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\"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:16:53.949560Z", - "iopub.status.busy": "2024-05-24T14:16:53.949098Z", - "iopub.status.idle": "2024-05-24T14:16:53.967220Z", - "shell.execute_reply": "2024-05-24T14:16:53.966766Z" + "iopub.execute_input": "2024-05-24T14:47:04.494561Z", + "iopub.status.busy": "2024-05-24T14:47:04.494190Z", + "iopub.status.idle": "2024-05-24T14:47:04.515327Z", + "shell.execute_reply": "2024-05-24T14:47:04.514753Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:53.969575Z", - "iopub.status.busy": "2024-05-24T14:16:53.969152Z", - "iopub.status.idle": "2024-05-24T14:16:53.972283Z", - "shell.execute_reply": "2024-05-24T14:16:53.971836Z" + "iopub.execute_input": "2024-05-24T14:47:04.518048Z", + "iopub.status.busy": "2024-05-24T14:47:04.517704Z", + "iopub.status.idle": "2024-05-24T14:47:04.521191Z", + "shell.execute_reply": "2024-05-24T14:47:04.520693Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:53.974240Z", - "iopub.status.busy": "2024-05-24T14:16:53.973959Z", - "iopub.status.idle": "2024-05-24T14:16:54.033719Z", - "shell.execute_reply": "2024-05-24T14:16:54.033211Z" + "iopub.execute_input": "2024-05-24T14:47:04.523189Z", + "iopub.status.busy": "2024-05-24T14:47:04.522996Z", + "iopub.status.idle": "2024-05-24T14:47:04.581897Z", + "shell.execute_reply": "2024-05-24T14:47:04.581240Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.036016Z", - "iopub.status.busy": "2024-05-24T14:16:54.035694Z", - "iopub.status.idle": "2024-05-24T14:16:54.217027Z", - "shell.execute_reply": "2024-05-24T14:16:54.216505Z" + "iopub.execute_input": "2024-05-24T14:47:04.584548Z", + "iopub.status.busy": "2024-05-24T14:47:04.584087Z", + "iopub.status.idle": "2024-05-24T14:47:04.785981Z", + "shell.execute_reply": "2024-05-24T14:47:04.785324Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.219569Z", - "iopub.status.busy": "2024-05-24T14:16:54.219138Z", - "iopub.status.idle": "2024-05-24T14:16:54.463668Z", - "shell.execute_reply": "2024-05-24T14:16:54.463047Z" + "iopub.execute_input": "2024-05-24T14:47:04.789188Z", + "iopub.status.busy": "2024-05-24T14:47:04.788666Z", + "iopub.status.idle": "2024-05-24T14:47:05.052163Z", + "shell.execute_reply": "2024-05-24T14:47:05.051482Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.466095Z", - "iopub.status.busy": "2024-05-24T14:16:54.465710Z", - "iopub.status.idle": "2024-05-24T14:16:54.470289Z", - "shell.execute_reply": "2024-05-24T14:16:54.469813Z" + "iopub.execute_input": "2024-05-24T14:47:05.054390Z", + "iopub.status.busy": "2024-05-24T14:47:05.054184Z", + "iopub.status.idle": "2024-05-24T14:47:05.059284Z", + "shell.execute_reply": "2024-05-24T14:47:05.058776Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.472358Z", - "iopub.status.busy": "2024-05-24T14:16:54.472018Z", - "iopub.status.idle": "2024-05-24T14:16:54.477733Z", - "shell.execute_reply": "2024-05-24T14:16:54.477284Z" + "iopub.execute_input": "2024-05-24T14:47:05.061589Z", + "iopub.status.busy": "2024-05-24T14:47:05.061256Z", + "iopub.status.idle": "2024-05-24T14:47:05.068596Z", + "shell.execute_reply": "2024-05-24T14:47:05.067935Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.479873Z", - "iopub.status.busy": "2024-05-24T14:16:54.479454Z", - "iopub.status.idle": "2024-05-24T14:16:54.482243Z", - "shell.execute_reply": "2024-05-24T14:16:54.481663Z" + "iopub.execute_input": "2024-05-24T14:47:05.071303Z", + "iopub.status.busy": "2024-05-24T14:47:05.070997Z", + "iopub.status.idle": "2024-05-24T14:47:05.073937Z", + "shell.execute_reply": "2024-05-24T14:47:05.073464Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.484266Z", - "iopub.status.busy": "2024-05-24T14:16:54.483860Z", - "iopub.status.idle": "2024-05-24T14:17:02.729964Z", - "shell.execute_reply": "2024-05-24T14:17:02.729231Z" + "iopub.execute_input": "2024-05-24T14:47:05.076227Z", + "iopub.status.busy": "2024-05-24T14:47:05.075865Z", + "iopub.status.idle": "2024-05-24T14:47:13.850907Z", + "shell.execute_reply": "2024-05-24T14:47:13.850220Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.732901Z", - "iopub.status.busy": "2024-05-24T14:17:02.732413Z", - "iopub.status.idle": "2024-05-24T14:17:02.740225Z", - "shell.execute_reply": "2024-05-24T14:17:02.739759Z" + "iopub.execute_input": "2024-05-24T14:47:13.854219Z", + "iopub.status.busy": "2024-05-24T14:47:13.853511Z", + "iopub.status.idle": "2024-05-24T14:47:13.861980Z", + "shell.execute_reply": "2024-05-24T14:47:13.861443Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.742257Z", - "iopub.status.busy": "2024-05-24T14:17:02.741934Z", - "iopub.status.idle": "2024-05-24T14:17:02.745669Z", - "shell.execute_reply": "2024-05-24T14:17:02.745111Z" + "iopub.execute_input": "2024-05-24T14:47:13.864301Z", + "iopub.status.busy": "2024-05-24T14:47:13.863924Z", + "iopub.status.idle": "2024-05-24T14:47:13.867723Z", + "shell.execute_reply": "2024-05-24T14:47:13.867267Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.747680Z", - "iopub.status.busy": "2024-05-24T14:17:02.747263Z", - "iopub.status.idle": "2024-05-24T14:17:02.750591Z", - "shell.execute_reply": "2024-05-24T14:17:02.750026Z" + "iopub.execute_input": "2024-05-24T14:47:13.869842Z", + "iopub.status.busy": "2024-05-24T14:47:13.869515Z", + "iopub.status.idle": "2024-05-24T14:47:13.872780Z", + "shell.execute_reply": "2024-05-24T14:47:13.872262Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.752711Z", - "iopub.status.busy": "2024-05-24T14:17:02.752316Z", - "iopub.status.idle": "2024-05-24T14:17:02.755447Z", - "shell.execute_reply": "2024-05-24T14:17:02.754903Z" + "iopub.execute_input": "2024-05-24T14:47:13.875008Z", + "iopub.status.busy": "2024-05-24T14:47:13.874481Z", + "iopub.status.idle": "2024-05-24T14:47:13.877784Z", + "shell.execute_reply": "2024-05-24T14:47:13.877349Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.757486Z", - "iopub.status.busy": "2024-05-24T14:17:02.757069Z", - "iopub.status.idle": "2024-05-24T14:17:02.765120Z", - "shell.execute_reply": "2024-05-24T14:17:02.764572Z" + "iopub.execute_input": "2024-05-24T14:47:13.879774Z", + "iopub.status.busy": "2024-05-24T14:47:13.879442Z", + "iopub.status.idle": "2024-05-24T14:47:13.887941Z", + "shell.execute_reply": "2024-05-24T14:47:13.887375Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.767095Z", - "iopub.status.busy": "2024-05-24T14:17:02.766782Z", - "iopub.status.idle": "2024-05-24T14:17:02.769456Z", - "shell.execute_reply": "2024-05-24T14:17:02.768910Z" + "iopub.execute_input": "2024-05-24T14:47:13.890345Z", + "iopub.status.busy": "2024-05-24T14:47:13.889921Z", + "iopub.status.idle": "2024-05-24T14:47:13.892738Z", + "shell.execute_reply": "2024-05-24T14:47:13.892285Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.771610Z", - "iopub.status.busy": "2024-05-24T14:17:02.771306Z", - "iopub.status.idle": "2024-05-24T14:17:02.892264Z", - "shell.execute_reply": "2024-05-24T14:17:02.891701Z" + "iopub.execute_input": "2024-05-24T14:47:13.894992Z", + "iopub.status.busy": "2024-05-24T14:47:13.894656Z", + "iopub.status.idle": "2024-05-24T14:47:14.017529Z", + "shell.execute_reply": "2024-05-24T14:47:14.016936Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:02.894664Z", - "iopub.status.busy": "2024-05-24T14:17:02.894275Z", - "iopub.status.idle": "2024-05-24T14:17:03.002392Z", - "shell.execute_reply": "2024-05-24T14:17:03.001779Z" + "iopub.execute_input": "2024-05-24T14:47:14.019893Z", + "iopub.status.busy": "2024-05-24T14:47:14.019511Z", + "iopub.status.idle": "2024-05-24T14:47:14.137961Z", + "shell.execute_reply": "2024-05-24T14:47:14.137305Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:03.004872Z", - "iopub.status.busy": "2024-05-24T14:17:03.004510Z", - "iopub.status.idle": "2024-05-24T14:17:03.493374Z", - "shell.execute_reply": "2024-05-24T14:17:03.492823Z" + "iopub.execute_input": "2024-05-24T14:47:14.140894Z", + "iopub.status.busy": "2024-05-24T14:47:14.140463Z", + "iopub.status.idle": "2024-05-24T14:47:14.633843Z", + "shell.execute_reply": "2024-05-24T14:47:14.633289Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:03.496039Z", - "iopub.status.busy": "2024-05-24T14:17:03.495639Z", - "iopub.status.idle": "2024-05-24T14:17:03.573307Z", - "shell.execute_reply": "2024-05-24T14:17:03.572719Z" + "iopub.execute_input": "2024-05-24T14:47:14.636657Z", + "iopub.status.busy": "2024-05-24T14:47:14.636193Z", + "iopub.status.idle": "2024-05-24T14:47:14.716923Z", + "shell.execute_reply": "2024-05-24T14:47:14.716269Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:03.575691Z", - "iopub.status.busy": "2024-05-24T14:17:03.575320Z", - "iopub.status.idle": "2024-05-24T14:17:03.584382Z", - "shell.execute_reply": "2024-05-24T14:17:03.583811Z" + "iopub.execute_input": "2024-05-24T14:47:14.719317Z", + "iopub.status.busy": "2024-05-24T14:47:14.718954Z", + "iopub.status.idle": "2024-05-24T14:47:14.727562Z", + "shell.execute_reply": "2024-05-24T14:47:14.727095Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:03.586594Z", - "iopub.status.busy": "2024-05-24T14:17:03.586417Z", - "iopub.status.idle": "2024-05-24T14:17:03.589005Z", - "shell.execute_reply": "2024-05-24T14:17:03.588558Z" + "iopub.execute_input": "2024-05-24T14:47:14.729643Z", + "iopub.status.busy": "2024-05-24T14:47:14.729308Z", + "iopub.status.idle": "2024-05-24T14:47:14.732057Z", + "shell.execute_reply": "2024-05-24T14:47:14.731603Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:03.590942Z", - "iopub.status.busy": "2024-05-24T14:17:03.590652Z", - "iopub.status.idle": "2024-05-24T14:17:09.050544Z", - "shell.execute_reply": "2024-05-24T14:17:09.049862Z" + "iopub.execute_input": "2024-05-24T14:47:14.734094Z", + "iopub.status.busy": "2024-05-24T14:47:14.733774Z", + "iopub.status.idle": "2024-05-24T14:47:20.229441Z", + "shell.execute_reply": "2024-05-24T14:47:20.228821Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:09.052925Z", - "iopub.status.busy": "2024-05-24T14:17:09.052604Z", - "iopub.status.idle": "2024-05-24T14:17:09.061472Z", - "shell.execute_reply": "2024-05-24T14:17:09.060902Z" + "iopub.execute_input": "2024-05-24T14:47:20.231843Z", + "iopub.status.busy": "2024-05-24T14:47:20.231472Z", + "iopub.status.idle": "2024-05-24T14:47:20.240396Z", + "shell.execute_reply": "2024-05-24T14:47:20.239835Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:09.063841Z", - "iopub.status.busy": "2024-05-24T14:17:09.063436Z", - "iopub.status.idle": "2024-05-24T14:17:09.128851Z", - "shell.execute_reply": "2024-05-24T14:17:09.128343Z" + "iopub.execute_input": "2024-05-24T14:47:20.242411Z", + "iopub.status.busy": "2024-05-24T14:47:20.242232Z", + "iopub.status.idle": "2024-05-24T14:47:20.308134Z", + "shell.execute_reply": "2024-05-24T14:47:20.307473Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index ec43b5fec..7c054d311 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": "2024-05-24T14:17:12.098975Z", - "iopub.status.busy": "2024-05-24T14:17:12.098794Z", - "iopub.status.idle": "2024-05-24T14:17:13.187472Z", - "shell.execute_reply": "2024-05-24T14:17:13.186859Z" + "iopub.execute_input": "2024-05-24T14:47:23.751320Z", + "iopub.status.busy": "2024-05-24T14:47:23.751138Z", + "iopub.status.idle": "2024-05-24T14:47:25.225197Z", + "shell.execute_reply": "2024-05-24T14:47:25.224396Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:13.190034Z", - "iopub.status.busy": "2024-05-24T14:17:13.189840Z", - "iopub.status.idle": "2024-05-24T14:17:56.244531Z", - "shell.execute_reply": "2024-05-24T14:17:56.243884Z" + "iopub.execute_input": "2024-05-24T14:47:25.228167Z", + "iopub.status.busy": "2024-05-24T14:47:25.227876Z", + "iopub.status.idle": "2024-05-24T14:48:06.900638Z", + "shell.execute_reply": "2024-05-24T14:48:06.899939Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:56.246941Z", - "iopub.status.busy": "2024-05-24T14:17:56.246597Z", - "iopub.status.idle": "2024-05-24T14:17:57.361537Z", - "shell.execute_reply": "2024-05-24T14:17:57.360976Z" + "iopub.execute_input": "2024-05-24T14:48:06.903639Z", + "iopub.status.busy": "2024-05-24T14:48:06.903118Z", + "iopub.status.idle": "2024-05-24T14:48:08.159074Z", + "shell.execute_reply": "2024-05-24T14:48:08.158455Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:17:57.363963Z", - "iopub.status.busy": "2024-05-24T14:17:57.363656Z", - "iopub.status.idle": "2024-05-24T14:17:57.366860Z", - "shell.execute_reply": "2024-05-24T14:17:57.366396Z" + "iopub.execute_input": "2024-05-24T14:48:08.161720Z", + "iopub.status.busy": "2024-05-24T14:48:08.161355Z", + "iopub.status.idle": "2024-05-24T14:48:08.164937Z", + "shell.execute_reply": "2024-05-24T14:48:08.164437Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.368955Z", - "iopub.status.busy": "2024-05-24T14:17:57.368625Z", - "iopub.status.idle": "2024-05-24T14:17:57.372418Z", - "shell.execute_reply": "2024-05-24T14:17:57.371945Z" + "iopub.execute_input": "2024-05-24T14:48:08.167013Z", + "iopub.status.busy": "2024-05-24T14:48:08.166830Z", + "iopub.status.idle": "2024-05-24T14:48:08.171171Z", + "shell.execute_reply": "2024-05-24T14:48:08.170623Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.374586Z", - "iopub.status.busy": "2024-05-24T14:17:57.374186Z", - "iopub.status.idle": "2024-05-24T14:17:57.377890Z", - "shell.execute_reply": "2024-05-24T14:17:57.377442Z" + "iopub.execute_input": "2024-05-24T14:48:08.173563Z", + "iopub.status.busy": "2024-05-24T14:48:08.173189Z", + "iopub.status.idle": "2024-05-24T14:48:08.177196Z", + "shell.execute_reply": "2024-05-24T14:48:08.176714Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.379908Z", - "iopub.status.busy": "2024-05-24T14:17:57.379577Z", - "iopub.status.idle": "2024-05-24T14:17:57.382337Z", - "shell.execute_reply": "2024-05-24T14:17:57.381872Z" + "iopub.execute_input": "2024-05-24T14:48:08.179576Z", + "iopub.status.busy": "2024-05-24T14:48:08.179106Z", + "iopub.status.idle": "2024-05-24T14:48:08.182081Z", + "shell.execute_reply": "2024-05-24T14:48:08.181644Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.384391Z", - "iopub.status.busy": "2024-05-24T14:17:57.384059Z", - "iopub.status.idle": "2024-05-24T14:18:32.166810Z", - "shell.execute_reply": "2024-05-24T14:18:32.166191Z" + "iopub.execute_input": "2024-05-24T14:48:08.184202Z", + "iopub.status.busy": "2024-05-24T14:48:08.184001Z", + "iopub.status.idle": "2024-05-24T14:48:43.717252Z", + "shell.execute_reply": "2024-05-24T14:48:43.716631Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b925d1eb16a4c0da4908e6c9ae20ddc", + "model_id": "3672ba1a4e5b46fc9b75c6e4927b369a", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e91fb6e7e4e41419c598fed5e03305a", + "model_id": "382983ea361a4f5a866d4601f539f91f", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:18:32.169305Z", - "iopub.status.busy": "2024-05-24T14:18:32.169043Z", - "iopub.status.idle": "2024-05-24T14:18:32.842974Z", - "shell.execute_reply": "2024-05-24T14:18:32.842486Z" + "iopub.execute_input": "2024-05-24T14:48:43.719940Z", + "iopub.status.busy": "2024-05-24T14:48:43.719611Z", + "iopub.status.idle": "2024-05-24T14:48:44.423578Z", + "shell.execute_reply": "2024-05-24T14:48:44.422996Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:18:32.845116Z", - "iopub.status.busy": "2024-05-24T14:18:32.844829Z", - "iopub.status.idle": "2024-05-24T14:18:35.564158Z", - "shell.execute_reply": "2024-05-24T14:18:35.563568Z" + "iopub.execute_input": "2024-05-24T14:48:44.426212Z", + "iopub.status.busy": "2024-05-24T14:48:44.425592Z", + "iopub.status.idle": "2024-05-24T14:48:47.282615Z", + "shell.execute_reply": "2024-05-24T14:48:47.281970Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:18:35.566509Z", - "iopub.status.busy": "2024-05-24T14:18:35.566169Z", - "iopub.status.idle": "2024-05-24T14:19:08.319151Z", - "shell.execute_reply": "2024-05-24T14:19:08.318671Z" + "iopub.execute_input": "2024-05-24T14:48:47.285179Z", + "iopub.status.busy": "2024-05-24T14:48:47.284689Z", + "iopub.status.idle": "2024-05-24T14:49:20.452475Z", + "shell.execute_reply": "2024-05-24T14:49:20.451973Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e49d3ef85b404defa7f4963e4e6629e0", + "model_id": "e16af912ee194d9193fa6f36331fb731", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:08.321371Z", - "iopub.status.busy": "2024-05-24T14:19:08.321035Z", - "iopub.status.idle": "2024-05-24T14:19:22.644386Z", - "shell.execute_reply": "2024-05-24T14:19:22.643806Z" + "iopub.execute_input": "2024-05-24T14:49:20.454694Z", + "iopub.status.busy": "2024-05-24T14:49:20.454407Z", + "iopub.status.idle": "2024-05-24T14:49:35.504423Z", + "shell.execute_reply": "2024-05-24T14:49:35.503798Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:22.647038Z", - "iopub.status.busy": "2024-05-24T14:19:22.646609Z", - "iopub.status.idle": "2024-05-24T14:19:26.330344Z", - "shell.execute_reply": "2024-05-24T14:19:26.329816Z" + "iopub.execute_input": "2024-05-24T14:49:35.507033Z", + "iopub.status.busy": "2024-05-24T14:49:35.506803Z", + "iopub.status.idle": "2024-05-24T14:49:39.497973Z", + "shell.execute_reply": "2024-05-24T14:49:39.497351Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:26.332507Z", - "iopub.status.busy": "2024-05-24T14:19:26.332175Z", - "iopub.status.idle": "2024-05-24T14:19:27.746150Z", - "shell.execute_reply": "2024-05-24T14:19:27.745559Z" + "iopub.execute_input": "2024-05-24T14:49:39.500229Z", + "iopub.status.busy": "2024-05-24T14:49:39.499893Z", + "iopub.status.idle": "2024-05-24T14:49:41.103191Z", + "shell.execute_reply": "2024-05-24T14:49:41.102597Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72283dda082f4ec0939a16c2b153ce67", + "model_id": "545e865005cc44008d0627654a77a00f", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:27.748653Z", - "iopub.status.busy": "2024-05-24T14:19:27.748259Z", - "iopub.status.idle": "2024-05-24T14:19:27.778181Z", - "shell.execute_reply": "2024-05-24T14:19:27.777661Z" + "iopub.execute_input": "2024-05-24T14:49:41.105631Z", + "iopub.status.busy": "2024-05-24T14:49:41.105265Z", + "iopub.status.idle": "2024-05-24T14:49:41.143677Z", + "shell.execute_reply": "2024-05-24T14:49:41.143107Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:27.780781Z", - "iopub.status.busy": "2024-05-24T14:19:27.780417Z", - "iopub.status.idle": "2024-05-24T14:19:33.885804Z", - "shell.execute_reply": "2024-05-24T14:19:33.885211Z" + "iopub.execute_input": "2024-05-24T14:49:41.146408Z", + "iopub.status.busy": "2024-05-24T14:49:41.145977Z", + "iopub.status.idle": "2024-05-24T14:49:47.552406Z", + "shell.execute_reply": "2024-05-24T14:49:47.551769Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:33.888133Z", - "iopub.status.busy": "2024-05-24T14:19:33.887792Z", - "iopub.status.idle": "2024-05-24T14:19:33.943798Z", - "shell.execute_reply": "2024-05-24T14:19:33.943306Z" + "iopub.execute_input": "2024-05-24T14:49:47.554765Z", + "iopub.status.busy": "2024-05-24T14:49:47.554389Z", + "iopub.status.idle": "2024-05-24T14:49:47.611617Z", + "shell.execute_reply": "2024-05-24T14:49:47.611045Z" }, "nbsphinx": "hidden" }, @@ -1038,25 +1038,60 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "024d84969eb0452e826811c05b19f723": { - "model_module": "@jupyter-widgets/controls", + "0440cad43c2a498fbd42bce297cdc34f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_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 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0b39b8abd42c455baba1e06abb8a2329": { + "04cd61cf624d403b860482e547b93418": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1074,7 +1109,7 @@ "text_color": null } }, - "0e8e060c4b254e9aa5d9aee1d4552593": { + "04fcab59994944f9aebbb74313208b51": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1089,15 +1124,15 @@ "_view_name": "HTMLView", "description": "", 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https://data.deepai.org/conll2003.zip\r\n", + "--2024-05-24 14:49:50-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,21 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.247, 2400:52e0:1a00::845:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" + "185.93.1.246, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -129,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.90MB/s in 0.2s \r\n", "\r\n", - "2024-05-24 14:19:36 (7.26 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-05-24 14:49:50 (5.90 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -151,9 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-24 14:19:37-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.219.169, 3.5.11.148, 3.5.27.172, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.219.169|:443... connected.\r\n", + "--2024-05-24 14:49:51-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.96.179, 52.217.49.108, 52.217.233.1, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.96.179|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,9 +168,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", + "pred_probs.npz 96%[==================> ] 15.71M 70.4MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 71.8MB/s in 0.2s \r\n", "\r\n", - "2024-05-24 14:19:37 (160 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-05-24 14:49:51 (71.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -193,10 +188,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:37.433469Z", - "iopub.status.busy": "2024-05-24T14:19:37.433289Z", - "iopub.status.idle": "2024-05-24T14:19:38.693567Z", - "shell.execute_reply": "2024-05-24T14:19:38.693013Z" + "iopub.execute_input": "2024-05-24T14:49:51.547444Z", + "iopub.status.busy": "2024-05-24T14:49:51.547170Z", + "iopub.status.idle": "2024-05-24T14:49:53.068431Z", + "shell.execute_reply": "2024-05-24T14:49:53.067845Z" }, "nbsphinx": "hidden" }, @@ -207,7 +202,7 @@ "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -233,10 +228,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.696151Z", - "iopub.status.busy": "2024-05-24T14:19:38.695736Z", - "iopub.status.idle": "2024-05-24T14:19:38.698953Z", - "shell.execute_reply": "2024-05-24T14:19:38.698536Z" + "iopub.execute_input": "2024-05-24T14:49:53.071590Z", + "iopub.status.busy": "2024-05-24T14:49:53.070873Z", + "iopub.status.idle": "2024-05-24T14:49:53.074970Z", + "shell.execute_reply": "2024-05-24T14:49:53.074360Z" } }, "outputs": [], @@ -286,10 +281,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.700791Z", - "iopub.status.busy": "2024-05-24T14:19:38.700617Z", - "iopub.status.idle": "2024-05-24T14:19:38.703466Z", - "shell.execute_reply": "2024-05-24T14:19:38.703042Z" + "iopub.execute_input": "2024-05-24T14:49:53.077640Z", + "iopub.status.busy": "2024-05-24T14:49:53.077268Z", + "iopub.status.idle": "2024-05-24T14:49:53.080839Z", + "shell.execute_reply": "2024-05-24T14:49:53.080227Z" }, "nbsphinx": "hidden" }, @@ -307,10 +302,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.705221Z", - "iopub.status.busy": "2024-05-24T14:19:38.705050Z", - "iopub.status.idle": "2024-05-24T14:19:47.741655Z", - "shell.execute_reply": "2024-05-24T14:19:47.741082Z" + "iopub.execute_input": "2024-05-24T14:49:53.083196Z", + "iopub.status.busy": "2024-05-24T14:49:53.082885Z", + "iopub.status.idle": "2024-05-24T14:50:02.197841Z", + "shell.execute_reply": "2024-05-24T14:50:02.197236Z" } }, "outputs": [], @@ -384,10 +379,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:47.744245Z", - "iopub.status.busy": "2024-05-24T14:19:47.743798Z", - "iopub.status.idle": "2024-05-24T14:19:47.749434Z", - "shell.execute_reply": "2024-05-24T14:19:47.748970Z" + "iopub.execute_input": "2024-05-24T14:50:02.200838Z", + "iopub.status.busy": "2024-05-24T14:50:02.200415Z", + "iopub.status.idle": "2024-05-24T14:50:02.206637Z", + "shell.execute_reply": "2024-05-24T14:50:02.206064Z" }, "nbsphinx": "hidden" }, @@ -427,10 +422,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:47.751530Z", - "iopub.status.busy": "2024-05-24T14:19:47.751102Z", - "iopub.status.idle": "2024-05-24T14:19:48.096364Z", - "shell.execute_reply": "2024-05-24T14:19:48.095809Z" + "iopub.execute_input": "2024-05-24T14:50:02.209145Z", + "iopub.status.busy": "2024-05-24T14:50:02.208748Z", + "iopub.status.idle": "2024-05-24T14:50:02.638158Z", + "shell.execute_reply": "2024-05-24T14:50:02.637534Z" } }, "outputs": [], @@ -467,10 +462,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:48.098890Z", - "iopub.status.busy": "2024-05-24T14:19:48.098535Z", - "iopub.status.idle": "2024-05-24T14:19:48.103037Z", - "shell.execute_reply": "2024-05-24T14:19:48.102561Z" + "iopub.execute_input": "2024-05-24T14:50:02.641122Z", + "iopub.status.busy": "2024-05-24T14:50:02.640673Z", + "iopub.status.idle": "2024-05-24T14:50:02.645692Z", + "shell.execute_reply": "2024-05-24T14:50:02.644970Z" } }, "outputs": [ @@ -542,10 +537,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:48.105040Z", - "iopub.status.busy": "2024-05-24T14:19:48.104729Z", - "iopub.status.idle": "2024-05-24T14:19:50.453604Z", - "shell.execute_reply": "2024-05-24T14:19:50.452960Z" + "iopub.execute_input": "2024-05-24T14:50:02.648427Z", + "iopub.status.busy": "2024-05-24T14:50:02.647997Z", + "iopub.status.idle": "2024-05-24T14:50:05.427039Z", + "shell.execute_reply": "2024-05-24T14:50:05.426330Z" } }, "outputs": [], @@ -567,10 +562,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.456521Z", - "iopub.status.busy": "2024-05-24T14:19:50.455956Z", - "iopub.status.idle": "2024-05-24T14:19:50.460042Z", - "shell.execute_reply": "2024-05-24T14:19:50.459521Z" + "iopub.execute_input": "2024-05-24T14:50:05.430476Z", + "iopub.status.busy": "2024-05-24T14:50:05.429663Z", + "iopub.status.idle": "2024-05-24T14:50:05.434064Z", + "shell.execute_reply": "2024-05-24T14:50:05.433453Z" } }, "outputs": [ @@ -606,10 +601,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.462133Z", - "iopub.status.busy": "2024-05-24T14:19:50.461793Z", - "iopub.status.idle": "2024-05-24T14:19:50.466784Z", - "shell.execute_reply": "2024-05-24T14:19:50.466230Z" + "iopub.execute_input": "2024-05-24T14:50:05.436615Z", + "iopub.status.busy": "2024-05-24T14:50:05.436042Z", + "iopub.status.idle": "2024-05-24T14:50:05.442446Z", + "shell.execute_reply": "2024-05-24T14:50:05.441825Z" } }, "outputs": [ @@ -787,10 +782,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.468850Z", - "iopub.status.busy": "2024-05-24T14:19:50.468522Z", - "iopub.status.idle": "2024-05-24T14:19:50.494333Z", - "shell.execute_reply": "2024-05-24T14:19:50.493853Z" + "iopub.execute_input": "2024-05-24T14:50:05.444740Z", + "iopub.status.busy": "2024-05-24T14:50:05.444488Z", + "iopub.status.idle": "2024-05-24T14:50:05.475833Z", + "shell.execute_reply": "2024-05-24T14:50:05.475164Z" } }, "outputs": [ @@ -892,10 +887,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.496514Z", - "iopub.status.busy": "2024-05-24T14:19:50.496124Z", - "iopub.status.idle": "2024-05-24T14:19:50.500485Z", - "shell.execute_reply": "2024-05-24T14:19:50.499944Z" + "iopub.execute_input": "2024-05-24T14:50:05.478414Z", + "iopub.status.busy": "2024-05-24T14:50:05.477949Z", + "iopub.status.idle": "2024-05-24T14:50:05.484291Z", + "shell.execute_reply": "2024-05-24T14:50:05.483674Z" } }, "outputs": [ @@ -969,10 +964,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.502462Z", - "iopub.status.busy": "2024-05-24T14:19:50.502137Z", - "iopub.status.idle": "2024-05-24T14:19:51.874676Z", - "shell.execute_reply": "2024-05-24T14:19:51.874134Z" + "iopub.execute_input": "2024-05-24T14:50:05.486636Z", + "iopub.status.busy": "2024-05-24T14:50:05.486295Z", + "iopub.status.idle": "2024-05-24T14:50:07.075328Z", + "shell.execute_reply": "2024-05-24T14:50:07.074681Z" } }, "outputs": [ @@ -1144,10 +1139,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:51.876917Z", - "iopub.status.busy": "2024-05-24T14:19:51.876550Z", - "iopub.status.idle": "2024-05-24T14:19:51.880651Z", - "shell.execute_reply": "2024-05-24T14:19:51.880202Z" + "iopub.execute_input": "2024-05-24T14:50:07.078297Z", + "iopub.status.busy": "2024-05-24T14:50:07.077744Z", + "iopub.status.idle": "2024-05-24T14:50:07.082976Z", + "shell.execute_reply": "2024-05-24T14:50:07.082309Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 63ef7492ad35b61696627bf71addfaf9f470ff36..b06d94c86d7077302f1f81475b35ea7d987bfd13 100644 GIT binary patch delta 62 zcmX>tep-A(E~8&=6Q^|TmWKv5&r-H delta 62 zcmX>tep-A(E~8;ag>jxqrbWJfvT?F$nxToMu~|~GSz@w*iG`_=simQ%iIH)tX{xay QP$<#Rz%+65JVsqE0AEKE4gdfE diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index 432b23a02647ca23c44c445af24a30d80be5707b..642b7051075e2acec0385e099faf3fb90344f692 100644 GIT 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Source code for cleanlab.internal.neighbor.knn_graph

 from __future__ import annotations
 from typing import List, Optional, TYPE_CHECKING, Tuple
-import warnings
 
 import numpy as np
 from scipy.sparse import csr_matrix
@@ -1085,47 +1084,11 @@ 
Source code for cleanlab.internal.neighbor.knn_graph
return sorted_first_k_duplicate_inds
 
