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zcmcb1mg(AArVTBOhABx#=0&+FM*2y{25BjYX2~YTsTN787AZz)24)84mWHMl=Ba6^ U#)d}8M#hOLCZ?OWGAb?w0MpqN(f|Me delta 64 zcmcb1mg(AArVTBOhM85#CMHScruvB~h6cu|21XW!=9cEhX=#bZDHcYSDQ1bumga_* U=1ImzrWVP`1{RyQGAb?w0LCK}W&i*H diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index b70c8c754b96b99abf094563524505c9f27fc76b..e19b7e5094af06f6b96a29917283005dc7b32757 100644 GIT binary patch delta 63 zcmV~$yA{A72mrvxL_zRF#u35FG+D_7G7O!|REzJ!r T&6A9cOf8a=4J;O4Vax#lz8(}% diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index 1b1f01adb..98abd1f6f 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": "2024-07-30T16:31:34.527671Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:23.433118Z", + "iopub.status.busy": "2024-08-02T23:17:23.432923Z", + "iopub.status.idle": "2024-08-02T23:17:24.941638Z", + "shell.execute_reply": "2024-08-02T23:17:24.941075Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:31:36.143586Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:24.944158Z", + "iopub.status.busy": "2024-08-02T23:17:24.943875Z", + "iopub.status.idle": "2024-08-02T23:17:24.963528Z", + "shell.execute_reply": "2024-08-02T23:17:24.962963Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:24.966010Z", + "iopub.status.busy": "2024-08-02T23:17:24.965604Z", + "iopub.status.idle": "2024-08-02T23:17:25.079442Z", + "shell.execute_reply": "2024-08-02T23:17:25.078863Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.111044Z", + "iopub.status.busy": "2024-08-02T23:17:25.110645Z", + "iopub.status.idle": "2024-08-02T23:17:25.114497Z", + "shell.execute_reply": "2024-08-02T23:17:25.114027Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.116536Z", + "iopub.status.busy": "2024-08-02T23:17:25.116200Z", + "iopub.status.idle": "2024-08-02T23:17:25.124454Z", + "shell.execute_reply": "2024-08-02T23:17:25.123892Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.126998Z", + "iopub.status.busy": "2024-08-02T23:17:25.126543Z", + "iopub.status.idle": "2024-08-02T23:17:25.129409Z", + "shell.execute_reply": "2024-08-02T23:17:25.128804Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.131344Z", + "iopub.status.busy": "2024-08-02T23:17:25.131035Z", + "iopub.status.idle": "2024-08-02T23:17:25.655338Z", + "shell.execute_reply": "2024-08-02T23:17:25.654793Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.657837Z", + "iopub.status.busy": "2024-08-02T23:17:25.657465Z", + "iopub.status.idle": "2024-08-02T23:17:27.751426Z", + "shell.execute_reply": "2024-08-02T23:17:27.750727Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.754463Z", + "iopub.status.busy": "2024-08-02T23:17:27.753684Z", + "iopub.status.idle": "2024-08-02T23:17:27.764911Z", + "shell.execute_reply": "2024-08-02T23:17:27.764361Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.767199Z", + "iopub.status.busy": "2024-08-02T23:17:27.766875Z", + "iopub.status.idle": "2024-08-02T23:17:27.770951Z", + "shell.execute_reply": "2024-08-02T23:17:27.770498Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.772966Z", + "iopub.status.busy": "2024-08-02T23:17:27.772625Z", + "iopub.status.idle": "2024-08-02T23:17:27.779796Z", + "shell.execute_reply": "2024-08-02T23:17:27.779212Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.781951Z", + "iopub.status.busy": "2024-08-02T23:17:27.781645Z", + "iopub.status.idle": "2024-08-02T23:17:27.895282Z", + "shell.execute_reply": "2024-08-02T23:17:27.894690Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.897585Z", + "iopub.status.busy": "2024-08-02T23:17:27.897260Z", + "iopub.status.idle": "2024-08-02T23:17:27.899963Z", + "shell.execute_reply": "2024-08-02T23:17:27.899515Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.902092Z", + "iopub.status.busy": "2024-08-02T23:17:27.901699Z", + "iopub.status.idle": "2024-08-02T23:17:30.041948Z", + "shell.execute_reply": "2024-08-02T23:17:30.041308Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:30.045213Z", + "iopub.status.busy": "2024-08-02T23:17:30.044360Z", + "iopub.status.idle": "2024-08-02T23:17:30.055915Z", + "shell.execute_reply": "2024-08-02T23:17:30.055449Z" } }, "outputs": [ @@ -766,15 +766,30 @@ "We can see that the test set accuracy slightly improved as a result of the data cleaning. Note that this will not always be the case, especially when we evaluate on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:30.057830Z", + "iopub.status.busy": "2024-08-02T23:17:30.057653Z", + "iopub.status.idle": "2024-08-02T23:17:30.088205Z", + "shell.execute_reply": "2024-08-02T23:17:30.087743Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index 0ca024c1d..a81564d50 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": "2024-07-30T16:31:45.538656Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:33.437374Z", + "iopub.status.busy": "2024-08-02T23:17:33.437203Z", + "iopub.status.idle": "2024-08-02T23:17:37.042923Z", + "shell.execute_reply": "2024-08-02T23:17:37.042233Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:31:49.401469Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:37.045817Z", + "iopub.status.busy": "2024-08-02T23:17:37.045322Z", + "iopub.status.idle": "2024-08-02T23:17:37.049160Z", + "shell.execute_reply": "2024-08-02T23:17:37.048563Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.051274Z", + "iopub.status.busy": "2024-08-02T23:17:37.051087Z", + "iopub.status.idle": "2024-08-02T23:17:37.054580Z", + "shell.execute_reply": "2024-08-02T23:17:37.054080Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.056632Z", + "iopub.status.busy": "2024-08-02T23:17:37.056445Z", + "iopub.status.idle": "2024-08-02T23:17:37.094103Z", + "shell.execute_reply": "2024-08-02T23:17:37.093554Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.096154Z", + "iopub.status.busy": "2024-08-02T23:17:37.095963Z", + "iopub.status.idle": "2024-08-02T23:17:37.099840Z", + "shell.execute_reply": "2024-08-02T23:17:37.099374Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.101701Z", + "iopub.status.busy": "2024-08-02T23:17:37.101517Z", + "iopub.status.idle": "2024-08-02T23:17:37.104926Z", + "shell.execute_reply": "2024-08-02T23:17:37.104415Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'change_pin', 'card_about_to_expire', 'beneficiary_not_allowed', 'visa_or_mastercard', 'getting_spare_card', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_payment_fee_charged'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.107157Z", + "iopub.status.busy": "2024-08-02T23:17:37.106820Z", + "iopub.status.idle": "2024-08-02T23:17:37.110074Z", + "shell.execute_reply": "2024-08-02T23:17:37.109475Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.112222Z", + "iopub.status.busy": "2024-08-02T23:17:37.111870Z", + "iopub.status.idle": "2024-08-02T23:17:37.115305Z", + "shell.execute_reply": "2024-08-02T23:17:37.114842Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.117435Z", + "iopub.status.busy": "2024-08-02T23:17:37.117091Z", + "iopub.status.idle": "2024-08-02T23:17:41.492569Z", + "shell.execute_reply": "2024-08-02T23:17:41.492001Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97c7a5a1b558446099d39e138e95bd3c", + "model_id": "79ca72fe19f9461bbd6eac4989f0d0e5", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91c91931728e42119a5ed1a8e771a320", + "model_id": "19931fa885eb4bfe9823463770d72209", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a58243eb6a194ea7957507b4c0fcd20c", + "model_id": "41323f0f880b493180c67d1f67ae6818", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f47d961a8a6443fdb97fc093198a3a37", + "model_id": "c5358d2af5f4413db78296cb4e822b6d", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd86dc9f79bf4ea6b32ccf1b10684fdd", + "model_id": "f95fa2a0418543fea006ef9489d34baf", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f07a08b8ebf486cbecde146931b6ae3", + "model_id": "3dcc386a355446078b0260d76f1f460b", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d9438aa4de0a49669ef6cc3e242dea8a", + "model_id": "48f13ab0fd1e4de9a23d90349ff0827c", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.495465Z", + "iopub.status.busy": "2024-08-02T23:17:41.495109Z", + "iopub.status.idle": "2024-08-02T23:17:41.498080Z", + "shell.execute_reply": "2024-08-02T23:17:41.497515Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.500217Z", + "iopub.status.busy": "2024-08-02T23:17:41.499902Z", + "iopub.status.idle": "2024-08-02T23:17:41.502619Z", + "shell.execute_reply": "2024-08-02T23:17:41.502156Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.504428Z", + "iopub.status.busy": "2024-08-02T23:17:41.504253Z", + "iopub.status.idle": "2024-08-02T23:17:44.256566Z", + "shell.execute_reply": "2024-08-02T23:17:44.255764Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:44.259923Z", + "iopub.status.busy": "2024-08-02T23:17:44.259087Z", + "iopub.status.idle": "2024-08-02T23:17:44.267224Z", + "shell.execute_reply": "2024-08-02T23:17:44.266727Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:44.269594Z", + "iopub.status.busy": "2024-08-02T23:17:44.268945Z", + "iopub.status.idle": "2024-08-02T23:17:44.273323Z", + "shell.execute_reply": "2024-08-02T23:17:44.272797Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:44.275271Z", + "iopub.status.busy": "2024-08-02T23:17:44.275085Z", + "iopub.status.idle": "2024-08-02T23:17:44.278514Z", + "shell.execute_reply": "2024-08-02T23:17:44.278038Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:56.868248Z", - "iopub.status.busy": "2024-07-30T16:31:56.867919Z", - "iopub.status.idle": "2024-07-30T16:31:56.871062Z", - "shell.execute_reply": "2024-07-30T16:31:56.870497Z" + "iopub.execute_input": "2024-08-02T23:17:44.280425Z", + "iopub.status.busy": "2024-08-02T23:17:44.280248Z", + "iopub.status.idle": "2024-08-02T23:17:44.283388Z", + "shell.execute_reply": "2024-08-02T23:17:44.282925Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:56.873122Z", - "iopub.status.busy": "2024-07-30T16:31:56.872946Z", - "iopub.status.idle": "2024-07-30T16:31:56.880133Z", - "shell.execute_reply": "2024-07-30T16:31:56.879673Z" + "iopub.execute_input": "2024-08-02T23:17:44.285353Z", + "iopub.status.busy": "2024-08-02T23:17:44.284986Z", + "iopub.status.idle": "2024-08-02T23:17:44.291834Z", + "shell.execute_reply": "2024-08-02T23:17:44.291282Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:56.882358Z", - "iopub.status.busy": "2024-07-30T16:31:56.881972Z", - "iopub.status.idle": "2024-07-30T16:31:57.113724Z", - "shell.execute_reply": "2024-07-30T16:31:57.113137Z" + "iopub.execute_input": "2024-08-02T23:17:44.294132Z", + "iopub.status.busy": "2024-08-02T23:17:44.293791Z", + "iopub.status.idle": "2024-08-02T23:17:44.521530Z", + "shell.execute_reply": "2024-08-02T23:17:44.520927Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:57.116220Z", - "iopub.status.busy": "2024-07-30T16:31:57.115868Z", - "iopub.status.idle": "2024-07-30T16:31:57.325117Z", - "shell.execute_reply": "2024-07-30T16:31:57.324552Z" + "iopub.execute_input": "2024-08-02T23:17:44.524094Z", + "iopub.status.busy": "2024-08-02T23:17:44.523689Z", + "iopub.status.idle": "2024-08-02T23:17:44.700348Z", + "shell.execute_reply": "2024-08-02T23:17:44.699773Z" }, "scrolled": true }, @@ -1053,15 +1053,30 @@ "We can see that the test set accuracy slightly improved as a result of the data cleaning. Note that this will not always be the case, especially when we are evaluating on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

