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zuj4s1972!h(EZ`$<2fC8>3Ic9k8XiCe6Y!0D)+&Mr!0L1-&3+w4!*WDHaYT+=lDmW zIhBHPsdGC8@#iw*f^unEnL)X9Il4b6mxfwWP%aJGO4V{(@ZE5Ti|q?tXMgzIk)2SM zhffAsngZS@S?UjNCs}gK($cr!Xp&h7c$hSPc5JJbd+lew(3oxA4a*mPBUBV-8QhEQ zwY9aF6PmxEYs@nza%mX6IKdWIIGIZeKjpfo&8X9_%?dIpT^7IF|TRqd=vAUj*|w~~`R#|JqU#J;}n&PG?V zCh|?-BXGQE+;G_c_u7XB&hlNaY27}u>vaHq6=lt_Rn~Pa0WXT|ZgBv3Pc-&AEW2MH zZOqfb$GXV8AM?5bXIf%wj*E)EIUF?ZzdJzFlKi@C;1#sE_!20EB=>bnN;LkOhZZL$ I4yyhC0JPTcfdBvi diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index a3a0e88627eca6d68239352f63d32cad8507bbb4..dc4a39baca514695c7760832d1a329f92e973481 100644 GIT binary patch delta 64 zcmeA<%hYq0X+sO6p_x%hL1so-k-nv+p?Qi)N@{9ylChalVzOCcqFJhexw%2Iv6;C^ Uih+Saa\n", " 2\n", " outlier\n", - " 0.356958\n", - " 362\n", + " 0.356924\n", + " 363\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619565\n", + " 0.619581\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356958 362\n", - "3 near_duplicate 0.619565 108\n", + "2 outlier 0.356924 363\n", + "3 near_duplicate 0.619581 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651929 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:10.841884Z", - "iopub.status.busy": "2024-07-01T15:06:10.841353Z", - "iopub.status.idle": "2024-07-01T15:06:18.620342Z", - "shell.execute_reply": "2024-07-01T15:06:18.619784Z" + "iopub.execute_input": "2024-07-02T12:04:51.683932Z", + "iopub.status.busy": "2024-07-02T12:04:51.683392Z", + "iopub.status.idle": "2024-07-02T12:04:59.515985Z", + "shell.execute_reply": "2024-07-02T12:04:59.515371Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:18.622535Z", - "iopub.status.busy": "2024-07-01T15:06:18.622344Z", - "iopub.status.idle": "2024-07-01T15:06:18.765943Z", - "shell.execute_reply": "2024-07-01T15:06:18.765367Z" + "iopub.execute_input": "2024-07-02T12:04:59.518078Z", + "iopub.status.busy": "2024-07-02T12:04:59.517894Z", + "iopub.status.idle": "2024-07-02T12:04:59.659289Z", + "shell.execute_reply": "2024-07-02T12:04:59.658739Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:18.768372Z", - "iopub.status.busy": "2024-07-01T15:06:18.768184Z", - "iopub.status.idle": "2024-07-01T15:06:20.089155Z", - "shell.execute_reply": "2024-07-01T15:06:20.088659Z" + "iopub.execute_input": "2024-07-02T12:04:59.661683Z", + "iopub.status.busy": "2024-07-02T12:04:59.661350Z", + "iopub.status.idle": "2024-07-02T12:05:00.957856Z", + "shell.execute_reply": "2024-07-02T12:05:00.957311Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.091428Z", - "iopub.status.busy": "2024-07-01T15:06:20.091067Z", - "iopub.status.idle": "2024-07-01T15:06:20.540474Z", - "shell.execute_reply": "2024-07-01T15:06:20.539777Z" + "iopub.execute_input": "2024-07-02T12:05:00.960128Z", + "iopub.status.busy": "2024-07-02T12:05:00.959785Z", + "iopub.status.idle": "2024-07-02T12:05:01.381421Z", + "shell.execute_reply": "2024-07-02T12:05:01.380807Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.543076Z", - "iopub.status.busy": "2024-07-01T15:06:20.542728Z", - "iopub.status.idle": "2024-07-01T15:06:20.551688Z", - "shell.execute_reply": "2024-07-01T15:06:20.551243Z" + "iopub.execute_input": "2024-07-02T12:05:01.383745Z", + "iopub.status.busy": "2024-07-02T12:05:01.383267Z", + "iopub.status.idle": "2024-07-02T12:05:01.392315Z", + "shell.execute_reply": "2024-07-02T12:05:01.391863Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.553781Z", - "iopub.status.busy": "2024-07-01T15:06:20.553342Z", - "iopub.status.idle": "2024-07-01T15:06:20.571023Z", - "shell.execute_reply": "2024-07-01T15:06:20.570476Z" + "iopub.execute_input": "2024-07-02T12:05:01.394282Z", + "iopub.status.busy": "2024-07-02T12:05:01.393956Z", + "iopub.status.idle": "2024-07-02T12:05:01.411562Z", + "shell.execute_reply": "2024-07-02T12:05:01.411139Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.573201Z", - "iopub.status.busy": "2024-07-01T15:06:20.572911Z", - "iopub.status.idle": "2024-07-01T15:06:20.803461Z", - "shell.execute_reply": "2024-07-01T15:06:20.802856Z" + "iopub.execute_input": "2024-07-02T12:05:01.413543Z", + "iopub.status.busy": "2024-07-02T12:05:01.413221Z", + "iopub.status.idle": "2024-07-02T12:05:01.630162Z", + "shell.execute_reply": "2024-07-02T12:05:01.629562Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.806333Z", - "iopub.status.busy": "2024-07-01T15:06:20.805945Z", - "iopub.status.idle": "2024-07-01T15:06:20.825160Z", - "shell.execute_reply": "2024-07-01T15:06:20.824612Z" + "iopub.execute_input": "2024-07-02T12:05:01.632639Z", + "iopub.status.busy": "2024-07-02T12:05:01.632236Z", + "iopub.status.idle": "2024-07-02T12:05:01.650528Z", + "shell.execute_reply": "2024-07-02T12:05:01.649988Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.827381Z", - "iopub.status.busy": "2024-07-01T15:06:20.826963Z", - "iopub.status.idle": "2024-07-01T15:06:20.993329Z", - "shell.execute_reply": "2024-07-01T15:06:20.992779Z" + "iopub.execute_input": "2024-07-02T12:05:01.652709Z", + "iopub.status.busy": "2024-07-02T12:05:01.652303Z", + "iopub.status.idle": "2024-07-02T12:05:01.816760Z", + "shell.execute_reply": "2024-07-02T12:05:01.816173Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:20.995655Z", - "iopub.status.busy": "2024-07-01T15:06:20.995315Z", - "iopub.status.idle": "2024-07-01T15:06:21.005096Z", - "shell.execute_reply": "2024-07-01T15:06:21.004627Z" + "iopub.execute_input": "2024-07-02T12:05:01.818813Z", + "iopub.status.busy": "2024-07-02T12:05:01.818633Z", + "iopub.status.idle": "2024-07-02T12:05:01.828263Z", + "shell.execute_reply": "2024-07-02T12:05:01.827827Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.007134Z", - "iopub.status.busy": "2024-07-01T15:06:21.006797Z", - "iopub.status.idle": "2024-07-01T15:06:21.015972Z", - "shell.execute_reply": "2024-07-01T15:06:21.015502Z" + "iopub.execute_input": "2024-07-02T12:05:01.830285Z", + "iopub.status.busy": "2024-07-02T12:05:01.830099Z", + "iopub.status.idle": "2024-07-02T12:05:01.839416Z", + "shell.execute_reply": "2024-07-02T12:05:01.838852Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.017811Z", - "iopub.status.busy": "2024-07-01T15:06:21.017637Z", - "iopub.status.idle": "2024-07-01T15:06:21.046279Z", - "shell.execute_reply": "2024-07-01T15:06:21.045826Z" + "iopub.execute_input": "2024-07-02T12:05:01.841444Z", + "iopub.status.busy": "2024-07-02T12:05:01.841118Z", + "iopub.status.idle": "2024-07-02T12:05:01.878960Z", + "shell.execute_reply": "2024-07-02T12:05:01.878541Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.048363Z", - "iopub.status.busy": "2024-07-01T15:06:21.048043Z", - "iopub.status.idle": "2024-07-01T15:06:21.050539Z", - "shell.execute_reply": "2024-07-01T15:06:21.050117Z" + "iopub.execute_input": "2024-07-02T12:05:01.881007Z", + "iopub.status.busy": "2024-07-02T12:05:01.880679Z", + "iopub.status.idle": "2024-07-02T12:05:01.883255Z", + "shell.execute_reply": "2024-07-02T12:05:01.882829Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.052583Z", - "iopub.status.busy": "2024-07-01T15:06:21.052269Z", - "iopub.status.idle": "2024-07-01T15:06:21.070692Z", - "shell.execute_reply": "2024-07-01T15:06:21.070162Z" + "iopub.execute_input": "2024-07-02T12:05:01.885223Z", + "iopub.status.busy": "2024-07-02T12:05:01.884900Z", + "iopub.status.idle": "2024-07-02T12:05:01.903469Z", + "shell.execute_reply": "2024-07-02T12:05:01.903010Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.072780Z", - "iopub.status.busy": "2024-07-01T15:06:21.072455Z", - "iopub.status.idle": "2024-07-01T15:06:21.076730Z", - "shell.execute_reply": "2024-07-01T15:06:21.076273Z" + "iopub.execute_input": "2024-07-02T12:05:01.905390Z", + "iopub.status.busy": "2024-07-02T12:05:01.905216Z", + "iopub.status.idle": "2024-07-02T12:05:01.909303Z", + "shell.execute_reply": "2024-07-02T12:05:01.908869Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.078798Z", - "iopub.status.busy": "2024-07-01T15:06:21.078478Z", - "iopub.status.idle": "2024-07-01T15:06:21.105961Z", - "shell.execute_reply": "2024-07-01T15:06:21.105424Z" + "iopub.execute_input": "2024-07-02T12:05:01.911113Z", + "iopub.status.busy": "2024-07-02T12:05:01.910943Z", + "iopub.status.idle": "2024-07-02T12:05:01.938117Z", + "shell.execute_reply": "2024-07-02T12:05:01.937659Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.108003Z", - 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Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n", + "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n", + "These correlations could lead to unreliable model predictions and poor generalization.\n", "\n", - "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models." + "\n", + "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n", + "\n", + "- Darkness\n", + "- Blurriness\n", + "- Aspect ratio anomalies\n", + "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n", + "\n", + "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. Load the dataset\n", + "### 1. Load the Dataset\n", + "\n", + "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n", "\n", - "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n", + "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n", + "- The correlation scores range from 0 to 1, where:\n", + " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n", + " - Scores close to 1 suggest little to no correlation between the property and class labels.\n", + "- By introducing this bias, we expect to see:\n", + " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n", + " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n", "\n", - "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height." + "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels." ] }, { @@ -3743,10 +3760,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:21.804166Z", - "iopub.status.busy": "2024-07-01T15:06:21.803993Z", - "iopub.status.idle": "2024-07-01T15:06:27.357122Z", - "shell.execute_reply": "2024-07-01T15:06:27.356570Z" + "iopub.execute_input": "2024-07-02T12:05:02.574589Z", + "iopub.status.busy": "2024-07-02T12:05:02.574422Z", + "iopub.status.idle": "2024-07-02T12:05:08.693945Z", + "shell.execute_reply": "2024-07-02T12:05:08.693406Z" } }, "outputs": [ @@ -3770,7 +3787,39 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 917504/170498071 [00:00<00:20, 8269545.10it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8347158.96it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 6%|▌ | 9601024/170498071 [00:00<00:03, 52614403.72it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 11%|█ | 18481152/170498071 [00:00<00:02, 68746962.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 15%|█▍ | 25493504/170498071 [00:00<00:02, 68028252.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 19%|█▉ | 32571392/170498071 [00:00<00:02, 68946396.69it/s]" ] }, { @@ -3778,7 +3827,7 @@ "output_type": "stream", "text": [ "\r", - 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"100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]" + " 90%|████████▉ | 152961024/170498071 [00:02<00:00, 70793844.72it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▍| 160071680/170498071 [00:02<00:00, 69055168.81it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 167608320/170498071 [00:02<00:00, 70630354.07it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]" ] }, { @@ -3964,7 +4037,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find vision-related issues" + "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find image-related issues" ] }, { @@ -3972,10 +4045,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:27.360097Z", - "iopub.status.busy": "2024-07-01T15:06:27.359363Z", - "iopub.status.idle": "2024-07-01T15:06:27.426972Z", - "shell.execute_reply": "2024-07-01T15:06:27.426512Z" + "iopub.execute_input": "2024-07-02T12:05:08.696598Z", + "iopub.status.busy": "2024-07-02T12:05:08.696059Z", + "iopub.status.idle": "2024-07-02T12:05:08.763426Z", + "shell.execute_reply": "2024-07-02T12:05:08.762929Z" } }, "outputs": [], @@ -3997,10 +4070,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:27.429234Z", - "iopub.status.busy": "2024-07-01T15:06:27.429051Z", - "iopub.status.idle": "2024-07-01T15:06:27.469917Z", - "shell.execute_reply": "2024-07-01T15:06:27.469464Z" + "iopub.execute_input": "2024-07-02T12:05:08.765613Z", + "iopub.status.busy": "2024-07-02T12:05:08.765285Z", + "iopub.status.idle": "2024-07-02T12:05:08.805806Z", + "shell.execute_reply": "2024-07-02T12:05:08.805281Z" } }, "outputs": [], @@ -4034,10 +4107,10 @@ "execution_count": 36, "metadata": { "execution": { - 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"model_id": "1fa7dd59335f4faa818bcf4e966c7c70", + "model_id": "ab730d681373436cbffc495350a9abe1", "version_major": 2, "version_minor": 0 }, @@ -4153,7 +4226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc30a28b7d6044ff9e4159420fbd7a37", + "model_id": "e44decacc70f4d08b59475e297136aab", "version_major": 2, "version_minor": 0 }, @@ -4176,7 +4249,7 @@ { "data": { "text/markdown": [ - "### Vision-specific property scores in the original dataset" + "### Image-specific property scores in the original dataset" ], "text/plain": [ "" @@ -4267,7 +4340,7 @@ { "data": { "text/markdown": [ - "### Vision-specific property scores in the transformed dataset" + "### Image-specific property scores in the transformed dataset" ], "text/plain": [ "" @@ -4372,9 +4445,9 @@ "transformed_property_scores = get_property_scores(transformed_dataset)\n", "\n", "# Displaying the scores dataframe\n", - "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n", + "display(Markdown(\"### Image-specific property scores in the original dataset\"))\n", "display(standard_property_scores)\n", - 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"_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0e8630e122434af98c6e33cb01b60aa5", - "placeholder": "​", - "style": "IPY_MODEL_f812040db2d642d08a03d0f598afe058", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 847.08it/s]" - } - }, - "981dd7e875cd448284efead3ec98c9ab": { + "5fccbfa0a7a94b55a6825fc52ecdeee3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4894,23 +4913,7 @@ "width": null } }, - "a1e2e6018ec643d38a6a7f43c30c3d0b": { - "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": "" - } - }, - "b80a9a6f3cc8460ba1eacd1eebdd1695": { + "6bdd7248294f4094a2da7c7af2e67e50": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4925,15 +4928,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ef85a24fdc2e4cf783b12b95dc654a54", + "layout": "IPY_MODEL_d6941ea7ad6a41efb80f48dde9923682", "placeholder": "​", - "style": "IPY_MODEL_7d1e93d6b2384e198a1aab9f26b5c1fa", + "style": "IPY_MODEL_a55c5a0d7aca4c16a982994a5595ca08", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 200/200 [00:00<00:00, 811.85it/s]" } }, - "ba235d6a06cb4c11a73df384a365fbe8": { + "797a5104afa24ca5b172ddc308a704ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4986,7 +4989,43 @@ "width": null } }, - "bc30a28b7d6044ff9e4159420fbd7a37": { + "9d67c6a8b80b4718975da970d5ba6be1": { + "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 + } + }, + "a55c5a0d7aca4c16a982994a5595ca08": { + "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 + } + }, + "ab730d681373436cbffc495350a9abe1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5001,16 +5040,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6cc234a88c8e4669af66844757fac7f7", - "IPY_MODEL_5fe8b6850a5246f4896ff6995970d3c4", - "IPY_MODEL_7df90a685b4747759b5a5df590246cc1" + "IPY_MODEL_37657cc47549425e81123fbc00061dcd", + "IPY_MODEL_ccd3930d3b25423fb8d520dc87205752", + "IPY_MODEL_6bdd7248294f4094a2da7c7af2e67e50" ], - "layout": "IPY_MODEL_ba235d6a06cb4c11a73df384a365fbe8", + "layout": "IPY_MODEL_440b53038a3d4c4c964a83e8b710361f", "tabbable": null, "tooltip": null } }, - "bd44e36d29ce400580eb9c39706e9764": { + "ccd3930d3b25423fb8d520dc87205752": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5026,17 +5065,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5daa3a2aa6bb4c9f84ea2674afd64ead", + "layout": "IPY_MODEL_302d670260304f5d973a1863227c2b38", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_fc145740c00f4075a0ddec1338af6a17", + "style": "IPY_MODEL_2ce33b586399430db7231ec582a8ad1c", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "ef85a24fdc2e4cf783b12b95dc654a54": { + "d6941ea7ad6a41efb80f48dde9923682": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5089,7 +5128,7 @@ "width": null } }, - "f812040db2d642d08a03d0f598afe058": { + "d6c64d036d3c464bba338c11b7d7e118": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5107,20 +5146,54 @@ "text_color": null } }, - "fc145740c00f4075a0ddec1338af6a17": { + "e44decacc70f4d08b59475e297136aab": { "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_3f75258f70194866856b4da554e4dbeb", + "IPY_MODEL_e621caf6c19d4d638ba32cd7caed9a15", + "IPY_MODEL_06e95a0f1df9408095248eef0924c604" + ], + "layout": "IPY_MODEL_22612fb7095f4323876a32fa6832ebee", + "tabbable": null, + "tooltip": null + } + }, + "e621caf6c19d4d638ba32cd7caed9a15": { + "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_797a5104afa24ca5b172ddc308a704ec", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_34fad403248e49fb9d7ed5541db4875e", + "tabbable": null, + "tooltip": null, + "value": 200.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 2c0bb8a06..05afc2f2e 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:34.327040Z", - "iopub.status.busy": "2024-07-01T15:06:34.326556Z", - "iopub.status.idle": "2024-07-01T15:06:35.430619Z", - "shell.execute_reply": "2024-07-01T15:06:35.430106Z" + "iopub.execute_input": "2024-07-02T12:05:14.883207Z", + "iopub.status.busy": "2024-07-02T12:05:14.882732Z", + "iopub.status.idle": "2024-07-02T12:05:15.976658Z", + "shell.execute_reply": "2024-07-02T12:05:15.976156Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:06:35.433295Z", - "iopub.status.busy": "2024-07-01T15:06:35.432846Z", - "iopub.status.idle": "2024-07-01T15:06:35.435562Z", - "shell.execute_reply": "2024-07-01T15:06:35.435139Z" + "iopub.execute_input": "2024-07-02T12:05:15.979210Z", + "iopub.status.busy": "2024-07-02T12:05:15.978822Z", + "iopub.status.idle": "2024-07-02T12:05:15.981689Z", + "shell.execute_reply": "2024-07-02T12:05:15.981162Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:35.437647Z", - "iopub.status.busy": "2024-07-01T15:06:35.437334Z", - "iopub.status.idle": "2024-07-01T15:06:35.448830Z", - "shell.execute_reply": "2024-07-01T15:06:35.448366Z" + "iopub.execute_input": "2024-07-02T12:05:15.983805Z", + "iopub.status.busy": "2024-07-02T12:05:15.983602Z", + "iopub.status.idle": "2024-07-02T12:05:15.994757Z", + "shell.execute_reply": "2024-07-02T12:05:15.994295Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:35.450965Z", - "iopub.status.busy": "2024-07-01T15:06:35.450626Z", - "iopub.status.idle": "2024-07-01T15:06:39.722894Z", - "shell.execute_reply": "2024-07-01T15:06:39.722312Z" + "iopub.execute_input": "2024-07-02T12:05:15.996851Z", + "iopub.status.busy": "2024-07-02T12:05:15.996526Z", + "iopub.status.idle": "2024-07-02T12:05:19.883673Z", + "shell.execute_reply": "2024-07-02T12:05:19.883072Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 86b92fc2a..964629f99 