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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index e860f069b..ba3e9b57d 100644
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index fb89290bd..15905d30d 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index 2c6f81ee6..e65894c84 100644
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diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
index 1596ab50a..36baab6a0 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:20.536553Z",
- "iopub.status.busy": "2024-08-29T17:07:20.536073Z",
- "iopub.status.idle": "2024-08-29T17:07:21.786062Z",
- "shell.execute_reply": "2024-08-29T17:07:21.785506Z"
+ "iopub.execute_input": "2024-09-04T16:36:33.494350Z",
+ "iopub.status.busy": "2024-09-04T16:36:33.493852Z",
+ "iopub.status.idle": "2024-09-04T16:36:34.726026Z",
+ "shell.execute_reply": "2024-09-04T16:36:34.725399Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:07:21.788700Z",
- "iopub.status.busy": "2024-08-29T17:07:21.788279Z",
- "iopub.status.idle": "2024-08-29T17:07:21.806586Z",
- "shell.execute_reply": "2024-08-29T17:07:21.806009Z"
+ "iopub.execute_input": "2024-09-04T16:36:34.729286Z",
+ "iopub.status.busy": "2024-09-04T16:36:34.728744Z",
+ "iopub.status.idle": "2024-09-04T16:36:34.747897Z",
+ "shell.execute_reply": "2024-09-04T16:36:34.747378Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.808803Z",
- "iopub.status.busy": "2024-08-29T17:07:21.808416Z",
- "iopub.status.idle": "2024-08-29T17:07:21.929772Z",
- "shell.execute_reply": "2024-08-29T17:07:21.929178Z"
+ "iopub.execute_input": "2024-09-04T16:36:34.750373Z",
+ "iopub.status.busy": "2024-09-04T16:36:34.749905Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.046021Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.045440Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.960601Z",
- "iopub.status.busy": "2024-08-29T17:07:21.960225Z",
- "iopub.status.idle": "2024-08-29T17:07:21.963941Z",
- "shell.execute_reply": "2024-08-29T17:07:21.963468Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.076604Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.076192Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.079864Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.079398Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.965925Z",
- "iopub.status.busy": "2024-08-29T17:07:21.965589Z",
- "iopub.status.idle": "2024-08-29T17:07:21.973807Z",
- "shell.execute_reply": "2024-08-29T17:07:21.973371Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.081865Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.081597Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.090286Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.089725Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.975916Z",
- "iopub.status.busy": "2024-08-29T17:07:21.975570Z",
- "iopub.status.idle": "2024-08-29T17:07:21.978067Z",
- "shell.execute_reply": "2024-08-29T17:07:21.977625Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.092459Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.092118Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.094778Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.094312Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.980150Z",
- "iopub.status.busy": "2024-08-29T17:07:21.979829Z",
- "iopub.status.idle": "2024-08-29T17:07:22.497459Z",
- "shell.execute_reply": "2024-08-29T17:07:22.496833Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.096769Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.096372Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.623436Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.622805Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:22.499970Z",
- "iopub.status.busy": "2024-08-29T17:07:22.499787Z",
- "iopub.status.idle": "2024-08-29T17:07:24.411694Z",
- "shell.execute_reply": "2024-08-29T17:07:24.411025Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.625916Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.625730Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.510736Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.510124Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.414319Z",
- "iopub.status.busy": "2024-08-29T17:07:24.413697Z",
- "iopub.status.idle": "2024-08-29T17:07:24.424172Z",
- "shell.execute_reply": "2024-08-29T17:07:24.423712Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.513479Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.512707Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.522816Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.522356Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.426313Z",
- "iopub.status.busy": "2024-08-29T17:07:24.425865Z",
- "iopub.status.idle": "2024-08-29T17:07:24.429917Z",
- "shell.execute_reply": "2024-08-29T17:07:24.429479Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.524923Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.524600Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.528591Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.528155Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.432009Z",
- "iopub.status.busy": "2024-08-29T17:07:24.431607Z",
- "iopub.status.idle": "2024-08-29T17:07:24.439758Z",
- "shell.execute_reply": "2024-08-29T17:07:24.439330Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.530787Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.530452Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.538844Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.538421Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.441674Z",
- "iopub.status.busy": "2024-08-29T17:07:24.441407Z",
- "iopub.status.idle": "2024-08-29T17:07:24.553494Z",
- "shell.execute_reply": "2024-08-29T17:07:24.553024Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.540782Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.540515Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.658571Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.658063Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.555730Z",
- "iopub.status.busy": "2024-08-29T17:07:24.555391Z",
- "iopub.status.idle": "2024-08-29T17:07:24.558029Z",
- "shell.execute_reply": "2024-08-29T17:07:24.557585Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.660665Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.660392Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.663359Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.662802Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.560040Z",
- "iopub.status.busy": "2024-08-29T17:07:24.559706Z",
- "iopub.status.idle": "2024-08-29T17:07:26.688998Z",
- "shell.execute_reply": "2024-08-29T17:07:26.688364Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.665583Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.665415Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.737444Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.736766Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:26.692110Z",
- "iopub.status.busy": "2024-08-29T17:07:26.691312Z",
- "iopub.status.idle": "2024-08-29T17:07:26.702522Z",
- "shell.execute_reply": "2024-08-29T17:07:26.702054Z"
+ "iopub.execute_input": "2024-09-04T16:36:39.740370Z",
+ "iopub.status.busy": "2024-09-04T16:36:39.739756Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.751066Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.750597Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:26.704548Z",
- "iopub.status.busy": "2024-08-29T17:07:26.704208Z",
- "iopub.status.idle": "2024-08-29T17:07:26.733958Z",
- "shell.execute_reply": "2024-08-29T17:07:26.733535Z"
+ "iopub.execute_input": "2024-09-04T16:36:39.753075Z",
