-07-30T16:37:11.842134Z", + "shell.execute_reply": "2024-07-30T16:37:11.841617Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.877652Z", - "iopub.status.busy": "2024-07-18T04:07:14.877284Z", - "iopub.status.idle": "2024-07-18T04:07:14.881329Z", - "shell.execute_reply": "2024-07-18T04:07:14.880859Z" + "iopub.execute_input": "2024-07-30T16:37:11.845557Z", + "iopub.status.busy": "2024-07-30T16:37:11.844646Z", + "iopub.status.idle": "2024-07-30T16:37:11.849988Z", + "shell.execute_reply": "2024-07-30T16:37:11.849571Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.883619Z", - "iopub.status.busy": "2024-07-18T04:07:14.883248Z", - "iopub.status.idle": "2024-07-18T04:07:14.887859Z", - "shell.execute_reply": "2024-07-18T04:07:14.887364Z" + "iopub.execute_input": "2024-07-30T16:37:11.852085Z", + "iopub.status.busy": "2024-07-30T16:37:11.851735Z", + "iopub.status.idle": "2024-07-30T16:37:11.856242Z", + "shell.execute_reply": "2024-07-30T16:37:11.855836Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.890172Z", - "iopub.status.busy": "2024-07-18T04:07:14.889802Z", - "iopub.status.idle": "2024-07-18T04:07:14.967278Z", - "shell.execute_reply": "2024-07-18T04:07:14.966734Z" + "iopub.execute_input": "2024-07-30T16:37:11.858461Z", + "iopub.status.busy": "2024-07-30T16:37:11.858025Z", + "iopub.status.idle": "2024-07-30T16:37:11.938221Z", + "shell.execute_reply": "2024-07-30T16:37:11.937709Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.969554Z", - "iopub.status.busy": "2024-07-18T04:07:14.969394Z", - "iopub.status.idle": "2024-07-18T04:07:14.980191Z", - "shell.execute_reply": "2024-07-18T04:07:14.979708Z" + "iopub.execute_input": "2024-07-30T16:37:11.940497Z", + "iopub.status.busy": "2024-07-30T16:37:11.940305Z", + "iopub.status.idle": "2024-07-30T16:37:11.950235Z", + "shell.execute_reply": "2024-07-30T16:37:11.949598Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.983421Z", - "iopub.status.busy": "2024-07-18T04:07:14.982704Z", - "iopub.status.idle": "2024-07-18T04:07:14.986309Z", - "shell.execute_reply": "2024-07-18T04:07:14.985259Z" + "iopub.execute_input": "2024-07-30T16:37:11.952707Z", + "iopub.status.busy": "2024-07-30T16:37:11.952413Z", + "iopub.status.idle": "2024-07-30T16:37:11.955525Z", + "shell.execute_reply": "2024-07-30T16:37:11.954939Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.988371Z", - "iopub.status.busy": "2024-07-18T04:07:14.988062Z", - "iopub.status.idle": "2024-07-18T04:07:14.997162Z", - "shell.execute_reply": "2024-07-18T04:07:14.996732Z" + "iopub.execute_input": "2024-07-30T16:37:11.957491Z", + "iopub.status.busy": "2024-07-30T16:37:11.957322Z", + "iopub.status.idle": "2024-07-30T16:37:11.968955Z", + "shell.execute_reply": "2024-07-30T16:37:11.968450Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:14.999345Z", - "iopub.status.busy": "2024-07-18T04:07:14.999017Z", - "iopub.status.idle": "2024-07-18T04:07:15.005252Z", - "shell.execute_reply": "2024-07-18T04:07:15.004791Z" + "iopub.execute_input": "2024-07-30T16:37:11.971083Z", + "iopub.status.busy": "2024-07-30T16:37:11.970902Z", + "iopub.status.idle": "2024-07-30T16:37:11.977806Z", + "shell.execute_reply": "2024-07-30T16:37:11.977330Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:15.007199Z", - "iopub.status.busy": "2024-07-18T04:07:15.006868Z", - "iopub.status.idle": "2024-07-18T04:07:15.009987Z", - "shell.execute_reply": "2024-07-18T04:07:15.009541Z" + "iopub.execute_input": "2024-07-30T16:37:11.979672Z", + "iopub.status.busy": "2024-07-30T16:37:11.979501Z", + "iopub.status.idle": "2024-07-30T16:37:11.982695Z", + "shell.execute_reply": "2024-07-30T16:37:11.982238Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:15.011951Z", - "iopub.status.busy": "2024-07-18T04:07:15.011621Z", - "iopub.status.idle": "2024-07-18T04:07:19.005757Z", - "shell.execute_reply": "2024-07-18T04:07:19.005204Z" + "iopub.execute_input": "2024-07-30T16:37:11.984563Z", + "iopub.status.busy": "2024-07-30T16:37:11.984385Z", + "iopub.status.idle": "2024-07-30T16:37:16.038898Z", + "shell.execute_reply": "2024-07-30T16:37:16.038334Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:19.009017Z", - "iopub.status.busy": "2024-07-18T04:07:19.008110Z", - "iopub.status.idle": "2024-07-18T04:07:19.011999Z", - "shell.execute_reply": "2024-07-18T04:07:19.011599Z" + "iopub.execute_input": "2024-07-30T16:37:16.041419Z", + "iopub.status.busy": "2024-07-30T16:37:16.041044Z", + "iopub.status.idle": "2024-07-30T16:37:16.044136Z", + "shell.execute_reply": "2024-07-30T16:37:16.043742Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:19.013973Z", - "iopub.status.busy": "2024-07-18T04:07:19.013518Z", - "iopub.status.idle": "2024-07-18T04:07:19.016164Z", - "shell.execute_reply": "2024-07-18T04:07:19.015768Z" + "iopub.execute_input": "2024-07-30T16:37:16.046136Z", + "iopub.status.busy": "2024-07-30T16:37:16.045835Z", + "iopub.status.idle": "2024-07-30T16:37:16.048832Z", + "shell.execute_reply": "2024-07-30T16:37:16.048207Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 773aea810..63d074d15 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": "2024-07-18T04:07:22.076336Z", - "iopub.status.busy": "2024-07-18T04:07:22.075827Z", - "iopub.status.idle": "2024-07-18T04:07:23.267351Z", - "shell.execute_reply": "2024-07-18T04:07:23.266808Z" + "iopub.execute_input": "2024-07-30T16:37:19.514665Z", + "iopub.status.busy": "2024-07-30T16:37:19.514193Z", + "iopub.status.idle": "2024-07-30T16:37:20.970203Z", + "shell.execute_reply": "2024-07-30T16:37:20.969599Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:23.269779Z", - "iopub.status.busy": "2024-07-18T04:07:23.269491Z", - "iopub.status.idle": "2024-07-18T04:07:23.449090Z", - "shell.execute_reply": "2024-07-18T04:07:23.448580Z" + "iopub.execute_input": "2024-07-30T16:37:20.972868Z", + "iopub.status.busy": "2024-07-30T16:37:20.972378Z", + "iopub.status.idle": "2024-07-30T16:37:20.975839Z", + "shell.execute_reply": "2024-07-30T16:37:20.975373Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.451531Z", - "iopub.status.busy": "2024-07-18T04:07:23.451185Z", - "iopub.status.idle": "2024-07-18T04:07:23.462992Z", - "shell.execute_reply": "2024-07-18T04:07:23.462568Z" + "iopub.execute_input": "2024-07-30T16:37:20.977983Z", + "iopub.status.busy": "2024-07-30T16:37:20.977647Z", + "iopub.status.idle": "2024-07-30T16:37:20.988855Z", + "shell.execute_reply": "2024-07-30T16:37:20.988422Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.465102Z", - "iopub.status.busy": "2024-07-18T04:07:23.464771Z", - "iopub.status.idle": "2024-07-18T04:07:23.699321Z", - "shell.execute_reply": "2024-07-18T04:07:23.698710Z" + "iopub.execute_input": "2024-07-30T16:37:20.990750Z", + "iopub.status.busy": "2024-07-30T16:37:20.990413Z", + "iopub.status.idle": "2024-07-30T16:37:21.236239Z", + "shell.execute_reply": "2024-07-30T16:37:21.235736Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.701468Z", - "iopub.status.busy": "2024-07-18T04:07:23.701288Z", - "iopub.status.idle": "2024-07-18T04:07:23.726925Z", - "shell.execute_reply": "2024-07-18T04:07:23.726344Z" + "iopub.execute_input": "2024-07-30T16:37:21.238707Z", + "iopub.status.busy": "2024-07-30T16:37:21.238345Z", + "iopub.status.idle": "2024-07-30T16:37:21.264617Z", + "shell.execute_reply": "2024-07-30T16:37:21.264131Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:23.729027Z", - "iopub.status.busy": "2024-07-18T04:07:23.728854Z", - "iopub.status.idle": "2024-07-18T04:07:25.797394Z", - "shell.execute_reply": "2024-07-18T04:07:25.796767Z" + "iopub.execute_input": "2024-07-30T16:37:21.266767Z", + "iopub.status.busy": "2024-07-30T16:37:21.266578Z", + "iopub.status.idle": "2024-07-30T16:37:23.611867Z", + "shell.execute_reply": "2024-07-30T16:37:23.611160Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:25.799776Z", - "iopub.status.busy": "2024-07-18T04:07:25.799466Z", - "iopub.status.idle": "2024-07-18T04:07:25.818394Z", - "shell.execute_reply": "2024-07-18T04:07:25.817813Z" + "iopub.execute_input": "2024-07-30T16:37:23.614599Z", + "iopub.status.busy": "2024-07-30T16:37:23.614210Z", + "iopub.status.idle": "2024-07-30T16:37:23.634028Z", + "shell.execute_reply": "2024-07-30T16:37:23.633465Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:25.820464Z", - "iopub.status.busy": "2024-07-18T04:07:25.820179Z", - "iopub.status.idle": "2024-07-18T04:07:27.394088Z", - "shell.execute_reply": "2024-07-18T04:07:27.393509Z" + "iopub.execute_input": "2024-07-30T16:37:23.636438Z", + "iopub.status.busy": "2024-07-30T16:37:23.635973Z", + "iopub.status.idle": "2024-07-30T16:37:25.305599Z", + "shell.execute_reply": "2024-07-30T16:37:25.304862Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.396818Z", - "iopub.status.busy": "2024-07-18T04:07:27.396136Z", - "iopub.status.idle": "2024-07-18T04:07:27.409817Z", - "shell.execute_reply": "2024-07-18T04:07:27.409253Z" + "iopub.execute_input": "2024-07-30T16:37:25.308828Z", + "iopub.status.busy": "2024-07-30T16:37:25.307885Z", + "iopub.status