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": "2024-07-01T15:06:41.824444Z", - "iopub.status.busy": "2024-07-01T15:06:41.824251Z", - "iopub.status.idle": "2024-07-01T15:06:42.924912Z", - "shell.execute_reply": "2024-07-01T15:06:42.924299Z" + "iopub.execute_input": "2024-07-02T12:05:21.944164Z", + "iopub.status.busy": "2024-07-02T12:05:21.943684Z", + "iopub.status.idle": "2024-07-02T12:05:23.029911Z", + "shell.execute_reply": "2024-07-02T12:05:23.029367Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:42.927716Z", - "iopub.status.busy": "2024-07-01T15:06:42.927441Z", - "iopub.status.idle": "2024-07-01T15:06:42.930770Z", - "shell.execute_reply": "2024-07-01T15:06:42.930234Z" + "iopub.execute_input": "2024-07-02T12:05:23.032775Z", + "iopub.status.busy": "2024-07-02T12:05:23.032157Z", + "iopub.status.idle": "2024-07-02T12:05:23.035645Z", + "shell.execute_reply": "2024-07-02T12:05:23.035092Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:42.932829Z", - "iopub.status.busy": "2024-07-01T15:06:42.932448Z", - "iopub.status.idle": "2024-07-01T15:06:46.102573Z", - "shell.execute_reply": "2024-07-01T15:06:46.101934Z" + "iopub.execute_input": "2024-07-02T12:05:23.037598Z", + "iopub.status.busy": "2024-07-02T12:05:23.037330Z", + "iopub.status.idle": "2024-07-02T12:05:26.140141Z", + "shell.execute_reply": "2024-07-02T12:05:26.139387Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.105551Z", - "iopub.status.busy": "2024-07-01T15:06:46.104970Z", - "iopub.status.idle": "2024-07-01T15:06:46.139232Z", - "shell.execute_reply": "2024-07-01T15:06:46.138546Z" + "iopub.execute_input": "2024-07-02T12:05:26.143157Z", + "iopub.status.busy": "2024-07-02T12:05:26.142519Z", + "iopub.status.idle": "2024-07-02T12:05:26.174588Z", + "shell.execute_reply": "2024-07-02T12:05:26.174022Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.141703Z", - "iopub.status.busy": "2024-07-01T15:06:46.141464Z", - "iopub.status.idle": "2024-07-01T15:06:46.166417Z", - "shell.execute_reply": "2024-07-01T15:06:46.165807Z" + "iopub.execute_input": "2024-07-02T12:05:26.177140Z", + "iopub.status.busy": "2024-07-02T12:05:26.176847Z", + "iopub.status.idle": "2024-07-02T12:05:26.205277Z", + "shell.execute_reply": "2024-07-02T12:05:26.204606Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.168919Z", - "iopub.status.busy": "2024-07-01T15:06:46.168681Z", - "iopub.status.idle": "2024-07-01T15:06:46.171591Z", - "shell.execute_reply": "2024-07-01T15:06:46.171158Z" + "iopub.execute_input": "2024-07-02T12:05:26.208173Z", + "iopub.status.busy": "2024-07-02T12:05:26.207802Z", + "iopub.status.idle": "2024-07-02T12:05:26.210662Z", + "shell.execute_reply": "2024-07-02T12:05:26.210230Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.173661Z", - "iopub.status.busy": "2024-07-01T15:06:46.173228Z", - "iopub.status.idle": "2024-07-01T15:06:46.175926Z", - "shell.execute_reply": "2024-07-01T15:06:46.175394Z" + "iopub.execute_input": "2024-07-02T12:05:26.212655Z", + "iopub.status.busy": "2024-07-02T12:05:26.212352Z", + "iopub.status.idle": "2024-07-02T12:05:26.214801Z", + "shell.execute_reply": "2024-07-02T12:05:26.214383Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.178276Z", - "iopub.status.busy": "2024-07-01T15:06:46.177828Z", - "iopub.status.idle": "2024-07-01T15:06:46.201962Z", - "shell.execute_reply": "2024-07-01T15:06:46.201387Z" + "iopub.execute_input": "2024-07-02T12:05:26.216825Z", + "iopub.status.busy": "2024-07-02T12:05:26.216567Z", + "iopub.status.idle": "2024-07-02T12:05:26.239503Z", + "shell.execute_reply": "2024-07-02T12:05:26.238989Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4fd339b3d01f445392d6c990fdab5a89", + "model_id": "b3fbed235b41419c8dcc7c6dc31f69a4", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e05780aa0694da2b04037e49f9ac6f9", + "model_id": "55f5d02e58414e189c4d35720f6593e4", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.208658Z", - "iopub.status.busy": "2024-07-01T15:06:46.208178Z", - "iopub.status.idle": "2024-07-01T15:06:46.214827Z", - "shell.execute_reply": "2024-07-01T15:06:46.214300Z" + "iopub.execute_input": "2024-07-02T12:05:26.245285Z", + "iopub.status.busy": "2024-07-02T12:05:26.244763Z", + "iopub.status.idle": "2024-07-02T12:05:26.251470Z", + "shell.execute_reply": "2024-07-02T12:05:26.251055Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:46.216919Z", - "iopub.status.busy": "2024-07-01T15:06:46.216531Z", - "iopub.status.idle": "2024-07-01T15:06:46.220056Z", - "shell.execute_reply": "2024-07-01T15:06:46.219618Z" + "iopub.execute_input": "2024-07-02T12:05:26.253486Z", + "iopub.status.busy": "2024-07-02T12:05:26.253192Z", + "iopub.status.idle": "2024-07-02T12:05:26.256606Z", + "shell.execute_reply": "2024-07-02T12:05:26.256082Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - 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"id": "c8e20eef", + "id": "9569bf2b", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "5c7c2dee", + "id": "570b1222", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:49.489589Z", - "iopub.status.busy": "2024-07-01T15:06:49.489256Z", - "iopub.status.idle": "2024-07-01T15:06:49.496732Z", - "shell.execute_reply": "2024-07-01T15:06:49.496311Z" + "iopub.execute_input": "2024-07-02T12:05:29.565181Z", + "iopub.status.busy": "2024-07-02T12:05:29.564854Z", + "iopub.status.idle": "2024-07-02T12:05:29.572447Z", + "shell.execute_reply": "2024-07-02T12:05:29.571983Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "02fbab1c", + "id": "a87b6fe0", "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": "80ce97c2", + "id": "26953078", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:49.498789Z", - "iopub.status.busy": "2024-07-01T15:06:49.498386Z", - "iopub.status.idle": "2024-07-01T15:06:49.516684Z", - "shell.execute_reply": "2024-07-01T15:06:49.516122Z" + "iopub.execute_input": "2024-07-02T12:05:29.574436Z", + "iopub.status.busy": "2024-07-02T12:05:29.574108Z", + "iopub.status.idle": "2024-07-02T12:05:29.592051Z", + "shell.execute_reply": "2024-07-02T12:05:29.591598Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "f382c568", + "id": "948c6a32", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:49.518671Z", - "iopub.status.busy": "2024-07-01T15:06:49.518371Z", - "iopub.status.idle": "2024-07-01T15:06:49.521416Z", - "shell.execute_reply": "2024-07-01T15:06:49.520910Z" + "iopub.execute_input": 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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 } }, - "852d38b7e7464bfb919e2fb9d8164e6e": { + "d554d48cbc1b4a5caca9da8c04018917": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2229,30 +2296,7 @@ "width": null } }, - "ac61742632624d8e99ae7cb5f710a566": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": 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"layout": "IPY_MODEL_460a43df0d3b417d8996679fabe135e8", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_762d2f4c294343aaac239f8510aee6d2", + "style": "IPY_MODEL_507bd342f43644e28c3e257c443121b3", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for checking labels: " } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 4c0344124..31db58268 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": "2024-07-01T15:06:52.670679Z", - "iopub.status.busy": "2024-07-01T15:06:52.670319Z", - "iopub.status.idle": "2024-07-01T15:06:53.828911Z", - "shell.execute_reply": "2024-07-01T15:06:53.828417Z" + "iopub.execute_input": "2024-07-02T12:05:32.646814Z", + "iopub.status.busy": "2024-07-02T12:05:32.646634Z", + "iopub.status.idle": "2024-07-02T12:05:33.799016Z", + "shell.execute_reply": "2024-07-02T12:05:33.798421Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:06:53.831480Z", - "iopub.status.busy": "2024-07-01T15:06:53.831210Z", - "iopub.status.idle": "2024-07-01T15:06:54.013309Z", - "shell.execute_reply": "2024-07-01T15:06:54.012774Z" + "iopub.execute_input": "2024-07-02T12:05:33.801518Z", + "iopub.status.busy": "2024-07-02T12:05:33.801117Z", + "iopub.status.idle": "2024-07-02T12:05:33.979293Z", + "shell.execute_reply": "2024-07-02T12:05:33.978808Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:54.015638Z", - "iopub.status.busy": "2024-07-01T15:06:54.015446Z", - "iopub.status.idle": "2024-07-01T15:06:54.026660Z", - "shell.execute_reply": "2024-07-01T15:06:54.026227Z" + "iopub.execute_input": "2024-07-02T12:05:33.981747Z", + "iopub.status.busy": "2024-07-02T12:05:33.981411Z", + "iopub.status.idle": "2024-07-02T12:05:33.992581Z", + "shell.execute_reply": "2024-07-02T12:05:33.992150Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:54.028614Z", - "iopub.status.busy": "2024-07-01T15:06:54.028276Z", - "iopub.status.idle": "2024-07-01T15:06:54.258453Z", - "shell.execute_reply": "2024-07-01T15:06:54.257871Z" + "iopub.execute_input": "2024-07-02T12:05:33.994624Z", + "iopub.status.busy": "2024-07-02T12:05:33.994295Z", + "iopub.status.idle": "2024-07-02T12:05:34.203292Z", + "shell.execute_reply": "2024-07-02T12:05:34.202749Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:54.260736Z", - "iopub.status.busy": "2024-07-01T15:06:54.260424Z", - "iopub.status.idle": "2024-07-01T15:06:54.286726Z", - "shell.execute_reply": "2024-07-01T15:06:54.286174Z" + "iopub.execute_input": "2024-07-02T12:05:34.205578Z", + "iopub.status.busy": "2024-07-02T12:05:34.205242Z", + "iopub.status.idle": "2024-07-02T12:05:34.231392Z", + "shell.execute_reply": "2024-07-02T12:05:34.230966Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:54.288988Z", - "iopub.status.busy": "2024-07-01T15:06:54.288686Z", - "iopub.status.idle": "2024-07-01T15:06:56.272596Z", - "shell.execute_reply": "2024-07-01T15:06:56.271981Z" + "iopub.execute_input": "2024-07-02T12:05:34.233560Z", + "iopub.status.busy": "2024-07-02T12:05:34.233135Z", + "iopub.status.idle": "2024-07-02T12:05:36.181908Z", + "shell.execute_reply": "2024-07-02T12:05:36.181255Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:56.275147Z", - "iopub.status.busy": "2024-07-01T15:06:56.274650Z", - "iopub.status.idle": "2024-07-01T15:06:56.292694Z", - "shell.execute_reply": "2024-07-01T15:06:56.292260Z" + "iopub.execute_input": "2024-07-02T12:05:36.184389Z", + "iopub.status.busy": "2024-07-02T12:05:36.183843Z", + "iopub.status.idle": "2024-07-02T12:05:36.201856Z", + "shell.execute_reply": "2024-07-02T12:05:36.201294Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:56.294630Z", - "iopub.status.busy": "2024-07-01T15:06:56.294450Z", - "iopub.status.idle": "2024-07-01T15:06:57.712997Z", - "shell.execute_reply": "2024-07-01T15:06:57.712387Z" + "iopub.execute_input": "2024-07-02T12:05:36.204241Z", + "iopub.status.busy": "2024-07-02T12:05:36.203963Z", + "iopub.status.idle": "2024-07-02T12:05:37.598285Z", + "shell.execute_reply": "2024-07-02T12:05:37.597675Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:57.715656Z", - "iopub.status.busy": "2024-07-01T15:06:57.715048Z", - "iopub.status.idle": "2024-07-01T15:06:57.728518Z", - "shell.execute_reply": "2024-07-01T15:06:57.728061Z" + "iopub.execute_input": "2024-07-02T12:05:37.600758Z", + "iopub.status.busy": "2024-07-02T12:05:37.600219Z", + "iopub.status.idle": "2024-07-02T12:05:37.613480Z", + "shell.execute_reply": "2024-07-02T12:05:37.612921Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:57.730397Z", - "iopub.status.busy": "2024-07-01T15:06:57.730225Z", - "iopub.status.idle": "2024-07-01T15:06:57.801036Z", - "shell.execute_reply": "2024-07-01T15:06:57.800476Z" + "iopub.execute_input": "2024-07-02T12:05:37.615558Z", + "iopub.status.busy": "2024-07-02T12:05:37.615275Z", + "iopub.status.idle": "2024-07-02T12:05:37.682573Z", + "shell.execute_reply": "2024-07-02T12:05:37.681981Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:57.803338Z", - "iopub.status.busy": "2024-07-01T15:06:57.802990Z", - "iopub.status.idle": "2024-07-01T15:06:58.016553Z", - "shell.execute_reply": "2024-07-01T15:06:58.016010Z" + "iopub.execute_input": "2024-07-02T12:05:37.685019Z", + "iopub.status.busy": "2024-07-02T12:05:37.684694Z", + "iopub.status.idle": "2024-07-02T12:05:37.893897Z", + "shell.execute_reply": "2024-07-02T12:05:37.893417Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.018803Z", - "iopub.status.busy": "2024-07-01T15:06:58.018381Z", - "iopub.status.idle": "2024-07-01T15:06:58.034965Z", - "shell.execute_reply": "2024-07-01T15:06:58.034432Z" + "iopub.execute_input": "2024-07-02T12:05:37.896031Z", + "iopub.status.busy": "2024-07-02T12:05:37.895697Z", + "iopub.status.idle": "2024-07-02T12:05:37.912159Z", + "shell.execute_reply": "2024-07-02T12:05:37.911619Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.037248Z", - "iopub.status.busy": "2024-07-01T15:06:58.036814Z", - "iopub.status.idle": "2024-07-01T15:06:58.046245Z", - "shell.execute_reply": "2024-07-01T15:06:58.045792Z" + "iopub.execute_input": "2024-07-02T12:05:37.914291Z", + "iopub.status.busy": "2024-07-02T12:05:37.913990Z", + "iopub.status.idle": "2024-07-02T12:05:37.923838Z", + "shell.execute_reply": "2024-07-02T12:05:37.923277Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.048367Z", - "iopub.status.busy": "2024-07-01T15:06:58.048051Z", - "iopub.status.idle": "2024-07-01T15:06:58.130967Z", - "shell.execute_reply": "2024-07-01T15:06:58.130377Z" + "iopub.execute_input": "2024-07-02T12:05:37.925873Z", + "iopub.status.busy": "2024-07-02T12:05:37.925449Z", + "iopub.status.idle": "2024-07-02T12:05:38.005405Z", + "shell.execute_reply": "2024-07-02T12:05:38.004805Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.133536Z", - "iopub.status.busy": "2024-07-01T15:06:58.133071Z", - "iopub.status.idle": "2024-07-01T15:06:58.249759Z", - "shell.execute_reply": "2024-07-01T15:06:58.249159Z" + "iopub.execute_input": "2024-07-02T12:05:38.007885Z", + "iopub.status.busy": "2024-07-02T12:05:38.007370Z", + "iopub.status.idle": "2024-07-02T12:05:38.126166Z", + "shell.execute_reply": "2024-07-02T12:05:38.125636Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.252274Z", - "iopub.status.busy": "2024-07-01T15:06:58.251852Z", - "iopub.status.idle": "2024-07-01T15:06:58.255729Z", - "shell.execute_reply": "2024-07-01T15:06:58.255184Z" + "iopub.execute_input": "2024-07-02T12:05:38.128463Z", + "iopub.status.busy": "2024-07-02T12:05:38.128096Z", + "iopub.status.idle": "2024-07-02T12:05:38.132029Z", + "shell.execute_reply": "2024-07-02T12:05:38.131380Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.257572Z", - "iopub.status.busy": "2024-07-01T15:06:58.257399Z", - "iopub.status.idle": "2024-07-01T15:06:58.261023Z", - "shell.execute_reply": "2024-07-01T15:06:58.260496Z" + "iopub.execute_input": "2024-07-02T12:05:38.134113Z", + "iopub.status.busy": "2024-07-02T12:05:38.133792Z", + "iopub.status.idle": "2024-07-02T12:05:38.137656Z", + "shell.execute_reply": "2024-07-02T12:05:38.137186Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.263055Z", - "iopub.status.busy": "2024-07-01T15:06:58.262734Z", - "iopub.status.idle": "2024-07-01T15:06:58.298627Z", - "shell.execute_reply": "2024-07-01T15:06:58.298174Z" + "iopub.execute_input": "2024-07-02T12:05:38.139628Z", + "iopub.status.busy": "2024-07-02T12:05:38.139306Z", + "iopub.status.idle": "2024-07-02T12:05:38.175873Z", + "shell.execute_reply": "2024-07-02T12:05:38.175335Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.300556Z", - "iopub.status.busy": "2024-07-01T15:06:58.300384Z", - "iopub.status.idle": "2024-07-01T15:06:58.341152Z", - "shell.execute_reply": "2024-07-01T15:06:58.340674Z" + "iopub.execute_input": "2024-07-02T12:05:38.177802Z", + "iopub.status.busy": "2024-07-02T12:05:38.177621Z", + "iopub.status.idle": "2024-07-02T12:05:38.222062Z", + "shell.execute_reply": "2024-07-02T12:05:38.221459Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.343232Z", - "iopub.status.busy": "2024-07-01T15:06:58.343056Z", - "iopub.status.idle": "2024-07-01T15:06:58.437535Z", - "shell.execute_reply": "2024-07-01T15:06:58.436855Z" + "iopub.execute_input": "2024-07-02T12:05:38.225715Z", + "iopub.status.busy": "2024-07-02T12:05:38.225497Z", + "iopub.status.idle": "2024-07-02T12:05:38.315625Z", + "shell.execute_reply": "2024-07-02T12:05:38.315082Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.440127Z", - "iopub.status.busy": "2024-07-01T15:06:58.439842Z", - "iopub.status.idle": "2024-07-01T15:06:58.527589Z", - "shell.execute_reply": "2024-07-01T15:06:58.526960Z" + "iopub.execute_input": "2024-07-02T12:05:38.318154Z", + "iopub.status.busy": "2024-07-02T12:05:38.317969Z", + "iopub.status.idle": "2024-07-02T12:05:38.405501Z", + "shell.execute_reply": "2024-07-02T12:05:38.404891Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.529972Z", - "iopub.status.busy": "2024-07-01T15:06:58.529737Z", - "iopub.status.idle": "2024-07-01T15:06:58.741167Z", - "shell.execute_reply": "2024-07-01T15:06:58.740717Z" + "iopub.execute_input": "2024-07-02T12:05:38.407826Z", + "iopub.status.busy": "2024-07-02T12:05:38.407489Z", + "iopub.status.idle": "2024-07-02T12:05:38.614829Z", + "shell.execute_reply": "2024-07-02T12:05:38.614370Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.743495Z", - "iopub.status.busy": "2024-07-01T15:06:58.743153Z", - "iopub.status.idle": "2024-07-01T15:06:58.920954Z", - "shell.execute_reply": "2024-07-01T15:06:58.920411Z" + "iopub.execute_input": "2024-07-02T12:05:38.617073Z", + "iopub.status.busy": "2024-07-02T12:05:38.616735Z", + "iopub.status.idle": "2024-07-02T12:05:38.796547Z", + "shell.execute_reply": "2024-07-02T12:05:38.796035Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.923453Z", - "iopub.status.busy": "2024-07-01T15:06:58.923009Z", - "iopub.status.idle": "2024-07-01T15:06:58.928872Z", - "shell.execute_reply": "2024-07-01T15:06:58.928426Z" + "iopub.execute_input": "2024-07-02T12:05:38.798843Z", + "iopub.status.busy": "2024-07-02T12:05:38.798472Z", + "iopub.status.idle": "2024-07-02T12:05:38.804480Z", + "shell.execute_reply": "2024-07-02T12:05:38.804052Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:58.930892Z", - "iopub.status.busy": "2024-07-01T15:06:58.930502Z", - "iopub.status.idle": "2024-07-01T15:06:59.148406Z", - "shell.execute_reply": "2024-07-01T15:06:59.147826Z" + "iopub.execute_input": "2024-07-02T12:05:38.806348Z", + "iopub.status.busy": "2024-07-02T12:05:38.806175Z", + "iopub.status.idle": "2024-07-02T12:05:39.020330Z", + "shell.execute_reply": "2024-07-02T12:05:39.019866Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:59.150754Z", - "iopub.status.busy": "2024-07-01T15:06:59.150391Z", - "iopub.status.idle": "2024-07-01T15:07:00.213417Z", - "shell.execute_reply": "2024-07-01T15:07:00.212813Z" + "iopub.execute_input": "2024-07-02T12:05:39.022452Z", + "iopub.status.busy": "2024-07-02T12:05:39.022256Z", + "iopub.status.idle": "2024-07-02T12:05:40.077777Z", + "shell.execute_reply": "2024-07-02T12:05:40.077247Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index b426f5b7a..dfb026440 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": "2024-07-01T15:07:03.695403Z", - "iopub.status.busy": "2024-07-01T15:07:03.695236Z", - "iopub.status.idle": "2024-07-01T15:07:04.786480Z", - "shell.execute_reply": "2024-07-01T15:07:04.785971Z" + "iopub.execute_input": "2024-07-02T12:05:43.484936Z", + "iopub.status.busy": "2024-07-02T12:05:43.484760Z", + "iopub.status.idle": "2024-07-02T12:05:44.574684Z", + "shell.execute_reply": "2024-07-02T12:05:44.574061Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:04.789244Z", - "iopub.status.busy": "2024-07-01T15:07:04.788665Z", - "iopub.status.idle": "2024-07-01T15:07:04.791892Z", - "shell.execute_reply": "2024-07-01T15:07:04.791444Z" + "iopub.execute_input": "2024-07-02T12:05:44.577417Z", + "iopub.status.busy": "2024-07-02T12:05:44.576983Z", + "iopub.status.idle": "2024-07-02T12:05:44.579868Z", + "shell.execute_reply": "2024-07-02T12:05:44.579405Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.793920Z", - "iopub.status.busy": "2024-07-01T15:07:04.793593Z", - "iopub.status.idle": "2024-07-01T15:07:04.801265Z", - "shell.execute_reply": "2024-07-01T15:07:04.800810Z" + "iopub.execute_input": "2024-07-02T12:05:44.581906Z", + "iopub.status.busy": "2024-07-02T12:05:44.581588Z", + "iopub.status.idle": "2024-07-02T12:05:44.588930Z", + "shell.execute_reply": "2024-07-02T12:05:44.588511Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.803286Z", - "iopub.status.busy": "2024-07-01T15:07:04.802900Z", - "iopub.status.idle": "2024-07-01T15:07:04.850047Z", - "shell.execute_reply": "2024-07-01T15:07:04.849570Z" + "iopub.execute_input": "2024-07-02T12:05:44.591022Z", + "iopub.status.busy": "2024-07-02T12:05:44.590587Z", + "iopub.status.idle": "2024-07-02T12:05:44.643404Z", + "shell.execute_reply": "2024-07-02T12:05:44.642882Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.852247Z", - "iopub.status.busy": "2024-07-01T15:07:04.852061Z", - "iopub.status.idle": "2024-07-01T15:07:04.869485Z", - "shell.execute_reply": "2024-07-01T15:07:04.869018Z" + "iopub.execute_input": "2024-07-02T12:05:44.645347Z", + "iopub.status.busy": "2024-07-02T12:05:44.645170Z", + "iopub.status.idle": "2024-07-02T12:05:44.661922Z", + "shell.execute_reply": "2024-07-02T12:05:44.661404Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.871391Z", - "iopub.status.busy": "2024-07-01T15:07:04.871213Z", - "iopub.status.idle": "2024-07-01T15:07:04.875222Z", - "shell.execute_reply": "2024-07-01T15:07:04.874787Z" + "iopub.execute_input": "2024-07-02T12:05:44.663786Z", + "iopub.status.busy": "2024-07-02T12:05:44.663593Z", + "iopub.status.idle": "2024-07-02T12:05:44.667360Z", + "shell.execute_reply": "2024-07-02T12:05:44.666837Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.877103Z", - "iopub.status.busy": "2024-07-01T15:07:04.876935Z", - "iopub.status.idle": "2024-07-01T15:07:04.890567Z", - "shell.execute_reply": "2024-07-01T15:07:04.890109Z" + "iopub.execute_input": "2024-07-02T12:05:44.669486Z", + "iopub.status.busy": "2024-07-02T12:05:44.669101Z", + "iopub.status.idle": "2024-07-02T12:05:44.685613Z", + "shell.execute_reply": "2024-07-02T12:05:44.685195Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.892340Z", - "iopub.status.busy": "2024-07-01T15:07:04.892165Z", - "iopub.status.idle": "2024-07-01T15:07:04.917921Z", - "shell.execute_reply": "2024-07-01T15:07:04.917510Z" + "iopub.execute_input": "2024-07-02T12:05:44.687438Z", + "iopub.status.busy": "2024-07-02T12:05:44.687261Z", + "iopub.status.idle": "2024-07-02T12:05:44.713068Z", + "shell.execute_reply": "2024-07-02T12:05:44.712511Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:04.919909Z", - "iopub.status.busy": "2024-07-01T15:07:04.919