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"IPY_MODEL_eebcc942ebba414e8985c53b0046690f", - "IPY_MODEL_753e8b7795c943afa31e39370262dac0", - "IPY_MODEL_f44abb2240724661a8f28fabeb7e6152" + "IPY_MODEL_110998629dbb4b208e73d47bf58de355", + "IPY_MODEL_e56c074b43db4299a289b48b848b8805", + "IPY_MODEL_39e900d03806496d9c99a7ae184fdf8f" ], - "layout": "IPY_MODEL_9e52acaf4e244873ab2a7f8db7a56f87", + "layout": "IPY_MODEL_c3c4f83d199041848bc14699273a6582", "tabbable": null, "tooltip": null } }, - "f492c8a7a79d41b082fc05a8a4fa2314": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f57043327c7a43d580fbd77e72e74801": { + "fd22a9571a294002baa926714a442d12": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3566,63 +3624,20 @@ "width": null } }, - "f9e39c8f34f044faabfd9b2d9c388e8e": { - "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 - } - }, - "fc3db8eb310345b3b1c927bd575ab50e": { - "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_5a71b8cd70d84f7ba31770899fe09c9b", - "placeholder": "​", - "style": "IPY_MODEL_c394f7a4870a4f5f8de748644e8df357", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" - } - }, - "fe392344d7834620bada4587f2dbf065": { + "ff310b8c1a574ff6af2c52581c6b20f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_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 + "bar_color": null, + "description_width": "" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index a2d329ad2..a38491bc9 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": "2024-07-30T16:32:01.430387Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:48.674521Z", + "iopub.status.busy": "2024-08-02T23:17:48.674006Z", + "iopub.status.idle": "2024-08-02T23:17:54.489657Z", + "shell.execute_reply": "2024-08-02T23:17:54.489092Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:07.510541Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:54.492364Z", + "iopub.status.busy": "2024-08-02T23:17:54.491862Z", + "iopub.status.idle": "2024-08-02T23:17:54.495158Z", + "shell.execute_reply": "2024-08-02T23:17:54.494599Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:54.497251Z", + "iopub.status.busy": "2024-08-02T23:17:54.496901Z", + "iopub.status.idle": "2024-08-02T23:17:54.501477Z", + "shell.execute_reply": "2024-08-02T23:17:54.501011Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:54.503439Z", + "iopub.status.busy": "2024-08-02T23:17:54.503137Z", + "iopub.status.idle": "2024-08-02T23:17:56.103711Z", + "shell.execute_reply": "2024-08-02T23:17:56.103032Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.106386Z", + "iopub.status.busy": "2024-08-02T23:17:56.106173Z", + "iopub.status.idle": "2024-08-02T23:17:56.117184Z", + "shell.execute_reply": "2024-08-02T23:17:56.116749Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.119274Z", + "iopub.status.busy": "2024-08-02T23:17:56.118919Z", + "iopub.status.idle": "2024-08-02T23:17:56.124342Z", + "shell.execute_reply": "2024-08-02T23:17:56.123884Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.126193Z", + "iopub.status.busy": "2024-08-02T23:17:56.126017Z", + "iopub.status.idle": "2024-08-02T23:17:56.587440Z", + "shell.execute_reply": "2024-08-02T23:17:56.586822Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.589528Z", + "iopub.status.busy": "2024-08-02T23:17:56.589339Z", + "iopub.status.idle": "2024-08-02T23:17:57.247247Z", + "shell.execute_reply": "2024-08-02T23:17:57.246625Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.249823Z", + "iopub.status.busy": "2024-08-02T23:17:57.249401Z", + "iopub.status.idle": "2024-08-02T23:17:57.268344Z", + "shell.execute_reply": "2024-08-02T23:17:57.267769Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.270433Z", + "iopub.status.busy": "2024-08-02T23:17:57.270112Z", + "iopub.status.idle": "2024-08-02T23:17:57.273390Z", + "shell.execute_reply": "2024-08-02T23:17:57.272811Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.275541Z", + "iopub.status.busy": "2024-08-02T23:17:57.275202Z", + "iopub.status.idle": "2024-08-02T23:18:11.647525Z", + "shell.execute_reply": "2024-08-02T23:18:11.646907Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:11.650258Z", + "iopub.status.busy": "2024-08-02T23:18:11.649858Z", + "iopub.status.idle": "2024-08-02T23:18:11.653962Z", + "shell.execute_reply": "2024-08-02T23:18:11.653485Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:11.656092Z", + "iopub.status.busy": "2024-08-02T23:18:11.655746Z", + "iopub.status.idle": "2024-08-02T23:18:12.346371Z", + "shell.execute_reply": "2024-08-02T23:18:12.345770Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:12.349358Z", + "iopub.status.busy": "2024-08-02T23:18:12.348955Z", + "iopub.status.idle": "2024-08-02T23:18:12.353747Z", + "shell.execute_reply": "2024-08-02T23:18:12.353244Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:12.356979Z", + "iopub.status.busy": "2024-08-02T23:18:12.356030Z", + "iopub.status.idle": "2024-08-02T23:18:12.482133Z", + "shell.execute_reply": "2024-08-02T23:18:12.481542Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - 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"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" + "iopub.execute_input": "2024-08-02T23:18:16.484336Z", + "iopub.status.busy": "2024-08-02T23:18:16.484165Z", + "iopub.status.idle": "2024-08-02T23:18:17.882451Z", + "shell.execute_reply": "2024-08-02T23:18:17.881898Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:34.121608Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:17.885156Z", + "iopub.status.busy": "2024-08-02T23:18:17.884677Z", + "iopub.status.idle": "2024-08-02T23:18:17.887758Z", + "shell.execute_reply": "2024-08-02T23:18:17.887295Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.889898Z", + "iopub.status.busy": "2024-08-02T23:18:17.889563Z", + "iopub.status.idle": "2024-08-02T23:18:17.898213Z", + "shell.execute_reply": "2024-08-02T23:18:17.897752Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.900225Z", + "iopub.status.busy": "2024-08-02T23:18:17.899877Z", + "iopub.status.idle": "2024-08-02T23:18:17.904388Z", + "shell.execute_reply": "2024-08-02T23:18:17.903975Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.906648Z", + "iopub.status.busy": "2024-08-02T23:18:17.906301Z", + "iopub.status.idle": "2024-08-02T23:18:17.914041Z", + "shell.execute_reply": "2024-08-02T23:18:17.913599Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - 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"_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a8759625ec944bbbac04db9cec87a317", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ee9027d19159444e8d46c66083251836", + "layout": "IPY_MODEL_0c1a082463244b57b51d61d7f6c72e71", + "placeholder": "​", + "style": "IPY_MODEL_3e3e5258de4f42cb8228d1fc6798c2f3", "tabbable": null, "tooltip": null, - "value": 132.0 + "value": " 132/132 [00:00<00:00, 11675.41 examples/s]" } }, - "fe623ece577749be8a8ce449c28b2074": { + "c73f2f5e1ae54b468441d578f7c2ba01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,6 +1765,47 @@ "visibility": null, "width": null } + }, + "c9798d892bbb4b4495225f4e0b80e70c": { + "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_527cb92bada44744a7822b43b03ee159", + "placeholder": "​", + "style": "IPY_MODEL_e0e9fbdf6956499f98167164d76e9bb4", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "e0e9fbdf6956499f98167164d76e9bb4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index dd27cd645..94e8a87f7 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": "2024-07-30T16:32:39.968392Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:23.440181Z", + "iopub.status.busy": "2024-08-02T23:18:23.440011Z", + "iopub.status.idle": "2024-08-02T23:18:24.873718Z", + "shell.execute_reply": "2024-08-02T23:18:24.873164Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:41.431407Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:24.876197Z", + "iopub.status.busy": "2024-08-02T23:18:24.875898Z", + "iopub.status.idle": "2024-08-02T23:18:24.879459Z", + "shell.execute_reply": "2024-08-02T23:18:24.879019Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.881728Z", + "iopub.status.busy": "2024-08-02T23:18:24.881284Z", + "iopub.status.idle": "2024-08-02T23:18:24.890527Z", + "shell.execute_reply": "2024-08-02T23:18:24.890092Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.892364Z", + "iopub.status.busy": "2024-08-02T23:18:24.892191Z", + "iopub.status.idle": "2024-08-02T23:18:24.897249Z", + "shell.execute_reply": "2024-08-02T23:18:24.896624Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.899657Z", + "iopub.status.busy": "2024-08-02T23:18:24.899315Z", + "iopub.status.idle": "2024-08-02T23:18:24.907713Z", + "shell.execute_reply": "2024-08-02T23:18:24.907270Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.909718Z", + "iopub.status.busy": "2024-08-02T23:18:24.909392Z", + "iopub.status.idle": "2024-08-02T23:18:25.284990Z", + "shell.execute_reply": "2024-08-02T23:18:25.284340Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.287244Z", + "iopub.status.busy": "2024-08-02T23:18:25.286882Z", + "iopub.status.idle": "2024-08-02T23:18:25.289594Z", + "shell.execute_reply": "2024-08-02T23:18:25.289144Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.291654Z", + "iopub.status.busy": "2024-08-02T23:18:25.291314Z", + "iopub.status.idle": "2024-08-02T23:18:25.325141Z", + "shell.execute_reply": "2024-08-02T23:18:25.324531Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.327400Z", + "iopub.status.busy": "2024-08-02T23:18:25.327081Z", + "iopub.status.idle": "2024-08-02T23:18:27.419844Z", + "shell.execute_reply": "2024-08-02T23:18:27.419221Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.422496Z", + "iopub.status.busy": "2024-08-02T23:18:27.421968Z", + "iopub.status.idle": "2024-08-02T23:18:27.440757Z", + "shell.execute_reply": "2024-08-02T23:18:27.440302Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.442868Z", + "iopub.status.busy": "2024-08-02T23:18:27.442525Z", + "iopub.status.idle": "2024-08-02T23:18:27.449073Z", + "shell.execute_reply": "2024-08-02T23:18:27.448585Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.451087Z", + "iopub.status.busy": "2024-08-02T23:18:27.450752Z", + "iopub.status.idle": "2024-08-02T23:18:27.456602Z", + "shell.execute_reply": "2024-08-02T23:18:27.456121Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.458665Z", + "iopub.status.busy": "2024-08-02T23:18:27.458311Z", + "iopub.status.idle": "2024-08-02T23:18:27.470031Z", + "shell.execute_reply": "2024-08-02T23:18:27.469562Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.472056Z", + "iopub.status.busy": "2024-08-02T23:18:27.471719Z", + "iopub.status.idle": "2024-08-02T23:18:27.480622Z", + "shell.execute_reply": "2024-08-02T23:18:27.480154Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.482811Z", + "iopub.status.busy": "2024-08-02T23:18:27.482463Z", + "iopub.status.idle": "2024-08-02T23:18:27.489531Z", + "shell.execute_reply": "2024-08-02T23:18:27.489014Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.491621Z", + "iopub.status.busy": "2024-08-02T23:18:27.491282Z", + "iopub.status.idle": "2024-08-02T23:18:27.500416Z", + "shell.execute_reply": "2024-08-02T23:18:27.499840Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.502452Z", + "iopub.status.busy": "2024-08-02T23:18:27.502275Z", + "iopub.status.idle": "2024-08-02T23:18:27.517561Z", + "shell.execute_reply": "2024-08-02T23:18:27.517118Z" }, "nbsphinx": "hidden" }, @@ -1615,6 +1615,21 @@ "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index 4228b95b6..b6b82bece 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": "2024-07-30T16:32:47.382198Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:30.187968Z", + "iopub.status.busy": "2024-08-02T23:18:30.187540Z", + "iopub.status.idle": "2024-08-02T23:18:33.174744Z", + "shell.execute_reply": "2024-08-02T23:18:33.174187Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:33.177406Z", + "iopub.status.busy": "2024-08-02T23:18:33.176910Z", + "iopub.status.idle": "2024-08-02T23:18:33.180538Z", + "shell.execute_reply": "2024-08-02T23:18:33.180061Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:33.182529Z", + "iopub.status.busy": "2024-08-02T23:18:33.182219Z", + "iopub.status.idle": "2024-08-02T23:18:44.664109Z", + "shell.execute_reply": "2024-08-02T23:18:44.663641Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cd770708ca5498492377d6a0fd76616", + "model_id": "a83344ed6e98426eabf329c5f44403a3", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4fa7fdeeb9446ddbf6516f8963fa52e", + "model_id": "eeb48354781f459d926f56b9d9f2d412", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6e2987ba28d48c28d884b33288562df", + "model_id": "53411696bfa143a2bdec30cc846c6549", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29ade62a53ac448198f24b5900001b05", + "model_id": "c17e6593dbb94d3a9ee695742a582d56", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "926846a8c6954c46acf37f4dd63e7eb9", + "model_id": "a26294fe97dc43f9be84fc17b73f9563", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6aaa8d39274f4cfea54a66eb8516a06f", + "model_id": "dac75bdcb107415d915bb9ad97029fe4", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96697881a93440babad369ae2e2fd4b8", + "model_id": "73b0c33bd83f44ad9d81965657542e7d", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efe64e5c44d94c6bb0bed3ad6e844c33", + "model_id": "ecb36eb02e7843c881d512c1e1980bfc", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:44.666249Z", + "iopub.status.busy": "2024-08-02T23:18:44.666064Z", + "iopub.status.idle": "2024-08-02T23:18:44.669866Z", + "shell.execute_reply": "2024-08-02T23:18:44.669332Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:44.671881Z", + "iopub.status.busy": "2024-08-02T23:18:44.671547Z", + "iopub.status.idle": "2024-08-02T23:18:56.267751Z", + "shell.execute_reply": "2024-08-02T23:18:56.267200Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c48d73b75d8543b7900f7e3a24c14ff0", + "model_id": "82fd82af78b445e7b64eeceba4a9b1cc", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:56.270597Z", + "iopub.status.busy": "2024-08-02T23:18:56.270197Z", + "iopub.status.idle": "2024-08-02T23:19:15.145228Z", + "shell.execute_reply": "2024-08-02T23:19:15.144654Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.147853Z", + "iopub.status.busy": "2024-08-02T23:19:15.147474Z", + "iopub.status.idle": "2024-08-02T23:19:15.153225Z", + "shell.execute_reply": "2024-08-02T23:19:15.152782Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.155015Z", + "iopub.status.busy": "2024-08-02T23:19:15.154828Z", + "iopub.status.idle": "2024-08-02T23:19:15.159184Z", + "shell.execute_reply": "2024-08-02T23:19:15.158778Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.161173Z", + "iopub.status.busy": "2024-08-02T23:19:15.160982Z", + "iopub.status.idle": "2024-08-02T23:19:15.169844Z", + "shell.execute_reply": "2024-08-02T23:19:15.169391Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.171692Z", + "iopub.status.busy": "2024-08-02T23:19:15.171519Z", + "iopub.status.idle": "2024-08-02T23:19:15.197718Z", + "shell.execute_reply": "2024-08-02T23:19:15.197156Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.200155Z", + "iopub.status.busy": "2024-08-02T23:19:15.199758Z", + "iopub.status.idle": "2024-08-02T23:19:48.490207Z", + "shell.execute_reply": "2024-08-02T23:19:48.489541Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.221\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.940\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.696\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4800d17f20734ee3900349a11b2585dc", + "model_id": "dcb047742e7c4146a32757077d93eb95", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21d92163462b4f67a981a814fcb48508", + "model_id": "64bb6421005c4259bdb6379773d89e83", "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: 5.233\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.990\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.598\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8af1aec52aef434b81a22b708073556f", + "model_id": "7b496615a26e4658af3e583f61bcdef9", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19a5e3a37b304b559df2c5101035122f", + "model_id": "cf7cc11f28ea46039cc95c145d1ce401", "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: 5.455\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.891\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.595\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2738be416ce480c95fff046962f1137", + "model_id": "4a80db32ba09418496242d3395cc72bf", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c2f40cf42cc413e8b1040c82a085028", + "model_id": "9e16783844c34794a2677bfd495b5109", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.492853Z", + "iopub.status.busy": "2024-08-02T23:19:48.492428Z", + "iopub.status.idle": "2024-08-02T23:19:48.507600Z", + "shell.execute_reply": "2024-08-02T23:19:48.507055Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.509976Z", + "iopub.status.busy": "2024-08-02T23:19:48.509546Z", + "iopub.status.idle": "2024-08-02T23:19:48.985893Z", + "shell.execute_reply": "2024-08-02T23:19:48.985338Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.988296Z", + "iopub.status.busy": "2024-08-02T23:19:48.987937Z", + "iopub.status.idle": "2024-08-02T23:21:27.529258Z", + "shell.execute_reply": "2024-08-02T23:21:27.528524Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e7ed9a8db3f47d499c32f8ab98695a3", + "model_id": "65b0b32c141e4ddeb98d61670fbf32bf", "version_major": 2, "version_minor": 0 }, @@ -1150,10 +1150,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:27.531774Z", + "iopub.status.busy": "2024-08-02T23:21:27.531386Z", + "iopub.status.idle": "2024-08-02T23:21:27.988639Z", + "shell.execute_reply": "2024-08-02T23:21:27.987974Z" } }, "outputs": [ @@ -1299,10 +1299,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:27.999558Z", + "iopub.status.busy": "2024-08-02T23:21:27.999325Z", + "iopub.status.idle": "2024-08-02T23:21:28.049571Z", + "shell.execute_reply": "2024-08-02T23:21:28.048979Z" } }, "outputs": [ @@ -1406,10 +1406,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.051857Z", + "iopub.status.busy": "2024-08-02T23:21:28.051505Z", + "iopub.status.idle": "2024-08-02T23:21:28.060658Z", + "shell.execute_reply": "2024-08-02T23:21:28.060215Z" } }, "outputs": [ @@ -1539,10 +1539,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.062728Z", + "iopub.status.busy": "2024-08-02T23:21:28.062400Z", + "iopub.status.idle": "2024-08-02T23:21:28.066951Z", + "shell.execute_reply": "2024-08-02T23:21:28.066486Z" }, "nbsphinx": "hidden" }, @@ -1588,10 +1588,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.069039Z", + "iopub.status.busy": "2024-08-02T23:21:28.068692Z", + "iopub.status.idle": "2024-08-02T23:21:28.571892Z", + "shell.execute_reply": "2024-08-02T23:21:28.571292Z" } }, "outputs": [ @@ -1626,10 +1626,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.574424Z", + "iopub.status.busy": "2024-08-02T23:21:28.574048Z", + "iopub.status.idle": "2024-08-02T23:21:28.582821Z", + "shell.execute_reply": "2024-08-02T23:21:28.582318Z" } }, "outputs": [ @@ -1796,10 +1796,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.585102Z", + "iopub.status.busy": "2024-08-02T23:21:28.584720Z", + "iopub.status.idle": "2024-08-02T23:21:28.592253Z", + "shell.execute_reply": "2024-08-02T23:21:28.591755Z" }, "nbsphinx": "hidden" }, @@ -1875,10 +1875,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.594385Z", + "iopub.status.busy": "2024-08-02T23:21:28.593990Z", + "iopub.status.idle": "2024-08-02T23:21:29.355028Z", + "shell.execute_reply": "2024-08-02T23:21:29.354474Z" } }, "outputs": [ @@ -1915,10 +1915,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.357774Z", + "iopub.status.busy": "2024-08-02T23:21:29.357407Z", + "iopub.status.idle": "2024-08-02T23:21:29.372810Z", + "shell.execute_reply": "2024-08-02T23:21:29.372351Z" } }, "outputs": [ @@ -2075,10 +2075,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.375092Z", + "iopub.status.busy": "2024-08-02T23:21:29.374753Z", + "iopub.status.idle": "2024-08-02T23:21:29.380139Z", + "shell.execute_reply": "2024-08-02T23:21:29.379692Z" }, "nbsphinx": "hidden" }, @@ -2123,10 +2123,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.382139Z", + "iopub.status.busy": "2024-08-02T23:21:29.381799Z", + "iopub.status.idle": "2024-08-02T23:21:29.845557Z", + "shell.execute_reply": "2024-08-02T23:21:29.844919Z" } }, "outputs": [ @@ -2208,10 +2208,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.848126Z", + "iopub.status.busy": "2024-08-02T23:21:29.847912Z", + "iopub.status.idle": "2024-08-02T23:21:29.857322Z", + "shell.execute_reply": "2024-08-02T23:21:29.856727Z" } }, "outputs": [ @@ -2236,47 +2236,47 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 34848\n", - " 0.203922\n", " True\n", + " 0.203922\n", " \n", " \n", " 50270\n", - " 0.204588\n", " True\n", + " 0.204588\n", " \n", " \n", " 3936\n", - " 0.213098\n", " True\n", + " 0.213098\n", " \n", " \n", " 733\n", - " 0.217686\n", " True\n", + " 0.217686\n", " \n", " \n", " 8094\n", - " 0.230118\n", " True\n", + " 0.230118\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 26, @@ -2339,10 +2339,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.863079Z", + "iopub.status.busy": "2024-08-02T23:21:29.862689Z", + "iopub.status.idle": "2024-08-02T23:21:29.868549Z", + "shell.execute_reply": "2024-08-02T23:21:29.868014Z" }, "nbsphinx": "hidden" }, @@ -2379,10 +2379,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:35:51.944959Z", - "iopub.status.busy": "2024-07-30T16:35:51.944785Z", - 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"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" + "iopub.execute_input": "2024-08-02T23:21:34.625401Z", + "iopub.status.busy": "2024-08-02T23:21:34.625214Z", + "iopub.status.idle": "2024-08-02T23:21:36.031325Z", + "shell.execute_reply": "2024-08-02T23:21:36.030762Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:35:57.512868Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:36.033876Z", + "iopub.status.busy": "2024-08-02T23:21:36.033576Z", + "iopub.status.idle": "2024-08-02T23:21:36.052522Z", + "shell.execute_reply": "2024-08-02T23:21:36.052073Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.054768Z", + "iopub.status.busy": "2024-08-02T23:21:36.054504Z", + "iopub.status.idle": "2024-08-02T23:21:36.078555Z", + "shell.execute_reply": "2024-08-02T23:21:36.078092Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.080474Z", + "iopub.status.busy": "2024-08-02T23:21:36.080294Z", + "iopub.status.idle": "2024-08-02T23:21:36.083816Z", + "shell.execute_reply": "2024-08-02T23:21:36.083361Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.085725Z", + "iopub.status.busy": "2024-08-02T23:21:36.085553Z", + "iopub.status.idle": "2024-08-02T23:21:36.092911Z", + "shell.execute_reply": "2024-08-02T23:21:36.092464Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.094839Z", + "iopub.status.busy": "2024-08-02T23:21:36.094665Z", + "iopub.status.idle": "2024-08-02T23:21:36.097347Z", + "shell.execute_reply": "2024-08-02T23:21:36.096835Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.099385Z", + "iopub.status.busy": "2024-08-02T23:21:36.099048Z", + "iopub.status.idle": "2024-08-02T23:21:39.178225Z", + "shell.execute_reply": "2024-08-02T23:21:39.177680Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:39.181086Z", + "iopub.status.busy": "2024-08-02T23:21:39.180688Z", + "iopub.status.idle": "2024-08-02T23:21:39.190195Z", + "shell.execute_reply": "2024-08-02T23:21:39.189607Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:39.192491Z", + "iopub.status.busy": "2024-08-02T23:21:39.192157Z", + "iopub.status.idle": "2024-08-02T23:21:41.385468Z", + "shell.execute_reply": "2024-08-02T23:21:41.384800Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.388190Z", + "iopub.status.busy": "2024-08-02T23:21:41.387567Z", + "iopub.status.idle": "2024-08-02T23:21:41.406849Z", + "shell.execute_reply": "2024-08-02T23:21:41.406379Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.408952Z", + "iopub.status.busy": "2024-08-02T23:21:41.408765Z", + "iopub.status.idle": "2024-08-02T23:21:41.417080Z", + "shell.execute_reply": "2024-08-02T23:21:41.416607Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.419162Z", + "iopub.status.busy": "2024-08-02T23:21:41.418912Z", + "iopub.status.idle": "2024-08-02T23:21:41.428332Z", + "shell.execute_reply": "2024-08-02T23:21:41.427869Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.430451Z", + "iopub.status.busy": "2024-08-02T23:21:41.430111Z", + "iopub.status.idle": "2024-08-02T23:21:41.437844Z", + "shell.execute_reply": "2024-08-02T23:21:41.437283Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.439955Z", + "iopub.status.busy": "2024-08-02T23:21:41.439619Z", + "iopub.status.idle": "2024-08-02T23:21:41.448535Z", + "shell.execute_reply": "2024-08-02T23:21:41.447977Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.450685Z", + "iopub.status.busy": "2024-08-02T23:21:41.450360Z", + "iopub.status.idle": "2024-08-02T23:21:41.457701Z", + "shell.execute_reply": "2024-08-02T23:21:41.457210Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.459831Z", + "iopub.status.busy": "2024-08-02T23:21:41.459473Z", + "iopub.status.idle": "2024-08-02T23:21:41.466693Z", + "shell.execute_reply": "2024-08-02T23:21:41.466243Z" } }, "outputs": [ @@ -1290,9 +1290,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" ] }, { @@ -1300,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.468785Z", + "iopub.status.busy": "2024-08-02T23:21:41.468507Z", + "iopub.status.idle": "2024-08-02T23:21:41.477200Z", + "shell.execute_reply": "2024-08-02T23:21:41.476679Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index b380e2cea..ae24736d8 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": "2024-07-30T16:36:05.906897Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:44.540425Z", + "iopub.status.busy": "2024-08-02T23:21:44.540253Z", + "iopub.status.idle": "2024-08-02T23:21:47.824794Z", + "shell.execute_reply": "2024-08-02T23:21:47.824202Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:36:09.213471Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:47.827543Z", + "iopub.status.busy": "2024-08-02T23:21:47.827067Z", + "iopub.status.idle": "2024-08-02T23:21:47.830552Z", + "shell.execute_reply": "2024-08-02T23:21:47.829969Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.832743Z", + "iopub.status.busy": "2024-08-02T23:21:47.832414Z", + "iopub.status.idle": "2024-08-02T23:21:47.835675Z", + "shell.execute_reply": "2024-08-02T23:21:47.835102Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.837826Z", + "iopub.status.busy": "2024-08-02T23:21:47.837474Z", + "iopub.status.idle": "2024-08-02T23:21:47.861187Z", + "shell.execute_reply": "2024-08-02T23:21:47.860585Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.863473Z", + "iopub.status.busy": "2024-08-02T23:21:47.863101Z", + "iopub.status.idle": "2024-08-02T23:21:47.867008Z", + "shell.execute_reply": "2024-08-02T23:21:47.866495Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.869127Z", + "iopub.status.busy": "2024-08-02T23:21:47.868786Z", + "iopub.status.idle": "2024-08-02T23:21:47.872006Z", + "shell.execute_reply": "2024-08-02T23:21:47.871455Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.874101Z", + "iopub.status.busy": "2024-08-02T23:21:47.873920Z", + "iopub.status.idle": "2024-08-02T23:21:51.371823Z", + "shell.execute_reply": "2024-08-02T23:21:51.371151Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:51.374609Z", + "iopub.status.busy": "2024-08-02T23:21:51.374204Z", + "iopub.status.idle": "2024-08-02T23:21:52.273008Z", + "shell.execute_reply": "2024-08-02T23:21:52.272406Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:52.276055Z", + "iopub.status.busy": "2024-08-02T23:21:52.275635Z", + "iopub.status.idle": "2024-08-02T23:21:52.278641Z", + "shell.execute_reply": "2024-08-02T23:21:52.278123Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:52.281114Z", + "iopub.status.busy": "2024-08-02T23:21:52.280693Z", + "iopub.status.idle": "2024-08-02T23:21:54.282307Z", + "shell.execute_reply": "2024-08-02T23:21:54.281655Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.285687Z", + "iopub.status.busy": "2024-08-02T23:21:54.284976Z", + "iopub.status.idle": "2024-08-02T23:21:54.309240Z", + "shell.execute_reply": "2024-08-02T23:21:54.308666Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.311719Z", + "iopub.status.busy": "2024-08-02T23:21:54.311308Z", + "iopub.status.idle": "2024-08-02T23:21:54.321181Z", + "shell.execute_reply": "2024-08-02T23:21:54.320680Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.323214Z", + "iopub.status.busy": "2024-08-02T23:21:54.322881Z", + "iopub.status.idle": "2024-08-02T23:21:54.327193Z", + "shell.execute_reply": "2024-08-02T23:21:54.326638Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.329227Z", + "iopub.status.busy": "2024-08-02T23:21:54.328892Z", + "iopub.status.idle": "2024-08-02T23:21:54.335223Z", + "shell.execute_reply": "2024-08-02T23:21:54.334737Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.337162Z", + "iopub.status.busy": "2024-08-02T23:21:54.336976Z", + "iopub.status.idle": "2024-08-02T23:21:54.343438Z", + "shell.execute_reply": "2024-08-02T23:21:54.342965Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.345616Z", + "iopub.status.busy": "2024-08-02T23:21:54.345242Z", + "iopub.status.idle": "2024-08-02T23:21:54.351019Z", + "shell.execute_reply": "2024-08-02T23:21:54.350470Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.353129Z", + "iopub.status.busy": "2024-08-02T23:21:54.352774Z", + "iopub.status.idle": "2024-08-02T23:21:54.361472Z", + "shell.execute_reply": "2024-08-02T23:21:54.360992Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.363537Z", + "iopub.status.busy": "2024-08-02T23:21:54.363200Z", + "iopub.status.idle": "2024-08-02T23:21:54.368517Z", + "shell.execute_reply": "2024-08-02T23:21:54.367955Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.370813Z", + "iopub.status.busy": "2024-08-02T23:21:54.370341Z", + "iopub.status.idle": "2024-08-02T23:21:54.376019Z", + "shell.execute_reply": "2024-08-02T23:21:54.375442Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.378125Z", + "iopub.status.busy": "2024-08-02T23:21:54.377805Z", + "iopub.status.idle": "2024-08-02T23:21:54.381485Z", + "shell.execute_reply": "2024-08-02T23:21:54.380895Z" } }, "outputs": [ @@ -1433,9 +1433,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" ] }, { @@ -1443,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.383731Z", + "iopub.status.busy": "2024-08-02T23:21:54.383385Z", + "iopub.status.idle": "2024-08-02T23:21:54.388743Z", + "shell.execute_reply": "2024-08-02T23:21:54.388162Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index d6d1d2769..b1d172e95 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": "2024-07-30T16:36:19.928818Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:58.540122Z", + "iopub.status.busy": "2024-08-02T23:21:58.539942Z", + "iopub.status.idle": "2024-08-02T23:21:58.972460Z", + "shell.execute_reply": "2024-08-02T23:21:58.971845Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:58.975240Z", + "iopub.status.busy": "2024-08-02T23:21:58.974829Z", + "iopub.status.idle": "2024-08-02T23:21:59.105776Z", + "shell.execute_reply": "2024-08-02T23:21:59.105181Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:59.108029Z", + "iopub.status.busy": "2024-08-02T23:21:59.107636Z", + "iopub.status.idle": "2024-08-02T23:21:59.130897Z", + "shell.execute_reply": "2024-08-02T23:21:59.130271Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:59.133602Z", + "iopub.status.busy": "2024-08-02T23:21:59.133095Z", + "iopub.status.idle": "2024-08-02T23:22:02.346575Z", + "shell.execute_reply": "2024-08-02T23:22:02.345989Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:02.349075Z", + "iopub.status.busy": "2024-08-02T23:22:02.348695Z", + "iopub.status.idle": "2024-08-02T23:22:10.790575Z", + "shell.execute_reply": "2024-08-02T23:22:10.790056Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:10.792934Z", + "iopub.status.busy": "2024-08-02T23:22:10.792557Z", + "iopub.status.idle": "2024-08-02T23:22:10.956046Z", + "shell.execute_reply": "2024-08-02T23:22:10.955515Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:10.958582Z", + "iopub.status.busy": "2024-08-02T23:22:10.958202Z", + "iopub.status.idle": "2024-08-02T23:22:12.292827Z", + "shell.execute_reply": "2024-08-02T23:22:12.292255Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.295279Z", + "iopub.status.busy": "2024-08-02T23:22:12.294790Z", + "iopub.status.idle": "2024-08-02T23:22:12.622073Z", + "shell.execute_reply": "2024-08-02T23:22:12.621483Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.624778Z", + "iopub.status.busy": "2024-08-02T23:22:12.624229Z", + "iopub.status.idle": "2024-08-02T23:22:12.637797Z", + "shell.execute_reply": "2024-08-02T23:22:12.637348Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.640065Z", + "iopub.status.busy": "2024-08-02T23:22:12.639723Z", + "iopub.status.idle": "2024-08-02T23:22:12.658542Z", + "shell.execute_reply": "2024-08-02T23:22:12.658085Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.660724Z", + "iopub.status.busy": "2024-08-02T23:22:12.660375Z", + "iopub.status.idle": "2024-08-02T23:22:12.877581Z", + "shell.execute_reply": "2024-08-02T23:22:12.877013Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.880604Z", + "iopub.status.busy": "2024-08-02T23:22:12.880140Z", + "iopub.status.idle": "2024-08-02T23:22:12.899913Z", + "shell.execute_reply": "2024-08-02T23:22:12.899342Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.902002Z", + "iopub.status.busy": "2024-08-02T23:22:12.901825Z", + "iopub.status.idle": "2024-08-02T23:22:13.071307Z", + "shell.execute_reply": "2024-08-02T23:22:13.070655Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.073549Z", + "iopub.status.busy": "2024-08-02T23:22:13.073366Z", + "iopub.status.idle": "2024-08-02T23:22:13.083592Z", + "shell.execute_reply": "2024-08-02T23:22:13.083032Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.085738Z", + "iopub.status.busy": "2024-08-02T23:22:13.085397Z", + "iopub.status.idle": "2024-08-02T23:22:13.094677Z", + "shell.execute_reply": "2024-08-02T23:22:13.094210Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.096577Z", + "iopub.status.busy": "2024-08-02T23:22:13.096407Z", + "iopub.status.idle": "2024-08-02T23:22:13.122565Z", + "shell.execute_reply": "2024-08-02T23:22:13.122066Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.124990Z", + "iopub.status.busy": "2024-08-02T23:22:13.124635Z", + "iopub.status.idle": "2024-08-02T23:22:13.127488Z", + "shell.execute_reply": "2024-08-02T23:22:13.127033Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.129612Z", + "iopub.status.busy": "2024-08-02T23:22:13.129258Z", + "iopub.status.idle": "2024-08-02T23:22:13.149348Z", + "shell.execute_reply": "2024-08-02T23:22:13.148753Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.151509Z", + "iopub.status.busy": "2024-08-02T23:22:13.151173Z", + "iopub.status.idle": "2024-08-02T23:22:13.155595Z", + "shell.execute_reply": "2024-08-02T23:22:13.155039Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.157749Z", + "iopub.status.busy": "2024-08-02T23:22:13.157403Z", + "iopub.status.idle": "2024-08-02T23:22:13.186218Z", + "shell.execute_reply": "2024-08-02T23:22:13.185603Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.188598Z", + "iopub.status.busy": "2024-08-02T23:22:13.188252Z", + "iopub.status.idle": "2024-08-02T23:22:13.558273Z", + "shell.execute_reply": "2024-08-02T23:22:13.557664Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.560412Z", + "iopub.status.busy": "2024-08-02T23:22:13.560221Z", + "iopub.status.idle": "2024-08-02T23:22:13.563654Z", + "shell.execute_reply": "2024-08-02T23:22:13.563075Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.565843Z", + "iopub.status.busy": "2024-08-02T23:22:13.565488Z", + "iopub.status.idle": "2024-08-02T23:22:13.578636Z", + "shell.execute_reply": "2024-08-02T23:22:13.578130Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.580749Z", + "iopub.status.busy": "2024-08-02T23:22:13.580400Z", + "iopub.status.idle": "2024-08-02T23:22:13.593962Z", + "shell.execute_reply": "2024-08-02T23:22:13.593384Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.596098Z", + "iopub.status.busy": "2024-08-02T23:22:13.595769Z", + "iopub.status.idle": "2024-08-02T23:22:13.606627Z", + "shell.execute_reply": "2024-08-02T23:22:13.606051Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:35.310642Z", - "iopub.status.busy": "2024-07-30T16:36:35.310307Z", - "iopub.status.idle": "2024-07-30T16:36:35.319369Z", - "shell.execute_reply": "2024-07-30T16:36:35.318850Z" + "iopub.execute_input": "2024-08-02T23:22:13.608671Z", + "iopub.status.busy": "2024-08-02T23:22:13.608346Z", + "iopub.status.idle": "2024-08-02T23:22:13.617864Z", + "shell.execute_reply": "2024-08-02T23:22:13.617309Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:35.321481Z", - "iopub.status.busy": "2024-07-30T16:36:35.321145Z", - "iopub.status.idle": "2024-07-30T16:36:35.324693Z", - "shell.execute_reply": "2024-07-30T16:36:35.324231Z" + "iopub.execute_input": "2024-08-02T23:22:13.619896Z", + "iopub.status.busy": "2024-08-02T23:22:13.619554Z", + "iopub.status.idle": "2024-08-02T23:22:13.623041Z", + "shell.execute_reply": "2024-08-02T23:22:13.622595Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:35.326670Z", - "iopub.status.busy": "2024-07-30T16:36:35.326496Z", - "iopub.status.idle": "2024-07-30T16:36:35.379645Z", - "shell.execute_reply": "2024-07-30T16:36:35.379123Z" + "iopub.execute_input": "2024-08-02T23:22:13.624963Z", + "iopub.status.busy": "2024-08-02T23:22:13.624789Z", + "iopub.status.idle": "2024-08-02T23:22:13.676686Z", + "shell.execute_reply": "2024-08-02T23:22:13.676151Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - 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 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:35.382058Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:13.679035Z", + "iopub.status.busy": "2024-08-02T23:22:13.678697Z", + "iopub.status.idle": "2024-08-02T23:22:13.685822Z", + "shell.execute_reply": "2024-08-02T23:22:13.685355Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.688074Z", + "iopub.status.busy": "2024-08-02T23:22:13.687619Z", + "iopub.status.idle": "2024-08-02T23:22:13.699375Z", + "shell.execute_reply": "2024-08-02T23:22:13.698786Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.701685Z", + "iopub.status.busy": "2024-08-02T23:22:13.701265Z", + "iopub.status.idle": "2024-08-02T23:22:13.918661Z", + "shell.execute_reply": "2024-08-02T23:22:13.918038Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.920933Z", + "iopub.status.busy": "2024-08-02T23:22:13.920727Z", + "iopub.status.idle": "2024-08-02T23:22:13.928739Z", + "shell.execute_reply": "2024-08-02T23:22:13.928289Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.930887Z", + "iopub.status.busy": "2024-08-02T23:22:13.930700Z", + "iopub.status.idle": "2024-08-02T23:22:14.283492Z", + "shell.execute_reply": "2024-08-02T23:22:14.282648Z" } }, "outputs": [ @@ -3767,32 +3767,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-30 16:36:35-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", - "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.108.153, 185.199.110.153, ...\r\n", + "--2024-08-02 23:22:13-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 986707 (964K) [application/zip]\r\n", "Saving to: ‘CIFAR-10-subset.zip’\r\n", "\r\n", "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.03s \r\n", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.007s \r\n", "\r\n", - "2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-08-02 23:22:14 (131 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3808,10 +3794,10 @@ "execution_count": 34, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:14.286320Z", + "iopub.status.busy": "2024-08-02T23:22:14.285986Z", + "iopub.status.idle": "2024-08-02T23:22:16.250080Z", + "shell.execute_reply": "2024-08-02T23:22:16.249517Z" } }, "outputs": [], @@ -3857,10 +3843,10 @@ "execution_count": 35, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:16.252933Z", + "iopub.status.busy": "2024-08-02T23:22:16.252320Z", + "iopub.status.idle": "2024-08-02T23:22:16.733355Z", + "shell.execute_reply": "2024-08-02T23:22:16.732689Z" } }, "outputs": [ @@ -3875,7 +3861,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ed4efbeb1874db0a5e2316cc6fdcc53", + "model_id": "71a4c08b9dfc456aa328fdeec90efbf7", "version_major": 2, "version_minor": 0 }, @@ -3957,10 +3943,10 @@ "execution_count": 36, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:16.737440Z", + "iopub.status.busy": "2024-08-02T23:22:16.736282Z", + "iopub.status.idle": "2024-08-02T23:22:16.754516Z", + "shell.execute_reply": "2024-08-02T23:22:16.754000Z" } }, "outputs": [ @@ -4079,35 +4065,35 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 0\n", - " True\n", " 0.237196\n", + " True\n", " \n", " \n", " 1\n", - " True\n", " 0.197229\n", + " True\n", " \n", " \n", " 2\n", - " True\n", " 0.254188\n", + " True\n", " \n", " \n", " 3\n", - " True\n", " 0.229170\n", + " True\n", " \n", " \n", " 4\n", - " True\n", " 0.208907\n", + " True\n", " \n", " \n", " ...\n", @@ -4116,28 +4102,28 @@ " \n", " \n", " 195\n", - " False\n", " 0.793840\n", + " False\n", " \n", " \n", " 196\n", - " False\n", " 1.000000\n", + " False\n", " \n", " \n", " 197\n", - " False\n", " 0.971560\n", + " False\n", " \n", " \n", " 198\n", - " False\n", " 0.862236\n", + " False\n", " \n", " \n", " 199\n", - " False\n", " 0.973533\n", + " False\n", " \n", " \n", "\n", @@ -4145,18 +4131,18 @@ "" ], "text/plain": [ - " 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", + " dark_score is_dark_issue\n", + "0 0.237196 True\n", + "1 0.197229 True\n", + "2 0.254188 True\n", + "3 0.229170 True\n", + "4 0.208907 True\n", + ".. ... ...\n", + "195 0.793840 False\n", + "196 1.000000 False\n", + "197 0.971560 False\n", + "198 0.862236 False\n", + "199 0.973533 False\n", "\n", "[200 rows x 2 columns]" ] @@ -4218,10 +4204,10 @@ "execution_count": 37, "metadata": { "execution": { - 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" False\n", " 0.773951\n", + " False\n", " \n", " \n", " 4\n", - " False\n", " 0.699518\n", + " False\n", " \n", " \n", " ...\n", @@ -4401,28 +4387,28 @@ " \n", " \n", " 195\n", - " False\n", " 0.793840\n", + " False\n", " \n", " \n", " 196\n", - " False\n", " 1.000000\n", + " False\n", " \n", " \n", " 197\n", - " False\n", " 0.971560\n", + " False\n", " \n", " \n", " 198\n", - " False\n", " 0.862236\n", + " False\n", " \n", " \n", " 199\n", - " False\n", " 0.973533\n", + " False\n", " \n", " \n", "\n", @@ -4430,18 +4416,18 @@ "" ], "text/plain": [ - " 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", + " dark_score is_dark_issue\n", + "0 0.797509 False\n", + "1 0.663760 False\n", + "2 0.849826 False\n", + "3 0.773951 False\n", + "4 0.699518 False\n", + ".. ... ...\n", + "195 0.793840 False\n", + "196 1.000000 False\n", + "197 0.971560 False\n", + "198 0.862236 False\n", + "199 0.973533 False\n", "\n", "[200 rows x 2 columns]" ] @@ -4504,7 +4490,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"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" + "iopub.execute_input": "2024-08-02T23:22:21.207713Z", + "iopub.status.busy": "2024-08-02T23:22:21.207533Z", + "iopub.status.idle": "2024-08-02T23:22:22.617403Z", + "shell.execute_reply": "2024-08-02T23:22:22.616703Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:36:44.679704Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:22.619987Z", + "iopub.status.busy": "2024-08-02T23:22:22.619693Z", + "iopub.status.idle": "2024-08-02T23:22:22.622687Z", + "shell.execute_reply": "2024-08-02T23:22:22.622224Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:22.624727Z", + "iopub.status.busy": "2024-08-02T23:22:22.624552Z", + "iopub.status.idle": "2024-08-02T23:22:22.636926Z", + "shell.execute_reply": "2024-08-02T23:22:22.636449Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:22.638863Z", + "iopub.status.busy": "2024-08-02T23:22:22.638690Z", + "iopub.status.idle": "2024-08-02T23:22:26.870323Z", + "shell.execute_reply": "2024-08-02T23:22:26.869834Z" }, "id": "dhTHOg8Pyv5G" }, @@ -3081,6 +3081,21 @@ " # run 1 line of code to evaluate the health of your dataset\n", " _ = cleanlab.dataset.health_summary(labels, pred_probs, class_names=class_names)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 0e282dd07..85b88d83d 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": "2024-07-30T16:36:53.364898Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:29.317927Z", + "iopub.status.busy": "2024-08-02T23:22:29.317762Z", + "iopub.status.idle": "2024-08-02T23:22:30.709755Z", + "shell.execute_reply": "2024-08-02T23:22:30.709204Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:30.712568Z", + "iopub.status.busy": "2024-08-02T23:22:30.712110Z", + "iopub.status.idle": "2024-08-02T23:22:30.715506Z", + "shell.execute_reply": "2024-08-02T23:22:30.715053Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:30.717656Z", + "iopub.status.busy": "2024-08-02T23:22:30.717203Z", + "iopub.status.idle": "2024-08-02T23:22:34.286623Z", + "shell.execute_reply": "2024-08-02T23:22:34.285962Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.290077Z", + "iopub.status.busy": "2024-08-02T23:22:34.289157Z", + "iopub.status.idle": "2024-08-02T23:22:34.334428Z", + "shell.execute_reply": "2024-08-02T23:22:34.333773Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.337263Z", + "iopub.status.busy": "2024-08-02T23:22:34.336802Z", + "iopub.status.idle": "2024-08-02T23:22:34.379369Z", + "shell.execute_reply": "2024-08-02T23:22:34.378671Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.382130Z", + "iopub.status.busy": "2024-08-02T23:22:34.381755Z", + "iopub.status.idle": "2024-08-02T23:22:34.384963Z", + "shell.execute_reply": "2024-08-02T23:22:34.384470Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.387036Z", + "iopub.status.busy": "2024-08-02T23:22:34.386739Z", + "iopub.status.idle": "2024-08-02T23:22:34.389652Z", + "shell.execute_reply": "2024-08-02T23:22:34.388895Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.391832Z", + "iopub.status.busy": "2024-08-02T23:22:34.391513Z", + "iopub.status.idle": "2024-08-02T23:22:34.417865Z", + "shell.execute_reply": "2024-08-02T23:22:34.417276Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57581a07cda143f5ae3947a8ceb2effa", + "model_id": "994bb101c48240bd91ac23c6d451faea", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8036196b7e194ee38336f33c15df9344", + "model_id": "75f8dda3bfd7411ab998335d777d0d77", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.423377Z", + "iopub.status.busy": "2024-08-02T23:22:34.423067Z", + "iopub.status.idle": "2024-08-02T23:22:34.429876Z", + "shell.execute_reply": "2024-08-02T23:22:34.429438Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.690192Z", - "iopub.status.busy": "2024-07-30T16:36:58.689845Z", - "iopub.status.idle": "2024-07-30T16:36:58.693407Z", - "shell.execute_reply": "2024-07-30T16:36:58.692931Z" + "iopub.execute_input": "2024-08-02T23:22:34.431811Z", + "iopub.status.busy": "2024-08-02T23:22:34.431637Z", + "iopub.status.idle": "2024-08-02T23:22:34.435115Z", + "shell.execute_reply": "2024-08-02T23:22:34.434650Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.695508Z", - "iopub.status.busy": "2024-07-30T16:36:58.695194Z", - "iopub.status.idle": "2024-07-30T16:36:58.701664Z", - "shell.execute_reply": "2024-07-30T16:36:58.701093Z" + "iopub.execute_input": "2024-08-02T23:22:34.437011Z", + "iopub.status.busy": "2024-08-02T23:22:34.436832Z", + "iopub.status.idle": "2024-08-02T23:22:34.443057Z", + "shell.execute_reply": "2024-08-02T23:22:34.442613Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.703807Z", - "iopub.status.busy": "2024-07-30T16:36:58.703473Z", - "iopub.status.idle": "2024-07-30T16:36:58.753286Z", - "shell.execute_reply": "2024-07-30T16:36:58.752623Z" + "iopub.execute_input": "2024-08-02T23:22:34.444958Z", + "iopub.status.busy": "2024-08-02T23:22:34.444777Z", + "iopub.status.idle": "2024-08-02T23:22:34.488386Z", + "shell.execute_reply": "2024-08-02T23:22:34.487771Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.756242Z", - "iopub.status.busy": "2024-07-30T16:36:58.755751Z", - "iopub.status.idle": "2024-07-30T16:36:58.810969Z", - "shell.execute_reply": "2024-07-30T16:36:58.810185Z" + "iopub.execute_input": "2024-08-02T23:22:34.491204Z", + "iopub.status.busy": "2024-08-02T23:22:34.490718Z", + "iopub.status.idle": "2024-08-02T23:22:34.533900Z", + "shell.execute_reply": "2024-08-02T23:22:34.533124Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.813764Z", - "iopub.status.busy": "2024-07-30T16:36:58.813494Z", - "iopub.status.idle": "2024-07-30T16:36:58.954189Z", - "shell.execute_reply": "2024-07-30T16:36:58.953479Z" + "iopub.execute_input": "2024-08-02T23:22:34.536698Z", + "iopub.status.busy": "2024-08-02T23:22:34.536347Z", + "iopub.status.idle": "2024-08-02T23:22:34.667900Z", + "shell.execute_reply": "2024-08-02T23:22:34.667248Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.956979Z", - "iopub.status.busy": "2024-07-30T16:36:58.956298Z", - "iopub.status.idle": "2024-07-30T16:37:02.028094Z", - "shell.execute_reply": "2024-07-30T16:37:02.027507Z" + "iopub.execute_input": "2024-08-02T23:22:34.670834Z", + "iopub.status.busy": "2024-08-02T23:22:34.670036Z", + "iopub.status.idle": "2024-08-02T23:22:37.696516Z", + "shell.execute_reply": "2024-08-02T23:22:37.695903Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:02.030600Z", - "iopub.status.busy": "2024-07-30T16:37:02.030207Z", - "iopub.status.idle": "2024-07-30T16:37:02.089072Z", - "shell.execute_reply": "2024-07-30T16:37:02.088458Z" + "iopub.execute_input": "2024-08-02T23:22:37.698762Z", + "iopub.status.busy": "2024-08-02T23:22:37.698568Z", + "iopub.status.idle": "2024-08-02T23:22:37.757100Z", + "shell.execute_reply": "2024-08-02T23:22:37.756458Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - 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"id": "1e0becd2", + "id": "a6f1d693", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "cba58da6", + "id": "2d3fc7a8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:37.802141Z", + "iopub.status.busy": "2024-08-02T23:22:37.801810Z", + "iopub.status.idle": "2024-08-02T23:22:37.809564Z", + "shell.execute_reply": "2024-08-02T23:22:37.808972Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "fea318fb", + "id": "1fd92d80", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. 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"_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e531205987048c482bafb4fc142f9ba", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_831e08b227234d0295aa11cd11e50c69", + "tabbable": null, + "tooltip": null, + "value": 50.