.idle": "2024-07-30T16:37:25.322222Z", + "shell.execute_reply": "2024-07-30T16:37:25.321727Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.411938Z", - "iopub.status.busy": "2024-07-18T04:07:27.411554Z", - "iopub.status.idle": "2024-07-18T04:07:27.487589Z", - "shell.execute_reply": "2024-07-18T04:07:27.486978Z" + "iopub.execute_input": "2024-07-30T16:37:25.324721Z", + "iopub.status.busy": "2024-07-30T16:37:25.324152Z", + "iopub.status.idle": "2024-07-30T16:37:25.419049Z", + "shell.execute_reply": "2024-07-30T16:37:25.418364Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.490046Z", - "iopub.status.busy": "2024-07-18T04:07:27.489661Z", - "iopub.status.idle": "2024-07-18T04:07:27.700426Z", - "shell.execute_reply": "2024-07-18T04:07:27.699861Z" + "iopub.execute_input": "2024-07-30T16:37:25.421430Z", + "iopub.status.busy": "2024-07-30T16:37:25.421173Z", + "iopub.status.idle": "2024-07-30T16:37:25.644270Z", + "shell.execute_reply": "2024-07-30T16:37:25.643645Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.702570Z", - "iopub.status.busy": "2024-07-18T04:07:27.702242Z", - "iopub.status.idle": "2024-07-18T04:07:27.719037Z", - "shell.execute_reply": "2024-07-18T04:07:27.718497Z" + "iopub.execute_input": "2024-07-30T16:37:25.646795Z", + "iopub.status.busy": "2024-07-30T16:37:25.646428Z", + "iopub.status.idle": "2024-07-30T16:37:25.665764Z", + "shell.execute_reply": "2024-07-30T16:37:25.665270Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.721046Z", - "iopub.status.busy": "2024-07-18T04:07:27.720735Z", - "iopub.status.idle": "2024-07-18T04:07:27.731534Z", - "shell.execute_reply": "2024-07-18T04:07:27.730958Z" + "iopub.execute_input": "2024-07-30T16:37:25.667885Z", + "iopub.status.busy": "2024-07-30T16:37:25.667692Z", + "iopub.status.idle": "2024-07-30T16:37:25.678270Z", + "shell.execute_reply": "2024-07-30T16:37:25.677775Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.733768Z", - "iopub.status.busy": "2024-07-18T04:07:27.733373Z", - "iopub.status.idle": "2024-07-18T04:07:27.825217Z", - "shell.execute_reply": "2024-07-18T04:07:27.824625Z" + "iopub.execute_input": "2024-07-30T16:37:25.680557Z", + "iopub.status.busy": "2024-07-30T16:37:25.680215Z", + "iopub.status.idle": "2024-07-30T16:37:25.783566Z", + "shell.execute_reply": "2024-07-30T16:37:25.782891Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.827744Z", - "iopub.status.busy": "2024-07-18T04:07:27.827350Z", - "iopub.status.idle": "2024-07-18T04:07:27.961216Z", - "shell.execute_reply": "2024-07-18T04:07:27.960600Z" + "iopub.execute_input": "2024-07-30T16:37:25.786394Z", + "iopub.status.busy": "2024-07-30T16:37:25.785963Z", + "iopub.status.idle": "2024-07-30T16:37:25.944890Z", + "shell.execute_reply": "2024-07-30T16:37:25.944224Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.963793Z", - "iopub.status.busy": "2024-07-18T04:07:27.963318Z", - "iopub.status.idle": "2024-07-18T04:07:27.967349Z", - "shell.execute_reply": "2024-07-18T04:07:27.966769Z" + "iopub.execute_input": "2024-07-30T16:37:25.947223Z", + "iopub.status.busy": "2024-07-30T16:37:25.947014Z", + "iopub.status.idle": "2024-07-30T16:37:25.951228Z", + "shell.execute_reply": "2024-07-30T16:37:25.950663Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.969456Z", - "iopub.status.busy": "2024-07-18T04:07:27.969185Z", - "iopub.status.idle": "2024-07-18T04:07:27.972925Z", - "shell.execute_reply": "2024-07-18T04:07:27.972378Z" + "iopub.execute_input": "2024-07-30T16:37:25.953418Z", + "iopub.status.busy": "2024-07-30T16:37:25.953075Z", + "iopub.status.idle": "2024-07-30T16:37:25.957102Z", + "shell.execute_reply": "2024-07-30T16:37:25.956520Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:27.974780Z", - "iopub.status.busy": "2024-07-18T04:07:27.974604Z", - "iopub.status.idle": "2024-07-18T04:07:28.011316Z", - "shell.execute_reply": "2024-07-18T04:07:28.010835Z" + "iopub.execute_input": "2024-07-30T16:37:25.959055Z", + "iopub.status.busy": "2024-07-30T16:37:25.958872Z", + "iopub.status.idle": "2024-07-30T16:37:25.996394Z", + "shell.execute_reply": "2024-07-30T16:37:25.995898Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.013167Z", - "iopub.status.busy": "2024-07-18T04:07:28.012994Z", - "iopub.status.idle": "2024-07-18T04:07:28.054350Z", - "shell.execute_reply": "2024-07-18T04:07:28.053903Z" + "iopub.execute_input": "2024-07-30T16:37:25.998305Z", + "iopub.status.busy": "2024-07-30T16:37:25.998128Z", + "iopub.status.idle": "2024-07-30T16:37:26.039427Z", + "shell.execute_reply": "2024-07-30T16:37:26.038868Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.056361Z", - "iopub.status.busy": "2024-07-18T04:07:28.056029Z", - "iopub.status.idle": "2024-07-18T04:07:28.151883Z", - "shell.execute_reply": "2024-07-18T04:07:28.151289Z" + "iopub.execute_input": "2024-07-30T16:37:26.041607Z", + "iopub.status.busy": "2024-07-30T16:37:26.041417Z", + "iopub.status.idle": "2024-07-30T16:37:26.162225Z", + "shell.execute_reply": "2024-07-30T16:37:26.161548Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.154465Z", - "iopub.status.busy": "2024-07-18T04:07:28.154094Z", - "iopub.status.idle": "2024-07-18T04:07:28.255502Z", - "shell.execute_reply": "2024-07-18T04:07:28.254823Z" + "iopub.execute_input": "2024-07-30T16:37:26.165084Z", + "iopub.status.busy": "2024-07-30T16:37:26.164618Z", + "iopub.status.idle": "2024-07-30T16:37:26.285845Z", + "shell.execute_reply": "2024-07-30T16:37:26.285184Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.258165Z", - "iopub.status.busy": "2024-07-18T04:07:28.257804Z", - "iopub.status.idle": "2024-07-18T04:07:28.470060Z", - "shell.execute_reply": "2024-07-18T04:07:28.469555Z" + "iopub.execute_input": "2024-07-30T16:37:26.288464Z", + "iopub.status.busy": "2024-07-30T16:37:26.288093Z", + "iopub.status.idle": "2024-07-30T16:37:26.502063Z", + "shell.execute_reply": "2024-07-30T16:37:26.501416Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.472345Z", - "iopub.status.busy": "2024-07-18T04:07:28.471991Z", - "iopub.status.idle": "2024-07-18T04:07:28.679158Z", - "shell.execute_reply": "2024-07-18T04:07:28.678514Z" + "iopub.execute_input": "2024-07-30T16:37:26.504441Z", + "iopub.status.busy": "2024-07-30T16:37:26.503981Z", + "iopub.status.idle": "2024-07-30T16:37:26.744760Z", + "shell.execute_reply": "2024-07-30T16:37:26.744174Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.681512Z", - "iopub.status.busy": "2024-07-18T04:07:28.681268Z", - "iopub.status.idle": "2024-07-18T04:07:28.687496Z", - "shell.execute_reply": "2024-07-18T04:07:28.686962Z" + "iopub.execute_input": "2024-07-30T16:37:26.747291Z", + "iopub.status.busy": "2024-07-30T16:37:26.746891Z", + "iopub.status.idle": "2024-07-30T16:37:26.752870Z", + "shell.execute_reply": "2024-07-30T16:37:26.752415Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.689401Z", - "iopub.status.busy": "2024-07-18T04:07:28.689228Z", - "iopub.status.idle": "2024-07-18T04:07:28.907545Z", - "shell.execute_reply": "2024-07-18T04:07:28.907027Z" + "iopub.execute_input": "2024-07-30T16:37:26.754943Z", + "iopub.status.busy": "2024-07-30T16:37:26.754598Z", + "iopub.status.idle": "2024-07-30T16:37:26.972039Z", + "shell.execute_reply": "2024-07-30T16:37:26.971400Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:28.909667Z", - "iopub.status.busy": "2024-07-18T04:07:28.909321Z", - "iopub.status.idle": "2024-07-18T04:07:29.967572Z", - "shell.execute_reply": "2024-07-18T04:07:29.967037Z" + "iopub.execute_input": "2024-07-30T16:37:26.974261Z", + "iopub.status.busy": "2024-07-30T16:37:26.974064Z", + "iopub.status.idle": "2024-07-30T16:37:28.066231Z", + "shell.execute_reply": "2024-07-30T16:37:28.065649Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 04b78b7b1..fd2c70404 