740Z", - "iopub.status.idle": "2024-07-01T15:07:06.770405Z", - "shell.execute_reply": "2024-07-01T15:07:06.769771Z" + "iopub.execute_input": "2024-07-02T12:05:44.714998Z", + "iopub.status.busy": "2024-07-02T12:05:44.714828Z", + "iopub.status.idle": "2024-07-02T12:05:46.561058Z", + "shell.execute_reply": "2024-07-02T12:05:46.560413Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.773094Z", - "iopub.status.busy": "2024-07-01T15:07:06.772569Z", - "iopub.status.idle": "2024-07-01T15:07:06.779189Z", - "shell.execute_reply": "2024-07-01T15:07:06.778660Z" + "iopub.execute_input": "2024-07-02T12:05:46.563695Z", + "iopub.status.busy": "2024-07-02T12:05:46.563390Z", + "iopub.status.idle": "2024-07-02T12:05:46.570695Z", + "shell.execute_reply": "2024-07-02T12:05:46.570276Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.781060Z", - "iopub.status.busy": "2024-07-01T15:07:06.780797Z", - "iopub.status.idle": "2024-07-01T15:07:06.793132Z", - "shell.execute_reply": "2024-07-01T15:07:06.792613Z" + "iopub.execute_input": "2024-07-02T12:05:46.572666Z", + "iopub.status.busy": "2024-07-02T12:05:46.572452Z", + "iopub.status.idle": "2024-07-02T12:05:46.585257Z", + "shell.execute_reply": "2024-07-02T12:05:46.584820Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.795311Z", - "iopub.status.busy": "2024-07-01T15:07:06.794896Z", - "iopub.status.idle": "2024-07-01T15:07:06.801219Z", - "shell.execute_reply": "2024-07-01T15:07:06.800801Z" + "iopub.execute_input": "2024-07-02T12:05:46.587355Z", + "iopub.status.busy": "2024-07-02T12:05:46.586953Z", + "iopub.status.idle": "2024-07-02T12:05:46.593328Z", + "shell.execute_reply": "2024-07-02T12:05:46.592850Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.803175Z", - "iopub.status.busy": "2024-07-01T15:07:06.802994Z", - "iopub.status.idle": "2024-07-01T15:07:06.805670Z", - "shell.execute_reply": "2024-07-01T15:07:06.805234Z" + "iopub.execute_input": "2024-07-02T12:05:46.595350Z", + "iopub.status.busy": "2024-07-02T12:05:46.595021Z", + "iopub.status.idle": "2024-07-02T12:05:46.597564Z", + "shell.execute_reply": "2024-07-02T12:05:46.597149Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.807492Z", - "iopub.status.busy": "2024-07-01T15:07:06.807328Z", - "iopub.status.idle": "2024-07-01T15:07:06.810895Z", - "shell.execute_reply": "2024-07-01T15:07:06.810453Z" + "iopub.execute_input": "2024-07-02T12:05:46.599508Z", + "iopub.status.busy": "2024-07-02T12:05:46.599184Z", + "iopub.status.idle": "2024-07-02T12:05:46.602546Z", + "shell.execute_reply": "2024-07-02T12:05:46.602058Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.812899Z", - "iopub.status.busy": "2024-07-01T15:07:06.812510Z", - "iopub.status.idle": "2024-07-01T15:07:06.815130Z", - "shell.execute_reply": "2024-07-01T15:07:06.814702Z" + "iopub.execute_input": "2024-07-02T12:05:46.604583Z", + "iopub.status.busy": "2024-07-02T12:05:46.604261Z", + "iopub.status.idle": "2024-07-02T12:05:46.606854Z", + "shell.execute_reply": "2024-07-02T12:05:46.606416Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.817057Z", - "iopub.status.busy": "2024-07-01T15:07:06.816734Z", - "iopub.status.idle": "2024-07-01T15:07:06.820893Z", - "shell.execute_reply": "2024-07-01T15:07:06.820448Z" + "iopub.execute_input": "2024-07-02T12:05:46.608809Z", + "iopub.status.busy": "2024-07-02T12:05:46.608533Z", + "iopub.status.idle": "2024-07-02T12:05:46.612540Z", + "shell.execute_reply": "2024-07-02T12:05:46.612106Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.822875Z", - "iopub.status.busy": "2024-07-01T15:07:06.822704Z", - "iopub.status.idle": "2024-07-01T15:07:06.851357Z", - "shell.execute_reply": "2024-07-01T15:07:06.850916Z" + "iopub.execute_input": "2024-07-02T12:05:46.614617Z", + "iopub.status.busy": "2024-07-02T12:05:46.614295Z", + "iopub.status.idle": "2024-07-02T12:05:46.642333Z", + "shell.execute_reply": "2024-07-02T12:05:46.641923Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:06.853186Z", - "iopub.status.busy": "2024-07-01T15:07:06.853017Z", - "iopub.status.idle": "2024-07-01T15:07:06.857526Z", - "shell.execute_reply": "2024-07-01T15:07:06.857095Z" + "iopub.execute_input": "2024-07-02T12:05:46.644398Z", + "iopub.status.busy": "2024-07-02T12:05:46.644076Z", + "iopub.status.idle": "2024-07-02T12:05:46.648349Z", + "shell.execute_reply": "2024-07-02T12:05:46.647909Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index f9fccade5..02d580b54 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": "2024-07-01T15:07:09.805934Z", - "iopub.status.busy": "2024-07-01T15:07:09.805760Z", - "iopub.status.idle": "2024-07-01T15:07:10.951874Z", - "shell.execute_reply": "2024-07-01T15:07:10.951332Z" + "iopub.execute_input": "2024-07-02T12:05:49.390201Z", + "iopub.status.busy": "2024-07-02T12:05:49.390029Z", + "iopub.status.idle": "2024-07-02T12:05:50.506272Z", + "shell.execute_reply": "2024-07-02T12:05:50.505689Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:10.954229Z", - "iopub.status.busy": "2024-07-01T15:07:10.953974Z", - "iopub.status.idle": "2024-07-01T15:07:11.145898Z", - "shell.execute_reply": "2024-07-01T15:07:11.145328Z" + "iopub.execute_input": "2024-07-02T12:05:50.508865Z", + "iopub.status.busy": "2024-07-02T12:05:50.508468Z", + "iopub.status.idle": "2024-07-02T12:05:50.696756Z", + "shell.execute_reply": "2024-07-02T12:05:50.696292Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:11.148952Z", - "iopub.status.busy": "2024-07-01T15:07:11.148446Z", - "iopub.status.idle": "2024-07-01T15:07:11.162195Z", - "shell.execute_reply": "2024-07-01T15:07:11.161701Z" + "iopub.execute_input": "2024-07-02T12:05:50.698941Z", + "iopub.status.busy": "2024-07-02T12:05:50.698699Z", + "iopub.status.idle": "2024-07-02T12:05:50.711704Z", + "shell.execute_reply": "2024-07-02T12:05:50.711226Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:11.164347Z", - "iopub.status.busy": "2024-07-01T15:07:11.163932Z", - "iopub.status.idle": "2024-07-01T15:07:13.785011Z", - "shell.execute_reply": "2024-07-01T15:07:13.784429Z" + "iopub.execute_input": "2024-07-02T12:05:50.713503Z", + "iopub.status.busy": "2024-07-02T12:05:50.713332Z", + "iopub.status.idle": "2024-07-02T12:05:53.318405Z", + "shell.execute_reply": "2024-07-02T12:05:53.317873Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:13.787263Z", - "iopub.status.busy": "2024-07-01T15:07:13.787077Z", - "iopub.status.idle": "2024-07-01T15:07:15.129078Z", - "shell.execute_reply": "2024-07-01T15:07:15.128523Z" + "iopub.execute_input": "2024-07-02T12:05:53.320633Z", + "iopub.status.busy": "2024-07-02T12:05:53.320318Z", + "iopub.status.idle": "2024-07-02T12:05:54.676476Z", + "shell.execute_reply": "2024-07-02T12:05:54.675931Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:15.131446Z", - "iopub.status.busy": "2024-07-01T15:07:15.131256Z", - "iopub.status.idle": "2024-07-01T15:07:15.135063Z", - "shell.execute_reply": "2024-07-01T15:07:15.134547Z" + "iopub.execute_input": "2024-07-02T12:05:54.678848Z", + "iopub.status.busy": "2024-07-02T12:05:54.678408Z", + "iopub.status.idle": "2024-07-02T12:05:54.682336Z", + "shell.execute_reply": "2024-07-02T12:05:54.681800Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:15.137004Z", - "iopub.status.busy": "2024-07-01T15:07:15.136825Z", - "iopub.status.idle": "2024-07-01T15:07:17.134343Z", - "shell.execute_reply": "2024-07-01T15:07:17.133735Z" + "iopub.execute_input": "2024-07-02T12:05:54.684325Z", + "iopub.status.busy": "2024-07-02T12:05:54.683937Z", + "iopub.status.idle": "2024-07-02T12:05:56.558099Z", + "shell.execute_reply": "2024-07-02T12:05:56.557479Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:17.136852Z", - "iopub.status.busy": "2024-07-01T15:07:17.136398Z", - "iopub.status.idle": "2024-07-01T15:07:17.144283Z", - "shell.execute_reply": "2024-07-01T15:07:17.143851Z" + "iopub.execute_input": "2024-07-02T12:05:56.560538Z", + "iopub.status.busy": "2024-07-02T12:05:56.560208Z", + "iopub.status.idle": "2024-07-02T12:05:56.567803Z", + "shell.execute_reply": "2024-07-02T12:05:56.567265Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:17.146377Z", - "iopub.status.busy": "2024-07-01T15:07:17.146026Z", - "iopub.status.idle": "2024-07-01T15:07:19.687541Z", - "shell.execute_reply": "2024-07-01T15:07:19.686980Z" + "iopub.execute_input": "2024-07-02T12:05:56.569739Z", + "iopub.status.busy": "2024-07-02T12:05:56.569446Z", + "iopub.status.idle": "2024-07-02T12:05:59.160999Z", + "shell.execute_reply": "2024-07-02T12:05:59.160450Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:19.689886Z", - "iopub.status.busy": "2024-07-01T15:07:19.689435Z", - "iopub.status.idle": "2024-07-01T15:07:19.692913Z", - "shell.execute_reply": "2024-07-01T15:07:19.692502Z" + "iopub.execute_input": "2024-07-02T12:05:59.163107Z", + "iopub.status.busy": "2024-07-02T12:05:59.162773Z", + "iopub.status.idle": "2024-07-02T12:05:59.166191Z", + "shell.execute_reply": "2024-07-02T12:05:59.165684Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:19.694737Z", - "iopub.status.busy": "2024-07-01T15:07:19.694568Z", - "iopub.status.idle": "2024-07-01T15:07:19.698060Z", - "shell.execute_reply": "2024-07-01T15:07:19.697520Z" + "iopub.execute_input": "2024-07-02T12:05:59.168252Z", + "iopub.status.busy": "2024-07-02T12:05:59.167849Z", + "iopub.status.idle": "2024-07-02T12:05:59.171322Z", + "shell.execute_reply": "2024-07-02T12:05:59.170794Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:19.700087Z", - "iopub.status.busy": "2024-07-01T15:07:19.699691Z", - "iopub.status.idle": "2024-07-01T15:07:19.702785Z", - "shell.execute_reply": "2024-07-01T15:07:19.702302Z" + "iopub.execute_input": "2024-07-02T12:05:59.173235Z", + "iopub.status.busy": "2024-07-02T12:05:59.172937Z", + "iopub.status.idle": "2024-07-02T12:05:59.176035Z", + "shell.execute_reply": "2024-07-02T12:05:59.175500Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 013f8cdd9..7ce8a7f2b 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": "2024-07-01T15:07:22.044165Z", - "iopub.status.busy": "2024-07-01T15:07:22.043990Z", - "iopub.status.idle": "2024-07-01T15:07:23.190619Z", - "shell.execute_reply": "2024-07-01T15:07:23.190110Z" + "iopub.execute_input": "2024-07-02T12:06:01.378322Z", + "iopub.status.busy": "2024-07-02T12:06:01.377923Z", + "iopub.status.idle": "2024-07-02T12:06:02.503419Z", + "shell.execute_reply": "2024-07-02T12:06:02.502819Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:23.192992Z", - "iopub.status.busy": "2024-07-01T15:07:23.192742Z", - "iopub.status.idle": "2024-07-01T15:07:24.642885Z", - "shell.execute_reply": "2024-07-01T15:07:24.642213Z" + "iopub.execute_input": "2024-07-02T12:06:02.505878Z", + "iopub.status.busy": "2024-07-02T12:06:02.505606Z", + "iopub.status.idle": "2024-07-02T12:06:03.484637Z", + "shell.execute_reply": "2024-07-02T12:06:03.483911Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:24.645453Z", - "iopub.status.busy": "2024-07-01T15:07:24.645208Z", - "iopub.status.idle": "2024-07-01T15:07:24.648320Z", - "shell.execute_reply": "2024-07-01T15:07:24.647888Z" + "iopub.execute_input": "2024-07-02T12:06:03.487478Z", + "iopub.status.busy": "2024-07-02T12:06:03.486983Z", + "iopub.status.idle": "2024-07-02T12:06:03.490372Z", + "shell.execute_reply": "2024-07-02T12:06:03.489937Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:24.650220Z", - "iopub.status.busy": "2024-07-01T15:07:24.650036Z", - "iopub.status.idle": "2024-07-01T15:07:24.656028Z", - "shell.execute_reply": "2024-07-01T15:07:24.655606Z" + "iopub.execute_input": "2024-07-02T12:06:03.492668Z", + "iopub.status.busy": "2024-07-02T12:06:03.492302Z", + "iopub.status.idle": "2024-07-02T12:06:03.499701Z", + "shell.execute_reply": "2024-07-02T12:06:03.499223Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:24.657894Z", - "iopub.status.busy": "2024-07-01T15:07:24.657721Z", - "iopub.status.idle": "2024-07-01T15:07:25.141231Z", - "shell.execute_reply": "2024-07-01T15:07:25.140651Z" + "iopub.execute_input": "2024-07-02T12:06:03.501657Z", + "iopub.status.busy": "2024-07-02T12:06:03.501478Z", + "iopub.status.idle": "2024-07-02T12:06:03.984496Z", + "shell.execute_reply": "2024-07-02T12:06:03.983911Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:25.144290Z", - "iopub.status.busy": "2024-07-01T15:07:25.143822Z", - "iopub.status.idle": "2024-07-01T15:07:25.149165Z", - "shell.execute_reply": "2024-07-01T15:07:25.148739Z" + "iopub.execute_input": "2024-07-02T12:06:03.987155Z", + "iopub.status.busy": "2024-07-02T12:06:03.986711Z", + "iopub.status.idle": "2024-07-02T12:06:03.992050Z", + "shell.execute_reply": "2024-07-02T12:06:03.991587Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:25.151192Z", - "iopub.status.busy": "2024-07-01T15:07:25.150870Z", - "iopub.status.idle": "2024-07-01T15:07:25.154548Z", - "shell.execute_reply": "2024-07-01T15:07:25.154108Z" + "iopub.execute_input": "2024-07-02T12:06:03.993958Z", + "iopub.status.busy": "2024-07-02T12:06:03.993639Z", + "iopub.status.idle": "2024-07-02T12:06:03.997330Z", + "shell.execute_reply": "2024-07-02T12:06:03.996906Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:25.156514Z", - "iopub.status.busy": "2024-07-01T15:07:25.156335Z", - "iopub.status.idle": "2024-07-01T15:07:26.038062Z", - "shell.execute_reply": "2024-07-01T15:07:26.037425Z" + "iopub.execute_input": "2024-07-02T12:06:03.999294Z", + "iopub.status.busy": "2024-07-02T12:06:03.998989Z", + "iopub.status.idle": "2024-07-02T12:06:04.886721Z", + "shell.execute_reply": "2024-07-02T12:06:04.886183Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:26.040299Z", - "iopub.status.busy": "2024-07-01T15:07:26.040059Z", - "iopub.status.idle": "2024-07-01T15:07:26.281733Z", - "shell.execute_reply": "2024-07-01T15:07:26.281238Z" + "iopub.execute_input": "2024-07-02T12:06:04.889094Z", + "iopub.status.busy": "2024-07-02T12:06:04.888730Z", + "iopub.status.idle": "2024-07-02T12:06:05.104977Z", + "shell.execute_reply": "2024-07-02T12:06:05.104560Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:26.284005Z", - "iopub.status.busy": "2024-07-01T15:07:26.283674Z", - "iopub.status.idle": "2024-07-01T15:07:26.287739Z", - "shell.execute_reply": "2024-07-01T15:07:26.287303Z" + "iopub.execute_input": "2024-07-02T12:06:05.107009Z", + "iopub.status.busy": "2024-07-02T12:06:05.106744Z", + "iopub.status.idle": "2024-07-02T12:06:05.111011Z", + "shell.execute_reply": "2024-07-02T12:06:05.110475Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:26.289717Z", - "iopub.status.busy": "2024-07-01T15:07:26.289415Z", - "iopub.status.idle": "2024-07-01T15:07:26.747330Z", - "shell.execute_reply": "2024-07-01T15:07:26.746844Z" + "iopub.execute_input": "2024-07-02T12:06:05.112841Z", + "iopub.status.busy": "2024-07-02T12:06:05.112667Z", + "iopub.status.idle": "2024-07-02T12:06:05.549544Z", + "shell.execute_reply": "2024-07-02T12:06:05.548895Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:26.749504Z", - "iopub.status.busy": "2024-07-01T15:07:26.749157Z", - "iopub.status.idle": "2024-07-01T15:07:27.049969Z", - "shell.execute_reply": "2024-07-01T15:07:27.049390Z" + "iopub.execute_input": "2024-07-02T12:06:05.552420Z", + "iopub.status.busy": "2024-07-02T12:06:05.552234Z", + "iopub.status.idle": "2024-07-02T12:06:05.880895Z", + "shell.execute_reply": "2024-07-02T12:06:05.880300Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:27.052016Z", - "iopub.status.busy": "2024-07-01T15:07:27.051834Z", - "iopub.status.idle": "2024-07-01T15:07:27.386953Z", - "shell.execute_reply": "2024-07-01T15:07:27.386354Z" + "iopub.execute_input": "2024-07-02T12:06:05.883106Z", + "iopub.status.busy": "2024-07-02T12:06:05.882705Z", + "iopub.status.idle": "2024-07-02T12:06:06.240971Z", + "shell.execute_reply": "2024-07-02T12:06:06.240404Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:27.390094Z", - "iopub.status.busy": "2024-07-01T15:07:27.389720Z", - "iopub.status.idle": "2024-07-01T15:07:27.826810Z", - "shell.execute_reply": "2024-07-01T15:07:27.826201Z" + "iopub.execute_input": "2024-07-02T12:06:06.243379Z", + "iopub.status.busy": "2024-07-02T12:06:06.243189Z", + "iopub.status.idle": "2024-07-02T12:06:06.680772Z", + "shell.execute_reply": "2024-07-02T12:06:06.680290Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:27.830888Z", - "iopub.status.busy": "2024-07-01T15:07:27.830547Z", - "iopub.status.idle": "2024-07-01T15:07:28.275927Z", - "shell.execute_reply": "2024-07-01T15:07:28.275306Z" + "iopub.execute_input": "2024-07-02T12:06:06.682984Z", + "iopub.status.busy": "2024-07-02T12:06:06.682675Z", + "iopub.status.idle": "2024-07-02T12:06:07.129389Z", + "shell.execute_reply": "2024-07-02T12:06:07.128744Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:28.278580Z", - "iopub.status.busy": "2024-07-01T15:07:28.278386Z", - "iopub.status.idle": "2024-07-01T15:07:28.478171Z", - "shell.execute_reply": "2024-07-01T15:07:28.477537Z" + "iopub.execute_input": "2024-07-02T12:06:07.132269Z", + "iopub.status.busy": "2024-07-02T12:06:07.132092Z", + "iopub.status.idle": "2024-07-02T12:06:07.345651Z", + "shell.execute_reply": "2024-07-02T12:06:07.345066Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:28.481029Z", - "iopub.status.busy": "2024-07-01T15:07:28.480514Z", - "iopub.status.idle": "2024-07-01T15:07:28.679630Z", - "shell.execute_reply": "2024-07-01T15:07:28.679032Z" + "iopub.execute_input": "2024-07-02T12:06:07.347943Z", + "iopub.status.busy": "2024-07-02T12:06:07.347569Z", + "iopub.status.idle": "2024-07-02T12:06:07.545897Z", + "shell.execute_reply": "2024-07-02T12:06:07.545303Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:28.681815Z", - "iopub.status.busy": "2024-07-01T15:07:28.681633Z", - "iopub.status.idle": "2024-07-01T15:07:28.684760Z", - "shell.execute_reply": "2024-07-01T15:07:28.684215Z" + "iopub.execute_input": "2024-07-02T12:06:07.548054Z", + "iopub.status.busy": "2024-07-02T12:06:07.547721Z", + "iopub.status.idle": "2024-07-02T12:06:07.550610Z", + "shell.execute_reply": "2024-07-02T12:06:07.550172Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:28.686736Z", - "iopub.status.busy": "2024-07-01T15:07:28.686404Z", - "iopub.status.idle": "2024-07-01T15:07:29.599883Z", - "shell.execute_reply": "2024-07-01T15:07:29.599378Z" + "iopub.execute_input": "2024-07-02T12:06:07.552606Z", + "iopub.status.busy": "2024-07-02T12:06:07.552209Z", + "iopub.status.idle": "2024-07-02T12:06:08.545283Z", + "shell.execute_reply": "2024-07-02T12:06:08.544691Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:29.602491Z", - "iopub.status.busy": "2024-07-01T15:07:29.602156Z", - "iopub.status.idle": "2024-07-01T15:07:29.724845Z", - "shell.execute_reply": "2024-07-01T15:07:29.724400Z" + "iopub.execute_input": "2024-07-02T12:06:08.550100Z", + "iopub.status.busy": "2024-07-02T12:06:08.549675Z", + "iopub.status.idle": "2024-07-02T12:06:08.692703Z", + "shell.execute_reply": "2024-07-02T12:06:08.692222Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:29.727049Z", - "iopub.status.busy": "2024-07-01T15:07:29.726723Z", - "iopub.status.idle": "2024-07-01T15:07:29.857120Z", - "shell.execute_reply": "2024-07-01T15:07:29.856610Z" + "iopub.execute_input": "2024-07-02T12:06:08.694865Z", + "iopub.status.busy": "2024-07-02T12:06:08.694525Z", + "iopub.status.idle": "2024-07-02T12:06:08.829794Z", + "shell.execute_reply": "2024-07-02T12:06:08.829310Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:29.859645Z", - "iopub.status.busy": "2024-07-01T15:07:29.859295Z", - "iopub.status.idle": "2024-07-01T15:07:30.599850Z", - "shell.execute_reply": "2024-07-01T15:07:30.599307Z" + "iopub.execute_input": "2024-07-02T12:06:08.832030Z", + "iopub.status.busy": "2024-07-02T12:06:08.831714Z", + "iopub.status.idle": "2024-07-02T12:06:09.569943Z", + "shell.execute_reply": "2024-07-02T12:06:09.569367Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:30.602022Z", - "iopub.status.busy": "2024-07-01T15:07:30.601697Z", - "iopub.status.idle": "2024-07-01T15:07:30.605345Z", - "shell.execute_reply": "2024-07-01T15:07:30.604899Z" + "iopub.execute_input": "2024-07-02T12:06:09.572191Z", + "iopub.status.busy": "2024-07-02T12:06:09.571856Z", + "iopub.status.idle": "2024-07-02T12:06:09.575442Z", + "shell.execute_reply": "2024-07-02T12:06:09.575034Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index de1ca9206..12c6da264 