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb index 839c45673..9ec229e44 100644 --- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:41.243159Z", + "iopub.status.busy": "2024-08-02T23:22:41.242848Z", + "iopub.status.idle": "2024-08-02T23:22:42.677514Z", + "shell.execute_reply": "2024-08-02T23:22:42.676864Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.303031Z", - "iopub.status.busy": "2024-07-30T16:37:08.302535Z", - "iopub.status.idle": "2024-07-30T16:37:08.306381Z", - "shell.execute_reply": "2024-07-30T16:37:08.305889Z" + "iopub.execute_input": "2024-08-02T23:22:42.680254Z", + "iopub.status.busy": "2024-08-02T23:22:42.679911Z", + "iopub.status.idle": "2024-08-02T23:22:42.683720Z", + "shell.execute_reply": "2024-08-02T23:22:42.683171Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.308567Z", - "iopub.status.busy": "2024-07-30T16:37:08.308093Z", - "iopub.status.idle": "2024-07-30T16:37:08.564617Z", - "shell.execute_reply": "2024-07-30T16:37:08.564044Z" + "iopub.execute_input": "2024-08-02T23:22:42.685860Z", + "iopub.status.busy": "2024-08-02T23:22:42.685596Z", + "iopub.status.idle": "2024-08-02T23:22:42.951861Z", + "shell.execute_reply": "2024-08-02T23:22:42.951252Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.567009Z", - "iopub.status.busy": "2024-07-30T16:37:08.566570Z", - "iopub.status.idle": "2024-07-30T16:37:08.572786Z", - "shell.execute_reply": "2024-07-30T16:37:08.572249Z" + "iopub.execute_input": "2024-08-02T23:22:42.954216Z", + "iopub.status.busy": "2024-08-02T23:22:42.953791Z", + "iopub.status.idle": "2024-08-02T23:22:42.959699Z", + "shell.execute_reply": "2024-08-02T23:22:42.959165Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.575053Z", - "iopub.status.busy": "2024-07-30T16:37:08.574648Z", - "iopub.status.idle": "2024-07-30T16:37:08.581553Z", - "shell.execute_reply": "2024-07-30T16:37:08.580995Z" + "iopub.execute_input": "2024-08-02T23:22:42.961766Z", + "iopub.status.busy": "2024-08-02T23:22:42.961424Z", + "iopub.status.idle": "2024-08-02T23:22:42.968221Z", + "shell.execute_reply": "2024-08-02T23:22:42.967660Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.583694Z", - "iopub.status.busy": "2024-07-30T16:37:08.583295Z", - "iopub.status.idle": "2024-07-30T16:37:08.588214Z", - "shell.execute_reply": "2024-07-30T16:37:08.587641Z" + "iopub.execute_input": "2024-08-02T23:22:42.970573Z", + "iopub.status.busy": "2024-08-02T23:22:42.970111Z", + "iopub.status.idle": "2024-08-02T23:22:42.975154Z", + "shell.execute_reply": "2024-08-02T23:22:42.974573Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.590172Z", - "iopub.status.busy": "2024-07-30T16:37:08.589849Z", - "iopub.status.idle": "2024-07-30T16:37:08.595724Z", - "shell.execute_reply": "2024-07-30T16:37:08.595139Z" + "iopub.execute_input": "2024-08-02T23:22:42.977356Z", + "iopub.status.busy": "2024-08-02T23:22:42.976989Z", + "iopub.status.idle": "2024-08-02T23:22:42.982565Z", + "shell.execute_reply": "2024-08-02T23:22:42.982093Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.597670Z", - "iopub.status.busy": "2024-07-30T16:37:08.597361Z", - "iopub.status.idle": "2024-07-30T16:37:08.601523Z", - "shell.execute_reply": "2024-07-30T16:37:08.600966Z" + "iopub.execute_input": "2024-08-02T23:22:42.984641Z", + "iopub.status.busy": "2024-08-02T23:22:42.984213Z", + "iopub.status.idle": "2024-08-02T23:22:42.988345Z", + "shell.execute_reply": "2024-08-02T23:22:42.987921Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.603521Z", - "iopub.status.busy": "2024-07-30T16:37:08.603187Z", - "iopub.status.idle": "2024-07-30T16:37:08.669967Z", - "shell.execute_reply": "2024-07-30T16:37:08.669384Z" + "iopub.execute_input": "2024-08-02T23:22:42.990465Z", + "iopub.status.busy": "2024-08-02T23:22:42.990022Z", + "iopub.status.idle": "2024-08-02T23:22:43.055914Z", + "shell.execute_reply": "2024-08-02T23:22:43.055064Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.672642Z", - "iopub.status.busy": "2024-07-30T16:37:08.672054Z", - "iopub.status.idle": "2024-07-30T16:37:08.683309Z", - "shell.execute_reply": "2024-07-30T16:37:08.682790Z" + "iopub.execute_input": "2024-08-02T23:22:43.059448Z", + "iopub.status.busy": "2024-08-02T23:22:43.058515Z", + "iopub.status.idle": "2024-08-02T23:22:43.071379Z", + "shell.execute_reply": "2024-08-02T23:22:43.070858Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.685772Z", - "iopub.status.busy": "2024-07-30T16:37:08.685236Z", - "iopub.status.idle": "2024-07-30T16:37:08.705410Z", - "shell.execute_reply": "2024-07-30T16:37:08.704880Z" + "iopub.execute_input": "2024-08-02T23:22:43.075030Z", + "iopub.status.busy": "2024-08-02T23:22:43.074109Z", + "iopub.status.idle": "2024-08-02T23:22:43.095778Z", + "shell.execute_reply": "2024-08-02T23:22:43.095284Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.099248Z", + "iopub.status.busy": "2024-08-02T23:22:43.098329Z", + "iopub.status.idle": "2024-08-02T23:22:43.104218Z", + "shell.execute_reply": "2024-08-02T23:22:43.103727Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.107706Z", + "iopub.status.busy": "2024-08-02T23:22:43.106779Z", + "iopub.status.idle": "2024-08-02T23:22:43.112886Z", + "shell.execute_reply": "2024-08-02T23:22:43.112392Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.116202Z", + "iopub.status.busy": "2024-08-02T23:22:43.115450Z", + "iopub.status.idle": "2024-08-02T23:22:43.125506Z", + "shell.execute_reply": "2024-08-02T23:22:43.125084Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.127473Z", + "iopub.status.busy": "2024-08-02T23:22:43.127141Z", + "iopub.status.idle": "2024-08-02T23:22:43.131395Z", + "shell.execute_reply": "2024-08-02T23:22:43.130979Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - 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"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" + "iopub.execute_input": "2024-08-02T23:22:43.257796Z", + "iopub.status.busy": "2024-08-02T23:22:43.257451Z", + "iopub.status.idle": "2024-08-02T23:22:45.391725Z", + "shell.execute_reply": "2024-08-02T23:22:45.391106Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.394492Z", + "iopub.status.busy": "2024-08-02T23:22:45.394017Z", + "iopub.status.idle": "2024-08-02T23:22:45.407717Z", + "shell.execute_reply": "2024-08-02T23:22:45.407211Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.409965Z", + "iopub.status.busy": "2024-08-02T23:22:45.409683Z", + "iopub.status.idle": "2024-08-02T23:22:45.412502Z", + "shell.execute_reply": "2024-08-02T23:22:45.411936Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.414713Z", + "iopub.status.busy": "2024-08-02T23:22:45.414480Z", + "iopub.status.idle": "2024-08-02T23:22:45.419479Z", + "shell.execute_reply": "2024-08-02T23:22:45.418922Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.421657Z", + "iopub.status.busy": "2024-08-02T23:22:45.421424Z", + "iopub.status.idle": "2024-08-02T23:22:45.459617Z", + "shell.execute_reply": "2024-08-02T23:22:45.459124Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.461965Z", + "iopub.status.busy": "2024-08-02T23:22:45.461590Z", + "iopub.status.idle": "2024-08-02T23:22:46.004492Z", + "shell.execute_reply": "2024-08-02T23:22:46.003940Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.007832Z", + "iopub.status.busy": "2024-08-02T23:22:46.006922Z", + "iopub.status.idle": "2024-08-02T23:22:46.139183Z", + "shell.execute_reply": "2024-08-02T23:22:46.138499Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.142862Z", + "iopub.status.busy": "2024-08-02T23:22:46.141903Z", + "iopub.status.idle": "2024-08-02T23:22:46.150567Z", + "shell.execute_reply": "2024-08-02T23:22:46.150071Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.154019Z", + "iopub.status.busy": "2024-08-02T23:22:46.153094Z", + "iopub.status.idle": "2024-08-02T23:22:46.160946Z", + "shell.execute_reply": "2024-08-02T23:22:46.160456Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.164386Z", + "iopub.status.busy": "2024-08-02T23:22:46.163465Z", + "iopub.status.idle": "2024-08-02T23:22:46.170712Z", + "shell.execute_reply": "2024-08-02T23:22:46.170223Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.174165Z", + "iopub.status.busy": "2024-08-02T23:22:46.173238Z", + "iopub.status.idle": "2024-08-02T23:22:46.179291Z", + "shell.execute_reply": "2024-08-02T23:22:46.178762Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.181687Z", + "iopub.status.busy": "2024-08-02T23:22:46.181513Z", + "iopub.status.idle": "2024-08-02T23:22:46.186322Z", + "shell.execute_reply": "2024-08-02T23:22:46.185868Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.188278Z", + "iopub.status.busy": "2024-08-02T23:22:46.188100Z", + "iopub.status.idle": "2024-08-02T23:22:46.265593Z", + "shell.execute_reply": "2024-08-02T23:22:46.264934Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.268378Z", + "iopub.status.busy": "2024-08-02T23:22:46.267931Z", + "iopub.status.idle": "2024-08-02T23:22:46.277696Z", + "shell.execute_reply": "2024-08-02T23:22:46.277149Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.280290Z", + "iopub.status.busy": "2024-08-02T23:22:46.279817Z", + "iopub.status.idle": "2024-08-02T23:22:46.282856Z", + "shell.execute_reply": "2024-08-02T23:22:46.282369Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.285113Z", + "iopub.status.busy": "2024-08-02T23:22:46.284905Z", + "iopub.status.idle": "2024-08-02T23:22:46.295023Z", + "shell.execute_reply": "2024-08-02T23:22:46.294587Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.297195Z", + "iopub.status.busy": "2024-08-02T23:22:46.297014Z", + "iopub.status.idle": "2024-08-02T23:22:46.303290Z", + "shell.execute_reply": "2024-08-02T23:22:46.302794Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.305390Z", + "iopub.status.busy": "2024-08-02T23:22:46.305229Z", + "iopub.status.idle": "2024-08-02T23:22:46.308203Z", + "shell.execute_reply": "2024-08-02T23:22:46.307753Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.310173Z", + "iopub.status.busy": "2024-08-02T23:22:46.310013Z", + "iopub.status.idle": "2024-08-02T23:22:50.335807Z", + "shell.execute_reply": "2024-08-02T23:22:50.335295Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:50.339016Z", + "iopub.status.busy": "2024-08-02T23:22:50.338110Z", + "iopub.status.idle": "2024-08-02T23:22:50.342873Z", + "shell.execute_reply": "2024-08-02T23:22:50.342277Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:50.345292Z", + "iopub.status.busy": "2024-08-02T23:22:50.344858Z", + "iopub.status.idle": "2024-08-02T23:22:50.347667Z", + "shell.execute_reply": "2024-08-02T23:22:50.347208Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 63d074d15..dbe76443f 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": "2024-07-30T16:37:19.514665Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:53.660990Z", + "iopub.status.busy": "2024-08-02T23:22:53.660815Z", + "iopub.status.idle": "2024-08-02T23:22:55.078462Z", + "shell.execute_reply": "2024-08-02T23:22:55.077902Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:20.972868Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:55.080870Z", + "iopub.status.busy": "2024-08-02T23:22:55.080574Z", + "iopub.status.idle": "2024-08-02T23:22:55.084143Z", + "shell.execute_reply": "2024-08-02T23:22:55.083572Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.086422Z", + "iopub.status.busy": "2024-08-02T23:22:55.086068Z", + "iopub.status.idle": "2024-08-02T23:22:55.097345Z", + "shell.execute_reply": "2024-08-02T23:22:55.096867Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.099137Z", + "iopub.status.busy": "2024-08-02T23:22:55.098958Z", + "iopub.status.idle": "2024-08-02T23:22:55.335579Z", + "shell.execute_reply": "2024-08-02T23:22:55.334975Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.337901Z", + "iopub.status.busy": "2024-08-02T23:22:55.337546Z", + "iopub.status.idle": "2024-08-02T23:22:55.363253Z", + "shell.execute_reply": "2024-08-02T23:22:55.362813Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.365173Z", + "iopub.status.busy": "2024-08-02T23:22:55.364984Z", + "iopub.status.idle": "2024-08-02T23:22:57.475466Z", + "shell.execute_reply": "2024-08-02T23:22:57.474804Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:57.477895Z", + "iopub.status.busy": "2024-08-02T23:22:57.477563Z", + "iopub.status.idle": "2024-08-02T23:22:57.495457Z", + "shell.execute_reply": "2024-08-02T23:22:57.494985Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:57.497360Z", + "iopub.status.busy": "2024-08-02T23:22:57.497175Z", + "iopub.status.idle": "2024-08-02T23:22:59.083356Z", + "shell.execute_reply": "2024-08-02T23:22:59.082741Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.086101Z", + "iopub.status.busy": "2024-08-02T23:22:59.085430Z", + "iopub.status.idle": "2024-08-02T23:22:59.099257Z", + "shell.execute_reply": "2024-08-02T23:22:59.098675Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.101459Z", + "iopub.status.busy": "2024-08-02T23:22:59.101073Z", + "iopub.status.idle": "2024-08-02T23:22:59.182514Z", + "shell.execute_reply": "2024-08-02T23:22:59.181863Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.185181Z", + "iopub.status.busy": "2024-08-02T23:22:59.184699Z", + "iopub.status.idle": "2024-08-02T23:22:59.399591Z", + "shell.execute_reply": "2024-08-02T23:22:59.399127Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.401855Z", + "iopub.status.busy": "2024-08-02T23:22:59.401500Z", + "iopub.status.idle": "2024-08-02T23:22:59.418383Z", + "shell.execute_reply": "2024-08-02T23:22:59.417944Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.420363Z", + "iopub.status.busy": "2024-08-02T23:22:59.420024Z", + "iopub.status.idle": "2024-08-02T23:22:59.429357Z", + "shell.execute_reply": "2024-08-02T23:22:59.428784Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.431554Z", + "iopub.status.busy": "2024-08-02T23:22:59.431121Z", + "iopub.status.idle": "2024-08-02T23:22:59.523557Z", + "shell.execute_reply": "2024-08-02T23:22:59.522973Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.525876Z", + "iopub.status.busy": "2024-08-02T23:22:59.525646Z", + "iopub.status.idle": "2024-08-02T23:22:59.668775Z", + "shell.execute_reply": "2024-08-02T23:22:59.668197Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.671509Z", + "iopub.status.busy": "2024-08-02T23:22:59.671116Z", + "iopub.status.idle": "2024-08-02T23:22:59.675170Z", + "shell.execute_reply": "2024-08-02T23:22:59.674673Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.677097Z", + "iopub.status.busy": "2024-08-02T23:22:59.676885Z", + "iopub.status.idle": "2024-08-02T23:22:59.680782Z", + "shell.execute_reply": "2024-08-02T23:22:59.680214Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.682941Z", + "iopub.status.busy": "2024-08-02T23:22:59.682612Z", + "iopub.status.idle": "2024-08-02T23:22:59.719665Z", + "shell.execute_reply": "2024-08-02T23:22:59.719195Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.721625Z", + "iopub.status.busy": "2024-08-02T23:22:59.721448Z", + "iopub.status.idle": "2024-08-02T23:22:59.762453Z", + "shell.execute_reply": "2024-08-02T23:22:59.761973Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.764348Z", + "iopub.status.busy": "2024-08-02T23:22:59.764176Z", + "iopub.status.idle": "2024-08-02T23:22:59.883238Z", + "shell.execute_reply": "2024-08-02T23:22:59.882497Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.885892Z", + "iopub.status.busy": "2024-08-02T23:22:59.885655Z", + "iopub.status.idle": "2024-08-02T23:22:59.991995Z", + "shell.execute_reply": "2024-08-02T23:22:59.991397Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.994517Z", + "iopub.status.busy": "2024-08-02T23:22:59.994166Z", + "iopub.status.idle": "2024-08-02T23:23:00.206479Z", + "shell.execute_reply": "2024-08-02T23:23:00.205902Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.208774Z", + "iopub.status.busy": "2024-08-02T23:23:00.208393Z", + "iopub.status.idle": "2024-08-02T23:23:00.425932Z", + "shell.execute_reply": "2024-08-02T23:23:00.425360Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.428347Z", + "iopub.status.busy": "2024-08-02T23:23:00.427962Z", + "iopub.status.idle": "2024-08-02T23:23:00.434288Z", + "shell.execute_reply": "2024-08-02T23:23:00.433838Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.436322Z", + "iopub.status.busy": "2024-08-02T23:23:00.435908Z", + "iopub.status.idle": "2024-08-02T23:23:00.651939Z", + "shell.execute_reply": "2024-08-02T23:23:00.651371Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.654276Z", + "iopub.status.busy": "2024-08-02T23:23:00.653821Z", + "iopub.status.idle": "2024-08-02T23:23:01.699320Z", + "shell.execute_reply": "2024-08-02T23:23:01.698766Z" }, "id": "wL3ngCnuLEWd" }, @@ -2381,6 +2381,21 @@ "source": [ "While ensembling different models' label quality scores (`label_quality_scores_best`) will often be superior to getting label quality scores from a single ensemble predictor (`label_quality_scores_better`), both approaches produce significantly better label quality scores than just using the predictions from a single model." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index fd2c70404..f2e838faf 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": "2024-07-30T16:37:32.718320Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:05.094466Z", + "iopub.status.busy": "2024-08-02T23:23:05.094291Z", + "iopub.status.idle": "2024-08-02T23:23:06.519188Z", + "shell.execute_reply": "2024-08-02T23:23:06.518592Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:34.163333Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:06.521921Z", + "iopub.status.busy": "2024-08-02T23:23:06.521450Z", + "iopub.status.idle": "2024-08-02T23:23:06.524559Z", + "shell.execute_reply": "2024-08-02T23:23:06.524087Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.526668Z", + "iopub.status.busy": "2024-08-02T23:23:06.526334Z", + "iopub.status.idle": "2024-08-02T23:23:06.534155Z", + "shell.execute_reply": "2024-08-02T23:23:06.533679Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.536079Z", + "iopub.status.busy": "2024-08-02T23:23:06.535779Z", + "iopub.status.idle": "2024-08-02T23:23:06.582288Z", + "shell.execute_reply": "2024-08-02T23:23:06.581795Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.584696Z", + "iopub.status.busy": "2024-08-02T23:23:06.584134Z", + "iopub.status.idle": "2024-08-02T23:23:06.601445Z", + "shell.execute_reply": "2024-08-02T23:23:06.600877Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.603369Z", + "iopub.status.busy": "2024-08-02T23:23:06.603188Z", + "iopub.status.idle": "2024-08-02T23:23:06.607249Z", + "shell.execute_reply": "2024-08-02T23:23:06.606678Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.609439Z", + "iopub.status.busy": "2024-08-02T23:23:06.608988Z", + "iopub.status.idle": "2024-08-02T23:23:06.625441Z", + "shell.execute_reply": "2024-08-02T23:23:06.624820Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.627733Z", + "iopub.status.busy": "2024-08-02T23:23:06.627381Z", + "iopub.status.idle": "2024-08-02T23:23:06.653432Z", + "shell.execute_reply": "2024-08-02T23:23:06.652979Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.655520Z", + "iopub.status.busy": "2024-08-02T23:23:06.655195Z", + "iopub.status.idle": "2024-08-02T23:23:08.797131Z", + "shell.execute_reply": "2024-08-02T23:23:08.796420Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.800999Z", + "iopub.status.busy": "2024-08-02T23:23:08.799464Z", + "iopub.status.idle": "2024-08-02T23:23:08.807486Z", + "shell.execute_reply": "2024-08-02T23:23:08.807012Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.809557Z", + "iopub.status.busy": "2024-08-02T23:23:08.809274Z", + "iopub.status.idle": "2024-08-02T23:23:08.821755Z", + "shell.execute_reply": "2024-08-02T23:23:08.821200Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.823744Z", + "iopub.status.busy": "2024-08-02T23:23:08.823567Z", + "iopub.status.idle": "2024-08-02T23:23:08.829911Z", + "shell.execute_reply": "2024-08-02T23:23:08.829473Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.831814Z", + "iopub.status.busy": "2024-08-02T23:23:08.831639Z", + "iopub.status.idle": "2024-08-02T23:23:08.834249Z", + "shell.execute_reply": "2024-08-02T23:23:08.833780Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.836353Z", + "iopub.status.busy": "2024-08-02T23:23:08.836022Z", + "iopub.status.idle": "2024-08-02T23:23:08.839391Z", + "shell.execute_reply": "2024-08-02T23:23:08.838879Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.841450Z", + "iopub.status.busy": "2024-08-02T23:23:08.841177Z", + "iopub.status.idle": "2024-08-02T23:23:08.843918Z", + "shell.execute_reply": "2024-08-02T23:23:08.843357Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.845923Z", + "iopub.status.busy": "2024-08-02T23:23:08.845589Z", + "iopub.status.idle": "2024-08-02T23:23:08.849887Z", + "shell.execute_reply": "2024-08-02T23:23:08.849429Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.852036Z", + "iopub.status.busy": "2024-08-02T23:23:08.851589Z", + "iopub.status.idle": "2024-08-02T23:23:08.880616Z", + "shell.execute_reply": "2024-08-02T23:23:08.879983Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.883256Z", + "iopub.status.busy": "2024-08-02T23:23:08.882881Z", + "iopub.status.idle": "2024-08-02T23:23:08.887623Z", + "shell.execute_reply": "2024-08-02T23:23:08.887175Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 85921d220..607a1ea32 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": "2024-07-30T16:37:39.759530Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:11.947982Z", + "iopub.status.busy": "2024-08-02T23:23:11.947507Z", + "iopub.status.idle": "2024-08-02T23:23:13.351095Z", + "shell.execute_reply": "2024-08-02T23:23:13.350543Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:41.228688Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:13.353619Z", + "iopub.status.busy": "2024-08-02T23:23:13.353223Z", + "iopub.status.idle": "2024-08-02T23:23:13.373148Z", + "shell.execute_reply": "2024-08-02T23:23:13.372529Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:13.375624Z", + "iopub.status.busy": "2024-08-02T23:23:13.375180Z", + "iopub.status.idle": "2024-08-02T23:23:13.388068Z", + "shell.execute_reply": "2024-08-02T23:23:13.387585Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:13.390138Z", + "iopub.status.busy": "2024-08-02T23:23:13.389833Z", + "iopub.status.idle": "2024-08-02T23:23:16.059034Z", + "shell.execute_reply": "2024-08-02T23:23:16.058466Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:16.061219Z", + "iopub.status.busy": "2024-08-02T23:23:16.060988Z", + "iopub.status.idle": "2024-08-02T23:23:17.421704Z", + "shell.execute_reply": "2024-08-02T23:23:17.421064Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:17.424281Z", + "iopub.status.busy": "2024-08-02T23:23:17.423964Z", + "iopub.status.idle": "2024-08-02T23:23:17.428263Z", + "shell.execute_reply": "2024-08-02T23:23:17.427671Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:17.430557Z", + "iopub.status.busy": "2024-08-02T23:23:17.430085Z", + "iopub.status.idle": "2024-08-02T23:23:19.529227Z", + "shell.execute_reply": "2024-08-02T23:23:19.528576Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:19.531878Z", + "iopub.status.busy": "2024-08-02T23:23:19.531352Z", + "iopub.status.idle": "2024-08-02T23:23:19.539973Z", + "shell.execute_reply": "2024-08-02T23:23:19.539495Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:19.541992Z", + "iopub.status.busy": "2024-08-02T23:23:19.541709Z", + "iopub.status.idle": "2024-08-02T23:23:22.157011Z", + "shell.execute_reply": "2024-08-02T23:23:22.156381Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.159325Z", + "iopub.status.busy": "2024-08-02T23:23:22.159129Z", + "iopub.status.idle": "2024-08-02T23:23:22.162534Z", + "shell.execute_reply": "2024-08-02T23:23:22.162022Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.164526Z", + "iopub.status.busy": "2024-08-02T23:23:22.164350Z", + "iopub.status.idle": "2024-08-02T23:23:22.167796Z", + "shell.execute_reply": "2024-08-02T23:23:22.167349Z" } }, "outputs": [], @@ -746,16 +746,33 @@ "To see cleanlab applied to a real image tagging dataset, check out our [example](https://github.com/cleanlab/examples) notebook [\"Find Label Errors in Multi-Label Classification Data (CelebA Image Tagging)\"](https://github.com/cleanlab/examples/blob/master/multilabel_classification/image_tagging.ipynb). That example also demonstrates how to use a state-of-the-art Pytorch neural network for multi-label classification with image data." ] }, + { + "cell_type": "markdown", + "id": "f1bd9f83", + "metadata": {}, + "source": [ + "\n", + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": 12, "id": "d1a2c008", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.169928Z", + "iopub.status.busy": "2024-08-02T23:23:22.169595Z", + "iopub.status.idle": "2024-08-02T23:23:22.173262Z", + "shell.execute_reply": "2024-08-02T23:23:22.172813Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index b908d214b..82a016874 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": "2024-07-30T16:37:52.967876Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:24.740098Z", + "iopub.status.busy": "2024-08-02T23:23:24.739916Z", + "iopub.status.idle": "2024-08-02T23:23:26.153388Z", + "shell.execute_reply": "2024-08-02T23:23:26.152727Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:54.426120Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:26.155926Z", + "iopub.status.busy": "2024-08-02T23:23:26.155520Z", + "iopub.status.idle": "2024-08-02T23:23:27.265000Z", + "shell.execute_reply": "2024-08-02T23:23:27.264184Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.267778Z", + "iopub.status.busy": "2024-08-02T23:23:27.267566Z", + "iopub.status.idle": "2024-08-02T23:23:27.271124Z", + "shell.execute_reply": "2024-08-02T23:23:27.270533Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.273453Z", + "iopub.status.busy": "2024-08-02T23:23:27.272991Z", + "iopub.status.idle": "2024-08-02T23:23:27.280260Z", + "shell.execute_reply": "2024-08-02T23:23:27.279681Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.282555Z", + "iopub.status.busy": "2024-08-02T23:23:27.282222Z", + "iopub.status.idle": "2024-08-02T23:23:27.602378Z", + "shell.execute_reply": "2024-08-02T23:23:27.601767Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.605245Z", + "iopub.status.busy": "2024-08-02T23:23:27.605028Z", + "iopub.status.idle": "2024-08-02T23:23:27.610491Z", + "shell.execute_reply": "2024-08-02T23:23:27.610005Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.612710Z", + "iopub.status.busy": "2024-08-02T23:23:27.612291Z", + "iopub.status.idle": "2024-08-02T23:23:27.616428Z", + "shell.execute_reply": "2024-08-02T23:23:27.615974Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.618678Z", + "iopub.status.busy": "2024-08-02T23:23:27.618223Z", + "iopub.status.idle": "2024-08-02T23:23:28.510375Z", + "shell.execute_reply": "2024-08-02T23:23:28.509695Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.512934Z", + "iopub.status.busy": "2024-08-02T23:23:28.512566Z", + "iopub.status.idle": "2024-08-02T23:23:28.729976Z", + "shell.execute_reply": "2024-08-02T23:23:28.729360Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.732357Z", + "iopub.status.busy": "2024-08-02T23:23:28.731916Z", + "iopub.status.idle": "2024-08-02T23:23:28.736334Z", + "shell.execute_reply": "2024-08-02T23:23:28.735894Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.738485Z", + "iopub.status.busy": "2024-08-02T23:23:28.738171Z", + "iopub.status.idle": "2024-08-02T23:23:29.208640Z", + "shell.execute_reply": "2024-08-02T23:23:29.207936Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.211755Z", + "iopub.status.busy": "2024-08-02T23:23:29.211376Z", + "iopub.status.idle": "2024-08-02T23:23:29.548019Z", + "shell.execute_reply": "2024-08-02T23:23:29.547410Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.550940Z", + "iopub.status.busy": "2024-08-02T23:23:29.550736Z", + "iopub.status.idle": "2024-08-02T23:23:29.920855Z", + "shell.execute_reply": "2024-08-02T23:23:29.920218Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.924419Z", + "iopub.status.busy": "2024-08-02T23:23:29.923997Z", + "iopub.status.idle": "2024-08-02T23:23:30.376104Z", + "shell.execute_reply": "2024-08-02T23:23:30.375480Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:30.380802Z", + "iopub.status.busy": "2024-08-02T23:23:30.380407Z", + "iopub.status.idle": "2024-08-02T23:23:30.837108Z", + "shell.execute_reply": "2024-08-02T23:23:30.836487Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:30.840640Z", + "iopub.status.busy": "2024-08-02T23:23:30.840263Z", + "iopub.status.idle": "2024-08-02T23:23:31.057636Z", + "shell.execute_reply": "2024-08-02T23:23:31.057057Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.059925Z", + "iopub.status.busy": "2024-08-02T23:23:31.059721Z", + "iopub.status.idle": "2024-08-02T23:23:31.259783Z", + "shell.execute_reply": "2024-08-02T23:23:31.259243Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.262327Z", + "iopub.status.busy": "2024-08-02T23:23:31.262133Z", + "iopub.status.idle": "2024-08-02T23:23:31.265004Z", + "shell.execute_reply": "2024-08-02T23:23:31.264548Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.267241Z", + "iopub.status.busy": "2024-08-02T23:23:31.266775Z", + "iopub.status.idle": "2024-08-02T23:23:32.193701Z", + "shell.execute_reply": "2024-08-02T23:23:32.193146Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.196208Z", + "iopub.status.busy": "2024-08-02T23:23:32.196016Z", + "iopub.status.idle": "2024-08-02T23:23:32.322609Z", + "shell.execute_reply": "2024-08-02T23:23:32.322075Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.324841Z", + "iopub.status.busy": "2024-08-02T23:23:32.324492Z", + "iopub.status.idle": "2024-08-02T23:23:32.514569Z", + "shell.execute_reply": "2024-08-02T23:23:32.514058Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.516894Z", + "iopub.status.busy": "2024-08-02T23:23:32.516525Z", + "iopub.status.idle": "2024-08-02T23:23:33.194488Z", + "shell.execute_reply": "2024-08-02T23:23:33.193856Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:33.196816Z", + "iopub.status.busy": "2024-08-02T23:23:33.196477Z", + "iopub.status.idle": "2024-08-02T23:23:33.200251Z", + "shell.execute_reply": "2024-08-02T23:23:33.199677Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 3df92007c..3ad8a9b33 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": "2024-07-30T16:38:03.978289Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:35.415873Z", + "iopub.status.busy": "2024-08-02T23:23:35.415697Z", + "iopub.status.idle": "2024-08-02T23:23:38.626597Z", + "shell.execute_reply": "2024-08-02T23:23:38.626029Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:38:07.299136Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:38.629407Z", + "iopub.status.busy": "2024-08-02T23:23:38.628808Z", + "iopub.status.idle": "2024-08-02T23:23:38.648318Z", + "shell.execute_reply": "2024-08-02T23:23:38.647845Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:38.650609Z", + "iopub.status.busy": "2024-08-02T23:23:38.650204Z", + "iopub.status.idle": "2024-08-02T23:23:38.654348Z", + "shell.execute_reply": "2024-08-02T23:23:38.653801Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:38.656499Z", + "iopub.status.busy": "2024-08-02T23:23:38.656188Z", + "iopub.status.idle": "2024-08-02T23:23:43.176479Z", + "shell.execute_reply": "2024-08-02T23:23:43.175945Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8226376.49it/s]" + " 1%| | 1703936/170498071 [00:00<00:10, 16570351.12it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10846208/170498071 [00:00<00:02, 59307309.31it/s]" + " 8%|▊ | 13139968/170498071 [00:00<00:02, 73382685.55it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22511616/170498071 [00:00<00:01, 84811001.30it/s]" + " 14%|█▎ | 23134208/170498071 [00:00<00:01, 85416938.17it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 34209792/170498071 [00:00<00:01, 97255735.33it/s]" + " 19%|█▉ | 33193984/170498071 [00:00<00:01, 91344444.59it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45875200/170498071 [00:00<00:01, 104168464.41it/s]" + " 26%|██▌ | 44728320/170498071 [00:00<00:01, 99919094.88it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 57573376/170498071 [00:00<00:01, 108391005.55it/s]" + " 32%|███▏ | 54788096/170498071 [00:00<00:01, 100132338.36it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 69271552/170498071 [00:00<00:00, 111144409.19it/s]" + " 38%|███▊ | 64847872/170498071 [00:00<00:01, 100262039.06it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 80936960/170498071 [00:00<00:00, 112866681.19it/s]" + " 45%|████▍ | 76316672/170498071 [00:00<00:00, 104840140.37it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 92635136/170498071 [00:00<00:00, 114109378.79it/s]" + " 51%|█████ | 86835200/170498071 [00:00<00:00, 104704399.11it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 104300544/170498071 [00:01<00:00, 114843936.89it/s]" + " 58%|█████▊ | 98369536/170498071 [00:01<00:00, 107943288.17it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 115933184/170498071 [00:01<00:00, 115260750.98it/s]" + " 65%|██████▍ | 110100480/170498071 [00:01<00:00, 110768998.54it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 127631360/170498071 [00:01<00:00, 115738038.46it/s]" + " 71%|███████▏ | 121634816/170498071 [00:01<00:00, 112151957.27it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 139296768/170498071 [00:01<00:00, 115934009.59it/s]" + " 78%|███████▊ | 133169152/170498071 [00:01<00:00, 113008402.64it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 150994944/170498071 [00:01<00:00, 116207199.22it/s]" + " 85%|████████▍ | 144703488/170498071 [00:01<00:00, 113634335.19it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162627584/170498071 [00:01<00:00, 116186659.71it/s]" + " 92%|█████████▏| 156172288/170498071 [00:01<00:00, 113937851.78it/s]" ] }, { @@ -372,7 +372,15 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 107805868.41it/s]" + " 98%|█████████▊| 167706624/170498071 [00:01<00:00, 114298938.25it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:01<00:00, 104612399.87it/s]" ] }, { @@ -490,10 +498,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:43.178843Z", + "iopub.status.busy": "2024-08-02T23:23:43.178435Z", + "iopub.status.idle": "2024-08-02T23:23:43.183358Z", + "shell.execute_reply": "2024-08-02T23:23:43.182774Z" }, "nbsphinx": "hidden" }, @@ -544,10 +552,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:43.185454Z", + "iopub.status.busy": "2024-08-02T23:23:43.185025Z", + "iopub.status.idle": "2024-08-02T23:23:43.734027Z", + "shell.execute_reply": "2024-08-02T23:23:43.733470Z" } }, "outputs": [ @@ -580,10 +588,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:43.736253Z", + "iopub.status.busy": "2024-08-02T23:23:43.735930Z", + "iopub.status.idle": "2024-08-02T23:23:44.250070Z", + "shell.execute_reply": "2024-08-02T23:23:44.249455Z" } }, "outputs": [ @@ -621,10 +629,10 @@ "id": "1cf25354", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:44.252258Z", + "iopub.status.busy": "2024-08-02T23:23:44.252058Z", + "iopub.status.idle": "2024-08-02T23:23:44.255571Z", + "shell.execute_reply": "2024-08-02T23:23:44.255129Z" } }, "outputs": [], @@ -647,17 +655,17 @@ "id": "85a58d41", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:44.257605Z", + "iopub.status.busy": "2024-08-02T23:23:44.257264Z", + "iopub.status.idle": "2024-08-02T23:23:56.709113Z", + "shell.execute_reply": "2024-08-02T23:23:56.708472Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23a512869c5e4f05a2356b8f464b1bcc", + "model_id": "d7f8f03577d54c03b0ecc33be697a44d", "version_major": 2, "version_minor": 0 }, @@ -716,10 +724,10 @@ "id": "feb0f519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:56.711676Z", + "iopub.status.busy": "2024-08-02T23:23:56.711249Z", + "iopub.status.idle": "2024-08-02T23:23:58.847600Z", + "shell.execute_reply": "2024-08-02T23:23:58.846962Z" } }, "outputs": [ @@ -763,10 +771,10 @@ "id": "089d5860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:58.850292Z", + "iopub.status.busy": "2024-08-02T23:23:58.849815Z", + "iopub.status.idle": "2024-08-02T23:23:59.089528Z", + "shell.execute_reply": "2024-08-02T23:23:59.088872Z" } }, "outputs": [ @@ -802,10 +810,10 @@ "id": "78b1951c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:59.092082Z", + "iopub.status.busy": "2024-08-02T23:23:59.091879Z", + "iopub.status.idle": "2024-08-02T23:23:59.733692Z", + "shell.execute_reply": "2024-08-02T23:23:59.733029Z" } }, "outputs": [ @@ -855,10 +863,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:59.736298Z", + "iopub.status.busy": "2024-08-02T23:23:59.736091Z", + "iopub.status.idle": "2024-08-02T23:24:00.029276Z", + "shell.execute_reply": "2024-08-02T23:24:00.028640Z" } }, "outputs": [ @@ -906,10 +914,10 @@ "id": "616769f8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:00.031475Z", + "iopub.status.busy": "2024-08-02T23:24:00.031284Z", + "iopub.status.idle": "2024-08-02T23:24:00.284506Z", + "shell.execute_reply": "2024-08-02T23:24:00.283911Z" } }, "outputs": [ @@ -965,10 +973,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:00.287194Z", + "iopub.status.busy": "2024-08-02T23:24:00.286847Z", + "iopub.status.idle": "2024-08-02T23:24:00.366770Z", + "shell.execute_reply": "2024-08-02T23:24:00.366293Z" } }, "outputs": [], @@ -989,10 +997,10 @@ "id": "89f9db72", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:00.369143Z", + "iopub.status.busy": "2024-08-02T23:24:00.368939Z", + "iopub.status.idle": "2024-08-02T23:24:10.565474Z", + "shell.execute_reply": "2024-08-02T23:24:10.564795Z" } }, "outputs": [ @@ -1029,10 +1037,10 @@ "id": "874c885a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:10.568135Z", + "iopub.status.busy": "2024-08-02T23:24:10.567718Z", + "iopub.status.idle": "2024-08-02T23:24:12.819254Z", + "shell.execute_reply": "2024-08-02T23:24:12.818651Z" } }, "outputs": [ @@ -1063,10 +1071,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:12.822039Z", + "iopub.status.busy": "2024-08-02T23:24:12.821413Z", + "iopub.status.idle": "2024-08-02T23:24:13.027440Z", + "shell.execute_reply": "2024-08-02T23:24:13.026901Z" } }, "outputs": [], @@ -1080,10 +1088,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:13.029812Z", + "iopub.status.busy": "2024-08-02T23:24:13.029518Z", + "iopub.status.idle": "2024-08-02T23:24:13.032865Z", + "shell.execute_reply": "2024-08-02T23:24:13.032400Z" } }, "outputs": [], @@ -1099,16 +1107,32 @@ "Detecting outliers based on feature embeddings can be done for arbitrary unlabeled datasets, but requires a meaningful numerical representation of the data. Detecting outliers based on predicted probabilities applies mainly for labeled classification datasets, but can be done with any effective classifier. The effectiveness of the latter approach depends on: how much auxiliary information captured in the feature values is lost in the predicted probabilities (determined by the particular set of labels in the classification task), the accuracy of our classifier, and how properly its predictions reflect epistemic uncertainty. Read more about it [here](https://pub.towardsai.net/a-simple-adjustment-improves-out-of-distribution-detection-for-any-classifier-5e96bbb2d627)." ] }, + { + "cell_type": "markdown", + "id": "03a5c870", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": 20, "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:38:42.508323Z", - "iopub.status.busy": "2024-07-30T16:38:42.508151Z", - "iopub.status.idle": "2024-07-30T16:38:42.517729Z", - "shell.execute_reply": "2024-07-30T16:38:42.517288Z" + "iopub.execute_input": "2024-08-02T23:24:13.034934Z", + "iopub.status.busy": "2024-08-02T23:24:13.034594Z", + "iopub.status.idle": "2024-08-02T23:24:13.043213Z", + "shell.execute_reply": "2024-08-02T23:24:13.042768Z" }, "nbsphinx": "hidden" }, @@ -1153,31 +1177,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "23a512869c5e4f05a2356b8f464b1bcc": { + "310b96bf6543469cabf0f55665ff25f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_58618ff0d8be415dbaa56326b7b1db8c", - "IPY_MODEL_633d743f49c44f93be1bfe7c09cb76e5", - "IPY_MODEL_313c673f5ae140548d908be43be34294" - ], - "layout": "IPY_MODEL_f1fa8803defc478f8f1a9688f96d5a79", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "3094116b83c34a98b8ed5ce27da55168": { + "330e053db6fe416e9722e5a98ab8d4ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1230,7 +1248,7 @@ "width": null } }, - "313c673f5ae140548d908be43be34294": { + "3408c76adf1949bfa8f227a1dca82da4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1245,38 +1263,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_afe5f4fb71e54d6b97c4b44ecea40c54", + "layout": "IPY_MODEL_330e053db6fe416e9722e5a98ab8d4ab", "placeholder": "​", - "style": "IPY_MODEL_59856c986feb4d3abc586fed584de5c0", + "style": "IPY_MODEL_9a1cc697a447421590eefe450375da25", "tabbable": null, "tooltip": null, - "value": " 102M/102M [00:00<00:00, 274MB/s]" + "value": "model.safetensors: 100%" } }, - "58618ff0d8be415dbaa56326b7b1db8c": { + "68e47a1386df476da98d7f630d7b8d6c": { "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_3094116b83c34a98b8ed5ce27da55168", - "placeholder": "​", - "style": "IPY_MODEL_7105d5b497e34139b8cba14426fdd044", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "59856c986feb4d3abc586fed584de5c0": { + "9a1cc697a447421590eefe450375da25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1294,7 +1305,7 @@ "text_color": null } }, - "633d743f49c44f93be1bfe7c09cb76e5": { + "9e08204f4c2845e1821cce00ebace48b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1310,17 +1321,40 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6eb5b857715449aaa4e92a9a9560a833", + "layout": "IPY_MODEL_e30e1ae7872c46d180e55263ed029b6a", "max": 102469840.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_dae8ad4cd0df4444bf5ff766e8012dc4", + "style": "IPY_MODEL_68e47a1386df476da98d7f630d7b8d6c", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "6eb5b857715449aaa4e92a9a9560a833": { + "a17403bdb20c4c13949ca9635c43e46e": { + "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_b6fd529321ad458292f2208a4d6fc71d", + "placeholder": "​", + "style": "IPY_MODEL_310b96bf6543469cabf0f55665ff25f5", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 297MB/s]" + } + }, + "a828ae2c754b4b3ba7b44cc7fe06cf4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1373,25 +1407,7 @@ "width": null } }, - "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": { + "b6fd529321ad458292f2208a4d6fc71d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1444,23 +1460,31 @@ "width": null } }, - "dae8ad4cd0df4444bf5ff766e8012dc4": { + "d7f8f03577d54c03b0ecc33be697a44d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3408c76adf1949bfa8f227a1dca82da4", + "IPY_MODEL_9e08204f4c2845e1821cce00ebace48b", + "IPY_MODEL_a17403bdb20c4c13949ca9635c43e46e" + ], + "layout": "IPY_MODEL_a828ae2c754b4b3ba7b44cc7fe06cf4f", + "tabbable": null, + "tooltip": null } }, - "f1fa8803defc478f8f1a9688f96d5a79": { + "e30e1ae7872c46d180e55263ed029b6a": { "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 7c5c07d39..099430c03 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": "2024-07-30T16:38:46.925237Z", - "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" + "iopub.execute_input": "2024-08-02T23:24:17.404200Z", + "iopub.status.busy": "2024-08-02T23:24:17.404031Z", + "iopub.status.idle": "2024-08-02T23:24:18.819393Z", + "shell.execute_reply": "2024-08-02T23:24:18.818751Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:38:48.348157Z", - "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" + "iopub.execute_input": "2024-08-02T23:24:18.822083Z", + "iopub.status.busy": "2024-08-02T23:24:18.821615Z", + "iopub.status.idle": "2024-08-02T23:24:18.839962Z", + "shell.execute_reply": "2024-08-02T23:24:18.839405Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.842424Z", + "iopub.status.busy": "2024-08-02T23:24:18.842013Z", + "iopub.status.idle": "2024-08-02T23:24:18.844900Z", + "shell.execute_reply": "2024-08-02T23:24:18.844448Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.846982Z", + "iopub.status.busy": "2024-08-02T23:24:18.846651Z", + "iopub.status.idle": "2024-08-02T23:24:18.905648Z", + "shell.execute_reply": "2024-08-02T23:24:18.905180Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.907939Z", + "iopub.status.busy": "2024-08-02T23:24:18.907494Z", + "iopub.status.idle": "2024-08-02T23:24:18.911937Z", + "shell.execute_reply": "2024-08-02T23:24:18.911430Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.913935Z", + "iopub.status.busy": "2024-08-02T23:24:18.913612Z", + "iopub.status.idle": "2024-08-02T23:24:19.156090Z", + "shell.execute_reply": "2024-08-02T23:24:19.155477Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.158339Z", + "iopub.status.busy": "2024-08-02T23:24:19.158149Z", + "iopub.status.idle": "2024-08-02T23:24:19.162504Z", + "shell.execute_reply": "2024-08-02T23:24:19.162045Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.164445Z", + "iopub.status.busy": "2024-08-02T23:24:19.164256Z", + "iopub.status.idle": "2024-08-02T23:24:19.170243Z", + "shell.execute_reply": "2024-08-02T23:24:19.169792Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.172217Z", + "iopub.status.busy": "2024-08-02T23:24:19.172043Z", + "iopub.status.idle": "2024-08-02T23:24:19.174763Z", + "shell.execute_reply": "2024-08-02T23:24:19.174300Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.176599Z", + "iopub.status.busy": "2024-08-02T23:24:19.176431Z", + "iopub.status.idle": "2024-08-02T23:24:28.206119Z", + "shell.execute_reply": "2024-08-02T23:24:28.205469Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.209000Z", + "iopub.status.busy": "2024-08-02T23:24:28.208362Z", + "iopub.status.idle": "2024-08-02T23:24:28.215857Z", + "shell.execute_reply": "2024-08-02T23:24:28.215396Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.217833Z", + "iopub.status.busy": "2024-08-02T23:24:28.217557Z", + "iopub.status.idle": "2024-08-02T23:24:28.221146Z", + "shell.execute_reply": "2024-08-02T23:24:28.220682Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.223157Z", + "iopub.status.busy": "2024-08-02T23:24:28.222839Z", + "iopub.status.idle": "2024-08-02T23:24:28.226202Z", + "shell.execute_reply": "2024-08-02T23:24:28.225642Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.228136Z", + "iopub.status.busy": "2024-08-02T23:24:28.227911Z", + "iopub.status.idle": "2024-08-02T23:24:28.230792Z", + "shell.execute_reply": "2024-08-02T23:24:28.230325Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.232772Z", + "iopub.status.busy": "2024-08-02T23:24:28.232437Z", + "iopub.status.idle": "2024-08-02T23:24:28.240280Z", + "shell.execute_reply": "2024-08-02T23:24:28.239831Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.242339Z", + "iopub.status.busy": "2024-08-02T23:24:28.241943Z", + "iopub.status.idle": "2024-08-02T23:24:28.244706Z", + "shell.execute_reply": "2024-08-02T23:24:28.244158Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.246759Z", + "iopub.status.busy": "2024-08-02T23:24:28.246451Z", + "iopub.status.idle": "2024-08-02T23:24:28.374401Z", + "shell.execute_reply": "2024-08-02T23:24:28.373775Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.376853Z", + "iopub.status.busy": "2024-08-02T23:24:28.376504Z", + "iopub.status.idle": "2024-08-02T23:24:28.483607Z", + "shell.execute_reply": "2024-08-02T23:24:28.483029Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.486071Z", + "iopub.status.busy": "2024-08-02T23:24:28.485733Z", + "iopub.status.idle": "2024-08-02T23:24:28.990646Z", + "shell.execute_reply": "2024-08-02T23:24:28.990027Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.993111Z", + "iopub.status.busy": "2024-08-02T23:24:28.992883Z", + "iopub.status.idle": "2024-08-02T23:24:29.089656Z", + "shell.execute_reply": "2024-08-02T23:24:29.088990Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:29.093755Z", + "iopub.status.busy": "2024-08-02T23:24:29.093401Z", + "iopub.status.idle": "2024-08-02T23:24:29.103300Z", + "shell.execute_reply": "2024-08-02T23:24:29.102788Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:29.105807Z", + "iopub.status.busy": "2024-08-02T23:24:29.105420Z", + "iopub.status.idle": "2024-08-02T23:24:29.108643Z", + "shell.execute_reply": "2024-08-02T23:24:29.108081Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:29.110806Z", + "iopub.status.busy": "2024-08-02T23:24:29.110492Z", + "iopub.status.idle": "2024-08-02T23:24:34.719376Z", + "shell.execute_reply": "2024-08-02T23:24:34.718784Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:34.721958Z", + "iopub.status.busy": "2024-08-02T23:24:34.721544Z", + "iopub.status.idle": "2024-08-02T23:24:34.730054Z", + "shell.execute_reply": "2024-08-02T23:24:34.729479Z" } }, "outputs": [ @@ -1370,16 +1370,32 @@ "**Summary:** To detect many types of issues in your regression dataset, we recommend using `Datalab` with provided `features` plus the best regression model you know for your data. If your goal is to train a robust regression model with noisy data rather than detect data/label issues, then use `CleanLearning`. Alternatively, if you don't have a sklearn-compatible regression model or already have pre-computed predictions from the model you'd like to rely on, you can pass these predictions into `Datalab` directly to find issues based on them instead of providing a regression model." ] }, + { + "cell_type": "markdown", + "id": "8a7a5387", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": 25, "id": "95531cda", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:34.732147Z", + "iopub.status.busy": "2024-08-02T23:24:34.731881Z", + "iopub.status.idle": "2024-08-02T23:24:34.800226Z", + "shell.execute_reply": "2024-08-02T23:24:34.799721Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index ddf315699..96da6f24e 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": "2024-07-30T16:39:08.949260Z", - "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" + "iopub.execute_input": "2024-08-02T23:24:38.145016Z", + "iopub.status.busy": "2024-08-02T23:24:38.144603Z", + "iopub.status.idle": "2024-08-02T23:24:40.117038Z", + "shell.execute_reply": "2024-08-02T23:24:40.116342Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:40.119515Z", + "iopub.status.busy": "2024-08-02T23:24:40.119338Z", + "iopub.status.idle": "2024-08-02T23:25:35.352783Z", + "shell.execute_reply": "2024-08-02T23:25:35.352105Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:35.355348Z", + "iopub.status.busy": "2024-08-02T23:25:35.354967Z", + "iopub.status.idle": "2024-08-02T23:25:36.767542Z", + "shell.execute_reply": "2024-08-02T23:25:36.766894Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:40:32.525566Z", - "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" + "iopub.execute_input": "2024-08-02T23:25:36.770200Z", + "iopub.status.busy": "2024-08-02T23:25:36.769889Z", + "iopub.status.idle": "2024-08-02T23:25:36.773141Z", + "shell.execute_reply": "2024-08-02T23:25:36.772658Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.775177Z", + "iopub.status.busy": "2024-08-02T23:25:36.774994Z", + "iopub.status.idle": "2024-08-02T23:25:36.779004Z", + "shell.execute_reply": "2024-08-02T23:25:36.778469Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.781185Z", + "iopub.status.busy": "2024-08-02T23:25:36.780850Z", + "iopub.status.idle": "2024-08-02T23:25:36.784519Z", + "shell.execute_reply": "2024-08-02T23:25:36.783988Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.786657Z", + "iopub.status.busy": "2024-08-02T23:25:36.786198Z", + "iopub.status.idle": "2024-08-02T23:25:36.789074Z", + "shell.execute_reply": "2024-08-02T23:25:36.788604Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.790984Z", + "iopub.status.busy": "2024-08-02T23:25:36.790806Z", + "iopub.status.idle": "2024-08-02T23:26:14.726533Z", + "shell.execute_reply": "2024-08-02T23:26:14.725867Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c621015e28040a280bd1034a80975dc", + "model_id": "a46521e429c5421aaf0cd8ac6b2244a7", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f880a204f2942c89dcc00391ef9c5e7", + "model_id": "bbb9477350a14f3eb3d95cb40f0405ee", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:26:14.729149Z", + "iopub.status.busy": "2024-08-02T23:26:14.728917Z", + "iopub.status.idle": "2024-08-02T23:26:15.176544Z", + "shell.execute_reply": "2024-08-02T23:26:15.175972Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:26:15.179034Z", + "iopub.status.busy": "2024-08-02T23:26:15.178572Z", + "iopub.status.idle": "2024-08-02T23:26:18.201528Z", + "shell.execute_reply": "2024-08-02T23:26:18.200962Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:26:18.203662Z", + "iopub.status.busy": "2024-08-02T23:26:18.203342Z", + "iopub.status.idle": "2024-08-02T23:26:51.154916Z", + "shell.execute_reply": "2024-08-02T23:26:51.154351Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 6a696df89..7588e441c 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:21.097374Z", + "iopub.status.busy": "2024-08-02T23:27:21.097213Z", + "iopub.status.idle": "2024-08-02T23:27:22.032475Z", + "shell.execute_reply": "2024-08-02T23:27:22.031763Z" } }, "outputs": [ @@ -86,22 +86,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-30 16:42:16-- https://data.deepai.org/conll2003.zip\r\n", - "Resolving data.deepai.org (data.deepai.org)... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" + "--2024-08-02 23:27:21-- https://data.deepai.org/conll2003.zip\r\n", + "Resolving data.deepai.org (data.deepai.org)... 185.93.1.246, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -124,7 +118,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-30 16:42:16 (6.62 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-08-02 23:27:21 (6.78 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +138,9 @@ "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", + "--2024-08-02 23:27:21-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.226.169, 54.231.137.105, 54.231.202.161, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.226.169|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -169,7 +163,7 @@ "\r", "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-30 16:42:17 (125 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-08-02 23:27:21 (135 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +180,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:22.035122Z", + "iopub.status.busy": "2024-08-02T23:27:22.034920Z", + "iopub.status.idle": "2024-08-02T23:27:23.618049Z", + "shell.execute_reply": "2024-08-02T23:27:23.617396Z" }, "nbsphinx": "hidden" }, @@ -200,7 +194,7 @@ "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +220,10 @@ "id": "a1349304", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.620722Z", + "iopub.status.busy": "2024-08-02T23:27:23.620416Z", + "iopub.status.idle": "2024-08-02T23:27:23.624004Z", + "shell.execute_reply": "2024-08-02T23:27:23.623532Z" } }, "outputs": [], @@ -279,10 +273,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.626173Z", + "iopub.status.busy": "2024-08-02T23:27:23.625726Z", + "iopub.status.idle": "2024-08-02T23:27:23.628865Z", + "shell.execute_reply": "2024-08-02T23:27:23.628333Z" }, "nbsphinx": "hidden" }, @@ -300,10 +294,10 @@ "id": "519cb80c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.631121Z", + "iopub.status.busy": "2024-08-02T23:27:23.630720Z", + "iopub.status.idle": "2024-08-02T23:27:32.801377Z", + "shell.execute_reply": "2024-08-02T23:27:32.800697Z" } }, "outputs": [], @@ -377,10 +371,10 @@ "id": "202f1526", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:32.803844Z", + "iopub.status.busy": "2024-08-02T23:27:32.803644Z", + "iopub.status.idle": "2024-08-02T23:27:32.809441Z", + "shell.execute_reply": "2024-08-02T23:27:32.808867Z" }, "nbsphinx": "hidden" }, @@ -420,10 +414,10 @@ "id": "a4381f03", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:32.811534Z", + "iopub.status.busy": "2024-08-02T23:27:32.811201Z", + "iopub.status.idle": "2024-08-02T23:27:33.180182Z", + "shell.execute_reply": "2024-08-02T23:27:33.179511Z" } }, "outputs": [], @@ -460,10 +454,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:33.182638Z", + "iopub.status.busy": "2024-08-02T23:27:33.182437Z", + "iopub.status.idle": "2024-08-02T23:27:33.187039Z", + "shell.execute_reply": "2024-08-02T23:27:33.186557Z" } }, "outputs": [ @@ -535,10 +529,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:33.189240Z", + "iopub.status.busy": "2024-08-02T23:27:33.188864Z", + "iopub.status.idle": "2024-08-02T23:27:35.925225Z", + "shell.execute_reply": "2024-08-02T23:27:35.924446Z" } }, "outputs": [], @@ -560,10 +554,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.928379Z", + "iopub.status.busy": "2024-08-02T23:27:35.927716Z", + "iopub.status.idle": "2024-08-02T23:27:35.931918Z", + "shell.execute_reply": "2024-08-02T23:27:35.931392Z" } }, "outputs": [ @@ -599,10 +593,10 @@ "id": "e13de188", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.933833Z", + "iopub.status.busy": "2024-08-02T23:27:35.933656Z", + "iopub.status.idle": "2024-08-02T23:27:35.939473Z", + "shell.execute_reply": "2024-08-02T23:27:35.939001Z" } }, "outputs": [ @@ -780,10 +774,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.941428Z", + "iopub.status.busy": "2024-08-02T23:27:35.941253Z", + "iopub.status.idle": "2024-08-02T23:27:35.967430Z", + "shell.execute_reply": "2024-08-02T23:27:35.966843Z" } }, "outputs": [ @@ -885,10 +879,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.969458Z", + "iopub.status.busy": "2024-08-02T23:27:35.969278Z", + "iopub.status.idle": "2024-08-02T23:27:35.973349Z", + "shell.execute_reply": "2024-08-02T23:27:35.972803Z" } }, "outputs": [ @@ -962,10 +956,10 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.975297Z", + "iopub.status.busy": "2024-08-02T23:27:35.975119Z", + "iopub.status.idle": "2024-08-02T23:27:37.460630Z", + "shell.execute_reply": "2024-08-02T23:27:37.460082Z" } }, "outputs": [ @@ -1137,10 +1131,10 @@ "id": "a18795eb", "metadata": { "execution": { - "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" + "iopub.execute_input": 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" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -458,6 +458,21 @@ "We can see that the test set accuracy slightly improved as a result of the data cleaning. Note that this will not always be the case, especially when we evaluate on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb index fcc9d1478..9a288faaa 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -535,6 +535,21 @@ "We can see that the test set accuracy slightly improved as a result of the data cleaning. Note that this will not always be the case, especially when we are evaluating on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb index 39ad45277..4a9fccb8a 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 aab33d6e3..d8e45a8ea 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 64e8b75c3..a4dcaa31e 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -808,6 +808,21 @@ "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index 9132d3724..199d71b39 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -478,9 +478,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" ] }, { diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb index 84937486a..b60b202be 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -545,9 +545,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" ] }, { diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index 1d2cfdc12..c0cf2cafe 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -282,6 +282,21 @@ " # run 1 line of code to evaluate the health of your dataset\n", " _ = cleanlab.dataset.health_summary(labels, pred_probs, class_names=class_names)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb index 438ca9320..5e5d96fb7 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 feb419f3f..276b2d857 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -1125,6 +1125,21 @@ "source": [ "While ensembling different models' label quality scores (`label_quality_scores_best`) will often be superior to getting label quality scores from a single ensemble predictor (`label_quality_scores_better`), both approaches produce significantly better label quality scores than just using the predictions from a single model." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] } ], "metadata": { diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb index cda239e55..d279ee9b0 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 bb30f1dc1..a352c03e2 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -601,6 +601,23 @@ "To see cleanlab applied to a real image tagging dataset, check out our [example](https://github.com/cleanlab/examples) notebook [\"Find Label Errors in Multi-Label Classification Data (CelebA Image Tagging)\"](https://github.com/cleanlab/examples/blob/master/multilabel_classification/image_tagging.ipynb). That example also demonstrates how to use a state-of-the-art Pytorch neural network for multi-label classification with image data." ] }, + { + "cell_type": "markdown", + "id": "f1bd9f83", + "metadata": {}, + "source": [ + "\n", + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb index bcff2400f..2e48dac87 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 a2fa23c15..bf2f1265b 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -667,6 +667,22 @@ "Detecting outliers based on feature embeddings can be done for arbitrary unlabeled datasets, but requires a meaningful numerical representation of the data. Detecting outliers based on predicted probabilities applies mainly for labeled classification datasets, but can be done with any effective classifier. The effectiveness of the latter approach depends on: how much auxiliary information captured in the feature values is lost in the predicted probabilities (determined by the particular set of labels in the classification task), the accuracy of our classifier, and how properly its predictions reflect epistemic uncertainty. Read more about it [here](https://pub.towardsai.net/a-simple-adjustment-improves-out-of-distribution-detection-for-any-classifier-5e96bbb2d627)." ] }, + { + "cell_type": "markdown", + "id": "03a5c870", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/master/_sources/tutorials/pred_probs_cross_val.rst b/master/_sources/tutorials/pred_probs_cross_val.rst index f5e7f9573..278befc59 100644 --- a/master/_sources/tutorials/pred_probs_cross_val.rst +++ b/master/_sources/tutorials/pred_probs_cross_val.rst @@ -58,3 +58,13 @@ Here is pseudocode for manually implementing K-fold cross-validation with K = 3: out_of_sample_pred_probs_for_B, out_of_sample_pred_probs_for_C, ]) + + +Spending too much time on data quality? +---- +This notebook demonstrates a toy ML model to quickly produce pred_probs. Because the performance of Cleanlab critically depends on the quality of your ML model, you should try to use a better ML model than the one demonstrated here. +If you are unsure how to produce a better ML model for your data, or want a platform to actually fix your issues automatically, use Cleanlab Studio which will automatically produce the best model for your dataset via cutting-edge AutoML with Foundation models. +`Try Cleanlab Studio for free! `_ + +.. image:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/ml-with-cleanlab-studio.png + :alt: The modern AI pipeline automated with Cleanlab Studio \ No newline at end of file diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb index 29bd5bde2..104969f7a 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -701,6 +701,22 @@ "**Summary:** To detect many types of issues in your regression dataset, we recommend using `Datalab` with provided `features` plus the best regression model you know for your data. If your goal is to train a robust regression model with noisy data rather than detect data/label issues, then use `CleanLearning`. Alternatively, if you don't have a sklearn-compatible regression model or already have pre-computed predictions from the model you'd like to rely on, you can pass these predictions into `Datalab` directly to find issues based on them instead of providing a regression model." ] }, + { + "cell_type": "markdown", + "id": "8a7a5387", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