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:34.296538Z", - "iopub.status.busy": "2024-07-18T04:07:34.296363Z", - "iopub.status.idle": "2024-07-18T04:07:35.422497Z", - "shell.execute_reply": "2024-07-18T04:07:35.421862Z" + "iopub.execute_input": "2024-07-30T16:37:32.718320Z", + "iopub.status.busy": "2024-07-30T16:37:32.718143Z", + "iopub.status.idle": "2024-07-30T16:37:34.160547Z", + "shell.execute_reply": "2024-07-30T16:37:34.159900Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:35.425676Z", - "iopub.status.busy": "2024-07-18T04:07:35.425102Z", - "iopub.status.idle": "2024-07-18T04:07:35.428437Z", - "shell.execute_reply": "2024-07-18T04:07:35.427876Z" + "iopub.execute_input": "2024-07-30T16:37:34.163333Z", + "iopub.status.busy": "2024-07-30T16:37:34.163023Z", + "iopub.status.idle": "2024-07-30T16:37:34.166128Z", + "shell.execute_reply": "2024-07-30T16:37:34.165659Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.430702Z", - "iopub.status.busy": "2024-07-18T04:07:35.430517Z", - "iopub.status.idle": "2024-07-18T04:07:35.438434Z", - "shell.execute_reply": "2024-07-18T04:07:35.437884Z" + "iopub.execute_input": "2024-07-30T16:37:34.168192Z", + "iopub.status.busy": "2024-07-30T16:37:34.168013Z", + "iopub.status.idle": "2024-07-30T16:37:34.175994Z", + "shell.execute_reply": "2024-07-30T16:37:34.175519Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.440572Z", - "iopub.status.busy": "2024-07-18T04:07:35.440110Z", - "iopub.status.idle": "2024-07-18T04:07:35.486384Z", - "shell.execute_reply": "2024-07-18T04:07:35.485828Z" + "iopub.execute_input": "2024-07-30T16:37:34.178122Z", + "iopub.status.busy": "2024-07-30T16:37:34.177688Z", + "iopub.status.idle": "2024-07-30T16:37:34.225806Z", + "shell.execute_reply": "2024-07-30T16:37:34.225161Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.488599Z", - "iopub.status.busy": "2024-07-18T04:07:35.488257Z", - "iopub.status.idle": "2024-07-18T04:07:35.504681Z", - "shell.execute_reply": "2024-07-18T04:07:35.504225Z" + "iopub.execute_input": "2024-07-30T16:37:34.228546Z", + "iopub.status.busy": "2024-07-30T16:37:34.228175Z", + "iopub.status.idle": "2024-07-30T16:37:34.246251Z", + "shell.execute_reply": "2024-07-30T16:37:34.245703Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.506559Z", - "iopub.status.busy": "2024-07-18T04:07:35.506378Z", - "iopub.status.idle": "2024-07-18T04:07:35.510269Z", - "shell.execute_reply": "2024-07-18T04:07:35.509744Z" + "iopub.execute_input": "2024-07-30T16:37:34.248429Z", + "iopub.status.busy": "2024-07-30T16:37:34.248067Z", + "iopub.status.idle": "2024-07-30T16:37:34.251958Z", + "shell.execute_reply": "2024-07-30T16:37:34.251523Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.512387Z", - "iopub.status.busy": "2024-07-18T04:07:35.511994Z", - "iopub.status.idle": "2024-07-18T04:07:35.528137Z", - "shell.execute_reply": "2024-07-18T04:07:35.527604Z" + "iopub.execute_input": "2024-07-30T16:37:34.254230Z", + "iopub.status.busy": "2024-07-30T16:37:34.253755Z", + "iopub.status.idle": "2024-07-30T16:37:34.270486Z", + "shell.execute_reply": "2024-07-30T16:37:34.269879Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.530221Z", - "iopub.status.busy": "2024-07-18T04:07:35.529800Z", - "iopub.status.idle": "2024-07-18T04:07:35.555407Z", - "shell.execute_reply": "2024-07-18T04:07:35.554844Z" + "iopub.execute_input": "2024-07-30T16:37:34.272682Z", + "iopub.status.busy": "2024-07-30T16:37:34.272503Z", + "iopub.status.idle": "2024-07-30T16:37:34.299362Z", + "shell.execute_reply": "2024-07-30T16:37:34.298706Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:35.557521Z", - "iopub.status.busy": "2024-07-18T04:07:35.557213Z", - "iopub.status.idle": "2024-07-18T04:07:37.509880Z", - "shell.execute_reply": "2024-07-18T04:07:37.509298Z" + "iopub.execute_input": "2024-07-30T16:37:34.302325Z", + "iopub.status.busy": "2024-07-30T16:37:34.301951Z", + "iopub.status.idle": "2024-07-30T16:37:36.536542Z", + "shell.execute_reply": "2024-07-30T16:37:36.535928Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.512502Z", - "iopub.status.busy": "2024-07-18T04:07:37.511961Z", - "iopub.status.idle": "2024-07-18T04:07:37.518638Z", - "shell.execute_reply": "2024-07-18T04:07:37.518085Z" + "iopub.execute_input": "2024-07-30T16:37:36.540387Z", + "iopub.status.busy": "2024-07-30T16:37:36.538845Z", + "iopub.status.idle": "2024-07-30T16:37:36.547424Z", + "shell.execute_reply": "2024-07-30T16:37:36.546819Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.520639Z", - "iopub.status.busy": "2024-07-18T04:07:37.520331Z", - "iopub.status.idle": "2024-07-18T04:07:37.532904Z", - "shell.execute_reply": "2024-07-18T04:07:37.532451Z" + "iopub.execute_input": "2024-07-30T16:37:36.549619Z", + "iopub.status.busy": "2024-07-30T16:37:36.549270Z", + "iopub.status.idle": "2024-07-30T16:37:36.562222Z", + "shell.execute_reply": "2024-07-30T16:37:36.561697Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.534769Z", - "iopub.status.busy": "2024-07-18T04:07:37.534595Z", - "iopub.status.idle": "2024-07-18T04:07:37.541067Z", - "shell.execute_reply": "2024-07-18T04:07:37.540607Z" + "iopub.execute_input": "2024-07-30T16:37:36.564431Z", + "iopub.status.busy": "2024-07-30T16:37:36.564072Z", + "iopub.status.idle": "2024-07-30T16:37:36.570665Z", + "shell.execute_reply": "2024-07-30T16:37:36.570168Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.543185Z", - "iopub.status.busy": "2024-07-18T04:07:37.542911Z", - "iopub.status.idle": "2024-07-18T04:07:37.545708Z", - "shell.execute_reply": "2024-07-18T04:07:37.545133Z" + "iopub.execute_input": "2024-07-30T16:37:36.572817Z", + "iopub.status.busy": "2024-07-30T16:37:36.572406Z", + "iopub.status.idle": "2024-07-30T16:37:36.575372Z", + "shell.execute_reply": "2024-07-30T16:37:36.574796Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.547886Z", - "iopub.status.busy": "2024-07-18T04:07:37.547445Z", - "iopub.status.idle": "2024-07-18T04:07:37.550823Z", - "shell.execute_reply": "2024-07-18T04:07:37.550384Z" + "iopub.execute_input": "2024-07-30T16:37:36.577427Z", + "iopub.status.busy": "2024-07-30T16:37:36.577104Z", + "iopub.status.idle": "2024-07-30T16:37:36.580747Z", + "shell.execute_reply": "2024-07-30T16:37:36.580200Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.552820Z", - "iopub.status.busy": "2024-07-18T04:07:37.552645Z", - "iopub.status.idle": "2024-07-18T04:07:37.555115Z", - "shell.execute_reply": "2024-07-18T04:07:37.554670Z" + "iopub.execute_input": "2024-07-30T16:37:36.582932Z", + "iopub.status.busy": "2024-07-30T16:37:36.582604Z", + "iopub.status.idle": "2024-07-30T16:37:36.585678Z", + "shell.execute_reply": "2024-07-30T16:37:36.585251Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.557056Z", - "iopub.status.busy": "2024-07-18T04:07:37.556883Z", - "iopub.status.idle": "2024-07-18T04:07:37.561041Z", - "shell.execute_reply": "2024-07-18T04:07:37.560482Z" + "iopub.execute_input": "2024-07-30T16:37:36.587663Z", + "iopub.status.busy": "2024-07-30T16:37:36.587336Z", + "iopub.status.idle": "2024-07-30T16:37:36.591506Z", + "shell.execute_reply": "2024-07-30T16:37:36.590945Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.563256Z", - "iopub.status.busy": "2024-07-18T04:07:37.562843Z", - "iopub.status.idle": "2024-07-18T04:07:37.591846Z", - "shell.execute_reply": "2024-07-18T04:07:37.591369Z" + "iopub.execute_input": "2024-07-30T16:37:36.593587Z", + "iopub.status.busy": "2024-07-30T16:37:36.593411Z", + "iopub.status.idle": "2024-07-30T16:37:36.622081Z", + "shell.execute_reply": "2024-07-30T16:37:36.621614Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:37.593815Z", - "iopub.status.busy": "2024-07-18T04:07:37.593607Z", - "iopub.status.idle": "2024-07-18T04:07:37.598269Z", - "shell.execute_reply": "2024-07-18T04:07:37.597815Z" + "iopub.execute_input": "2024-07-30T16:37:36.624325Z", + "iopub.status.busy": "2024-07-30T16:37:36.623993Z", + "iopub.status.idle": "2024-07-30T16:37:36.628891Z", + "shell.execute_reply": "2024-07-30T16:37:36.628307Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 2d0d39b6e..85921d220 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": "2024-07-18T04:07:40.549664Z", - "iopub.status.busy": "2024-07-18T04:07:40.549497Z", - "iopub.status.idle": "2024-07-18T04:07:41.730292Z", - "shell.execute_reply": "2024-07-18T04:07:41.729739Z" + "iopub.execute_input": "2024-07-30T16:37:39.759530Z", + "iopub.status.busy": "2024-07-30T16:37:39.759170Z", + "iopub.status.idle": "2024-07-30T16:37:41.225938Z", + "shell.execute_reply": "2024-07-30T16:37:41.225361Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:41.732831Z", - "iopub.status.busy": "2024-07-18T04:07:41.732430Z", - "iopub.status.idle": "2024-07-18T04:07:41.924995Z", - "shell.execute_reply": "2024-07-18T04:07:41.924470Z" + "iopub.execute_input": "2024-07-30T16:37:41.228688Z", + "iopub.status.busy": "2024-07-30T16:37:41.228204Z", + "iopub.status.idle": "2024-07-30T16:37:41.249656Z", + "shell.execute_reply": "2024-07-30T16:37:41.249163Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:41.927419Z", - "iopub.status.busy": "2024-07-18T04:07:41.927009Z", - "iopub.status.idle": "2024-07-18T04:07:41.940256Z", - "shell.execute_reply": "2024-07-18T04:07:41.939813Z" + "iopub.execute_input": "2024-07-30T16:37:41.252375Z", + "iopub.status.busy": "2024-07-30T16:37:41.251838Z", + "iopub.status.idle": "2024-07-30T16:37:41.265158Z", + "shell.execute_reply": "2024-07-30T16:37:41.264726Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:41.942329Z", - "iopub.status.busy": "2024-07-18T04:07:41.942007Z", - "iopub.status.idle": "2024-07-18T04:07:44.554754Z", - "shell.execute_reply": "2024-07-18T04:07:44.554282Z" + "iopub.execute_input": "2024-07-30T16:37:41.267369Z", + "iopub.status.busy": "2024-07-30T16:37:41.266961Z", + "iopub.status.idle": "2024-07-30T16:37:43.948010Z", + "shell.execute_reply": "2024-07-30T16:37:43.947423Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:44.557154Z", - "iopub.status.busy": "2024-07-18T04:07:44.556790Z", - "iopub.status.idle": "2024-07-18T04:07:45.887262Z", - "shell.execute_reply": "2024-07-18T04:07:45.886610Z" + "iopub.execute_input": "2024-07-30T16:37:43.950421Z", + "iopub.status.busy": "2024-07-30T16:37:43.950035Z", + "iopub.status.idle": "2024-07-30T16:37:45.317858Z", + "shell.execute_reply": "2024-07-30T16:37:45.317234Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:45.889687Z", - "iopub.status.busy": "2024-07-18T04:07:45.889483Z", - "iopub.status.idle": "2024-07-18T04:07:45.893592Z", - "shell.execute_reply": "2024-07-18T04:07:45.893123Z" + "iopub.execute_input": "2024-07-30T16:37:45.320689Z", + "iopub.status.busy": "2024-07-30T16:37:45.320261Z", + "iopub.status.idle": "2024-07-30T16:37:45.325116Z", + "shell.execute_reply": "2024-07-30T16:37:45.324609Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:45.895685Z", - "iopub.status.busy": "2024-07-18T04:07:45.895358Z", - "iopub.status.idle": "2024-07-18T04:07:47.938953Z", - "shell.execute_reply": "2024-07-18T04:07:47.938261Z" + "iopub.execute_input": "2024-07-30T16:37:45.327504Z", + "iopub.status.busy": "2024-07-30T16:37:45.327099Z", + "iopub.status.idle": "2024-07-30T16:37:47.549771Z", + "shell.execute_reply": "2024-07-30T16:37:47.549091Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:47.941361Z", - "iopub.status.busy": "2024-07-18T04:07:47.941011Z", - "iopub.status.idle": "2024-07-18T04:07:47.949262Z", - "shell.execute_reply": "2024-07-18T04:07:47.948769Z" + "iopub.execute_input": "2024-07-30T16:37:47.552479Z", + "iopub.status.busy": "2024-07-30T16:37:47.551972Z", + "iopub.status.idle": "2024-07-30T16:37:47.560612Z", + "shell.execute_reply": "2024-07-30T16:37:47.560120Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:47.951205Z", - "iopub.status.busy": "2024-07-18T04:07:47.950934Z", - "iopub.status.idle": "2024-07-18T04:07:50.493764Z", - "shell.execute_reply": "2024-07-18T04:07:50.493249Z" + "iopub.execute_input": "2024-07-30T16:37:47.562648Z", + "iopub.status.busy": "2024-07-30T16:37:47.562368Z", + "iopub.status.idle": "2024-07-30T16:37:50.183671Z", + "shell.execute_reply": "2024-07-30T16:37:50.183000Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.495904Z", - "iopub.status.busy": "2024-07-18T04:07:50.495718Z", - "iopub.status.idle": "2024-07-18T04:07:50.499462Z", - "shell.execute_reply": "2024-07-18T04:07:50.498995Z" + "iopub.execute_input": "2024-07-30T16:37:50.186114Z", + "iopub.status.busy": "2024-07-30T16:37:50.185699Z", + "iopub.status.idle": "2024-07-30T16:37:50.189636Z", + "shell.execute_reply": "2024-07-30T16:37:50.189140Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.501330Z", - "iopub.status.busy": "2024-07-18T04:07:50.501162Z", - "iopub.status.idle": "2024-07-18T04:07:50.504431Z", - "shell.execute_reply": "2024-07-18T04:07:50.503994Z" + "iopub.execute_input": "2024-07-30T16:37:50.191867Z", + "iopub.status.busy": "2024-07-30T16:37:50.191497Z", + "iopub.status.idle": "2024-07-30T16:37:50.195269Z", + "shell.execute_reply": "2024-07-30T16:37:50.194765Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:50.506301Z", - "iopub.status.busy": "2024-07-18T04:07:50.506126Z", - "iopub.status.idle": "2024-07-18T04:07:50.509100Z", - "shell.execute_reply": "2024-07-18T04:07:50.508661Z" + "iopub.execute_input": "2024-07-30T16:37:50.197502Z", + "iopub.status.busy": "2024-07-30T16:37:50.197113Z", + "iopub.status.idle": "2024-07-30T16:37:50.200387Z", + "shell.execute_reply": "2024-07-30T16:37:50.199867Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index c73a5e42a..b908d214b 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:53.027507Z", - "iopub.status.busy": "2024-07-18T04:07:53.027336Z", - "iopub.status.idle": "2024-07-18T04:07:54.203836Z", - "shell.execute_reply": "2024-07-18T04:07:54.203291Z" + "iopub.execute_input": "2024-07-30T16:37:52.967876Z", + "iopub.status.busy": "2024-07-30T16:37:52.967697Z", + "iopub.status.idle": "2024-07-30T16:37:54.423334Z", + "shell.execute_reply": "2024-07-30T16:37:54.422716Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:07:54.206499Z", - "iopub.status.busy": "2024-07-18T04:07:54.205989Z", - "iopub.status.idle": "2024-07-18T04:07:56.950673Z", - "shell.execute_reply": "2024-07-18T04:07:56.949957Z" + "iopub.execute_input": "2024-07-30T16:37:54.426120Z", + "iopub.status.busy": "2024-07-30T16:37:54.425563Z", + "iopub.status.idle": "2024-07-30T16:37:55.812519Z", + "shell.execute_reply": "2024-07-30T16:37:55.811700Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.953257Z", - "iopub.status.busy": "2024-07-18T04:07:56.953039Z", - "iopub.status.idle": "2024-07-18T04:07:56.956605Z", - "shell.execute_reply": "2024-07-18T04:07:56.956021Z" + "iopub.execute_input": "2024-07-30T16:37:55.815457Z", + "iopub.status.busy": "2024-07-30T16:37:55.815048Z", + "iopub.status.idle": "2024-07-30T16:37:55.818533Z", + "shell.execute_reply": "2024-07-30T16:37:55.817973Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.958812Z", - "iopub.status.busy": "2024-07-18T04:07:56.958471Z", - "iopub.status.idle": "2024-07-18T04:07:56.965097Z", - "shell.execute_reply": "2024-07-18T04:07:56.964666Z" + "iopub.execute_input": "2024-07-30T16:37:55.820681Z", + "iopub.status.busy": "2024-07-30T16:37:55.820334Z", + "iopub.status.idle": "2024-07-30T16:37:55.827147Z", + "shell.execute_reply": "2024-07-30T16:37:55.826691Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:56.967310Z", - "iopub.status.busy": "2024-07-18T04:07:56.966946Z", - "iopub.status.idle": "2024-07-18T04:07:57.458211Z", - "shell.execute_reply": "2024-07-18T04:07:57.457607Z" + "iopub.execute_input": "2024-07-30T16:37:55.829268Z", + "iopub.status.busy": "2024-07-30T16:37:55.828920Z", + "iopub.status.idle": "2024-07-30T16:37:56.150888Z", + "shell.execute_reply": "2024-07-30T16:37:56.150240Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.460514Z", - "iopub.status.busy": "2024-07-18T04:07:57.460329Z", - "iopub.status.idle": "2024-07-18T04:07:57.465713Z", - "shell.execute_reply": "2024-07-18T04:07:57.465150Z" + "iopub.execute_input": "2024-07-30T16:37:56.154023Z", + "iopub.status.busy": "2024-07-30T16:37:56.153563Z", + "iopub.status.idle": "2024-07-30T16:37:56.159171Z", + "shell.execute_reply": "2024-07-30T16:37:56.158713Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.467728Z", - "iopub.status.busy": "2024-07-18T04:07:57.467432Z", - "iopub.status.idle": "2024-07-18T04:07:57.471282Z", - "shell.execute_reply": "2024-07-18T04:07:57.470724Z" + "iopub.execute_input": "2024-07-30T16:37:56.161294Z", + "iopub.status.busy": "2024-07-30T16:37:56.160941Z", + "iopub.status.idle": "2024-07-30T16:37:56.164954Z", + "shell.execute_reply": "2024-07-30T16:37:56.164403Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:57.473417Z", - "iopub.status.busy": "2024-07-18T04:07:57.473021Z", - "iopub.status.idle": "2024-07-18T04:07:58.320437Z", - "shell.execute_reply": "2024-07-18T04:07:58.319767Z" + "iopub.execute_input": "2024-07-30T16:37:56.166946Z", + "iopub.status.busy": "2024-07-30T16:37:56.166762Z", + "iopub.status.idle": "2024-07-30T16:37:57.061837Z", + "shell.execute_reply": "2024-07-30T16:37:57.061214Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.322734Z", - "iopub.status.busy": "2024-07-18T04:07:58.322537Z", - "iopub.status.idle": "2024-07-18T04:07:58.596192Z", - "shell.execute_reply": "2024-07-18T04:07:58.595728Z" + "iopub.execute_input": "2024-07-30T16:37:57.064231Z", + "iopub.status.busy": "2024-07-30T16:37:57.064020Z", + "iopub.status.idle": "2024-07-30T16:37:57.269680Z", + "shell.execute_reply": "2024-07-30T16:37:57.269071Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.598116Z", - "iopub.status.busy": "2024-07-18T04:07:58.597937Z", - "iopub.status.idle": "2024-07-18T04:07:58.602256Z", - "shell.execute_reply": "2024-07-18T04:07:58.601801Z" + "iopub.execute_input": "2024-07-30T16:37:57.271779Z", + "iopub.status.busy": "2024-07-30T16:37:57.271589Z", + "iopub.status.idle": "2024-07-30T16:37:57.276069Z", + "shell.execute_reply": "2024-07-30T16:37:57.275620Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:58.604102Z", - "iopub.status.busy": "2024-07-18T04:07:58.603930Z", - "iopub.status.idle": "2024-07-18T04:07:59.051714Z", - "shell.execute_reply": "2024-07-18T04:07:59.051138Z" + "iopub.execute_input": "2024-07-30T16:37:57.277956Z", + "iopub.status.busy": "2024-07-30T16:37:57.277779Z", + "iopub.status.idle": "2024-07-30T16:37:57.741717Z", + "shell.execute_reply": "2024-07-30T16:37:57.741080Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.054893Z", - "iopub.status.busy": "2024-07-18T04:07:59.054683Z", - "iopub.status.idle": "2024-07-18T04:07:59.362985Z", - "shell.execute_reply": "2024-07-18T04:07:59.362380Z" + "iopub.execute_input": "2024-07-30T16:37:57.744943Z", + "iopub.status.busy": "2024-07-30T16:37:57.744706Z", + "iopub.status.idle": "2024-07-30T16:37:58.080727Z", + "shell.execute_reply": "2024-07-30T16:37:58.080133Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.365227Z", - "iopub.status.busy": "2024-07-18T04:07:59.364819Z", - "iopub.status.idle": "2024-07-18T04:07:59.721287Z", - "shell.execute_reply": "2024-07-18T04:07:59.720689Z" + "iopub.execute_input": "2024-07-30T16:37:58.083717Z", + "iopub.status.busy": "2024-07-30T16:37:58.083475Z", + "iopub.status.idle": "2024-07-30T16:37:58.448789Z", + "shell.execute_reply": "2024-07-30T16:37:58.448130Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:07:59.723711Z", - "iopub.status.busy": "2024-07-18T04:07:59.723524Z", - "iopub.status.idle": "2024-07-18T04:08:00.129832Z", - "shell.execute_reply": "2024-07-18T04:08:00.129234Z" + "iopub.execute_input": "2024-07-30T16:37:58.451979Z", + "iopub.status.busy": "2024-07-30T16:37:58.451737Z", + "iopub.status.idle": "2024-07-30T16:37:58.897902Z", + "shell.execute_reply": "2024-07-30T16:37:58.897266Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.134389Z", - "iopub.status.busy": "2024-07-18T04:08:00.134195Z", - "iopub.status.idle": "2024-07-18T04:08:00.555058Z", - "shell.execute_reply": "2024-07-18T04:08:00.554472Z" + "iopub.execute_input": "2024-07-30T16:37:58.902565Z", + "iopub.status.busy": "2024-07-30T16:37:58.902206Z", + "iopub.status.idle": "2024-07-30T16:37:59.331261Z", + "shell.execute_reply": "2024-07-30T16:37:59.330642Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.557791Z", - "iopub.status.busy": "2024-07-18T04:08:00.557601Z", - "iopub.status.idle": "2024-07-18T04:08:00.745423Z", - "shell.execute_reply": "2024-07-18T04:08:00.744861Z" + "iopub.execute_input": "2024-07-30T16:37:59.334413Z", + "iopub.status.busy": "2024-07-30T16:37:59.334051Z", + "iopub.status.idle": "2024-07-30T16:37:59.529024Z", + "shell.execute_reply": "2024-07-30T16:37:59.528352Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.747619Z", - "iopub.status.busy": "2024-07-18T04:08:00.747438Z", - "iopub.status.idle": "2024-07-18T04:08:00.951881Z", - "shell.execute_reply": "2024-07-18T04:08:00.951263Z" + "iopub.execute_input": "2024-07-30T16:37:59.531621Z", + "iopub.status.busy": "2024-07-30T16:37:59.531147Z", + "iopub.status.idle": "2024-07-30T16:37:59.713867Z", + "shell.execute_reply": "2024-07-30T16:37:59.713268Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.954083Z", - "iopub.status.busy": "2024-07-18T04:08:00.953900Z", - "iopub.status.idle": "2024-07-18T04:08:00.956913Z", - "shell.execute_reply": "2024-07-18T04:08:00.956455Z" + "iopub.execute_input": "2024-07-30T16:37:59.716604Z", + "iopub.status.busy": "2024-07-30T16:37:59.716372Z", + "iopub.status.idle": "2024-07-30T16:37:59.719701Z", + "shell.execute_reply": "2024-07-30T16:37:59.719240Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:00.958720Z", - "iopub.status.busy": "2024-07-18T04:08:00.958549Z", - "iopub.status.idle": "2024-07-18T04:08:01.900993Z", - "shell.execute_reply": "2024-07-18T04:08:01.900441Z" + "iopub.execute_input": "2024-07-30T16:37:59.721469Z", + "iopub.status.busy": "2024-07-30T16:37:59.721297Z", + "iopub.status.idle": "2024-07-30T16:38:00.653952Z", + "shell.execute_reply": "2024-07-30T16:38:00.653313Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:01.903897Z", - "iopub.status.busy": "2024-07-18T04:08:01.903502Z", - "iopub.status.idle": "2024-07-18T04:08:02.024595Z", - "shell.execute_reply": "2024-07-18T04:08:02.024142Z" + "iopub.execute_input": "2024-07-30T16:38:00.656659Z", + "iopub.status.busy": "2024-07-30T16:38:00.656200Z", + "iopub.status.idle": "2024-07-30T16:38:00.806657Z", + "shell.execute_reply": "2024-07-30T16:38:00.806013Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.026835Z", - "iopub.status.busy": "2024-07-18T04:08:02.026498Z", - "iopub.status.idle": "2024-07-18T04:08:02.149086Z", - "shell.execute_reply": "2024-07-18T04:08:02.148602Z" + "iopub.execute_input": "2024-07-30T16:38:00.809105Z", + "iopub.status.busy": "2024-07-30T16:38:00.808873Z", + "iopub.status.idle": "2024-07-30T16:38:01.017879Z", + "shell.execute_reply": "2024-07-30T16:38:01.017223Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.151149Z", - "iopub.status.busy": "2024-07-18T04:08:02.150799Z", - "iopub.status.idle": "2024-07-18T04:08:02.892035Z", - "shell.execute_reply": "2024-07-18T04:08:02.891450Z" + "iopub.execute_input": "2024-07-30T16:38:01.020087Z", + "iopub.status.busy": "2024-07-30T16:38:01.019752Z", + "iopub.status.idle": "2024-07-30T16:38:01.734744Z", + "shell.execute_reply": "2024-07-30T16:38:01.734246Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:02.894338Z", - "iopub.status.busy": "2024-07-18T04:08:02.894143Z", - "iopub.status.idle": "2024-07-18T04:08:02.898103Z", - "shell.execute_reply": "2024-07-18T04:08:02.897554Z" + "iopub.execute_input": "2024-07-30T16:38:01.737160Z", + "iopub.status.busy": "2024-07-30T16:38:01.736731Z", + "iopub.status.idle": "2024-07-30T16:38:01.740592Z", + "shell.execute_reply": "2024-07-30T16:38:01.740039Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index b4b348024..51298fe0f 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

2. Pre-process the Cifar10 dataset 
-100%|██████████| 170498071/170498071 [00:04<00:00, 41662854.99it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 107805868.41it/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 db08980fc..3df92007c 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:05.194442Z", - "iopub.status.busy": "2024-07-18T04:08:05.194273Z", - "iopub.status.idle": "2024-07-18T04:08:07.990167Z", - "shell.execute_reply": "2024-07-18T04:08:07.989536Z" + "iopub.execute_input": "2024-07-30T16:38:03.978289Z", + "iopub.status.busy": "2024-07-30T16:38:03.977787Z", + "iopub.status.idle": "2024-07-30T16:38:07.296478Z", + "shell.execute_reply": "2024-07-30T16:38:07.295899Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:08:07.993089Z", - "iopub.status.busy": "2024-07-18T04:08:07.992487Z", - "iopub.status.idle": "2024-07-18T04:08:08.306676Z", - "shell.execute_reply": "2024-07-18T04:08:08.306053Z" + "iopub.execute_input": "2024-07-30T16:38:07.299136Z", + "iopub.status.busy": "2024-07-30T16:38:07.298701Z", + "iopub.status.idle": "2024-07-30T16:38:07.318355Z", + "shell.execute_reply": "2024-07-30T16:38:07.317750Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:08.309275Z", - "iopub.status.busy": "2024-07-18T04:08:08.308985Z", - "iopub.status.idle": "2024-07-18T04:08:08.313469Z", - "shell.execute_reply": "2024-07-18T04:08:08.312921Z" + "iopub.execute_input": "2024-07-30T16:38:07.320466Z", + "iopub.status.busy": "2024-07-30T16:38:07.320062Z", + "iopub.status.idle": "2024-07-30T16:38:07.324323Z", + "shell.execute_reply": "2024-07-30T16:38:07.323777Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:08.315749Z", - "iopub.status.busy": "2024-07-18T04:08:08.315344Z", - "iopub.status.idle": "2024-07-18T04:08:15.710803Z", - "shell.execute_reply": "2024-07-18T04:08:15.710242Z" + "iopub.execute_input": "2024-07-30T16:38:07.326455Z", + "iopub.status.busy": "2024-07-30T16:38:07.325959Z", + "iopub.status.idle": "2024-07-30T16:38:11.831429Z", + "shell.execute_reply": "2024-07-30T16:38:11.830839Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 65536/170498071 [00:00<05:54, 480174.66it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8226376.49it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<02:57, 956605.73it/s]" + " 6%|▋ | 10846208/170498071 [00:00<00:02, 59307309.31it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:59, 2869713.68it/s]" + " 13%|█▎ | 22511616/170498071 [00:00<00:01, 84811001.30it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-07-18T04:08:15.719428Z", - "iopub.status.busy": "2024-07-18T04:08:15.719259Z", - "iopub.status.idle": "2024-07-18T04:08:16.258630Z", - "shell.execute_reply": "2024-07-18T04:08:16.258056Z" + "iopub.execute_input": "2024-07-30T16:38:11.840562Z", + "iopub.status.busy": "2024-07-30T16:38:11.840251Z", + "iopub.status.idle": "2024-07-30T16:38:12.371586Z", + "shell.execute_reply": "2024-07-30T16:38:12.371033Z" } }, "outputs": [ @@ -764,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.260848Z", - "iopub.status.busy": "2024-07-18T04:08:16.260520Z", - "iopub.status.idle": "2024-07-18T04:08:16.767687Z", - "shell.execute_reply": "2024-07-18T04:08:16.767215Z" + "iopub.execute_input": "2024-07-30T16:38:12.373894Z", + "iopub.status.busy": "2024-07-30T16:38:12.373541Z", + "iopub.status.idle": "2024-07-30T16:38:12.887091Z", + "shell.execute_reply": "2024-07-30T16:38:12.886524Z" } }, "outputs": [ @@ -805,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.769863Z", - "iopub.status.busy": "2024-07-18T04:08:16.769508Z", - "iopub.status.idle": "2024-07-18T04:08:16.772822Z", - "shell.execute_reply": "2024-07-18T04:08:16.772376Z" + "iopub.execute_input": "2024-07-30T16:38:12.889299Z", + "iopub.status.busy": "2024-07-30T16:38:12.888937Z", + "iopub.status.idle": "2024-07-30T16:38:12.892536Z", + "shell.execute_reply": "2024-07-30T16:38:12.892076Z" } }, "outputs": [], @@ -831,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:16.774860Z", - "iopub.status.busy": "2024-07-18T04:08:16.774532Z", - "iopub.status.idle": "2024-07-18T04:08:29.229998Z", - "shell.execute_reply": "2024-07-18T04:08:29.229404Z" + "iopub.execute_input": "2024-07-30T16:38:12.894534Z", + "iopub.status.busy": "2024-07-30T16:38:12.894200Z", + "iopub.status.idle": "2024-07-30T16:38:25.488449Z", + "shell.execute_reply": "2024-07-30T16:38:25.487794Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "424409b5a3c248919a596aef89b959d3", + "model_id": "23a512869c5e4f05a2356b8f464b1bcc", "version_major": 