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": "2024-07-01T15:07:32.630513Z", - "iopub.status.busy": "2024-07-01T15:07:32.630022Z", - "iopub.status.idle": "2024-07-01T15:07:35.339624Z", - "shell.execute_reply": "2024-07-01T15:07:35.338990Z" + "iopub.execute_input": "2024-07-02T12:06:11.678697Z", + "iopub.status.busy": "2024-07-02T12:06:11.678521Z", + "iopub.status.idle": "2024-07-02T12:06:14.408240Z", + "shell.execute_reply": "2024-07-02T12:06:14.407674Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:07:35.342314Z", - "iopub.status.busy": "2024-07-01T15:07:35.341942Z", - "iopub.status.idle": "2024-07-01T15:07:35.677074Z", - "shell.execute_reply": "2024-07-01T15:07:35.676543Z" + "iopub.execute_input": "2024-07-02T12:06:14.410934Z", + "iopub.status.busy": "2024-07-02T12:06:14.410443Z", + "iopub.status.idle": "2024-07-02T12:06:14.735244Z", + "shell.execute_reply": "2024-07-02T12:06:14.734679Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:35.679709Z", - "iopub.status.busy": "2024-07-01T15:07:35.679377Z", - "iopub.status.idle": "2024-07-01T15:07:35.683566Z", - "shell.execute_reply": "2024-07-01T15:07:35.683132Z" + "iopub.execute_input": "2024-07-02T12:06:14.737835Z", + "iopub.status.busy": "2024-07-02T12:06:14.737360Z", + "iopub.status.idle": "2024-07-02T12:06:14.741543Z", + "shell.execute_reply": "2024-07-02T12:06:14.741013Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:35.685686Z", - "iopub.status.busy": "2024-07-01T15:07:35.685365Z", - "iopub.status.idle": "2024-07-01T15:07:42.517988Z", - "shell.execute_reply": "2024-07-01T15:07:42.517428Z" + "iopub.execute_input": "2024-07-02T12:06:14.743746Z", + "iopub.status.busy": "2024-07-02T12:06:14.743385Z", + "iopub.status.idle": "2024-07-02T12:06:25.921071Z", + "shell.execute_reply": "2024-07-02T12:06:25.920486Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 786432/170498071 [00:00<00:21, 7820176.68it/s]" + " 0%| | 458752/170498071 [00:00<00:37, 4550205.38it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 4980736/170498071 [00:00<00:05, 27792797.45it/s]" + " 2%|▏ | 2686976/170498071 [00:00<00:11, 14867624.00it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10944512/170498071 [00:00<00:03, 42298222.53it/s]" + " 3%|▎ | 4915200/170498071 [00:00<00:09, 18176569.25it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 16449536/170498071 [00:00<00:03, 47151386.97it/s]" + " 4%|▍ | 7110656/170498071 [00:00<00:08, 19525356.25it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 21168128/170498071 [00:00<00:03, 41939697.62it/s]" + " 5%|▌ | 9273344/170498071 [00:00<00:08, 20138060.31it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25460736/170498071 [00:00<00:04, 36250418.01it/s]" + " 7%|▋ | 11468800/170498071 [00:00<00:07, 20583296.62it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 29261824/170498071 [00:00<00:04, 31518178.43it/s]" + " 8%|▊ | 13565952/170498071 [00:00<00:07, 20618122.34it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32604160/170498071 [00:00<00:04, 29639430.93it/s]" + " 9%|▉ | 15695872/170498071 [00:00<00:07, 20684064.34it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 35684352/170498071 [00:01<00:04, 28240309.80it/s]" + " 10%|█ | 17793024/170498071 [00:00<00:07, 20210099.70it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 38600704/170498071 [00:01<00:04, 27686377.59it/s]" + " 12%|█▏ | 19857408/170498071 [00:01<00:07, 20157298.26it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 41451520/170498071 [00:01<00:04, 27880418.29it/s]" + " 13%|█▎ | 21889024/170498071 [00:01<00:07, 19580366.36it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 44302336/170498071 [00:01<00:04, 27339381.04it/s]" + " 14%|█▍ | 23887872/170498071 [00:01<00:07, 19689752.59it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 47153152/170498071 [00:01<00:04, 27588406.47it/s]" + " 15%|█▌ | 26148864/170498071 [00:01<00:07, 20522936.05it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49938432/170498071 [00:01<00:04, 27519027.46it/s]" + " 17%|█▋ | 28901376/170498071 [00:01<00:06, 22488420.49it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52723712/170498071 [00:01<00:04, 26308629.52it/s]" + " 18%|█▊ | 31260672/170498071 [00:01<00:06, 22666713.87it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 55377920/170498071 [00:01<00:04, 25864377.41it/s]" + " 20%|█▉ | 33783808/170498071 [00:01<00:05, 23420171.66it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 58884096/170498071 [00:01<00:03, 28360977.09it/s]" + " 21%|██ | 36143104/170498071 [00:01<00:05, 23367837.44it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 62717952/170498071 [00:02<00:03, 31182568.80it/s]" + " 23%|██▎ | 38567936/170498071 [00:01<00:05, 23628433.32it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 66846720/170498071 [00:02<00:03, 34093308.02it/s]" + " 24%|██▍ | 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+ " 37%|███▋ | 63602688/170498071 [00:03<00:05, 20984454.80it/s]" ] }, { @@ -484,7 +484,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 118554624/170498071 [00:03<00:01, 46326838.71it/s]" + " 39%|███▊ | 65732608/170498071 [00:03<00:05, 20040214.69it/s]" ] }, { @@ -492,7 +492,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 123207680/170498071 [00:03<00:01, 46221454.72it/s]" + " 40%|███▉ | 67764224/170498071 [00:03<00:05, 19617119.66it/s]" ] }, { @@ -500,7 +500,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 127860736/170498071 [00:03<00:00, 45331471.71it/s]" + " 41%|████ | 69763072/170498071 [00:03<00:05, 19368566.16it/s]" ] }, { @@ -508,7 +508,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 132775936/170498071 [00:03<00:00, 46431225.49it/s]" + " 42%|████▏ | 71729152/170498071 [00:03<00:05, 18942200.76it/s]" ] }, { @@ -516,7 +516,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 137756672/170498071 [00:03<00:00, 47136533.89it/s]" + " 43%|████▎ | 73760768/170498071 [00:03<00:05, 19136506.47it/s]" ] }, { @@ -524,7 +524,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 142966784/170498071 [00:03<00:00, 48566305.03it/s]" + " 44%|████▍ | 75694080/170498071 [00:03<00:05, 18546539.77it/s]" ] }, { @@ -532,7 +532,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 147849216/170498071 [00:03<00:00, 47837503.41it/s]" + " 46%|████▌ | 77856768/170498071 [00:03<00:04, 19310897.00it/s]" ] }, { @@ -540,7 +540,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 153649152/170498071 [00:03<00:00, 50699005.24it/s]" + " 47%|████▋ | 79855616/170498071 [00:03<00:04, 19370411.60it/s]" ] }, { @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 160006144/170498071 [00:04<00:00, 54457105.34it/s]" + " 48%|████▊ | 81821696/170498071 [00:03<00:04, 18841681.57it/s]" ] }, { @@ -556,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - " 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"2024-07-01T15:07:42.524205Z" + "iopub.execute_input": "2024-07-02T12:06:25.923304Z", + "iopub.status.busy": "2024-07-02T12:06:25.922962Z", + "iopub.status.idle": "2024-07-02T12:06:25.927532Z", + "shell.execute_reply": "2024-07-02T12:06:25.927116Z" }, "nbsphinx": "hidden" }, @@ -736,10 +1072,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:42.526805Z", - "iopub.status.busy": "2024-07-01T15:07:42.526366Z", - "iopub.status.idle": "2024-07-01T15:07:43.068980Z", - "shell.execute_reply": "2024-07-01T15:07:43.068376Z" + "iopub.execute_input": "2024-07-02T12:06:25.929617Z", + "iopub.status.busy": "2024-07-02T12:06:25.929294Z", + "iopub.status.idle": "2024-07-02T12:06:26.466020Z", + "shell.execute_reply": "2024-07-02T12:06:26.465500Z" } }, "outputs": [ @@ -772,10 +1108,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:43.071278Z", - "iopub.status.busy": "2024-07-01T15:07:43.070855Z", - "iopub.status.idle": "2024-07-01T15:07:43.585255Z", - "shell.execute_reply": "2024-07-01T15:07:43.584665Z" + "iopub.execute_input": "2024-07-02T12:06:26.468274Z", + "iopub.status.busy": "2024-07-02T12:06:26.467846Z", + "iopub.status.idle": "2024-07-02T12:06:26.973804Z", + "shell.execute_reply": "2024-07-02T12:06:26.973190Z" } }, "outputs": [ @@ -813,10 +1149,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:43.587754Z", - "iopub.status.busy": "2024-07-01T15:07:43.587325Z", - "iopub.status.idle": "2024-07-01T15:07:43.591527Z", - "shell.execute_reply": "2024-07-01T15:07:43.591015Z" + "iopub.execute_input": "2024-07-02T12:06:26.976024Z", + "iopub.status.busy": "2024-07-02T12:06:26.975702Z", + "iopub.status.idle": "2024-07-02T12:06:26.979191Z", + "shell.execute_reply": "2024-07-02T12:06:26.978654Z" } }, "outputs": [], @@ -839,17 +1175,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:43.593739Z", - "iopub.status.busy": "2024-07-01T15:07:43.593377Z", - "iopub.status.idle": "2024-07-01T15:07:56.035606Z", - "shell.execute_reply": "2024-07-01T15:07:56.035038Z" + "iopub.execute_input": "2024-07-02T12:06:26.981120Z", + "iopub.status.busy": "2024-07-02T12:06:26.980808Z", + "iopub.status.idle": "2024-07-02T12:06:39.219368Z", + "shell.execute_reply": "2024-07-02T12:06:39.218785Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e0af51d1d7c41f6b28e94e107a2e2dd", + "model_id": "e62048d58b1a436fa16544b9ecbd1a17", "version_major": 2, "version_minor": 0 }, @@ -908,10 +1244,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:56.037942Z", - "iopub.status.busy": "2024-07-01T15:07:56.037579Z", - "iopub.status.idle": "2024-07-01T15:07:58.157290Z", - "shell.execute_reply": "2024-07-01T15:07:58.156719Z" + "iopub.execute_input": "2024-07-02T12:06:39.221701Z", + "iopub.status.busy": "2024-07-02T12:06:39.221327Z", + "iopub.status.idle": "2024-07-02T12:06:41.264255Z", + "shell.execute_reply": "2024-07-02T12:06:41.263645Z" } }, "outputs": [ @@ -955,10 +1291,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:58.160021Z", - "iopub.status.busy": "2024-07-01T15:07:58.159723Z", - "iopub.status.idle": "2024-07-01T15:07:58.413397Z", - "shell.execute_reply": "2024-07-01T15:07:58.412322Z" + "iopub.execute_input": "2024-07-02T12:06:41.266829Z", + "iopub.status.busy": "2024-07-02T12:06:41.266301Z", + "iopub.status.idle": "2024-07-02T12:06:41.492927Z", + "shell.execute_reply": "2024-07-02T12:06:41.492268Z" } }, "outputs": [ @@ -994,10 +1330,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:07:58.415968Z", - "iopub.status.busy": "2024-07-01T15:07:58.415746Z", - "iopub.status.idle": "2024-07-01T15:07:59.087448Z", - "shell.execute_reply": "2024-07-01T15:07:59.086838Z" + "iopub.execute_input": "2024-07-02T12:06:41.495155Z", + 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"f2d6b576288e4f7fbed42581aafbf977": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1668,42 +2040,6 @@ "visibility": null, "width": null } - }, - "e010df9fbdef472aa0c2e3f8a393bb55": { - "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 - } - }, - "f100e8e508354e3a998f09e41481fe4a": { - "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/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 46926446f..75e02e92c 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": "2024-07-01T15:08:16.815662Z", - "iopub.status.busy": "2024-07-01T15:08:16.815212Z", - "iopub.status.idle": "2024-07-01T15:08:18.061264Z", - "shell.execute_reply": "2024-07-01T15:08:18.060688Z" + "iopub.execute_input": "2024-07-02T12:06:59.101052Z", + "iopub.status.busy": "2024-07-02T12:06:59.100876Z", + "iopub.status.idle": "2024-07-02T12:07:00.258136Z", + "shell.execute_reply": "2024-07-02T12:07:00.257587Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:08:18.064012Z", - "iopub.status.busy": "2024-07-01T15:08:18.063557Z", - "iopub.status.idle": "2024-07-01T15:08:18.081205Z", - "shell.execute_reply": "2024-07-01T15:08:18.080744Z" + "iopub.execute_input": "2024-07-02T12:07:00.260745Z", + "iopub.status.busy": "2024-07-02T12:07:00.260339Z", + "iopub.status.idle": "2024-07-02T12:07:00.277570Z", + "shell.execute_reply": "2024-07-02T12:07:00.277011Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.083492Z", - "iopub.status.busy": "2024-07-01T15:08:18.083103Z", - "iopub.status.idle": "2024-07-01T15:08:18.086376Z", - "shell.execute_reply": "2024-07-01T15:08:18.085828Z" + "iopub.execute_input": "2024-07-02T12:07:00.280398Z", + "iopub.status.busy": "2024-07-02T12:07:00.279700Z", + "iopub.status.idle": "2024-07-02T12:07:00.283630Z", + "shell.execute_reply": "2024-07-02T12:07:00.282919Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.088481Z", - "iopub.status.busy": "2024-07-01T15:08:18.088156Z", - "iopub.status.idle": "2024-07-01T15:08:18.174903Z", - "shell.execute_reply": "2024-07-01T15:08:18.174413Z" + "iopub.execute_input": "2024-07-02T12:07:00.286415Z", + "iopub.status.busy": "2024-07-02T12:07:00.285840Z", + "iopub.status.idle": "2024-07-02T12:07:00.351880Z", + "shell.execute_reply": "2024-07-02T12:07:00.350456Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.177216Z", - "iopub.status.busy": "2024-07-01T15:08:18.176851Z", - "iopub.status.idle": "2024-07-01T15:08:18.363421Z", - "shell.execute_reply": "2024-07-01T15:08:18.362759Z" + "iopub.execute_input": "2024-07-02T12:07:00.354191Z", + "iopub.status.busy": "2024-07-02T12:07:00.353874Z", + "iopub.status.idle": "2024-07-02T12:07:00.543757Z", + "shell.execute_reply": "2024-07-02T12:07:00.543276Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.366181Z", - "iopub.status.busy": "2024-07-01T15:08:18.365737Z", - "iopub.status.idle": "2024-07-01T15:08:18.613007Z", - "shell.execute_reply": "2024-07-01T15:08:18.612399Z" + "iopub.execute_input": "2024-07-02T12:07:00.545894Z", + "iopub.status.busy": "2024-07-02T12:07:00.545559Z", + "iopub.status.idle": "2024-07-02T12:07:00.784978Z", + "shell.execute_reply": "2024-07-02T12:07:00.784416Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.615413Z", - "iopub.status.busy": "2024-07-01T15:08:18.615032Z", - "iopub.status.idle": "2024-07-01T15:08:18.619703Z", - "shell.execute_reply": "2024-07-01T15:08:18.619083Z" + "iopub.execute_input": "2024-07-02T12:07:00.787127Z", + "iopub.status.busy": "2024-07-02T12:07:00.786946Z", + "iopub.status.idle": "2024-07-02T12:07:00.791220Z", + "shell.execute_reply": "2024-07-02T12:07:00.790792Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.621922Z", - "iopub.status.busy": "2024-07-01T15:08:18.621693Z", - "iopub.status.idle": "2024-07-01T15:08:18.629203Z", - "shell.execute_reply": "2024-07-01T15:08:18.628679Z" + "iopub.execute_input": "2024-07-02T12:07:00.793213Z", + "iopub.status.busy": "2024-07-02T12:07:00.792887Z", + "iopub.status.idle": "2024-07-02T12:07:00.798368Z", + "shell.execute_reply": "2024-07-02T12:07:00.797958Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.631920Z", - "iopub.status.busy": "2024-07-01T15:08:18.631513Z", - "iopub.status.idle": "2024-07-01T15:08:18.634527Z", - "shell.execute_reply": "2024-07-01T15:08:18.633964Z" + "iopub.execute_input": "2024-07-02T12:07:00.800409Z", + "iopub.status.busy": "2024-07-02T12:07:00.800087Z", + "iopub.status.idle": "2024-07-02T12:07:00.802550Z", + "shell.execute_reply": "2024-07-02T12:07:00.802117Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:18.636916Z", - "iopub.status.busy": "2024-07-01T15:08:18.636464Z", - "iopub.status.idle": "2024-07-01T15:08:27.681165Z", - "shell.execute_reply": "2024-07-01T15:08:27.680590Z" + "iopub.execute_input": "2024-07-02T12:07:00.804548Z", + "iopub.status.busy": "2024-07-02T12:07:00.804231Z", + "iopub.status.idle": "2024-07-02T12:07:09.170648Z", + "shell.execute_reply": "2024-07-02T12:07:09.170087Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.683915Z", - "iopub.status.busy": "2024-07-01T15:08:27.683447Z", - "iopub.status.idle": "2024-07-01T15:08:27.691061Z", - "shell.execute_reply": "2024-07-01T15:08:27.690544Z" + "iopub.execute_input": "2024-07-02T12:07:09.173635Z", + "iopub.status.busy": "2024-07-02T12:07:09.172986Z", + "iopub.status.idle": "2024-07-02T12:07:09.180628Z", + "shell.execute_reply": "2024-07-02T12:07:09.180165Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.693260Z", - "iopub.status.busy": "2024-07-01T15:08:27.692915Z", - "iopub.status.idle": "2024-07-01T15:08:27.696508Z", - "shell.execute_reply": "2024-07-01T15:08:27.696074Z" + "iopub.execute_input": "2024-07-02T12:07:09.182718Z", + "iopub.status.busy": "2024-07-02T12:07:09.182401Z", + "iopub.status.idle": "2024-07-02T12:07:09.186064Z", + "shell.execute_reply": "2024-07-02T12:07:09.185614Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.698591Z", - "iopub.status.busy": "2024-07-01T15:08:27.698265Z", - "iopub.status.idle": "2024-07-01T15:08:27.701394Z", - "shell.execute_reply": "2024-07-01T15:08:27.700844Z" + "iopub.execute_input": "2024-07-02T12:07:09.188065Z", + "iopub.status.busy": "2024-07-02T12:07:09.187765Z", + "iopub.status.idle": "2024-07-02T12:07:09.191124Z", + "shell.execute_reply": "2024-07-02T12:07:09.190682Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.703468Z", - "iopub.status.busy": "2024-07-01T15:08:27.703137Z", - "iopub.status.idle": "2024-07-01T15:08:27.706217Z", - "shell.execute_reply": "2024-07-01T15:08:27.705750Z" + "iopub.execute_input": "2024-07-02T12:07:09.193018Z", + "iopub.status.busy": "2024-07-02T12:07:09.192715Z", + "iopub.status.idle": "2024-07-02T12:07:09.195753Z", + "shell.execute_reply": "2024-07-02T12:07:09.195211Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.708232Z", - "iopub.status.busy": "2024-07-01T15:08:27.707899Z", - "iopub.status.idle": "2024-07-01T15:08:27.715999Z", - "shell.execute_reply": "2024-07-01T15:08:27.715525Z" + "iopub.execute_input": "2024-07-02T12:07:09.197818Z", + "iopub.status.busy": "2024-07-02T12:07:09.197511Z", + "iopub.status.idle": "2024-07-02T12:07:09.205619Z", + "shell.execute_reply": "2024-07-02T12:07:09.205180Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.718018Z", - "iopub.status.busy": "2024-07-01T15:08:27.717680Z", - "iopub.status.idle": "2024-07-01T15:08:27.720242Z", - "shell.execute_reply": "2024-07-01T15:08:27.719798Z" + "iopub.execute_input": "2024-07-02T12:07:09.207503Z", + "iopub.status.busy": "2024-07-02T12:07:09.207209Z", + "iopub.status.idle": "2024-07-02T12:07:09.209820Z", + "shell.execute_reply": "2024-07-02T12:07:09.209307Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.722277Z", - "iopub.status.busy": "2024-07-01T15:08:27.721939Z", - "iopub.status.idle": "2024-07-01T15:08:27.850295Z", - "shell.execute_reply": "2024-07-01T15:08:27.849694Z" + "iopub.execute_input": "2024-07-02T12:07:09.211933Z", + "iopub.status.busy": "2024-07-02T12:07:09.211620Z", + "iopub.status.idle": "2024-07-02T12:07:09.330539Z", + "shell.execute_reply": "2024-07-02T12:07:09.329946Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.852484Z", - "iopub.status.busy": "2024-07-01T15:08:27.852300Z", - "iopub.status.idle": "2024-07-01T15:08:27.955847Z", - "shell.execute_reply": "2024-07-01T15:08:27.955257Z" + "iopub.execute_input": "2024-07-02T12:07:09.332913Z", + "iopub.status.busy": "2024-07-02T12:07:09.332537Z", + "iopub.status.idle": "2024-07-02T12:07:09.439546Z", + "shell.execute_reply": "2024-07-02T12:07:09.438879Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:27.958252Z", - "iopub.status.busy": "2024-07-01T15:08:27.957880Z", - "iopub.status.idle": "2024-07-01T15:08:28.451750Z", - "shell.execute_reply": "2024-07-01T15:08:28.451203Z" + "iopub.execute_input": "2024-07-02T12:07:09.441953Z", + "iopub.status.busy": "2024-07-02T12:07:09.441731Z", + "iopub.status.idle": "2024-07-02T12:07:09.926340Z", + "shell.execute_reply": "2024-07-02T12:07:09.925811Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:28.454335Z", - "iopub.status.busy": "2024-07-01T15:08:28.454151Z", - "iopub.status.idle": "2024-07-01T15:08:28.527356Z", - "shell.execute_reply": "2024-07-01T15:08:28.526736Z" + "iopub.execute_input": "2024-07-02T12:07:09.928918Z", + "iopub.status.busy": "2024-07-02T12:07:09.928531Z", + "iopub.status.idle": "2024-07-02T12:07:10.007223Z", + "shell.execute_reply": "2024-07-02T12:07:10.006669Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:28.529697Z", - "iopub.status.busy": "2024-07-01T15:08:28.529341Z", - "iopub.status.idle": "2024-07-01T15:08:28.538428Z", - "shell.execute_reply": "2024-07-01T15:08:28.537958Z" + "iopub.execute_input": "2024-07-02T12:07:10.009492Z", + "iopub.status.busy": "2024-07-02T12:07:10.009118Z", + "iopub.status.idle": "2024-07-02T12:07:10.017415Z", + "shell.execute_reply": "2024-07-02T12:07:10.016968Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:28.540454Z", - "iopub.status.busy": "2024-07-01T15:08:28.540269Z", - "iopub.status.idle": "2024-07-01T15:08:28.542883Z", - "shell.execute_reply": "2024-07-01T15:08:28.542447Z" + "iopub.execute_input": "2024-07-02T12:07:10.019396Z", + "iopub.status.busy": "2024-07-02T12:07:10.019069Z", + "iopub.status.idle": "2024-07-02T12:07:10.021767Z", + "shell.execute_reply": "2024-07-02T12:07:10.021319Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:28.544877Z", - "iopub.status.busy": "2024-07-01T15:08:28.544701Z", - "iopub.status.idle": "2024-07-01T15:08:33.972038Z", - "shell.execute_reply": "2024-07-01T15:08:33.971430Z" + "iopub.execute_input": "2024-07-02T12:07:10.023754Z", + "iopub.status.busy": "2024-07-02T12:07:10.023447Z", + "iopub.status.idle": "2024-07-02T12:07:15.333825Z", + "shell.execute_reply": "2024-07-02T12:07:15.333229Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:33.974153Z", - "iopub.status.busy": "2024-07-01T15:08:33.973956Z", - "iopub.status.idle": "2024-07-01T15:08:33.982773Z", - "shell.execute_reply": "2024-07-01T15:08:33.982320Z" + "iopub.execute_input": "2024-07-02T12:07:15.336220Z", + "iopub.status.busy": "2024-07-02T12:07:15.335826Z", + "iopub.status.idle": "2024-07-02T12:07:15.344270Z", + "shell.execute_reply": "2024-07-02T12:07:15.343811Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:33.984722Z", - "iopub.status.busy": "2024-07-01T15:08:33.984546Z", - "iopub.status.idle": "2024-07-01T15:08:34.049986Z", - "shell.execute_reply": "2024-07-01T15:08:34.049478Z" + "iopub.execute_input": "2024-07-02T12:07:15.346339Z", + "iopub.status.busy": "2024-07-02T12:07:15.346012Z", + "iopub.status.idle": "2024-07-02T12:07:15.414442Z", + "shell.execute_reply": "2024-07-02T12:07:15.413948Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 