\n", + " \"The\n", + "

" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb index 64d2a9d9e..3bc1dd5cf 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 16a5dc3ee..9e499cdb0 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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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 5d4ef7f2df33692e369facb40470ff0096a7c288..a67bbb38d12c0e56cbe03943162cd728006f67ec 100644 GIT binary patch literal 38671 zcmV*8Kykk#AX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkoY-M3? zY++&wBOq2~a&u{KZaN?eBOp|0Wgv28ZDDC{WMy(7Z)PBLXlZjGW@&6?AZc?TV{dJ6 za%FRKWn>_Ab7^j8AbMcHB6UE_&Zj!96}_wLPn-YRh(2jn|sf%OlAy zt(GN^WtVsF8#Xf&sbq;sW)Y-p_0_z?x!QN<>O9DKlzEba06-FCf+T*K>HX7XB@yvO 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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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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[86, "Spending-too-much-time-on-data-quality?"], [87, "Spending-too-much-time-on-data-quality?"], [90, "Spending-too-much-time-on-data-quality?"], [93, "Spending-too-much-time-on-data-quality?"], [94, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [99, "Spending-too-much-time-on-data-quality?"], [102, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [105, "spending-too-much-time-on-data-quality"], [106, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "4. (Optional) Compare with a Dataset Without Spurious Correlations": [[95, "4.-(Optional)-Compare-with-a-Dataset-Without-Spurious-Correlations"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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(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.html b/master/tutorials/clean_learning/tabular.html index 8219bec1d..27d691acd 100644 --- a/master/tutorials/clean_learning/tabular.html +++ b/master/tutorials/clean_learning/tabular.html @@ -1081,6 +1081,12 @@