2, "version_minor": 0 }, @@ -900,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:29.232469Z", - "iopub.status.busy": "2024-07-18T04:08:29.232033Z", - "iopub.status.idle": "2024-07-18T04:08:31.261531Z", - "shell.execute_reply": "2024-07-18T04:08:31.260905Z" + "iopub.execute_input": "2024-07-30T16:38:25.490754Z", + "iopub.status.busy": "2024-07-30T16:38:25.490545Z", + "iopub.status.idle": "2024-07-30T16:38:27.681301Z", + "shell.execute_reply": "2024-07-30T16:38:27.680552Z" } }, "outputs": [ @@ -947,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:31.264147Z", - "iopub.status.busy": "2024-07-18T04:08:31.263800Z", - "iopub.status.idle": "2024-07-18T04:08:31.486008Z", - "shell.execute_reply": "2024-07-18T04:08:31.485290Z" + "iopub.execute_input": "2024-07-30T16:38:27.684463Z", + "iopub.status.busy": "2024-07-30T16:38:27.683946Z", + "iopub.status.idle": "2024-07-30T16:38:27.951193Z", + "shell.execute_reply": "2024-07-30T16:38:27.950604Z" } }, "outputs": [ @@ -986,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:31.488468Z", - "iopub.status.busy": "2024-07-18T04:08:31.488018Z", - "iopub.status.idle": "2024-07-18T04:08:32.142236Z", - "shell.execute_reply": "2024-07-18T04:08:32.141607Z" + "iopub.execute_input": "2024-07-30T16:38:27.953780Z", + "iopub.status.busy": "2024-07-30T16:38:27.953567Z", + "iopub.status.idle": "2024-07-30T16:38:28.631392Z", + "shell.execute_reply": "2024-07-30T16:38:28.630768Z" } }, "outputs": [ @@ -1039,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.144771Z", - "iopub.status.busy": "2024-07-18T04:08:32.144585Z", - "iopub.status.idle": "2024-07-18T04:08:32.436148Z", - "shell.execute_reply": "2024-07-18T04:08:32.435672Z" + "iopub.execute_input": "2024-07-30T16:38:28.634456Z", + "iopub.status.busy": "2024-07-30T16:38:28.633952Z", + "iopub.status.idle": "2024-07-30T16:38:28.975662Z", + "shell.execute_reply": "2024-07-30T16:38:28.975098Z" } }, "outputs": [ @@ -1090,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.438256Z", - "iopub.status.busy": "2024-07-18T04:08:32.437904Z", - "iopub.status.idle": "2024-07-18T04:08:32.675912Z", - "shell.execute_reply": "2024-07-18T04:08:32.675301Z" + "iopub.execute_input": "2024-07-30T16:38:28.978011Z", + "iopub.status.busy": "2024-07-30T16:38:28.977574Z", + "iopub.status.idle": "2024-07-30T16:38:29.207618Z", + "shell.execute_reply": "2024-07-30T16:38:29.206996Z" } }, "outputs": [ @@ -1149,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.678608Z", - "iopub.status.busy": "2024-07-18T04:08:32.678123Z", - "iopub.status.idle": "2024-07-18T04:08:32.775298Z", - "shell.execute_reply": "2024-07-18T04:08:32.774751Z" + "iopub.execute_input": "2024-07-30T16:38:29.209892Z", + "iopub.status.busy": "2024-07-30T16:38:29.209709Z", + "iopub.status.idle": "2024-07-30T16:38:29.298647Z", + "shell.execute_reply": "2024-07-30T16:38:29.297971Z" } }, "outputs": [], @@ -1173,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:32.778213Z", - "iopub.status.busy": "2024-07-18T04:08:32.777804Z", - "iopub.status.idle": "2024-07-18T04:08:43.316731Z", - "shell.execute_reply": "2024-07-18T04:08:43.316056Z" + "iopub.execute_input": "2024-07-30T16:38:29.301052Z", + "iopub.status.busy": "2024-07-30T16:38:29.300869Z", + "iopub.status.idle": "2024-07-30T16:38:39.931040Z", + "shell.execute_reply": "2024-07-30T16:38:39.930336Z" } }, "outputs": [ @@ -1213,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:43.319383Z", - "iopub.status.busy": "2024-07-18T04:08:43.319007Z", - "iopub.status.idle": "2024-07-18T04:08:45.511352Z", - "shell.execute_reply": "2024-07-18T04:08:45.510818Z" + "iopub.execute_input": "2024-07-30T16:38:39.933469Z", + "iopub.status.busy": "2024-07-30T16:38:39.933256Z", + "iopub.status.idle": "2024-07-30T16:38:42.292073Z", + "shell.execute_reply": "2024-07-30T16:38:42.291503Z" } }, "outputs": [ @@ -1247,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:45.513955Z", - "iopub.status.busy": "2024-07-18T04:08:45.513429Z", - "iopub.status.idle": "2024-07-18T04:08:45.711540Z", - "shell.execute_reply": "2024-07-18T04:08:45.711028Z" + "iopub.execute_input": "2024-07-30T16:38:42.295015Z", + "iopub.status.busy": "2024-07-30T16:38:42.294328Z", + "iopub.status.idle": "2024-07-30T16:38:42.501084Z", + "shell.execute_reply": "2024-07-30T16:38:42.500563Z" } }, "outputs": [], @@ -1264,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:45.713788Z", - "iopub.status.busy": "2024-07-18T04:08:45.713606Z", - "iopub.status.idle": "2024-07-18T04:08:45.716870Z", - "shell.execute_reply": "2024-07-18T04:08:45.716441Z" + "iopub.execute_input": "2024-07-30T16:38:42.503445Z", + "iopub.status.busy": "2024-07-30T16:38:42.503259Z", + "iopub.status.idle": "2024-07-30T16:38:42.506470Z", + "shell.execute_reply": "2024-07-30T16:38:42.506025Z" } }, "outputs": [], @@ -1289,10 +1105,10 @@ "id": "17f96fa6", "metadata": { "execution": { - 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"font_size": null, - "text_color": null - } - }, - "424409b5a3c248919a596aef89b959d3": { + "23a512869c5e4f05a2356b8f464b1bcc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1404,16 +1168,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_eb3b2b87a32446c892767b6727cf7c96", - "IPY_MODEL_62d287d26948474db68cc5ea75df7b81", - "IPY_MODEL_e35529740133431397715ff5e470a55e" + "IPY_MODEL_58618ff0d8be415dbaa56326b7b1db8c", + "IPY_MODEL_633d743f49c44f93be1bfe7c09cb76e5", + "IPY_MODEL_313c673f5ae140548d908be43be34294" ], - "layout": "IPY_MODEL_5955528c2a2f4be686f4bb6106813bff", + "layout": "IPY_MODEL_f1fa8803defc478f8f1a9688f96d5a79", "tabbable": null, "tooltip": null } }, - "5955528c2a2f4be686f4bb6106813bff": { + "3094116b83c34a98b8ed5ce27da55168": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1466,7 +1230,71 @@ "width": null } }, - "62d287d26948474db68cc5ea75df7b81": { + "313c673f5ae140548d908be43be34294": { + "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_afe5f4fb71e54d6b97c4b44ecea40c54", + "placeholder": "​", + "style": "IPY_MODEL_59856c986feb4d3abc586fed584de5c0", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 274MB/s]" + } + }, + "58618ff0d8be415dbaa56326b7b1db8c": { + "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_3094116b83c34a98b8ed5ce27da55168", + "placeholder": "​", + "style": "IPY_MODEL_7105d5b497e34139b8cba14426fdd044", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "59856c986feb4d3abc586fed584de5c0": { + "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 + } + }, + "633d743f49c44f93be1bfe7c09cb76e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1482,17 +1310,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7d11920d1e3a4b0f923cc702ffaaadcd", + "layout": "IPY_MODEL_6eb5b857715449aaa4e92a9a9560a833", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_1ea943e162c94baba190703922291391", + "style": "IPY_MODEL_dae8ad4cd0df4444bf5ff766e8012dc4", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "7d11920d1e3a4b0f923cc702ffaaadcd": { + "6eb5b857715449aaa4e92a9a9560a833": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,7 +1373,25 @@ "width": null } }, - "83d24ca021e7439990012fc1cfae5ebc": { + "7105d5b497e34139b8cba14426fdd044": { + "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 + } + }, + "afe5f4fb71e54d6b97c4b44ecea40c54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1598,53 +1444,23 @@ "width": null } }, - "e35529740133431397715ff5e470a55e": { + "dae8ad4cd0df4444bf5ff766e8012dc4": { "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_f169574227104d7b94f6e1a96b5ff27b", - "placeholder": "​", - "style": "IPY_MODEL_13b75646e4ae45bb8738a8342199ed8d", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 305MB/s]" - } - }, - "eb3b2b87a32446c892767b6727cf7c96": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_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_83d24ca021e7439990012fc1cfae5ebc", - "placeholder": "​", - "style": "IPY_MODEL_26b0e201445143b6ad5363d6f61e02c4", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f169574227104d7b94f6e1a96b5ff27b": { + "f1fa8803defc478f8f1a9688f96d5a79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 28ed3a546..7c5c07d39 