 
-def _warn_missing_exact_duplicates(indices: np.ndarray, exact_duplicate_sets: list) -> None:
-    """Go through all sets of points in the sets of exact duplicates, and ensure that none are missing in the indices array.
-
-    Raises
-    ------
-    UserWarning :
-        A warning may be raised if there were some slots available for an exact duplicate that were missed.
-        This may happen if the number of exact duplicates in the existing KNN graph is lower than k,
-        but the set of exact duplicates is larger than what was included in the KNN graph.
-    """
-    # The number of neighbors to consider
-    k = indices.shape[0]
-
-    points_missing_exact_duplicates = []
-    for duplicate_inds in exact_duplicate_sets:
-        for i in duplicate_inds:
-            inds = indices[i]
-
-            same_point_indices = np.setdiff1d(duplicate_inds, i)
-
-            # Figure out how many were already included in the original knn graph
-            pre_existing_same_point_indices = np.intersect1d(same_point_indices, inds)
-
-            # Optionally warn the user if there are more identical points than slots available in the existing knn graph
-            same_point_indices_set_is_larger = len(pre_existing_same_point_indices) < len(
-                same_point_indices
-            )
-            slots_larger = len(pre_existing_same_point_indices) < k
-            if same_point_indices_set_is_larger and slots_larger:
-                points_missing_exact_duplicates.append(i)
-
-    if points_missing_exact_duplicates:
-        warnings.warn("There were some slots available for an exact duplicate that were missed.")
-
-
 
[docs]def correct_knn_distances_and_indices(
     features: FeatureArray,
     distances: np.ndarray,
     indices: np.ndarray,
     exact_duplicate_sets: Optional[List[np.ndarray]] = None,
-    enable_warning: bool = False,
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Corrects the distances and indices of a k-nearest neighbors (KNN) graph
@@ -1141,8 +1104,6 @@ 
Source code for cleanlab.internal.neighbor.knn_graph
        The indices of the k nearest neighbors for each point.
     exact_duplicate_sets:
         A list of numpy arrays, where each array contains the indices of exact duplicates in the feature array. If not provided, it will be computed from the feature array.
-    enable_warning :
-        Whether to enable warning messages if any row underestimates the number of exact duplicates.
 
     Returns
     -------
@@ -1151,15 +1112,6 @@ 
Source code for cleanlab.internal.neighbor.knn_graph
    corrected_indices :
         The corrected indices of the k nearest neighbors for each point. Exact duplicates are ensured to be included in the k nearest neighbors, unless the number of exact duplicates exceeds k.
 
-    Raises
-    ------
-    UserWarning :
-        A warning may be raised if there were some slots available for an exact duplicate that were missed.
-        This may happen if the number of exact duplicates in the existing KNN graph is lower than k,
-        but the set of exact duplicates is larger than what was included in the KNN graph.
-        This warning may be disabled by setting enable_warning=False.
-
-
     Example
     -------
     >>> import numpy as np
@@ -1189,20 +1141,11 @@ 
Source code for cleanlab.internal.neighbor.knn_graph
    array([[0.], [0.], [1.41421356]])
     >>> corrected_indices
     array([[1], [0], [0]])
-
-
-    Clearly, the first point misses its exact duplicate in the KNN graph. To raise a warning for such cases, set enable_warning=True.
-
-    >>> corrected_distances, corrected_indices = correct_knn_distances_and_indices(X, distances, indices, enable_warning=True)
-    UserWarning: There were some slots available for an exact duplicate that were missed.
     """
 
     if exact_duplicate_sets is None:
         exact_duplicate_sets = _compute_exact_duplicate_sets(features)
 
-    if enable_warning:
-        _warn_missing_exact_duplicates(indices, exact_duplicate_sets)
-
     # Prepare the output arrays
     corrected_distances = np.copy(distances)
     corrected_indices = np.copy(indices)
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 342c09192..603f34f5f 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index 9988a7dde..51aeae808 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 92880598a..c365ffb16 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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/data_monitor.ipynb b/master/_sources/tutorials/datalab/data_monitor.ipynb
index 5cde8f1e8..0ce8c1c80 100644
--- a/master/_sources/tutorials/datalab/data_monitor.ipynb
+++ b/master/_sources/tutorials/datalab/data_monitor.ipynb
@@ -83,7 +83,7 @@
     "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 49ddb70d1..fe2f723fc 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 54b13fc15..3ff0b13c1 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 5ce0fefcd..4c4d23729 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 ce84077a6..d2f8ba229 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 4b6e07cda..1b208f7cb 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 0f9e81e4e..b2cabe536 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 66c071254..6ed37b0aa 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 39cfd67eb..ca55af704 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 825b1365a..d3385f068 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 5cf24eb78..a3a353f89 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 f9cb78905..0d5d692da 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 31331c392..05b9d7e16 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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 c5aa95ecf..477a7e275 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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/cleanlab/internal/neighbor/knn_graph.html b/master/cleanlab/internal/neighbor/knn_graph.html
index 390d8eb5b..4f48703c8 100644
--- a/master/cleanlab/internal/neighbor/knn_graph.html
+++ b/master/cleanlab/internal/neighbor/knn_graph.html
@@ -876,7 +876,7 @@
 
 

 

-cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices(features, distances, indices, exact_duplicate_sets=None, enable_warning=False)[source]#

+cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices(features, distances, indices, exact_duplicate_sets=None)[source]#
 
Corrects the distances and indices of a k-nearest neighbors (KNN) graph
 based on all exact duplicates detected in the feature array.

 

@@ -886,7 +886,6 @@
 
  • distances (ndarray) – The distances between each point and its k nearest neighbors.
    • 
 
  • indices (ndarray) – The indices of the k nearest neighbors for each point.
    • 
 
  • exact_duplicate_sets (Optional[List[ndarray]]) – A list of numpy arrays, where each array contains the indices of exact duplicates in the feature array. If not provided, it will be computed from the feature array.
    • 
-
  • enable_warning (bool) – Whether to enable warning messages if any row underestimates the number of exact duplicates.
    • 
 
 
    
 
    Return type:
    
@@ -899,12 +898,6 @@
 
 
    
 
-
    Raises:
    
-
    UserWarning : – A warning may be raised if there were some slots available for an exact duplicate that were missed.
-    This may happen if the number of exact duplicates in the existing KNN graph is lower than k,
-    but the set of exact duplicates is larger than what was included in the KNN graph.
-    This warning may be disabled by setting enable_warning=False.
    
-
    
 
    
 
    Example
    
 
    >>> import numpy as np
@@ -936,11 +929,6 @@
 array([[1], [0], [0]])
 
    
 
    
-
    Clearly, the first point misses its exact duplicate in the KNN graph. To raise a warning for such cases, set enable_warning=True.
    
-
    >>> corrected_distances, corrected_indices = correct_knn_distances_and_indices(X, distances, indices, enable_warning=True)
-UserWarning: There were some slots available for an exact duplicate that were missed.
-
    
-
    
 
 
 
diff --git a/master/searchindex.js b/master/searchindex.js
index de3de28cf..2601923a6 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", 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"cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/data_monitor", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", 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"cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Label Issue": [[10, "label-issue"]], "Outlier Issue": [[10, "outlier-issue"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "Non-IID Issue": [[10, "non-iid-issue"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "Null Issue": [[10, "null-issue"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, 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[[34, "task"]], "dataset": [[36, "module-cleanlab.dataset"], [62, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "experimental": [[39, "experimental"]], "label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "filter": [[43, "module-cleanlab.filter"], [63, "module-cleanlab.multilabel_classification.filter"], [66, "filter"], [75, "filter"], [79, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[50, "neighbor"]], "knn_graph": [[51, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[52, "module-cleanlab.internal.neighbor.metric"]], "search": [[53, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[55, "module-cleanlab.internal.token_classification_utils"]], "util": [[56, "module-cleanlab.internal.util"]], "validation": [[57, "module-cleanlab.internal.validation"]], "fasttext": [[58, "fasttext"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [105, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [105, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [105, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[89, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[89, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [100, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[89, "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": [[89, "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": [[89, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[89, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[89, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "7. Finding outliers in new data": [[89, "7.-Finding-outliers-in-new-data"]], "8. Looking for both label issues and outliers": [[89, "8.-Looking-for-both-label-issues-and-outliers"]], "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"]], "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)"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [98, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[97, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[98, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[98, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[98, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[98, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[98, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[98, "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.": [[98, "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": [[98, "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": [[98, "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!": [[98, "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": [[98, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[98, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[98, "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)": [[98, "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:": [[98, "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": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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?": [[98, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[98, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[99, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[100, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[100, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[100, "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": [[100, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[100, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[100, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[100, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[100, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[100, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[101, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[101, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[101, "2.-Format-data,-labels,-and-model-predictions"], [102, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[101, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [106, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[101, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[101, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[101, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[101, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[101, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[102, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[102, "1.-Install-required-dependencies-and-download-data"], [106, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[102, "Get-label-quality-scores"], [106, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[102, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[102, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[102, "Other-uses-of-visualize"]], "Exploratory data analysis": [[102, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[103, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[103, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[103, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[103, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[103, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[103, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[104, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[104, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[104, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[105, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[105, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[105, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[106, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[106, "2.-Get-data,-labels,-and-pred_probs"], [107, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[106, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[106, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[106, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[107, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[107, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[107, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[107, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[107, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Label Issue": [[10, "label-issue"]], "Outlier Issue": [[10, "outlier-issue"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "Non-IID Issue": [[10, "non-iid-issue"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "Null Issue": [[10, "null-issue"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[11, "getting-started"]], "Guides": [[11, "guides"]], "API Reference": [[11, "api-reference"]], "data": [[12, "module-cleanlab.datalab.internal.data"]], "data_issues": [[13, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[14, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[15, "internal"], [44, "internal"]], "issue_finder": [[16, "issue-finder"]], "duplicate": [[19, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[20, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[21, "issue-manager"], [22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[21, "registered-issue-managers"]], "ML task-specific issue managers": [[21, "ml-task-specific-issue-managers"]], "label": [[23, "module-cleanlab.datalab.internal.issue_manager.label"], [25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [30, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[24, "multilabel"]], "noniid": [[26, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[27, "null"]], "outlier": [[28, "module-cleanlab.datalab.internal.issue_manager.outlier"], [54, "module-cleanlab.internal.outlier"], [70, "module-cleanlab.outlier"]], "regression": [[29, "regression"], [72, "regression"]], "Priority Order for finding issues:": [[30, null]], "underperforming_group": [[31, "underperforming-group"]], "model_outputs": [[32, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[33, "report"]], "task": [[34, "task"]], "dataset": [[36, "module-cleanlab.dataset"], [62, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "experimental": [[39, "experimental"]], "label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "filter": [[43, "module-cleanlab.filter"], [63, "module-cleanlab.multilabel_classification.filter"], [66, "filter"], [75, "filter"], [79, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[50, "neighbor"]], "knn_graph": [[51, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[52, "module-cleanlab.internal.neighbor.metric"]], "search": [[53, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[55, "module-cleanlab.internal.token_classification_utils"]], "util": [[56, "module-cleanlab.internal.util"]], "validation": [[57, "module-cleanlab.internal.validation"]], "fasttext": [[58, "fasttext"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Find common issues in your data": [[83, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [105, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [105, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [105, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[89, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[89, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [100, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[89, "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": [[89, "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": [[89, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[89, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[89, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "7. Finding outliers in new data": [[89, "7.-Finding-outliers-in-new-data"]], "8. Looking for both label issues and outliers": [[89, "8.-Looking-for-both-label-issues-and-outliers"]], "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"]], "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)"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [98, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[97, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[98, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[98, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[98, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[98, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[98, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[98, "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.": [[98, "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": [[98, "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": [[98, "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!": [[98, "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": [[98, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[98, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[98, "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)": [[98, "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:": [[98, "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": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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.": [[98, "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?": [[98, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[98, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[99, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[100, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[100, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[100, "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": [[100, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[100, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[100, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[100, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[100, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[100, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[101, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[101, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[101, "2.-Format-data,-labels,-and-model-predictions"], [102, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[101, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [106, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[101, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[101, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[101, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[101, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[101, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[102, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[102, "1.-Install-required-dependencies-and-download-data"], [106, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[102, "Get-label-quality-scores"], [106, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[102, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[102, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[102, "Other-uses-of-visualize"]], "Exploratory data analysis": [[102, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[103, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[103, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[103, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[103, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[103, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[103, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[104, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[104, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[104, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[105, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[105, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[105, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[106, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[106, "2.-Get-data,-labels,-and-pred_probs"], [107, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[106, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[106, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[106, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[107, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[107, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[107, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[107, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[107, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[56, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[57, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[57, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[59, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[60, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[60, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[60, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[60, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[61, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[61, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[62, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[62, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[63, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[63, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[64, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[65, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[65, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[66, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[66, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[67, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[68, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[68, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[69, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[69, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[70, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[70, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[70, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[71, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[71, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[71, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[72, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[73, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[73, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[73, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[73, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[74, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[74, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[75, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[75, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[76, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[77, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[77, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[78, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[78, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[79, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[79, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[80, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[81, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[81, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[82, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[82, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 80c985082..8cc902e01 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": "2024-05-24T14:08:26.493704Z",
-     "iopub.status.busy": "2024-05-24T14:08:26.493360Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.679203Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.678594Z"
+     "iopub.execute_input": "2024-05-24T14:38:02.264239Z",
+     "iopub.status.busy": "2024-05-24T14:38:02.264008Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.644677Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.644088Z"
     },
     "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:08:27.682002Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.681482Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.699948Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.699539Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.647964Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.647279Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.667766Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.667215Z"
     }
    },
    "outputs": [],
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:27.702243Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.701841Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.893550Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.893000Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.670956Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.670239Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.784377Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.783768Z"
     }
    },
    "outputs": [
@@ -305,10 +305,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:27.924466Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.924250Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.927908Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.927443Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.817907Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.817331Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.821631Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.821138Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:27.929957Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.929626Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.937567Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.937163Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.823973Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.823607Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.832808Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.832300Z"
     }
    },
    "outputs": [],
@@ -384,10 +384,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:27.939583Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.939260Z",
-     "iopub.status.idle": "2024-05-24T14:08:27.941863Z",
-     "shell.execute_reply": "2024-05-24T14:08:27.941419Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.835418Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.835184Z",
+     "iopub.status.idle": "2024-05-24T14:38:03.838193Z",
+     "shell.execute_reply": "2024-05-24T14:38:03.837715Z"
     }
    },
    "outputs": [],
@@ -409,10 +409,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:27.943877Z",
-     "iopub.status.busy": "2024-05-24T14:08:27.943569Z",
-     "iopub.status.idle": "2024-05-24T14:08:28.460584Z",
-     "shell.execute_reply": "2024-05-24T14:08:28.459965Z"
+     "iopub.execute_input": "2024-05-24T14:38:03.840358Z",
+     "iopub.status.busy": "2024-05-24T14:38:03.840169Z",
+     "iopub.status.idle": "2024-05-24T14:38:04.384563Z",
+     "shell.execute_reply": "2024-05-24T14:38:04.383974Z"
     }
    },
    "outputs": [],
@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:28.463019Z",
-     "iopub.status.busy": "2024-05-24T14:08:28.462840Z",
-     "iopub.status.idle": "2024-05-24T14:08:30.052137Z",
-     "shell.execute_reply": "2024-05-24T14:08:30.051487Z"
+     "iopub.execute_input": "2024-05-24T14:38:04.387188Z",
+     "iopub.status.busy": "2024-05-24T14:38:04.386978Z",
+     "iopub.status.idle": "2024-05-24T14:38:06.298800Z",
+     "shell.execute_reply": "2024-05-24T14:38:06.298193Z"
     }
    },
    "outputs": [
@@ -481,10 +481,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:30.054764Z",
-     "iopub.status.busy": "2024-05-24T14:08:30.054150Z",
-     "iopub.status.idle": "2024-05-24T14:08:30.064272Z",
-     "shell.execute_reply": "2024-05-24T14:08:30.063816Z"
+     "iopub.execute_input": "2024-05-24T14:38:06.301592Z",
+     "iopub.status.busy": "2024-05-24T14:38:06.300905Z",
+     "iopub.status.idle": "2024-05-24T14:38:06.312244Z",
+     "shell.execute_reply": "2024-05-24T14:38:06.311621Z"
     }
    },
    "outputs": [
@@ -605,10 +605,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-24T14:08:30.066384Z",
-     "iopub.status.busy": "2024-05-24T14:08:30.065951Z",
-     "iopub.status.idle": "2024-05-24T14:08:30.069955Z",
-     "shell.execute_reply": "2024-05-24T14:08:30.069422Z"
+     "iopub.execute_input": "2024-05-24T14:38:06.314697Z",
+     "iopub.status.busy": "2024-05-24T14:38:06.314234Z",
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@@ -633,10 +633,10 @@
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    "metadata": {
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@@ -658,10 +658,10 @@
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@@ -691,10 +691,10 @@
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@@ -715,10 +715,10 @@
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@@ -738,10 +738,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-05-24T14:08:32.114469Z"
+     "iopub.execute_input": "2024-05-24T14:38:08.635619Z",
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@@ -771,10 +771,10 @@
    "execution_count": 16,
    "metadata": {
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-     "iopub.execute_input": "2024-05-24T14:08:32.116759Z",
-     "iopub.status.busy": "2024-05-24T14:08:32.116586Z",
-     "iopub.status.idle": "2024-05-24T14:08:32.147680Z",
-     "shell.execute_reply": "2024-05-24T14:08:32.147262Z"
+     "iopub.execute_input": "2024-05-24T14:38:08.652403Z",
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diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 1d4d193cf..0a8fcec5c 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -798,7 +798,7 @@ 
    2. Load and format the text dataset
 
     This dataset has 10 classes.
-Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed'}
+Classes: {'change_pin', 'cancel_transfer', 'getting_spare_card', 'card_payment_fee_charged', 'card_about_to_expire', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard'}
 
    
 
    
 
    Let’s print the first example in the train set.
    
@@ -861,43 +861,43 @@ 
    2. Load and format the text dataset
 
    
 
    
-
    
+
    
 
    
 
    
 
    
 
    
 
    
-
    
+
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    @@ -1196,7 +1196,7 @@

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"2024-05-24T14:08:35.115607Z", - "iopub.status.idle": "2024-05-24T14:08:38.077286Z", - "shell.execute_reply": "2024-05-24T14:08:38.076555Z" + "iopub.execute_input": "2024-05-24T14:38:12.171246Z", + "iopub.status.busy": "2024-05-24T14:38:12.171056Z", + "iopub.status.idle": "2024-05-24T14:38:15.365150Z", + "shell.execute_reply": "2024-05-24T14:38:15.364467Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:08:38.080144Z", - "iopub.status.busy": "2024-05-24T14:08:38.079630Z", - "iopub.status.idle": "2024-05-24T14:08:38.083651Z", - "shell.execute_reply": "2024-05-24T14:08:38.083143Z" + "iopub.execute_input": "2024-05-24T14:38:15.368291Z", + "iopub.status.busy": "2024-05-24T14:38:15.367880Z", + "iopub.status.idle": "2024-05-24T14:38:15.371800Z", + "shell.execute_reply": "2024-05-24T14:38:15.371297Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.085755Z", - "iopub.status.busy": "2024-05-24T14:08:38.085394Z", - "iopub.status.idle": "2024-05-24T14:08:38.089036Z", - "shell.execute_reply": "2024-05-24T14:08:38.088539Z" + "iopub.execute_input": "2024-05-24T14:38:15.373880Z", + "iopub.status.busy": "2024-05-24T14:38:15.373558Z", + "iopub.status.idle": "2024-05-24T14:38:15.376903Z", + "shell.execute_reply": "2024-05-24T14:38:15.376353Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.091351Z", - "iopub.status.busy": "2024-05-24T14:08:38.091029Z", - "iopub.status.idle": "2024-05-24T14:08:38.125892Z", - "shell.execute_reply": "2024-05-24T14:08:38.125420Z" + "iopub.execute_input": "2024-05-24T14:38:15.379040Z", + "iopub.status.busy": "2024-05-24T14:38:15.378763Z", + "iopub.status.idle": "2024-05-24T14:38:15.420024Z", + "shell.execute_reply": "2024-05-24T14:38:15.419401Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.127989Z", - "iopub.status.busy": "2024-05-24T14:08:38.127584Z", - "iopub.status.idle": "2024-05-24T14:08:38.131233Z", - "shell.execute_reply": "2024-05-24T14:08:38.130793Z" + "iopub.execute_input": "2024-05-24T14:38:15.422269Z", + "iopub.status.busy": "2024-05-24T14:38:15.422072Z", + "iopub.status.idle": "2024-05-24T14:38:15.426041Z", + "shell.execute_reply": "2024-05-24T14:38:15.425544Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.133246Z", - "iopub.status.busy": "2024-05-24T14:08:38.132834Z", - "iopub.status.idle": "2024-05-24T14:08:38.136037Z", - "shell.execute_reply": "2024-05-24T14:08:38.135608Z" + "iopub.execute_input": "2024-05-24T14:38:15.428098Z", + "iopub.status.busy": "2024-05-24T14:38:15.427914Z", + "iopub.status.idle": "2024-05-24T14:38:15.431751Z", + "shell.execute_reply": "2024-05-24T14:38:15.431246Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed'}\n" + "Classes: {'change_pin', 'cancel_transfer', 'getting_spare_card', 'card_payment_fee_charged', 'card_about_to_expire', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.137819Z", - "iopub.status.busy": "2024-05-24T14:08:38.137651Z", - "iopub.status.idle": "2024-05-24T14:08:38.140898Z", - "shell.execute_reply": "2024-05-24T14:08:38.140444Z" + "iopub.execute_input": "2024-05-24T14:38:15.433907Z", + "iopub.status.busy": "2024-05-24T14:38:15.433573Z", + "iopub.status.idle": "2024-05-24T14:38:15.437075Z", + "shell.execute_reply": "2024-05-24T14:38:15.436573Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.142964Z", - "iopub.status.busy": "2024-05-24T14:08:38.142558Z", - "iopub.status.idle": "2024-05-24T14:08:38.145918Z", - "shell.execute_reply": "2024-05-24T14:08:38.145330Z" + "iopub.execute_input": "2024-05-24T14:38:15.439170Z", + "iopub.status.busy": "2024-05-24T14:38:15.438838Z", + "iopub.status.idle": "2024-05-24T14:38:15.442193Z", + "shell.execute_reply": "2024-05-24T14:38:15.441741Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:38.147920Z", - "iopub.status.busy": "2024-05-24T14:08:38.147550Z", - "iopub.status.idle": "2024-05-24T14:08:42.333653Z", - "shell.execute_reply": "2024-05-24T14:08:42.332993Z" + "iopub.execute_input": "2024-05-24T14:38:15.444398Z", + "iopub.status.busy": "2024-05-24T14:38:15.444073Z", + "iopub.status.idle": "2024-05-24T14:38:20.697132Z", + "shell.execute_reply": "2024-05-24T14:38:20.696477Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61902606363948da8d5d110cc7550cb1", + "model_id": "f53e9db2d70d48c98d3bc0e8ba7f98c3", "version_major": 2, 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"description": "", "description_allow_html": false, "layout": "IPY_MODEL_49b225c8d6e448c5bbd609161017414f", "placeholder": "\u200b", "style": "IPY_MODEL_ffe46cc511494999b76236397b3c95ba", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200717.1MB/s]"}}, "dc356c9f53224c6a8dc009826a6b6a29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "abc602ff50c54f9d8142d235dc0bffb0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_aa4f5f51e5c44065a27e7af4b4212268", "IPY_MODEL_fe1eea6073a54a6582c3f73596fde981", "IPY_MODEL_61dadabdaa0143739b1f9e8623a0e0c5"], "layout": "IPY_MODEL_dc356c9f53224c6a8dc009826a6b6a29", "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 09eafb085..57eb80416 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": "2024-05-24T14:08:48.995726Z", - "iopub.status.busy": "2024-05-24T14:08:48.995556Z", - "iopub.status.idle": "2024-05-24T14:08:53.517187Z", - "shell.execute_reply": "2024-05-24T14:08:53.516675Z" + "iopub.execute_input": "2024-05-24T14:38:28.042324Z", + "iopub.status.busy": "2024-05-24T14:38:28.041898Z", + "iopub.status.idle": "2024-05-24T14:38:33.240485Z", + "shell.execute_reply": "2024-05-24T14:38:33.239900Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:08:53.519894Z", - "iopub.status.busy": "2024-05-24T14:08:53.519414Z", - "iopub.status.idle": "2024-05-24T14:08:53.522499Z", - "shell.execute_reply": "2024-05-24T14:08:53.522031Z" + "iopub.execute_input": "2024-05-24T14:38:33.243443Z", + "iopub.status.busy": "2024-05-24T14:38:33.242824Z", + "iopub.status.idle": "2024-05-24T14:38:33.246235Z", + "shell.execute_reply": "2024-05-24T14:38:33.245774Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:53.524545Z", - "iopub.status.busy": "2024-05-24T14:08:53.524153Z", - "iopub.status.idle": "2024-05-24T14:08:53.528763Z", - "shell.execute_reply": "2024-05-24T14:08:53.528217Z" + "iopub.execute_input": "2024-05-24T14:38:33.248299Z", + "iopub.status.busy": "2024-05-24T14:38:33.247955Z", + "iopub.status.idle": "2024-05-24T14:38:33.252886Z", + "shell.execute_reply": "2024-05-24T14:38:33.252292Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-24T14:08:53.530968Z", - "iopub.status.busy": "2024-05-24T14:08:53.530655Z", - "iopub.status.idle": "2024-05-24T14:08:55.084232Z", - "shell.execute_reply": "2024-05-24T14:08:55.083611Z" + "iopub.execute_input": "2024-05-24T14:38:33.255233Z", + "iopub.status.busy": "2024-05-24T14:38:33.254911Z", + "iopub.status.idle": "2024-05-24T14:38:34.854793Z", + "shell.execute_reply": "2024-05-24T14:38:34.854046Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-24T14:08:55.086798Z", - "iopub.status.busy": "2024-05-24T14:08:55.086600Z", - "iopub.status.idle": "2024-05-24T14:08:55.097194Z", - "shell.execute_reply": "2024-05-24T14:08:55.096748Z" + "iopub.execute_input": "2024-05-24T14:38:34.857798Z", + "iopub.status.busy": "2024-05-24T14:38:34.857421Z", + "iopub.status.idle": "2024-05-24T14:38:34.868250Z", + "shell.execute_reply": "2024-05-24T14:38:34.867584Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:55.099250Z", - "iopub.status.busy": "2024-05-24T14:08:55.099075Z", - "iopub.status.idle": "2024-05-24T14:08:55.104375Z", - "shell.execute_reply": "2024-05-24T14:08:55.103947Z" + "iopub.execute_input": "2024-05-24T14:38:34.870543Z", + "iopub.status.busy": "2024-05-24T14:38:34.870213Z", + "iopub.status.idle": "2024-05-24T14:38:34.875971Z", + "shell.execute_reply": "2024-05-24T14:38:34.875430Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-05-24T14:08:55.106381Z", - "iopub.status.busy": "2024-05-24T14:08:55.106057Z", - "iopub.status.idle": "2024-05-24T14:08:55.547649Z", - "shell.execute_reply": "2024-05-24T14:08:55.547069Z" + "iopub.execute_input": "2024-05-24T14:38:34.878179Z", + "iopub.status.busy": "2024-05-24T14:38:34.877833Z", + "iopub.status.idle": "2024-05-24T14:38:35.348718Z", + "shell.execute_reply": "2024-05-24T14:38:35.348162Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:55.550115Z", - "iopub.status.busy": "2024-05-24T14:08:55.549638Z", - "iopub.status.idle": "2024-05-24T14:08:56.233340Z", - "shell.execute_reply": "2024-05-24T14:08:56.232788Z" + "iopub.execute_input": "2024-05-24T14:38:35.351094Z", + "iopub.status.busy": "2024-05-24T14:38:35.350724Z", + "iopub.status.idle": "2024-05-24T14:38:36.041522Z", + "shell.execute_reply": "2024-05-24T14:38:36.040976Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-05-24T14:08:56.235741Z", - "iopub.status.busy": "2024-05-24T14:08:56.235551Z", - "iopub.status.idle": "2024-05-24T14:08:56.254015Z", - "shell.execute_reply": "2024-05-24T14:08:56.253441Z" + "iopub.execute_input": "2024-05-24T14:38:36.044224Z", + "iopub.status.busy": "2024-05-24T14:38:36.043983Z", + "iopub.status.idle": "2024-05-24T14:38:36.063883Z", + "shell.execute_reply": "2024-05-24T14:38:36.063276Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:56.256130Z", - "iopub.status.busy": "2024-05-24T14:08:56.255809Z", - "iopub.status.idle": "2024-05-24T14:08:56.258959Z", - "shell.execute_reply": "2024-05-24T14:08:56.258410Z" + "iopub.execute_input": "2024-05-24T14:38:36.066307Z", + "iopub.status.busy": "2024-05-24T14:38:36.066070Z", + "iopub.status.idle": "2024-05-24T14:38:36.069866Z", + "shell.execute_reply": "2024-05-24T14:38:36.069372Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:08:56.260853Z", - "iopub.status.busy": "2024-05-24T14:08:56.260554Z", - "iopub.status.idle": "2024-05-24T14:09:10.581253Z", - "shell.execute_reply": "2024-05-24T14:09:10.580698Z" + "iopub.execute_input": "2024-05-24T14:38:36.072411Z", + "iopub.status.busy": "2024-05-24T14:38:36.071951Z", + "iopub.status.idle": "2024-05-24T14:38:52.669673Z", + "shell.execute_reply": "2024-05-24T14:38:52.668932Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-24T14:09:10.584167Z", - "iopub.status.busy": "2024-05-24T14:09:10.583543Z", - "iopub.status.idle": "2024-05-24T14:09:10.587413Z", - "shell.execute_reply": "2024-05-24T14:09:10.586942Z" + "iopub.execute_input": "2024-05-24T14:38:52.672954Z", + "iopub.status.busy": "2024-05-24T14:38:52.672471Z", + "iopub.status.idle": "2024-05-24T14:38:52.676746Z", + "shell.execute_reply": "2024-05-24T14:38:52.676197Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:10.589560Z", - "iopub.status.busy": "2024-05-24T14:09:10.589267Z", - "iopub.status.idle": "2024-05-24T14:09:11.310739Z", - "shell.execute_reply": "2024-05-24T14:09:11.310119Z" + "iopub.execute_input": "2024-05-24T14:38:52.678834Z", + "iopub.status.busy": "2024-05-24T14:38:52.678652Z", + "iopub.status.idle": "2024-05-24T14:38:53.397581Z", + "shell.execute_reply": "2024-05-24T14:38:53.396992Z" }, "id": "i_drkY9YOcw4" }, @@ -727,10 +727,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.313523Z", - "iopub.status.busy": "2024-05-24T14:09:11.313143Z", - "iopub.status.idle": "2024-05-24T14:09:11.318232Z", - "shell.execute_reply": "2024-05-24T14:09:11.317680Z" + "iopub.execute_input": "2024-05-24T14:38:53.400407Z", + "iopub.status.busy": "2024-05-24T14:38:53.400006Z", + "iopub.status.idle": "2024-05-24T14:38:53.405174Z", + "shell.execute_reply": "2024-05-24T14:38:53.404662Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -777,10 +777,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.320653Z", - "iopub.status.busy": "2024-05-24T14:09:11.320263Z", - "iopub.status.idle": "2024-05-24T14:09:11.415255Z", - "shell.execute_reply": "2024-05-24T14:09:11.414628Z" + "iopub.execute_input": "2024-05-24T14:38:53.407647Z", + "iopub.status.busy": "2024-05-24T14:38:53.407247Z", + "iopub.status.idle": "2024-05-24T14:38:53.504566Z", + "shell.execute_reply": "2024-05-24T14:38:53.503989Z" } }, "outputs": [ @@ -817,10 +817,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.417703Z", - "iopub.status.busy": "2024-05-24T14:09:11.417392Z", - "iopub.status.idle": "2024-05-24T14:09:11.430300Z", - "shell.execute_reply": "2024-05-24T14:09:11.429696Z" + "iopub.execute_input": "2024-05-24T14:38:53.507054Z", + "iopub.status.busy": "2024-05-24T14:38:53.506688Z", + "iopub.status.idle": "2024-05-24T14:38:53.519402Z", + "shell.execute_reply": "2024-05-24T14:38:53.518899Z" }, "scrolled": true }, @@ -875,10 +875,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.432519Z", - "iopub.status.busy": "2024-05-24T14:09:11.432109Z", - "iopub.status.idle": "2024-05-24T14:09:11.440123Z", - "shell.execute_reply": "2024-05-24T14:09:11.439659Z" + "iopub.execute_input": "2024-05-24T14:38:53.521736Z", + "iopub.status.busy": "2024-05-24T14:38:53.521387Z", + "iopub.status.idle": "2024-05-24T14:38:53.529281Z", + "shell.execute_reply": "2024-05-24T14:38:53.528730Z" } }, "outputs": [ @@ -982,10 +982,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.442299Z", - "iopub.status.busy": "2024-05-24T14:09:11.441844Z", - "iopub.status.idle": "2024-05-24T14:09:11.446136Z", - "shell.execute_reply": "2024-05-24T14:09:11.445643Z" + "iopub.execute_input": "2024-05-24T14:38:53.531513Z", + "iopub.status.busy": "2024-05-24T14:38:53.531171Z", + "iopub.status.idle": "2024-05-24T14:38:53.535292Z", + "shell.execute_reply": "2024-05-24T14:38:53.534752Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-05-24T14:09:11.447986Z", - 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index 1992cfd54..f66bb24da 100644 --- a/master/tutorials/datalab/data_monitor.html +++ b/master/tutorials/datalab/data_monitor.html @@ -686,7 +686,7 @@