5. Train a more robust model from noisy labels3. Select a classification model and compute out-of-sample predicted probabilities
  • 4. Use cleanlab to find label issues
  • 5. Train a more robust model from noisy labels
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index 1b1f01adb..98abd1f6f 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": "2024-07-30T16:31:34.527671Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:23.433118Z", + "iopub.status.busy": "2024-08-02T23:17:23.432923Z", + "iopub.status.idle": "2024-08-02T23:17:24.941638Z", + "shell.execute_reply": "2024-08-02T23:17:24.941075Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:31:36.143586Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:24.944158Z", + "iopub.status.busy": "2024-08-02T23:17:24.943875Z", + "iopub.status.idle": "2024-08-02T23:17:24.963528Z", + "shell.execute_reply": "2024-08-02T23:17:24.962963Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:24.966010Z", + "iopub.status.busy": "2024-08-02T23:17:24.965604Z", + "iopub.status.idle": "2024-08-02T23:17:25.079442Z", + "shell.execute_reply": "2024-08-02T23:17:25.078863Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.111044Z", + "iopub.status.busy": "2024-08-02T23:17:25.110645Z", + "iopub.status.idle": "2024-08-02T23:17:25.114497Z", + "shell.execute_reply": "2024-08-02T23:17:25.114027Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.116536Z", + "iopub.status.busy": "2024-08-02T23:17:25.116200Z", + "iopub.status.idle": "2024-08-02T23:17:25.124454Z", + "shell.execute_reply": "2024-08-02T23:17:25.123892Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.126998Z", + "iopub.status.busy": "2024-08-02T23:17:25.126543Z", + "iopub.status.idle": "2024-08-02T23:17:25.129409Z", + "shell.execute_reply": "2024-08-02T23:17:25.128804Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.131344Z", + "iopub.status.busy": "2024-08-02T23:17:25.131035Z", + "iopub.status.idle": "2024-08-02T23:17:25.655338Z", + "shell.execute_reply": "2024-08-02T23:17:25.654793Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:25.657837Z", + "iopub.status.busy": "2024-08-02T23:17:25.657465Z", + "iopub.status.idle": "2024-08-02T23:17:27.751426Z", + "shell.execute_reply": "2024-08-02T23:17:27.750727Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.754463Z", + "iopub.status.busy": "2024-08-02T23:17:27.753684Z", + "iopub.status.idle": "2024-08-02T23:17:27.764911Z", + "shell.execute_reply": "2024-08-02T23:17:27.764361Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.767199Z", + "iopub.status.busy": "2024-08-02T23:17:27.766875Z", + "iopub.status.idle": "2024-08-02T23:17:27.770951Z", + "shell.execute_reply": "2024-08-02T23:17:27.770498Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.772966Z", + "iopub.status.busy": "2024-08-02T23:17:27.772625Z", + "iopub.status.idle": "2024-08-02T23:17:27.779796Z", + "shell.execute_reply": "2024-08-02T23:17:27.779212Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.781951Z", + "iopub.status.busy": "2024-08-02T23:17:27.781645Z", + "iopub.status.idle": "2024-08-02T23:17:27.895282Z", + "shell.execute_reply": "2024-08-02T23:17:27.894690Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.897585Z", + "iopub.status.busy": "2024-08-02T23:17:27.897260Z", + "iopub.status.idle": "2024-08-02T23:17:27.899963Z", + "shell.execute_reply": "2024-08-02T23:17:27.899515Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:27.902092Z", + "iopub.status.busy": "2024-08-02T23:17:27.901699Z", + "iopub.status.idle": "2024-08-02T23:17:30.041948Z", + "shell.execute_reply": "2024-08-02T23:17:30.041308Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:30.045213Z", + "iopub.status.busy": "2024-08-02T23:17:30.044360Z", + "iopub.status.idle": "2024-08-02T23:17:30.055915Z", + "shell.execute_reply": "2024-08-02T23:17:30.055449Z" } }, "outputs": [ @@ -766,15 +766,30 @@ "We can see that the test set accuracy slightly improved as a result of the data cleaning. Note that this will not always be the case, especially when we evaluate on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] + }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:30.057830Z", + "iopub.status.busy": "2024-08-02T23:17:30.057653Z", + "iopub.status.idle": "2024-08-02T23:17:30.088205Z", + "shell.execute_reply": "2024-08-02T23:17:30.087743Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index 5f3a2c5ca..82424a63b 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -817,7 +817,7 @@

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

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

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

    2. Load and format the text dataset
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    @@ -1212,8 +1212,14 @@

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"children": ["IPY_MODEL_80b423d8da65443590ae4c634d0b83a7", "IPY_MODEL_6c5f50cde5734e65ae80022e95c8a7d2", "IPY_MODEL_45c8dc232e4c422a8454eafb546ca6d8"], "layout": "IPY_MODEL_8da99fc57db34318a69f0a2f7aa23f9e", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} + +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1297,6 +1303,7 @@

    4. Train a more robust model from noisy labels2. Load and format the text dataset
  • 3. Define a classification model and use cleanlab to find potential label errors
  • 4. Train a more robust model from noisy labels
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index 0ca024c1d..a81564d50 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:45.538656Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:33.437374Z", + "iopub.status.busy": "2024-08-02T23:17:33.437203Z", + "iopub.status.idle": "2024-08-02T23:17:37.042923Z", + "shell.execute_reply": "2024-08-02T23:17:37.042233Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:31:49.401469Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:37.045817Z", + "iopub.status.busy": "2024-08-02T23:17:37.045322Z", + "iopub.status.idle": "2024-08-02T23:17:37.049160Z", + "shell.execute_reply": "2024-08-02T23:17:37.048563Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.051274Z", + "iopub.status.busy": "2024-08-02T23:17:37.051087Z", + "iopub.status.idle": "2024-08-02T23:17:37.054580Z", + "shell.execute_reply": "2024-08-02T23:17:37.054080Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.056632Z", + "iopub.status.busy": "2024-08-02T23:17:37.056445Z", + "iopub.status.idle": "2024-08-02T23:17:37.094103Z", + "shell.execute_reply": "2024-08-02T23:17:37.093554Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.096154Z", + "iopub.status.busy": "2024-08-02T23:17:37.095963Z", + "iopub.status.idle": "2024-08-02T23:17:37.099840Z", + "shell.execute_reply": "2024-08-02T23:17:37.099374Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.101701Z", + "iopub.status.busy": "2024-08-02T23:17:37.101517Z", + "iopub.status.idle": "2024-08-02T23:17:37.104926Z", + "shell.execute_reply": "2024-08-02T23:17:37.104415Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'change_pin', 'card_about_to_expire', 'beneficiary_not_allowed', 'visa_or_mastercard', 'getting_spare_card', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_payment_fee_charged'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.107157Z", + "iopub.status.busy": "2024-08-02T23:17:37.106820Z", + "iopub.status.idle": "2024-08-02T23:17:37.110074Z", + "shell.execute_reply": "2024-08-02T23:17:37.109475Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.112222Z", + "iopub.status.busy": "2024-08-02T23:17:37.111870Z", + "iopub.status.idle": "2024-08-02T23:17:37.115305Z", + "shell.execute_reply": "2024-08-02T23:17:37.114842Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:37.117435Z", + "iopub.status.busy": "2024-08-02T23:17:37.117091Z", + "iopub.status.idle": "2024-08-02T23:17:41.492569Z", + "shell.execute_reply": "2024-08-02T23:17:41.492001Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97c7a5a1b558446099d39e138e95bd3c", + "model_id": "79ca72fe19f9461bbd6eac4989f0d0e5", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91c91931728e42119a5ed1a8e771a320", + "model_id": "19931fa885eb4bfe9823463770d72209", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a58243eb6a194ea7957507b4c0fcd20c", + "model_id": "41323f0f880b493180c67d1f67ae6818", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f47d961a8a6443fdb97fc093198a3a37", + "model_id": "c5358d2af5f4413db78296cb4e822b6d", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd86dc9f79bf4ea6b32ccf1b10684fdd", + "model_id": "f95fa2a0418543fea006ef9489d34baf", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f07a08b8ebf486cbecde146931b6ae3", + "model_id": "3dcc386a355446078b0260d76f1f460b", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d9438aa4de0a49669ef6cc3e242dea8a", + "model_id": "48f13ab0fd1e4de9a23d90349ff0827c", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.495465Z", + "iopub.status.busy": "2024-08-02T23:17:41.495109Z", + "iopub.status.idle": "2024-08-02T23:17:41.498080Z", + "shell.execute_reply": "2024-08-02T23:17:41.497515Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.500217Z", + "iopub.status.busy": "2024-08-02T23:17:41.499902Z", + "iopub.status.idle": "2024-08-02T23:17:41.502619Z", + "shell.execute_reply": "2024-08-02T23:17:41.502156Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:41.504428Z", + "iopub.status.busy": "2024-08-02T23:17:41.504253Z", + "iopub.status.idle": "2024-08-02T23:17:44.256566Z", + "shell.execute_reply": "2024-08-02T23:17:44.255764Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - 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Note that this will not always be the case, especially when we are evaluating on test data that are themselves noisy. The best practice is to run cleanlab to identify potential label issues and then manually review them, before blindly trusting any accuracy metrics. In particular, the most effort should be made to ensure high-quality test data, which is supposed to reflect the expected performance of our model during deployment.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] + }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:31:57.328218Z", - "iopub.status.busy": "2024-07-30T16:31:57.327844Z", - "iopub.status.idle": "2024-07-30T16:31:57.332176Z", - "shell.execute_reply": "2024-07-30T16:31:57.331645Z" + "iopub.execute_input": "2024-08-02T23:17:44.703922Z", + "iopub.status.busy": "2024-08-02T23:17:44.702968Z", + "iopub.status.idle": "2024-08-02T23:17:44.707974Z", + "shell.execute_reply": "2024-08-02T23:17:44.707467Z" }, "nbsphinx": "hidden" }, @@ -1105,86 +1120,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0243ca7793174c8d987c19c51daa3a90": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - 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"IPY_MODEL_0b8a7b3b6ee5417c892f888aed2c46c1"], "layout": "IPY_MODEL_48bb90edac5840838e6781305996eb88", "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 a2d329ad2..a38491bc9 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": "2024-07-30T16:32:01.430387Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:48.674521Z", + "iopub.status.busy": "2024-08-02T23:17:48.674006Z", + "iopub.status.idle": "2024-08-02T23:17:54.489657Z", + "shell.execute_reply": "2024-08-02T23:17:54.489092Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:07.510541Z", - "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" + "iopub.execute_input": "2024-08-02T23:17:54.492364Z", + "iopub.status.busy": "2024-08-02T23:17:54.491862Z", + "iopub.status.idle": "2024-08-02T23:17:54.495158Z", + "shell.execute_reply": "2024-08-02T23:17:54.494599Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:54.497251Z", + "iopub.status.busy": "2024-08-02T23:17:54.496901Z", + "iopub.status.idle": "2024-08-02T23:17:54.501477Z", + "shell.execute_reply": "2024-08-02T23:17:54.501011Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:54.503439Z", + "iopub.status.busy": "2024-08-02T23:17:54.503137Z", + "iopub.status.idle": "2024-08-02T23:17:56.103711Z", + "shell.execute_reply": "2024-08-02T23:17:56.103032Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.106386Z", + "iopub.status.busy": "2024-08-02T23:17:56.106173Z", + "iopub.status.idle": "2024-08-02T23:17:56.117184Z", + "shell.execute_reply": "2024-08-02T23:17:56.116749Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.119274Z", + "iopub.status.busy": "2024-08-02T23:17:56.118919Z", + "iopub.status.idle": "2024-08-02T23:17:56.124342Z", + "shell.execute_reply": "2024-08-02T23:17:56.123884Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.126193Z", + "iopub.status.busy": "2024-08-02T23:17:56.126017Z", + "iopub.status.idle": "2024-08-02T23:17:56.587440Z", + "shell.execute_reply": "2024-08-02T23:17:56.586822Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:56.589528Z", + "iopub.status.busy": "2024-08-02T23:17:56.589339Z", + "iopub.status.idle": "2024-08-02T23:17:57.247247Z", + "shell.execute_reply": "2024-08-02T23:17:57.246625Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.249823Z", + "iopub.status.busy": "2024-08-02T23:17:57.249401Z", + "iopub.status.idle": "2024-08-02T23:17:57.268344Z", + "shell.execute_reply": "2024-08-02T23:17:57.267769Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.270433Z", + "iopub.status.busy": "2024-08-02T23:17:57.270112Z", + "iopub.status.idle": "2024-08-02T23:17:57.273390Z", + "shell.execute_reply": "2024-08-02T23:17:57.272811Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:17:57.275541Z", + "iopub.status.busy": "2024-08-02T23:17:57.275202Z", + "iopub.status.idle": "2024-08-02T23:18:11.647525Z", + "shell.execute_reply": "2024-08-02T23:18:11.646907Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:11.650258Z", + "iopub.status.busy": "2024-08-02T23:18:11.649858Z", + "iopub.status.idle": "2024-08-02T23:18:11.653962Z", + "shell.execute_reply": "2024-08-02T23:18:11.653485Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:11.656092Z", + "iopub.status.busy": "2024-08-02T23:18:11.655746Z", + "iopub.status.idle": "2024-08-02T23:18:12.346371Z", + "shell.execute_reply": "2024-08-02T23:18:12.345770Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:12.349358Z", + "iopub.status.busy": "2024-08-02T23:18:12.348955Z", + "iopub.status.idle": "2024-08-02T23:18:12.353747Z", + "shell.execute_reply": "2024-08-02T23:18:12.353244Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:12.356979Z", + "iopub.status.busy": "2024-08-02T23:18:12.356030Z", + "iopub.status.idle": "2024-08-02T23:18:12.482133Z", + "shell.execute_reply": "2024-08-02T23:18:12.481542Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:12.484630Z", + "iopub.status.busy": "2024-08-02T23:18:12.484204Z", + "iopub.status.idle": "2024-08-02T23:18:12.496863Z", + "shell.execute_reply": "2024-08-02T23:18:12.496354Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:32:27.749097Z", - "iopub.status.busy": "2024-07-30T16:32:27.748752Z", - 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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 899e02b72..282b62c0d 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": "2024-07-30T16:32:32.656232Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:16.484336Z", + "iopub.status.busy": "2024-08-02T23:18:16.484165Z", + "iopub.status.idle": "2024-08-02T23:18:17.882451Z", + "shell.execute_reply": "2024-08-02T23:18:17.881898Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:34.121608Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:17.885156Z", + "iopub.status.busy": "2024-08-02T23:18:17.884677Z", + "iopub.status.idle": "2024-08-02T23:18:17.887758Z", + "shell.execute_reply": "2024-08-02T23:18:17.887295Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.889898Z", + "iopub.status.busy": "2024-08-02T23:18:17.889563Z", + "iopub.status.idle": "2024-08-02T23:18:17.898213Z", + "shell.execute_reply": "2024-08-02T23:18:17.897752Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.900225Z", + "iopub.status.busy": "2024-08-02T23:18:17.899877Z", + "iopub.status.idle": "2024-08-02T23:18:17.904388Z", + "shell.execute_reply": "2024-08-02T23:18:17.903975Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.906648Z", + "iopub.status.busy": "2024-08-02T23:18:17.906301Z", + "iopub.status.idle": "2024-08-02T23:18:17.914041Z", + "shell.execute_reply": "2024-08-02T23:18:17.913599Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:17.916042Z", + "iopub.status.busy": "2024-08-02T23:18:17.915708Z", + "iopub.status.idle": "2024-08-02T23:18:18.290994Z", + "shell.execute_reply": 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"2024-08-02T23:18:20.500213Z", + "shell.execute_reply": "2024-08-02T23:18:20.499712Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:32:36.870048Z", - "iopub.status.busy": "2024-07-30T16:32:36.869719Z", - "iopub.status.idle": "2024-07-30T16:32:36.889337Z", - "shell.execute_reply": "2024-07-30T16:32:36.888743Z" + "iopub.execute_input": "2024-08-02T23:18:20.502287Z", + "iopub.status.busy": "2024-08-02T23:18:20.501963Z", + "iopub.status.idle": "2024-08-02T23:18:20.520731Z", + "shell.execute_reply": "2024-08-02T23:18:20.520197Z" } }, "outputs": [ @@ -1447,30 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "24e5bfd20a30498b87f31ca923bcaee0": { - "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": 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+ "527cb92bada44744a7822b43b03ee159": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1600,7 +1595,23 @@ "width": null } }, - "c9a94c2705dc4c6888c5b9bcb593ec3b": { + "52f1b8f2c74a4bb6a99bf9934c7b2337": { + "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": "" + } + }, + "5707965d1e434f65bdddd79f785196a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1653,108 +1664,56 @@ "width": null } }, - "d477eb478231404692d7fabdeafcfa6e": { + "a744c826589e4e2d9f2285146f0341d8": { "model_module": 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"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 + "value": 132.0 } }, - "fa38f1de6f7849ee9f7cd7ad7a504555": { + "b4e9ca667f8b4ad98d99a1c1d4cad9ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_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_a8759625ec944bbbac04db9cec87a317", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ee9027d19159444e8d46c66083251836", + "layout": "IPY_MODEL_0c1a082463244b57b51d61d7f6c72e71", + "placeholder": "​", + "style": "IPY_MODEL_3e3e5258de4f42cb8228d1fc6798c2f3", "tabbable": null, "tooltip": null, - "value": 132.0 + "value": " 132/132 [00:00<00:00, 11675.41 examples/s]" } }, - "fe623ece577749be8a8ce449c28b2074": { + "c73f2f5e1ae54b468441d578f7c2ba01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,6 +1765,47 @@ "visibility": null, "width": null } + }, + "c9798d892bbb4b4495225f4e0b80e70c": { + "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_527cb92bada44744a7822b43b03ee159", + "placeholder": "​", + "style": "IPY_MODEL_e0e9fbdf6956499f98167164d76e9bb4", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "e0e9fbdf6956499f98167164d76e9bb4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.html b/master/tutorials/datalab/datalab_quickstart.html index 01ec56c9f..d00b92717 100644 --- a/master/tutorials/datalab/datalab_quickstart.html +++ b/master/tutorials/datalab/datalab_quickstart.html @@ -1586,6 +1586,12 @@

    Near duplicate issuesTo learn more, check out this example notebook (demonstrates Datalab applied to a real dataset) and the advanced Datalab tutorial (demonstrates configuration and customization options to exert greater control).

    +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1673,6 +1679,7 @@

    Near duplicate issuesNear duplicate issues +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index dd27cd645..94e8a87f7 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": "2024-07-30T16:32:39.968392Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:23.440181Z", + "iopub.status.busy": "2024-08-02T23:18:23.440011Z", + "iopub.status.idle": "2024-08-02T23:18:24.873718Z", + "shell.execute_reply": "2024-08-02T23:18:24.873164Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:32:41.431407Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:24.876197Z", + "iopub.status.busy": "2024-08-02T23:18:24.875898Z", + "iopub.status.idle": "2024-08-02T23:18:24.879459Z", + "shell.execute_reply": "2024-08-02T23:18:24.879019Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.881728Z", + "iopub.status.busy": "2024-08-02T23:18:24.881284Z", + "iopub.status.idle": "2024-08-02T23:18:24.890527Z", + "shell.execute_reply": "2024-08-02T23:18:24.890092Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.892364Z", + "iopub.status.busy": "2024-08-02T23:18:24.892191Z", + "iopub.status.idle": "2024-08-02T23:18:24.897249Z", + "shell.execute_reply": "2024-08-02T23:18:24.896624Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.899657Z", + "iopub.status.busy": "2024-08-02T23:18:24.899315Z", + "iopub.status.idle": "2024-08-02T23:18:24.907713Z", + "shell.execute_reply": "2024-08-02T23:18:24.907270Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:24.909718Z", + "iopub.status.busy": "2024-08-02T23:18:24.909392Z", + "iopub.status.idle": "2024-08-02T23:18:25.284990Z", + "shell.execute_reply": "2024-08-02T23:18:25.284340Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.287244Z", + "iopub.status.busy": "2024-08-02T23:18:25.286882Z", + "iopub.status.idle": "2024-08-02T23:18:25.289594Z", + "shell.execute_reply": "2024-08-02T23:18:25.289144Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.291654Z", + "iopub.status.busy": "2024-08-02T23:18:25.291314Z", + "iopub.status.idle": "2024-08-02T23:18:25.325141Z", + "shell.execute_reply": "2024-08-02T23:18:25.324531Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:25.327400Z", + "iopub.status.busy": "2024-08-02T23:18:25.327081Z", + "iopub.status.idle": "2024-08-02T23:18:27.419844Z", + "shell.execute_reply": "2024-08-02T23:18:27.419221Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.422496Z", + "iopub.status.busy": "2024-08-02T23:18:27.421968Z", + "iopub.status.idle": "2024-08-02T23:18:27.440757Z", + "shell.execute_reply": "2024-08-02T23:18:27.440302Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.442868Z", + "iopub.status.busy": "2024-08-02T23:18:27.442525Z", + "iopub.status.idle": "2024-08-02T23:18:27.449073Z", + "shell.execute_reply": "2024-08-02T23:18:27.448585Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.451087Z", + "iopub.status.busy": "2024-08-02T23:18:27.450752Z", + "iopub.status.idle": "2024-08-02T23:18:27.456602Z", + "shell.execute_reply": "2024-08-02T23:18:27.456121Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.458665Z", + "iopub.status.busy": "2024-08-02T23:18:27.458311Z", + "iopub.status.idle": "2024-08-02T23:18:27.470031Z", + "shell.execute_reply": "2024-08-02T23:18:27.469562Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.472056Z", + "iopub.status.busy": "2024-08-02T23:18:27.471719Z", + "iopub.status.idle": "2024-08-02T23:18:27.480622Z", + "shell.execute_reply": "2024-08-02T23:18:27.480154Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.482811Z", + "iopub.status.busy": "2024-08-02T23:18:27.482463Z", + "iopub.status.idle": "2024-08-02T23:18:27.489531Z", + "shell.execute_reply": "2024-08-02T23:18:27.489014Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.491621Z", + "iopub.status.busy": "2024-08-02T23:18:27.491282Z", + "iopub.status.idle": "2024-08-02T23:18:27.500416Z", + "shell.execute_reply": "2024-08-02T23:18:27.499840Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:27.502452Z", + "iopub.status.busy": "2024-08-02T23:18:27.502275Z", + "iopub.status.idle": "2024-08-02T23:18:27.517561Z", + "shell.execute_reply": "2024-08-02T23:18:27.517118Z" }, "nbsphinx": "hidden" }, @@ -1615,6 +1615,21 @@ "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] } ], "metadata": { diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index c0c41a388..8fd98bf6e 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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    [3]:
@@ -824,7 +824,7 @@ 
    2. Fetch and normalize the Fashion-MNIST dataset
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    +

    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: 5.221
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.940
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.696
 Computing feature embeddings ...

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

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

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.233
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.990
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.598
 Computing feature embeddings ...

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

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

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

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

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.455
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.891
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.595
 Computing feature embeddings ...