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:49.857100Z", - "iopub.status.busy": "2024-07-18T04:08:49.856925Z", - "iopub.status.idle": "2024-07-18T04:08:51.033463Z", - "shell.execute_reply": "2024-07-18T04:08:51.032826Z" + "iopub.execute_input": "2024-07-30T16:38:46.925237Z", + "iopub.status.busy": "2024-07-30T16:38:46.925067Z", + "iopub.status.idle": "2024-07-30T16:38:48.345531Z", + "shell.execute_reply": "2024-07-30T16:38:48.344960Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:08:51.036042Z", - "iopub.status.busy": "2024-07-18T04:08:51.035764Z", - "iopub.status.idle": "2024-07-18T04:08:51.053474Z", - "shell.execute_reply": "2024-07-18T04:08:51.052908Z" + "iopub.execute_input": "2024-07-30T16:38:48.348157Z", + "iopub.status.busy": "2024-07-30T16:38:48.347674Z", + "iopub.status.idle": "2024-07-30T16:38:48.365919Z", + "shell.execute_reply": "2024-07-30T16:38:48.365467Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.055718Z", - "iopub.status.busy": "2024-07-18T04:08:51.055333Z", - "iopub.status.idle": "2024-07-18T04:08:51.058387Z", - "shell.execute_reply": "2024-07-18T04:08:51.057846Z" + "iopub.execute_input": "2024-07-30T16:38:48.368246Z", + "iopub.status.busy": "2024-07-30T16:38:48.367803Z", + "iopub.status.idle": "2024-07-30T16:38:48.370780Z", + "shell.execute_reply": "2024-07-30T16:38:48.370332Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.060619Z", - "iopub.status.busy": "2024-07-18T04:08:51.060155Z", - "iopub.status.idle": "2024-07-18T04:08:51.265981Z", - "shell.execute_reply": "2024-07-18T04:08:51.265535Z" + "iopub.execute_input": "2024-07-30T16:38:48.372766Z", + "iopub.status.busy": "2024-07-30T16:38:48.372450Z", + "iopub.status.idle": "2024-07-30T16:38:48.468454Z", + "shell.execute_reply": "2024-07-30T16:38:48.467839Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.268127Z", - "iopub.status.busy": "2024-07-18T04:08:51.267788Z", - "iopub.status.idle": "2024-07-18T04:08:51.448821Z", - "shell.execute_reply": "2024-07-18T04:08:51.448311Z" + "iopub.execute_input": "2024-07-30T16:38:48.471122Z", + "iopub.status.busy": "2024-07-30T16:38:48.470653Z", + "iopub.status.idle": "2024-07-30T16:38:48.475521Z", + "shell.execute_reply": "2024-07-30T16:38:48.475049Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.450979Z", - "iopub.status.busy": "2024-07-18T04:08:51.450781Z", - "iopub.status.idle": "2024-07-18T04:08:51.660929Z", - "shell.execute_reply": "2024-07-18T04:08:51.660315Z" + "iopub.execute_input": "2024-07-30T16:38:48.477468Z", + "iopub.status.busy": "2024-07-30T16:38:48.477131Z", + "iopub.status.idle": "2024-07-30T16:38:48.720327Z", + "shell.execute_reply": "2024-07-30T16:38:48.719696Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.663179Z", - "iopub.status.busy": "2024-07-18T04:08:51.662774Z", - "iopub.status.idle": "2024-07-18T04:08:51.667262Z", - "shell.execute_reply": "2024-07-18T04:08:51.666686Z" + "iopub.execute_input": "2024-07-30T16:38:48.722633Z", + "iopub.status.busy": "2024-07-30T16:38:48.722278Z", + "iopub.status.idle": "2024-07-30T16:38:48.726622Z", + "shell.execute_reply": "2024-07-30T16:38:48.726163Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.669382Z", - "iopub.status.busy": "2024-07-18T04:08:51.669036Z", - "iopub.status.idle": "2024-07-18T04:08:51.674698Z", - "shell.execute_reply": "2024-07-18T04:08:51.674244Z" + "iopub.execute_input": "2024-07-30T16:38:48.728701Z", + "iopub.status.busy": "2024-07-30T16:38:48.728352Z", + "iopub.status.idle": "2024-07-30T16:38:48.734485Z", + "shell.execute_reply": "2024-07-30T16:38:48.734046Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.676720Z", - "iopub.status.busy": "2024-07-18T04:08:51.676391Z", - "iopub.status.idle": "2024-07-18T04:08:51.678866Z", - "shell.execute_reply": "2024-07-18T04:08:51.678427Z" + "iopub.execute_input": "2024-07-30T16:38:48.736597Z", + "iopub.status.busy": "2024-07-30T16:38:48.736263Z", + "iopub.status.idle": "2024-07-30T16:38:48.738985Z", + "shell.execute_reply": "2024-07-30T16:38:48.738429Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:08:51.680800Z", - "iopub.status.busy": "2024-07-18T04:08:51.680488Z", - "iopub.status.idle": "2024-07-18T04:09:00.539921Z", - "shell.execute_reply": "2024-07-18T04:09:00.539355Z" + "iopub.execute_input": "2024-07-30T16:38:48.741068Z", + "iopub.status.busy": "2024-07-30T16:38:48.740746Z", + "iopub.status.idle": "2024-07-30T16:38:57.890643Z", + "shell.execute_reply": "2024-07-30T16:38:57.890064Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.542544Z", - "iopub.status.busy": "2024-07-18T04:09:00.542172Z", - "iopub.status.idle": "2024-07-18T04:09:00.549395Z", - "shell.execute_reply": "2024-07-18T04:09:00.548943Z" + "iopub.execute_input": "2024-07-30T16:38:57.893640Z", + "iopub.status.busy": "2024-07-30T16:38:57.893011Z", + "iopub.status.idle": "2024-07-30T16:38:57.900759Z", + "shell.execute_reply": "2024-07-30T16:38:57.900288Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.551482Z", - "iopub.status.busy": "2024-07-18T04:09:00.551165Z", - "iopub.status.idle": "2024-07-18T04:09:00.554829Z", - "shell.execute_reply": "2024-07-18T04:09:00.554351Z" + "iopub.execute_input": "2024-07-30T16:38:57.903196Z", + "iopub.status.busy": "2024-07-30T16:38:57.902725Z", + "iopub.status.idle": "2024-07-30T16:38:57.906622Z", + "shell.execute_reply": "2024-07-30T16:38:57.906179Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.556730Z", - "iopub.status.busy": "2024-07-18T04:09:00.556561Z", - "iopub.status.idle": "2024-07-18T04:09:00.559924Z", - "shell.execute_reply": "2024-07-18T04:09:00.559463Z" + "iopub.execute_input": "2024-07-30T16:38:57.908608Z", + "iopub.status.busy": "2024-07-30T16:38:57.908262Z", + "iopub.status.idle": "2024-07-30T16:38:57.911716Z", + "shell.execute_reply": "2024-07-30T16:38:57.911253Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.561758Z", - "iopub.status.busy": "2024-07-18T04:09:00.561590Z", - "iopub.status.idle": "2024-07-18T04:09:00.564614Z", - "shell.execute_reply": "2024-07-18T04:09:00.564159Z" + "iopub.execute_input": "2024-07-30T16:38:57.913595Z", + "iopub.status.busy": "2024-07-30T16:38:57.913317Z", + "iopub.status.idle": "2024-07-30T16:38:57.916287Z", + "shell.execute_reply": "2024-07-30T16:38:57.915835Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:09:00.566608Z", - "iopub.status.busy": "2024-07-18T04:09:00.566208Z", - "iopub.status.idle": "2024-07-18T04:09:00.574230Z", - "shell.execute_reply": "2024-07-18T04:09:00.573776Z" + "iopub.execute_input": "2024-07-30T16:38:57.918353Z", + "iopub.status.busy": "2024-07-30T16:38:57.918022Z", + "iopub.status.idle": "2024-07-30T16:38:57.925798Z", + "shell.execute_reply": "2024-07-30T16:38:57.925354Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - 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3. Use cleanlab to find label issues

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

Classes which are commonly mislabeled overall

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

Get label quality scores

-
+

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

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

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"2024-07-18T04:10:29.405039Z" + "iopub.execute_input": "2024-07-30T16:40:31.014861Z", + "iopub.status.busy": "2024-07-30T16:40:31.014482Z", + "iopub.status.idle": "2024-07-30T16:40:32.523099Z", + "shell.execute_reply": "2024-07-30T16:40:32.522526Z" }, "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\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": "2024-07-18T04:10:29.408244Z", - "iopub.status.busy": "2024-07-18T04:10:29.407724Z", - "iopub.status.idle": "2024-07-18T04:10:29.411085Z", - "shell.execute_reply": "2024-07-18T04:10:29.410512Z" + "iopub.execute_input": "2024-07-30T16:40:32.525566Z", + "iopub.status.busy": "2024-07-30T16:40:32.525262Z", + "iopub.status.idle": "2024-07-30T16:40:32.528712Z", + "shell.execute_reply": "2024-07-30T16:40:32.528246Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.413314Z", - "iopub.status.busy": "2024-07-18T04:10:29.413010Z", - "iopub.status.idle": "2024-07-18T04:10:29.416803Z", - "shell.execute_reply": "2024-07-18T04:10:29.416373Z" + "iopub.execute_input": "2024-07-30T16:40:32.530916Z", + "iopub.status.busy": "2024-07-30T16:40:32.530497Z", + "iopub.status.idle": "2024-07-30T16:40:32.534386Z", + "shell.execute_reply": "2024-07-30T16:40:32.533915Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.418928Z", - "iopub.status.busy": "2024-07-18T04:10:29.418571Z", - "iopub.status.idle": "2024-07-18T04:10:29.422140Z", - "shell.execute_reply": "2024-07-18T04:10:29.421708Z" + "iopub.execute_input": "2024-07-30T16:40:32.536602Z", + "iopub.status.busy": "2024-07-30T16:40:32.536175Z", + "iopub.status.idle": "2024-07-30T16:40:32.539968Z", + "shell.execute_reply": "2024-07-30T16:40:32.539531Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.424233Z", - "iopub.status.busy": "2024-07-18T04:10:29.423908Z", - "iopub.status.idle": "2024-07-18T04:10:29.426602Z", - "shell.execute_reply": "2024-07-18T04:10:29.426181Z" + "iopub.execute_input": "2024-07-30T16:40:32.542052Z", + "iopub.status.busy": "2024-07-30T16:40:32.541706Z", + "iopub.status.idle": "2024-07-30T16:40:32.544446Z", + "shell.execute_reply": "2024-07-30T16:40:32.544021Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:10:29.428591Z", - 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1. Install required dependencies and download data 
---2024-07-18 04:12:13--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 143.244.49.183, 2400:52e0:1a01::1001:1
-Connecting to data.deepai.org (data.deepai.org)|143.244.49.183|:443... connected.