    1. Install and import required dependenciesdependencies = ["cleanlab", "matplotlib", "datasets"] # TODO: make sure this list is updated if "google.colab" in str(get_ipython()): # Check if it's running in Google Colab - %pip install git+https://github.com/cleanlab/cleanlab.git@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970 + %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e cmd = ' '.join([dep for dep in dependencies if dep != "cleanlab"]) %pip install $cmd else: @@ -1169,7 +1169,7 @@

    5. Use DataMonitor to find issues in new data

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

    5. Use DataMonitor to find issues in new data

    -
    +
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    7. Finding outliers in new data

    -
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    7. Finding outliers in new data

    -
    +
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    8. Looking for both label issues and outliers

    -
    +
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    8. Looking for both label issues and outliers

    -
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f68290024..15be77829 100644 --- a/master/tutorials/datalab/data_monitor.ipynb +++ b/master/tutorials/datalab/data_monitor.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:16.449649Z", - "iopub.status.busy": "2024-05-24T14:09:16.449474Z", - "iopub.status.idle": "2024-05-24T14:09:16.460318Z", - "shell.execute_reply": "2024-05-24T14:09:16.459782Z" + "iopub.execute_input": "2024-05-24T14:38:57.981492Z", + "iopub.status.busy": "2024-05-24T14:38:57.981324Z", + "iopub.status.idle": "2024-05-24T14:38:57.992789Z", + "shell.execute_reply": "2024-05-24T14:38:57.992293Z" } }, "outputs": [], @@ -85,10 +85,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:16.462477Z", - "iopub.status.busy": "2024-05-24T14:09:16.462171Z", - "iopub.status.idle": "2024-05-24T14:09:17.620745Z", - "shell.execute_reply": "2024-05-24T14:09:17.620123Z" + "iopub.execute_input": "2024-05-24T14:38:57.995161Z", + "iopub.status.busy": "2024-05-24T14:38:57.994868Z", + "iopub.status.idle": "2024-05-24T14:38:59.304681Z", + "shell.execute_reply": "2024-05-24T14:38:59.304001Z" } }, "outputs": [], @@ -97,7 +97,7 @@ "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -122,10 +122,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.623321Z", - "iopub.status.busy": "2024-05-24T14:09:17.623063Z", - "iopub.status.idle": "2024-05-24T14:09:17.640219Z", - "shell.execute_reply": "2024-05-24T14:09:17.639689Z" + "iopub.execute_input": "2024-05-24T14:38:59.307618Z", + "iopub.status.busy": "2024-05-24T14:38:59.307228Z", + "iopub.status.idle": "2024-05-24T14:38:59.328136Z", + "shell.execute_reply": "2024-05-24T14:38:59.327593Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.642620Z", - "iopub.status.busy": "2024-05-24T14:09:17.642295Z", - "iopub.status.idle": "2024-05-24T14:09:17.660253Z", - "shell.execute_reply": "2024-05-24T14:09:17.659809Z" + "iopub.execute_input": "2024-05-24T14:38:59.330873Z", + "iopub.status.busy": "2024-05-24T14:38:59.330435Z", + "iopub.status.idle": "2024-05-24T14:38:59.353547Z", + "shell.execute_reply": "2024-05-24T14:38:59.352999Z" } }, "outputs": [], @@ -353,10 +353,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.662298Z", - "iopub.status.busy": "2024-05-24T14:09:17.661967Z", - "iopub.status.idle": "2024-05-24T14:09:17.675804Z", - "shell.execute_reply": "2024-05-24T14:09:17.675395Z" + "iopub.execute_input": "2024-05-24T14:38:59.356582Z", + "iopub.status.busy": "2024-05-24T14:38:59.356174Z", + "iopub.status.idle": "2024-05-24T14:38:59.375458Z", + "shell.execute_reply": "2024-05-24T14:38:59.374898Z" } }, "outputs": [], @@ -369,10 +369,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.677890Z", - "iopub.status.busy": "2024-05-24T14:09:17.677601Z", - "iopub.status.idle": "2024-05-24T14:09:17.692021Z", - "shell.execute_reply": "2024-05-24T14:09:17.691455Z" + "iopub.execute_input": "2024-05-24T14:38:59.378223Z", + "iopub.status.busy": "2024-05-24T14:38:59.377957Z", + "iopub.status.idle": "2024-05-24T14:38:59.395545Z", + "shell.execute_reply": "2024-05-24T14:38:59.395001Z" } }, "outputs": [], @@ -450,10 +450,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.694402Z", - "iopub.status.busy": "2024-05-24T14:09:17.693875Z", - "iopub.status.idle": "2024-05-24T14:09:17.885204Z", - "shell.execute_reply": "2024-05-24T14:09:17.884607Z" + "iopub.execute_input": "2024-05-24T14:38:59.398159Z", + "iopub.status.busy": "2024-05-24T14:38:59.397943Z", + "iopub.status.idle": "2024-05-24T14:38:59.612356Z", + "shell.execute_reply": "2024-05-24T14:38:59.611821Z" } }, "outputs": [], @@ -507,10 +507,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:17.887679Z", - "iopub.status.busy": "2024-05-24T14:09:17.887492Z", - "iopub.status.idle": "2024-05-24T14:09:18.244192Z", - "shell.execute_reply": "2024-05-24T14:09:18.243633Z" + "iopub.execute_input": "2024-05-24T14:38:59.614879Z", + "iopub.status.busy": "2024-05-24T14:38:59.614679Z", + "iopub.status.idle": "2024-05-24T14:38:59.996105Z", + "shell.execute_reply": "2024-05-24T14:38:59.995490Z" } }, "outputs": [ @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:18.246419Z", - "iopub.status.busy": "2024-05-24T14:09:18.246154Z", - "iopub.status.idle": "2024-05-24T14:09:18.282191Z", - "shell.execute_reply": "2024-05-24T14:09:18.281765Z" + "iopub.execute_input": "2024-05-24T14:38:59.998486Z", + "iopub.status.busy": "2024-05-24T14:38:59.998279Z", + "iopub.status.idle": "2024-05-24T14:39:00.038131Z", + "shell.execute_reply": "2024-05-24T14:39:00.037603Z" } }, "outputs": [], @@ -581,10 +581,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:18.284298Z", - "iopub.status.busy": "2024-05-24T14:09:18.283974Z", - "iopub.status.idle": "2024-05-24T14:09:19.882878Z", - "shell.execute_reply": "2024-05-24T14:09:19.882238Z" + "iopub.execute_input": "2024-05-24T14:39:00.040758Z", + "iopub.status.busy": "2024-05-24T14:39:00.040556Z", + "iopub.status.idle": "2024-05-24T14:39:02.025079Z", + "shell.execute_reply": "2024-05-24T14:39:02.024246Z" } }, "outputs": [ @@ -667,10 +667,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:19.885497Z", - "iopub.status.busy": "2024-05-24T14:09:19.885034Z", - "iopub.status.idle": "2024-05-24T14:09:19.913727Z", - "shell.execute_reply": "2024-05-24T14:09:19.913155Z" + "iopub.execute_input": "2024-05-24T14:39:02.028410Z", + "iopub.status.busy": "2024-05-24T14:39:02.027681Z", + "iopub.status.idle": "2024-05-24T14:39:02.071965Z", + "shell.execute_reply": "2024-05-24T14:39:02.071183Z" } }, "outputs": [], @@ -701,10 +701,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:19.916113Z", - "iopub.status.busy": "2024-05-24T14:09:19.915712Z", - "iopub.status.idle": "2024-05-24T14:09:19.947061Z", - "shell.execute_reply": "2024-05-24T14:09:19.946493Z" + "iopub.execute_input": "2024-05-24T14:39:02.075014Z", + "iopub.status.busy": "2024-05-24T14:39:02.074550Z", + "iopub.status.idle": "2024-05-24T14:39:02.118523Z", + "shell.execute_reply": "2024-05-24T14:39:02.117865Z" } }, "outputs": [], @@ -741,17 +741,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:19.949374Z", - "iopub.status.busy": "2024-05-24T14:09:19.949067Z", - "iopub.status.idle": "2024-05-24T14:09:25.042990Z", - "shell.execute_reply": "2024-05-24T14:09:25.042388Z" + "iopub.execute_input": "2024-05-24T14:39:02.121571Z", + "iopub.status.busy": "2024-05-24T14:39:02.121213Z", + "iopub.status.idle": "2024-05-24T14:39:07.244006Z", + "shell.execute_reply": "2024-05-24T14:39:07.243239Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "662be85c83fb4299927b984583f2a0aa", + "model_id": "6250e7600adc4b8db8a1048afdf59bd5", "version_major": 2, "version_minor": 0 }, @@ -811,17 +811,17 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:25.045136Z", - "iopub.status.busy": "2024-05-24T14:09:25.044800Z", - "iopub.status.idle": "2024-05-24T14:09:30.366345Z", - "shell.execute_reply": "2024-05-24T14:09:30.365706Z" + "iopub.execute_input": "2024-05-24T14:39:07.246643Z", + "iopub.status.busy": "2024-05-24T14:39:07.246370Z", + "iopub.status.idle": "2024-05-24T14:39:12.575525Z", + "shell.execute_reply": "2024-05-24T14:39:12.574812Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab646e85b72c41ca8755ce66562dedc7", + "model_id": "18f319d617234733a734397a965d8ff0", "version_major": 2, "version_minor": 0 }, @@ -949,10 +949,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.368601Z", - "iopub.status.busy": "2024-05-24T14:09:30.368420Z", - "iopub.status.idle": "2024-05-24T14:09:30.403863Z", - "shell.execute_reply": "2024-05-24T14:09:30.403442Z" + "iopub.execute_input": "2024-05-24T14:39:12.578565Z", + "iopub.status.busy": "2024-05-24T14:39:12.578268Z", + "iopub.status.idle": "2024-05-24T14:39:12.616079Z", + "shell.execute_reply": "2024-05-24T14:39:12.615604Z" } }, "outputs": [ @@ -1185,10 +1185,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.405718Z", - "iopub.status.busy": "2024-05-24T14:09:30.405542Z", - "iopub.status.idle": "2024-05-24T14:09:30.436207Z", - "shell.execute_reply": "2024-05-24T14:09:30.435785Z" + "iopub.execute_input": "2024-05-24T14:39:12.618239Z", + "iopub.status.busy": "2024-05-24T14:39:12.617904Z", + "iopub.status.idle": "2024-05-24T14:39:12.650787Z", + "shell.execute_reply": "2024-05-24T14:39:12.650206Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.438199Z", - "iopub.status.busy": "2024-05-24T14:09:30.438012Z", - "iopub.status.idle": "2024-05-24T14:09:30.482833Z", - "shell.execute_reply": "2024-05-24T14:09:30.482403Z" + "iopub.execute_input": "2024-05-24T14:39:12.653120Z", + "iopub.status.busy": "2024-05-24T14:39:12.652762Z", + "iopub.status.idle": "2024-05-24T14:39:12.701538Z", + "shell.execute_reply": "2024-05-24T14:39:12.701013Z" } }, "outputs": [ @@ -1314,10 +1314,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.484752Z", - "iopub.status.busy": "2024-05-24T14:09:30.484577Z", - "iopub.status.idle": "2024-05-24T14:09:30.510315Z", - "shell.execute_reply": "2024-05-24T14:09:30.509877Z" + "iopub.execute_input": "2024-05-24T14:39:12.703797Z", + "iopub.status.busy": "2024-05-24T14:39:12.703449Z", + "iopub.status.idle": "2024-05-24T14:39:12.731481Z", + "shell.execute_reply": "2024-05-24T14:39:12.730963Z" } }, "outputs": [], @@ -1331,10 +1331,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.512385Z", - "iopub.status.busy": "2024-05-24T14:09:30.511996Z", - "iopub.status.idle": "2024-05-24T14:09:30.535231Z", - "shell.execute_reply": "2024-05-24T14:09:30.534669Z" + "iopub.execute_input": "2024-05-24T14:39:12.734070Z", + "iopub.status.busy": "2024-05-24T14:39:12.733697Z", + "iopub.status.idle": "2024-05-24T14:39:12.765523Z", + "shell.execute_reply": "2024-05-24T14:39:12.764993Z" } }, "outputs": [], @@ -1363,17 +1363,17 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:30.537330Z", - "iopub.status.busy": "2024-05-24T14:09:30.537032Z", - "iopub.status.idle": "2024-05-24T14:09:40.946389Z", - "shell.execute_reply": "2024-05-24T14:09:40.945821Z" + "iopub.execute_input": "2024-05-24T14:39:12.768178Z", + "iopub.status.busy": "2024-05-24T14:39:12.767814Z", + "iopub.status.idle": "2024-05-24T14:39:23.214616Z", + "shell.execute_reply": "2024-05-24T14:39:23.213897Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2a6e4b076ef4712a9b5d3b5de511099", + "model_id": "64269642e57f4cef9fee7191358f0f27", "version_major": 2, "version_minor": 0 }, @@ -1397,7 +1397,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d4293023da84faf8077ec698a653354", + "model_id": "fd77b343f785457d98ccfd14dc8b67bb", "version_major": 2, "version_minor": 0 }, @@ -1463,10 +1463,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:40.949165Z", - "iopub.status.busy": "2024-05-24T14:09:40.948833Z", - "iopub.status.idle": "2024-05-24T14:09:41.026535Z", - "shell.execute_reply": "2024-05-24T14:09:41.025963Z" + "iopub.execute_input": "2024-05-24T14:39:23.217587Z", + "iopub.status.busy": "2024-05-24T14:39:23.217230Z", + "iopub.status.idle": "2024-05-24T14:39:23.313142Z", + "shell.execute_reply": "2024-05-24T14:39:23.312414Z" } }, "outputs": [ @@ -1546,10 +1546,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:41.028776Z", - "iopub.status.busy": "2024-05-24T14:09:41.028410Z", - "iopub.status.idle": "2024-05-24T14:09:41.058692Z", - "shell.execute_reply": "2024-05-24T14:09:41.058266Z" + "iopub.execute_input": "2024-05-24T14:39:23.315468Z", + "iopub.status.busy": "2024-05-24T14:39:23.315092Z", + "iopub.status.idle": "2024-05-24T14:39:23.348251Z", + "shell.execute_reply": "2024-05-24T14:39:23.347609Z" } }, "outputs": [], @@ -1562,10 +1562,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:41.060943Z", - "iopub.status.busy": "2024-05-24T14:09:41.060580Z", - "iopub.status.idle": "2024-05-24T14:09:41.089864Z", - "shell.execute_reply": "2024-05-24T14:09:41.089259Z" + "iopub.execute_input": "2024-05-24T14:39:23.350971Z", + "iopub.status.busy": "2024-05-24T14:39:23.350594Z", + "iopub.status.idle": "2024-05-24T14:39:23.383047Z", + "shell.execute_reply": "2024-05-24T14:39:23.382442Z" } }, "outputs": [], @@ -1594,17 +1594,17 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:41.092266Z", - 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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 127b1552c..fc496a8e6 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": "2024-05-24T14:09:54.043523Z", - "iopub.status.busy": "2024-05-24T14:09:54.043116Z", - "iopub.status.idle": "2024-05-24T14:09:55.169795Z", - "shell.execute_reply": "2024-05-24T14:09:55.169246Z" + "iopub.execute_input": "2024-05-24T14:39:36.929115Z", + "iopub.status.busy": "2024-05-24T14:39:36.928639Z", + "iopub.status.idle": "2024-05-24T14:39:38.226330Z", + "shell.execute_reply": "2024-05-24T14:39:38.225794Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:09:55.172429Z", - "iopub.status.busy": "2024-05-24T14:09:55.172016Z", - "iopub.status.idle": "2024-05-24T14:09:55.174896Z", - "shell.execute_reply": "2024-05-24T14:09:55.174467Z" + "iopub.execute_input": "2024-05-24T14:39:38.229131Z", + "iopub.status.busy": "2024-05-24T14:39:38.228651Z", + "iopub.status.idle": "2024-05-24T14:39:38.231818Z", + "shell.execute_reply": "2024-05-24T14:39:38.231287Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.176987Z", - "iopub.status.busy": "2024-05-24T14:09:55.176690Z", - "iopub.status.idle": "2024-05-24T14:09:55.185625Z", - "shell.execute_reply": "2024-05-24T14:09:55.185073Z" + "iopub.execute_input": "2024-05-24T14:39:38.234187Z", + "iopub.status.busy": "2024-05-24T14:39:38.233896Z", + "iopub.status.idle": "2024-05-24T14:39:38.243902Z", + "shell.execute_reply": "2024-05-24T14:39:38.243261Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.187691Z", - "iopub.status.busy": "2024-05-24T14:09:55.187493Z", - "iopub.status.idle": "2024-05-24T14:09:55.191831Z", - "shell.execute_reply": "2024-05-24T14:09:55.191420Z" + "iopub.execute_input": "2024-05-24T14:39:38.246119Z", + "iopub.status.busy": "2024-05-24T14:39:38.245921Z", + "iopub.status.idle": "2024-05-24T14:39:38.251040Z", + "shell.execute_reply": "2024-05-24T14:39:38.250596Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.193910Z", - "iopub.status.busy": "2024-05-24T14:09:55.193528Z", - "iopub.status.idle": "2024-05-24T14:09:55.375800Z", - "shell.execute_reply": "2024-05-24T14:09:55.375203Z" + "iopub.execute_input": "2024-05-24T14:39:38.253466Z", + "iopub.status.busy": "2024-05-24T14:39:38.253105Z", + "iopub.status.idle": "2024-05-24T14:39:38.458836Z", + "shell.execute_reply": "2024-05-24T14:39:38.458095Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.378201Z", - "iopub.status.busy": "2024-05-24T14:09:55.377830Z", - "iopub.status.idle": "2024-05-24T14:09:55.692204Z", - "shell.execute_reply": "2024-05-24T14:09:55.691600Z" + "iopub.execute_input": "2024-05-24T14:39:38.461763Z", + "iopub.status.busy": "2024-05-24T14:39:38.461497Z", + "iopub.status.idle": "2024-05-24T14:39:38.806779Z", + "shell.execute_reply": "2024-05-24T14:39:38.806168Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.694467Z", - "iopub.status.busy": "2024-05-24T14:09:55.694114Z", - "iopub.status.idle": "2024-05-24T14:09:55.716962Z", - "shell.execute_reply": "2024-05-24T14:09:55.716525Z" + "iopub.execute_input": "2024-05-24T14:39:38.809290Z", + "iopub.status.busy": "2024-05-24T14:39:38.808860Z", + "iopub.status.idle": "2024-05-24T14:39:38.833522Z", + "shell.execute_reply": "2024-05-24T14:39:38.832882Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.719050Z", - "iopub.status.busy": "2024-05-24T14:09:55.718712Z", - "iopub.status.idle": "2024-05-24T14:09:55.729381Z", - "shell.execute_reply": "2024-05-24T14:09:55.728948Z" + "iopub.execute_input": "2024-05-24T14:39:38.836340Z", + "iopub.status.busy": "2024-05-24T14:39:38.835866Z", + "iopub.status.idle": "2024-05-24T14:39:38.848683Z", + "shell.execute_reply": "2024-05-24T14:39:38.848097Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:55.731475Z", - "iopub.status.busy": "2024-05-24T14:09:55.731141Z", - "iopub.status.idle": "2024-05-24T14:09:57.314769Z", - "shell.execute_reply": "2024-05-24T14:09:57.314154Z" + "iopub.execute_input": "2024-05-24T14:39:38.851602Z", + "iopub.status.busy": "2024-05-24T14:39:38.851195Z", + "iopub.status.idle": "2024-05-24T14:39:40.763015Z", + "shell.execute_reply": "2024-05-24T14:39:40.762333Z" } }, "outputs": [ @@ -709,10 +709,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.317204Z", - "iopub.status.busy": "2024-05-24T14:09:57.316886Z", - "iopub.status.idle": "2024-05-24T14:09:57.338989Z", - "shell.execute_reply": "2024-05-24T14:09:57.338430Z" + "iopub.execute_input": "2024-05-24T14:39:40.765720Z", + "iopub.status.busy": "2024-05-24T14:39:40.765229Z", + "iopub.status.idle": "2024-05-24T14:39:40.791460Z", + "shell.execute_reply": "2024-05-24T14:39:40.790815Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.341145Z", - "iopub.status.busy": "2024-05-24T14:09:57.340712Z", - "iopub.status.idle": "2024-05-24T14:09:57.360701Z", - "shell.execute_reply": "2024-05-24T14:09:57.360172Z" + "iopub.execute_input": "2024-05-24T14:39:40.793882Z", + "iopub.status.busy": "2024-05-24T14:39:40.793549Z", + "iopub.status.idle": "2024-05-24T14:39:40.816473Z", + "shell.execute_reply": "2024-05-24T14:39:40.815869Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.362754Z", - "iopub.status.busy": "2024-05-24T14:09:57.362421Z", - "iopub.status.idle": "2024-05-24T14:09:57.376547Z", - "shell.execute_reply": "2024-05-24T14:09:57.375987Z" + "iopub.execute_input": "2024-05-24T14:39:40.818822Z", + "iopub.status.busy": "2024-05-24T14:39:40.818387Z", + "iopub.status.idle": "2024-05-24T14:39:40.834172Z", + "shell.execute_reply": "2024-05-24T14:39:40.833556Z" } }, "outputs": [ @@ -1069,17 +1069,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.378777Z", - "iopub.status.busy": "2024-05-24T14:09:57.378444Z", - "iopub.status.idle": "2024-05-24T14:09:57.396845Z", - "shell.execute_reply": "2024-05-24T14:09:57.396278Z" + "iopub.execute_input": "2024-05-24T14:39:40.836529Z", + "iopub.status.busy": "2024-05-24T14:39:40.836095Z", + "iopub.status.idle": "2024-05-24T14:39:40.856343Z", + "shell.execute_reply": "2024-05-24T14:39:40.855768Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb3555b3247844ec83c188729d1bd005", + "model_id": "96a1502e04e4437cadfa82c07e418096", "version_major": 2, "version_minor": 0 }, @@ -1115,10 +1115,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.399055Z", - "iopub.status.busy": "2024-05-24T14:09:57.398674Z", - "iopub.status.idle": "2024-05-24T14:09:57.413328Z", - "shell.execute_reply": "2024-05-24T14:09:57.412798Z" + "iopub.execute_input": "2024-05-24T14:39:40.858486Z", + "iopub.status.busy": "2024-05-24T14:39:40.858250Z", + "iopub.status.idle": "2024-05-24T14:39:40.874621Z", + "shell.execute_reply": "2024-05-24T14:39:40.874035Z" } }, "outputs": [ @@ -1236,10 +1236,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.415426Z", - "iopub.status.busy": "2024-05-24T14:09:57.415022Z", - "iopub.status.idle": "2024-05-24T14:09:57.420803Z", - "shell.execute_reply": "2024-05-24T14:09:57.420257Z" + "iopub.execute_input": "2024-05-24T14:39:40.877065Z", + "iopub.status.busy": "2024-05-24T14:39:40.876647Z", + "iopub.status.idle": "2024-05-24T14:39:40.882914Z", + "shell.execute_reply": "2024-05-24T14:39:40.882327Z" } }, "outputs": [], @@ -1296,10 +1296,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:09:57.422755Z", - "iopub.status.busy": "2024-05-24T14:09:57.422434Z", - "iopub.status.idle": "2024-05-24T14:09:57.440428Z", - "shell.execute_reply": "2024-05-24T14:09:57.439980Z" + "iopub.execute_input": "2024-05-24T14:39:40.885157Z", + "iopub.status.busy": "2024-05-24T14:39:40.884821Z", + "iopub.status.idle": "2024-05-24T14:39:40.904555Z", + "shell.execute_reply": "2024-05-24T14:39:40.903946Z" } }, "outputs": [ @@ -1431,33 +1431,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "020e151cb9a843eb829e6f2d5471ee41": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - 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"@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_f6c54b2a53ad40e8ba17c8f74bb18a33", + "placeholder": "​", + "style": "IPY_MODEL_3d0f6b651acf458b9b46f33f226b10a4", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "3d0f6b651acf458b9b46f33f226b10a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1567,7 +1541,7 @@ "text_color": null } }, - "7ed627a7e944436ab831a963233c3c3d": { + "3f3f21850a164eae9a851d6c6586542b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1620,7 +1594,49 @@ "width": null } }, - "9a5e82581968475aac694f13ac10da0b": { + 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"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 + } + }, + "cf1ef50a0a974fb3bcb57ecac66ad5db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1635,15 +1651,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7ed627a7e944436ab831a963233c3c3d", + "layout": "IPY_MODEL_3f3f21850a164eae9a851d6c6586542b", "placeholder": "​", - "style": "IPY_MODEL_ea9a3f7c705846b09a501e5d189aebe5", + "style": "IPY_MODEL_982e2c84d37a4caf8b4749d94c770790", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 13715.02 examples/s]" + "value": " 132/132 [00:00<00:00, 12548.97 examples/s]" } }, - "ab1de18cbd944c67a3bad60547b64da5": { + "e0bf868747574641a87e3c4b0870dba2": { + "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_3256cbd3b8804115be539812715c7445", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0f3fcbca71ca4ac2bc4a766a4fd82dba", + "tabbable": null, + "tooltip": null, + "value": 132.0 + } + }, + "f6c54b2a53ad40e8ba17c8f74bb18a33": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1696,7 +1738,7 @@ "width": null } }, - "b9f02f78bf384af9bfd0ae5756b1cd18": { + "f9e9974ca3744482b197a014d79e041c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1748,48 +1790,6 @@ "visibility": null, "width": null } - }, - "ea9a3f7c705846b09a501e5d189aebe5": { - "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 - } - }, - "eb3555b3247844ec83c188729d1bd005": { - "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_5dce0e4496f6438a81b1b7df6a62a9d4", - "IPY_MODEL_020e151cb9a843eb829e6f2d5471ee41", - "IPY_MODEL_9a5e82581968475aac694f13ac10da0b" - ], - "layout": "IPY_MODEL_ab1de18cbd944c67a3bad60547b64da5", - "tabbable": null, - "tooltip": null - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index aa349e5f1..0a1d6f071 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": "2024-05-24T14:10:00.027159Z", - "iopub.status.busy": "2024-05-24T14:10:00.026986Z", - "iopub.status.idle": "2024-05-24T14:10:01.179160Z", - "shell.execute_reply": "2024-05-24T14:10:01.178566Z" + "iopub.execute_input": "2024-05-24T14:39:44.078513Z", + "iopub.status.busy": "2024-05-24T14:39:44.077961Z", + "iopub.status.idle": "2024-05-24T14:39:45.421679Z", + "shell.execute_reply": "2024-05-24T14:39:45.421075Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:10:01.181762Z", - "iopub.status.busy": "2024-05-24T14:10:01.181269Z", - "iopub.status.idle": "2024-05-24T14:10:01.184305Z", - "shell.execute_reply": "2024-05-24T14:10:01.183875Z" + "iopub.execute_input": "2024-05-24T14:39:45.424447Z", + "iopub.status.busy": "2024-05-24T14:39:45.423965Z", + "iopub.status.idle": "2024-05-24T14:39:45.427303Z", + "shell.execute_reply": "2024-05-24T14:39:45.426743Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.186447Z", - "iopub.status.busy": "2024-05-24T14:10:01.186120Z", - "iopub.status.idle": "2024-05-24T14:10:01.195461Z", - "shell.execute_reply": "2024-05-24T14:10:01.195009Z" + "iopub.execute_input": "2024-05-24T14:39:45.429645Z", + "iopub.status.busy": "2024-05-24T14:39:45.429327Z", + "iopub.status.idle": "2024-05-24T14:39:45.440139Z", + "shell.execute_reply": "2024-05-24T14:39:45.439471Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.197374Z", - "iopub.status.busy": "2024-05-24T14:10:01.197051Z", - "iopub.status.idle": "2024-05-24T14:10:01.201383Z", - "shell.execute_reply": "2024-05-24T14:10:01.200983Z" + "iopub.execute_input": "2024-05-24T14:39:45.442704Z", + "iopub.status.busy": "2024-05-24T14:39:45.442225Z", + "iopub.status.idle": "2024-05-24T14:39:45.447705Z", + "shell.execute_reply": "2024-05-24T14:39:45.447168Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.203437Z", - "iopub.status.busy": "2024-05-24T14:10:01.203054Z", - "iopub.status.idle": "2024-05-24T14:10:01.385015Z", - "shell.execute_reply": "2024-05-24T14:10:01.384416Z" + "iopub.execute_input": "2024-05-24T14:39:45.450299Z", + "iopub.status.busy": "2024-05-24T14:39:45.449900Z", + "iopub.status.idle": "2024-05-24T14:39:45.653918Z", + "shell.execute_reply": "2024-05-24T14:39:45.653327Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.387593Z", - "iopub.status.busy": "2024-05-24T14:10:01.387402Z", - "iopub.status.idle": "2024-05-24T14:10:01.752479Z", - "shell.execute_reply": "2024-05-24T14:10:01.751898Z" + "iopub.execute_input": "2024-05-24T14:39:45.656640Z", + "iopub.status.busy": "2024-05-24T14:39:45.656260Z", + "iopub.status.idle": "2024-05-24T14:39:46.054293Z", + "shell.execute_reply": "2024-05-24T14:39:46.053627Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.754875Z", - "iopub.status.busy": "2024-05-24T14:10:01.754513Z", - "iopub.status.idle": "2024-05-24T14:10:01.757205Z", - "shell.execute_reply": "2024-05-24T14:10:01.756759Z" + "iopub.execute_input": "2024-05-24T14:39:46.056959Z", + "iopub.status.busy": "2024-05-24T14:39:46.056512Z", + "iopub.status.idle": "2024-05-24T14:39:46.060530Z", + "shell.execute_reply": "2024-05-24T14:39:46.059818Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.759373Z", - "iopub.status.busy": "2024-05-24T14:10:01.759037Z", - "iopub.status.idle": "2024-05-24T14:10:01.794293Z", - "shell.execute_reply": "2024-05-24T14:10:01.793762Z" + "iopub.execute_input": "2024-05-24T14:39:46.063440Z", + "iopub.status.busy": "2024-05-24T14:39:46.062903Z", + "iopub.status.idle": "2024-05-24T14:39:46.102038Z", + "shell.execute_reply": "2024-05-24T14:39:46.101383Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:01.796390Z", - "iopub.status.busy": "2024-05-24T14:10:01.796053Z", - "iopub.status.idle": "2024-05-24T14:10:03.399924Z", - "shell.execute_reply": "2024-05-24T14:10:03.399219Z" + "iopub.execute_input": "2024-05-24T14:39:46.104587Z", + "iopub.status.busy": "2024-05-24T14:39:46.104203Z", + "iopub.status.idle": "2024-05-24T14:39:48.022349Z", + "shell.execute_reply": "2024-05-24T14:39:48.021617Z" } }, "outputs": [ @@ -711,10 +711,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.402306Z", - "iopub.status.busy": "2024-05-24T14:10:03.401892Z", - "iopub.status.idle": "2024-05-24T14:10:03.420281Z", - "shell.execute_reply": "2024-05-24T14:10:03.419811Z" + "iopub.execute_input": "2024-05-24T14:39:48.024948Z", + "iopub.status.busy": "2024-05-24T14:39:48.024531Z", + "iopub.status.idle": "2024-05-24T14:39:48.045962Z", + "shell.execute_reply": "2024-05-24T14:39:48.045375Z" } }, "outputs": [ @@ -842,10 +842,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.422372Z", - "iopub.status.busy": "2024-05-24T14:10:03.422033Z", - "iopub.status.idle": "2024-05-24T14:10:03.428350Z", - "shell.execute_reply": "2024-05-24T14:10:03.427908Z" + "iopub.execute_input": "2024-05-24T14:39:48.048457Z", + "iopub.status.busy": "2024-05-24T14:39:48.048031Z", + "iopub.status.idle": "2024-05-24T14:39:48.056632Z", + "shell.execute_reply": "2024-05-24T14:39:48.056055Z" } }, "outputs": [ @@ -956,10 +956,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.430386Z", - "iopub.status.busy": "2024-05-24T14:10:03.430038Z", - "iopub.status.idle": "2024-05-24T14:10:03.435518Z", - "shell.execute_reply": "2024-05-24T14:10:03.435052Z" + "iopub.execute_input": "2024-05-24T14:39:48.059219Z", + "iopub.status.busy": "2024-05-24T14:39:48.058782Z", + "iopub.status.idle": "2024-05-24T14:39:48.066010Z", + "shell.execute_reply": "2024-05-24T14:39:48.065383Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.437512Z", - "iopub.status.busy": "2024-05-24T14:10:03.437188Z", - "iopub.status.idle": "2024-05-24T14:10:03.447302Z", - "shell.execute_reply": "2024-05-24T14:10:03.446856Z" + "iopub.execute_input": "2024-05-24T14:39:48.068572Z", + "iopub.status.busy": "2024-05-24T14:39:48.068202Z", + "iopub.status.idle": "2024-05-24T14:39:48.080549Z", + "shell.execute_reply": "2024-05-24T14:39:48.079915Z" } }, "outputs": [ @@ -1221,10 +1221,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.449231Z", - "iopub.status.busy": "2024-05-24T14:10:03.448925Z", - "iopub.status.idle": "2024-05-24T14:10:03.457750Z", - "shell.execute_reply": "2024-05-24T14:10:03.457229Z" + "iopub.execute_input": "2024-05-24T14:39:48.083117Z", + "iopub.status.busy": "2024-05-24T14:39:48.082731Z", + "iopub.status.idle": "2024-05-24T14:39:48.093131Z", + "shell.execute_reply": "2024-05-24T14:39:48.092524Z" } }, "outputs": [ @@ -1340,10 +1340,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.459762Z", - "iopub.status.busy": "2024-05-24T14:10:03.459439Z", - "iopub.status.idle": "2024-05-24T14:10:03.466329Z", - "shell.execute_reply": "2024-05-24T14:10:03.465754Z" + "iopub.execute_input": "2024-05-24T14:39:48.095468Z", + "iopub.status.busy": "2024-05-24T14:39:48.095103Z", + "iopub.status.idle": "2024-05-24T14:39:48.102708Z", + "shell.execute_reply": "2024-05-24T14:39:48.102137Z" }, "scrolled": true }, @@ -1468,10 +1468,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.468334Z", - "iopub.status.busy": "2024-05-24T14:10:03.468016Z", - "iopub.status.idle": "2024-05-24T14:10:03.476918Z", - "shell.execute_reply": "2024-05-24T14:10:03.476414Z" + "iopub.execute_input": "2024-05-24T14:39:48.105035Z", + "iopub.status.busy": "2024-05-24T14:39:48.104667Z", + "iopub.status.idle": "2024-05-24T14:39:48.115478Z", + "shell.execute_reply": "2024-05-24T14:39:48.114843Z" } }, "outputs": [ @@ -1574,10 +1574,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:03.479054Z", - "iopub.status.busy": "2024-05-24T14:10:03.478651Z", - "iopub.status.idle": "2024-05-24T14:10:03.490503Z", - "shell.execute_reply": "2024-05-24T14:10:03.489934Z" + "iopub.execute_input": "2024-05-24T14:39:48.117804Z", + "iopub.status.busy": "2024-05-24T14:39:48.117583Z", + "iopub.status.idle": "2024-05-24T14:39:48.132209Z", + "shell.execute_reply": "2024-05-24T14:39:48.131685Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 93a0a739e..e874415bb 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -708,25 +708,25 @@