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

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

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

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    @@ -1222,7 +1222,7 @@

    7. Use cleanlab to find issues

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    @@ -1937,35 +1937,35 @@

    Dark images - dark_score is_dark_issue + dark_score 34848 - 0.203922 True + 0.203922 50270 - 0.204588 True + 0.204588 3936 - 0.213098 True + 0.213098 733 - 0.217686 True + 0.217686 8094 - 0.230118 True + 0.230118 @@ -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 4228b95b6..b6b82bece 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": "2024-07-30T16:32:47.382198Z", - "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" + "iopub.execute_input": "2024-08-02T23:18:30.187968Z", + "iopub.status.busy": "2024-08-02T23:18:30.187540Z", + "iopub.status.idle": "2024-08-02T23:18:33.174744Z", + "shell.execute_reply": "2024-08-02T23:18:33.174187Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:33.177406Z", + "iopub.status.busy": "2024-08-02T23:18:33.176910Z", + "iopub.status.idle": "2024-08-02T23:18:33.180538Z", + "shell.execute_reply": "2024-08-02T23:18:33.180061Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:33.182529Z", + "iopub.status.busy": "2024-08-02T23:18:33.182219Z", + "iopub.status.idle": "2024-08-02T23:18:44.664109Z", + "shell.execute_reply": "2024-08-02T23:18:44.663641Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cd770708ca5498492377d6a0fd76616", + "model_id": "a83344ed6e98426eabf329c5f44403a3", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4fa7fdeeb9446ddbf6516f8963fa52e", + "model_id": "eeb48354781f459d926f56b9d9f2d412", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6e2987ba28d48c28d884b33288562df", + "model_id": "53411696bfa143a2bdec30cc846c6549", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29ade62a53ac448198f24b5900001b05", + "model_id": "c17e6593dbb94d3a9ee695742a582d56", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "926846a8c6954c46acf37f4dd63e7eb9", + "model_id": "a26294fe97dc43f9be84fc17b73f9563", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6aaa8d39274f4cfea54a66eb8516a06f", + "model_id": "dac75bdcb107415d915bb9ad97029fe4", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96697881a93440babad369ae2e2fd4b8", + "model_id": "73b0c33bd83f44ad9d81965657542e7d", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efe64e5c44d94c6bb0bed3ad6e844c33", + "model_id": "ecb36eb02e7843c881d512c1e1980bfc", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:44.666249Z", + "iopub.status.busy": "2024-08-02T23:18:44.666064Z", + "iopub.status.idle": "2024-08-02T23:18:44.669866Z", + "shell.execute_reply": "2024-08-02T23:18:44.669332Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:44.671881Z", + "iopub.status.busy": "2024-08-02T23:18:44.671547Z", + "iopub.status.idle": "2024-08-02T23:18:56.267751Z", + "shell.execute_reply": "2024-08-02T23:18:56.267200Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c48d73b75d8543b7900f7e3a24c14ff0", + "model_id": "82fd82af78b445e7b64eeceba4a9b1cc", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:18:56.270597Z", + "iopub.status.busy": "2024-08-02T23:18:56.270197Z", + "iopub.status.idle": "2024-08-02T23:19:15.145228Z", + "shell.execute_reply": "2024-08-02T23:19:15.144654Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.147853Z", + "iopub.status.busy": "2024-08-02T23:19:15.147474Z", + "iopub.status.idle": "2024-08-02T23:19:15.153225Z", + "shell.execute_reply": "2024-08-02T23:19:15.152782Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.155015Z", + "iopub.status.busy": "2024-08-02T23:19:15.154828Z", + "iopub.status.idle": "2024-08-02T23:19:15.159184Z", + "shell.execute_reply": "2024-08-02T23:19:15.158778Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.161173Z", + "iopub.status.busy": "2024-08-02T23:19:15.160982Z", + "iopub.status.idle": "2024-08-02T23:19:15.169844Z", + "shell.execute_reply": "2024-08-02T23:19:15.169391Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.171692Z", + "iopub.status.busy": "2024-08-02T23:19:15.171519Z", + "iopub.status.idle": "2024-08-02T23:19:15.197718Z", + "shell.execute_reply": "2024-08-02T23:19:15.197156Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:15.200155Z", + "iopub.status.busy": "2024-08-02T23:19:15.199758Z", + "iopub.status.idle": "2024-08-02T23:19:48.490207Z", + "shell.execute_reply": "2024-08-02T23:19:48.489541Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.221\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.940\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.922\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.696\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4800d17f20734ee3900349a11b2585dc", + "model_id": "dcb047742e7c4146a32757077d93eb95", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21d92163462b4f67a981a814fcb48508", + "model_id": "64bb6421005c4259bdb6379773d89e83", "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: 5.233\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.990\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.913\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.598\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8af1aec52aef434b81a22b708073556f", + "model_id": "7b496615a26e4658af3e583f61bcdef9", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19a5e3a37b304b559df2c5101035122f", + "model_id": "cf7cc11f28ea46039cc95c145d1ce401", "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: 5.455\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.891\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.031\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.595\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2738be416ce480c95fff046962f1137", + "model_id": "4a80db32ba09418496242d3395cc72bf", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c2f40cf42cc413e8b1040c82a085028", + "model_id": "9e16783844c34794a2677bfd495b5109", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.492853Z", + "iopub.status.busy": "2024-08-02T23:19:48.492428Z", + "iopub.status.idle": "2024-08-02T23:19:48.507600Z", + "shell.execute_reply": "2024-08-02T23:19:48.507055Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.509976Z", + "iopub.status.busy": "2024-08-02T23:19:48.509546Z", + "iopub.status.idle": "2024-08-02T23:19:48.985893Z", + "shell.execute_reply": "2024-08-02T23:19:48.985338Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:19:48.988296Z", + "iopub.status.busy": "2024-08-02T23:19:48.987937Z", + "iopub.status.idle": "2024-08-02T23:21:27.529258Z", + "shell.execute_reply": "2024-08-02T23:21:27.528524Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e7ed9a8db3f47d499c32f8ab98695a3", + "model_id": "65b0b32c141e4ddeb98d61670fbf32bf", "version_major": 2, "version_minor": 0 }, @@ -1150,10 +1150,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:27.531774Z", + "iopub.status.busy": "2024-08-02T23:21:27.531386Z", + "iopub.status.idle": "2024-08-02T23:21:27.988639Z", + "shell.execute_reply": "2024-08-02T23:21:27.987974Z" } }, "outputs": [ @@ -1299,10 +1299,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:27.999558Z", + "iopub.status.busy": "2024-08-02T23:21:27.999325Z", + "iopub.status.idle": "2024-08-02T23:21:28.049571Z", + "shell.execute_reply": "2024-08-02T23:21:28.048979Z" } }, "outputs": [ @@ -1406,10 +1406,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.051857Z", + "iopub.status.busy": "2024-08-02T23:21:28.051505Z", + "iopub.status.idle": "2024-08-02T23:21:28.060658Z", + "shell.execute_reply": "2024-08-02T23:21:28.060215Z" } }, "outputs": [ @@ -1539,10 +1539,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.062728Z", + "iopub.status.busy": "2024-08-02T23:21:28.062400Z", + "iopub.status.idle": "2024-08-02T23:21:28.066951Z", + "shell.execute_reply": "2024-08-02T23:21:28.066486Z" }, "nbsphinx": "hidden" }, @@ -1588,10 +1588,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.069039Z", + "iopub.status.busy": "2024-08-02T23:21:28.068692Z", + "iopub.status.idle": "2024-08-02T23:21:28.571892Z", + "shell.execute_reply": "2024-08-02T23:21:28.571292Z" } }, "outputs": [ @@ -1626,10 +1626,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.574424Z", + "iopub.status.busy": "2024-08-02T23:21:28.574048Z", + "iopub.status.idle": "2024-08-02T23:21:28.582821Z", + "shell.execute_reply": "2024-08-02T23:21:28.582318Z" } }, "outputs": [ @@ -1796,10 +1796,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.585102Z", + "iopub.status.busy": "2024-08-02T23:21:28.584720Z", + "iopub.status.idle": "2024-08-02T23:21:28.592253Z", + "shell.execute_reply": "2024-08-02T23:21:28.591755Z" }, "nbsphinx": "hidden" }, @@ -1875,10 +1875,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:28.594385Z", + "iopub.status.busy": "2024-08-02T23:21:28.593990Z", + "iopub.status.idle": "2024-08-02T23:21:29.355028Z", + "shell.execute_reply": "2024-08-02T23:21:29.354474Z" } }, "outputs": [ @@ -1915,10 +1915,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.357774Z", + "iopub.status.busy": "2024-08-02T23:21:29.357407Z", + "iopub.status.idle": "2024-08-02T23:21:29.372810Z", + "shell.execute_reply": "2024-08-02T23:21:29.372351Z" } }, "outputs": [ @@ -2075,10 +2075,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.375092Z", + "iopub.status.busy": "2024-08-02T23:21:29.374753Z", + "iopub.status.idle": "2024-08-02T23:21:29.380139Z", + "shell.execute_reply": "2024-08-02T23:21:29.379692Z" }, "nbsphinx": "hidden" }, @@ -2123,10 +2123,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.382139Z", + "iopub.status.busy": "2024-08-02T23:21:29.381799Z", + "iopub.status.idle": "2024-08-02T23:21:29.845557Z", + "shell.execute_reply": "2024-08-02T23:21:29.844919Z" } }, "outputs": [ @@ -2208,10 +2208,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.848126Z", + "iopub.status.busy": "2024-08-02T23:21:29.847912Z", + "iopub.status.idle": "2024-08-02T23:21:29.857322Z", + "shell.execute_reply": "2024-08-02T23:21:29.856727Z" } }, "outputs": [ @@ -2236,47 +2236,47 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 34848\n", - " 0.203922\n", " True\n", + " 0.203922\n", " \n", " \n", " 50270\n", - " 0.204588\n", " True\n", + " 0.204588\n", " \n", " \n", " 3936\n", - " 0.213098\n", " True\n", + " 0.213098\n", " \n", " \n", " 733\n", - " 0.217686\n", " True\n", + " 0.217686\n", " \n", " \n", " 8094\n", - " 0.230118\n", " True\n", + " 0.230118\n", " \n", " \n", "\n", "

    " ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 26, @@ -2339,10 +2339,10 @@ "execution_count": 27, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:29.863079Z", + "iopub.status.busy": "2024-08-02T23:21:29.862689Z", + "iopub.status.idle": "2024-08-02T23:21:29.868549Z", + "shell.execute_reply": "2024-08-02T23:21:29.868014Z" }, "nbsphinx": "hidden" }, @@ -2379,10 +2379,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:35:51.944959Z", - 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"tooltip": null + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.49MB/s]" } }, - "f3268349f66e4fca99547204d4fed8cf": { + "fd69a19b7d0147feafb879a2901669a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8499,74 +8610,7 @@ "width": null } }, - "f36e7eec2ef6494ea5dfe75f34bb946e": { - "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 - } - }, - "f4570a2a6a834672b816a3c3c92674d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - 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"_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": { + "fe373b9b0fd449b5b8ebf17e3268d1d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8619,51 +8663,7 @@ "width": null } }, - "faaac1dac8fa4eccb56fbb10b45393c3": { - "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 - } - }, - "fabe89de1f3f41f493f2490c661f6d02": { - "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_c72eded270c04594b9cd5ab865ec18ee", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_01972cf3b8f94fab866c986e21f7f91f", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "fd82af84285840158e600b4d0204c84e": { + "ff995d6039774b1795125f0d38e2290c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/datalab/tabular.html b/master/tutorials/datalab/tabular.html index 383216817..1e43c3bd8 100644 --- a/master/tutorials/datalab/tabular.html +++ b/master/tutorials/datalab/tabular.html @@ -1521,12 +1521,13 @@

    Near-duplicate issuesWe identified another set of exact duplicates in our dataset! Including near/exact duplicates in a dataset may have unintended effects on models; be wary about splitting them across training/test sets. Learn more about handling near duplicates detected in a dataset from the FAQ.

    This tutorial highlighted a straightforward approach to detect potentially incorrect information in any tabular dataset. Just use Datalab with any ML model – the better the model, the more accurate the data errors detected by Datalab will be!

    -
    -

    Easy Mode#

    -

    Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don’t know the best ML model for your data, try Cleanlab 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!

    -
    +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1613,9 +1614,9 @@

    Easy ModeLabel issues
  • Outlier issues
  • Near-duplicate issues
    • -
  • Easy Mode
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index a6ed7ee61..fa39299c4 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": "2024-07-30T16:35:56.051172Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:34.625401Z", + "iopub.status.busy": "2024-08-02T23:21:34.625214Z", + "iopub.status.idle": "2024-08-02T23:21:36.031325Z", + "shell.execute_reply": "2024-08-02T23:21:36.030762Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:35:57.512868Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:36.033876Z", + "iopub.status.busy": "2024-08-02T23:21:36.033576Z", + "iopub.status.idle": "2024-08-02T23:21:36.052522Z", + "shell.execute_reply": "2024-08-02T23:21:36.052073Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.054768Z", + "iopub.status.busy": "2024-08-02T23:21:36.054504Z", + "iopub.status.idle": "2024-08-02T23:21:36.078555Z", + "shell.execute_reply": "2024-08-02T23:21:36.078092Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.080474Z", + "iopub.status.busy": "2024-08-02T23:21:36.080294Z", + "iopub.status.idle": "2024-08-02T23:21:36.083816Z", + "shell.execute_reply": "2024-08-02T23:21:36.083361Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.085725Z", + "iopub.status.busy": "2024-08-02T23:21:36.085553Z", + "iopub.status.idle": "2024-08-02T23:21:36.092911Z", + "shell.execute_reply": "2024-08-02T23:21:36.092464Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.094839Z", + "iopub.status.busy": "2024-08-02T23:21:36.094665Z", + "iopub.status.idle": "2024-08-02T23:21:36.097347Z", + "shell.execute_reply": "2024-08-02T23:21:36.096835Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:36.099385Z", + "iopub.status.busy": "2024-08-02T23:21:36.099048Z", + "iopub.status.idle": "2024-08-02T23:21:39.178225Z", + "shell.execute_reply": "2024-08-02T23:21:39.177680Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:39.181086Z", + "iopub.status.busy": "2024-08-02T23:21:39.180688Z", + "iopub.status.idle": "2024-08-02T23:21:39.190195Z", + "shell.execute_reply": "2024-08-02T23:21:39.189607Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:39.192491Z", + "iopub.status.busy": "2024-08-02T23:21:39.192157Z", + "iopub.status.idle": "2024-08-02T23:21:41.385468Z", + "shell.execute_reply": "2024-08-02T23:21:41.384800Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.388190Z", + "iopub.status.busy": "2024-08-02T23:21:41.387567Z", + "iopub.status.idle": "2024-08-02T23:21:41.406849Z", + "shell.execute_reply": "2024-08-02T23:21:41.406379Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.408952Z", + "iopub.status.busy": "2024-08-02T23:21:41.408765Z", + "iopub.status.idle": "2024-08-02T23:21:41.417080Z", + "shell.execute_reply": "2024-08-02T23:21:41.416607Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.419162Z", + "iopub.status.busy": "2024-08-02T23:21:41.418912Z", + "iopub.status.idle": "2024-08-02T23:21:41.428332Z", + "shell.execute_reply": "2024-08-02T23:21:41.427869Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.430451Z", + "iopub.status.busy": "2024-08-02T23:21:41.430111Z", + "iopub.status.idle": "2024-08-02T23:21:41.437844Z", + "shell.execute_reply": "2024-08-02T23:21:41.437283Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.439955Z", + "iopub.status.busy": "2024-08-02T23:21:41.439619Z", + "iopub.status.idle": "2024-08-02T23:21:41.448535Z", + "shell.execute_reply": "2024-08-02T23:21:41.447977Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.450685Z", + "iopub.status.busy": "2024-08-02T23:21:41.450360Z", + "iopub.status.idle": "2024-08-02T23:21:41.457701Z", + "shell.execute_reply": "2024-08-02T23:21:41.457210Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.459831Z", + "iopub.status.busy": "2024-08-02T23:21:41.459473Z", + "iopub.status.idle": "2024-08-02T23:21:41.466693Z", + "shell.execute_reply": "2024-08-02T23:21:41.466243Z" } }, "outputs": [ @@ -1290,9 +1290,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " ] }, { @@ -1300,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:41.468785Z", + "iopub.status.busy": "2024-08-02T23:21:41.468507Z", + "iopub.status.idle": "2024-08-02T23:21:41.477200Z", + "shell.execute_reply": "2024-08-02T23:21:41.476679Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index ae13fe500..e92c42578 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: {'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'}
+Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire'}

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

    @@ -1548,12 +1548,13 @@

    Non-IID issues (data drift) -

    Easy Mode#

    -

    Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don’t know the best ML model for your data, try Cleanlab 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!

    - +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1640,9 +1641,9 @@

    Easy ModeOutlier issues
  • Near-duplicate issues
  • Non-IID issues (data drift)
    • -
  • Easy Mode
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index b380e2cea..ae24736d8 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": "2024-07-30T16:36:05.906897Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:44.540425Z", + "iopub.status.busy": "2024-08-02T23:21:44.540253Z", + "iopub.status.idle": "2024-08-02T23:21:47.824794Z", + "shell.execute_reply": "2024-08-02T23:21:47.824202Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:36:09.213471Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:47.827543Z", + "iopub.status.busy": "2024-08-02T23:21:47.827067Z", + "iopub.status.idle": "2024-08-02T23:21:47.830552Z", + "shell.execute_reply": "2024-08-02T23:21:47.829969Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.832743Z", + "iopub.status.busy": "2024-08-02T23:21:47.832414Z", + "iopub.status.idle": "2024-08-02T23:21:47.835675Z", + "shell.execute_reply": "2024-08-02T23:21:47.835102Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.837826Z", + "iopub.status.busy": "2024-08-02T23:21:47.837474Z", + "iopub.status.idle": "2024-08-02T23:21:47.861187Z", + "shell.execute_reply": "2024-08-02T23:21:47.860585Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.863473Z", + "iopub.status.busy": "2024-08-02T23:21:47.863101Z", + "iopub.status.idle": "2024-08-02T23:21:47.867008Z", + "shell.execute_reply": "2024-08-02T23:21:47.866495Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\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" + "Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.869127Z", + "iopub.status.busy": "2024-08-02T23:21:47.868786Z", + "iopub.status.idle": "2024-08-02T23:21:47.872006Z", + "shell.execute_reply": "2024-08-02T23:21:47.871455Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:47.874101Z", + "iopub.status.busy": "2024-08-02T23:21:47.873920Z", + "iopub.status.idle": "2024-08-02T23:21:51.371823Z", + "shell.execute_reply": "2024-08-02T23:21:51.371151Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:51.374609Z", + "iopub.status.busy": "2024-08-02T23:21:51.374204Z", + "iopub.status.idle": "2024-08-02T23:21:52.273008Z", + "shell.execute_reply": "2024-08-02T23:21:52.272406Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:52.276055Z", + "iopub.status.busy": "2024-08-02T23:21:52.275635Z", + "iopub.status.idle": "2024-08-02T23:21:52.278641Z", + "shell.execute_reply": "2024-08-02T23:21:52.278123Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:52.281114Z", + "iopub.status.busy": "2024-08-02T23:21:52.280693Z", + "iopub.status.idle": "2024-08-02T23:21:54.282307Z", + "shell.execute_reply": "2024-08-02T23:21:54.281655Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.285687Z", + "iopub.status.busy": "2024-08-02T23:21:54.284976Z", + "iopub.status.idle": "2024-08-02T23:21:54.309240Z", + "shell.execute_reply": "2024-08-02T23:21:54.308666Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.311719Z", + "iopub.status.busy": "2024-08-02T23:21:54.311308Z", + "iopub.status.idle": "2024-08-02T23:21:54.321181Z", + "shell.execute_reply": "2024-08-02T23:21:54.320680Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.323214Z", + "iopub.status.busy": "2024-08-02T23:21:54.322881Z", + "iopub.status.idle": "2024-08-02T23:21:54.327193Z", + "shell.execute_reply": "2024-08-02T23:21:54.326638Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.329227Z", + "iopub.status.busy": "2024-08-02T23:21:54.328892Z", + "iopub.status.idle": "2024-08-02T23:21:54.335223Z", + "shell.execute_reply": "2024-08-02T23:21:54.334737Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.337162Z", + "iopub.status.busy": "2024-08-02T23:21:54.336976Z", + "iopub.status.idle": "2024-08-02T23:21:54.343438Z", + "shell.execute_reply": "2024-08-02T23:21:54.342965Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.345616Z", + "iopub.status.busy": "2024-08-02T23:21:54.345242Z", + "iopub.status.idle": "2024-08-02T23:21:54.351019Z", + "shell.execute_reply": "2024-08-02T23:21:54.350470Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.353129Z", + "iopub.status.busy": "2024-08-02T23:21:54.352774Z", + "iopub.status.idle": "2024-08-02T23:21:54.361472Z", + "shell.execute_reply": "2024-08-02T23:21:54.360992Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.363537Z", + "iopub.status.busy": "2024-08-02T23:21:54.363200Z", + "iopub.status.idle": "2024-08-02T23:21:54.368517Z", + "shell.execute_reply": "2024-08-02T23:21:54.367955Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.370813Z", + "iopub.status.busy": "2024-08-02T23:21:54.370341Z", + "iopub.status.idle": "2024-08-02T23:21:54.376019Z", + "shell.execute_reply": "2024-08-02T23:21:54.375442Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.378125Z", + "iopub.status.busy": "2024-08-02T23:21:54.377805Z", + "iopub.status.idle": "2024-08-02T23:21:54.381485Z", + "shell.execute_reply": "2024-08-02T23:21:54.380895Z" } }, "outputs": [ @@ -1433,9 +1433,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Easy Mode \n", + "## Spending too much time on data quality?\n", "\n", - "Cleanlab is most effective when you run this code with a good ML model. Try to produce the best ML model you can for your data (instead of the basic model from this tutorial). If you don't know the best ML model for your data, try [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) which will automatically produce one for you. Super easy to use, [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) 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!](https://cleanlab.ai/signup/)" + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " ] }, { @@ -1443,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:54.383731Z", + "iopub.status.busy": "2024-08-02T23:21:54.383385Z", + "iopub.status.idle": "2024-08-02T23:21:54.388743Z", + "shell.execute_reply": "2024-08-02T23:21:54.388162Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index ab5136da2..03cfeeb36 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
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    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
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    @@ -3503,16 +3503,16 @@

    1. Load the Dataset 
    ---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, ...
+--2024-08-02 23:22:13--  https://s.cleanlab.ai/CIFAR-10-subset.zip
+Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...
 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
+CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.007s
 
-2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
+2024-08-02 23:22:14 (131 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
    @@ -3582,7 +3582,7 @@

    2. Run Datalab Analysis
    -
    +
    @@ -3753,35 +3753,35 @@

    3. Interpret the Results - is_dark_issue dark_score + is_dark_issue 0 - True 0.237196 + True 1 - True 0.197229 + True 2 - True 0.254188 + True 3 - True 0.229170 + True 4 - True 0.208907 + True ... @@ -3790,28 +3790,28 @@

    3. Interpret the Results

    -
    +
    @@ -3986,35 +3986,35 @@

    4. (Optional) Compare with a Dataset Without Spurious Correlations - is_dark_issue dark_score + is_dark_issue 0 - False 0.797509 + False 1 - False 0.663760 + False 2 - False 0.849826 + False 3 - False 0.773951 + False 4 - False 0.699518 + False ... @@ -4023,28 +4023,28 @@

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

    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/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index d6d1d2769..b1d172e95 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": "2024-07-30T16:36:19.928818Z", - "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" + "iopub.execute_input": "2024-08-02T23:21:58.540122Z", + "iopub.status.busy": "2024-08-02T23:21:58.539942Z", + "iopub.status.idle": "2024-08-02T23:21:58.972460Z", + "shell.execute_reply": "2024-08-02T23:21:58.971845Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:58.975240Z", + "iopub.status.busy": "2024-08-02T23:21:58.974829Z", + "iopub.status.idle": "2024-08-02T23:21:59.105776Z", + "shell.execute_reply": "2024-08-02T23:21:59.105181Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:59.108029Z", + "iopub.status.busy": "2024-08-02T23:21:59.107636Z", + "iopub.status.idle": "2024-08-02T23:21:59.130897Z", + "shell.execute_reply": "2024-08-02T23:21:59.130271Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:21:59.133602Z", + "iopub.status.busy": "2024-08-02T23:21:59.133095Z", + "iopub.status.idle": "2024-08-02T23:22:02.346575Z", + "shell.execute_reply": "2024-08-02T23:22:02.345989Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:02.349075Z", + "iopub.status.busy": "2024-08-02T23:22:02.348695Z", + "iopub.status.idle": "2024-08-02T23:22:10.790575Z", + "shell.execute_reply": "2024-08-02T23:22:10.790056Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:10.792934Z", + "iopub.status.busy": "2024-08-02T23:22:10.792557Z", + "iopub.status.idle": "2024-08-02T23:22:10.956046Z", + "shell.execute_reply": "2024-08-02T23:22:10.955515Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:10.958582Z", + "iopub.status.busy": "2024-08-02T23:22:10.958202Z", + "iopub.status.idle": "2024-08-02T23:22:12.292827Z", + "shell.execute_reply": "2024-08-02T23:22:12.292255Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.295279Z", + "iopub.status.busy": "2024-08-02T23:22:12.294790Z", + "iopub.status.idle": "2024-08-02T23:22:12.622073Z", + "shell.execute_reply": "2024-08-02T23:22:12.621483Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.624778Z", + "iopub.status.busy": "2024-08-02T23:22:12.624229Z", + "iopub.status.idle": "2024-08-02T23:22:12.637797Z", + "shell.execute_reply": "2024-08-02T23:22:12.637348Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.640065Z", + "iopub.status.busy": "2024-08-02T23:22:12.639723Z", + "iopub.status.idle": "2024-08-02T23:22:12.658542Z", + "shell.execute_reply": "2024-08-02T23:22:12.658085Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.660724Z", + "iopub.status.busy": "2024-08-02T23:22:12.660375Z", + "iopub.status.idle": "2024-08-02T23:22:12.877581Z", + "shell.execute_reply": "2024-08-02T23:22:12.877013Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.880604Z", + "iopub.status.busy": "2024-08-02T23:22:12.880140Z", + "iopub.status.idle": "2024-08-02T23:22:12.899913Z", + "shell.execute_reply": "2024-08-02T23:22:12.899342Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:12.902002Z", + "iopub.status.busy": "2024-08-02T23:22:12.901825Z", + "iopub.status.idle": "2024-08-02T23:22:13.071307Z", + "shell.execute_reply": "2024-08-02T23:22:13.070655Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.073549Z", + "iopub.status.busy": "2024-08-02T23:22:13.073366Z", + "iopub.status.idle": "2024-08-02T23:22:13.083592Z", + "shell.execute_reply": "2024-08-02T23:22:13.083032Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.085738Z", + "iopub.status.busy": "2024-08-02T23:22:13.085397Z", + "iopub.status.idle": "2024-08-02T23:22:13.094677Z", + "shell.execute_reply": "2024-08-02T23:22:13.094210Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.096577Z", + "iopub.status.busy": "2024-08-02T23:22:13.096407Z", + "iopub.status.idle": "2024-08-02T23:22:13.122565Z", + "shell.execute_reply": "2024-08-02T23:22:13.122066Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.124990Z", + "iopub.status.busy": "2024-08-02T23:22:13.124635Z", + "iopub.status.idle": "2024-08-02T23:22:13.127488Z", + "shell.execute_reply": "2024-08-02T23:22:13.127033Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.129612Z", + "iopub.status.busy": "2024-08-02T23:22:13.129258Z", + "iopub.status.idle": "2024-08-02T23:22:13.149348Z", + "shell.execute_reply": "2024-08-02T23:22:13.148753Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.151509Z", + "iopub.status.busy": "2024-08-02T23:22:13.151173Z", + "iopub.status.idle": "2024-08-02T23:22:13.155595Z", + "shell.execute_reply": "2024-08-02T23:22:13.155039Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.157749Z", + "iopub.status.busy": "2024-08-02T23:22:13.157403Z", + "iopub.status.idle": "2024-08-02T23:22:13.186218Z", + "shell.execute_reply": "2024-08-02T23:22:13.185603Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.188598Z", + "iopub.status.busy": "2024-08-02T23:22:13.188252Z", + "iopub.status.idle": "2024-08-02T23:22:13.558273Z", + "shell.execute_reply": "2024-08-02T23:22:13.557664Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.560412Z", + "iopub.status.busy": "2024-08-02T23:22:13.560221Z", + "iopub.status.idle": "2024-08-02T23:22:13.563654Z", + "shell.execute_reply": "2024-08-02T23:22:13.563075Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.565843Z", + "iopub.status.busy": "2024-08-02T23:22:13.565488Z", + "iopub.status.idle": "2024-08-02T23:22:13.578636Z", + "shell.execute_reply": "2024-08-02T23:22:13.578130Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:13.580749Z", + "iopub.status.busy": "2024-08-02T23:22:13.580400Z", + "iopub.status.idle": "2024-08-02T23:22:13.593962Z", + "shell.execute_reply": "2024-08-02T23:22:13.593384Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - 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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 986707 (964K) [application/zip]\r\n", "Saving to: ‘CIFAR-10-subset.zip’\r\n", "\r\n", "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.03s \r\n", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.007s \r\n", "\r\n", - "2024-07-30 16:36:35 (36.4 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-08-02 23:22:14 (131 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3808,10 +3794,10 @@ "execution_count": 34, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:14.286320Z", + "iopub.status.busy": "2024-08-02T23:22:14.285986Z", + "iopub.status.idle": "2024-08-02T23:22:16.250080Z", + "shell.execute_reply": "2024-08-02T23:22:16.249517Z" } }, "outputs": [], @@ -3857,10 +3843,10 @@ "execution_count": 35, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:16.252933Z", + "iopub.status.busy": "2024-08-02T23:22:16.252320Z", + "iopub.status.idle": "2024-08-02T23:22:16.733355Z", + "shell.execute_reply": "2024-08-02T23:22:16.732689Z" } }, "outputs": [ @@ -3875,7 +3861,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ed4efbeb1874db0a5e2316cc6fdcc53", + "model_id": "71a4c08b9dfc456aa328fdeec90efbf7", "version_major": 2, "version_minor": 0 }, @@ -3957,10 +3943,10 @@ "execution_count": 36, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:16.737440Z", + "iopub.status.busy": "2024-08-02T23:22:16.736282Z", + "iopub.status.idle": "2024-08-02T23:22:16.754516Z", + "shell.execute_reply": "2024-08-02T23:22:16.754000Z" } }, "outputs": [ @@ -4079,35 +4065,35 @@ " \n", " \n", " \n", - " is_dark_issue\n", " dark_score\n", + " is_dark_issue\n", " \n", " \n", " \n", " \n", " 0\n", - " True\n", " 0.237196\n", + " True\n", " \n", " \n", " 1\n", - " True\n", " 0.197229\n", + " True\n", " \n", " \n", " 2\n", - " True\n", " 0.254188\n", + " True\n", " \n", " \n", " 3\n", - " True\n", " 0.229170\n", + " True\n", " \n", " \n", " 4\n", - " True\n", " 0.208907\n", + " True\n", " \n", " \n", " ...\n", @@ -4116,28 +4102,28 @@ " \n", " \n", " 195\n", - " False\n", " 0.793840\n", + " False\n", " \n", " \n", " 196\n", - " False\n", " 1.000000\n", + " False\n", " \n", " \n", " 197\n", - " False\n", " 0.971560\n", + " False\n", " \n", " \n", " 198\n", - " False\n", " 0.862236\n", + " False\n", " \n", " \n", " 199\n", - " False\n", " 0.973533\n", + " False\n", " \n", " \n", "\n", @@ -4145,18 +4131,18 @@ "