+--2024-07-30 16:42:16--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.250, 2400:52e0:1a00::1070:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  5.71MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
 
-2024-07-18 04:12:13 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-07-30 16:42:16 (6.62 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 1cfa9de2e..6a696df89 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:13.563318Z", - "iopub.status.busy": "2024-07-18T04:12:13.563162Z", - "iopub.status.idle": "2024-07-18T04:12:15.665934Z", - "shell.execute_reply": "2024-07-18T04:12:15.665276Z" + "iopub.execute_input": "2024-07-30T16:42:16.108435Z", + "iopub.status.busy": "2024-07-30T16:42:16.108277Z", + "iopub.status.idle": "2024-07-30T16:42:17.473595Z", + "shell.execute_reply": "2024-07-30T16:42:17.472949Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-18 04:12:13-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-30 16:42:16-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "143.244.49.183, 2400:52e0:1a01::1001:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|143.244.49.183|:443... connected.\r\n", + "185.93.1.250, 2400:52e0:1a00::1070:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -116,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-18 04:12:13 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-30 16:42:16 (6.62 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -138,22 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-18 04:12:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.140.161, 52.217.200.145, 16.182.74.81, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.140.161|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-07-30 16:42:16-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.138.89, 52.217.134.249, 52.216.41.17, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.138.89|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,34 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 0%[ ] 160.53K 750KB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 8%[> ] 1.42M 3.31MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 49%[========> ] 7.97M 12.4MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 99%[==================> ] 16.12M 18.8MB/s \r", - "pred_probs.npz 100%[===================>] 16.26M 19.0MB/s in 0.9s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-18 04:12:15 (19.0 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-30 16:42:17 (125 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -218,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:15.668596Z", - "iopub.status.busy": "2024-07-18T04:12:15.668398Z", - "iopub.status.idle": "2024-07-18T04:12:16.911759Z", - "shell.execute_reply": "2024-07-18T04:12:16.911145Z" + "iopub.execute_input": "2024-07-30T16:42:17.476282Z", + "iopub.status.busy": "2024-07-30T16:42:17.475905Z", + "iopub.status.idle": "2024-07-30T16:42:18.926532Z", + "shell.execute_reply": "2024-07-30T16:42:18.925850Z" }, "nbsphinx": "hidden" }, @@ -232,7 +200,7 @@ "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@cebb53dd00e3df7a864b21f23652f08e0654101d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@774f5b4625f50853a4527b3bf0414f14a7116208\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -258,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.914442Z", - "iopub.status.busy": "2024-07-18T04:12:16.914008Z", - "iopub.status.idle": "2024-07-18T04:12:16.917267Z", - "shell.execute_reply": "2024-07-18T04:12:16.916832Z" + "iopub.execute_input": "2024-07-30T16:42:18.929007Z", + "iopub.status.busy": "2024-07-30T16:42:18.928712Z", + "iopub.status.idle": "2024-07-30T16:42:18.932103Z", + "shell.execute_reply": "2024-07-30T16:42:18.931658Z" } }, "outputs": [], @@ -311,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.919339Z", - "iopub.status.busy": "2024-07-18T04:12:16.919000Z", - "iopub.status.idle": "2024-07-18T04:12:16.922078Z", - "shell.execute_reply": "2024-07-18T04:12:16.921530Z" + "iopub.execute_input": "2024-07-30T16:42:18.934243Z", + "iopub.status.busy": "2024-07-30T16:42:18.933903Z", + "iopub.status.idle": "2024-07-30T16:42:18.937344Z", + "shell.execute_reply": "2024-07-30T16:42:18.936919Z" }, "nbsphinx": "hidden" }, @@ -332,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:16.924213Z", - "iopub.status.busy": "2024-07-18T04:12:16.923864Z", - "iopub.status.idle": "2024-07-18T04:12:26.037149Z", - "shell.execute_reply": "2024-07-18T04:12:26.036588Z" + "iopub.execute_input": "2024-07-30T16:42:18.939414Z", + "iopub.status.busy": "2024-07-30T16:42:18.939071Z", + "iopub.status.idle": "2024-07-30T16:42:28.307360Z", + "shell.execute_reply": "2024-07-30T16:42:28.306819Z" } }, "outputs": [], @@ -409,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.039560Z", - "iopub.status.busy": "2024-07-18T04:12:26.039344Z", - "iopub.status.idle": "2024-07-18T04:12:26.044846Z", - "shell.execute_reply": "2024-07-18T04:12:26.044413Z" + "iopub.execute_input": "2024-07-30T16:42:28.310072Z", + "iopub.status.busy": "2024-07-30T16:42:28.309609Z", + "iopub.status.idle": "2024-07-30T16:42:28.315308Z", + "shell.execute_reply": "2024-07-30T16:42:28.314851Z" }, "nbsphinx": "hidden" }, @@ -452,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.046633Z", - "iopub.status.busy": "2024-07-18T04:12:26.046462Z", - "iopub.status.idle": "2024-07-18T04:12:26.386018Z", - "shell.execute_reply": "2024-07-18T04:12:26.385389Z" + "iopub.execute_input": "2024-07-30T16:42:28.317438Z", + "iopub.status.busy": "2024-07-30T16:42:28.317037Z", + "iopub.status.idle": "2024-07-30T16:42:28.691191Z", + "shell.execute_reply": "2024-07-30T16:42:28.690527Z" } }, "outputs": [], @@ -492,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.388817Z", - "iopub.status.busy": "2024-07-18T04:12:26.388359Z", - "iopub.status.idle": "2024-07-18T04:12:26.392513Z", - "shell.execute_reply": "2024-07-18T04:12:26.392066Z" + "iopub.execute_input": "2024-07-30T16:42:28.693775Z", + "iopub.status.busy": "2024-07-30T16:42:28.693565Z", + "iopub.status.idle": "2024-07-30T16:42:28.698316Z", + "shell.execute_reply": "2024-07-30T16:42:28.697696Z" } }, "outputs": [ @@ -567,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:26.394754Z", - "iopub.status.busy": "2024-07-18T04:12:26.394386Z", - "iopub.status.idle": "2024-07-18T04:12:29.007823Z", - "shell.execute_reply": "2024-07-18T04:12:29.007030Z" + "iopub.execute_input": "2024-07-30T16:42:28.700484Z", + "iopub.status.busy": "2024-07-30T16:42:28.700147Z", + "iopub.status.idle": "2024-07-30T16:42:31.466890Z", + "shell.execute_reply": "2024-07-30T16:42:31.466180Z" } }, "outputs": [], @@ -592,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.011403Z", - "iopub.status.busy": "2024-07-18T04:12:29.010454Z", - "iopub.status.idle": "2024-07-18T04:12:29.015308Z", - "shell.execute_reply": "2024-07-18T04:12:29.014770Z" + "iopub.execute_input": "2024-07-30T16:42:31.470029Z", + "iopub.status.busy": "2024-07-30T16:42:31.469337Z", + "iopub.status.idle": "2024-07-30T16:42:31.473767Z", + "shell.execute_reply": "2024-07-30T16:42:31.473222Z" } }, "outputs": [ @@ -631,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.017563Z", - "iopub.status.busy": "2024-07-18T04:12:29.017204Z", - "iopub.status.idle": "2024-07-18T04:12:29.023529Z", - "shell.execute_reply": "2024-07-18T04:12:29.022965Z" + "iopub.execute_input": "2024-07-30T16:42:31.476028Z", + "iopub.status.busy": "2024-07-30T16:42:31.475684Z", + "iopub.status.idle": "2024-07-30T16:42:31.481390Z", + "shell.execute_reply": "2024-07-30T16:42:31.480918Z" } }, "outputs": [ @@ -812,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.025886Z", - "iopub.status.busy": "2024-07-18T04:12:29.025543Z", - "iopub.status.idle": "2024-07-18T04:12:29.053000Z", - "shell.execute_reply": "2024-07-18T04:12:29.052460Z" + "iopub.execute_input": "2024-07-30T16:42:31.483594Z", + "iopub.status.busy": "2024-07-30T16:42:31.483253Z", + "iopub.status.idle": "2024-07-30T16:42:31.509722Z", + "shell.execute_reply": "2024-07-30T16:42:31.509269Z" } }, "outputs": [ @@ -917,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.055238Z", - "iopub.status.busy": "2024-07-18T04:12:29.054781Z", - "iopub.status.idle": "2024-07-18T04:12:29.059137Z", - "shell.execute_reply": "2024-07-18T04:12:29.058579Z" + "iopub.execute_input": "2024-07-30T16:42:31.511897Z", + "iopub.status.busy": "2024-07-30T16:42:31.511537Z", + "iopub.status.idle": "2024-07-30T16:42:31.516114Z", + "shell.execute_reply": "2024-07-30T16:42:31.515643Z" } }, "outputs": [ @@ -994,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:29.061173Z", - "iopub.status.busy": "2024-07-18T04:12:29.061001Z", - "iopub.status.idle": "2024-07-18T04:12:30.455263Z", - "shell.execute_reply": "2024-07-18T04:12:30.454724Z" + "iopub.execute_input": "2024-07-30T16:42:31.518017Z", + "iopub.status.busy": "2024-07-30T16:42:31.517821Z", + "iopub.status.idle": "2024-07-30T16:42:33.009420Z", + "shell.execute_reply": "2024-07-30T16:42:33.008849Z" } }, "outputs": [ @@ -1169,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-07-18T04:12:30.457343Z", - "iopub.status.busy": "2024-07-18T04:12:30.457166Z", - "iopub.status.idle": "2024-07-18T04:12:30.461345Z", - "shell.execute_reply": "2024-07-18T04:12:30.460894Z" + "iopub.execute_input": "2024-07-30T16:42:33.011845Z", + "iopub.status.busy": "2024-07-30T16:42:33.011502Z", + "iopub.status.idle": "2024-07-30T16:42:33.015539Z", + "shell.execute_reply": "2024-07-30T16:42:33.015098Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 53671a1bb..b023d4814 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "cebb53dd00e3df7a864b21f23652f08e0654101d", + commit_hash: "774f5b4625f50853a4527b3bf0414f14a7116208", }; \ No newline at end of file