    2. Fetch and normalize the Fashion-MNIST dataset

    -
    +
    -
    +
    -
    +
    -
    +
    [3]:
@@ -781,7 +781,7 @@ 
    2. Fetch and normalize the Fashion-MNIST dataset
    -
    +

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

    @@ -1030,8 +1030,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.673
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.435
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.532
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 5.226
 Computing feature embeddings ...

    @@ -1039,7 +1039,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1053,7 +1053,7 @@

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

    -
    +
    @@ -1062,8 +1062,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.659
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.469
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.334
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 5.031
 Computing feature embeddings ...

    @@ -1071,7 +1071,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1085,7 +1085,7 @@

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

    -
    +
    @@ -1094,8 +1094,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.590
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.372
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.344
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.913
 Computing feature embeddings ...

    @@ -1103,7 +1103,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1117,7 +1117,7 @@

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

    -
    +
    @@ -1189,7 +1189,7 @@

    7. Use cleanlab to find issues

    -
    +
    @@ -2077,7 +2077,7 @@

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

    diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index c30ae4dee..a782ae45c 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": "2024-05-24T14:10:06.073310Z", - "iopub.status.busy": "2024-05-24T14:10:06.072907Z", - "iopub.status.idle": "2024-05-24T14:10:08.920738Z", - "shell.execute_reply": "2024-05-24T14:10:08.920191Z" + "iopub.execute_input": "2024-05-24T14:39:51.401534Z", + "iopub.status.busy": "2024-05-24T14:39:51.401113Z", + "iopub.status.idle": "2024-05-24T14:39:54.721030Z", + "shell.execute_reply": "2024-05-24T14:39:54.720360Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:08.923304Z", - "iopub.status.busy": "2024-05-24T14:10:08.922858Z", - "iopub.status.idle": "2024-05-24T14:10:08.926527Z", - "shell.execute_reply": "2024-05-24T14:10:08.926059Z" + "iopub.execute_input": "2024-05-24T14:39:54.724172Z", + "iopub.status.busy": "2024-05-24T14:39:54.723586Z", + "iopub.status.idle": "2024-05-24T14:39:54.727697Z", + "shell.execute_reply": "2024-05-24T14:39:54.727140Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:08.928488Z", - "iopub.status.busy": "2024-05-24T14:10:08.928160Z", - "iopub.status.idle": "2024-05-24T14:10:10.113056Z", - "shell.execute_reply": "2024-05-24T14:10:10.112471Z" + "iopub.execute_input": "2024-05-24T14:39:54.730054Z", + "iopub.status.busy": "2024-05-24T14:39:54.729681Z", + "iopub.status.idle": "2024-05-24T14:39:56.012074Z", + "shell.execute_reply": "2024-05-24T14:39:56.011470Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20928c42639c40d19008649453d96b2d", + "model_id": "eb73512244bd426c86fb781343718302", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e9ede0b093f468f8f3fd69c4f80a03d", + "model_id": "016a7a75a2b645a4be4fc7565a3feffb", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "acbb7092c3624d1db7690371aa52add2", + "model_id": "06eebd2cb0b94c568a5db381a8530569", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7564a055b104dcfad12b4171b8c013a", + "model_id": "ce9901284426434a9b5f9a77243c05c6", "version_major": 2, "version_minor": 0 }, @@ -246,10 +246,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:10.115249Z", - "iopub.status.busy": "2024-05-24T14:10:10.115062Z", - "iopub.status.idle": "2024-05-24T14:10:10.119023Z", - "shell.execute_reply": "2024-05-24T14:10:10.118570Z" + "iopub.execute_input": "2024-05-24T14:39:56.014461Z", + "iopub.status.busy": "2024-05-24T14:39:56.014103Z", + "iopub.status.idle": "2024-05-24T14:39:56.018169Z", + "shell.execute_reply": "2024-05-24T14:39:56.017636Z" } }, "outputs": [ @@ -274,17 +274,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:10.120914Z", - "iopub.status.busy": "2024-05-24T14:10:10.120736Z", - "iopub.status.idle": "2024-05-24T14:10:20.994419Z", - "shell.execute_reply": "2024-05-24T14:10:20.993838Z" + "iopub.execute_input": "2024-05-24T14:39:56.020642Z", + "iopub.status.busy": "2024-05-24T14:39:56.020238Z", + "iopub.status.idle": "2024-05-24T14:40:07.664607Z", + "shell.execute_reply": "2024-05-24T14:40:07.663851Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2b82540d1d36435d8d3f12c70565158d", + "model_id": "0311a66b3899492e96791b9ec51f70dc", "version_major": 2, "version_minor": 0 }, @@ -322,10 +322,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:20.996883Z", - "iopub.status.busy": "2024-05-24T14:10:20.996528Z", - "iopub.status.idle": "2024-05-24T14:10:39.351446Z", - "shell.execute_reply": "2024-05-24T14:10:39.350906Z" + "iopub.execute_input": "2024-05-24T14:40:07.668254Z", + "iopub.status.busy": "2024-05-24T14:40:07.667800Z", + "iopub.status.idle": "2024-05-24T14:40:26.400120Z", + "shell.execute_reply": "2024-05-24T14:40:26.399309Z" } }, "outputs": [], @@ -358,10 +358,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:39.353941Z", - "iopub.status.busy": "2024-05-24T14:10:39.353763Z", - "iopub.status.idle": "2024-05-24T14:10:39.358516Z", - "shell.execute_reply": "2024-05-24T14:10:39.358093Z" + "iopub.execute_input": "2024-05-24T14:40:26.403345Z", + "iopub.status.busy": "2024-05-24T14:40:26.402927Z", + "iopub.status.idle": "2024-05-24T14:40:26.408261Z", + "shell.execute_reply": "2024-05-24T14:40:26.407613Z" } }, "outputs": [], @@ -399,10 +399,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:39.360304Z", - "iopub.status.busy": "2024-05-24T14:10:39.360126Z", - "iopub.status.idle": "2024-05-24T14:10:39.364229Z", - "shell.execute_reply": "2024-05-24T14:10:39.363697Z" + "iopub.execute_input": "2024-05-24T14:40:26.410749Z", + "iopub.status.busy": "2024-05-24T14:40:26.410358Z", + "iopub.status.idle": "2024-05-24T14:40:26.414977Z", + "shell.execute_reply": "2024-05-24T14:40:26.414497Z" }, "nbsphinx": "hidden" }, @@ -539,10 +539,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:39.366381Z", - "iopub.status.busy": "2024-05-24T14:10:39.366055Z", - "iopub.status.idle": "2024-05-24T14:10:39.374892Z", - "shell.execute_reply": "2024-05-24T14:10:39.374422Z" + "iopub.execute_input": "2024-05-24T14:40:26.417519Z", + "iopub.status.busy": "2024-05-24T14:40:26.417128Z", + "iopub.status.idle": "2024-05-24T14:40:26.426990Z", + "shell.execute_reply": "2024-05-24T14:40:26.426373Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:39.376897Z", - "iopub.status.busy": "2024-05-24T14:10:39.376573Z", - "iopub.status.idle": "2024-05-24T14:10:39.402869Z", - "shell.execute_reply": "2024-05-24T14:10:39.402406Z" + "iopub.execute_input": "2024-05-24T14:40:26.429640Z", + "iopub.status.busy": "2024-05-24T14:40:26.429171Z", + "iopub.status.idle": "2024-05-24T14:40:26.457392Z", + "shell.execute_reply": "2024-05-24T14:40:26.456837Z" } }, "outputs": [], @@ -707,10 +707,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:10:39.404868Z", - "iopub.status.busy": "2024-05-24T14:10:39.404539Z", - "iopub.status.idle": "2024-05-24T14:11:10.629979Z", - "shell.execute_reply": "2024-05-24T14:11:10.629317Z" + "iopub.execute_input": "2024-05-24T14:40:26.460392Z", + "iopub.status.busy": "2024-05-24T14:40:26.459839Z", + "iopub.status.idle": "2024-05-24T14:41:02.324665Z", + "shell.execute_reply": "2024-05-24T14:41:02.323997Z" } }, "outputs": [ @@ -726,21 +726,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.673\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.532\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.435\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 5.226\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f98d33dc3b54e0bb570418033290a10", + "model_id": "df3eb5942e94463d8ef4681e64d74811", "version_major": 2, "version_minor": 0 }, @@ -761,7 +761,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "941ce9ad8c97424cb79a597244bec856", + "model_id": "0c96a3f2323b4792a6a2959bdfd9966c", "version_major": 2, "version_minor": 0 }, @@ -784,21 +784,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.659\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.334\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.469\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 5.031\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2365870067d44d6b5105d0c496b2126", + "model_id": "bce4e34808954d08a06d5d174764aee5", "version_major": 2, "version_minor": 0 }, @@ -819,7 +819,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40b4bb6b0525445faa3a71aab418104a", + "model_id": "075a356c128b421097ba7828f20c2808", "version_major": 2, "version_minor": 0 }, @@ -842,21 +842,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.590\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.344\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.372\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.913\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02cfa49961f9497cb9ecbe65e96f8441", + "model_id": "0e6de3035fd743809c30b1e2f209bd90", "version_major": 2, "version_minor": 0 }, @@ -877,7 +877,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd83501a7a8245c7ac9fda5f0e9df22d", + "model_id": "c3a7eece24944ea4981012166d0b749a", "version_major": 2, "version_minor": 0 }, @@ -956,10 +956,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:11:10.632628Z", - "iopub.status.busy": "2024-05-24T14:11:10.632390Z", - "iopub.status.idle": "2024-05-24T14:11:10.649457Z", - "shell.execute_reply": "2024-05-24T14:11:10.648901Z" + "iopub.execute_input": "2024-05-24T14:41:02.327216Z", + "iopub.status.busy": "2024-05-24T14:41:02.326883Z", + "iopub.status.idle": "2024-05-24T14:41:02.344507Z", + "shell.execute_reply": "2024-05-24T14:41:02.343999Z" } }, "outputs": [], @@ -984,10 +984,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:11:10.651614Z", - "iopub.status.busy": "2024-05-24T14:11:10.651222Z", - "iopub.status.idle": "2024-05-24T14:11:11.097614Z", - "shell.execute_reply": "2024-05-24T14:11:11.096994Z" + "iopub.execute_input": "2024-05-24T14:41:02.347058Z", + "iopub.status.busy": "2024-05-24T14:41:02.346641Z", + "iopub.status.idle": "2024-05-24T14:41:02.846262Z", + "shell.execute_reply": "2024-05-24T14:41:02.845616Z" } }, "outputs": [], @@ -1007,10 +1007,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:11:11.100272Z", - "iopub.status.busy": "2024-05-24T14:11:11.099945Z", - "iopub.status.idle": "2024-05-24T14:14:48.052908Z", - "shell.execute_reply": "2024-05-24T14:14:48.052297Z" + "iopub.execute_input": "2024-05-24T14:41:02.849042Z", + "iopub.status.busy": "2024-05-24T14:41:02.848683Z", + "iopub.status.idle": "2024-05-24T14:44:45.648449Z", + "shell.execute_reply": "2024-05-24T14:44:45.647817Z" } }, "outputs": [ @@ -1058,7 +1058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31134bbba8f44b82a41291a10ff5c70d", + "model_id": "6c4fa066416d431b934093b5c1c09e92", "version_major": 2, "version_minor": 0 }, @@ -1097,10 +1097,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:48.055446Z", - "iopub.status.busy": "2024-05-24T14:14:48.055024Z", - "iopub.status.idle": "2024-05-24T14:14:48.517330Z", - "shell.execute_reply": "2024-05-24T14:14:48.516784Z" + "iopub.execute_input": "2024-05-24T14:44:45.650979Z", + "iopub.status.busy": "2024-05-24T14:44:45.650479Z", + "iopub.status.idle": "2024-05-24T14:44:46.123901Z", + "shell.execute_reply": "2024-05-24T14:44:46.123320Z" } }, "outputs": [ @@ -1241,10 +1241,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:48.519979Z", - "iopub.status.busy": "2024-05-24T14:14:48.519653Z", - "iopub.status.idle": "2024-05-24T14:14:48.581345Z", - "shell.execute_reply": "2024-05-24T14:14:48.580825Z" + "iopub.execute_input": "2024-05-24T14:44:46.126867Z", + "iopub.status.busy": "2024-05-24T14:44:46.126350Z", + "iopub.status.idle": "2024-05-24T14:44:46.188788Z", + "shell.execute_reply": "2024-05-24T14:44:46.188215Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:48.583708Z", - "iopub.status.busy": "2024-05-24T14:14:48.583376Z", - "iopub.status.idle": "2024-05-24T14:14:48.592238Z", - "shell.execute_reply": "2024-05-24T14:14:48.591802Z" + "iopub.execute_input": "2024-05-24T14:44:46.191154Z", + "iopub.status.busy": "2024-05-24T14:44:46.190843Z", + "iopub.status.idle": "2024-05-24T14:44:46.200732Z", + "shell.execute_reply": "2024-05-24T14:44:46.200202Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:48.594350Z", - "iopub.status.busy": "2024-05-24T14:14:48.593998Z", - "iopub.status.idle": "2024-05-24T14:14:48.598612Z", - "shell.execute_reply": "2024-05-24T14:14:48.598154Z" + "iopub.execute_input": "2024-05-24T14:44:46.203135Z", + "iopub.status.busy": "2024-05-24T14:44:46.202741Z", + "iopub.status.idle": "2024-05-24T14:44:46.207957Z", + "shell.execute_reply": "2024-05-24T14:44:46.207400Z" }, "nbsphinx": "hidden" }, @@ -1530,10 +1530,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:48.600526Z", - "iopub.status.busy": "2024-05-24T14:14:48.600354Z", - "iopub.status.idle": "2024-05-24T14:14:49.096890Z", - "shell.execute_reply": "2024-05-24T14:14:49.096358Z" + "iopub.execute_input": "2024-05-24T14:44:46.210322Z", + "iopub.status.busy": "2024-05-24T14:44:46.209948Z", + "iopub.status.idle": "2024-05-24T14:44:46.759504Z", + "shell.execute_reply": "2024-05-24T14:44:46.758890Z" } }, "outputs": [ @@ -1568,10 +1568,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.098963Z", - "iopub.status.busy": "2024-05-24T14:14:49.098785Z", - "iopub.status.idle": "2024-05-24T14:14:49.107340Z", - "shell.execute_reply": "2024-05-24T14:14:49.106910Z" + "iopub.execute_input": "2024-05-24T14:44:46.761720Z", + "iopub.status.busy": "2024-05-24T14:44:46.761534Z", + "iopub.status.idle": "2024-05-24T14:44:46.770434Z", + "shell.execute_reply": "2024-05-24T14:44:46.769873Z" } }, "outputs": [ @@ -1738,10 +1738,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.109526Z", - "iopub.status.busy": "2024-05-24T14:14:49.109117Z", - "iopub.status.idle": "2024-05-24T14:14:49.116331Z", - "shell.execute_reply": "2024-05-24T14:14:49.115802Z" + "iopub.execute_input": "2024-05-24T14:44:46.772713Z", + "iopub.status.busy": "2024-05-24T14:44:46.772399Z", + "iopub.status.idle": "2024-05-24T14:44:46.779606Z", + "shell.execute_reply": "2024-05-24T14:44:46.779126Z" }, "nbsphinx": "hidden" }, @@ -1817,10 +1817,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.118535Z", - "iopub.status.busy": "2024-05-24T14:14:49.118010Z", - "iopub.status.idle": "2024-05-24T14:14:49.583947Z", - "shell.execute_reply": "2024-05-24T14:14:49.583351Z" + "iopub.execute_input": "2024-05-24T14:44:46.781638Z", + "iopub.status.busy": "2024-05-24T14:44:46.781307Z", + "iopub.status.idle": "2024-05-24T14:44:47.272826Z", + "shell.execute_reply": "2024-05-24T14:44:47.272237Z" } }, "outputs": [ @@ -1857,10 +1857,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.586205Z", - "iopub.status.busy": "2024-05-24T14:14:49.585797Z", - "iopub.status.idle": "2024-05-24T14:14:49.601251Z", - "shell.execute_reply": "2024-05-24T14:14:49.600784Z" + "iopub.execute_input": "2024-05-24T14:44:47.275640Z", + "iopub.status.busy": "2024-05-24T14:44:47.275262Z", + "iopub.status.idle": "2024-05-24T14:44:47.292474Z", + "shell.execute_reply": "2024-05-24T14:44:47.291889Z" } }, "outputs": [ @@ -2017,10 +2017,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.603488Z", - "iopub.status.busy": "2024-05-24T14:14:49.603154Z", - "iopub.status.idle": "2024-05-24T14:14:49.608600Z", - "shell.execute_reply": "2024-05-24T14:14:49.608100Z" + "iopub.execute_input": "2024-05-24T14:44:47.294879Z", + "iopub.status.busy": "2024-05-24T14:44:47.294488Z", + "iopub.status.idle": "2024-05-24T14:44:47.300729Z", + "shell.execute_reply": "2024-05-24T14:44:47.300111Z" }, "nbsphinx": "hidden" }, @@ -2065,10 +2065,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:49.610524Z", - "iopub.status.busy": "2024-05-24T14:14:49.610265Z", - "iopub.status.idle": "2024-05-24T14:14:50.068831Z", - "shell.execute_reply": "2024-05-24T14:14:50.068303Z" + "iopub.execute_input": "2024-05-24T14:44:47.303050Z", + "iopub.status.busy": "2024-05-24T14:44:47.302711Z", + "iopub.status.idle": "2024-05-24T14:44:47.802911Z", + "shell.execute_reply": "2024-05-24T14:44:47.802299Z" } }, "outputs": [ @@ -2150,10 +2150,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:50.071357Z", - "iopub.status.busy": "2024-05-24T14:14:50.070986Z", - "iopub.status.idle": "2024-05-24T14:14:50.080349Z", - "shell.execute_reply": "2024-05-24T14:14:50.079849Z" + "iopub.execute_input": "2024-05-24T14:44:47.805882Z", + "iopub.status.busy": "2024-05-24T14:44:47.805637Z", + "iopub.status.idle": "2024-05-24T14:44:47.815882Z", + "shell.execute_reply": "2024-05-24T14:44:47.815245Z" } }, "outputs": [ @@ -2281,10 +2281,10 @@ "execution_count": 27, "metadata": { "execution": { - 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"ff4f3fe8e99c407baa5b103a8cfc74b4": { + "fd3228089d8c403f82370ef418e46dd0": { + "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 + } + }, + "fd63d5555e5b49eb8659ed29c6eb5088": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index d94a4b865..1e9fa48cd 