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"a8c94e907c12444e8d0364601f96e159": { + "c3884d048fe24b41af28a41df38517ea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4973,7 +5012,7 @@ "text_color": null } }, - "b7913d94eed4408cb306a3e2b47761cb": { + "c38bfaecbf7147b693a82b0b3e772612": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5026,83 +5065,7 @@ "width": null } }, - "bd875ac3a62042068a132488eecbb1c4": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - 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"_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_9ab790ab579f415bbc804c1f992660a0", - "placeholder": "​", - "style": "IPY_MODEL_72b98c9996864aa9abad1934ce4b27c3", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "df924bb590a44f27a4f68916d0e77c53": { + "c5b4b4b00b804232a9ff311ead8306fc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5118,7 +5081,7 @@ "description_width": "" } }, - "f2cb72313c294a56bc0221871a2d5717": { + "d48001ab3b6148e6a9e0fa4aceed791e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5171,7 +5134,7 @@ "width": null } }, - "f59fcaa8e8d04931980396c1bdc42425": { + "d97ac03902bf40949b08b5f65f538140": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5223,6 +5186,29 @@ "visibility": null, "width": null } + }, + "e66b4a469d0d4eadb2566c3d7c697ecd": { + "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_c38bfaecbf7147b693a82b0b3e772612", + "placeholder": "​", + "style": "IPY_MODEL_4222b0643f044a5f9e8426072332e919", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 689.10it/s]" + } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.html b/master/tutorials/dataset_health.html index b149fd0f1..31a94df7c 100644 --- a/master/tutorials/dataset_health.html +++ b/master/tutorials/dataset_health.html @@ -3078,6 +3078,12 @@

    Start of tutorial: Evaluate the health of 8 popular dataset +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -3159,6 +3165,7 @@

    Start of tutorial: Evaluate the health of 8 popular dataset
  • Install dependencies and import them
  • Fetch the data (can skip these details)
  • Start of tutorial: Evaluate the health of 8 popular datasets
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index b41d28ba4..464e6d818 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": "2024-07-30T16:36:43.263935Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:21.207713Z", + "iopub.status.busy": "2024-08-02T23:22:21.207533Z", + "iopub.status.idle": "2024-08-02T23:22:22.617403Z", + "shell.execute_reply": "2024-08-02T23:22:22.616703Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:36:44.679704Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:22.619987Z", + "iopub.status.busy": "2024-08-02T23:22:22.619693Z", + "iopub.status.idle": "2024-08-02T23:22:22.622687Z", + "shell.execute_reply": "2024-08-02T23:22:22.622224Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:22.624727Z", + "iopub.status.busy": "2024-08-02T23:22:22.624552Z", + "iopub.status.idle": "2024-08-02T23:22:22.636926Z", + "shell.execute_reply": "2024-08-02T23:22:22.636449Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:22.638863Z", + "iopub.status.busy": "2024-08-02T23:22:22.638690Z", + "iopub.status.idle": "2024-08-02T23:22:26.870323Z", + "shell.execute_reply": "2024-08-02T23:22:26.869834Z" }, "id": "dhTHOg8Pyv5G" }, @@ -3081,6 +3081,21 @@ " # run 1 line of code to evaluate the health of your dataset\n", " _ = cleanlab.dataset.health_summary(labels, pred_probs, class_names=class_names)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] } ], "metadata": { diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 3a4b6f1a2..8d757350b 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 0e282dd07..85b88d83d 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:29.317927Z", + "iopub.status.busy": "2024-08-02T23:22:29.317762Z", + "iopub.status.idle": "2024-08-02T23:22:30.709755Z", + "shell.execute_reply": "2024-08-02T23:22:30.709204Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:30.712568Z", + "iopub.status.busy": "2024-08-02T23:22:30.712110Z", + "iopub.status.idle": "2024-08-02T23:22:30.715506Z", + "shell.execute_reply": "2024-08-02T23:22:30.715053Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:30.717656Z", + "iopub.status.busy": "2024-08-02T23:22:30.717203Z", + "iopub.status.idle": "2024-08-02T23:22:34.286623Z", + "shell.execute_reply": "2024-08-02T23:22:34.285962Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.290077Z", + "iopub.status.busy": "2024-08-02T23:22:34.289157Z", + "iopub.status.idle": "2024-08-02T23:22:34.334428Z", + "shell.execute_reply": "2024-08-02T23:22:34.333773Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.337263Z", + "iopub.status.busy": "2024-08-02T23:22:34.336802Z", + "iopub.status.idle": "2024-08-02T23:22:34.379369Z", + "shell.execute_reply": "2024-08-02T23:22:34.378671Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.382130Z", + "iopub.status.busy": "2024-08-02T23:22:34.381755Z", + "iopub.status.idle": "2024-08-02T23:22:34.384963Z", + "shell.execute_reply": "2024-08-02T23:22:34.384470Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.387036Z", + "iopub.status.busy": "2024-08-02T23:22:34.386739Z", + "iopub.status.idle": "2024-08-02T23:22:34.389652Z", + "shell.execute_reply": "2024-08-02T23:22:34.388895Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.391832Z", + "iopub.status.busy": "2024-08-02T23:22:34.391513Z", + "iopub.status.idle": "2024-08-02T23:22:34.417865Z", + "shell.execute_reply": "2024-08-02T23:22:34.417276Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57581a07cda143f5ae3947a8ceb2effa", + "model_id": "994bb101c48240bd91ac23c6d451faea", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8036196b7e194ee38336f33c15df9344", + "model_id": "75f8dda3bfd7411ab998335d777d0d77", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:34.423377Z", + "iopub.status.busy": "2024-08-02T23:22:34.423067Z", + "iopub.status.idle": "2024-08-02T23:22:34.429876Z", + "shell.execute_reply": "2024-08-02T23:22:34.429438Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:36:58.690192Z", - 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"id": "984213fa", + "id": "12afe868", "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": "2618e545", + "id": "8634f5cf", "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": "1e0becd2", + "id": "a6f1d693", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "cba58da6", + "id": "2d3fc7a8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:37.802141Z", + "iopub.status.busy": "2024-08-02T23:22:37.801810Z", + "iopub.status.idle": "2024-08-02T23:22:37.809564Z", + "shell.execute_reply": "2024-08-02T23:22:37.808972Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "fea318fb", + "id": "1fd92d80", "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": "6afc3734", + "id": "c2a27eb0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:37.811723Z", + "iopub.status.busy": "2024-08-02T23:22:37.811326Z", + "iopub.status.idle": "2024-08-02T23:22:37.831346Z", + "shell.execute_reply": "2024-08-02T23:22:37.830864Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "b8513ca9", + "id": "4c46c839", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:37.833309Z", + "iopub.status.busy": "2024-08-02T23:22:37.833133Z", + "iopub.status.idle": "2024-08-02T23:22:37.836625Z", + "shell.execute_reply": "2024-08-02T23:22:37.836148Z" } }, "outputs": [ @@ -1622,7 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e531205987048c482bafb4fc142f9ba", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_831e08b227234d0295aa11cd11e50c69", + "tabbable": null, + "tooltip": null, + "value": 50.0 } } }, diff --git a/master/tutorials/improving_ml_performance.ipynb b/master/tutorials/improving_ml_performance.ipynb index 839c45673..9ec229e44 100644 --- a/master/tutorials/improving_ml_performance.ipynb +++ b/master/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:41.243159Z", + "iopub.status.busy": "2024-08-02T23:22:41.242848Z", + "iopub.status.idle": "2024-08-02T23:22:42.677514Z", + "shell.execute_reply": "2024-08-02T23:22:42.676864Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.303031Z", - "iopub.status.busy": "2024-07-30T16:37:08.302535Z", - "iopub.status.idle": "2024-07-30T16:37:08.306381Z", - "shell.execute_reply": "2024-07-30T16:37:08.305889Z" + "iopub.execute_input": "2024-08-02T23:22:42.680254Z", + "iopub.status.busy": "2024-08-02T23:22:42.679911Z", + "iopub.status.idle": "2024-08-02T23:22:42.683720Z", + "shell.execute_reply": "2024-08-02T23:22:42.683171Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.308567Z", - 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"iopub.execute_input": "2024-07-30T16:37:08.590172Z", - "iopub.status.busy": "2024-07-30T16:37:08.589849Z", - "iopub.status.idle": "2024-07-30T16:37:08.595724Z", - "shell.execute_reply": "2024-07-30T16:37:08.595139Z" + "iopub.execute_input": "2024-08-02T23:22:42.977356Z", + "iopub.status.busy": "2024-08-02T23:22:42.976989Z", + "iopub.status.idle": "2024-08-02T23:22:42.982565Z", + "shell.execute_reply": "2024-08-02T23:22:42.982093Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:08.597670Z", - "iopub.status.busy": "2024-07-30T16:37:08.597361Z", - "iopub.status.idle": "2024-07-30T16:37:08.601523Z", - "shell.execute_reply": "2024-07-30T16:37:08.600966Z" + "iopub.execute_input": "2024-08-02T23:22:42.984641Z", + "iopub.status.busy": "2024-08-02T23:22:42.984213Z", + "iopub.status.idle": "2024-08-02T23:22:42.988345Z", + "shell.execute_reply": "2024-08-02T23:22:42.987921Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - 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"iopub.execute_input": "2024-07-30T16:37:08.685772Z", - "iopub.status.busy": "2024-07-30T16:37:08.685236Z", - "iopub.status.idle": "2024-07-30T16:37:08.705410Z", - "shell.execute_reply": "2024-07-30T16:37:08.704880Z" + "iopub.execute_input": "2024-08-02T23:22:43.075030Z", + "iopub.status.busy": "2024-08-02T23:22:43.074109Z", + "iopub.status.idle": "2024-08-02T23:22:43.095778Z", + "shell.execute_reply": "2024-08-02T23:22:43.095284Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.099248Z", + "iopub.status.busy": "2024-08-02T23:22:43.098329Z", + "iopub.status.idle": "2024-08-02T23:22:43.104218Z", + "shell.execute_reply": "2024-08-02T23:22:43.103727Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.107706Z", + "iopub.status.busy": "2024-08-02T23:22:43.106779Z", + "iopub.status.idle": "2024-08-02T23:22:43.112886Z", + "shell.execute_reply": "2024-08-02T23:22:43.112392Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.116202Z", + "iopub.status.busy": "2024-08-02T23:22:43.115450Z", + "iopub.status.idle": "2024-08-02T23:22:43.125506Z", + "shell.execute_reply": "2024-08-02T23:22:43.125084Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.127473Z", + "iopub.status.busy": "2024-08-02T23:22:43.127141Z", + "iopub.status.idle": "2024-08-02T23:22:43.131395Z", + "shell.execute_reply": "2024-08-02T23:22:43.130979Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.133376Z", + "iopub.status.busy": "2024-08-02T23:22:43.133027Z", + "iopub.status.idle": "2024-08-02T23:22:43.247301Z", + "shell.execute_reply": "2024-08-02T23:22:43.246697Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.249758Z", + "iopub.status.busy": "2024-08-02T23:22:43.249222Z", + "iopub.status.idle": "2024-08-02T23:22:43.255494Z", + "shell.execute_reply": "2024-08-02T23:22:43.255010Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:43.257796Z", + "iopub.status.busy": "2024-08-02T23:22:43.257451Z", + "iopub.status.idle": "2024-08-02T23:22:45.391725Z", + "shell.execute_reply": "2024-08-02T23:22:45.391106Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.394492Z", + "iopub.status.busy": "2024-08-02T23:22:45.394017Z", + "iopub.status.idle": "2024-08-02T23:22:45.407717Z", + "shell.execute_reply": "2024-08-02T23:22:45.407211Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.409965Z", + "iopub.status.busy": "2024-08-02T23:22:45.409683Z", + "iopub.status.idle": "2024-08-02T23:22:45.412502Z", + "shell.execute_reply": "2024-08-02T23:22:45.411936Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.414713Z", + "iopub.status.busy": "2024-08-02T23:22:45.414480Z", + "iopub.status.idle": "2024-08-02T23:22:45.419479Z", + "shell.execute_reply": "2024-08-02T23:22:45.418922Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.421657Z", + "iopub.status.busy": "2024-08-02T23:22:45.421424Z", + "iopub.status.idle": "2024-08-02T23:22:45.459617Z", + "shell.execute_reply": "2024-08-02T23:22:45.459124Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:45.461965Z", + "iopub.status.busy": "2024-08-02T23:22:45.461590Z", + "iopub.status.idle": "2024-08-02T23:22:46.004492Z", + "shell.execute_reply": "2024-08-02T23:22:46.003940Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.007832Z", + "iopub.status.busy": "2024-08-02T23:22:46.006922Z", + "iopub.status.idle": "2024-08-02T23:22:46.139183Z", + "shell.execute_reply": "2024-08-02T23:22:46.138499Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.142862Z", + "iopub.status.busy": "2024-08-02T23:22:46.141903Z", + "iopub.status.idle": "2024-08-02T23:22:46.150567Z", + "shell.execute_reply": "2024-08-02T23:22:46.150071Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.154019Z", + "iopub.status.busy": "2024-08-02T23:22:46.153094Z", + "iopub.status.idle": "2024-08-02T23:22:46.160946Z", + "shell.execute_reply": "2024-08-02T23:22:46.160456Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.164386Z", + "iopub.status.busy": "2024-08-02T23:22:46.163465Z", + "iopub.status.idle": "2024-08-02T23:22:46.170712Z", + "shell.execute_reply": "2024-08-02T23:22:46.170223Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.174165Z", + "iopub.status.busy": "2024-08-02T23:22:46.173238Z", + "iopub.status.idle": "2024-08-02T23:22:46.179291Z", + "shell.execute_reply": "2024-08-02T23:22:46.178762Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.181687Z", + "iopub.status.busy": "2024-08-02T23:22:46.181513Z", + "iopub.status.idle": "2024-08-02T23:22:46.186322Z", + "shell.execute_reply": "2024-08-02T23:22:46.185868Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.188278Z", + "iopub.status.busy": "2024-08-02T23:22:46.188100Z", + "iopub.status.idle": "2024-08-02T23:22:46.265593Z", + "shell.execute_reply": "2024-08-02T23:22:46.264934Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.268378Z", + "iopub.status.busy": "2024-08-02T23:22:46.267931Z", + "iopub.status.idle": "2024-08-02T23:22:46.277696Z", + "shell.execute_reply": "2024-08-02T23:22:46.277149Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.280290Z", + "iopub.status.busy": "2024-08-02T23:22:46.279817Z", + "iopub.status.idle": "2024-08-02T23:22:46.282856Z", + "shell.execute_reply": "2024-08-02T23:22:46.282369Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.285113Z", + "iopub.status.busy": "2024-08-02T23:22:46.284905Z", + "iopub.status.idle": "2024-08-02T23:22:46.295023Z", + "shell.execute_reply": "2024-08-02T23:22:46.294587Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.297195Z", + "iopub.status.busy": "2024-08-02T23:22:46.297014Z", + "iopub.status.idle": "2024-08-02T23:22:46.303290Z", + "shell.execute_reply": "2024-08-02T23:22:46.302794Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.305390Z", + "iopub.status.busy": "2024-08-02T23:22:46.305229Z", + "iopub.status.idle": "2024-08-02T23:22:46.308203Z", + "shell.execute_reply": "2024-08-02T23:22:46.307753Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:46.310173Z", + "iopub.status.busy": "2024-08-02T23:22:46.310013Z", + "iopub.status.idle": "2024-08-02T23:22:50.335807Z", + "shell.execute_reply": "2024-08-02T23:22:50.335295Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:50.339016Z", + "iopub.status.busy": "2024-08-02T23:22:50.338110Z", + "iopub.status.idle": "2024-08-02T23:22:50.342873Z", + "shell.execute_reply": "2024-08-02T23:22:50.342277Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:50.345292Z", + "iopub.status.busy": "2024-08-02T23:22:50.344858Z", + "iopub.status.idle": "2024-08-02T23:22:50.347667Z", + "shell.execute_reply": "2024-08-02T23:22:50.347208Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/indepth_overview.html b/master/tutorials/indepth_overview.html index 025a90aac..59b924a32 100644 --- a/master/tutorials/indepth_overview.html +++ b/master/tutorials/indepth_overview.html @@ -2363,6 +2363,12 @@

    Workflow 8: Ensembling label quality scores from multiple p

    While ensembling different models’ label quality scores (label_quality_scores_best) will often be superior to getting label quality scores from a single ensemble predictor (label_quality_scores_better), both approaches produce significantly better label quality scores than just using the predictions from a single model.

    +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -2479,6 +2485,7 @@

    Workflow 8: Ensembling label quality scores from multiple p
  • Workflow 8: Ensembling label quality scores from multiple predictors
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 63d074d15..dbe76443f 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": "2024-07-30T16:37:19.514665Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:53.660990Z", + "iopub.status.busy": "2024-08-02T23:22:53.660815Z", + "iopub.status.idle": "2024-08-02T23:22:55.078462Z", + "shell.execute_reply": "2024-08-02T23:22:55.077902Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:20.972868Z", - "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" + "iopub.execute_input": "2024-08-02T23:22:55.080870Z", + "iopub.status.busy": "2024-08-02T23:22:55.080574Z", + "iopub.status.idle": "2024-08-02T23:22:55.084143Z", + "shell.execute_reply": "2024-08-02T23:22:55.083572Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.086422Z", + "iopub.status.busy": "2024-08-02T23:22:55.086068Z", + "iopub.status.idle": "2024-08-02T23:22:55.097345Z", + "shell.execute_reply": "2024-08-02T23:22:55.096867Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.099137Z", + "iopub.status.busy": "2024-08-02T23:22:55.098958Z", + "iopub.status.idle": "2024-08-02T23:22:55.335579Z", + "shell.execute_reply": "2024-08-02T23:22:55.334975Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.337901Z", + "iopub.status.busy": "2024-08-02T23:22:55.337546Z", + "iopub.status.idle": "2024-08-02T23:22:55.363253Z", + "shell.execute_reply": "2024-08-02T23:22:55.362813Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:55.365173Z", + "iopub.status.busy": "2024-08-02T23:22:55.364984Z", + "iopub.status.idle": "2024-08-02T23:22:57.475466Z", + "shell.execute_reply": "2024-08-02T23:22:57.474804Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:57.477895Z", + "iopub.status.busy": "2024-08-02T23:22:57.477563Z", + "iopub.status.idle": "2024-08-02T23:22:57.495457Z", + "shell.execute_reply": "2024-08-02T23:22:57.494985Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:57.497360Z", + "iopub.status.busy": "2024-08-02T23:22:57.497175Z", + "iopub.status.idle": "2024-08-02T23:22:59.083356Z", + "shell.execute_reply": "2024-08-02T23:22:59.082741Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.086101Z", + "iopub.status.busy": "2024-08-02T23:22:59.085430Z", + "iopub.status.idle": "2024-08-02T23:22:59.099257Z", + "shell.execute_reply": "2024-08-02T23:22:59.098675Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.101459Z", + "iopub.status.busy": "2024-08-02T23:22:59.101073Z", + "iopub.status.idle": "2024-08-02T23:22:59.182514Z", + "shell.execute_reply": "2024-08-02T23:22:59.181863Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.185181Z", + "iopub.status.busy": "2024-08-02T23:22:59.184699Z", + "iopub.status.idle": "2024-08-02T23:22:59.399591Z", + "shell.execute_reply": "2024-08-02T23:22:59.399127Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.401855Z", + "iopub.status.busy": "2024-08-02T23:22:59.401500Z", + "iopub.status.idle": "2024-08-02T23:22:59.418383Z", + "shell.execute_reply": "2024-08-02T23:22:59.417944Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.420363Z", + "iopub.status.busy": "2024-08-02T23:22:59.420024Z", + "iopub.status.idle": "2024-08-02T23:22:59.429357Z", + "shell.execute_reply": "2024-08-02T23:22:59.428784Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.431554Z", + "iopub.status.busy": "2024-08-02T23:22:59.431121Z", + "iopub.status.idle": "2024-08-02T23:22:59.523557Z", + "shell.execute_reply": "2024-08-02T23:22:59.522973Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.525876Z", + "iopub.status.busy": "2024-08-02T23:22:59.525646Z", + "iopub.status.idle": "2024-08-02T23:22:59.668775Z", + "shell.execute_reply": "2024-08-02T23:22:59.668197Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.671509Z", + "iopub.status.busy": "2024-08-02T23:22:59.671116Z", + "iopub.status.idle": "2024-08-02T23:22:59.675170Z", + "shell.execute_reply": "2024-08-02T23:22:59.674673Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.677097Z", + "iopub.status.busy": "2024-08-02T23:22:59.676885Z", + "iopub.status.idle": "2024-08-02T23:22:59.680782Z", + "shell.execute_reply": "2024-08-02T23:22:59.680214Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.682941Z", + "iopub.status.busy": "2024-08-02T23:22:59.682612Z", + "iopub.status.idle": "2024-08-02T23:22:59.719665Z", + "shell.execute_reply": "2024-08-02T23:22:59.719195Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.721625Z", + "iopub.status.busy": "2024-08-02T23:22:59.721448Z", + "iopub.status.idle": "2024-08-02T23:22:59.762453Z", + "shell.execute_reply": "2024-08-02T23:22:59.761973Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.764348Z", + "iopub.status.busy": "2024-08-02T23:22:59.764176Z", + "iopub.status.idle": "2024-08-02T23:22:59.883238Z", + "shell.execute_reply": "2024-08-02T23:22:59.882497Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.885892Z", + "iopub.status.busy": "2024-08-02T23:22:59.885655Z", + "iopub.status.idle": "2024-08-02T23:22:59.991995Z", + "shell.execute_reply": "2024-08-02T23:22:59.991397Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:22:59.994517Z", + "iopub.status.busy": "2024-08-02T23:22:59.994166Z", + "iopub.status.idle": "2024-08-02T23:23:00.206479Z", + "shell.execute_reply": "2024-08-02T23:23:00.205902Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.208774Z", + "iopub.status.busy": "2024-08-02T23:23:00.208393Z", + "iopub.status.idle": "2024-08-02T23:23:00.425932Z", + "shell.execute_reply": "2024-08-02T23:23:00.425360Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.428347Z", + "iopub.status.busy": "2024-08-02T23:23:00.427962Z", + "iopub.status.idle": "2024-08-02T23:23:00.434288Z", + "shell.execute_reply": "2024-08-02T23:23:00.433838Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.436322Z", + "iopub.status.busy": "2024-08-02T23:23:00.435908Z", + "iopub.status.idle": "2024-08-02T23:23:00.651939Z", + "shell.execute_reply": "2024-08-02T23:23:00.651371Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:00.654276Z", + "iopub.status.busy": "2024-08-02T23:23:00.653821Z", + "iopub.status.idle": "2024-08-02T23:23:01.699320Z", + "shell.execute_reply": "2024-08-02T23:23:01.698766Z" }, "id": "wL3ngCnuLEWd" }, @@ -2381,6 +2381,21 @@ "source": [ "While ensembling different models' label quality scores (`label_quality_scores_best`) will often be superior to getting label quality scores from a single ensemble predictor (`label_quality_scores_better`), both approaches produce significantly better label quality scores than just using the predictions from a single model." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] } ], "metadata": { diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index fd2c70404..f2e838faf 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:05.094466Z", + "iopub.status.busy": "2024-08-02T23:23:05.094291Z", + "iopub.status.idle": "2024-08-02T23:23:06.519188Z", + "shell.execute_reply": "2024-08-02T23:23:06.518592Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:34.163333Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:06.521921Z", + "iopub.status.busy": "2024-08-02T23:23:06.521450Z", + "iopub.status.idle": "2024-08-02T23:23:06.524559Z", + "shell.execute_reply": "2024-08-02T23:23:06.524087Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.526668Z", + "iopub.status.busy": "2024-08-02T23:23:06.526334Z", + "iopub.status.idle": "2024-08-02T23:23:06.534155Z", + "shell.execute_reply": "2024-08-02T23:23:06.533679Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.536079Z", + "iopub.status.busy": "2024-08-02T23:23:06.535779Z", + "iopub.status.idle": "2024-08-02T23:23:06.582288Z", + "shell.execute_reply": "2024-08-02T23:23:06.581795Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.584696Z", + "iopub.status.busy": "2024-08-02T23:23:06.584134Z", + "iopub.status.idle": "2024-08-02T23:23:06.601445Z", + "shell.execute_reply": "2024-08-02T23:23:06.600877Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.603369Z", + "iopub.status.busy": "2024-08-02T23:23:06.603188Z", + "iopub.status.idle": "2024-08-02T23:23:06.607249Z", + "shell.execute_reply": "2024-08-02T23:23:06.606678Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.609439Z", + "iopub.status.busy": "2024-08-02T23:23:06.608988Z", + "iopub.status.idle": "2024-08-02T23:23:06.625441Z", + "shell.execute_reply": "2024-08-02T23:23:06.624820Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.627733Z", + "iopub.status.busy": "2024-08-02T23:23:06.627381Z", + "iopub.status.idle": "2024-08-02T23:23:06.653432Z", + "shell.execute_reply": "2024-08-02T23:23:06.652979Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:06.655520Z", + "iopub.status.busy": "2024-08-02T23:23:06.655195Z", + "iopub.status.idle": "2024-08-02T23:23:08.797131Z", + "shell.execute_reply": "2024-08-02T23:23:08.796420Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.800999Z", + "iopub.status.busy": "2024-08-02T23:23:08.799464Z", + "iopub.status.idle": "2024-08-02T23:23:08.807486Z", + "shell.execute_reply": "2024-08-02T23:23:08.807012Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.809557Z", + "iopub.status.busy": "2024-08-02T23:23:08.809274Z", + "iopub.status.idle": "2024-08-02T23:23:08.821755Z", + "shell.execute_reply": "2024-08-02T23:23:08.821200Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.823744Z", + "iopub.status.busy": "2024-08-02T23:23:08.823567Z", + "iopub.status.idle": "2024-08-02T23:23:08.829911Z", + "shell.execute_reply": "2024-08-02T23:23:08.829473Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.831814Z", + "iopub.status.busy": "2024-08-02T23:23:08.831639Z", + "iopub.status.idle": "2024-08-02T23:23:08.834249Z", + "shell.execute_reply": "2024-08-02T23:23:08.833780Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.836353Z", + "iopub.status.busy": "2024-08-02T23:23:08.836022Z", + "iopub.status.idle": "2024-08-02T23:23:08.839391Z", + "shell.execute_reply": "2024-08-02T23:23:08.838879Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.841450Z", + "iopub.status.busy": "2024-08-02T23:23:08.841177Z", + "iopub.status.idle": "2024-08-02T23:23:08.843918Z", + "shell.execute_reply": "2024-08-02T23:23:08.843357Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.845923Z", + "iopub.status.busy": "2024-08-02T23:23:08.845589Z", + "iopub.status.idle": "2024-08-02T23:23:08.849887Z", + "shell.execute_reply": "2024-08-02T23:23:08.849429Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.852036Z", + "iopub.status.busy": "2024-08-02T23:23:08.851589Z", + "iopub.status.idle": "2024-08-02T23:23:08.880616Z", + "shell.execute_reply": "2024-08-02T23:23:08.879983Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:08.883256Z", + "iopub.status.busy": "2024-08-02T23:23:08.882881Z", + "iopub.status.idle": "2024-08-02T23:23:08.887623Z", + "shell.execute_reply": "2024-08-02T23:23:08.887175Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.html b/master/tutorials/multilabel_classification.html index 9eba8aeee..4f9d904e9 100644 --- a/master/tutorials/multilabel_classification.html +++ b/master/tutorials/multilabel_classification.html @@ -1021,6 +1021,12 @@

    Application to Real Dataexample notebook “Find Label Errors in Multi-Label Classification Data (CelebA Image Tagging)”. That example also demonstrates how to use a state-of-the-art Pytorch neural network for multi-label classification with image data.