3b1c6435d..fdafb004b 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": "2024-07-01T15:08:37.513049Z", - "iopub.status.busy": "2024-07-01T15:08:37.512824Z", - "iopub.status.idle": "2024-07-01T15:08:39.014630Z", - "shell.execute_reply": "2024-07-01T15:08:39.013915Z" + "iopub.execute_input": "2024-07-02T12:07:18.593560Z", + "iopub.status.busy": "2024-07-02T12:07:18.593400Z", + "iopub.status.idle": "2024-07-02T12:07:20.263944Z", + "shell.execute_reply": "2024-07-02T12:07:20.263270Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:08:39.017371Z", - "iopub.status.busy": "2024-07-01T15:08:39.017127Z", - "iopub.status.idle": "2024-07-01T15:09:39.584116Z", - "shell.execute_reply": "2024-07-01T15:09:39.583459Z" + "iopub.execute_input": "2024-07-02T12:07:20.266581Z", + "iopub.status.busy": "2024-07-02T12:07:20.266205Z", + "iopub.status.idle": "2024-07-02T12:08:06.109041Z", + "shell.execute_reply": "2024-07-02T12:08:06.108401Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:09:39.586690Z", - "iopub.status.busy": "2024-07-01T15:09:39.586340Z", - "iopub.status.idle": "2024-07-01T15:09:40.720146Z", - "shell.execute_reply": "2024-07-01T15:09:40.719576Z" + "iopub.execute_input": "2024-07-02T12:08:06.111457Z", + "iopub.status.busy": "2024-07-02T12:08:06.111270Z", + "iopub.status.idle": "2024-07-02T12:08:07.194905Z", + "shell.execute_reply": "2024-07-02T12:08:07.194300Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:09:40.722658Z", - "iopub.status.busy": "2024-07-01T15:09:40.722386Z", - "iopub.status.idle": "2024-07-01T15:09:40.725657Z", - "shell.execute_reply": "2024-07-01T15:09:40.725218Z" + "iopub.execute_input": "2024-07-02T12:08:07.197493Z", + "iopub.status.busy": "2024-07-02T12:08:07.197237Z", + "iopub.status.idle": "2024-07-02T12:08:07.200309Z", + "shell.execute_reply": "2024-07-02T12:08:07.199874Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:09:40.727650Z", - "iopub.status.busy": "2024-07-01T15:09:40.727470Z", - "iopub.status.idle": "2024-07-01T15:09:40.731254Z", - "shell.execute_reply": "2024-07-01T15:09:40.730747Z" + "iopub.execute_input": "2024-07-02T12:08:07.202276Z", + "iopub.status.busy": "2024-07-02T12:08:07.202097Z", + "iopub.status.idle": "2024-07-02T12:08:07.205874Z", + "shell.execute_reply": "2024-07-02T12:08:07.205417Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:09:40.733340Z", - "iopub.status.busy": "2024-07-01T15:09:40.733016Z", - "iopub.status.idle": "2024-07-01T15:09:40.736638Z", - "shell.execute_reply": "2024-07-01T15:09:40.736162Z" + "iopub.execute_input": "2024-07-02T12:08:07.207818Z", + "iopub.status.busy": "2024-07-02T12:08:07.207520Z", + "iopub.status.idle": "2024-07-02T12:08:07.211075Z", + "shell.execute_reply": "2024-07-02T12:08:07.210551Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:09:40.738709Z", - "iopub.status.busy": "2024-07-01T15:09:40.738283Z", - "iopub.status.idle": "2024-07-01T15:09:40.741139Z", - "shell.execute_reply": "2024-07-01T15:09:40.740716Z" + "iopub.execute_input": "2024-07-02T12:08:07.213131Z", + "iopub.status.busy": "2024-07-02T12:08:07.212769Z", + "iopub.status.idle": "2024-07-02T12:08:07.215484Z", + "shell.execute_reply": "2024-07-02T12:08:07.215039Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:09:40.743089Z", - "iopub.status.busy": "2024-07-01T15:09:40.742768Z", - "iopub.status.idle": "2024-07-01T15:10:14.851046Z", - "shell.execute_reply": "2024-07-01T15:10:14.850360Z" + "iopub.execute_input": "2024-07-02T12:08:07.217418Z", + "iopub.status.busy": "2024-07-02T12:08:07.217121Z", + "iopub.status.idle": "2024-07-02T12:08:41.707148Z", + "shell.execute_reply": "2024-07-02T12:08:41.706563Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e93b88c996c44feeb3673439eaaea41d", + "model_id": "9e20fdede857444e8054f80d2f1060d4", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cae016dc953549ce807817682c42dc87", + "model_id": "301ab18342ea43859b3e69cf6784234e", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:10:14.853695Z", - "iopub.status.busy": "2024-07-01T15:10:14.853439Z", - "iopub.status.idle": "2024-07-01T15:10:15.523116Z", - "shell.execute_reply": "2024-07-01T15:10:15.522614Z" + "iopub.execute_input": "2024-07-02T12:08:41.710056Z", + "iopub.status.busy": "2024-07-02T12:08:41.709655Z", + "iopub.status.idle": "2024-07-02T12:08:42.388632Z", + "shell.execute_reply": "2024-07-02T12:08:42.388139Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:10:15.525482Z", - "iopub.status.busy": "2024-07-01T15:10:15.525022Z", - "iopub.status.idle": "2024-07-01T15:10:18.415066Z", - "shell.execute_reply": "2024-07-01T15:10:18.414464Z" + "iopub.execute_input": "2024-07-02T12:08:42.390931Z", + "iopub.status.busy": "2024-07-02T12:08:42.390474Z", + "iopub.status.idle": "2024-07-02T12:08:45.214722Z", + "shell.execute_reply": "2024-07-02T12:08:45.214183Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:10:18.417219Z", - 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"iopub.status.idle": "2024-07-02T12:09:46.521891Z", + "shell.execute_reply": "2024-07-02T12:09:46.521319Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-01 15:11:18-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-02 12:09:45-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,15 +94,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.98, 2400:52e0:1a00::871:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... " + "185.93.1.249, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.249|:443... connected.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -123,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-02 12:09:45 (6.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-07-01 15:11:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.28.244, 3.5.24.72, 52.217.13.252, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.28.244|:443... connected.\r\n", + "--2024-07-02 12:09:46-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.236.81, 16.182.109.113, 3.5.9.115, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.236.81|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -168,17 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 35%[======> ] 5.78M 28.9MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 100%[===================>] 16.26M 52.3MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-07-01 15:11:19 (52.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-02 12:09:46 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -195,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:19.978352Z", - "iopub.status.busy": "2024-07-01T15:11:19.977882Z", - "iopub.status.idle": "2024-07-01T15:11:21.215995Z", - "shell.execute_reply": "2024-07-01T15:11:21.215505Z" + "iopub.execute_input": "2024-07-02T12:09:46.524639Z", + "iopub.status.busy": "2024-07-02T12:09:46.524272Z", + "iopub.status.idle": "2024-07-02T12:09:47.827762Z", + "shell.execute_reply": "2024-07-02T12:09:47.827179Z" }, "nbsphinx": "hidden" }, @@ -209,7 +200,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -235,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:21.218513Z", - "iopub.status.busy": "2024-07-01T15:11:21.218132Z", - "iopub.status.idle": "2024-07-01T15:11:21.221470Z", - "shell.execute_reply": "2024-07-01T15:11:21.221045Z" + "iopub.execute_input": "2024-07-02T12:09:47.830413Z", + "iopub.status.busy": "2024-07-02T12:09:47.829987Z", + "iopub.status.idle": "2024-07-02T12:09:47.833472Z", + "shell.execute_reply": "2024-07-02T12:09:47.833017Z" } }, "outputs": [], @@ -288,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:21.223694Z", - "iopub.status.busy": "2024-07-01T15:11:21.223257Z", - "iopub.status.idle": "2024-07-01T15:11:21.226332Z", - "shell.execute_reply": "2024-07-01T15:11:21.225848Z" + "iopub.execute_input": "2024-07-02T12:09:47.835687Z", + "iopub.status.busy": "2024-07-02T12:09:47.835327Z", + "iopub.status.idle": "2024-07-02T12:09:47.838382Z", + "shell.execute_reply": "2024-07-02T12:09:47.837903Z" }, "nbsphinx": "hidden" }, @@ -309,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:21.228084Z", - "iopub.status.busy": "2024-07-01T15:11:21.227917Z", - "iopub.status.idle": "2024-07-01T15:11:30.310755Z", - "shell.execute_reply": "2024-07-01T15:11:30.310211Z" + "iopub.execute_input": "2024-07-02T12:09:47.840488Z", + "iopub.status.busy": "2024-07-02T12:09:47.840076Z", + "iopub.status.idle": "2024-07-02T12:09:56.981305Z", + "shell.execute_reply": "2024-07-02T12:09:56.980685Z" } }, "outputs": [], @@ -386,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:30.313310Z", - "iopub.status.busy": "2024-07-01T15:11:30.313004Z", - "iopub.status.idle": "2024-07-01T15:11:30.318459Z", - "shell.execute_reply": "2024-07-01T15:11:30.318009Z" + "iopub.execute_input": "2024-07-02T12:09:56.983968Z", + "iopub.status.busy": "2024-07-02T12:09:56.983751Z", + "iopub.status.idle": "2024-07-02T12:09:56.989422Z", + "shell.execute_reply": "2024-07-02T12:09:56.988975Z" }, "nbsphinx": "hidden" }, @@ -429,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:30.320517Z", - "iopub.status.busy": "2024-07-01T15:11:30.320198Z", - "iopub.status.idle": "2024-07-01T15:11:30.659248Z", - "shell.execute_reply": "2024-07-01T15:11:30.658770Z" + "iopub.execute_input": "2024-07-02T12:09:56.991449Z", + "iopub.status.busy": "2024-07-02T12:09:56.991142Z", + "iopub.status.idle": "2024-07-02T12:09:57.333959Z", + "shell.execute_reply": "2024-07-02T12:09:57.333418Z" } }, "outputs": [], @@ -469,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:30.661698Z", - "iopub.status.busy": "2024-07-01T15:11:30.661301Z", - "iopub.status.idle": "2024-07-01T15:11:30.665925Z", - "shell.execute_reply": "2024-07-01T15:11:30.665448Z" + "iopub.execute_input": "2024-07-02T12:09:57.336408Z", + "iopub.status.busy": "2024-07-02T12:09:57.336047Z", + "iopub.status.idle": "2024-07-02T12:09:57.340566Z", + "shell.execute_reply": "2024-07-02T12:09:57.340088Z" } }, "outputs": [ @@ -544,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:30.667958Z", - "iopub.status.busy": "2024-07-01T15:11:30.667632Z", - "iopub.status.idle": "2024-07-01T15:11:33.481219Z", - "shell.execute_reply": "2024-07-01T15:11:33.480521Z" + "iopub.execute_input": "2024-07-02T12:09:57.342536Z", + "iopub.status.busy": "2024-07-02T12:09:57.342207Z", + "iopub.status.idle": "2024-07-02T12:09:59.889796Z", + "shell.execute_reply": "2024-07-02T12:09:59.889167Z" } }, "outputs": [], @@ -569,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:33.484626Z", - "iopub.status.busy": "2024-07-01T15:11:33.483787Z", - "iopub.status.idle": "2024-07-01T15:11:33.488254Z", - "shell.execute_reply": "2024-07-01T15:11:33.487491Z" + "iopub.execute_input": "2024-07-02T12:09:59.892826Z", + "iopub.status.busy": "2024-07-02T12:09:59.892074Z", + "iopub.status.idle": "2024-07-02T12:09:59.896257Z", + "shell.execute_reply": "2024-07-02T12:09:59.895794Z" } }, "outputs": [ @@ -608,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:33.490509Z", - "iopub.status.busy": "2024-07-01T15:11:33.490170Z", - "iopub.status.idle": "2024-07-01T15:11:33.496148Z", - "shell.execute_reply": "2024-07-01T15:11:33.495591Z" + "iopub.execute_input": "2024-07-02T12:09:59.898108Z", + "iopub.status.busy": "2024-07-02T12:09:59.897930Z", + "iopub.status.idle": "2024-07-02T12:09:59.903451Z", + "shell.execute_reply": "2024-07-02T12:09:59.902896Z" } }, "outputs": [ @@ -789,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:33.498276Z", - "iopub.status.busy": "2024-07-01T15:11:33.497940Z", - "iopub.status.idle": "2024-07-01T15:11:33.525403Z", - "shell.execute_reply": "2024-07-01T15:11:33.524817Z" + "iopub.execute_input": "2024-07-02T12:09:59.905627Z", + "iopub.status.busy": "2024-07-02T12:09:59.905242Z", + "iopub.status.idle": "2024-07-02T12:09:59.932087Z", + "shell.execute_reply": "2024-07-02T12:09:59.931495Z" } }, "outputs": [ @@ -894,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:33.527747Z", - "iopub.status.busy": "2024-07-01T15:11:33.527322Z", - "iopub.status.idle": "2024-07-01T15:11:33.532159Z", - "shell.execute_reply": "2024-07-01T15:11:33.531610Z" + "iopub.execute_input": "2024-07-02T12:09:59.934435Z", + "iopub.status.busy": "2024-07-02T12:09:59.934079Z", + "iopub.status.idle": "2024-07-02T12:09:59.939450Z", + "shell.execute_reply": "2024-07-02T12:09:59.938896Z" } }, "outputs": [ @@ -971,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:33.534578Z", - "iopub.status.busy": "2024-07-01T15:11:33.534006Z", - "iopub.status.idle": "2024-07-01T15:11:34.915561Z", - "shell.execute_reply": "2024-07-01T15:11:34.914971Z" + "iopub.execute_input": "2024-07-02T12:09:59.941692Z", + "iopub.status.busy": "2024-07-02T12:09:59.941362Z", + "iopub.status.idle": "2024-07-02T12:10:01.337767Z", + "shell.execute_reply": "2024-07-02T12:10:01.337179Z" } }, "outputs": [ @@ -1146,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:11:34.917877Z", - "iopub.status.busy": "2024-07-01T15:11:34.917550Z", - "iopub.status.idle": "2024-07-01T15:11:34.921657Z", - "shell.execute_reply": "2024-07-01T15:11:34.921191Z" + "iopub.execute_input": "2024-07-02T12:10:01.339986Z", + "iopub.status.busy": "2024-07-02T12:10:01.339664Z", + "iopub.status.idle": "2024-07-02T12:10:01.343749Z", + "shell.execute_reply": "2024-07-02T12:10:01.343244Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index bcc71851e3b5a1787b65bcceff90d82e2fc56ae0..56f3b4982de97e84ccef583f6d5f701af07b67c0 100644 GIT binary patch delta 62 zcmX>tep-A(E~BBDQAt5&Mp==*rKO>Hib+apYI2gXnNec0Sz@ADs)4z=L9(%#xk-wF RfkASziFuOg=6Q^|TmXE)66pW{ delta 62 zcmX>tep-A(E~8;`RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x=6Q^|TmX_A6Se>V diff --git 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index d6b47ffef..af77ff1a5 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb index d52a1f814..b012c3b83 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 744175c39..40b596a7c 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 4b8205657..6d03ae333 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index 2ba045367..ac39104cf 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb index 51da2d9a6..3e5552460 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/workflows.ipynb b/master/_sources/tutorials/datalab/workflows.ipynb index 6bd4ee5cf..0a17e353b 100644 --- a/master/_sources/tutorials/datalab/workflows.ipynb +++ b/master/_sources/tutorials/datalab/workflows.ipynb @@ -1331,22 +1331,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Find Spurious Correlation between Vision Dataset features and class labels\n", + "## Identify Spurious Correlations in Image Datasets\n", "\n", - "In this section, we demonstrate how to identify spurious correlations in a vision dataset using the `cleanlab` library. Spurious correlations are unintended associations in the data that do not reflect the true underlying relationships, potentially leading to misleading model predictions and poor generalization.\n", + "This section demonstrates how to detect spurious correlations in image datasets by measuring how strongly individual image properties correlate with class labels.\n", + "These correlations could lead to unreliable model predictions and poor generalization.\n", "\n", - "We will utilize the `Datalab` class from cleanlab with the `image_key` attribute to pinpoint vision-specific issues such as `dark_score`, `blurry_score`, `odd_aspect_ratio_score`, and more in the dataset. By analyzing these correlations, we can understand their impact on model performance and take steps to enhance the robustness and reliability of our machine learning models." + "\n", + "By providing an `image_key` argument, we can analyze image-specific attributes such as:\n", + "\n", + "- Darkness\n", + "- Blurriness\n", + "- Aspect ratio anomalies\n", + "- More image-specific features from [CleanVision](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html#What-is-CleanVision?)\n", + "\n", + "This analysis helps us identify unintended biases in our datasets and guides steps to enhance the robustness and reliability of our machine learning models.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. Load the dataset\n", + "### 1. Load the Dataset\n", + "\n", + "We'll use a subset of the CIFAR-10 dataset for this demonstration, selecting 100 images from two random classes. To illustrate spurious correlations:\n", "\n", - "We will demonstrate this workflow using the CIFAR-10 dataset by selecting 100 images from two random classes. To illustrate the impact of spurious correlations between image features and class labels, we will showcase how altering all images of a class, such as darkening them, significantly reduces the `dark_score`. This demonstrates the strong correlation detection of darkness within the dataset.\n", + "- We'll artificially introduce a bias by altering all images of one class (e.g., darkening them).\n", + "- The correlation scores range from 0 to 1, where:\n", + " - Scores close to 0 indicate a strong correlation between an image property and class labels, suggesting a likely spurious correlation.\n", + " - Scores close to 1 suggest little to no correlation between the property and class labels.\n", + "- By introducing this bias, we expect to see:\n", + " - A decrease in the `dark_score` for the darkened class, indicating an increased correlation between darkness and that class label.\n", + " - Similar effects can be observed with `blurry_score` or `odd_aspect_ratio_score` by introducing corresponding characteristics to one class.\n", "\n", - "Similarly, we can observe significant reductions in `blurry_score` and `odd_aspect_ratio_score` when one of the classes contains images with corresponding characteristics such as blurriness or an unusual aspect ratio between width and height." + "This setup allows us to demonstrate how Datalab detects strong correlations between image features and class labels." ] }, { @@ -1402,7 +1419,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find vision-related issues" + "### 2. Creating `Dataset` object to be passed to the `Datalab` object to find image-related issues" ] }, { @@ -1523,9 +1540,9 @@ "transformed_property_scores = get_property_scores(transformed_dataset)\n", "\n", "# Displaying the scores dataframe\n", - "display(Markdown(\"### Vision-specific property scores in the original dataset\"))\n", + "display(Markdown(\"### Image-specific property scores in the original dataset\"))\n", "display(standard_property_scores)\n", - "display(Markdown(\"### Vision-specific property scores in the transformed dataset\"))\n", + "display(Markdown(\"### Image-specific property scores in the transformed dataset\"))\n", "display(transformed_property_scores)\n", "\n", "# Smaller 'dark_score' value for modified dataframe shows strong correlation with the class labels in the transformed dataset\n", diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index ce8be3372..7af5e7f6e 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 7d89efb39..e036f973f 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb index ddf90b86b..56543bad0 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 542b9cfd4..348a544a8 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb index 79a87033f..2d80f1068 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 4f7e0427a..b6dbc6271 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb index c0d2c1d07..fe223cf83 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb index e65d991e6..f5ced067f 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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 2777086e9..f02e0094c 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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/objects.inv b/master/objects.inv index 0a47f7ce7c9e2779eaeb81a2940724df2a13627a..a324b74dd982674c5a72abde611e32fd5dff7400 100644 GIT binary patch literal 37577 zcmZU)Q;;Z46D>NnZQHhO+qP}&vF+Jo?Xhj!wrzXveCLn*aN@jFcXd`|cSUA)R@PcY zOrY#wVQJ?f~%`Z%4ocMNFXJWN&3^?7~H0Y-4I@XJcse`%LYB zaxQ}3dld^)0w+^jLrXgoQzrs@GXfV2OJ@QzOB+*y->b2`t%H-Pv$LrQfvdBnojHM* 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2. Load and format the text dataset 
 This dataset has 10 classes.
-Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}
+Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}