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": "2024-05-24T14:14:54.098841Z", - "iopub.status.busy": "2024-05-24T14:14:54.098352Z", - "iopub.status.idle": "2024-05-24T14:14:55.207876Z", - "shell.execute_reply": "2024-05-24T14:14:55.207310Z" + "iopub.execute_input": "2024-05-24T14:44:52.322424Z", + "iopub.status.busy": "2024-05-24T14:44:52.322241Z", + "iopub.status.idle": "2024-05-24T14:44:53.606935Z", + "shell.execute_reply": "2024-05-24T14:44:53.606311Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:14:55.210580Z", - "iopub.status.busy": "2024-05-24T14:14:55.210160Z", - "iopub.status.idle": "2024-05-24T14:14:55.228856Z", - "shell.execute_reply": "2024-05-24T14:14:55.228428Z" + "iopub.execute_input": "2024-05-24T14:44:53.609669Z", + "iopub.status.busy": "2024-05-24T14:44:53.609194Z", + "iopub.status.idle": "2024-05-24T14:44:53.628346Z", + "shell.execute_reply": "2024-05-24T14:44:53.627851Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:55.231119Z", - "iopub.status.busy": "2024-05-24T14:14:55.230735Z", - "iopub.status.idle": "2024-05-24T14:14:55.254383Z", - "shell.execute_reply": "2024-05-24T14:14:55.253810Z" + "iopub.execute_input": "2024-05-24T14:44:53.630740Z", + "iopub.status.busy": "2024-05-24T14:44:53.630459Z", + "iopub.status.idle": "2024-05-24T14:44:53.654246Z", + "shell.execute_reply": "2024-05-24T14:44:53.653651Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:55.256653Z", - "iopub.status.busy": "2024-05-24T14:14:55.256294Z", - "iopub.status.idle": "2024-05-24T14:14:55.259887Z", - "shell.execute_reply": "2024-05-24T14:14:55.259428Z" + "iopub.execute_input": "2024-05-24T14:44:53.656447Z", + "iopub.status.busy": "2024-05-24T14:44:53.656255Z", + "iopub.status.idle": "2024-05-24T14:44:53.659848Z", + "shell.execute_reply": "2024-05-24T14:44:53.659404Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:55.261807Z", - "iopub.status.busy": "2024-05-24T14:14:55.261630Z", - "iopub.status.idle": "2024-05-24T14:14:55.269136Z", - "shell.execute_reply": "2024-05-24T14:14:55.268703Z" + "iopub.execute_input": "2024-05-24T14:44:53.661755Z", + "iopub.status.busy": "2024-05-24T14:44:53.661577Z", + "iopub.status.idle": "2024-05-24T14:44:53.669393Z", + "shell.execute_reply": "2024-05-24T14:44:53.668933Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:55.271101Z", - "iopub.status.busy": "2024-05-24T14:14:55.270927Z", - "iopub.status.idle": "2024-05-24T14:14:55.273579Z", - "shell.execute_reply": "2024-05-24T14:14:55.273122Z" + "iopub.execute_input": "2024-05-24T14:44:53.671545Z", + "iopub.status.busy": "2024-05-24T14:44:53.671333Z", + "iopub.status.idle": "2024-05-24T14:44:53.674178Z", + "shell.execute_reply": "2024-05-24T14:44:53.673621Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:55.275435Z", - "iopub.status.busy": "2024-05-24T14:14:55.275265Z", - "iopub.status.idle": "2024-05-24T14:14:58.280354Z", - "shell.execute_reply": "2024-05-24T14:14:58.279815Z" + "iopub.execute_input": "2024-05-24T14:44:53.676469Z", + "iopub.status.busy": "2024-05-24T14:44:53.676144Z", + "iopub.status.idle": "2024-05-24T14:44:56.693344Z", + "shell.execute_reply": "2024-05-24T14:44:56.692777Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:58.282716Z", - "iopub.status.busy": "2024-05-24T14:14:58.282530Z", - "iopub.status.idle": "2024-05-24T14:14:58.292216Z", - "shell.execute_reply": "2024-05-24T14:14:58.291659Z" + "iopub.execute_input": "2024-05-24T14:44:56.696218Z", + "iopub.status.busy": "2024-05-24T14:44:56.695783Z", + "iopub.status.idle": "2024-05-24T14:44:56.706071Z", + "shell.execute_reply": "2024-05-24T14:44:56.705482Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:58.294636Z", - "iopub.status.busy": "2024-05-24T14:14:58.294203Z", - "iopub.status.idle": "2024-05-24T14:14:59.988951Z", - "shell.execute_reply": "2024-05-24T14:14:59.988348Z" + "iopub.execute_input": "2024-05-24T14:44:56.708278Z", + "iopub.status.busy": "2024-05-24T14:44:56.707953Z", + "iopub.status.idle": "2024-05-24T14:44:58.804239Z", + "shell.execute_reply": "2024-05-24T14:44:58.803588Z" } }, "outputs": [ @@ -484,10 +484,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:14:59.992630Z", - "iopub.status.busy": "2024-05-24T14:14:59.991299Z", - "iopub.status.idle": "2024-05-24T14:15:00.016536Z", - "shell.execute_reply": "2024-05-24T14:15:00.016018Z" + "iopub.execute_input": "2024-05-24T14:44:58.807444Z", + "iopub.status.busy": "2024-05-24T14:44:58.806724Z", + "iopub.status.idle": "2024-05-24T14:44:58.832941Z", + "shell.execute_reply": "2024-05-24T14:44:58.832373Z" }, "scrolled": true }, @@ -612,10 +612,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.020287Z", - "iopub.status.busy": "2024-05-24T14:15:00.019355Z", - "iopub.status.idle": "2024-05-24T14:15:00.030684Z", - "shell.execute_reply": "2024-05-24T14:15:00.030180Z" + "iopub.execute_input": "2024-05-24T14:44:58.835905Z", + "iopub.status.busy": "2024-05-24T14:44:58.835448Z", + "iopub.status.idle": "2024-05-24T14:44:58.846516Z", + "shell.execute_reply": "2024-05-24T14:44:58.845849Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.034213Z", - "iopub.status.busy": "2024-05-24T14:15:00.033273Z", - "iopub.status.idle": "2024-05-24T14:15:00.045968Z", - "shell.execute_reply": "2024-05-24T14:15:00.045484Z" + "iopub.execute_input": "2024-05-24T14:44:58.850825Z", + "iopub.status.busy": "2024-05-24T14:44:58.849766Z", + "iopub.status.idle": "2024-05-24T14:44:58.862770Z", + "shell.execute_reply": "2024-05-24T14:44:58.862129Z" } }, "outputs": [ @@ -851,10 +851,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.048338Z", - "iopub.status.busy": "2024-05-24T14:15:00.048161Z", - "iopub.status.idle": "2024-05-24T14:15:00.056107Z", - "shell.execute_reply": "2024-05-24T14:15:00.055668Z" + "iopub.execute_input": "2024-05-24T14:44:58.865319Z", + "iopub.status.busy": "2024-05-24T14:44:58.865073Z", + "iopub.status.idle": "2024-05-24T14:44:58.874937Z", + "shell.execute_reply": "2024-05-24T14:44:58.874304Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.058355Z", - "iopub.status.busy": "2024-05-24T14:15:00.058000Z", - "iopub.status.idle": "2024-05-24T14:15:00.066445Z", - "shell.execute_reply": "2024-05-24T14:15:00.065994Z" + "iopub.execute_input": "2024-05-24T14:44:58.877314Z", + "iopub.status.busy": "2024-05-24T14:44:58.877102Z", + "iopub.status.idle": "2024-05-24T14:44:58.887489Z", + "shell.execute_reply": "2024-05-24T14:44:58.886910Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.068436Z", - "iopub.status.busy": "2024-05-24T14:15:00.068115Z", - "iopub.status.idle": "2024-05-24T14:15:00.074549Z", - "shell.execute_reply": "2024-05-24T14:15:00.074120Z" + "iopub.execute_input": "2024-05-24T14:44:58.889910Z", + "iopub.status.busy": "2024-05-24T14:44:58.889415Z", + "iopub.status.idle": "2024-05-24T14:44:58.897311Z", + "shell.execute_reply": "2024-05-24T14:44:58.896787Z" } }, "outputs": [ @@ -1169,10 +1169,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.076621Z", - "iopub.status.busy": "2024-05-24T14:15:00.076315Z", - "iopub.status.idle": "2024-05-24T14:15:00.082670Z", - "shell.execute_reply": "2024-05-24T14:15:00.082227Z" + "iopub.execute_input": "2024-05-24T14:44:58.899745Z", + "iopub.status.busy": "2024-05-24T14:44:58.899204Z", + "iopub.status.idle": "2024-05-24T14:44:58.906944Z", + "shell.execute_reply": "2024-05-24T14:44:58.906340Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:00.084552Z", - "iopub.status.busy": "2024-05-24T14:15:00.084382Z", - "iopub.status.idle": "2024-05-24T14:15:00.090947Z", - "shell.execute_reply": "2024-05-24T14:15:00.090502Z" + "iopub.execute_input": "2024-05-24T14:44:58.909472Z", + "iopub.status.busy": "2024-05-24T14:44:58.909111Z", + "iopub.status.idle": "2024-05-24T14:44:58.916288Z", + "shell.execute_reply": "2024-05-24T14:44:58.915759Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 9845a47bc..6a1776511 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -772,7 +772,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone'}
+Classes: {'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'change_pin', 'lost_or_stolen_phone', 'getting_spare_card', 'cancel_transfer', '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 119953017..3b3ed73cf 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": "2024-05-24T14:15:02.537859Z", - "iopub.status.busy": "2024-05-24T14:15:02.537681Z", - "iopub.status.idle": "2024-05-24T14:15:05.194925Z", - "shell.execute_reply": "2024-05-24T14:15:05.194366Z" + "iopub.execute_input": "2024-05-24T14:45:02.150694Z", + "iopub.status.busy": "2024-05-24T14:45:02.150495Z", + "iopub.status.idle": "2024-05-24T14:45:05.178795Z", + "shell.execute_reply": "2024-05-24T14:45:05.178169Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:15:05.197442Z", - "iopub.status.busy": "2024-05-24T14:15:05.197040Z", - "iopub.status.idle": "2024-05-24T14:15:05.200272Z", - "shell.execute_reply": "2024-05-24T14:15:05.199820Z" + "iopub.execute_input": "2024-05-24T14:45:05.181684Z", + "iopub.status.busy": "2024-05-24T14:45:05.181189Z", + "iopub.status.idle": "2024-05-24T14:45:05.184914Z", + "shell.execute_reply": "2024-05-24T14:45:05.184370Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:05.202109Z", - "iopub.status.busy": "2024-05-24T14:15:05.201833Z", - "iopub.status.idle": "2024-05-24T14:15:05.204818Z", - "shell.execute_reply": "2024-05-24T14:15:05.204389Z" + "iopub.execute_input": "2024-05-24T14:45:05.187312Z", + "iopub.status.busy": "2024-05-24T14:45:05.186967Z", + "iopub.status.idle": "2024-05-24T14:45:05.190776Z", + "shell.execute_reply": "2024-05-24T14:45:05.190181Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:05.206788Z", - "iopub.status.busy": "2024-05-24T14:15:05.206459Z", - "iopub.status.idle": "2024-05-24T14:15:05.228334Z", - "shell.execute_reply": "2024-05-24T14:15:05.227878Z" + "iopub.execute_input": "2024-05-24T14:45:05.193180Z", + "iopub.status.busy": "2024-05-24T14:45:05.192739Z", + "iopub.status.idle": "2024-05-24T14:45:05.219200Z", + "shell.execute_reply": "2024-05-24T14:45:05.218614Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:05.230281Z", - "iopub.status.busy": "2024-05-24T14:15:05.230100Z", - "iopub.status.idle": "2024-05-24T14:15:05.233644Z", - "shell.execute_reply": "2024-05-24T14:15:05.233136Z" + "iopub.execute_input": "2024-05-24T14:45:05.221441Z", + "iopub.status.busy": "2024-05-24T14:45:05.221197Z", + "iopub.status.idle": "2024-05-24T14:45:05.225544Z", + "shell.execute_reply": "2024-05-24T14:45:05.225046Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone'}\n" + "Classes: {'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'change_pin', 'lost_or_stolen_phone', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:05.235829Z", - "iopub.status.busy": "2024-05-24T14:15:05.235382Z", - "iopub.status.idle": "2024-05-24T14:15:05.238702Z", - "shell.execute_reply": "2024-05-24T14:15:05.238142Z" + "iopub.execute_input": "2024-05-24T14:45:05.227793Z", + "iopub.status.busy": "2024-05-24T14:45:05.227467Z", + "iopub.status.idle": "2024-05-24T14:45:05.230799Z", + "shell.execute_reply": "2024-05-24T14:45:05.230239Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:05.240705Z", - "iopub.status.busy": "2024-05-24T14:15:05.240437Z", - "iopub.status.idle": "2024-05-24T14:15:08.883034Z", - "shell.execute_reply": "2024-05-24T14:15:08.882468Z" + "iopub.execute_input": "2024-05-24T14:45:05.232988Z", + "iopub.status.busy": "2024-05-24T14:45:05.232655Z", + "iopub.status.idle": "2024-05-24T14:45:09.138023Z", + "shell.execute_reply": "2024-05-24T14:45:09.137388Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:08.885682Z", - "iopub.status.busy": "2024-05-24T14:15:08.885287Z", - "iopub.status.idle": "2024-05-24T14:15:09.760168Z", - "shell.execute_reply": "2024-05-24T14:15:09.759557Z" + "iopub.execute_input": "2024-05-24T14:45:09.141512Z", + "iopub.status.busy": "2024-05-24T14:45:09.141060Z", + "iopub.status.idle": "2024-05-24T14:45:10.072192Z", + "shell.execute_reply": "2024-05-24T14:45:10.071573Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:09.763180Z", - "iopub.status.busy": "2024-05-24T14:15:09.762785Z", - "iopub.status.idle": "2024-05-24T14:15:09.765729Z", - "shell.execute_reply": "2024-05-24T14:15:09.765234Z" + "iopub.execute_input": "2024-05-24T14:45:10.075252Z", + "iopub.status.busy": "2024-05-24T14:45:10.074858Z", + "iopub.status.idle": "2024-05-24T14:45:10.077888Z", + "shell.execute_reply": "2024-05-24T14:45:10.077385Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:09.768167Z", - "iopub.status.busy": "2024-05-24T14:15:09.767774Z", - "iopub.status.idle": "2024-05-24T14:15:11.328288Z", - "shell.execute_reply": "2024-05-24T14:15:11.327690Z" + "iopub.execute_input": "2024-05-24T14:45:10.081242Z", + "iopub.status.busy": "2024-05-24T14:45:10.080262Z", + "iopub.status.idle": "2024-05-24T14:45:11.865544Z", + "shell.execute_reply": "2024-05-24T14:45:11.864902Z" }, "scrolled": true }, @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.331273Z", - "iopub.status.busy": "2024-05-24T14:15:11.330654Z", - "iopub.status.idle": "2024-05-24T14:15:11.354286Z", - "shell.execute_reply": "2024-05-24T14:15:11.353755Z" + "iopub.execute_input": "2024-05-24T14:45:11.868779Z", + "iopub.status.busy": "2024-05-24T14:45:11.868082Z", + "iopub.status.idle": "2024-05-24T14:45:11.895768Z", + "shell.execute_reply": "2024-05-24T14:45:11.895204Z" }, "scrolled": true }, @@ -666,10 +666,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.356729Z", - "iopub.status.busy": "2024-05-24T14:15:11.356350Z", - "iopub.status.idle": "2024-05-24T14:15:11.365835Z", - "shell.execute_reply": "2024-05-24T14:15:11.365356Z" + "iopub.execute_input": "2024-05-24T14:45:11.898606Z", + "iopub.status.busy": "2024-05-24T14:45:11.898179Z", + "iopub.status.idle": "2024-05-24T14:45:11.907425Z", + "shell.execute_reply": "2024-05-24T14:45:11.906959Z" }, "scrolled": true }, @@ -779,10 +779,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.368224Z", - "iopub.status.busy": "2024-05-24T14:15:11.367853Z", - "iopub.status.idle": "2024-05-24T14:15:11.372217Z", - "shell.execute_reply": "2024-05-24T14:15:11.371648Z" + "iopub.execute_input": "2024-05-24T14:45:11.909748Z", + "iopub.status.busy": "2024-05-24T14:45:11.909552Z", + "iopub.status.idle": "2024-05-24T14:45:11.914142Z", + "shell.execute_reply": "2024-05-24T14:45:11.913672Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.374035Z", - "iopub.status.busy": "2024-05-24T14:15:11.373865Z", - "iopub.status.idle": "2024-05-24T14:15:11.380076Z", - "shell.execute_reply": "2024-05-24T14:15:11.379533Z" + "iopub.execute_input": "2024-05-24T14:45:11.916328Z", + "iopub.status.busy": "2024-05-24T14:45:11.915996Z", + "iopub.status.idle": "2024-05-24T14:45:11.922967Z", + "shell.execute_reply": "2024-05-24T14:45:11.922373Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.381912Z", - "iopub.status.busy": "2024-05-24T14:15:11.381737Z", - "iopub.status.idle": "2024-05-24T14:15:11.387963Z", - "shell.execute_reply": "2024-05-24T14:15:11.387516Z" + "iopub.execute_input": "2024-05-24T14:45:11.925259Z", + "iopub.status.busy": "2024-05-24T14:45:11.924925Z", + "iopub.status.idle": "2024-05-24T14:45:11.931878Z", + "shell.execute_reply": "2024-05-24T14:45:11.931307Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.390016Z", - "iopub.status.busy": "2024-05-24T14:15:11.389607Z", - "iopub.status.idle": "2024-05-24T14:15:11.395689Z", - "shell.execute_reply": "2024-05-24T14:15:11.395139Z" + "iopub.execute_input": "2024-05-24T14:45:11.934094Z", + "iopub.status.busy": "2024-05-24T14:45:11.933743Z", + "iopub.status.idle": "2024-05-24T14:45:11.940383Z", + "shell.execute_reply": "2024-05-24T14:45:11.939877Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.397792Z", - "iopub.status.busy": "2024-05-24T14:15:11.397394Z", - "iopub.status.idle": "2024-05-24T14:15:11.405737Z", - "shell.execute_reply": "2024-05-24T14:15:11.405184Z" + "iopub.execute_input": "2024-05-24T14:45:11.942819Z", + "iopub.status.busy": "2024-05-24T14:45:11.942451Z", + "iopub.status.idle": "2024-05-24T14:45:11.951803Z", + "shell.execute_reply": "2024-05-24T14:45:11.951216Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.407654Z", - "iopub.status.busy": "2024-05-24T14:15:11.407389Z", - "iopub.status.idle": "2024-05-24T14:15:11.412728Z", - "shell.execute_reply": "2024-05-24T14:15:11.412190Z" + "iopub.execute_input": "2024-05-24T14:45:11.954216Z", + "iopub.status.busy": "2024-05-24T14:45:11.953756Z", + "iopub.status.idle": "2024-05-24T14:45:11.960334Z", + "shell.execute_reply": "2024-05-24T14:45:11.959731Z" } }, "outputs": [ @@ -1322,10 +1322,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.414846Z", - "iopub.status.busy": "2024-05-24T14:15:11.414441Z", - "iopub.status.idle": "2024-05-24T14:15:11.419708Z", - "shell.execute_reply": "2024-05-24T14:15:11.419162Z" + "iopub.execute_input": "2024-05-24T14:45:11.962552Z", + "iopub.status.busy": "2024-05-24T14:45:11.962215Z", + "iopub.status.idle": "2024-05-24T14:45:11.968585Z", + "shell.execute_reply": "2024-05-24T14:45:11.968064Z" } }, "outputs": [ @@ -1404,10 +1404,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.421651Z", - "iopub.status.busy": "2024-05-24T14:15:11.421361Z", - "iopub.status.idle": "2024-05-24T14:15:11.424923Z", - "shell.execute_reply": "2024-05-24T14:15:11.424391Z" + "iopub.execute_input": "2024-05-24T14:45:11.970907Z", + "iopub.status.busy": "2024-05-24T14:45:11.970540Z", + "iopub.status.idle": "2024-05-24T14:45:11.974631Z", + "shell.execute_reply": "2024-05-24T14:45:11.974041Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:11.426952Z", - "iopub.status.busy": "2024-05-24T14:15:11.426663Z", - "iopub.status.idle": "2024-05-24T14:15:11.431827Z", - "shell.execute_reply": "2024-05-24T14:15:11.431290Z" + "iopub.execute_input": "2024-05-24T14:45:11.976819Z", + "iopub.status.busy": "2024-05-24T14:45:11.976633Z", + "iopub.status.idle": "2024-05-24T14:45:11.982658Z", + "shell.execute_reply": "2024-05-24T14:45:11.982008Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 36d924f66..0a647eebb 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:14.610940Z", - "iopub.status.busy": "2024-05-24T14:15:14.610753Z", - "iopub.status.idle": "2024-05-24T14:15:15.728888Z", - "shell.execute_reply": "2024-05-24T14:15:15.728288Z" + "iopub.execute_input": "2024-05-24T14:45:15.697439Z", + "iopub.status.busy": "2024-05-24T14:45:15.696972Z", + "iopub.status.idle": "2024-05-24T14:45:16.966680Z", + "shell.execute_reply": "2024-05-24T14:45:16.966064Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:15:15.731379Z", - "iopub.status.busy": "2024-05-24T14:15:15.731080Z", - "iopub.status.idle": "2024-05-24T14:15:15.733909Z", - "shell.execute_reply": "2024-05-24T14:15:15.733384Z" + "iopub.execute_input": "2024-05-24T14:45:16.969529Z", + "iopub.status.busy": "2024-05-24T14:45:16.969028Z", + "iopub.status.idle": "2024-05-24T14:45:16.972074Z", + "shell.execute_reply": "2024-05-24T14:45:16.971598Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:15.736141Z", - "iopub.status.busy": "2024-05-24T14:15:15.735843Z", - "iopub.status.idle": "2024-05-24T14:15:15.747936Z", - "shell.execute_reply": "2024-05-24T14:15:15.747398Z" + "iopub.execute_input": "2024-05-24T14:45:16.974146Z", + "iopub.status.busy": "2024-05-24T14:45:16.973963Z", + "iopub.status.idle": "2024-05-24T14:45:16.987306Z", + "shell.execute_reply": "2024-05-24T14:45:16.986785Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:15.750031Z", - "iopub.status.busy": "2024-05-24T14:15:15.749628Z", - "iopub.status.idle": "2024-05-24T14:15:19.525154Z", - "shell.execute_reply": "2024-05-24T14:15:19.524608Z" + "iopub.execute_input": "2024-05-24T14:45:16.989658Z", + "iopub.status.busy": "2024-05-24T14:45:16.989286Z", + "iopub.status.idle": "2024-05-24T14:45:21.303475Z", + "shell.execute_reply": "2024-05-24T14:45:21.302944Z" }, "id": "dhTHOg8Pyv5G" }, @@ -694,6 +694,9 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", + "\n", + "\n", + "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n" ] }, @@ -701,9 +704,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", - "\n", "------------------------------------------------------------\n", "| Generating a Cleanlab Dataset Health Summary |\n", "| for your dataset with 10,000 examples and 10 classes. |\n", diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 9170b51c3..bdfc5a617 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -812,13 +812,13 @@

    How can I find label issues in big datasets with limited memory?
    -
    +
    -
    +
    @@ -1721,7 +1721,7 @@

    How to handle near-duplicate data identified by cleanlab? 
    -/tmp/ipykernel_7826/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_7751/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values