    +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1109,6 +1115,7 @@

    Application to Real DataApplication to Real Data +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 85921d220..607a1ea32 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": "2024-07-30T16:37:39.759530Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:11.947982Z", + "iopub.status.busy": "2024-08-02T23:23:11.947507Z", + "iopub.status.idle": "2024-08-02T23:23:13.351095Z", + "shell.execute_reply": "2024-08-02T23:23:13.350543Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:41.228688Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:13.353619Z", + "iopub.status.busy": "2024-08-02T23:23:13.353223Z", + "iopub.status.idle": "2024-08-02T23:23:13.373148Z", + "shell.execute_reply": "2024-08-02T23:23:13.372529Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:13.375624Z", + "iopub.status.busy": "2024-08-02T23:23:13.375180Z", + "iopub.status.idle": "2024-08-02T23:23:13.388068Z", + "shell.execute_reply": "2024-08-02T23:23:13.387585Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:13.390138Z", + "iopub.status.busy": "2024-08-02T23:23:13.389833Z", + "iopub.status.idle": "2024-08-02T23:23:16.059034Z", + "shell.execute_reply": "2024-08-02T23:23:16.058466Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:16.061219Z", + "iopub.status.busy": "2024-08-02T23:23:16.060988Z", + "iopub.status.idle": "2024-08-02T23:23:17.421704Z", + "shell.execute_reply": "2024-08-02T23:23:17.421064Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:17.424281Z", + "iopub.status.busy": "2024-08-02T23:23:17.423964Z", + "iopub.status.idle": "2024-08-02T23:23:17.428263Z", + "shell.execute_reply": "2024-08-02T23:23:17.427671Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:17.430557Z", + "iopub.status.busy": "2024-08-02T23:23:17.430085Z", + "iopub.status.idle": "2024-08-02T23:23:19.529227Z", + "shell.execute_reply": "2024-08-02T23:23:19.528576Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:19.531878Z", + "iopub.status.busy": "2024-08-02T23:23:19.531352Z", + "iopub.status.idle": "2024-08-02T23:23:19.539973Z", + "shell.execute_reply": "2024-08-02T23:23:19.539495Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:19.541992Z", + "iopub.status.busy": "2024-08-02T23:23:19.541709Z", + "iopub.status.idle": "2024-08-02T23:23:22.157011Z", + "shell.execute_reply": "2024-08-02T23:23:22.156381Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.159325Z", + "iopub.status.busy": "2024-08-02T23:23:22.159129Z", + "iopub.status.idle": "2024-08-02T23:23:22.162534Z", + "shell.execute_reply": "2024-08-02T23:23:22.162022Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.164526Z", + "iopub.status.busy": "2024-08-02T23:23:22.164350Z", + "iopub.status.idle": "2024-08-02T23:23:22.167796Z", + "shell.execute_reply": "2024-08-02T23:23:22.167349Z" } }, "outputs": [], @@ -746,16 +746,33 @@ "To see cleanlab applied to a real image tagging dataset, check out our [example](https://github.com/cleanlab/examples) notebook [\"Find Label Errors in Multi-Label Classification Data (CelebA Image Tagging)\"](https://github.com/cleanlab/examples/blob/master/multilabel_classification/image_tagging.ipynb). That example also demonstrates how to use a state-of-the-art Pytorch neural network for multi-label classification with image data." ] }, + { + "cell_type": "markdown", + "id": "f1bd9f83", + "metadata": {}, + "source": [ + "\n", + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] + }, { "cell_type": "code", "execution_count": 12, "id": "d1a2c008", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:22.169928Z", + "iopub.status.busy": "2024-08-02T23:23:22.169595Z", + "iopub.status.idle": "2024-08-02T23:23:22.173262Z", + "shell.execute_reply": "2024-08-02T23:23:22.172813Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index b908d214b..82a016874 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:37:52.967876Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:24.740098Z", + "iopub.status.busy": "2024-08-02T23:23:24.739916Z", + "iopub.status.idle": "2024-08-02T23:23:26.153388Z", + "shell.execute_reply": "2024-08-02T23:23:26.152727Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:37:54.426120Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:26.155926Z", + "iopub.status.busy": "2024-08-02T23:23:26.155520Z", + "iopub.status.idle": "2024-08-02T23:23:27.265000Z", + "shell.execute_reply": "2024-08-02T23:23:27.264184Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.267778Z", + "iopub.status.busy": "2024-08-02T23:23:27.267566Z", + "iopub.status.idle": "2024-08-02T23:23:27.271124Z", + "shell.execute_reply": "2024-08-02T23:23:27.270533Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.273453Z", + "iopub.status.busy": "2024-08-02T23:23:27.272991Z", + "iopub.status.idle": "2024-08-02T23:23:27.280260Z", + "shell.execute_reply": "2024-08-02T23:23:27.279681Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.282555Z", + "iopub.status.busy": "2024-08-02T23:23:27.282222Z", + "iopub.status.idle": "2024-08-02T23:23:27.602378Z", + "shell.execute_reply": "2024-08-02T23:23:27.601767Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.605245Z", + "iopub.status.busy": "2024-08-02T23:23:27.605028Z", + "iopub.status.idle": "2024-08-02T23:23:27.610491Z", + "shell.execute_reply": "2024-08-02T23:23:27.610005Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.612710Z", + "iopub.status.busy": "2024-08-02T23:23:27.612291Z", + "iopub.status.idle": "2024-08-02T23:23:27.616428Z", + "shell.execute_reply": "2024-08-02T23:23:27.615974Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:27.618678Z", + "iopub.status.busy": "2024-08-02T23:23:27.618223Z", + "iopub.status.idle": "2024-08-02T23:23:28.510375Z", + "shell.execute_reply": "2024-08-02T23:23:28.509695Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.512934Z", + "iopub.status.busy": "2024-08-02T23:23:28.512566Z", + "iopub.status.idle": "2024-08-02T23:23:28.729976Z", + "shell.execute_reply": "2024-08-02T23:23:28.729360Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.732357Z", + "iopub.status.busy": "2024-08-02T23:23:28.731916Z", + "iopub.status.idle": "2024-08-02T23:23:28.736334Z", + "shell.execute_reply": "2024-08-02T23:23:28.735894Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:28.738485Z", + "iopub.status.busy": "2024-08-02T23:23:28.738171Z", + "iopub.status.idle": "2024-08-02T23:23:29.208640Z", + "shell.execute_reply": "2024-08-02T23:23:29.207936Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.211755Z", + "iopub.status.busy": "2024-08-02T23:23:29.211376Z", + "iopub.status.idle": "2024-08-02T23:23:29.548019Z", + "shell.execute_reply": "2024-08-02T23:23:29.547410Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.550940Z", + "iopub.status.busy": "2024-08-02T23:23:29.550736Z", + "iopub.status.idle": "2024-08-02T23:23:29.920855Z", + "shell.execute_reply": "2024-08-02T23:23:29.920218Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:29.924419Z", + "iopub.status.busy": "2024-08-02T23:23:29.923997Z", + "iopub.status.idle": "2024-08-02T23:23:30.376104Z", + "shell.execute_reply": "2024-08-02T23:23:30.375480Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:30.380802Z", + "iopub.status.busy": "2024-08-02T23:23:30.380407Z", + "iopub.status.idle": "2024-08-02T23:23:30.837108Z", + "shell.execute_reply": "2024-08-02T23:23:30.836487Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:30.840640Z", + "iopub.status.busy": "2024-08-02T23:23:30.840263Z", + "iopub.status.idle": "2024-08-02T23:23:31.057636Z", + "shell.execute_reply": "2024-08-02T23:23:31.057057Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.059925Z", + "iopub.status.busy": "2024-08-02T23:23:31.059721Z", + "iopub.status.idle": "2024-08-02T23:23:31.259783Z", + "shell.execute_reply": "2024-08-02T23:23:31.259243Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.262327Z", + "iopub.status.busy": "2024-08-02T23:23:31.262133Z", + "iopub.status.idle": "2024-08-02T23:23:31.265004Z", + "shell.execute_reply": "2024-08-02T23:23:31.264548Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:31.267241Z", + "iopub.status.busy": "2024-08-02T23:23:31.266775Z", + "iopub.status.idle": "2024-08-02T23:23:32.193701Z", + "shell.execute_reply": "2024-08-02T23:23:32.193146Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.196208Z", + "iopub.status.busy": "2024-08-02T23:23:32.196016Z", + "iopub.status.idle": "2024-08-02T23:23:32.322609Z", + "shell.execute_reply": "2024-08-02T23:23:32.322075Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.324841Z", + "iopub.status.busy": "2024-08-02T23:23:32.324492Z", + "iopub.status.idle": "2024-08-02T23:23:32.514569Z", + "shell.execute_reply": "2024-08-02T23:23:32.514058Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:32.516894Z", + "iopub.status.busy": "2024-08-02T23:23:32.516525Z", + "iopub.status.idle": "2024-08-02T23:23:33.194488Z", + "shell.execute_reply": "2024-08-02T23:23:33.193856Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:33.196816Z", + "iopub.status.busy": "2024-08-02T23:23:33.196477Z", + "iopub.status.idle": "2024-08-02T23:23:33.200251Z", + "shell.execute_reply": "2024-08-02T23:23:33.199677Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 51298fe0f..a012e364a 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

    2. Pre-process the Cifar10 dataset 
    -100%|██████████| 170498071/170498071 [00:01<00:00, 107805868.41it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 104612399.87it/s]

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

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

    -
    +
    @@ -1123,8 +1123,14 @@

    4. Use cleanlab and here.

    - @@ -1211,6 +1217,7 @@

    4. Use cleanlab and 3. Use cleanlab and feature embeddings to find outliers in the data
  • 4. Use cleanlab and pred_probs to find outliers in the data
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 3df92007c..3ad8a9b33 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:35.415873Z", + "iopub.status.busy": "2024-08-02T23:23:35.415697Z", + "iopub.status.idle": "2024-08-02T23:23:38.626597Z", + "shell.execute_reply": "2024-08-02T23:23:38.626029Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:38:07.299136Z", - "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" + "iopub.execute_input": "2024-08-02T23:23:38.629407Z", + "iopub.status.busy": "2024-08-02T23:23:38.628808Z", + "iopub.status.idle": "2024-08-02T23:23:38.648318Z", + "shell.execute_reply": "2024-08-02T23:23:38.647845Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:38.650609Z", + "iopub.status.busy": "2024-08-02T23:23:38.650204Z", + "iopub.status.idle": "2024-08-02T23:23:38.654348Z", + "shell.execute_reply": "2024-08-02T23:23:38.653801Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:38.656499Z", + "iopub.status.busy": "2024-08-02T23:23:38.656188Z", + "iopub.status.idle": "2024-08-02T23:23:43.176479Z", + "shell.execute_reply": "2024-08-02T23:23:43.175945Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8226376.49it/s]" + " 1%| | 1703936/170498071 [00:00<00:10, 16570351.12it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10846208/170498071 [00:00<00:02, 59307309.31it/s]" + " 8%|▊ | 13139968/170498071 [00:00<00:02, 73382685.55it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22511616/170498071 [00:00<00:01, 84811001.30it/s]" + " 14%|█▎ | 23134208/170498071 [00:00<00:01, 85416938.17it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 34209792/170498071 [00:00<00:01, 97255735.33it/s]" + " 19%|█▉ | 33193984/170498071 [00:00<00:01, 91344444.59it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 45875200/170498071 [00:00<00:01, 104168464.41it/s]" + " 26%|██▌ | 44728320/170498071 [00:00<00:01, 99919094.88it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 57573376/170498071 [00:00<00:01, 108391005.55it/s]" + " 32%|███▏ | 54788096/170498071 [00:00<00:01, 100132338.36it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 69271552/170498071 [00:00<00:00, 111144409.19it/s]" + " 38%|███▊ | 64847872/170498071 [00:00<00:01, 100262039.06it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 80936960/170498071 [00:00<00:00, 112866681.19it/s]" + " 45%|████▍ | 76316672/170498071 [00:00<00:00, 104840140.37it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 92635136/170498071 [00:00<00:00, 114109378.79it/s]" + " 51%|█████ | 86835200/170498071 [00:00<00:00, 104704399.11it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 104300544/170498071 [00:01<00:00, 114843936.89it/s]" + " 58%|█████▊ | 98369536/170498071 [00:01<00:00, 107943288.17it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 115933184/170498071 [00:01<00:00, 115260750.98it/s]" + " 65%|██████▍ | 110100480/170498071 [00:01<00:00, 110768998.54it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 127631360/170498071 [00:01<00:00, 115738038.46it/s]" + " 71%|███████▏ | 121634816/170498071 [00:01<00:00, 112151957.27it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 139296768/170498071 [00:01<00:00, 115934009.59it/s]" + " 78%|███████▊ | 133169152/170498071 [00:01<00:00, 113008402.64it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 150994944/170498071 [00:01<00:00, 116207199.22it/s]" + " 85%|████████▍ | 144703488/170498071 [00:01<00:00, 113634335.19it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162627584/170498071 [00:01<00:00, 116186659.71it/s]" + " 92%|█████████▏| 156172288/170498071 [00:01<00:00, 113937851.78it/s]" ] }, { @@ -372,7 +372,15 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 107805868.41it/s]" + " 98%|█████████▊| 167706624/170498071 [00:01<00:00, 114298938.25it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:01<00:00, 104612399.87it/s]" ] }, { @@ -490,10 +498,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:23:43.178843Z", + "iopub.status.busy": "2024-08-02T23:23:43.178435Z", + "iopub.status.idle": "2024-08-02T23:23:43.183358Z", + "shell.execute_reply": "2024-08-02T23:23:43.182774Z" }, "nbsphinx": "hidden" }, @@ -544,10 +552,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - 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"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" + "iopub.execute_input": "2024-08-02T23:24:12.822039Z", + "iopub.status.busy": "2024-08-02T23:24:12.821413Z", + "iopub.status.idle": "2024-08-02T23:24:13.027440Z", + "shell.execute_reply": "2024-08-02T23:24:13.026901Z" } }, "outputs": [], @@ -1080,10 +1088,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:13.029812Z", + "iopub.status.busy": "2024-08-02T23:24:13.029518Z", + "iopub.status.idle": "2024-08-02T23:24:13.032865Z", + "shell.execute_reply": "2024-08-02T23:24:13.032400Z" } }, "outputs": [], @@ -1099,16 +1107,32 @@ "Detecting outliers based on feature embeddings can be done for arbitrary unlabeled datasets, but requires a meaningful numerical representation of the data. Detecting outliers based on predicted probabilities applies mainly for labeled classification datasets, but can be done with any effective classifier. The effectiveness of the latter approach depends on: how much auxiliary information captured in the feature values is lost in the predicted probabilities (determined by the particular set of labels in the classification task), the accuracy of our classifier, and how properly its predictions reflect epistemic uncertainty. Read more about it [here](https://pub.towardsai.net/a-simple-adjustment-improves-out-of-distribution-detection-for-any-classifier-5e96bbb2d627)." ] }, + { + "cell_type": "markdown", + "id": "03a5c870", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] + }, { "cell_type": "code", "execution_count": 20, "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:38:42.508323Z", - "iopub.status.busy": "2024-07-30T16:38:42.508151Z", - "iopub.status.idle": "2024-07-30T16:38:42.517729Z", - "shell.execute_reply": "2024-07-30T16:38:42.517288Z" + "iopub.execute_input": "2024-08-02T23:24:13.034934Z", + "iopub.status.busy": "2024-08-02T23:24:13.034594Z", + "iopub.status.idle": "2024-08-02T23:24:13.043213Z", + "shell.execute_reply": "2024-08-02T23:24:13.042768Z" }, "nbsphinx": "hidden" }, @@ -1153,31 +1177,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "23a512869c5e4f05a2356b8f464b1bcc": { + "310b96bf6543469cabf0f55665ff25f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - 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"style": "IPY_MODEL_dae8ad4cd0df4444bf5ff766e8012dc4", + "style": "IPY_MODEL_68e47a1386df476da98d7f630d7b8d6c", "tabbable": null, "tooltip": null, "value": 102469840.0 } }, - "6eb5b857715449aaa4e92a9a9560a833": { + "a17403bdb20c4c13949ca9635c43e46e": { + "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_b6fd529321ad458292f2208a4d6fc71d", + "placeholder": "​", + "style": "IPY_MODEL_310b96bf6543469cabf0f55665ff25f5", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 297MB/s]" + } + }, + "a828ae2c754b4b3ba7b44cc7fe06cf4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1373,25 +1407,7 @@ "width": null } }, - "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": { + "b6fd529321ad458292f2208a4d6fc71d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1444,23 +1460,31 @@ "width": null } }, - "dae8ad4cd0df4444bf5ff766e8012dc4": { + "d7f8f03577d54c03b0ecc33be697a44d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3408c76adf1949bfa8f227a1dca82da4", + "IPY_MODEL_9e08204f4c2845e1821cce00ebace48b", + "IPY_MODEL_a17403bdb20c4c13949ca9635c43e46e" + ], + "layout": "IPY_MODEL_a828ae2c754b4b3ba7b44cc7fe06cf4f", + "tabbable": null, + "tooltip": null } }, - "f1fa8803defc478f8f1a9688f96d5a79": { + "e30e1ae7872c46d180e55263ed029b6a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/pred_probs_cross_val.html b/master/tutorials/pred_probs_cross_val.html index 5cec9763f..02df34979 100644 --- a/master/tutorials/pred_probs_cross_val.html +++ b/master/tutorials/pred_probs_cross_val.html @@ -665,6 +665,13 @@

    What is K-fold cross-validation? +

    Spending too much time on data quality?#

    +

    This notebook demonstrates a toy ML model to quickly produce pred_probs. Because the performance of Cleanlab critically depends on the quality of your ML model, you should try to use a better ML model than the one demonstrated here. +If you are unsure how to produce a better ML model for your data, or want a platform to actually fix your issues automatically, use Cleanlab Studio which will automatically produce the best model for your dataset via cutting-edge AutoML with Foundation models. +Try Cleanlab Studio for free!

    +The modern AI pipeline automated with Cleanlab Studio + @@ -745,6 +752,7 @@

    What is K-fold cross-validation?Computing Out-of-Sample Predicted Probabilities with Cross-Validation diff --git a/master/tutorials/regression.html b/master/tutorials/regression.html index f2d06fe3e..496fcd8f6 100644 --- a/master/tutorials/regression.html +++ b/master/tutorials/regression.html @@ -1421,6 +1421,12 @@

    5. Other ways to find noisy labels in regression datasetsDatalab with provided features plus the best regression model you know for your data. If your goal is to train a robust regression model with noisy data rather than detect data/label issues, then use CleanLearning. Alternatively, if you don’t have a sklearn-compatible regression model or already have pre-computed predictions from the model you’d like to rely on, you can pass these predictions into Datalab directly to find issues based on them instead of providing a regression model.

    +
    +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1504,6 +1510,7 @@

    5. Other ways to find noisy labels in regression datasets3. Define a regression model and use cleanlab to find potential label errors
  • 4. Train a more robust model from noisy labels
  • 5. Other ways to find noisy labels in regression datasets
    • +
  • Spending too much time on data quality?
    • diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 7c5c07d39..099430c03 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:17.404200Z", + "iopub.status.busy": "2024-08-02T23:24:17.404031Z", + "iopub.status.idle": "2024-08-02T23:24:18.819393Z", + "shell.execute_reply": "2024-08-02T23:24:18.818751Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:38:48.348157Z", - "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" + "iopub.execute_input": "2024-08-02T23:24:18.822083Z", + "iopub.status.busy": "2024-08-02T23:24:18.821615Z", + "iopub.status.idle": "2024-08-02T23:24:18.839962Z", + "shell.execute_reply": "2024-08-02T23:24:18.839405Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.842424Z", + "iopub.status.busy": "2024-08-02T23:24:18.842013Z", + "iopub.status.idle": "2024-08-02T23:24:18.844900Z", + "shell.execute_reply": "2024-08-02T23:24:18.844448Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.846982Z", + "iopub.status.busy": "2024-08-02T23:24:18.846651Z", + "iopub.status.idle": "2024-08-02T23:24:18.905648Z", + "shell.execute_reply": "2024-08-02T23:24:18.905180Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.907939Z", + "iopub.status.busy": "2024-08-02T23:24:18.907494Z", + "iopub.status.idle": "2024-08-02T23:24:18.911937Z", + "shell.execute_reply": "2024-08-02T23:24:18.911430Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:18.913935Z", + "iopub.status.busy": "2024-08-02T23:24:18.913612Z", + "iopub.status.idle": "2024-08-02T23:24:19.156090Z", + "shell.execute_reply": "2024-08-02T23:24:19.155477Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.158339Z", + "iopub.status.busy": "2024-08-02T23:24:19.158149Z", + "iopub.status.idle": "2024-08-02T23:24:19.162504Z", + "shell.execute_reply": "2024-08-02T23:24:19.162045Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.164445Z", + "iopub.status.busy": "2024-08-02T23:24:19.164256Z", + "iopub.status.idle": "2024-08-02T23:24:19.170243Z", + "shell.execute_reply": "2024-08-02T23:24:19.169792Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.172217Z", + "iopub.status.busy": "2024-08-02T23:24:19.172043Z", + "iopub.status.idle": "2024-08-02T23:24:19.174763Z", + "shell.execute_reply": "2024-08-02T23:24:19.174300Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:19.176599Z", + "iopub.status.busy": "2024-08-02T23:24:19.176431Z", + "iopub.status.idle": "2024-08-02T23:24:28.206119Z", + "shell.execute_reply": "2024-08-02T23:24:28.205469Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.209000Z", + "iopub.status.busy": "2024-08-02T23:24:28.208362Z", + "iopub.status.idle": "2024-08-02T23:24:28.215857Z", + "shell.execute_reply": "2024-08-02T23:24:28.215396Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.217833Z", + "iopub.status.busy": "2024-08-02T23:24:28.217557Z", + "iopub.status.idle": "2024-08-02T23:24:28.221146Z", + "shell.execute_reply": "2024-08-02T23:24:28.220682Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.223157Z", + "iopub.status.busy": "2024-08-02T23:24:28.222839Z", + "iopub.status.idle": "2024-08-02T23:24:28.226202Z", + "shell.execute_reply": "2024-08-02T23:24:28.225642Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.228136Z", + "iopub.status.busy": "2024-08-02T23:24:28.227911Z", + "iopub.status.idle": "2024-08-02T23:24:28.230792Z", + "shell.execute_reply": "2024-08-02T23:24:28.230325Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.232772Z", + "iopub.status.busy": "2024-08-02T23:24:28.232437Z", + "iopub.status.idle": "2024-08-02T23:24:28.240280Z", + "shell.execute_reply": "2024-08-02T23:24:28.239831Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.242339Z", + "iopub.status.busy": "2024-08-02T23:24:28.241943Z", + "iopub.status.idle": "2024-08-02T23:24:28.244706Z", + "shell.execute_reply": "2024-08-02T23:24:28.244158Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.246759Z", + "iopub.status.busy": "2024-08-02T23:24:28.246451Z", + "iopub.status.idle": "2024-08-02T23:24:28.374401Z", + "shell.execute_reply": "2024-08-02T23:24:28.373775Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.376853Z", + "iopub.status.busy": "2024-08-02T23:24:28.376504Z", + "iopub.status.idle": "2024-08-02T23:24:28.483607Z", + "shell.execute_reply": "2024-08-02T23:24:28.483029Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:28.486071Z", + "iopub.status.busy": "2024-08-02T23:24:28.485733Z", + "iopub.status.idle": "2024-08-02T23:24:28.990646Z", + "shell.execute_reply": "2024-08-02T23:24:28.990027Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - 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"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" + "iopub.execute_input": "2024-08-02T23:24:34.721958Z", + "iopub.status.busy": "2024-08-02T23:24:34.721544Z", + "iopub.status.idle": "2024-08-02T23:24:34.730054Z", + "shell.execute_reply": "2024-08-02T23:24:34.729479Z" } }, "outputs": [ @@ -1370,16 +1370,32 @@ "**Summary:** To detect many types of issues in your regression dataset, we recommend using `Datalab` with provided `features` plus the best regression model you know for your data. If your goal is to train a robust regression model with noisy data rather than detect data/label issues, then use `CleanLearning`. Alternatively, if you don't have a sklearn-compatible regression model or already have pre-computed predictions from the model you'd like to rely on, you can pass these predictions into `Datalab` directly to find issues based on them instead of providing a regression model." ] }, + { + "cell_type": "markdown", + "id": "8a7a5387", + "metadata": {}, + "source": [ + "## Spending too much time on data quality?\n", + "\n", + "Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.\n", + "\n", + "That’s why we built [Cleanlab Studio](https://cleanlab.ai/blog/data-centric-ai/) -- an automated platform to find **and fix** issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. [Try it](https://cleanlab.ai/) for free!\n", + "\n", + "

    \n", + " \"The\n", + "

    " + ] + }, { "cell_type": "code", "execution_count": 25, "id": "95531cda", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:34.732147Z", + "iopub.status.busy": "2024-08-02T23:24:34.731881Z", + "iopub.status.idle": "2024-08-02T23:24:34.800226Z", + "shell.execute_reply": "2024-08-02T23:24:34.799721Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index c4e78a813..9887fea2c 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -800,13 +800,13 @@

    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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"2024-08-02T23:24:38.145016Z", + "iopub.status.busy": "2024-08-02T23:24:38.144603Z", + "iopub.status.idle": "2024-08-02T23:24:40.117038Z", + "shell.execute_reply": "2024-08-02T23:24:40.116342Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:24:40.119515Z", + "iopub.status.busy": "2024-08-02T23:24:40.119338Z", + "iopub.status.idle": "2024-08-02T23:25:35.352783Z", + "shell.execute_reply": "2024-08-02T23:25:35.352105Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:35.355348Z", + "iopub.status.busy": "2024-08-02T23:25:35.354967Z", + "iopub.status.idle": "2024-08-02T23:25:36.767542Z", + "shell.execute_reply": "2024-08-02T23:25:36.766894Z" }, "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\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": "2024-07-30T16:40:32.525566Z", - "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" + "iopub.execute_input": "2024-08-02T23:25:36.770200Z", + "iopub.status.busy": "2024-08-02T23:25:36.769889Z", + "iopub.status.idle": "2024-08-02T23:25:36.773141Z", + "shell.execute_reply": "2024-08-02T23:25:36.772658Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.775177Z", + "iopub.status.busy": "2024-08-02T23:25:36.774994Z", + "iopub.status.idle": "2024-08-02T23:25:36.779004Z", + "shell.execute_reply": "2024-08-02T23:25:36.778469Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.781185Z", + "iopub.status.busy": "2024-08-02T23:25:36.780850Z", + "iopub.status.idle": "2024-08-02T23:25:36.784519Z", + "shell.execute_reply": "2024-08-02T23:25:36.783988Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:25:36.786657Z", + "iopub.status.busy": "2024-08-02T23:25:36.786198Z", + "iopub.status.idle": "2024-08-02T23:25:36.789074Z", + "shell.execute_reply": "2024-08-02T23:25:36.788604Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:40:32.546293Z", - 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"_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } } }, "version_major": 2, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index fb54a4777..9ef900ddf 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

    1. Install required dependencies and download data 
    ---2024-07-30 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.
+--2024-08-02 23:27:21--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.246, 2400:52e0:1a00::871:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
 conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
 
-2024-07-30 16:42:16 (6.62 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-08-02 23:27:21 (6.78 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 6a696df89..7588e441c 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-30T16:42:16.108435Z", - "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" + "iopub.execute_input": "2024-08-02T23:27:21.097374Z", + "iopub.status.busy": "2024-08-02T23:27:21.097213Z", + "iopub.status.idle": "2024-08-02T23:27:22.032475Z", + "shell.execute_reply": "2024-08-02T23:27:22.031763Z" } }, "outputs": [ @@ -86,22 +86,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-30 16:42:16-- https://data.deepai.org/conll2003.zip\r\n", - "Resolving data.deepai.org (data.deepai.org)... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" + "--2024-08-02 23:27:21-- https://data.deepai.org/conll2003.zip\r\n", + "Resolving data.deepai.org (data.deepai.org)... 185.93.1.246, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -124,7 +118,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-30 16:42:16 (6.62 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-08-02 23:27:21 (6.78 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -144,9 +138,9 @@ "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", + "--2024-08-02 23:27:21-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.226.169, 54.231.137.105, 54.231.202.161, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.226.169|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -169,7 +163,7 @@ "\r", "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-30 16:42:17 (125 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-08-02 23:27:21 (135 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -186,10 +180,10 @@ "id": "439b0305", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:22.035122Z", + "iopub.status.busy": "2024-08-02T23:27:22.034920Z", + "iopub.status.idle": "2024-08-02T23:27:23.618049Z", + "shell.execute_reply": "2024-08-02T23:27:23.617396Z" }, "nbsphinx": "hidden" }, @@ -200,7 +194,7 @@ "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@774f5b4625f50853a4527b3bf0414f14a7116208\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@b699edd9acff56a96f5d8635fc51bcc94bc9a1ed\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -226,10 +220,10 @@ "id": "a1349304", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.620722Z", + "iopub.status.busy": "2024-08-02T23:27:23.620416Z", + "iopub.status.idle": "2024-08-02T23:27:23.624004Z", + "shell.execute_reply": "2024-08-02T23:27:23.623532Z" } }, "outputs": [], @@ -279,10 +273,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.626173Z", + "iopub.status.busy": "2024-08-02T23:27:23.625726Z", + "iopub.status.idle": "2024-08-02T23:27:23.628865Z", + "shell.execute_reply": "2024-08-02T23:27:23.628333Z" }, "nbsphinx": "hidden" }, @@ -300,10 +294,10 @@ "id": "519cb80c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:23.631121Z", + "iopub.status.busy": "2024-08-02T23:27:23.630720Z", + "iopub.status.idle": "2024-08-02T23:27:32.801377Z", + "shell.execute_reply": "2024-08-02T23:27:32.800697Z" } }, "outputs": [], @@ -377,10 +371,10 @@ "id": "202f1526", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:32.803844Z", + "iopub.status.busy": "2024-08-02T23:27:32.803644Z", + "iopub.status.idle": "2024-08-02T23:27:32.809441Z", + "shell.execute_reply": "2024-08-02T23:27:32.808867Z" }, "nbsphinx": "hidden" }, @@ -420,10 +414,10 @@ "id": "a4381f03", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:32.811534Z", + "iopub.status.busy": "2024-08-02T23:27:32.811201Z", + "iopub.status.idle": "2024-08-02T23:27:33.180182Z", + "shell.execute_reply": "2024-08-02T23:27:33.179511Z" } }, "outputs": [], @@ -460,10 +454,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:33.182638Z", + "iopub.status.busy": "2024-08-02T23:27:33.182437Z", + "iopub.status.idle": "2024-08-02T23:27:33.187039Z", + "shell.execute_reply": "2024-08-02T23:27:33.186557Z" } }, "outputs": [ @@ -535,10 +529,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:33.189240Z", + "iopub.status.busy": "2024-08-02T23:27:33.188864Z", + "iopub.status.idle": "2024-08-02T23:27:35.925225Z", + "shell.execute_reply": "2024-08-02T23:27:35.924446Z" } }, "outputs": [], @@ -560,10 +554,10 @@ "id": "95dc7268", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.928379Z", + "iopub.status.busy": "2024-08-02T23:27:35.927716Z", + "iopub.status.idle": "2024-08-02T23:27:35.931918Z", + "shell.execute_reply": "2024-08-02T23:27:35.931392Z" } }, "outputs": [ @@ -599,10 +593,10 @@ "id": "e13de188", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.933833Z", + "iopub.status.busy": "2024-08-02T23:27:35.933656Z", + "iopub.status.idle": "2024-08-02T23:27:35.939473Z", + "shell.execute_reply": "2024-08-02T23:27:35.939001Z" } }, "outputs": [ @@ -780,10 +774,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.941428Z", + "iopub.status.busy": "2024-08-02T23:27:35.941253Z", + "iopub.status.idle": "2024-08-02T23:27:35.967430Z", + "shell.execute_reply": "2024-08-02T23:27:35.966843Z" } }, "outputs": [ @@ -885,10 +879,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.969458Z", + "iopub.status.busy": "2024-08-02T23:27:35.969278Z", + "iopub.status.idle": "2024-08-02T23:27:35.973349Z", + "shell.execute_reply": "2024-08-02T23:27:35.972803Z" } }, "outputs": [ @@ -962,10 +956,10 @@ "id": "db0b5179", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:35.975297Z", + "iopub.status.busy": "2024-08-02T23:27:35.975119Z", + "iopub.status.idle": "2024-08-02T23:27:37.460630Z", + "shell.execute_reply": "2024-08-02T23:27:37.460082Z" } }, "outputs": [ @@ -1137,10 +1131,10 @@ "id": "a18795eb", "metadata": { "execution": { - "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" + "iopub.execute_input": "2024-08-02T23:27:37.462970Z", + "iopub.status.busy": "2024-08-02T23:27:37.462558Z", + "iopub.status.idle": "2024-08-02T23:27:37.466740Z", + "shell.execute_reply": "2024-08-02T23:27:37.466280Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index b023d4814..870b6615b 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "774f5b4625f50853a4527b3bf0414f14a7116208", + commit_hash: "b699edd9acff56a96f5d8635fc51bcc94bc9a1ed", }; \ No newline at end of file

    5. Train a more robust model from noisy labels +

    Spending too much time on data quality?#

    +

    Using this open-source package effectively can require significant ML expertise and experimentation, plus handling detected data issues can be cumbersome.

    +

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

    +

    The modern AI pipeline automated with Cleanlab Studio

    +

    @@ -1164,6 +1170,7 @@

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