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

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

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

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"2024-07-01T15:01:48.389202Z", - "iopub.status.idle": "2024-07-01T15:01:51.596566Z", - "shell.execute_reply": "2024-07-01T15:01:51.595964Z" + "iopub.execute_input": "2024-07-02T12:00:34.059784Z", + "iopub.status.busy": "2024-07-02T12:00:34.059279Z", + "iopub.status.idle": "2024-07-02T12:00:36.809187Z", + "shell.execute_reply": "2024-07-02T12:00:36.808623Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:01:51.599757Z", - "iopub.status.busy": "2024-07-01T15:01:51.599136Z", - "iopub.status.idle": "2024-07-01T15:01:51.603065Z", - "shell.execute_reply": "2024-07-01T15:01:51.602415Z" + "iopub.execute_input": "2024-07-02T12:00:36.811854Z", + "iopub.status.busy": "2024-07-02T12:00:36.811437Z", + "iopub.status.idle": "2024-07-02T12:00:36.814737Z", + "shell.execute_reply": "2024-07-02T12:00:36.814309Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.605582Z", - "iopub.status.busy": "2024-07-01T15:01:51.605171Z", - "iopub.status.idle": "2024-07-01T15:01:51.608781Z", - "shell.execute_reply": "2024-07-01T15:01:51.608196Z" + "iopub.execute_input": "2024-07-02T12:00:36.816857Z", + "iopub.status.busy": "2024-07-02T12:00:36.816534Z", + "iopub.status.idle": "2024-07-02T12:00:36.819520Z", + "shell.execute_reply": "2024-07-02T12:00:36.819089Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.611405Z", - "iopub.status.busy": "2024-07-01T15:01:51.610984Z", - "iopub.status.idle": "2024-07-01T15:01:51.666636Z", - "shell.execute_reply": "2024-07-01T15:01:51.666058Z" + "iopub.execute_input": "2024-07-02T12:00:36.821601Z", + "iopub.status.busy": "2024-07-02T12:00:36.821264Z", + "iopub.status.idle": "2024-07-02T12:00:36.862716Z", + "shell.execute_reply": "2024-07-02T12:00:36.862142Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.668846Z", - "iopub.status.busy": "2024-07-01T15:01:51.668483Z", - "iopub.status.idle": "2024-07-01T15:01:51.672233Z", - "shell.execute_reply": "2024-07-01T15:01:51.671774Z" + "iopub.execute_input": "2024-07-02T12:00:36.864907Z", + "iopub.status.busy": "2024-07-02T12:00:36.864568Z", + "iopub.status.idle": "2024-07-02T12:00:36.868079Z", + "shell.execute_reply": "2024-07-02T12:00:36.867616Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.674498Z", - "iopub.status.busy": "2024-07-01T15:01:51.674053Z", - "iopub.status.idle": "2024-07-01T15:01:51.677796Z", - "shell.execute_reply": "2024-07-01T15:01:51.677326Z" + "iopub.execute_input": "2024-07-02T12:00:36.870408Z", + "iopub.status.busy": "2024-07-02T12:00:36.870073Z", + "iopub.status.idle": "2024-07-02T12:00:36.873573Z", + "shell.execute_reply": "2024-07-02T12:00:36.873016Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}\n" + "Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.679875Z", - "iopub.status.busy": "2024-07-01T15:01:51.679530Z", - "iopub.status.idle": "2024-07-01T15:01:51.682840Z", - "shell.execute_reply": "2024-07-01T15:01:51.682369Z" + "iopub.execute_input": "2024-07-02T12:00:36.875763Z", + "iopub.status.busy": "2024-07-02T12:00:36.875423Z", + "iopub.status.idle": "2024-07-02T12:00:36.878670Z", + "shell.execute_reply": "2024-07-02T12:00:36.878216Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:51.684949Z", - "iopub.status.busy": "2024-07-01T15:01:51.684614Z", - "iopub.status.idle": "2024-07-01T15:01:51.687925Z", - 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"IPY_MODEL_743444b6670b4fa99cc0396e4b32f5e4"], "layout": "IPY_MODEL_f70f441fdd5147c685302ccf8dbb2370", "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 2f2d2a40d..9db139a3f 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": "2024-07-01T15:02:05.352362Z", - "iopub.status.busy": "2024-07-01T15:02:05.351840Z", - "iopub.status.idle": "2024-07-01T15:02:11.364535Z", - "shell.execute_reply": "2024-07-01T15:02:11.364016Z" + "iopub.execute_input": "2024-07-02T12:00:48.153712Z", + "iopub.status.busy": "2024-07-02T12:00:48.153535Z", + "iopub.status.idle": "2024-07-02T12:00:53.266339Z", + "shell.execute_reply": "2024-07-02T12:00:53.265786Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:11.367286Z", - "iopub.status.busy": "2024-07-01T15:02:11.366756Z", - "iopub.status.idle": "2024-07-01T15:02:11.369937Z", - "shell.execute_reply": "2024-07-01T15:02:11.369499Z" + "iopub.execute_input": "2024-07-02T12:00:53.268847Z", + "iopub.status.busy": "2024-07-02T12:00:53.268512Z", + "iopub.status.idle": "2024-07-02T12:00:53.271688Z", + "shell.execute_reply": "2024-07-02T12:00:53.271237Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:11.372033Z", - "iopub.status.busy": "2024-07-01T15:02:11.371712Z", - "iopub.status.idle": "2024-07-01T15:02:11.376772Z", - "shell.execute_reply": "2024-07-01T15:02:11.376263Z" + "iopub.execute_input": "2024-07-02T12:00:53.273790Z", + "iopub.status.busy": "2024-07-02T12:00:53.273468Z", + "iopub.status.idle": "2024-07-02T12:00:53.277843Z", + "shell.execute_reply": "2024-07-02T12:00:53.277413Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:11.378959Z", - "iopub.status.busy": "2024-07-01T15:02:11.378766Z", - "iopub.status.idle": "2024-07-01T15:02:12.901153Z", - "shell.execute_reply": "2024-07-01T15:02:12.900530Z" + "iopub.execute_input": "2024-07-02T12:00:53.279840Z", + "iopub.status.busy": "2024-07-02T12:00:53.279499Z", + "iopub.status.idle": "2024-07-02T12:00:54.884749Z", + "shell.execute_reply": "2024-07-02T12:00:54.884125Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:12.904092Z", - "iopub.status.busy": "2024-07-01T15:02:12.903651Z", - "iopub.status.idle": "2024-07-01T15:02:12.914311Z", - "shell.execute_reply": "2024-07-01T15:02:12.913807Z" + "iopub.execute_input": "2024-07-02T12:00:54.887464Z", + "iopub.status.busy": "2024-07-02T12:00:54.887081Z", + "iopub.status.idle": "2024-07-02T12:00:54.897463Z", + "shell.execute_reply": "2024-07-02T12:00:54.897041Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:12.916523Z", - "iopub.status.busy": "2024-07-01T15:02:12.916188Z", - "iopub.status.idle": "2024-07-01T15:02:12.921874Z", - "shell.execute_reply": "2024-07-01T15:02:12.921422Z" + "iopub.execute_input": "2024-07-02T12:00:54.899593Z", + "iopub.status.busy": "2024-07-02T12:00:54.899256Z", + "iopub.status.idle": "2024-07-02T12:00:54.904661Z", + "shell.execute_reply": "2024-07-02T12:00:54.904214Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:12.923867Z", - "iopub.status.busy": "2024-07-01T15:02:12.923684Z", - "iopub.status.idle": "2024-07-01T15:02:13.374643Z", - "shell.execute_reply": "2024-07-01T15:02:13.374029Z" + "iopub.execute_input": "2024-07-02T12:00:54.906699Z", + "iopub.status.busy": "2024-07-02T12:00:54.906445Z", + "iopub.status.idle": "2024-07-02T12:00:55.370547Z", + "shell.execute_reply": "2024-07-02T12:00:55.370054Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:13.376868Z", - "iopub.status.busy": "2024-07-01T15:02:13.376659Z", - "iopub.status.idle": "2024-07-01T15:02:14.191014Z", - "shell.execute_reply": "2024-07-01T15:02:14.190519Z" + "iopub.execute_input": "2024-07-02T12:00:55.372729Z", + "iopub.status.busy": "2024-07-02T12:00:55.372455Z", + "iopub.status.idle": "2024-07-02T12:00:56.373788Z", + "shell.execute_reply": "2024-07-02T12:00:56.373190Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:14.193499Z", - "iopub.status.busy": "2024-07-01T15:02:14.193141Z", - "iopub.status.idle": "2024-07-01T15:02:14.211506Z", - "shell.execute_reply": "2024-07-01T15:02:14.210918Z" + "iopub.execute_input": "2024-07-02T12:00:56.376073Z", + "iopub.status.busy": "2024-07-02T12:00:56.375890Z", + "iopub.status.idle": "2024-07-02T12:00:56.393884Z", + "shell.execute_reply": "2024-07-02T12:00:56.393321Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:14.213644Z", - "iopub.status.busy": "2024-07-01T15:02:14.213459Z", - "iopub.status.idle": "2024-07-01T15:02:14.216713Z", - "shell.execute_reply": "2024-07-01T15:02:14.216187Z" + "iopub.execute_input": "2024-07-02T12:00:56.396057Z", + "iopub.status.busy": "2024-07-02T12:00:56.395720Z", + "iopub.status.idle": "2024-07-02T12:00:56.398930Z", + "shell.execute_reply": "2024-07-02T12:00:56.398478Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:14.218727Z", - "iopub.status.busy": "2024-07-01T15:02:14.218427Z", - "iopub.status.idle": "2024-07-01T15:02:28.819416Z", - "shell.execute_reply": "2024-07-01T15:02:28.818802Z" + "iopub.execute_input": "2024-07-02T12:00:56.400749Z", + "iopub.status.busy": "2024-07-02T12:00:56.400581Z", + "iopub.status.idle": "2024-07-02T12:01:10.956584Z", + "shell.execute_reply": "2024-07-02T12:01:10.955969Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:28.822324Z", - "iopub.status.busy": "2024-07-01T15:02:28.821907Z", - "iopub.status.idle": "2024-07-01T15:02:28.825931Z", - "shell.execute_reply": "2024-07-01T15:02:28.825366Z" + "iopub.execute_input": "2024-07-02T12:01:10.959440Z", + "iopub.status.busy": "2024-07-02T12:01:10.959028Z", + "iopub.status.idle": "2024-07-02T12:01:10.962902Z", + "shell.execute_reply": "2024-07-02T12:01:10.962374Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:28.827995Z", - "iopub.status.busy": "2024-07-01T15:02:28.827681Z", - "iopub.status.idle": "2024-07-01T15:02:29.521211Z", - "shell.execute_reply": "2024-07-01T15:02:29.520635Z" + "iopub.execute_input": "2024-07-02T12:01:10.964878Z", + "iopub.status.busy": "2024-07-02T12:01:10.964705Z", + "iopub.status.idle": "2024-07-02T12:01:11.664747Z", + "shell.execute_reply": "2024-07-02T12:01:11.664181Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-07-01T15:02:29.524947Z", - "iopub.status.busy": "2024-07-01T15:02:29.524000Z", - "iopub.status.idle": "2024-07-01T15:02:29.530833Z", - "shell.execute_reply": "2024-07-01T15:02:29.530342Z" + "iopub.execute_input": "2024-07-02T12:01:11.667592Z", + "iopub.status.busy": "2024-07-02T12:01:11.667207Z", + "iopub.status.idle": "2024-07-02T12:01:11.671960Z", + "shell.execute_reply": "2024-07-02T12:01:11.671464Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:29.534459Z", - "iopub.status.busy": "2024-07-01T15:02:29.533509Z", - "iopub.status.idle": "2024-07-01T15:02:29.635169Z", - "shell.execute_reply": 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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 1e7141136..58bbdaa8a 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": "2024-07-01T15:02:34.100510Z", - "iopub.status.busy": "2024-07-01T15:02:34.100309Z", - "iopub.status.idle": "2024-07-01T15:02:35.344393Z", - "shell.execute_reply": "2024-07-01T15:02:35.343853Z" + "iopub.execute_input": "2024-07-02T12:01:15.541042Z", + "iopub.status.busy": "2024-07-02T12:01:15.540869Z", + "iopub.status.idle": "2024-07-02T12:01:16.706079Z", + "shell.execute_reply": "2024-07-02T12:01:16.705546Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:35.347246Z", - "iopub.status.busy": "2024-07-01T15:02:35.346746Z", - "iopub.status.idle": "2024-07-01T15:02:35.349837Z", - "shell.execute_reply": "2024-07-01T15:02:35.349391Z" + "iopub.execute_input": "2024-07-02T12:01:16.708528Z", + "iopub.status.busy": "2024-07-02T12:01:16.708127Z", + "iopub.status.idle": "2024-07-02T12:01:16.711112Z", + "shell.execute_reply": "2024-07-02T12:01:16.710676Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:35.352173Z", - "iopub.status.busy": "2024-07-01T15:02:35.351845Z", - "iopub.status.idle": "2024-07-01T15:02:35.361048Z", - "shell.execute_reply": "2024-07-01T15:02:35.360394Z" + "iopub.execute_input": "2024-07-02T12:01:16.713182Z", + "iopub.status.busy": "2024-07-02T12:01:16.712867Z", + "iopub.status.idle": "2024-07-02T12:01:16.721179Z", + "shell.execute_reply": "2024-07-02T12:01:16.720739Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:35.363704Z", - "iopub.status.busy": "2024-07-01T15:02:35.363238Z", - "iopub.status.idle": "2024-07-01T15:02:35.368807Z", - "shell.execute_reply": "2024-07-01T15:02:35.368166Z" + "iopub.execute_input": "2024-07-02T12:01:16.723125Z", + "iopub.status.busy": "2024-07-02T12:01:16.722823Z", + "iopub.status.idle": "2024-07-02T12:01:16.727946Z", + "shell.execute_reply": "2024-07-02T12:01:16.727497Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:35.371407Z", - "iopub.status.busy": "2024-07-01T15:02:35.370950Z", - "iopub.status.idle": "2024-07-01T15:02:35.579618Z", - "shell.execute_reply": "2024-07-01T15:02:35.578902Z" + "iopub.execute_input": "2024-07-02T12:01:16.730061Z", + "iopub.status.busy": "2024-07-02T12:01:16.729738Z", + "iopub.status.idle": "2024-07-02T12:01:16.910261Z", + "shell.execute_reply": "2024-07-02T12:01:16.909774Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:35.582660Z", - "iopub.status.busy": "2024-07-01T15:02:35.582270Z", - "iopub.status.idle": "2024-07-01T15:02:35.982874Z", - "shell.execute_reply": "2024-07-01T15:02:35.982240Z" + "iopub.execute_input": "2024-07-02T12:01:16.912657Z", + "iopub.status.busy": "2024-07-02T12:01:16.912383Z", + "iopub.status.idle": "2024-07-02T12:01:17.280864Z", + "shell.execute_reply": "2024-07-02T12:01:17.280305Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:35.985209Z", - "iopub.status.busy": "2024-07-01T15:02:35.984882Z", - "iopub.status.idle": "2024-07-01T15:02:36.009092Z", - "shell.execute_reply": "2024-07-01T15:02:36.008586Z" + "iopub.execute_input": "2024-07-02T12:01:17.283183Z", + "iopub.status.busy": "2024-07-02T12:01:17.282742Z", + "iopub.status.idle": "2024-07-02T12:01:17.305912Z", + "shell.execute_reply": "2024-07-02T12:01:17.305342Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:36.011743Z", - "iopub.status.busy": "2024-07-01T15:02:36.011310Z", - "iopub.status.idle": "2024-07-01T15:02:36.023407Z", - "shell.execute_reply": "2024-07-01T15:02:36.022817Z" + "iopub.execute_input": "2024-07-02T12:01:17.308190Z", + "iopub.status.busy": "2024-07-02T12:01:17.307876Z", + "iopub.status.idle": "2024-07-02T12:01:17.318887Z", + "shell.execute_reply": "2024-07-02T12:01:17.318342Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:36.025951Z", - "iopub.status.busy": "2024-07-01T15:02:36.025605Z", - "iopub.status.idle": "2024-07-01T15:02:38.178872Z", - "shell.execute_reply": "2024-07-01T15:02:38.178173Z" + "iopub.execute_input": "2024-07-02T12:01:17.321139Z", + "iopub.status.busy": "2024-07-02T12:01:17.320805Z", + "iopub.status.idle": "2024-07-02T12:01:19.303196Z", + "shell.execute_reply": "2024-07-02T12:01:19.302567Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:38.181613Z", - "iopub.status.busy": "2024-07-01T15:02:38.181161Z", - "iopub.status.idle": "2024-07-01T15:02:38.204407Z", - "shell.execute_reply": "2024-07-01T15:02:38.203790Z" + "iopub.execute_input": 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"shell.execute_reply": "2024-07-01T15:02:38.242861Z" + "iopub.execute_input": "2024-07-02T12:01:19.349172Z", + "iopub.status.busy": "2024-07-02T12:01:19.348833Z", + "iopub.status.idle": "2024-07-02T12:01:19.364109Z", + "shell.execute_reply": "2024-07-02T12:01:19.363523Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:38.245863Z", - "iopub.status.busy": "2024-07-01T15:02:38.245474Z", - "iopub.status.idle": "2024-07-01T15:02:38.267061Z", - "shell.execute_reply": "2024-07-01T15:02:38.266454Z" + "iopub.execute_input": "2024-07-02T12:01:19.366447Z", + "iopub.status.busy": "2024-07-02T12:01:19.366041Z", + "iopub.status.idle": "2024-07-02T12:01:19.385525Z", + "shell.execute_reply": "2024-07-02T12:01:19.384972Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d16dede2bb2e40b282d000f989523e41", + "model_id": "59b4478dd8e7455d94d80c6cac5956e7", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:38.269410Z", - "iopub.status.busy": "2024-07-01T15:02:38.269038Z", - "iopub.status.idle": "2024-07-01T15:02:38.286735Z", - "shell.execute_reply": "2024-07-01T15:02:38.286110Z" + "iopub.execute_input": "2024-07-02T12:01:19.387568Z", + "iopub.status.busy": "2024-07-02T12:01:19.387355Z", + "iopub.status.idle": "2024-07-02T12:01:19.403995Z", + "shell.execute_reply": "2024-07-02T12:01:19.403416Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:38.289163Z", - "iopub.status.busy": "2024-07-01T15:02:38.288765Z", - "iopub.status.idle": "2024-07-01T15:02:38.295067Z", - "shell.execute_reply": "2024-07-01T15:02:38.294503Z" + "iopub.execute_input": "2024-07-02T12:01:19.406166Z", + "iopub.status.busy": "2024-07-02T12:01:19.405840Z", + "iopub.status.idle": "2024-07-02T12:01:19.411828Z", + "shell.execute_reply": "2024-07-02T12:01:19.411266Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:38.297383Z", - "iopub.status.busy": "2024-07-01T15:02:38.296989Z", - "iopub.status.idle": "2024-07-01T15:02:38.317743Z", - "shell.execute_reply": "2024-07-01T15:02:38.317117Z" + "iopub.execute_input": "2024-07-02T12:01:19.414062Z", + "iopub.status.busy": "2024-07-02T12:01:19.413631Z", + "iopub.status.idle": "2024-07-02T12:01:19.432239Z", + "shell.execute_reply": "2024-07-02T12:01:19.431665Z" } }, "outputs": [ @@ -1447,7 +1447,23 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0dc84865474c4bf7a312e538bc8f4a74": { + "160374201c2049b98c39d1da42e6f09d": { + "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": "" + } + }, + "23609831ef654449b59fb8c4f8a2bb30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,7 +1516,53 @@ "width": null } }, - "39e0d0ff92854bb5b45f8340a9c5c5eb": { + "2c61a80b080b4e158a20edb5c4a1ac84": { + "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_23609831ef654449b59fb8c4f8a2bb30", + "placeholder": "​", + "style": "IPY_MODEL_ada4493def764ffa859a5d6ba4d315fb", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "430e528b6e30444ea44c9f7dacbfcc30": { + "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_8addd7af612b43d395a8dfcfeb6287ef", + "placeholder": "​", + "style": "IPY_MODEL_bd9b705b24884f74a14e8bfdd7ee8634", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 13162.98 examples/s]" + } + }, + "4d30844fcfff423583118cba2ebebe1b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1516,17 +1578,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0dc84865474c4bf7a312e538bc8f4a74", + "layout": "IPY_MODEL_92d343740ab348028d512cbabde596de", "max": 132.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_bd9368c5e9b842ca818c69f779cd5276", + "style": "IPY_MODEL_160374201c2049b98c39d1da42e6f09d", "tabbable": null, "tooltip": null, "value": 132.0 } }, - "746118eed9d445c9a37e681cbacd9674": { + "59b4478dd8e7455d94d80c6cac5956e7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2c61a80b080b4e158a20edb5c4a1ac84", + "IPY_MODEL_4d30844fcfff423583118cba2ebebe1b", + "IPY_MODEL_430e528b6e30444ea44c9f7dacbfcc30" + ], + "layout": "IPY_MODEL_5e818fd01e87406a87c87fc7bc810095", + "tabbable": null, + "tooltip": null + } + }, + "5e818fd01e87406a87c87fc7bc810095": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1579,7 +1665,7 @@ "width": null } }, - "890c2c0c04564f5da3221229c05800df": { + "8addd7af612b43d395a8dfcfeb6287ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1632,30 +1718,7 @@ "width": null } }, - "979503323663435eae635a194817476f": { - "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_746118eed9d445c9a37e681cbacd9674", - "placeholder": "​", - "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5", - "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } - }, - "af633ab6f6924af0b3f4ac3691d76422": { + "92d343740ab348028d512cbabde596de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1708,7 +1771,7 @@ "width": null } }, - "bcb7b5d047f845978925e2ef6da3385e": { + "ada4493def764ffa859a5d6ba4d315fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1726,70 +1789,7 @@ "text_color": null } }, - "bd9368c5e9b842ca818c69f779cd5276": { - "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": "" - } - }, - "cac3b162735445c0915d9ecfed155f4c": { - "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_af633ab6f6924af0b3f4ac3691d76422", - "placeholder": "​", - "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e", - "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 11804.36 examples/s]" - } - }, - "d16dede2bb2e40b282d000f989523e41": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_979503323663435eae635a194817476f", - "IPY_MODEL_39e0d0ff92854bb5b45f8340a9c5c5eb", - "IPY_MODEL_cac3b162735445c0915d9ecfed155f4c" - ], - "layout": "IPY_MODEL_890c2c0c04564f5da3221229c05800df", - "tabbable": null, - "tooltip": null - } - }, - "d57ca1f4799d45229ae2f7c720c262f5": { + "bd9b705b24884f74a14e8bfdd7ee8634": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index e8c4bda9d..61c4891f1 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": "2024-07-01T15:02:41.409044Z", - "iopub.status.busy": "2024-07-01T15:02:41.408875Z", - "iopub.status.idle": "2024-07-01T15:02:42.611044Z", - "shell.execute_reply": "2024-07-01T15:02:42.610498Z" + "iopub.execute_input": "2024-07-02T12:01:22.152510Z", + "iopub.status.busy": "2024-07-02T12:01:22.152333Z", + "iopub.status.idle": "2024-07-02T12:01:23.345486Z", + "shell.execute_reply": "2024-07-02T12:01:23.344925Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:02:42.613616Z", - "iopub.status.busy": "2024-07-01T15:02:42.613300Z", - "iopub.status.idle": "2024-07-01T15:02:42.616526Z", - "shell.execute_reply": "2024-07-01T15:02:42.616068Z" + "iopub.execute_input": "2024-07-02T12:01:23.348223Z", + "iopub.status.busy": "2024-07-02T12:01:23.347674Z", + "iopub.status.idle": "2024-07-02T12:01:23.350818Z", + "shell.execute_reply": "2024-07-02T12:01:23.350357Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:42.618748Z", - "iopub.status.busy": "2024-07-01T15:02:42.618428Z", - "iopub.status.idle": "2024-07-01T15:02:42.627446Z", - "shell.execute_reply": "2024-07-01T15:02:42.626999Z" + "iopub.execute_input": "2024-07-02T12:01:23.352826Z", + "iopub.status.busy": "2024-07-02T12:01:23.352642Z", + "iopub.status.idle": "2024-07-02T12:01:23.361928Z", + "shell.execute_reply": "2024-07-02T12:01:23.361407Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:42.629537Z", - "iopub.status.busy": "2024-07-01T15:02:42.629203Z", - "iopub.status.idle": "2024-07-01T15:02:42.633941Z", - "shell.execute_reply": "2024-07-01T15:02:42.633516Z" + "iopub.execute_input": "2024-07-02T12:01:23.363999Z", + "iopub.status.busy": "2024-07-02T12:01:23.363568Z", + "iopub.status.idle": "2024-07-02T12:01:23.368394Z", + "shell.execute_reply": "2024-07-02T12:01:23.367822Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:42.636177Z", - "iopub.status.busy": "2024-07-01T15:02:42.635851Z", - "iopub.status.idle": "2024-07-01T15:02:42.823356Z", - "shell.execute_reply": "2024-07-01T15:02:42.822807Z" + "iopub.execute_input": "2024-07-02T12:01:23.370691Z", + "iopub.status.busy": "2024-07-02T12:01:23.370280Z", + "iopub.status.idle": "2024-07-02T12:01:23.560449Z", + "shell.execute_reply": "2024-07-02T12:01:23.559925Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:42.826055Z", - "iopub.status.busy": "2024-07-01T15:02:42.825690Z", - "iopub.status.idle": "2024-07-01T15:02:43.206067Z", - "shell.execute_reply": "2024-07-01T15:02:43.205474Z" + "iopub.execute_input": "2024-07-02T12:01:23.563109Z", + "iopub.status.busy": "2024-07-02T12:01:23.562666Z", + "iopub.status.idle": "2024-07-02T12:01:23.933479Z", + "shell.execute_reply": "2024-07-02T12:01:23.932844Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:43.208532Z", - "iopub.status.busy": "2024-07-01T15:02:43.208145Z", - "iopub.status.idle": "2024-07-01T15:02:43.211102Z", - "shell.execute_reply": "2024-07-01T15:02:43.210626Z" + "iopub.execute_input": "2024-07-02T12:01:23.935860Z", + "iopub.status.busy": "2024-07-02T12:01:23.935411Z", + "iopub.status.idle": "2024-07-02T12:01:23.938217Z", + "shell.execute_reply": "2024-07-02T12:01:23.937776Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:43.213282Z", - "iopub.status.busy": "2024-07-01T15:02:43.212936Z", - "iopub.status.idle": "2024-07-01T15:02:43.248404Z", - "shell.execute_reply": "2024-07-01T15:02:43.247768Z" + "iopub.execute_input": "2024-07-02T12:01:23.940195Z", + "iopub.status.busy": "2024-07-02T12:01:23.940017Z", + "iopub.status.idle": "2024-07-02T12:01:23.974114Z", + "shell.execute_reply": "2024-07-02T12:01:23.973647Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:43.251350Z", - "iopub.status.busy": "2024-07-01T15:02:43.250964Z", - "iopub.status.idle": "2024-07-01T15:02:45.296650Z", - "shell.execute_reply": "2024-07-01T15:02:45.296009Z" + "iopub.execute_input": "2024-07-02T12:01:23.976287Z", + "iopub.status.busy": "2024-07-02T12:01:23.976112Z", + "iopub.status.idle": "2024-07-02T12:01:26.051828Z", + "shell.execute_reply": "2024-07-02T12:01:26.051244Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.298975Z", - "iopub.status.busy": "2024-07-01T15:02:45.298607Z", - "iopub.status.idle": "2024-07-01T15:02:45.317301Z", - "shell.execute_reply": "2024-07-01T15:02:45.316762Z" + "iopub.execute_input": "2024-07-02T12:01:26.054329Z", + "iopub.status.busy": "2024-07-02T12:01:26.053806Z", + "iopub.status.idle": "2024-07-02T12:01:26.073654Z", + "shell.execute_reply": "2024-07-02T12:01:26.073152Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.319610Z", - "iopub.status.busy": "2024-07-01T15:02:45.319291Z", - "iopub.status.idle": "2024-07-01T15:02:45.325606Z", - "shell.execute_reply": "2024-07-01T15:02:45.325097Z" + "iopub.execute_input": "2024-07-02T12:01:26.075978Z", + "iopub.status.busy": "2024-07-02T12:01:26.075603Z", + "iopub.status.idle": "2024-07-02T12:01:26.082158Z", + "shell.execute_reply": "2024-07-02T12:01:26.081661Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.327815Z", - "iopub.status.busy": "2024-07-01T15:02:45.327438Z", - "iopub.status.idle": "2024-07-01T15:02:45.333038Z", - "shell.execute_reply": "2024-07-01T15:02:45.332566Z" + "iopub.execute_input": "2024-07-02T12:01:26.084369Z", + "iopub.status.busy": "2024-07-02T12:01:26.084032Z", + "iopub.status.idle": "2024-07-02T12:01:26.090027Z", + "shell.execute_reply": "2024-07-02T12:01:26.089524Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.335093Z", - "iopub.status.busy": "2024-07-01T15:02:45.334787Z", - "iopub.status.idle": "2024-07-01T15:02:45.345460Z", - "shell.execute_reply": "2024-07-01T15:02:45.344912Z" + "iopub.execute_input": "2024-07-02T12:01:26.092307Z", + "iopub.status.busy": "2024-07-02T12:01:26.091888Z", + "iopub.status.idle": "2024-07-02T12:01:26.102686Z", + "shell.execute_reply": "2024-07-02T12:01:26.102114Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.347438Z", - "iopub.status.busy": "2024-07-01T15:02:45.347138Z", - "iopub.status.idle": "2024-07-01T15:02:45.356126Z", - "shell.execute_reply": "2024-07-01T15:02:45.355581Z" + "iopub.execute_input": "2024-07-02T12:01:26.104843Z", + "iopub.status.busy": "2024-07-02T12:01:26.104499Z", + "iopub.status.idle": "2024-07-02T12:01:26.113923Z", + "shell.execute_reply": "2024-07-02T12:01:26.113353Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.358103Z", - "iopub.status.busy": "2024-07-01T15:02:45.357792Z", - "iopub.status.idle": "2024-07-01T15:02:45.364571Z", - "shell.execute_reply": "2024-07-01T15:02:45.364114Z" + "iopub.execute_input": "2024-07-02T12:01:26.116196Z", + "iopub.status.busy": "2024-07-02T12:01:26.115857Z", + "iopub.status.idle": "2024-07-02T12:01:26.122959Z", + "shell.execute_reply": "2024-07-02T12:01:26.122462Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.366559Z", - "iopub.status.busy": "2024-07-01T15:02:45.366255Z", - "iopub.status.idle": "2024-07-01T15:02:45.375353Z", - "shell.execute_reply": "2024-07-01T15:02:45.374817Z" + "iopub.execute_input": "2024-07-02T12:01:26.125128Z", + "iopub.status.busy": "2024-07-02T12:01:26.124796Z", + "iopub.status.idle": "2024-07-02T12:01:26.134864Z", + "shell.execute_reply": "2024-07-02T12:01:26.134300Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:45.377315Z", - "iopub.status.busy": "2024-07-01T15:02:45.377010Z", - "iopub.status.idle": "2024-07-01T15:02:45.392963Z", - "shell.execute_reply": "2024-07-01T15:02:45.392390Z" + "iopub.execute_input": "2024-07-02T12:01:26.137332Z", + "iopub.status.busy": "2024-07-02T12:01:26.136913Z", + "iopub.status.idle": "2024-07-02T12:01:26.152852Z", + "shell.execute_reply": "2024-07-02T12:01:26.152376Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index a1388c4c1..7f856f6ea 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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[3]:
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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.