    @@ -1763,7 +1763,7 @@

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

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

    diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 674094b79..73d86c7aa 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:21.500899Z", - "iopub.status.busy": "2024-05-24T14:15:21.500721Z", - "iopub.status.idle": "2024-05-24T14:15:22.604994Z", - "shell.execute_reply": "2024-05-24T14:15:22.604376Z" + "iopub.execute_input": "2024-05-24T14:45:23.942301Z", + "iopub.status.busy": "2024-05-24T14:45:23.941937Z", + "iopub.status.idle": "2024-05-24T14:45:25.204076Z", + "shell.execute_reply": "2024-05-24T14:45:25.203401Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:22.607764Z", - "iopub.status.busy": "2024-05-24T14:15:22.607483Z", - "iopub.status.idle": "2024-05-24T14:15:22.610976Z", - "shell.execute_reply": "2024-05-24T14:15:22.610514Z" + "iopub.execute_input": "2024-05-24T14:45:25.206952Z", + "iopub.status.busy": "2024-05-24T14:45:25.206597Z", + "iopub.status.idle": "2024-05-24T14:45:25.210512Z", + "shell.execute_reply": "2024-05-24T14:45:25.209901Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:22.613047Z", - "iopub.status.busy": "2024-05-24T14:15:22.612738Z", - "iopub.status.idle": "2024-05-24T14:15:25.566940Z", - "shell.execute_reply": "2024-05-24T14:15:25.566322Z" + "iopub.execute_input": "2024-05-24T14:45:25.213081Z", + "iopub.status.busy": "2024-05-24T14:45:25.212553Z", + "iopub.status.idle": "2024-05-24T14:45:28.515815Z", + "shell.execute_reply": "2024-05-24T14:45:28.515016Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.569953Z", - "iopub.status.busy": "2024-05-24T14:15:25.569272Z", - "iopub.status.idle": "2024-05-24T14:15:25.603615Z", - "shell.execute_reply": "2024-05-24T14:15:25.603034Z" + "iopub.execute_input": "2024-05-24T14:45:28.518988Z", + "iopub.status.busy": "2024-05-24T14:45:28.518284Z", + "iopub.status.idle": "2024-05-24T14:45:28.559173Z", + "shell.execute_reply": "2024-05-24T14:45:28.558547Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.606054Z", - "iopub.status.busy": "2024-05-24T14:15:25.605823Z", - "iopub.status.idle": "2024-05-24T14:15:25.636179Z", - "shell.execute_reply": "2024-05-24T14:15:25.635606Z" + "iopub.execute_input": "2024-05-24T14:45:28.562136Z", + "iopub.status.busy": "2024-05-24T14:45:28.561744Z", + "iopub.status.idle": "2024-05-24T14:45:28.600682Z", + "shell.execute_reply": "2024-05-24T14:45:28.599924Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.638766Z", - "iopub.status.busy": "2024-05-24T14:15:25.638538Z", - "iopub.status.idle": "2024-05-24T14:15:25.641621Z", - "shell.execute_reply": "2024-05-24T14:15:25.641086Z" + "iopub.execute_input": "2024-05-24T14:45:28.603843Z", + "iopub.status.busy": "2024-05-24T14:45:28.603149Z", + "iopub.status.idle": "2024-05-24T14:45:28.606730Z", + "shell.execute_reply": "2024-05-24T14:45:28.606244Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.643838Z", - "iopub.status.busy": "2024-05-24T14:15:25.643422Z", - "iopub.status.idle": "2024-05-24T14:15:25.646459Z", - "shell.execute_reply": "2024-05-24T14:15:25.645873Z" + "iopub.execute_input": "2024-05-24T14:45:28.608686Z", + "iopub.status.busy": "2024-05-24T14:45:28.608509Z", + "iopub.status.idle": "2024-05-24T14:45:28.611331Z", + "shell.execute_reply": "2024-05-24T14:45:28.610784Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.648605Z", - "iopub.status.busy": "2024-05-24T14:15:25.648313Z", - "iopub.status.idle": "2024-05-24T14:15:25.671140Z", - "shell.execute_reply": "2024-05-24T14:15:25.670588Z" + "iopub.execute_input": "2024-05-24T14:45:28.613651Z", + "iopub.status.busy": "2024-05-24T14:45:28.613169Z", + "iopub.status.idle": "2024-05-24T14:45:28.637572Z", + "shell.execute_reply": "2024-05-24T14:45:28.637012Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95be6d8825d74eb086ac77473c4733d7", + "model_id": "c25ee9ca03ae4190a30c70dc003f572b", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "75451c5e653745b5a4c5f8a0da3c982c", + "model_id": "b37157a1177d4e14b65a669721bbce04", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.678485Z", - "iopub.status.busy": "2024-05-24T14:15:25.678305Z", - "iopub.status.idle": "2024-05-24T14:15:25.684711Z", - "shell.execute_reply": "2024-05-24T14:15:25.684289Z" + "iopub.execute_input": "2024-05-24T14:45:28.643467Z", + "iopub.status.busy": "2024-05-24T14:45:28.643026Z", + "iopub.status.idle": "2024-05-24T14:45:28.649817Z", + "shell.execute_reply": "2024-05-24T14:45:28.649281Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.686576Z", - "iopub.status.busy": "2024-05-24T14:15:25.686401Z", - "iopub.status.idle": "2024-05-24T14:15:25.689865Z", - "shell.execute_reply": "2024-05-24T14:15:25.689433Z" + "iopub.execute_input": "2024-05-24T14:45:28.652191Z", + "iopub.status.busy": "2024-05-24T14:45:28.651745Z", + "iopub.status.idle": "2024-05-24T14:45:28.655343Z", + "shell.execute_reply": "2024-05-24T14:45:28.654789Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.691988Z", - "iopub.status.busy": "2024-05-24T14:15:25.691734Z", - "iopub.status.idle": "2024-05-24T14:15:25.698101Z", - "shell.execute_reply": "2024-05-24T14:15:25.697624Z" + "iopub.execute_input": "2024-05-24T14:45:28.657662Z", + "iopub.status.busy": "2024-05-24T14:45:28.657267Z", + "iopub.status.idle": "2024-05-24T14:45:28.663917Z", + "shell.execute_reply": "2024-05-24T14:45:28.663352Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.700111Z", - "iopub.status.busy": "2024-05-24T14:15:25.699757Z", - "iopub.status.idle": "2024-05-24T14:15:25.734461Z", - "shell.execute_reply": "2024-05-24T14:15:25.733858Z" + "iopub.execute_input": "2024-05-24T14:45:28.666041Z", + "iopub.status.busy": "2024-05-24T14:45:28.665632Z", + "iopub.status.idle": "2024-05-24T14:45:28.704346Z", + "shell.execute_reply": "2024-05-24T14:45:28.703704Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.736986Z", - "iopub.status.busy": "2024-05-24T14:15:25.736621Z", - "iopub.status.idle": "2024-05-24T14:15:25.769327Z", - "shell.execute_reply": "2024-05-24T14:15:25.768748Z" + "iopub.execute_input": "2024-05-24T14:45:28.707066Z", + "iopub.status.busy": "2024-05-24T14:45:28.706771Z", + "iopub.status.idle": "2024-05-24T14:45:28.747826Z", + "shell.execute_reply": "2024-05-24T14:45:28.747158Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.772172Z", - "iopub.status.busy": "2024-05-24T14:15:25.771811Z", - "iopub.status.idle": "2024-05-24T14:15:25.893611Z", - "shell.execute_reply": "2024-05-24T14:15:25.892952Z" + "iopub.execute_input": "2024-05-24T14:45:28.750570Z", + "iopub.status.busy": "2024-05-24T14:45:28.750275Z", + "iopub.status.idle": "2024-05-24T14:45:28.875889Z", + "shell.execute_reply": "2024-05-24T14:45:28.875200Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:25.896270Z", - "iopub.status.busy": "2024-05-24T14:15:25.895732Z", - "iopub.status.idle": "2024-05-24T14:15:28.879430Z", - "shell.execute_reply": "2024-05-24T14:15:28.878794Z" + "iopub.execute_input": "2024-05-24T14:45:28.878977Z", + "iopub.status.busy": "2024-05-24T14:45:28.878163Z", + "iopub.status.idle": "2024-05-24T14:45:31.985806Z", + "shell.execute_reply": "2024-05-24T14:45:31.985131Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:28.881868Z", - "iopub.status.busy": "2024-05-24T14:15:28.881501Z", - "iopub.status.idle": "2024-05-24T14:15:28.938411Z", - "shell.execute_reply": "2024-05-24T14:15:28.937818Z" + "iopub.execute_input": "2024-05-24T14:45:31.988121Z", + "iopub.status.busy": "2024-05-24T14:45:31.987923Z", + "iopub.status.idle": "2024-05-24T14:45:32.051701Z", + "shell.execute_reply": "2024-05-24T14:45:32.051096Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:28.940739Z", - "iopub.status.busy": "2024-05-24T14:15:28.940435Z", - "iopub.status.idle": "2024-05-24T14:15:28.980230Z", - "shell.execute_reply": "2024-05-24T14:15:28.979695Z" + "iopub.execute_input": "2024-05-24T14:45:32.054153Z", + "iopub.status.busy": "2024-05-24T14:45:32.053648Z", + "iopub.status.idle": "2024-05-24T14:45:32.096076Z", + "shell.execute_reply": "2024-05-24T14:45:32.095399Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "b75573c2", + "id": "ff7e47bd", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "9fd8d8a2", + "id": "a545f6ae", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1340,13 +1340,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "08344775", + "id": "50a70af7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:28.982386Z", - "iopub.status.busy": "2024-05-24T14:15:28.982037Z", - "iopub.status.idle": "2024-05-24T14:15:29.079330Z", - "shell.execute_reply": "2024-05-24T14:15:29.078815Z" + "iopub.execute_input": "2024-05-24T14:45:32.098568Z", + "iopub.status.busy": "2024-05-24T14:45:32.098115Z", + "iopub.status.idle": "2024-05-24T14:45:32.196782Z", + "shell.execute_reply": "2024-05-24T14:45:32.196225Z" } }, "outputs": [ @@ -1354,7 +1354,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ...\n", + "Finding underperforming_group issues ..." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1387,7 +1394,7 @@ }, { "cell_type": "markdown", - "id": "02c6bb31", + "id": "a691acfb", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1396,13 +1403,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "9ec9741d", + "id": "42a1edf9", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:29.081929Z", - "iopub.status.busy": "2024-05-24T14:15:29.081580Z", - "iopub.status.idle": "2024-05-24T14:15:29.148290Z", - "shell.execute_reply": "2024-05-24T14:15:29.147695Z" + "iopub.execute_input": "2024-05-24T14:45:32.199476Z", + "iopub.status.busy": "2024-05-24T14:45:32.199146Z", + "iopub.status.idle": "2024-05-24T14:45:32.267744Z", + "shell.execute_reply": "2024-05-24T14:45:32.266765Z" } }, "outputs": [ @@ -1438,7 +1445,7 @@ }, { "cell_type": "markdown", - "id": "347508a2", + "id": "4e418536", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1449,13 +1456,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "9516494e", + "id": "fbeae666", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:29.150776Z", - "iopub.status.busy": "2024-05-24T14:15:29.150395Z", - "iopub.status.idle": "2024-05-24T14:15:29.157995Z", - "shell.execute_reply": "2024-05-24T14:15:29.157454Z" + "iopub.execute_input": "2024-05-24T14:45:32.270605Z", + "iopub.status.busy": "2024-05-24T14:45:32.270108Z", + "iopub.status.idle": "2024-05-24T14:45:32.277724Z", + "shell.execute_reply": "2024-05-24T14:45:32.277295Z" } }, "outputs": [], @@ -1557,7 +1564,7 @@ }, { "cell_type": "markdown", - "id": "ff19877a", + "id": "9c68285e", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1572,13 +1579,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "de4255d6", + "id": "dcd5866d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:29.159891Z", - "iopub.status.busy": "2024-05-24T14:15:29.159717Z", - "iopub.status.idle": "2024-05-24T14:15:29.178542Z", - "shell.execute_reply": "2024-05-24T14:15:29.177993Z" + "iopub.execute_input": "2024-05-24T14:45:32.280067Z", + "iopub.status.busy": "2024-05-24T14:45:32.279633Z", + "iopub.status.idle": "2024-05-24T14:45:32.299756Z", + "shell.execute_reply": "2024-05-24T14:45:32.299136Z" } }, "outputs": [ @@ -1595,7 +1602,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7826/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_7751/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1629,13 +1636,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "5e24a20c", + "id": "1b1d5a8e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:29.180403Z", - "iopub.status.busy": "2024-05-24T14:15:29.180233Z", - "iopub.status.idle": "2024-05-24T14:15:29.183704Z", - "shell.execute_reply": "2024-05-24T14:15:29.183243Z" + "iopub.execute_input": "2024-05-24T14:45:32.302026Z", + "iopub.status.busy": "2024-05-24T14:45:32.301839Z", + "iopub.status.idle": "2024-05-24T14:45:32.305204Z", + "shell.execute_reply": "2024-05-24T14:45:32.304598Z" } }, "outputs": [ @@ -1730,49 +1737,43 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03afadb4c6c4471e9dc7c82ebe9425f4": { + "0a07dd6e5b154aa5b2b3e48d0d1ea5b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2055afcae0104786b552ba4cb44d0ce3": { + "0bdb86b88ecf4162923f9d8f32bfe265": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_543c1dc50c924a2faaa03f85b3e31748", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_03afadb4c6c4471e9dc7c82ebe9425f4", - "tabbable": null, - "tooltip": null, - "value": 50.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "41a0b74e527d4f439023f22628f1ce21": { + "0fd0312a616141b99a22d183bb6e8ee0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1825,30 +1826,7 @@ "width": null } }, - "4d4b416cbfc24e9dab45e68aa55b1085": { - "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, - 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"iopub.execute_input": "2024-05-24T14:15:32.588557Z", - "iopub.status.busy": "2024-05-24T14:15:32.588136Z", - "iopub.status.idle": "2024-05-24T14:15:33.743753Z", - "shell.execute_reply": "2024-05-24T14:15:33.743131Z" + "iopub.execute_input": "2024-05-24T14:45:35.902783Z", + "iopub.status.busy": "2024-05-24T14:45:35.902368Z", + "iopub.status.idle": "2024-05-24T14:45:37.237474Z", + "shell.execute_reply": "2024-05-24T14:45:37.236809Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:15:33.746551Z", - "iopub.status.busy": "2024-05-24T14:15:33.746128Z", - "iopub.status.idle": "2024-05-24T14:15:33.925358Z", - "shell.execute_reply": "2024-05-24T14:15:33.924834Z" + "iopub.execute_input": "2024-05-24T14:45:37.240637Z", + "iopub.status.busy": "2024-05-24T14:45:37.240091Z", + "iopub.status.idle": "2024-05-24T14:45:37.441541Z", + "shell.execute_reply": "2024-05-24T14:45:37.440936Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:33.927957Z", - "iopub.status.busy": "2024-05-24T14:15:33.927603Z", - "iopub.status.idle": "2024-05-24T14:15:33.940006Z", - "shell.execute_reply": "2024-05-24T14:15:33.939555Z" + "iopub.execute_input": "2024-05-24T14:45:37.444513Z", + "iopub.status.busy": "2024-05-24T14:45:37.444091Z", + "iopub.status.idle": "2024-05-24T14:45:37.458230Z", + "shell.execute_reply": "2024-05-24T14:45:37.457708Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:33.942061Z", - "iopub.status.busy": "2024-05-24T14:15:33.941722Z", - "iopub.status.idle": "2024-05-24T14:15:34.175429Z", - "shell.execute_reply": "2024-05-24T14:15:34.174892Z" + "iopub.execute_input": "2024-05-24T14:45:37.460856Z", + "iopub.status.busy": "2024-05-24T14:45:37.460470Z", + "iopub.status.idle": "2024-05-24T14:45:37.709353Z", + "shell.execute_reply": "2024-05-24T14:45:37.708715Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:34.177843Z", - "iopub.status.busy": "2024-05-24T14:15:34.177499Z", - "iopub.status.idle": "2024-05-24T14:15:34.203711Z", - "shell.execute_reply": "2024-05-24T14:15:34.203271Z" + "iopub.execute_input": "2024-05-24T14:45:37.711923Z", + "iopub.status.busy": "2024-05-24T14:45:37.711481Z", + "iopub.status.idle": "2024-05-24T14:45:37.739036Z", + "shell.execute_reply": "2024-05-24T14:45:37.738482Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:34.205820Z", - "iopub.status.busy": "2024-05-24T14:15:34.205489Z", - "iopub.status.idle": "2024-05-24T14:15:35.845385Z", - "shell.execute_reply": "2024-05-24T14:15:35.844697Z" + "iopub.execute_input": "2024-05-24T14:45:37.741959Z", + "iopub.status.busy": "2024-05-24T14:45:37.741456Z", + "iopub.status.idle": "2024-05-24T14:45:39.656313Z", + "shell.execute_reply": "2024-05-24T14:45:39.655526Z" } }, "outputs": [ @@ -483,10 +483,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:35.847771Z", - "iopub.status.busy": "2024-05-24T14:15:35.847394Z", - "iopub.status.idle": "2024-05-24T14:15:35.865283Z", - "shell.execute_reply": "2024-05-24T14:15:35.864728Z" + "iopub.execute_input": "2024-05-24T14:45:39.658832Z", + "iopub.status.busy": "2024-05-24T14:45:39.658447Z", + "iopub.status.idle": "2024-05-24T14:45:39.677418Z", + "shell.execute_reply": "2024-05-24T14:45:39.676923Z" }, "scrolled": true }, @@ -611,10 +611,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:35.867487Z", - "iopub.status.busy": "2024-05-24T14:15:35.867136Z", - "iopub.status.idle": "2024-05-24T14:15:37.270536Z", - "shell.execute_reply": "2024-05-24T14:15:37.269735Z" + "iopub.execute_input": "2024-05-24T14:45:39.679629Z", + "iopub.status.busy": "2024-05-24T14:45:39.679434Z", + "iopub.status.idle": "2024-05-24T14:45:41.217735Z", + "shell.execute_reply": "2024-05-24T14:45:41.217102Z" }, "id": "AaHC5MRKjruT" }, @@ -733,10 +733,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.273122Z", - "iopub.status.busy": "2024-05-24T14:15:37.272502Z", - "iopub.status.idle": "2024-05-24T14:15:37.286252Z", - "shell.execute_reply": "2024-05-24T14:15:37.285684Z" + "iopub.execute_input": "2024-05-24T14:45:41.220608Z", + "iopub.status.busy": "2024-05-24T14:45:41.219914Z", + "iopub.status.idle": "2024-05-24T14:45:41.234865Z", + "shell.execute_reply": "2024-05-24T14:45:41.234268Z" }, "id": "Wy27rvyhjruU" }, @@ -785,10 +785,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.288317Z", - "iopub.status.busy": "2024-05-24T14:15:37.288019Z", - "iopub.status.idle": "2024-05-24T14:15:37.359164Z", - "shell.execute_reply": "2024-05-24T14:15:37.358617Z" + "iopub.execute_input": "2024-05-24T14:45:41.237385Z", + "iopub.status.busy": "2024-05-24T14:45:41.237029Z", + "iopub.status.idle": "2024-05-24T14:45:41.322296Z", + "shell.execute_reply": "2024-05-24T14:45:41.321660Z" }, "id": "Db8YHnyVjruU" }, @@ -895,10 +895,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.361379Z", - "iopub.status.busy": "2024-05-24T14:15:37.361095Z", - "iopub.status.idle": "2024-05-24T14:15:37.570682Z", - "shell.execute_reply": "2024-05-24T14:15:37.570119Z" + "iopub.execute_input": "2024-05-24T14:45:41.324748Z", + "iopub.status.busy": "2024-05-24T14:45:41.324436Z", + "iopub.status.idle": "2024-05-24T14:45:41.542708Z", + "shell.execute_reply": "2024-05-24T14:45:41.542057Z" }, "id": "iJqAHuS2jruV" }, @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.573097Z", - "iopub.status.busy": "2024-05-24T14:15:37.572578Z", - "iopub.status.idle": "2024-05-24T14:15:37.589292Z", - "shell.execute_reply": "2024-05-24T14:15:37.588853Z" + "iopub.execute_input": "2024-05-24T14:45:41.545535Z", + "iopub.status.busy": "2024-05-24T14:45:41.545041Z", + "iopub.status.idle": "2024-05-24T14:45:41.564088Z", + "shell.execute_reply": "2024-05-24T14:45:41.563509Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1404,10 +1404,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.591309Z", - "iopub.status.busy": "2024-05-24T14:15:37.591132Z", - "iopub.status.idle": "2024-05-24T14:15:37.600708Z", - "shell.execute_reply": "2024-05-24T14:15:37.600257Z" + "iopub.execute_input": "2024-05-24T14:45:41.566407Z", + "iopub.status.busy": "2024-05-24T14:45:41.566075Z", + "iopub.status.idle": "2024-05-24T14:45:41.577153Z", + "shell.execute_reply": "2024-05-24T14:45:41.576625Z" }, "id": "0lonvOYvjruV" }, @@ -1554,10 +1554,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.602825Z", - "iopub.status.busy": "2024-05-24T14:15:37.602401Z", - "iopub.status.idle": "2024-05-24T14:15:37.685066Z", - "shell.execute_reply": "2024-05-24T14:15:37.684453Z" + "iopub.execute_input": "2024-05-24T14:45:41.579244Z", + "iopub.status.busy": "2024-05-24T14:45:41.579057Z", + "iopub.status.idle": "2024-05-24T14:45:41.674263Z", + "shell.execute_reply": "2024-05-24T14:45:41.673605Z" }, "id": "MfqTCa3kjruV" }, @@ -1638,10 +1638,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.687480Z", - "iopub.status.busy": "2024-05-24T14:15:37.687256Z", - "iopub.status.idle": "2024-05-24T14:15:37.810902Z", - "shell.execute_reply": "2024-05-24T14:15:37.810280Z" + "iopub.execute_input": "2024-05-24T14:45:41.676743Z", + "iopub.status.busy": "2024-05-24T14:45:41.676488Z", + "iopub.status.idle": "2024-05-24T14:45:41.824363Z", + "shell.execute_reply": "2024-05-24T14:45:41.823652Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1701,10 +1701,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.813085Z", - "iopub.status.busy": "2024-05-24T14:15:37.812859Z", - "iopub.status.idle": "2024-05-24T14:15:37.816644Z", - "shell.execute_reply": "2024-05-24T14:15:37.816193Z" + "iopub.execute_input": "2024-05-24T14:45:41.827312Z", + "iopub.status.busy": "2024-05-24T14:45:41.826779Z", + "iopub.status.idle": "2024-05-24T14:45:41.831665Z", + "shell.execute_reply": "2024-05-24T14:45:41.830972Z" }, "id": "0rXP3ZPWjruW" }, @@ -1742,10 +1742,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.818609Z", - "iopub.status.busy": "2024-05-24T14:15:37.818277Z", - "iopub.status.idle": "2024-05-24T14:15:37.821878Z", - "shell.execute_reply": "2024-05-24T14:15:37.821322Z" + "iopub.execute_input": "2024-05-24T14:45:41.834209Z", + "iopub.status.busy": "2024-05-24T14:45:41.833704Z", + "iopub.status.idle": "2024-05-24T14:45:41.838333Z", + "shell.execute_reply": "2024-05-24T14:45:41.837802Z" }, "id": "-iRPe8KXjruW" }, @@ -1800,10 +1800,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.823971Z", - "iopub.status.busy": "2024-05-24T14:15:37.823650Z", - "iopub.status.idle": "2024-05-24T14:15:37.860389Z", - "shell.execute_reply": "2024-05-24T14:15:37.859899Z" + "iopub.execute_input": "2024-05-24T14:45:41.840850Z", + "iopub.status.busy": "2024-05-24T14:45:41.840480Z", + "iopub.status.idle": "2024-05-24T14:45:41.882153Z", + "shell.execute_reply": "2024-05-24T14:45:41.881494Z" }, "id": "ZpipUliyjruW" }, @@ -1854,10 +1854,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.862545Z", - "iopub.status.busy": "2024-05-24T14:15:37.862184Z", - "iopub.status.idle": "2024-05-24T14:15:37.904027Z", - "shell.execute_reply": "2024-05-24T14:15:37.903552Z" + "iopub.execute_input": "2024-05-24T14:45:41.884720Z", + "iopub.status.busy": "2024-05-24T14:45:41.884260Z", + "iopub.status.idle": "2024-05-24T14:45:41.932073Z", + "shell.execute_reply": "2024-05-24T14:45:41.931491Z" }, "id": "SLq-3q4xjruX" }, @@ -1926,10 +1926,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:37.905989Z", - "iopub.status.busy": "2024-05-24T14:15:37.905806Z", - "iopub.status.idle": "2024-05-24T14:15:38.001606Z", - "shell.execute_reply": "2024-05-24T14:15:38.001029Z" + "iopub.execute_input": "2024-05-24T14:45:41.934601Z", + "iopub.status.busy": "2024-05-24T14:45:41.934080Z", + "iopub.status.idle": "2024-05-24T14:45:42.039810Z", + "shell.execute_reply": "2024-05-24T14:45:42.039091Z" }, "id": "g5LHhhuqFbXK" }, @@ -1961,10 +1961,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.004225Z", - "iopub.status.busy": "2024-05-24T14:15:38.003843Z", - "iopub.status.idle": "2024-05-24T14:15:38.095198Z", - "shell.execute_reply": "2024-05-24T14:15:38.094594Z" + "iopub.execute_input": "2024-05-24T14:45:42.042892Z", + "iopub.status.busy": "2024-05-24T14:45:42.042477Z", + "iopub.status.idle": "2024-05-24T14:45:42.155162Z", + "shell.execute_reply": "2024-05-24T14:45:42.154527Z" }, "id": "p7w8F8ezBcet" }, @@ -2021,10 +2021,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.097529Z", - "iopub.status.busy": "2024-05-24T14:15:38.097295Z", - "iopub.status.idle": "2024-05-24T14:15:38.305739Z", - "shell.execute_reply": "2024-05-24T14:15:38.305163Z" + "iopub.execute_input": "2024-05-24T14:45:42.157567Z", + "iopub.status.busy": "2024-05-24T14:45:42.157196Z", + "iopub.status.idle": "2024-05-24T14:45:42.376022Z", + "shell.execute_reply": "2024-05-24T14:45:42.375460Z" }, "id": "WETRL74tE_sU" }, @@ -2059,10 +2059,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.307858Z", - "iopub.status.busy": "2024-05-24T14:15:38.307675Z", - "iopub.status.idle": "2024-05-24T14:15:38.488216Z", - "shell.execute_reply": "2024-05-24T14:15:38.487658Z" + "iopub.execute_input": "2024-05-24T14:45:42.378461Z", + "iopub.status.busy": "2024-05-24T14:45:42.378058Z", + "iopub.status.idle": "2024-05-24T14:45:42.596412Z", + "shell.execute_reply": "2024-05-24T14:45:42.595745Z" }, "id": "kCfdx2gOLmXS" }, @@ -2224,10 +2224,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.490384Z", - "iopub.status.busy": "2024-05-24T14:15:38.490152Z", - "iopub.status.idle": "2024-05-24T14:15:38.496486Z", - "shell.execute_reply": "2024-05-24T14:15:38.495924Z" + "iopub.execute_input": "2024-05-24T14:45:42.599281Z", + "iopub.status.busy": "2024-05-24T14:45:42.598781Z", + "iopub.status.idle": "2024-05-24T14:45:42.605708Z", + "shell.execute_reply": "2024-05-24T14:45:42.605222Z" }, "id": "-uogYRWFYnuu" }, @@ -2281,10 +2281,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.498775Z", - "iopub.status.busy": "2024-05-24T14:15:38.498349Z", - "iopub.status.idle": "2024-05-24T14:15:38.712574Z", - "shell.execute_reply": "2024-05-24T14:15:38.711990Z" + "iopub.execute_input": "2024-05-24T14:45:42.607916Z", + "iopub.status.busy": "2024-05-24T14:45:42.607570Z", + "iopub.status.idle": "2024-05-24T14:45:42.830768Z", + "shell.execute_reply": "2024-05-24T14:45:42.830187Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2331,10 +2331,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:38.714681Z", - "iopub.status.busy": "2024-05-24T14:15:38.714499Z", - "iopub.status.idle": "2024-05-24T14:15:39.775177Z", - "shell.execute_reply": "2024-05-24T14:15:39.774623Z" + "iopub.execute_input": "2024-05-24T14:45:42.833197Z", + "iopub.status.busy": "2024-05-24T14:45:42.832842Z", + "iopub.status.idle": "2024-05-24T14:45:43.936085Z", + "shell.execute_reply": "2024-05-24T14:45:43.935497Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index ab2d9d25a..3c82ba2a8 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:42.948422Z", - "iopub.status.busy": "2024-05-24T14:15:42.948240Z", - "iopub.status.idle": "2024-05-24T14:15:44.047144Z", - "shell.execute_reply": "2024-05-24T14:15:44.046578Z" + "iopub.execute_input": "2024-05-24T14:45:47.637995Z", + "iopub.status.busy": "2024-05-24T14:45:47.637636Z", + "iopub.status.idle": "2024-05-24T14:45:48.881560Z", + "shell.execute_reply": "2024-05-24T14:45:48.880914Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:15:44.049662Z", - "iopub.status.busy": "2024-05-24T14:15:44.049291Z", - "iopub.status.idle": "2024-05-24T14:15:44.052288Z", - "shell.execute_reply": "2024-05-24T14:15:44.051869Z" + "iopub.execute_input": "2024-05-24T14:45:48.884508Z", + "iopub.status.busy": "2024-05-24T14:45:48.884182Z", + "iopub.status.idle": "2024-05-24T14:45:48.887469Z", + "shell.execute_reply": "2024-05-24T14:45:48.886957Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.054326Z", - "iopub.status.busy": "2024-05-24T14:15:44.054035Z", - "iopub.status.idle": "2024-05-24T14:15:44.061975Z", - "shell.execute_reply": "2024-05-24T14:15:44.061546Z" + "iopub.execute_input": "2024-05-24T14:45:48.889806Z", + "iopub.status.busy": "2024-05-24T14:45:48.889421Z", + "iopub.status.idle": "2024-05-24T14:45:48.897739Z", + "shell.execute_reply": "2024-05-24T14:45:48.897155Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.063960Z", - "iopub.status.busy": "2024-05-24T14:15:44.063573Z", - "iopub.status.idle": "2024-05-24T14:15:44.111546Z", - "shell.execute_reply": "2024-05-24T14:15:44.111028Z" + "iopub.execute_input": "2024-05-24T14:45:48.899888Z", + "iopub.status.busy": "2024-05-24T14:45:48.899666Z", + "iopub.status.idle": "2024-05-24T14:45:48.949890Z", + "shell.execute_reply": "2024-05-24T14:45:48.949240Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.113831Z", - "iopub.status.busy": "2024-05-24T14:15:44.113608Z", - "iopub.status.idle": "2024-05-24T14:15:44.131538Z", - "shell.execute_reply": "2024-05-24T14:15:44.131084Z" + "iopub.execute_input": "2024-05-24T14:45:48.952943Z", + "iopub.status.busy": "2024-05-24T14:45:48.952490Z", + "iopub.status.idle": "2024-05-24T14:45:48.971410Z", + "shell.execute_reply": "2024-05-24T14:45:48.970800Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.133764Z", - "iopub.status.busy": "2024-05-24T14:15:44.133360Z", - "iopub.status.idle": "2024-05-24T14:15:44.137171Z", - "shell.execute_reply": "2024-05-24T14:15:44.136746Z" + "iopub.execute_input": "2024-05-24T14:45:48.973982Z", + "iopub.status.busy": "2024-05-24T14:45:48.973594Z", + "iopub.status.idle": "2024-05-24T14:45:48.978134Z", + "shell.execute_reply": "2024-05-24T14:45:48.977650Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.139391Z", - "iopub.status.busy": "2024-05-24T14:15:44.138987Z", - "iopub.status.idle": "2024-05-24T14:15:44.152537Z", - "shell.execute_reply": "2024-05-24T14:15:44.152126Z" + "iopub.execute_input": "2024-05-24T14:45:48.980444Z", + "iopub.status.busy": "2024-05-24T14:45:48.980066Z", + "iopub.status.idle": "2024-05-24T14:45:48.995202Z", + "shell.execute_reply": "2024-05-24T14:45:48.994573Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.154417Z", - "iopub.status.busy": "2024-05-24T14:15:44.154241Z", - "iopub.status.idle": "2024-05-24T14:15:44.179924Z", - "shell.execute_reply": "2024-05-24T14:15:44.179460Z" + "iopub.execute_input": "2024-05-24T14:45:48.997879Z", + "iopub.status.busy": "2024-05-24T14:45:48.997502Z", + "iopub.status.idle": "2024-05-24T14:45:49.025680Z", + "shell.execute_reply": "2024-05-24T14:45:49.025176Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:44.181797Z", - "iopub.status.busy": "2024-05-24T14:15:44.181626Z", - "iopub.status.idle": "2024-05-24T14:15:45.859864Z", - "shell.execute_reply": "2024-05-24T14:15:45.859306Z" + "iopub.execute_input": "2024-05-24T14:45:49.028402Z", + "iopub.status.busy": "2024-05-24T14:45:49.027995Z", + "iopub.status.idle": "2024-05-24T14:45:50.936044Z", + "shell.execute_reply": "2024-05-24T14:45:50.935399Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.862345Z", - "iopub.status.busy": "2024-05-24T14:15:45.862010Z", - "iopub.status.idle": "2024-05-24T14:15:45.869001Z", - "shell.execute_reply": "2024-05-24T14:15:45.868520Z" + "iopub.execute_input": "2024-05-24T14:45:50.939063Z", + "iopub.status.busy": "2024-05-24T14:45:50.938427Z", + "iopub.status.idle": "2024-05-24T14:45:50.946316Z", + "shell.execute_reply": "2024-05-24T14:45:50.945818Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.870916Z", - "iopub.status.busy": "2024-05-24T14:15:45.870736Z", - "iopub.status.idle": "2024-05-24T14:15:45.883500Z", - "shell.execute_reply": "2024-05-24T14:15:45.882950Z" + "iopub.execute_input": "2024-05-24T14:45:50.948648Z", + "iopub.status.busy": "2024-05-24T14:45:50.948273Z", + "iopub.status.idle": "2024-05-24T14:45:50.962319Z", + "shell.execute_reply": "2024-05-24T14:45:50.961789Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.885635Z", - "iopub.status.busy": "2024-05-24T14:15:45.885334Z", - "iopub.status.idle": "2024-05-24T14:15:45.891765Z", - "shell.execute_reply": "2024-05-24T14:15:45.891314Z" + "iopub.execute_input": "2024-05-24T14:45:50.964650Z", + "iopub.status.busy": "2024-05-24T14:45:50.964272Z", + "iopub.status.idle": "2024-05-24T14:45:50.971681Z", + "shell.execute_reply": "2024-05-24T14:45:50.971152Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.893806Z", - "iopub.status.busy": "2024-05-24T14:15:45.893473Z", - "iopub.status.idle": "2024-05-24T14:15:45.896635Z", - "shell.execute_reply": "2024-05-24T14:15:45.896206Z" + "iopub.execute_input": "2024-05-24T14:45:50.973911Z", + "iopub.status.busy": "2024-05-24T14:45:50.973538Z", + "iopub.status.idle": "2024-05-24T14:45:50.976449Z", + "shell.execute_reply": "2024-05-24T14:45:50.975941Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.898530Z", - "iopub.status.busy": "2024-05-24T14:15:45.898269Z", - "iopub.status.idle": "2024-05-24T14:15:45.901697Z", - "shell.execute_reply": "2024-05-24T14:15:45.901200Z" + "iopub.execute_input": "2024-05-24T14:45:50.978617Z", + "iopub.status.busy": "2024-05-24T14:45:50.978256Z", + "iopub.status.idle": "2024-05-24T14:45:50.981995Z", + "shell.execute_reply": "2024-05-24T14:45:50.981445Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.903722Z", - "iopub.status.busy": "2024-05-24T14:15:45.903400Z", - "iopub.status.idle": "2024-05-24T14:15:45.906438Z", - "shell.execute_reply": "2024-05-24T14:15:45.906017Z" + "iopub.execute_input": "2024-05-24T14:45:50.984351Z", + "iopub.status.busy": "2024-05-24T14:45:50.983986Z", + "iopub.status.idle": "2024-05-24T14:45:50.986919Z", + "shell.execute_reply": "2024-05-24T14:45:50.986446Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.908414Z", - "iopub.status.busy": "2024-05-24T14:15:45.908088Z", - "iopub.status.idle": "2024-05-24T14:15:45.912358Z", - "shell.execute_reply": "2024-05-24T14:15:45.911912Z" + "iopub.execute_input": "2024-05-24T14:45:50.989114Z", + "iopub.status.busy": "2024-05-24T14:45:50.988780Z", + "iopub.status.idle": "2024-05-24T14:45:50.993475Z", + "shell.execute_reply": "2024-05-24T14:45:50.992965Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.914449Z", - "iopub.status.busy": "2024-05-24T14:15:45.914129Z", - "iopub.status.idle": "2024-05-24T14:15:45.942613Z", - "shell.execute_reply": "2024-05-24T14:15:45.942038Z" + "iopub.execute_input": "2024-05-24T14:45:50.995845Z", + "iopub.status.busy": "2024-05-24T14:45:50.995468Z", + "iopub.status.idle": "2024-05-24T14:45:51.025569Z", + "shell.execute_reply": "2024-05-24T14:45:51.025055Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:45.944630Z", - "iopub.status.busy": "2024-05-24T14:15:45.944333Z", - "iopub.status.idle": "2024-05-24T14:15:45.948968Z", - "shell.execute_reply": "2024-05-24T14:15:45.948421Z" + "iopub.execute_input": "2024-05-24T14:45:51.028002Z", + "iopub.status.busy": "2024-05-24T14:45:51.027804Z", + "iopub.status.idle": "2024-05-24T14:45:51.033097Z", + "shell.execute_reply": "2024-05-24T14:45:51.032490Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 9e04bc5c4..1335742a2 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:48.738014Z", - "iopub.status.busy": "2024-05-24T14:15:48.737837Z", - "iopub.status.idle": "2024-05-24T14:15:49.916412Z", - "shell.execute_reply": "2024-05-24T14:15:49.915848Z" + "iopub.execute_input": "2024-05-24T14:45:53.967692Z", + "iopub.status.busy": "2024-05-24T14:45:53.967509Z", + "iopub.status.idle": "2024-05-24T14:45:55.209043Z", + "shell.execute_reply": "2024-05-24T14:45:55.208396Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:15:49.919035Z", - "iopub.status.busy": "2024-05-24T14:15:49.918581Z", - "iopub.status.idle": "2024-05-24T14:15:50.114354Z", - "shell.execute_reply": "2024-05-24T14:15:50.113720Z" + "iopub.execute_input": "2024-05-24T14:45:55.211808Z", + "iopub.status.busy": "2024-05-24T14:45:55.211288Z", + "iopub.status.idle": "2024-05-24T14:45:55.424110Z", + "shell.execute_reply": "2024-05-24T14:45:55.423521Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:50.117044Z", - "iopub.status.busy": "2024-05-24T14:15:50.116712Z", - "iopub.status.idle": "2024-05-24T14:15:50.130485Z", - "shell.execute_reply": "2024-05-24T14:15:50.129994Z" + "iopub.execute_input": "2024-05-24T14:45:55.427220Z", + "iopub.status.busy": "2024-05-24T14:45:55.426641Z", + "iopub.status.idle": "2024-05-24T14:45:55.441786Z", + "shell.execute_reply": "2024-05-24T14:45:55.441219Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:50.132653Z", - "iopub.status.busy": "2024-05-24T14:15:50.132306Z", - "iopub.status.idle": "2024-05-24T14:15:52.832041Z", - "shell.execute_reply": "2024-05-24T14:15:52.831417Z" + "iopub.execute_input": "2024-05-24T14:45:55.444528Z", + "iopub.status.busy": "2024-05-24T14:45:55.444066Z", + "iopub.status.idle": "2024-05-24T14:45:58.188392Z", + "shell.execute_reply": "2024-05-24T14:45:58.187789Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:52.834561Z", - "iopub.status.busy": "2024-05-24T14:15:52.834210Z", - "iopub.status.idle": "2024-05-24T14:15:54.187652Z", - "shell.execute_reply": "2024-05-24T14:15:54.187090Z" + "iopub.execute_input": "2024-05-24T14:45:58.190697Z", + "iopub.status.busy": "2024-05-24T14:45:58.190350Z", + "iopub.status.idle": "2024-05-24T14:45:59.557721Z", + "shell.execute_reply": "2024-05-24T14:45:59.557126Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:54.190141Z", - "iopub.status.busy": "2024-05-24T14:15:54.189749Z", - "iopub.status.idle": "2024-05-24T14:15:54.193570Z", - "shell.execute_reply": "2024-05-24T14:15:54.193060Z" + "iopub.execute_input": "2024-05-24T14:45:59.560465Z", + "iopub.status.busy": "2024-05-24T14:45:59.560079Z", + "iopub.status.idle": "2024-05-24T14:45:59.564016Z", + "shell.execute_reply": "2024-05-24T14:45:59.563443Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:54.195503Z", - "iopub.status.busy": "2024-05-24T14:15:54.195327Z", - "iopub.status.idle": "2024-05-24T14:15:55.934562Z", - "shell.execute_reply": "2024-05-24T14:15:55.933945Z" + "iopub.execute_input": "2024-05-24T14:45:59.566139Z", + "iopub.status.busy": "2024-05-24T14:45:59.565861Z", + "iopub.status.idle": "2024-05-24T14:46:01.646644Z", + "shell.execute_reply": "2024-05-24T14:46:01.645899Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:55.937052Z", - "iopub.status.busy": "2024-05-24T14:15:55.936537Z", - "iopub.status.idle": "2024-05-24T14:15:55.945614Z", - "shell.execute_reply": "2024-05-24T14:15:55.945139Z" + "iopub.execute_input": "2024-05-24T14:46:01.649903Z", + "iopub.status.busy": "2024-05-24T14:46:01.649295Z", + "iopub.status.idle": "2024-05-24T14:46:01.659529Z", + "shell.execute_reply": "2024-05-24T14:46:01.658852Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:55.947699Z", - "iopub.status.busy": "2024-05-24T14:15:55.947359Z", - "iopub.status.idle": "2024-05-24T14:15:58.564133Z", - "shell.execute_reply": "2024-05-24T14:15:58.563509Z" + "iopub.execute_input": "2024-05-24T14:46:01.661781Z", + "iopub.status.busy": "2024-05-24T14:46:01.661584Z", + "iopub.status.idle": "2024-05-24T14:46:04.373517Z", + "shell.execute_reply": "2024-05-24T14:46:04.372906Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:58.566507Z", - "iopub.status.busy": "2024-05-24T14:15:58.566092Z", - "iopub.status.idle": "2024-05-24T14:15:58.569829Z", - "shell.execute_reply": "2024-05-24T14:15:58.569273Z" + "iopub.execute_input": "2024-05-24T14:46:04.376108Z", + "iopub.status.busy": "2024-05-24T14:46:04.375734Z", + "iopub.status.idle": "2024-05-24T14:46:04.379696Z", + "shell.execute_reply": "2024-05-24T14:46:04.379207Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:58.571967Z", - "iopub.status.busy": "2024-05-24T14:15:58.571532Z", - "iopub.status.idle": "2024-05-24T14:15:58.575279Z", - "shell.execute_reply": "2024-05-24T14:15:58.574729Z" + "iopub.execute_input": "2024-05-24T14:46:04.381809Z", + "iopub.status.busy": "2024-05-24T14:46:04.381457Z", + "iopub.status.idle": "2024-05-24T14:46:04.385124Z", + "shell.execute_reply": "2024-05-24T14:46:04.384648Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:15:58.577360Z", - "iopub.status.busy": "2024-05-24T14:15:58.577097Z", - "iopub.status.idle": "2024-05-24T14:15:58.580337Z", - "shell.execute_reply": "2024-05-24T14:15:58.579784Z" + "iopub.execute_input": "2024-05-24T14:46:04.387243Z", + "iopub.status.busy": "2024-05-24T14:46:04.386890Z", + "iopub.status.idle": "2024-05-24T14:46:04.390163Z", + "shell.execute_reply": "2024-05-24T14:46:04.389700Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index d2275a329..853843d7c 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:00.912813Z", - "iopub.status.busy": "2024-05-24T14:16:00.912327Z", - "iopub.status.idle": "2024-05-24T14:16:02.056639Z", - "shell.execute_reply": "2024-05-24T14:16:02.056025Z" + "iopub.execute_input": "2024-05-24T14:46:07.173433Z", + "iopub.status.busy": "2024-05-24T14:46:07.173246Z", + "iopub.status.idle": "2024-05-24T14:46:08.474509Z", + "shell.execute_reply": "2024-05-24T14:46:08.473904Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:16:02.059313Z", - "iopub.status.busy": "2024-05-24T14:16:02.058896Z", - "iopub.status.idle": "2024-05-24T14:16:03.166033Z", - "shell.execute_reply": "2024-05-24T14:16:03.165344Z" + "iopub.execute_input": "2024-05-24T14:46:08.477400Z", + "iopub.status.busy": "2024-05-24T14:46:08.476950Z", + "iopub.status.idle": "2024-05-24T14:46:09.724570Z", + "shell.execute_reply": "2024-05-24T14:46:09.723744Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.168667Z", - "iopub.status.busy": "2024-05-24T14:16:03.168281Z", - "iopub.status.idle": "2024-05-24T14:16:03.171587Z", - "shell.execute_reply": "2024-05-24T14:16:03.171121Z" + "iopub.execute_input": "2024-05-24T14:46:09.727568Z", + "iopub.status.busy": "2024-05-24T14:46:09.727280Z", + "iopub.status.idle": "2024-05-24T14:46:09.731029Z", + "shell.execute_reply": "2024-05-24T14:46:09.730449Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.173698Z", - "iopub.status.busy": "2024-05-24T14:16:03.173375Z", - "iopub.status.idle": "2024-05-24T14:16:03.180500Z", - "shell.execute_reply": "2024-05-24T14:16:03.180058Z" + "iopub.execute_input": "2024-05-24T14:46:09.733594Z", + "iopub.status.busy": "2024-05-24T14:46:09.733185Z", + "iopub.status.idle": "2024-05-24T14:46:09.741406Z", + "shell.execute_reply": "2024-05-24T14:46:09.740748Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.182474Z", - "iopub.status.busy": "2024-05-24T14:16:03.182218Z", - "iopub.status.idle": "2024-05-24T14:16:03.671365Z", - "shell.execute_reply": "2024-05-24T14:16:03.670824Z" + "iopub.execute_input": "2024-05-24T14:46:09.744393Z", + "iopub.status.busy": "2024-05-24T14:46:09.743983Z", + "iopub.status.idle": "2024-05-24T14:46:10.270026Z", + "shell.execute_reply": "2024-05-24T14:46:10.269428Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.673954Z", - "iopub.status.busy": "2024-05-24T14:16:03.673571Z", - "iopub.status.idle": "2024-05-24T14:16:03.679123Z", - "shell.execute_reply": "2024-05-24T14:16:03.678681Z" + "iopub.execute_input": "2024-05-24T14:46:10.273035Z", + "iopub.status.busy": "2024-05-24T14:46:10.272631Z", + "iopub.status.idle": "2024-05-24T14:46:10.278397Z", + "shell.execute_reply": "2024-05-24T14:46:10.277803Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.681161Z", - "iopub.status.busy": "2024-05-24T14:16:03.680791Z", - "iopub.status.idle": "2024-05-24T14:16:03.684731Z", - "shell.execute_reply": "2024-05-24T14:16:03.684295Z" + "iopub.execute_input": "2024-05-24T14:46:10.280970Z", + "iopub.status.busy": "2024-05-24T14:46:10.280452Z", + "iopub.status.idle": "2024-05-24T14:46:10.284937Z", + "shell.execute_reply": "2024-05-24T14:46:10.284364Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:03.686802Z", - "iopub.status.busy": "2024-05-24T14:16:03.686476Z", - "iopub.status.idle": "2024-05-24T14:16:04.586199Z", - "shell.execute_reply": "2024-05-24T14:16:04.585609Z" + "iopub.execute_input": "2024-05-24T14:46:10.287335Z", + "iopub.status.busy": "2024-05-24T14:46:10.287002Z", + "iopub.status.idle": "2024-05-24T14:46:11.190950Z", + "shell.execute_reply": "2024-05-24T14:46:11.190246Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:04.588329Z", - "iopub.status.busy": "2024-05-24T14:16:04.588140Z", - "iopub.status.idle": "2024-05-24T14:16:04.875812Z", - "shell.execute_reply": "2024-05-24T14:16:04.875240Z" + "iopub.execute_input": "2024-05-24T14:46:11.193473Z", + "iopub.status.busy": "2024-05-24T14:46:11.193247Z", + "iopub.status.idle": "2024-05-24T14:46:11.425193Z", + "shell.execute_reply": "2024-05-24T14:46:11.424691Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:04.877878Z", - "iopub.status.busy": "2024-05-24T14:16:04.877696Z", - "iopub.status.idle": "2024-05-24T14:16:04.882053Z", - "shell.execute_reply": "2024-05-24T14:16:04.881517Z" + "iopub.execute_input": "2024-05-24T14:46:11.427553Z", + "iopub.status.busy": "2024-05-24T14:46:11.427204Z", + "iopub.status.idle": "2024-05-24T14:46:11.431770Z", + "shell.execute_reply": "2024-05-24T14:46:11.431206Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:04.884207Z", - "iopub.status.busy": "2024-05-24T14:16:04.883902Z", - "iopub.status.idle": "2024-05-24T14:16:05.330815Z", - "shell.execute_reply": "2024-05-24T14:16:05.330232Z" + "iopub.execute_input": "2024-05-24T14:46:11.433931Z", + "iopub.status.busy": "2024-05-24T14:46:11.433511Z", + "iopub.status.idle": "2024-05-24T14:46:11.920009Z", + "shell.execute_reply": "2024-05-24T14:46:11.919271Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:05.333725Z", - "iopub.status.busy": "2024-05-24T14:16:05.333435Z", - "iopub.status.idle": "2024-05-24T14:16:05.663318Z", - "shell.execute_reply": "2024-05-24T14:16:05.662809Z" + "iopub.execute_input": "2024-05-24T14:46:11.922824Z", + "iopub.status.busy": "2024-05-24T14:46:11.922353Z", + "iopub.status.idle": "2024-05-24T14:46:12.261495Z", + "shell.execute_reply": "2024-05-24T14:46:12.260885Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:05.666018Z", - "iopub.status.busy": "2024-05-24T14:16:05.665635Z", - "iopub.status.idle": "2024-05-24T14:16:06.026847Z", - "shell.execute_reply": "2024-05-24T14:16:06.026259Z" + "iopub.execute_input": "2024-05-24T14:46:12.264390Z", + "iopub.status.busy": "2024-05-24T14:46:12.264070Z", + "iopub.status.idle": "2024-05-24T14:46:12.638315Z", + "shell.execute_reply": "2024-05-24T14:46:12.637673Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:06.029956Z", - "iopub.status.busy": "2024-05-24T14:16:06.029599Z", - "iopub.status.idle": "2024-05-24T14:16:06.468037Z", - "shell.execute_reply": "2024-05-24T14:16:06.467512Z" + "iopub.execute_input": "2024-05-24T14:46:12.641801Z", + "iopub.status.busy": "2024-05-24T14:46:12.641375Z", + "iopub.status.idle": "2024-05-24T14:46:13.093205Z", + "shell.execute_reply": "2024-05-24T14:46:13.092623Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:06.472386Z", - "iopub.status.busy": "2024-05-24T14:16:06.471995Z", - "iopub.status.idle": "2024-05-24T14:16:06.916734Z", - "shell.execute_reply": "2024-05-24T14:16:06.916140Z" + "iopub.execute_input": "2024-05-24T14:46:13.097729Z", + "iopub.status.busy": "2024-05-24T14:46:13.097309Z", + "iopub.status.idle": "2024-05-24T14:46:13.559987Z", + "shell.execute_reply": "2024-05-24T14:46:13.559288Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:06.919806Z", - "iopub.status.busy": "2024-05-24T14:16:06.919470Z", - "iopub.status.idle": "2024-05-24T14:16:07.133822Z", - "shell.execute_reply": "2024-05-24T14:16:07.133237Z" + "iopub.execute_input": "2024-05-24T14:46:13.563261Z", + "iopub.status.busy": "2024-05-24T14:46:13.562869Z", + "iopub.status.idle": "2024-05-24T14:46:13.762453Z", + "shell.execute_reply": "2024-05-24T14:46:13.761824Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:07.135965Z", - "iopub.status.busy": "2024-05-24T14:16:07.135622Z", - "iopub.status.idle": "2024-05-24T14:16:07.333455Z", - "shell.execute_reply": "2024-05-24T14:16:07.332868Z" + "iopub.execute_input": "2024-05-24T14:46:13.765036Z", + "iopub.status.busy": "2024-05-24T14:46:13.764810Z", + "iopub.status.idle": "2024-05-24T14:46:13.968757Z", + "shell.execute_reply": "2024-05-24T14:46:13.968144Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:07.335696Z", - "iopub.status.busy": "2024-05-24T14:16:07.335279Z", - "iopub.status.idle": "2024-05-24T14:16:07.338343Z", - "shell.execute_reply": "2024-05-24T14:16:07.337807Z" + "iopub.execute_input": "2024-05-24T14:46:13.971601Z", + "iopub.status.busy": "2024-05-24T14:46:13.971379Z", + "iopub.status.idle": "2024-05-24T14:46:13.974421Z", + "shell.execute_reply": "2024-05-24T14:46:13.973948Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:07.340901Z", - "iopub.status.busy": "2024-05-24T14:16:07.340531Z", - "iopub.status.idle": "2024-05-24T14:16:08.304885Z", - "shell.execute_reply": "2024-05-24T14:16:08.304325Z" + "iopub.execute_input": "2024-05-24T14:46:13.976652Z", + "iopub.status.busy": "2024-05-24T14:46:13.976300Z", + "iopub.status.idle": "2024-05-24T14:46:14.990895Z", + "shell.execute_reply": "2024-05-24T14:46:14.990254Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:08.307745Z", - "iopub.status.busy": "2024-05-24T14:16:08.307417Z", - "iopub.status.idle": "2024-05-24T14:16:08.464656Z", - "shell.execute_reply": "2024-05-24T14:16:08.464068Z" + "iopub.execute_input": "2024-05-24T14:46:14.993723Z", + "iopub.status.busy": "2024-05-24T14:46:14.993329Z", + "iopub.status.idle": "2024-05-24T14:46:15.138071Z", + "shell.execute_reply": "2024-05-24T14:46:15.137450Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:08.467040Z", - "iopub.status.busy": "2024-05-24T14:16:08.466699Z", - "iopub.status.idle": "2024-05-24T14:16:08.642825Z", - "shell.execute_reply": "2024-05-24T14:16:08.642374Z" + "iopub.execute_input": "2024-05-24T14:46:15.140472Z", + "iopub.status.busy": "2024-05-24T14:46:15.140112Z", + "iopub.status.idle": "2024-05-24T14:46:15.290996Z", + "shell.execute_reply": "2024-05-24T14:46:15.290440Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:08.645002Z", - "iopub.status.busy": "2024-05-24T14:16:08.644678Z", - "iopub.status.idle": "2024-05-24T14:16:09.307872Z", - "shell.execute_reply": "2024-05-24T14:16:09.307275Z" + "iopub.execute_input": "2024-05-24T14:46:15.293307Z", + "iopub.status.busy": "2024-05-24T14:46:15.292887Z", + "iopub.status.idle": "2024-05-24T14:46:16.088741Z", + "shell.execute_reply": "2024-05-24T14:46:16.088193Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:09.310087Z", - "iopub.status.busy": "2024-05-24T14:16:09.309757Z", - "iopub.status.idle": "2024-05-24T14:16:09.313450Z", - "shell.execute_reply": "2024-05-24T14:16:09.312898Z" + "iopub.execute_input": "2024-05-24T14:46:16.091390Z", + "iopub.status.busy": "2024-05-24T14:46:16.090899Z", + "iopub.status.idle": "2024-05-24T14:46:16.095068Z", + "shell.execute_reply": "2024-05-24T14:46:16.094470Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index f20efe12c..a49cca22c 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -761,7 +761,7 @@

    2. Pre-process the Cifar10 dataset 
    -100%|██████████| 170498071/170498071 [00:01<00:00, 90014750.01it/s]
+100%|██████████| 170498071/170498071 [00:02<00:00, 58138838.87it/s]
    @@ -891,7 +891,7 @@

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

    -
    +
    @@ -1105,7 +1105,7 @@

    4. Use cleanlab and here.

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index a56824c91..e2d5eff53 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:11.688100Z", - "iopub.status.busy": "2024-05-24T14:16:11.687945Z", - "iopub.status.idle": "2024-05-24T14:16:14.407752Z", - "shell.execute_reply": "2024-05-24T14:16:14.407183Z" + "iopub.execute_input": "2024-05-24T14:46:18.616530Z", + "iopub.status.busy": "2024-05-24T14:46:18.616000Z", + "iopub.status.idle": "2024-05-24T14:46:21.670169Z", + "shell.execute_reply": "2024-05-24T14:46:21.669557Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:16:14.410321Z", - "iopub.status.busy": "2024-05-24T14:16:14.409839Z", - "iopub.status.idle": "2024-05-24T14:16:14.733469Z", - "shell.execute_reply": "2024-05-24T14:16:14.732853Z" + "iopub.execute_input": "2024-05-24T14:46:21.673336Z", + "iopub.status.busy": "2024-05-24T14:46:21.672748Z", + "iopub.status.idle": "2024-05-24T14:46:22.043426Z", + "shell.execute_reply": "2024-05-24T14:46:22.042787Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:14.736052Z", - "iopub.status.busy": "2024-05-24T14:16:14.735743Z", - "iopub.status.idle": "2024-05-24T14:16:14.740039Z", - "shell.execute_reply": "2024-05-24T14:16:14.739503Z" + "iopub.execute_input": "2024-05-24T14:46:22.046320Z", + "iopub.status.busy": "2024-05-24T14:46:22.045760Z", + "iopub.status.idle": "2024-05-24T14:46:22.050404Z", + "shell.execute_reply": "2024-05-24T14:46:22.049952Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:14.742331Z", - "iopub.status.busy": "2024-05-24T14:16:14.741874Z", - "iopub.status.idle": "2024-05-24T14:16:19.327125Z", - "shell.execute_reply": "2024-05-24T14:16:19.326531Z" + "iopub.execute_input": "2024-05-24T14:46:22.052792Z", + "iopub.status.busy": "2024-05-24T14:46:22.052444Z", + "iopub.status.idle": "2024-05-24T14:46:27.691858Z", + "shell.execute_reply": "2024-05-24T14:46:27.691198Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 2064384/170498071 [00:00<00:08, 20638102.69it/s]" + " 1%| | 1671168/170498071 [00:00<00:10, 16554608.13it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 12582912/170498071 [00:00<00:02, 70272073.82it/s]" + " 6%|▋ | 10780672/170498071 [00:00<00:02, 60117265.25it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22642688/170498071 [00:00<00:01, 84106618.69it/s]" + " 11%|█▏ | 19234816/170498071 [00:00<00:02, 71155863.70it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32702464/170498071 [00:00<00:01, 90553063.45it/s]" + " 16%|█▌ | 27066368/170498071 [00:00<00:01, 73940657.98it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 42926080/170498071 [00:00<00:01, 94741973.48it/s]" + " 21%|██ | 36044800/170498071 [00:00<00:01, 79613933.70it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52428800/170498071 [00:00<00:01, 94578568.50it/s]" + " 26%|██▋ | 44957696/170498071 [00:00<00:01, 82832072.79it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 62488576/170498071 [00:00<00:01, 96511460.38it/s]" + " 31%|███ | 53248000/170498071 [00:00<00:01, 80485950.48it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 72155136/170498071 [00:00<00:01, 95383426.91it/s]" + " 36%|███▋ | 62062592/170498071 [00:00<00:01, 82862402.20it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 81723392/170498071 [00:00<00:00, 95428872.81it/s]" + " 42%|████▏ | 70778880/170498071 [00:00<00:01, 84133225.36it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 91291648/170498071 [00:01<00:00, 95171323.87it/s]" + " 46%|████▋ | 79233024/170498071 [00:01<00:01, 81687827.67it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100859904/170498071 [00:01<00:00, 95302166.21it/s]" + " 52%|█████▏ | 88014848/170498071 [00:01<00:00, 83501780.14it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 110395392/170498071 [00:01<00:00, 93422347.07it/s]" + " 57%|█████▋ | 96403456/170498071 [00:01<00:00, 83570453.10it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 119767040/170498071 [00:01<00:00, 91723547.25it/s]" + " 61%|██████▏ | 104792064/170498071 [00:01<00:01, 65205251.33it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 128974848/170498071 [00:01<00:00, 91141533.70it/s]" + " 66%|██████▌ | 111935488/170498071 [00:01<00:01, 56992286.89it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - 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"iopub.execute_input": "2024-05-24T14:16:19.329395Z", - "iopub.status.busy": "2024-05-24T14:16:19.329031Z", - "iopub.status.idle": "2024-05-24T14:16:19.333764Z", - "shell.execute_reply": "2024-05-24T14:16:19.333329Z" + "iopub.execute_input": "2024-05-24T14:46:27.694384Z", + "iopub.status.busy": "2024-05-24T14:46:27.694012Z", + "iopub.status.idle": "2024-05-24T14:46:27.698831Z", + "shell.execute_reply": "2024-05-24T14:46:27.698388Z" }, "nbsphinx": "hidden" }, @@ -568,10 +624,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:19.335815Z", - "iopub.status.busy": "2024-05-24T14:16:19.335494Z", - "iopub.status.idle": "2024-05-24T14:16:19.880137Z", - "shell.execute_reply": "2024-05-24T14:16:19.879615Z" + "iopub.execute_input": "2024-05-24T14:46:27.701241Z", + "iopub.status.busy": "2024-05-24T14:46:27.700839Z", + "iopub.status.idle": "2024-05-24T14:46:28.248072Z", + "shell.execute_reply": "2024-05-24T14:46:28.247462Z" } }, "outputs": [ @@ -604,10 +660,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:19.882526Z", - "iopub.status.busy": "2024-05-24T14:16:19.882114Z", - "iopub.status.idle": "2024-05-24T14:16:20.366451Z", - "shell.execute_reply": "2024-05-24T14:16:20.365796Z" + "iopub.execute_input": "2024-05-24T14:46:28.250338Z", + "iopub.status.busy": "2024-05-24T14:46:28.249980Z", + "iopub.status.idle": "2024-05-24T14:46:28.813343Z", + "shell.execute_reply": "2024-05-24T14:46:28.812763Z" } }, "outputs": [ @@ -645,10 +701,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:20.368718Z", - "iopub.status.busy": "2024-05-24T14:16:20.368365Z", - "iopub.status.idle": "2024-05-24T14:16:20.371722Z", - "shell.execute_reply": "2024-05-24T14:16:20.371299Z" + "iopub.execute_input": "2024-05-24T14:46:28.816073Z", + "iopub.status.busy": "2024-05-24T14:46:28.815474Z", + "iopub.status.idle": "2024-05-24T14:46:28.819642Z", + "shell.execute_reply": "2024-05-24T14:46:28.819002Z" } }, "outputs": [], @@ -671,17 +727,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:20.373745Z", - "iopub.status.busy": "2024-05-24T14:16:20.373421Z", - "iopub.status.idle": "2024-05-24T14:16:32.763761Z", - "shell.execute_reply": "2024-05-24T14:16:32.763164Z" + "iopub.execute_input": "2024-05-24T14:46:28.821885Z", + "iopub.status.busy": "2024-05-24T14:46:28.821587Z", + "iopub.status.idle": "2024-05-24T14:46:41.796645Z", + "shell.execute_reply": "2024-05-24T14:46:41.796033Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a13802b931c1441fbd9fa1b4f5a0e8e7", + "model_id": "c0e7b1e78e4d48debdd9b3fa2a4a6538", "version_major": 2, "version_minor": 0 }, @@ -740,10 +796,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:32.766253Z", - "iopub.status.busy": "2024-05-24T14:16:32.765814Z", - "iopub.status.idle": "2024-05-24T14:16:34.853811Z", - "shell.execute_reply": "2024-05-24T14:16:34.853249Z" + "iopub.execute_input": "2024-05-24T14:46:41.798877Z", + "iopub.status.busy": "2024-05-24T14:46:41.798694Z", + "iopub.status.idle": "2024-05-24T14:46:43.760734Z", + "shell.execute_reply": "2024-05-24T14:46:43.760138Z" } }, "outputs": [ @@ -787,10 +843,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:34.856139Z", - "iopub.status.busy": "2024-05-24T14:16:34.855947Z", - "iopub.status.idle": "2024-05-24T14:16:35.097513Z", - "shell.execute_reply": "2024-05-24T14:16:35.096932Z" + "iopub.execute_input": "2024-05-24T14:46:43.763793Z", + "iopub.status.busy": "2024-05-24T14:46:43.763217Z", + "iopub.status.idle": "2024-05-24T14:46:44.251017Z", + "shell.execute_reply": "2024-05-24T14:46:44.250425Z" } }, "outputs": [ @@ -826,10 +882,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:35.100275Z", - "iopub.status.busy": "2024-05-24T14:16:35.100071Z", - "iopub.status.idle": "2024-05-24T14:16:35.753850Z", - "shell.execute_reply": "2024-05-24T14:16:35.753305Z" + "iopub.execute_input": "2024-05-24T14:46:44.253975Z", + "iopub.status.busy": "2024-05-24T14:46:44.253394Z", + "iopub.status.idle": "2024-05-24T14:46:44.917167Z", + "shell.execute_reply": "2024-05-24T14:46:44.916556Z" } }, "outputs": [ @@ -879,10 +935,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:35.756692Z", - "iopub.status.busy": "2024-05-24T14:16:35.756491Z", - "iopub.status.idle": "2024-05-24T14:16:36.097212Z", - "shell.execute_reply": "2024-05-24T14:16:36.096602Z" + "iopub.execute_input": "2024-05-24T14:46:44.920057Z", + "iopub.status.busy": "2024-05-24T14:46:44.919583Z", + "iopub.status.idle": "2024-05-24T14:46:45.275418Z", + "shell.execute_reply": "2024-05-24T14:46:45.274649Z" } }, "outputs": [ @@ -930,10 +986,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:36.099489Z", - "iopub.status.busy": "2024-05-24T14:16:36.099296Z", - "iopub.status.idle": "2024-05-24T14:16:36.343391Z", - "shell.execute_reply": "2024-05-24T14:16:36.342756Z" + "iopub.execute_input": "2024-05-24T14:46:45.277880Z", + "iopub.status.busy": "2024-05-24T14:46:45.277530Z", + "iopub.status.idle": "2024-05-24T14:46:45.534771Z", + "shell.execute_reply": "2024-05-24T14:46:45.534105Z" } }, "outputs": [ @@ -989,10 +1045,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:36.345756Z", - "iopub.status.busy": "2024-05-24T14:16:36.345430Z", - "iopub.status.idle": "2024-05-24T14:16:36.425607Z", - "shell.execute_reply": "2024-05-24T14:16:36.425104Z" + "iopub.execute_input": "2024-05-24T14:46:45.537718Z", + "iopub.status.busy": "2024-05-24T14:46:45.537249Z", + "iopub.status.idle": "2024-05-24T14:46:45.625688Z", + "shell.execute_reply": "2024-05-24T14:46:45.625167Z" } }, "outputs": [], @@ -1013,10 +1069,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:36.428195Z", - "iopub.status.busy": "2024-05-24T14:16:36.427727Z", - "iopub.status.idle": "2024-05-24T14:16:46.677567Z", - "shell.execute_reply": "2024-05-24T14:16:46.676872Z" + "iopub.execute_input": "2024-05-24T14:46:45.628107Z", + "iopub.status.busy": "2024-05-24T14:46:45.627809Z", + "iopub.status.idle": "2024-05-24T14:46:56.731248Z", + "shell.execute_reply": "2024-05-24T14:46:56.730579Z" } }, "outputs": [ @@ -1053,10 +1109,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:46.680205Z", - "iopub.status.busy": "2024-05-24T14:16:46.679686Z", - "iopub.status.idle": "2024-05-24T14:16:48.395328Z", - "shell.execute_reply": "2024-05-24T14:16:48.394769Z" + "iopub.execute_input": "2024-05-24T14:46:56.733869Z", + "iopub.status.busy": "2024-05-24T14:46:56.733436Z", + "iopub.status.idle": "2024-05-24T14:46:58.689076Z", + "shell.execute_reply": "2024-05-24T14:46:58.688461Z" } }, "outputs": [ @@ -1087,10 +1143,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:48.397868Z", - "iopub.status.busy": "2024-05-24T14:16:48.397498Z", - "iopub.status.idle": "2024-05-24T14:16:48.598915Z", - "shell.execute_reply": "2024-05-24T14:16:48.598398Z" + "iopub.execute_input": "2024-05-24T14:46:58.692092Z", + "iopub.status.busy": "2024-05-24T14:46:58.691521Z", + "iopub.status.idle": "2024-05-24T14:46:58.902592Z", + "shell.execute_reply": "2024-05-24T14:46:58.901966Z" } }, "outputs": [], @@ -1104,10 +1160,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:48.601274Z", - "iopub.status.busy": "2024-05-24T14:16:48.601092Z", - "iopub.status.idle": "2024-05-24T14:16:48.604255Z", - "shell.execute_reply": "2024-05-24T14:16:48.603726Z" + "iopub.execute_input": "2024-05-24T14:46:58.905326Z", + "iopub.status.busy": "2024-05-24T14:46:58.904994Z", + "iopub.status.idle": "2024-05-24T14:46:58.908370Z", + "shell.execute_reply": "2024-05-24T14:46:58.907885Z" } }, "outputs": [], @@ -1129,10 +1185,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:48.606348Z", - "iopub.status.busy": "2024-05-24T14:16:48.606178Z", - "iopub.status.idle": "2024-05-24T14:16:48.614219Z", - "shell.execute_reply": "2024-05-24T14:16:48.613770Z" + "iopub.execute_input": "2024-05-24T14:46:58.910424Z", + "iopub.status.busy": "2024-05-24T14:46:58.910228Z", + "iopub.status.idle": "2024-05-24T14:46:58.919221Z", + "shell.execute_reply": "2024-05-24T14:46:58.918591Z" }, "nbsphinx": "hidden" }, @@ -1177,7 +1233,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - 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"description_allow_html": false, - "layout": "IPY_MODEL_9f9192f6a8d247bb91e739d85ce207f2", - "placeholder": "​", - "style": "IPY_MODEL_3bad845962c04bedacf325296c890a66", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "d03b5f33c33548279fc21e436325b613": { + "feb7bc37d8f846678670890f63dfec6d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_777a465a4ba544778b8ae91f632a33f7", - "placeholder": "​", - "style": "IPY_MODEL_376b676656294388b8d316d0cf28299b", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 253MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index ca32abf63..e62da478d 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:52.781728Z", - "iopub.status.busy": "2024-05-24T14:16:52.781355Z", - "iopub.status.idle": "2024-05-24T14:16:53.946904Z", - "shell.execute_reply": "2024-05-24T14:16:53.946274Z" + "iopub.execute_input": "2024-05-24T14:47:03.153952Z", + "iopub.status.busy": "2024-05-24T14:47:03.153748Z", + "iopub.status.idle": "2024-05-24T14:47:04.491788Z", + "shell.execute_reply": "2024-05-24T14:47:04.491145Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:16:53.949560Z", - "iopub.status.busy": "2024-05-24T14:16:53.949098Z", - "iopub.status.idle": "2024-05-24T14:16:53.967220Z", - "shell.execute_reply": "2024-05-24T14:16:53.966766Z" + "iopub.execute_input": "2024-05-24T14:47:04.494561Z", + "iopub.status.busy": "2024-05-24T14:47:04.494190Z", + "iopub.status.idle": "2024-05-24T14:47:04.515327Z", + "shell.execute_reply": "2024-05-24T14:47:04.514753Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:53.969575Z", - "iopub.status.busy": "2024-05-24T14:16:53.969152Z", - "iopub.status.idle": "2024-05-24T14:16:53.972283Z", - "shell.execute_reply": "2024-05-24T14:16:53.971836Z" + "iopub.execute_input": "2024-05-24T14:47:04.518048Z", + "iopub.status.busy": "2024-05-24T14:47:04.517704Z", + "iopub.status.idle": "2024-05-24T14:47:04.521191Z", + "shell.execute_reply": "2024-05-24T14:47:04.520693Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:53.974240Z", - "iopub.status.busy": "2024-05-24T14:16:53.973959Z", - "iopub.status.idle": "2024-05-24T14:16:54.033719Z", - "shell.execute_reply": "2024-05-24T14:16:54.033211Z" + "iopub.execute_input": "2024-05-24T14:47:04.523189Z", + "iopub.status.busy": "2024-05-24T14:47:04.522996Z", + "iopub.status.idle": "2024-05-24T14:47:04.581897Z", + "shell.execute_reply": "2024-05-24T14:47:04.581240Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.036016Z", - "iopub.status.busy": "2024-05-24T14:16:54.035694Z", - "iopub.status.idle": "2024-05-24T14:16:54.217027Z", - "shell.execute_reply": "2024-05-24T14:16:54.216505Z" + "iopub.execute_input": "2024-05-24T14:47:04.584548Z", + "iopub.status.busy": "2024-05-24T14:47:04.584087Z", + "iopub.status.idle": "2024-05-24T14:47:04.785981Z", + "shell.execute_reply": "2024-05-24T14:47:04.785324Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.219569Z", - "iopub.status.busy": "2024-05-24T14:16:54.219138Z", - "iopub.status.idle": "2024-05-24T14:16:54.463668Z", - "shell.execute_reply": "2024-05-24T14:16:54.463047Z" + "iopub.execute_input": "2024-05-24T14:47:04.789188Z", + "iopub.status.busy": "2024-05-24T14:47:04.788666Z", + "iopub.status.idle": "2024-05-24T14:47:05.052163Z", + "shell.execute_reply": "2024-05-24T14:47:05.051482Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.466095Z", - "iopub.status.busy": "2024-05-24T14:16:54.465710Z", - "iopub.status.idle": "2024-05-24T14:16:54.470289Z", - "shell.execute_reply": "2024-05-24T14:16:54.469813Z" + "iopub.execute_input": "2024-05-24T14:47:05.054390Z", + "iopub.status.busy": "2024-05-24T14:47:05.054184Z", + "iopub.status.idle": "2024-05-24T14:47:05.059284Z", + "shell.execute_reply": "2024-05-24T14:47:05.058776Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:16:54.472358Z", - "iopub.status.busy": "2024-05-24T14:16:54.472018Z", - "iopub.status.idle": "2024-05-24T14:16:54.477733Z", - "shell.execute_reply": "2024-05-24T14:16:54.477284Z" + "iopub.execute_input": "2024-05-24T14:47:05.061589Z", + "iopub.status.busy": "2024-05-24T14:47:05.061256Z", + "iopub.status.idle": "2024-05-24T14:47:05.068596Z", + "shell.execute_reply": "2024-05-24T14:47:05.067935Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - 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    3. Use cleanlab to find label issues

    -
    +
    -
    +
    @@ -872,7 +872,7 @@

    Classes which are commonly mislabeled overall

    -
    +
    @@ -1121,7 +1121,7 @@

    Get label quality scores

    -
    +

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

    @@ -1177,7 +1177,7 @@

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"_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_763410d50a8e47a0a66c8d256feff144", "IPY_MODEL_2fdfbb9e9b6d40af915a9b427e9ba1ce", "IPY_MODEL_05f052e21b754028a3fc7b4cf7efcb2c"], "layout": "IPY_MODEL_f0325b816faa4f9cb226a3bf092f6bbb", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index ec43b5fec..7c054d311 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:12.098975Z", - "iopub.status.busy": "2024-05-24T14:17:12.098794Z", - "iopub.status.idle": "2024-05-24T14:17:13.187472Z", - "shell.execute_reply": "2024-05-24T14:17:13.186859Z" + "iopub.execute_input": "2024-05-24T14:47:23.751320Z", + "iopub.status.busy": "2024-05-24T14:47:23.751138Z", + "iopub.status.idle": "2024-05-24T14:47:25.225197Z", + "shell.execute_reply": "2024-05-24T14:47:25.224396Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:13.190034Z", - "iopub.status.busy": "2024-05-24T14:17:13.189840Z", - "iopub.status.idle": "2024-05-24T14:17:56.244531Z", - "shell.execute_reply": "2024-05-24T14:17:56.243884Z" + "iopub.execute_input": "2024-05-24T14:47:25.228167Z", + "iopub.status.busy": "2024-05-24T14:47:25.227876Z", + "iopub.status.idle": "2024-05-24T14:48:06.900638Z", + "shell.execute_reply": "2024-05-24T14:48:06.899939Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:56.246941Z", - "iopub.status.busy": "2024-05-24T14:17:56.246597Z", - "iopub.status.idle": "2024-05-24T14:17:57.361537Z", - "shell.execute_reply": "2024-05-24T14:17:57.360976Z" + "iopub.execute_input": "2024-05-24T14:48:06.903639Z", + "iopub.status.busy": "2024-05-24T14:48:06.903118Z", + "iopub.status.idle": "2024-05-24T14:48:08.159074Z", + "shell.execute_reply": "2024-05-24T14:48:08.158455Z" }, "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\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": "2024-05-24T14:17:57.363963Z", - "iopub.status.busy": "2024-05-24T14:17:57.363656Z", - "iopub.status.idle": "2024-05-24T14:17:57.366860Z", - "shell.execute_reply": "2024-05-24T14:17:57.366396Z" + "iopub.execute_input": "2024-05-24T14:48:08.161720Z", + "iopub.status.busy": "2024-05-24T14:48:08.161355Z", + "iopub.status.idle": "2024-05-24T14:48:08.164937Z", + "shell.execute_reply": "2024-05-24T14:48:08.164437Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.368955Z", - "iopub.status.busy": "2024-05-24T14:17:57.368625Z", - "iopub.status.idle": "2024-05-24T14:17:57.372418Z", - "shell.execute_reply": "2024-05-24T14:17:57.371945Z" + "iopub.execute_input": "2024-05-24T14:48:08.167013Z", + "iopub.status.busy": "2024-05-24T14:48:08.166830Z", + "iopub.status.idle": "2024-05-24T14:48:08.171171Z", + "shell.execute_reply": "2024-05-24T14:48:08.170623Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.374586Z", - "iopub.status.busy": "2024-05-24T14:17:57.374186Z", - "iopub.status.idle": "2024-05-24T14:17:57.377890Z", - "shell.execute_reply": "2024-05-24T14:17:57.377442Z" + "iopub.execute_input": "2024-05-24T14:48:08.173563Z", + "iopub.status.busy": "2024-05-24T14:48:08.173189Z", + "iopub.status.idle": "2024-05-24T14:48:08.177196Z", + "shell.execute_reply": "2024-05-24T14:48:08.176714Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.379908Z", - "iopub.status.busy": "2024-05-24T14:17:57.379577Z", - "iopub.status.idle": "2024-05-24T14:17:57.382337Z", - "shell.execute_reply": "2024-05-24T14:17:57.381872Z" + "iopub.execute_input": "2024-05-24T14:48:08.179576Z", + "iopub.status.busy": "2024-05-24T14:48:08.179106Z", + "iopub.status.idle": "2024-05-24T14:48:08.182081Z", + "shell.execute_reply": "2024-05-24T14:48:08.181644Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:17:57.384391Z", - 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    1. Install required dependencies and download data 
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-Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.
+--2024-05-24 14:49:50--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.246, 2400:52e0:1a00::1068:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
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-conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
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-2024-05-24 14:19:36 (7.26 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-05-24 14:49:50 (5.90 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
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 Archive:  conll2003.zip
@@ -708,16 +708,16 @@ 
    1. Install required dependencies and download data

    diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 022342012..73542985c 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:36.205155Z", - "iopub.status.busy": "2024-05-24T14:19:36.204981Z", - "iopub.status.idle": "2024-05-24T14:19:37.430982Z", - "shell.execute_reply": "2024-05-24T14:19:37.430385Z" + "iopub.execute_input": "2024-05-24T14:49:50.346505Z", + "iopub.status.busy": "2024-05-24T14:49:50.345913Z", + "iopub.status.idle": "2024-05-24T14:49:51.544534Z", + "shell.execute_reply": "2024-05-24T14:49:51.543857Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-24 14:19:36-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-05-24 14:49:50-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,21 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.247, 2400:52e0:1a00::845:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" + "185.93.1.246, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -129,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.90MB/s in 0.2s \r\n", "\r\n", - "2024-05-24 14:19:36 (7.26 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-05-24 14:49:50 (5.90 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -151,9 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-05-24 14:19:37-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.219.169, 3.5.11.148, 3.5.27.172, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.219.169|:443... connected.\r\n", + "--2024-05-24 14:49:51-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.96.179, 52.217.49.108, 52.217.233.1, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.96.179|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,9 +168,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", + "pred_probs.npz 96%[==================> ] 15.71M 70.4MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 71.8MB/s in 0.2s \r\n", "\r\n", - "2024-05-24 14:19:37 (160 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-05-24 14:49:51 (71.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -193,10 +188,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:37.433469Z", - "iopub.status.busy": "2024-05-24T14:19:37.433289Z", - "iopub.status.idle": "2024-05-24T14:19:38.693567Z", - "shell.execute_reply": "2024-05-24T14:19:38.693013Z" + "iopub.execute_input": "2024-05-24T14:49:51.547444Z", + "iopub.status.busy": "2024-05-24T14:49:51.547170Z", + "iopub.status.idle": "2024-05-24T14:49:53.068431Z", + "shell.execute_reply": "2024-05-24T14:49:53.067845Z" }, "nbsphinx": "hidden" }, @@ -207,7 +202,7 @@ "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@25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@438899286262e4635f83b7d60da7bfdd1035aa5e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -233,10 +228,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.696151Z", - "iopub.status.busy": "2024-05-24T14:19:38.695736Z", - "iopub.status.idle": "2024-05-24T14:19:38.698953Z", - "shell.execute_reply": "2024-05-24T14:19:38.698536Z" + "iopub.execute_input": "2024-05-24T14:49:53.071590Z", + "iopub.status.busy": "2024-05-24T14:49:53.070873Z", + "iopub.status.idle": "2024-05-24T14:49:53.074970Z", + "shell.execute_reply": "2024-05-24T14:49:53.074360Z" } }, "outputs": [], @@ -286,10 +281,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.700791Z", - "iopub.status.busy": "2024-05-24T14:19:38.700617Z", - "iopub.status.idle": "2024-05-24T14:19:38.703466Z", - "shell.execute_reply": "2024-05-24T14:19:38.703042Z" + "iopub.execute_input": "2024-05-24T14:49:53.077640Z", + "iopub.status.busy": "2024-05-24T14:49:53.077268Z", + "iopub.status.idle": "2024-05-24T14:49:53.080839Z", + "shell.execute_reply": "2024-05-24T14:49:53.080227Z" }, "nbsphinx": "hidden" }, @@ -307,10 +302,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:38.705221Z", - "iopub.status.busy": "2024-05-24T14:19:38.705050Z", - "iopub.status.idle": "2024-05-24T14:19:47.741655Z", - "shell.execute_reply": "2024-05-24T14:19:47.741082Z" + "iopub.execute_input": "2024-05-24T14:49:53.083196Z", + "iopub.status.busy": "2024-05-24T14:49:53.082885Z", + "iopub.status.idle": "2024-05-24T14:50:02.197841Z", + "shell.execute_reply": "2024-05-24T14:50:02.197236Z" } }, "outputs": [], @@ -384,10 +379,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:47.744245Z", - "iopub.status.busy": "2024-05-24T14:19:47.743798Z", - "iopub.status.idle": "2024-05-24T14:19:47.749434Z", - "shell.execute_reply": "2024-05-24T14:19:47.748970Z" + "iopub.execute_input": "2024-05-24T14:50:02.200838Z", + "iopub.status.busy": "2024-05-24T14:50:02.200415Z", + "iopub.status.idle": "2024-05-24T14:50:02.206637Z", + "shell.execute_reply": "2024-05-24T14:50:02.206064Z" }, "nbsphinx": "hidden" }, @@ -427,10 +422,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:47.751530Z", - "iopub.status.busy": "2024-05-24T14:19:47.751102Z", - "iopub.status.idle": "2024-05-24T14:19:48.096364Z", - "shell.execute_reply": "2024-05-24T14:19:48.095809Z" + "iopub.execute_input": "2024-05-24T14:50:02.209145Z", + "iopub.status.busy": "2024-05-24T14:50:02.208748Z", + "iopub.status.idle": "2024-05-24T14:50:02.638158Z", + "shell.execute_reply": "2024-05-24T14:50:02.637534Z" } }, "outputs": [], @@ -467,10 +462,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:48.098890Z", - "iopub.status.busy": "2024-05-24T14:19:48.098535Z", - "iopub.status.idle": "2024-05-24T14:19:48.103037Z", - "shell.execute_reply": "2024-05-24T14:19:48.102561Z" + "iopub.execute_input": "2024-05-24T14:50:02.641122Z", + "iopub.status.busy": "2024-05-24T14:50:02.640673Z", + "iopub.status.idle": "2024-05-24T14:50:02.645692Z", + "shell.execute_reply": "2024-05-24T14:50:02.644970Z" } }, "outputs": [ @@ -542,10 +537,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:48.105040Z", - "iopub.status.busy": "2024-05-24T14:19:48.104729Z", - "iopub.status.idle": "2024-05-24T14:19:50.453604Z", - "shell.execute_reply": "2024-05-24T14:19:50.452960Z" + "iopub.execute_input": "2024-05-24T14:50:02.648427Z", + "iopub.status.busy": "2024-05-24T14:50:02.647997Z", + "iopub.status.idle": "2024-05-24T14:50:05.427039Z", + "shell.execute_reply": "2024-05-24T14:50:05.426330Z" } }, "outputs": [], @@ -567,10 +562,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.456521Z", - "iopub.status.busy": "2024-05-24T14:19:50.455956Z", - "iopub.status.idle": "2024-05-24T14:19:50.460042Z", - "shell.execute_reply": "2024-05-24T14:19:50.459521Z" + "iopub.execute_input": "2024-05-24T14:50:05.430476Z", + "iopub.status.busy": "2024-05-24T14:50:05.429663Z", + "iopub.status.idle": "2024-05-24T14:50:05.434064Z", + "shell.execute_reply": "2024-05-24T14:50:05.433453Z" } }, "outputs": [ @@ -606,10 +601,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.462133Z", - "iopub.status.busy": "2024-05-24T14:19:50.461793Z", - "iopub.status.idle": "2024-05-24T14:19:50.466784Z", - "shell.execute_reply": "2024-05-24T14:19:50.466230Z" + "iopub.execute_input": "2024-05-24T14:50:05.436615Z", + "iopub.status.busy": "2024-05-24T14:50:05.436042Z", + "iopub.status.idle": "2024-05-24T14:50:05.442446Z", + "shell.execute_reply": "2024-05-24T14:50:05.441825Z" } }, "outputs": [ @@ -787,10 +782,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.468850Z", - "iopub.status.busy": "2024-05-24T14:19:50.468522Z", - "iopub.status.idle": "2024-05-24T14:19:50.494333Z", - "shell.execute_reply": "2024-05-24T14:19:50.493853Z" + "iopub.execute_input": "2024-05-24T14:50:05.444740Z", + "iopub.status.busy": "2024-05-24T14:50:05.444488Z", + "iopub.status.idle": "2024-05-24T14:50:05.475833Z", + "shell.execute_reply": "2024-05-24T14:50:05.475164Z" } }, "outputs": [ @@ -892,10 +887,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.496514Z", - "iopub.status.busy": "2024-05-24T14:19:50.496124Z", - "iopub.status.idle": "2024-05-24T14:19:50.500485Z", - "shell.execute_reply": "2024-05-24T14:19:50.499944Z" + "iopub.execute_input": "2024-05-24T14:50:05.478414Z", + "iopub.status.busy": "2024-05-24T14:50:05.477949Z", + "iopub.status.idle": "2024-05-24T14:50:05.484291Z", + "shell.execute_reply": "2024-05-24T14:50:05.483674Z" } }, "outputs": [ @@ -969,10 +964,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:50.502462Z", - "iopub.status.busy": "2024-05-24T14:19:50.502137Z", - "iopub.status.idle": "2024-05-24T14:19:51.874676Z", - "shell.execute_reply": "2024-05-24T14:19:51.874134Z" + "iopub.execute_input": "2024-05-24T14:50:05.486636Z", + "iopub.status.busy": "2024-05-24T14:50:05.486295Z", + "iopub.status.idle": "2024-05-24T14:50:07.075328Z", + "shell.execute_reply": "2024-05-24T14:50:07.074681Z" } }, "outputs": [ @@ -1144,10 +1139,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-05-24T14:19:51.876917Z", - "iopub.status.busy": "2024-05-24T14:19:51.876550Z", - "iopub.status.idle": "2024-05-24T14:19:51.880651Z", - "shell.execute_reply": "2024-05-24T14:19:51.880202Z" + "iopub.execute_input": "2024-05-24T14:50:07.078297Z", + "iopub.status.busy": "2024-05-24T14:50:07.077744Z", + "iopub.status.idle": "2024-05-24T14:50:07.082976Z", + "shell.execute_reply": "2024-05-24T14:50:07.082309Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 62b57a255..c7b9f8d1e 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.4", - commit_hash: "25b7aaba7e53fe99c871c9c33e0c6d8c3c5f2970", + commit_hash: "438899286262e4635f83b7d60da7bfdd1035aa5e", }; \ No newline at end of file