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5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468
 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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5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570
 Computing feature embeddings ...

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

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5. Compute out-of-sample predicted probabilities and feature embeddings
 
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476
 Computing feature embeddings ...

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

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7. Use cleanlab to find issues

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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 03d847503..3baceeb0b 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": "2024-07-01T15:02:48.074971Z", - "iopub.status.busy": "2024-07-01T15:02:48.074723Z", - "iopub.status.idle": "2024-07-01T15:02:51.342353Z", - "shell.execute_reply": "2024-07-01T15:02:51.341605Z" + "iopub.execute_input": "2024-07-02T12:01:28.896200Z", + "iopub.status.busy": "2024-07-02T12:01:28.896023Z", + "iopub.status.idle": "2024-07-02T12:01:31.827318Z", + "shell.execute_reply": "2024-07-02T12:01:31.826688Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:51.345614Z", - "iopub.status.busy": "2024-07-01T15:02:51.345060Z", - "iopub.status.idle": "2024-07-01T15:02:51.349168Z", - "shell.execute_reply": "2024-07-01T15:02:51.348682Z" + "iopub.execute_input": "2024-07-02T12:01:31.829957Z", + "iopub.status.busy": "2024-07-02T12:01:31.829648Z", + "iopub.status.idle": "2024-07-02T12:01:31.833462Z", + "shell.execute_reply": "2024-07-02T12:01:31.833002Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:02:51.351438Z", - "iopub.status.busy": "2024-07-01T15:02:51.351054Z", - "iopub.status.idle": "2024-07-01T15:03:02.526470Z", - "shell.execute_reply": "2024-07-01T15:03:02.525870Z" + "iopub.execute_input": "2024-07-02T12:01:31.835341Z", + "iopub.status.busy": "2024-07-02T12:01:31.835170Z", + "iopub.status.idle": "2024-07-02T12:01:42.989836Z", + "shell.execute_reply": "2024-07-02T12:01:42.989362Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f", + "model_id": "d4c59b0bfa86424a8c95a71f890f5454", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9efee99388e4bd987cba82e4c249be5", + "model_id": "2ffbe85316974d029eab626642378580", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b13b21c3b7544706aacfbba4f3504a8b", + "model_id": "1a9f98ff0f0446e7b89c4fe4fffc3418", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dcfb76cdced842fd810c0329fa0f1c7f", + "model_id": "39838b65ab134d2a9a445437586fec98", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0febc72cf36d4d939a7991cbb880240e", + "model_id": "4d801b30b791427d9103f41505cf1a3e", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6296fc9f1a3947edb989ab3a35afbefe", + "model_id": "0d1f1b12cc3545b0b78b6f64afe61ba8", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc98754b340343f594559442ba450aa4", + "model_id": "495daf880acd479da7fa63fedf1e1368", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d60f32b2907d4a288385a30c717ef39d", + "model_id": "96b3b9a948504544be06e5692d10926d", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:02.528967Z", - "iopub.status.busy": "2024-07-01T15:03:02.528621Z", - "iopub.status.idle": "2024-07-01T15:03:02.532647Z", - "shell.execute_reply": "2024-07-01T15:03:02.532080Z" + "iopub.execute_input": "2024-07-02T12:01:42.992144Z", + "iopub.status.busy": "2024-07-02T12:01:42.991695Z", + "iopub.status.idle": "2024-07-02T12:01:42.995507Z", + "shell.execute_reply": "2024-07-02T12:01:42.995062Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:02.534910Z", - "iopub.status.busy": "2024-07-01T15:03:02.534585Z", - "iopub.status.idle": "2024-07-01T15:03:13.866603Z", - "shell.execute_reply": "2024-07-01T15:03:13.865937Z" + "iopub.execute_input": "2024-07-02T12:01:42.997511Z", + "iopub.status.busy": "2024-07-02T12:01:42.997189Z", + "iopub.status.idle": "2024-07-02T12:01:54.313084Z", + "shell.execute_reply": "2024-07-02T12:01:54.312563Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70b6c17f51c948158afefdd56830a23f", + "model_id": "5191d0744a454151b8fae157e5a21ef4", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:13.869049Z", - "iopub.status.busy": "2024-07-01T15:03:13.868821Z", - "iopub.status.idle": "2024-07-01T15:03:31.582919Z", - "shell.execute_reply": "2024-07-01T15:03:31.582298Z" + "iopub.execute_input": "2024-07-02T12:01:54.315561Z", + "iopub.status.busy": "2024-07-02T12:01:54.315315Z", + "iopub.status.idle": "2024-07-02T12:02:13.013990Z", + "shell.execute_reply": "2024-07-02T12:02:13.013360Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:31.585953Z", - "iopub.status.busy": "2024-07-01T15:03:31.585389Z", - "iopub.status.idle": "2024-07-01T15:03:31.591279Z", - "shell.execute_reply": "2024-07-01T15:03:31.590830Z" + "iopub.execute_input": "2024-07-02T12:02:13.016850Z", + "iopub.status.busy": "2024-07-02T12:02:13.016410Z", + "iopub.status.idle": "2024-07-02T12:02:13.021208Z", + "shell.execute_reply": "2024-07-02T12:02:13.020777Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:31.593306Z", - "iopub.status.busy": "2024-07-01T15:03:31.592981Z", - "iopub.status.idle": "2024-07-01T15:03:31.596855Z", - "shell.execute_reply": "2024-07-01T15:03:31.596450Z" + "iopub.execute_input": "2024-07-02T12:02:13.023194Z", + "iopub.status.busy": "2024-07-02T12:02:13.022869Z", + "iopub.status.idle": "2024-07-02T12:02:13.027182Z", + "shell.execute_reply": "2024-07-02T12:02:13.026649Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:31.598838Z", - "iopub.status.busy": "2024-07-01T15:03:31.598577Z", - "iopub.status.idle": "2024-07-01T15:03:31.607398Z", - "shell.execute_reply": "2024-07-01T15:03:31.606925Z" + "iopub.execute_input": "2024-07-02T12:02:13.029208Z", + "iopub.status.busy": "2024-07-02T12:02:13.028904Z", + "iopub.status.idle": "2024-07-02T12:02:13.037801Z", + "shell.execute_reply": "2024-07-02T12:02:13.037284Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:31.609325Z", - "iopub.status.busy": "2024-07-01T15:03:31.609007Z", - "iopub.status.idle": "2024-07-01T15:03:31.635278Z", - "shell.execute_reply": "2024-07-01T15:03:31.634840Z" + "iopub.execute_input": "2024-07-02T12:02:13.039783Z", + "iopub.status.busy": "2024-07-02T12:02:13.039463Z", + "iopub.status.idle": "2024-07-02T12:02:13.066102Z", + "shell.execute_reply": "2024-07-02T12:02:13.065500Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:03:31.637322Z", - "iopub.status.busy": "2024-07-01T15:03:31.636996Z", - "iopub.status.idle": "2024-07-01T15:04:03.652341Z", - "shell.execute_reply": "2024-07-01T15:04:03.651742Z" + "iopub.execute_input": "2024-07-02T12:02:13.068543Z", + "iopub.status.busy": "2024-07-02T12:02:13.068350Z", + "iopub.status.idle": "2024-07-02T12:02:45.178356Z", + "shell.execute_reply": "2024-07-02T12:02:45.177789Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b69aa5fb137444eb962d31f239578d65", + "model_id": "ec86bd0afa46422aa85bf2778e427f2a", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ca247bf72f54f03aabdd5d72546025f", + "model_id": "a0b406e9eaf143599fd4e302b57381b4", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6465626e3264fa58f44ddccd18cfef2", + "model_id": "bfd46491d1764708be24b2103e5e6cb5", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fa46dee97a14f9594eb60312b03e045", + "model_id": "1696a28972cf4c1c95e3e3bf755c8d21", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76cd9d157bf74d6e93db6f5727c6f900", + "model_id": "32f22fc4e23745929d001d9647682786", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9717f3b4aaae491d9cb2e07d49a003a5", + "model_id": "846e19cb26a94bdba7b363dce398b69c", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:04:03.654962Z", - "iopub.status.busy": "2024-07-01T15:04:03.654720Z", - "iopub.status.idle": "2024-07-01T15:04:03.668632Z", - "shell.execute_reply": "2024-07-01T15:04:03.668209Z" + "iopub.execute_input": "2024-07-02T12:02:45.181036Z", + "iopub.status.busy": "2024-07-02T12:02:45.180584Z", + "iopub.status.idle": "2024-07-02T12:02:45.194402Z", + "shell.execute_reply": "2024-07-02T12:02:45.193957Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:04:03.670732Z", - "iopub.status.busy": "2024-07-01T15:04:03.670344Z", - "iopub.status.idle": "2024-07-01T15:04:04.150524Z", - "shell.execute_reply": "2024-07-01T15:04:04.149791Z" + "iopub.execute_input": "2024-07-02T12:02:45.196378Z", + "iopub.status.busy": "2024-07-02T12:02:45.196060Z", + "iopub.status.idle": "2024-07-02T12:02:45.659461Z", + "shell.execute_reply": "2024-07-02T12:02:45.658926Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:04:04.153028Z", - "iopub.status.busy": "2024-07-01T15:04:04.152825Z", - "iopub.status.idle": "2024-07-01T15:05:40.110641Z", - "shell.execute_reply": "2024-07-01T15:05:40.110011Z" + "iopub.execute_input": "2024-07-02T12:02:45.661921Z", + "iopub.status.busy": "2024-07-02T12:02:45.661522Z", + "iopub.status.idle": "2024-07-02T12:04:21.084670Z", + "shell.execute_reply": "2024-07-02T12:04:21.084011Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b242b3757014ca08c0be26603c856e5", + "model_id": "683ea97790a64507b71e617e6bb1960f", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:40.113143Z", - "iopub.status.busy": "2024-07-01T15:05:40.112512Z", - "iopub.status.idle": "2024-07-01T15:05:40.560298Z", - "shell.execute_reply": "2024-07-01T15:05:40.559714Z" + "iopub.execute_input": "2024-07-02T12:04:21.087384Z", + "iopub.status.busy": "2024-07-02T12:04:21.086898Z", + "iopub.status.idle": "2024-07-02T12:04:21.530187Z", + "shell.execute_reply": "2024-07-02T12:04:21.529650Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:40.563315Z", - "iopub.status.busy": "2024-07-01T15:05:40.562801Z", - "iopub.status.idle": "2024-07-01T15:05:40.624738Z", - "shell.execute_reply": "2024-07-01T15:05:40.624116Z" + "iopub.execute_input": "2024-07-02T12:04:21.532970Z", + "iopub.status.busy": "2024-07-02T12:04:21.532489Z", + "iopub.status.idle": "2024-07-02T12:04:21.594306Z", + "shell.execute_reply": "2024-07-02T12:04:21.593726Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:40.627071Z", - "iopub.status.busy": "2024-07-01T15:05:40.626639Z", - "iopub.status.idle": "2024-07-01T15:05:40.635299Z", - "shell.execute_reply": "2024-07-01T15:05:40.634756Z" + "iopub.execute_input": "2024-07-02T12:04:21.597613Z", + "iopub.status.busy": "2024-07-02T12:04:21.597278Z", + "iopub.status.idle": "2024-07-02T12:04:21.605873Z", + "shell.execute_reply": "2024-07-02T12:04:21.605434Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:40.637389Z", - "iopub.status.busy": "2024-07-01T15:05:40.636989Z", - "iopub.status.idle": "2024-07-01T15:05:40.641711Z", - "shell.execute_reply": "2024-07-01T15:05:40.641175Z" + "iopub.execute_input": "2024-07-02T12:04:21.607881Z", + "iopub.status.busy": "2024-07-02T12:04:21.607595Z", + "iopub.status.idle": "2024-07-02T12:04:21.612387Z", + "shell.execute_reply": "2024-07-02T12:04:21.611934Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:40.643683Z", - "iopub.status.busy": "2024-07-01T15:05:40.643498Z", - "iopub.status.idle": "2024-07-01T15:05:41.152016Z", - "shell.execute_reply": "2024-07-01T15:05:41.151428Z" + "iopub.execute_input": "2024-07-02T12:04:21.614443Z", + "iopub.status.busy": "2024-07-02T12:04:21.614030Z", + "iopub.status.idle": "2024-07-02T12:04:22.120240Z", + "shell.execute_reply": "2024-07-02T12:04:22.119680Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.154341Z", - "iopub.status.busy": "2024-07-01T15:05:41.154029Z", - "iopub.status.idle": "2024-07-01T15:05:41.162706Z", - "shell.execute_reply": "2024-07-01T15:05:41.162252Z" + "iopub.execute_input": "2024-07-02T12:04:22.122526Z", + "iopub.status.busy": "2024-07-02T12:04:22.122160Z", + "iopub.status.idle": "2024-07-02T12:04:22.130544Z", + "shell.execute_reply": "2024-07-02T12:04:22.130091Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.164766Z", - "iopub.status.busy": "2024-07-01T15:05:41.164446Z", - "iopub.status.idle": "2024-07-01T15:05:41.171486Z", - "shell.execute_reply": "2024-07-01T15:05:41.171059Z" + "iopub.execute_input": "2024-07-02T12:04:22.132648Z", + "iopub.status.busy": "2024-07-02T12:04:22.132322Z", + "iopub.status.idle": "2024-07-02T12:04:22.139582Z", + "shell.execute_reply": "2024-07-02T12:04:22.139132Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.173399Z", - "iopub.status.busy": "2024-07-01T15:05:41.173075Z", - "iopub.status.idle": "2024-07-01T15:05:41.934946Z", - "shell.execute_reply": "2024-07-01T15:05:41.934291Z" + "iopub.execute_input": "2024-07-02T12:04:22.141499Z", + "iopub.status.busy": "2024-07-02T12:04:22.141182Z", + "iopub.status.idle": "2024-07-02T12:04:22.871798Z", + "shell.execute_reply": "2024-07-02T12:04:22.871228Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.937509Z", - "iopub.status.busy": "2024-07-01T15:05:41.937076Z", - "iopub.status.idle": "2024-07-01T15:05:41.952809Z", - "shell.execute_reply": "2024-07-01T15:05:41.952240Z" + "iopub.execute_input": "2024-07-02T12:04:22.874107Z", + "iopub.status.busy": "2024-07-02T12:04:22.873751Z", + "iopub.status.idle": "2024-07-02T12:04:22.889160Z", + "shell.execute_reply": "2024-07-02T12:04:22.888693Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.954986Z", - "iopub.status.busy": "2024-07-01T15:05:41.954646Z", - "iopub.status.idle": "2024-07-01T15:05:41.960097Z", - "shell.execute_reply": "2024-07-01T15:05:41.959674Z" + "iopub.execute_input": "2024-07-02T12:04:22.891280Z", + "iopub.status.busy": "2024-07-02T12:04:22.890945Z", + "iopub.status.idle": "2024-07-02T12:04:22.896314Z", + "shell.execute_reply": "2024-07-02T12:04:22.895869Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:41.961941Z", - "iopub.status.busy": "2024-07-01T15:05:41.961770Z", - "iopub.status.idle": "2024-07-01T15:05:42.348365Z", - "shell.execute_reply": "2024-07-01T15:05:42.347794Z" + "iopub.execute_input": "2024-07-02T12:04:22.898366Z", + "iopub.status.busy": "2024-07-02T12:04:22.898042Z", + "iopub.status.idle": "2024-07-02T12:04:23.354782Z", + "shell.execute_reply": "2024-07-02T12:04:23.354256Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:42.350754Z", - "iopub.status.busy": "2024-07-01T15:05:42.350573Z", - "iopub.status.idle": "2024-07-01T15:05:42.359462Z", - "shell.execute_reply": "2024-07-01T15:05:42.358866Z" + "iopub.execute_input": "2024-07-02T12:04:23.357430Z", + "iopub.status.busy": "2024-07-02T12:04:23.357055Z", + "iopub.status.idle": "2024-07-02T12:04:23.366373Z", + "shell.execute_reply": "2024-07-02T12:04:23.365890Z" } }, "outputs": [ @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:42.361756Z", - "iopub.status.busy": "2024-07-01T15:05:42.361579Z", - "iopub.status.idle": "2024-07-01T15:05:42.366530Z", - "shell.execute_reply": "2024-07-01T15:05:42.365855Z" + "iopub.execute_input": "2024-07-02T12:04:23.368851Z", + "iopub.status.busy": "2024-07-02T12:04:23.368495Z", + "iopub.status.idle": "2024-07-02T12:04:23.374119Z", + "shell.execute_reply": "2024-07-02T12:04:23.373635Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:42.368583Z", - "iopub.status.busy": "2024-07-01T15:05:42.368393Z", - "iopub.status.idle": "2024-07-01T15:05:42.547146Z", - "shell.execute_reply": "2024-07-01T15:05:42.546558Z" + "iopub.execute_input": "2024-07-02T12:04:23.376452Z", + "iopub.status.busy": "2024-07-02T12:04:23.376105Z", + "iopub.status.idle": "2024-07-02T12:04:23.576168Z", + "shell.execute_reply": "2024-07-02T12:04:23.575585Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:42.549711Z", - "iopub.status.busy": "2024-07-01T15:05:42.549286Z", - "iopub.status.idle": "2024-07-01T15:05:42.557834Z", - "shell.execute_reply": "2024-07-01T15:05:42.557185Z" + "iopub.execute_input": "2024-07-02T12:04:23.578422Z", + "iopub.status.busy": "2024-07-02T12:04:23.578237Z", + "iopub.status.idle": "2024-07-02T12:04:23.586182Z", + "shell.execute_reply": "2024-07-02T12:04:23.585742Z" } }, "outputs": [ @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:42.559948Z", - "iopub.status.busy": "2024-07-01T15:05:42.559775Z", - "iopub.status.idle": "2024-07-01T15:05:42.735572Z", - "shell.execute_reply": "2024-07-01T15:05:42.734991Z" + "iopub.execute_input": "2024-07-02T12:04:23.588296Z", + "iopub.status.busy": "2024-07-02T12:04:23.587874Z", + "iopub.status.idle": "2024-07-02T12:04:23.783615Z", + "shell.execute_reply": "2024-07-02T12:04:23.783030Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - 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"_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": 10000.0 } }, - "ff86e317738e43de99250a83cfb7ad7f": { + "fffb62594db04599b3628dceafda46f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8702,12 +8702,12 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8767f058e5b649bfb0c2d444b49f3f9d", + "layout": "IPY_MODEL_30c3b868ba4b46ea9bcdb05e1c6d5613", "placeholder": "​", - "style": "IPY_MODEL_01c1fefd1d1549a1ac3d1168746bc81e", + "style": "IPY_MODEL_46552aea691e492084a7278f7a059830", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Map (num_proc=4): 100%" } } }, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 452755a26..32831e810 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": "2024-07-01T15:05:46.317874Z", - "iopub.status.busy": "2024-07-01T15:05:46.317719Z", - "iopub.status.idle": "2024-07-01T15:05:47.417876Z", - "shell.execute_reply": "2024-07-01T15:05:47.417402Z" + "iopub.execute_input": "2024-07-02T12:04:27.356934Z", + "iopub.status.busy": "2024-07-02T12:04:27.356523Z", + "iopub.status.idle": "2024-07-02T12:04:28.474290Z", + "shell.execute_reply": "2024-07-02T12:04:28.473753Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:05:47.420276Z", - "iopub.status.busy": "2024-07-01T15:05:47.420000Z", - "iopub.status.idle": "2024-07-01T15:05:47.437670Z", - "shell.execute_reply": "2024-07-01T15:05:47.437224Z" + "iopub.execute_input": "2024-07-02T12:04:28.476781Z", + "iopub.status.busy": "2024-07-02T12:04:28.476419Z", + "iopub.status.idle": "2024-07-02T12:04:28.493512Z", + "shell.execute_reply": "2024-07-02T12:04:28.493079Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:47.439750Z", - "iopub.status.busy": "2024-07-01T15:05:47.439498Z", - "iopub.status.idle": "2024-07-01T15:05:47.478024Z", - "shell.execute_reply": "2024-07-01T15:05:47.477526Z" + "iopub.execute_input": "2024-07-02T12:04:28.495747Z", + "iopub.status.busy": "2024-07-02T12:04:28.495323Z", + "iopub.status.idle": "2024-07-02T12:04:28.552204Z", + "shell.execute_reply": "2024-07-02T12:04:28.551635Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:47.480262Z", - "iopub.status.busy": "2024-07-01T15:05:47.479916Z", - "iopub.status.idle": "2024-07-01T15:05:47.483182Z", - "shell.execute_reply": "2024-07-01T15:05:47.482737Z" + "iopub.execute_input": "2024-07-02T12:04:28.554311Z", + "iopub.status.busy": "2024-07-02T12:04:28.553993Z", + "iopub.status.idle": "2024-07-02T12:04:28.557548Z", + "shell.execute_reply": "2024-07-02T12:04:28.557017Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:47.485314Z", - "iopub.status.busy": "2024-07-01T15:05:47.484937Z", - "iopub.status.idle": "2024-07-01T15:05:47.492797Z", - "shell.execute_reply": "2024-07-01T15:05:47.492370Z" + "iopub.execute_input": "2024-07-02T12:04:28.559563Z", + "iopub.status.busy": "2024-07-02T12:04:28.559241Z", + "iopub.status.idle": "2024-07-02T12:04:28.566506Z", + "shell.execute_reply": "2024-07-02T12:04:28.566080Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:47.494826Z", - "iopub.status.busy": "2024-07-01T15:05:47.494542Z", - "iopub.status.idle": "2024-07-01T15:05:47.497119Z", - "shell.execute_reply": "2024-07-01T15:05:47.496587Z" + "iopub.execute_input": "2024-07-02T12:04:28.568485Z", + "iopub.status.busy": "2024-07-02T12:04:28.568190Z", + "iopub.status.idle": "2024-07-02T12:04:28.570814Z", + "shell.execute_reply": "2024-07-02T12:04:28.570270Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:47.499036Z", - "iopub.status.busy": "2024-07-01T15:05:47.498842Z", - "iopub.status.idle": "2024-07-01T15:05:50.430868Z", - "shell.execute_reply": "2024-07-01T15:05:50.430331Z" + "iopub.execute_input": "2024-07-02T12:04:28.572815Z", + "iopub.status.busy": "2024-07-02T12:04:28.572491Z", + "iopub.status.idle": "2024-07-02T12:04:31.525677Z", + "shell.execute_reply": "2024-07-02T12:04:31.525153Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:50.433520Z", - "iopub.status.busy": "2024-07-01T15:05:50.433131Z", - "iopub.status.idle": "2024-07-01T15:05:50.442780Z", - "shell.execute_reply": "2024-07-01T15:05:50.442322Z" + "iopub.execute_input": "2024-07-02T12:04:31.528465Z", + "iopub.status.busy": "2024-07-02T12:04:31.528045Z", + "iopub.status.idle": "2024-07-02T12:04:31.537314Z", + "shell.execute_reply": "2024-07-02T12:04:31.536783Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:50.444757Z", - "iopub.status.busy": "2024-07-01T15:05:50.444440Z", - "iopub.status.idle": "2024-07-01T15:05:52.320323Z", - "shell.execute_reply": "2024-07-01T15:05:52.319680Z" + "iopub.execute_input": "2024-07-02T12:04:31.539264Z", + "iopub.status.busy": "2024-07-02T12:04:31.539089Z", + "iopub.status.idle": "2024-07-02T12:04:33.395993Z", + "shell.execute_reply": "2024-07-02T12:04:33.395329Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:52.322868Z", - "iopub.status.busy": "2024-07-01T15:05:52.322271Z", - "iopub.status.idle": "2024-07-01T15:05:52.341011Z", - "shell.execute_reply": "2024-07-01T15:05:52.340483Z" + "iopub.execute_input": "2024-07-02T12:04:33.398417Z", + "iopub.status.busy": "2024-07-02T12:04:33.397878Z", + "iopub.status.idle": "2024-07-02T12:04:33.416211Z", + "shell.execute_reply": "2024-07-02T12:04:33.415751Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:52.343025Z", - "iopub.status.busy": "2024-07-01T15:05:52.342731Z", - "iopub.status.idle": "2024-07-01T15:05:52.350595Z", - "shell.execute_reply": "2024-07-01T15:05:52.350103Z" + "iopub.execute_input": "2024-07-02T12:04:33.418164Z", + "iopub.status.busy": "2024-07-02T12:04:33.417840Z", + "iopub.status.idle": "2024-07-02T12:04:33.425514Z", + "shell.execute_reply": "2024-07-02T12:04:33.425080Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - 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"iopub.execute_input": "2024-07-01T15:05:52.372696Z", - "iopub.status.busy": "2024-07-01T15:05:52.372359Z", - "iopub.status.idle": "2024-07-01T15:05:52.380928Z", - "shell.execute_reply": "2024-07-01T15:05:52.380440Z" + "iopub.execute_input": "2024-07-02T12:04:33.447029Z", + "iopub.status.busy": "2024-07-02T12:04:33.446852Z", + "iopub.status.idle": "2024-07-02T12:04:33.455323Z", + "shell.execute_reply": "2024-07-02T12:04:33.454897Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:52.382940Z", - "iopub.status.busy": "2024-07-01T15:05:52.382568Z", - "iopub.status.idle": "2024-07-01T15:05:52.389986Z", - "shell.execute_reply": "2024-07-01T15:05:52.389445Z" + "iopub.execute_input": "2024-07-02T12:04:33.457305Z", + "iopub.status.busy": "2024-07-02T12:04:33.457003Z", + "iopub.status.idle": "2024-07-02T12:04:33.464266Z", + "shell.execute_reply": "2024-07-02T12:04:33.463800Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:52.392057Z", - "iopub.status.busy": "2024-07-01T15:05:52.391736Z", - "iopub.status.idle": "2024-07-01T15:05:52.398743Z", - "shell.execute_reply": "2024-07-01T15:05:52.398311Z" + "iopub.execute_input": "2024-07-02T12:04:33.466390Z", + "iopub.status.busy": "2024-07-02T12:04:33.465996Z", + "iopub.status.idle": "2024-07-02T12:04:33.473134Z", + "shell.execute_reply": "2024-07-02T12:04:33.472705Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:52.400864Z", - "iopub.status.busy": "2024-07-01T15:05:52.400548Z", - "iopub.status.idle": "2024-07-01T15:05:52.408413Z", - "shell.execute_reply": "2024-07-01T15:05:52.407979Z" + "iopub.execute_input": "2024-07-02T12:04:33.475300Z", + "iopub.status.busy": "2024-07-02T12:04:33.474982Z", + "iopub.status.idle": "2024-07-02T12:04:33.482977Z", + "shell.execute_reply": "2024-07-02T12:04:33.482536Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index cc4207eec..2bf6c15a0 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', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}
+Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}

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

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 94ec2b5de..8395c410d 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": "2024-07-01T15:05:55.109624Z", - "iopub.status.busy": "2024-07-01T15:05:55.109456Z", - "iopub.status.idle": "2024-07-01T15:05:57.756143Z", - "shell.execute_reply": "2024-07-01T15:05:57.755510Z" + "iopub.execute_input": "2024-07-02T12:04:36.240740Z", + "iopub.status.busy": "2024-07-02T12:04:36.240404Z", + "iopub.status.idle": "2024-07-02T12:04:38.828958Z", + "shell.execute_reply": "2024-07-02T12:04:38.828416Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:05:57.758704Z", - "iopub.status.busy": "2024-07-01T15:05:57.758362Z", - "iopub.status.idle": "2024-07-01T15:05:57.761689Z", - "shell.execute_reply": "2024-07-01T15:05:57.761157Z" + "iopub.execute_input": "2024-07-02T12:04:38.831414Z", + "iopub.status.busy": "2024-07-02T12:04:38.831139Z", + "iopub.status.idle": "2024-07-02T12:04:38.834207Z", + "shell.execute_reply": "2024-07-02T12:04:38.833787Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:57.763881Z", - "iopub.status.busy": "2024-07-01T15:05:57.763378Z", - "iopub.status.idle": "2024-07-01T15:05:57.766675Z", - "shell.execute_reply": "2024-07-01T15:05:57.766123Z" + "iopub.execute_input": "2024-07-02T12:04:38.836176Z", + "iopub.status.busy": "2024-07-02T12:04:38.835855Z", + "iopub.status.idle": "2024-07-02T12:04:38.838727Z", + "shell.execute_reply": "2024-07-02T12:04:38.838306Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:57.768614Z", - "iopub.status.busy": "2024-07-01T15:05:57.768315Z", - "iopub.status.idle": "2024-07-01T15:05:57.808437Z", - "shell.execute_reply": "2024-07-01T15:05:57.807887Z" + "iopub.execute_input": "2024-07-02T12:04:38.840549Z", + "iopub.status.busy": "2024-07-02T12:04:38.840377Z", + "iopub.status.idle": "2024-07-02T12:04:38.923955Z", + "shell.execute_reply": "2024-07-02T12:04:38.923459Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:57.810722Z", - "iopub.status.busy": "2024-07-01T15:05:57.810309Z", - "iopub.status.idle": "2024-07-01T15:05:57.814281Z", - "shell.execute_reply": "2024-07-01T15:05:57.813706Z" + "iopub.execute_input": "2024-07-02T12:04:38.926011Z", + "iopub.status.busy": "2024-07-02T12:04:38.925614Z", + "iopub.status.idle": "2024-07-02T12:04:38.929422Z", + "shell.execute_reply": "2024-07-02T12:04:38.928857Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n" + "Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:57.816292Z", - "iopub.status.busy": "2024-07-01T15:05:57.816001Z", - "iopub.status.idle": "2024-07-01T15:05:57.819153Z", - "shell.execute_reply": "2024-07-01T15:05:57.818607Z" + "iopub.execute_input": "2024-07-02T12:04:38.931544Z", + "iopub.status.busy": "2024-07-02T12:04:38.931095Z", + "iopub.status.idle": "2024-07-02T12:04:38.934251Z", + "shell.execute_reply": "2024-07-02T12:04:38.933726Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:05:57.821168Z", - "iopub.status.busy": "2024-07-01T15:05:57.820783Z", - "iopub.status.idle": "2024-07-01T15:06:01.454864Z", - "shell.execute_reply": "2024-07-01T15:06:01.454218Z" + "iopub.execute_input": "2024-07-02T12:04:38.936534Z", + "iopub.status.busy": "2024-07-02T12:04:38.936327Z", + "iopub.status.idle": "2024-07-02T12:04:42.537806Z", + "shell.execute_reply": "2024-07-02T12:04:42.537162Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:01.457576Z", - "iopub.status.busy": "2024-07-01T15:06:01.457191Z", - "iopub.status.idle": "2024-07-01T15:06:02.359759Z", - "shell.execute_reply": "2024-07-01T15:06:02.359194Z" + "iopub.execute_input": "2024-07-02T12:04:42.540458Z", + "iopub.status.busy": "2024-07-02T12:04:42.540268Z", + "iopub.status.idle": "2024-07-02T12:04:43.423626Z", + "shell.execute_reply": "2024-07-02T12:04:43.423064Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:02.362504Z", - "iopub.status.busy": "2024-07-01T15:06:02.362099Z", - "iopub.status.idle": "2024-07-01T15:06:02.365173Z", - "shell.execute_reply": "2024-07-01T15:06:02.364692Z" + "iopub.execute_input": "2024-07-02T12:04:43.426912Z", + "iopub.status.busy": "2024-07-02T12:04:43.426508Z", + "iopub.status.idle": "2024-07-02T12:04:43.429416Z", + "shell.execute_reply": "2024-07-02T12:04:43.428926Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:02.368303Z", - "iopub.status.busy": "2024-07-01T15:06:02.367393Z", - "iopub.status.idle": "2024-07-01T15:06:04.354878Z", - "shell.execute_reply": "2024-07-01T15:06:04.354255Z" + "iopub.execute_input": "2024-07-02T12:04:43.431781Z", + "iopub.status.busy": "2024-07-02T12:04:43.431407Z", + "iopub.status.idle": "2024-07-02T12:04:45.304891Z", + "shell.execute_reply": "2024-07-02T12:04:45.304275Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.359326Z", - "iopub.status.busy": "2024-07-01T15:06:04.358175Z", - "iopub.status.idle": "2024-07-01T15:06:04.383863Z", - "shell.execute_reply": "2024-07-01T15:06:04.383356Z" + "iopub.execute_input": "2024-07-02T12:04:45.309001Z", + "iopub.status.busy": "2024-07-02T12:04:45.307874Z", + "iopub.status.idle": "2024-07-02T12:04:45.333199Z", + "shell.execute_reply": "2024-07-02T12:04:45.332708Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.387331Z", - "iopub.status.busy": "2024-07-01T15:06:04.386438Z", - "iopub.status.idle": "2024-07-01T15:06:04.396138Z", - "shell.execute_reply": "2024-07-01T15:06:04.395755Z" + "iopub.execute_input": "2024-07-02T12:04:45.336771Z", + "iopub.status.busy": "2024-07-02T12:04:45.335844Z", + "iopub.status.idle": "2024-07-02T12:04:45.346004Z", + "shell.execute_reply": "2024-07-02T12:04:45.345452Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.398058Z", - "iopub.status.busy": "2024-07-01T15:06:04.397776Z", - "iopub.status.idle": "2024-07-01T15:06:04.401475Z", - "shell.execute_reply": "2024-07-01T15:06:04.401092Z" + "iopub.execute_input": "2024-07-02T12:04:45.348315Z", + "iopub.status.busy": "2024-07-02T12:04:45.347931Z", + "iopub.status.idle": "2024-07-02T12:04:45.352195Z", + "shell.execute_reply": "2024-07-02T12:04:45.351669Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.403322Z", - "iopub.status.busy": "2024-07-01T15:06:04.403036Z", - "iopub.status.idle": "2024-07-01T15:06:04.408720Z", - "shell.execute_reply": "2024-07-01T15:06:04.408332Z" + "iopub.execute_input": "2024-07-02T12:04:45.354318Z", + "iopub.status.busy": "2024-07-02T12:04:45.354009Z", + "iopub.status.idle": "2024-07-02T12:04:45.360212Z", + "shell.execute_reply": "2024-07-02T12:04:45.359737Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.410591Z", - "iopub.status.busy": "2024-07-01T15:06:04.410423Z", - "iopub.status.idle": "2024-07-01T15:06:04.416683Z", - "shell.execute_reply": "2024-07-01T15:06:04.416154Z" + "iopub.execute_input": "2024-07-02T12:04:45.362212Z", + "iopub.status.busy": "2024-07-02T12:04:45.361899Z", + "iopub.status.idle": "2024-07-02T12:04:45.368332Z", + "shell.execute_reply": "2024-07-02T12:04:45.367912Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.418724Z", - "iopub.status.busy": "2024-07-01T15:06:04.418385Z", - "iopub.status.idle": "2024-07-01T15:06:04.424043Z", - "shell.execute_reply": "2024-07-01T15:06:04.423521Z" + "iopub.execute_input": "2024-07-02T12:04:45.370347Z", + "iopub.status.busy": "2024-07-02T12:04:45.370035Z", + "iopub.status.idle": "2024-07-02T12:04:45.375916Z", + "shell.execute_reply": "2024-07-02T12:04:45.375352Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.426089Z", - "iopub.status.busy": "2024-07-01T15:06:04.425788Z", - "iopub.status.idle": "2024-07-01T15:06:04.434068Z", - "shell.execute_reply": "2024-07-01T15:06:04.433526Z" + "iopub.execute_input": "2024-07-02T12:04:45.377933Z", + "iopub.status.busy": "2024-07-02T12:04:45.377533Z", + "iopub.status.idle": "2024-07-02T12:04:45.386285Z", + "shell.execute_reply": "2024-07-02T12:04:45.385744Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.435974Z", - "iopub.status.busy": "2024-07-01T15:06:04.435800Z", - "iopub.status.idle": "2024-07-01T15:06:04.441070Z", - "shell.execute_reply": "2024-07-01T15:06:04.440586Z" + "iopub.execute_input": "2024-07-02T12:04:45.388235Z", + "iopub.status.busy": "2024-07-02T12:04:45.387909Z", + "iopub.status.idle": "2024-07-02T12:04:45.393341Z", + "shell.execute_reply": "2024-07-02T12:04:45.392791Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.443100Z", - "iopub.status.busy": "2024-07-01T15:06:04.442719Z", - "iopub.status.idle": "2024-07-01T15:06:04.447928Z", - "shell.execute_reply": "2024-07-01T15:06:04.447468Z" + "iopub.execute_input": "2024-07-02T12:04:45.395404Z", + "iopub.status.busy": "2024-07-02T12:04:45.395057Z", + "iopub.status.idle": "2024-07-02T12:04:45.400341Z", + "shell.execute_reply": "2024-07-02T12:04:45.399863Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.450005Z", - "iopub.status.busy": "2024-07-01T15:06:04.449608Z", - "iopub.status.idle": "2024-07-01T15:06:04.453217Z", - "shell.execute_reply": "2024-07-01T15:06:04.452674Z" + "iopub.execute_input": "2024-07-02T12:04:45.402359Z", + "iopub.status.busy": "2024-07-02T12:04:45.402038Z", + "iopub.status.idle": "2024-07-02T12:04:45.405437Z", + "shell.execute_reply": "2024-07-02T12:04:45.405020Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:06:04.455383Z", - "iopub.status.busy": "2024-07-01T15:06:04.455056Z", - "iopub.status.idle": "2024-07-01T15:06:04.460142Z", - "shell.execute_reply": "2024-07-01T15:06:04.459596Z" + "iopub.execute_input": "2024-07-02T12:04:45.407623Z", + "iopub.status.busy": "2024-07-02T12:04:45.407307Z", + "iopub.status.idle": "2024-07-02T12:04:45.412091Z", + "shell.execute_reply": "2024-07-02T12:04:45.411668Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index 4720c4579..cef347d5d 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -833,7 +833,7 @@

4. Identify Data Issues Using Datalab @@ -879,13 +879,13 @@

4. Identify Data Issues Using Datalab - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
@@ -3473,14 +3473,36 @@

3. (Optional) Visualize class imbalance issues -

Find Spurious Correlation between Vision Dataset features and class labels#

-

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

-

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

-
-

1. Load the dataset#

-

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

-

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

+
+

Identify Spurious Correlations in Image Datasets#

+

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

+

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

+
    +

  • Darkness

    • +

  • Blurriness

    • +

  • Aspect ratio anomalies

    • +

  • More image-specific features from CleanVision

    • +
+

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

+
+

1. Load the Dataset#

+

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

+
    +

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

    • +

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

    +
      +

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

      • +

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

      • +
    +
    • +

  • By introducing this bias, we expect to see:

    +
      +

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

      • +

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

      • +
    +
    • +
+

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

[33]:
@@ -3542,7 +3564,7 @@

1. Load the dataset 
-100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]
+100%|██████████| 170498071/170498071 [00:02<00:00, 69520911.78it/s]

@@ -3554,8 +3576,8 @@

1. Load the dataset -

2. Creating Dataset object to be passed to the Datalab object to find vision-related issues#

+

@@ -3711,7 +3733,7 @@

5. Finding image-specific property scores

-
+
@@ -3727,8 +3749,8 @@

5. Finding image-specific property scores

-
-

Vision-specific property scores in the original dataset#

+
+

Image-specific property scores in the original dataset#

@@ -3802,8 +3824,8 @@

Vision-specific property scores in the original dataset
-
-

Vision-specific property scores in the transformed dataset#

+
+

Image-specific property scores in the transformed dataset#

@@ -3874,7 +3896,7 @@

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@@ -4008,9 +4030,9 @@

Vision-specific property scores in the transformed dataset3. (Optional) Visualize class imbalance issues -
  • Find Spurious Correlation between Vision Dataset features and class labels

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Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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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"get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module 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"correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module 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"cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, 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"cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, 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"module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index 835b9297f..0c56c3881 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": "2024-07-01T15:01:38.704463Z", - "iopub.status.busy": "2024-07-01T15:01:38.704282Z", - "iopub.status.idle": "2024-07-01T15:01:39.968773Z", - "shell.execute_reply": "2024-07-01T15:01:39.968140Z" + "iopub.execute_input": "2024-07-02T12:00:24.117516Z", + "iopub.status.busy": "2024-07-02T12:00:24.117048Z", + "iopub.status.idle": "2024-07-02T12:00:25.333194Z", + "shell.execute_reply": "2024-07-02T12:00:25.332647Z" }, "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@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\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": "2024-07-01T15:01:39.971457Z", - "iopub.status.busy": "2024-07-01T15:01:39.971069Z", - "iopub.status.idle": "2024-07-01T15:01:39.990015Z", - "shell.execute_reply": "2024-07-01T15:01:39.989387Z" + "iopub.execute_input": "2024-07-02T12:00:25.335570Z", + "iopub.status.busy": "2024-07-02T12:00:25.335300Z", + "iopub.status.idle": "2024-07-02T12:00:25.352966Z", + "shell.execute_reply": "2024-07-02T12:00:25.352544Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:39.992806Z", - "iopub.status.busy": "2024-07-01T15:01:39.992402Z", - "iopub.status.idle": "2024-07-01T15:01:40.303536Z", - "shell.execute_reply": "2024-07-01T15:01:40.302965Z" + "iopub.execute_input": "2024-07-02T12:00:25.355177Z", + "iopub.status.busy": "2024-07-02T12:00:25.354929Z", + "iopub.status.idle": "2024-07-02T12:00:25.498882Z", + "shell.execute_reply": "2024-07-02T12:00:25.498315Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:40.336204Z", - "iopub.status.busy": "2024-07-01T15:01:40.335666Z", - "iopub.status.idle": "2024-07-01T15:01:40.340138Z", - "shell.execute_reply": "2024-07-01T15:01:40.339623Z" + "iopub.execute_input": "2024-07-02T12:00:25.528732Z", + "iopub.status.busy": "2024-07-02T12:00:25.528329Z", + "iopub.status.idle": "2024-07-02T12:00:25.532259Z", + "shell.execute_reply": "2024-07-02T12:00:25.531790Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:40.342354Z", - "iopub.status.busy": "2024-07-01T15:01:40.342145Z", - "iopub.status.idle": "2024-07-01T15:01:40.351148Z", - "shell.execute_reply": "2024-07-01T15:01:40.350569Z" + "iopub.execute_input": "2024-07-02T12:00:25.534236Z", + "iopub.status.busy": "2024-07-02T12:00:25.534064Z", + "iopub.status.idle": "2024-07-02T12:00:25.542721Z", + "shell.execute_reply": "2024-07-02T12:00:25.542178Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:40.353562Z", - "iopub.status.busy": "2024-07-01T15:01:40.353231Z", - "iopub.status.idle": "2024-07-01T15:01:40.356046Z", - "shell.execute_reply": "2024-07-01T15:01:40.355491Z" + "iopub.execute_input": "2024-07-02T12:00:25.544841Z", + "iopub.status.busy": "2024-07-02T12:00:25.544667Z", + "iopub.status.idle": "2024-07-02T12:00:25.547142Z", + "shell.execute_reply": "2024-07-02T12:00:25.546723Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:40.358053Z", - "iopub.status.busy": "2024-07-01T15:01:40.357874Z", - "iopub.status.idle": "2024-07-01T15:01:40.885000Z", - "shell.execute_reply": "2024-07-01T15:01:40.884377Z" + "iopub.execute_input": "2024-07-02T12:00:25.549121Z", + "iopub.status.busy": "2024-07-02T12:00:25.548952Z", + "iopub.status.idle": "2024-07-02T12:00:26.069775Z", + "shell.execute_reply": "2024-07-02T12:00:26.069166Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:40.887806Z", - "iopub.status.busy": "2024-07-01T15:01:40.887346Z", - "iopub.status.idle": "2024-07-01T15:01:42.858439Z", - "shell.execute_reply": "2024-07-01T15:01:42.857751Z" + "iopub.execute_input": "2024-07-02T12:00:26.072294Z", + "iopub.status.busy": "2024-07-02T12:00:26.072111Z", + "iopub.status.idle": "2024-07-02T12:00:27.964122Z", + "shell.execute_reply": "2024-07-02T12:00:27.963476Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:42.861505Z", - "iopub.status.busy": "2024-07-01T15:01:42.860685Z", - "iopub.status.idle": "2024-07-01T15:01:42.872129Z", - "shell.execute_reply": "2024-07-01T15:01:42.871534Z" + "iopub.execute_input": "2024-07-02T12:00:27.966793Z", + "iopub.status.busy": "2024-07-02T12:00:27.966128Z", + "iopub.status.idle": "2024-07-02T12:00:27.975803Z", + "shell.execute_reply": "2024-07-02T12:00:27.975266Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:42.874722Z", - "iopub.status.busy": "2024-07-01T15:01:42.874312Z", - "iopub.status.idle": "2024-07-01T15:01:42.879185Z", - "shell.execute_reply": "2024-07-01T15:01:42.878651Z" + "iopub.execute_input": "2024-07-02T12:00:27.977956Z", + "iopub.status.busy": "2024-07-02T12:00:27.977648Z", + "iopub.status.idle": "2024-07-02T12:00:27.981829Z", + "shell.execute_reply": "2024-07-02T12:00:27.981303Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:42.881719Z", - "iopub.status.busy": "2024-07-01T15:01:42.881293Z", - "iopub.status.idle": "2024-07-01T15:01:42.890936Z", - "shell.execute_reply": "2024-07-01T15:01:42.890441Z" + "iopub.execute_input": "2024-07-02T12:00:27.984025Z", + "iopub.status.busy": "2024-07-02T12:00:27.983701Z", + "iopub.status.idle": "2024-07-02T12:00:27.990825Z", + "shell.execute_reply": "2024-07-02T12:00:27.990380Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:42.893152Z", - "iopub.status.busy": "2024-07-01T15:01:42.892940Z", - "iopub.status.idle": "2024-07-01T15:01:43.010191Z", - "shell.execute_reply": "2024-07-01T15:01:43.009566Z" + "iopub.execute_input": "2024-07-02T12:00:27.992803Z", + "iopub.status.busy": "2024-07-02T12:00:27.992505Z", + "iopub.status.idle": "2024-07-02T12:00:28.104238Z", + "shell.execute_reply": "2024-07-02T12:00:28.103750Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:43.012877Z", - "iopub.status.busy": "2024-07-01T15:01:43.012678Z", - "iopub.status.idle": "2024-07-01T15:01:43.015881Z", - "shell.execute_reply": "2024-07-01T15:01:43.015414Z" + "iopub.execute_input": "2024-07-02T12:00:28.106465Z", + "iopub.status.busy": "2024-07-02T12:00:28.106127Z", + "iopub.status.idle": "2024-07-02T12:00:28.108811Z", + "shell.execute_reply": "2024-07-02T12:00:28.108400Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:43.017749Z", - "iopub.status.busy": "2024-07-01T15:01:43.017574Z", - "iopub.status.idle": "2024-07-01T15:01:45.116344Z", - "shell.execute_reply": "2024-07-01T15:01:45.115698Z" + "iopub.execute_input": "2024-07-02T12:00:28.110759Z", + "iopub.status.busy": "2024-07-02T12:00:28.110457Z", + "iopub.status.idle": "2024-07-02T12:00:30.104044Z", + "shell.execute_reply": "2024-07-02T12:00:30.103432Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:45.119290Z", - "iopub.status.busy": "2024-07-01T15:01:45.118731Z", - "iopub.status.idle": "2024-07-01T15:01:45.130593Z", - "shell.execute_reply": "2024-07-01T15:01:45.130118Z" + "iopub.execute_input": "2024-07-02T12:00:30.106906Z", + "iopub.status.busy": "2024-07-02T12:00:30.106328Z", + "iopub.status.idle": "2024-07-02T12:00:30.117548Z", + "shell.execute_reply": "2024-07-02T12:00:30.117099Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-01T15:01:45.132594Z", - "iopub.status.busy": "2024-07-01T15:01:45.132413Z", - "iopub.status.idle": "2024-07-01T15:01:45.200709Z", - "shell.execute_reply": "2024-07-01T15:01:45.200202Z" + "iopub.execute_input": "2024-07-02T12:00:30.119573Z", + "iopub.status.busy": "2024-07-02T12:00:30.119249Z", + "iopub.status.idle": "2024-07-02T12:00:30.150922Z", + "shell.execute_reply": "2024-07-02T12:00:30.150454Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index 87f58e815..c0155c6ba 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -817,7 +817,7 @@