+ "iopub.status.busy": "2024-09-04T16:36:39.752736Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.921485Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.920960Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index 3eab5a92c..707ac8b48 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:29.992566Z",
- "iopub.status.busy": "2024-08-29T17:07:29.992403Z",
- "iopub.status.idle": "2024-08-29T17:07:33.011464Z",
- "shell.execute_reply": "2024-08-29T17:07:33.010881Z"
+ "iopub.execute_input": "2024-09-04T16:36:42.886651Z",
+ "iopub.status.busy": "2024-09-04T16:36:42.886468Z",
+ "iopub.status.idle": "2024-09-04T16:36:45.659157Z",
+ "shell.execute_reply": "2024-09-04T16:36:45.658599Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:07:33.014357Z",
- "iopub.status.busy": "2024-08-29T17:07:33.013943Z",
- "iopub.status.idle": "2024-08-29T17:07:33.017670Z",
- "shell.execute_reply": "2024-08-29T17:07:33.017129Z"
+ "iopub.execute_input": "2024-09-04T16:36:45.662011Z",
+ "iopub.status.busy": "2024-09-04T16:36:45.661488Z",
+ "iopub.status.idle": "2024-09-04T16:36:45.665636Z",
+ "shell.execute_reply": "2024-09-04T16:36:45.665005Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:33.020073Z",
- "iopub.status.busy": "2024-08-29T17:07:33.019617Z",
- "iopub.status.idle": "2024-08-29T17:07:33.022756Z",
- "shell.execute_reply": "2024-08-29T17:07:33.022302Z"
+ "iopub.execute_input": "2024-09-04T16:36:45.667904Z",
+ "iopub.status.busy": "2024-09-04T16:36:45.667542Z",
+ "iopub.status.idle": "2024-09-04T16:36:45.670793Z",
+ "shell.execute_reply": "2024-09-04T16:36:45.670227Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
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@@ -312,10 +312,10 @@
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@@ -330,10 +330,10 @@
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@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'getting_spare_card', 'beneficiary_not_allowed', 'cancel_transfer', 'change_pin', 'supported_cards_and_currencies'}\n"
+ "Classes: {'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'card_payment_fee_charged', 'getting_spare_card', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed'}\n"
]
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@@ -409,10 +409,10 @@
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@@ -453,17 +453,17 @@
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@@ -1272,7 +1265,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index ac2ee08e5..c207e9188 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-08-29T17:07:45.313757Z",
- "iopub.status.idle": "2024-08-29T17:07:50.822059Z",
- "shell.execute_reply": "2024-08-29T17:07:50.821392Z"
+ "iopub.execute_input": "2024-09-04T16:36:57.240536Z",
+ "iopub.status.busy": "2024-09-04T16:36:57.240126Z",
+ "iopub.status.idle": "2024-09-04T16:37:02.508553Z",
+ "shell.execute_reply": "2024-09-04T16:37:02.507910Z"
},
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},
@@ -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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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,
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},
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},
@@ -157,10 +157,10 @@
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},
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@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
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- "iopub.status.idle": "2024-08-29T17:07:52.427284Z",
- "shell.execute_reply": "2024-08-29T17:07:52.426602Z"
+ "iopub.execute_input": "2024-09-04T16:37:02.522528Z",
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+ "shell.execute_reply": "2024-09-04T16:37:04.464182Z"
},
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"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
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- "shell.execute_reply": "2024-08-29T17:07:52.441839Z"
+ "iopub.execute_input": "2024-09-04T16:37:04.467875Z",
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},
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"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
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@@ -380,10 +380,10 @@
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"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
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@@ -474,10 +474,10 @@
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- "shell.execute_reply": "2024-08-29T17:07:53.942329Z"
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"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
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- "shell.execute_reply": "2024-08-29T17:07:53.947232Z"
+ "iopub.execute_input": "2024-09-04T16:37:06.060898Z",
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@@ -582,10 +582,10 @@
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@@ -617,10 +617,10 @@
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+ "iopub.execute_input": "2024-09-04T16:37:19.891159Z",
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@@ -680,10 +680,10 @@
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@@ -717,10 +717,10 @@
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@@ -767,10 +767,10 @@
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@@ -807,10 +807,10 @@
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@@ -870,10 +870,10 @@
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@@ -977,10 +977,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 161d64427..9a4d8b34f 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -517,10 +517,10 @@
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@@ -569,10 +569,10 @@
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@@ -714,10 +714,10 @@
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@@ -830,10 +830,10 @@
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@@ -937,10 +937,10 @@
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- "value": " 132/132 [00:00<00:00, 11638.35 examples/s]"
+ "value": "Saving the dataset (1/1 shards): 100%"
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@@ -1647,23 +1633,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index c8c193157..c29ef173d 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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@@ -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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -602,10 +602,10 @@
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@@ -638,10 +638,10 @@
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@@ -685,10 +685,10 @@
"execution_count": 10,
"metadata": {
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@@ -821,10 +821,10 @@
"execution_count": 11,
"metadata": {
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@@ -935,10 +935,10 @@
"execution_count": 12,
"metadata": {
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"outputs": [
@@ -1005,10 +1005,10 @@
"execution_count": 13,
"metadata": {
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}
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@@ -1200,10 +1200,10 @@
"execution_count": 14,
"metadata": {
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- "shell.execute_reply": "2024-08-29T17:08:24.088724Z"
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+ "shell.execute_reply": "2024-09-04T16:37:35.617603Z"
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"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
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+ "shell.execute_reply": "2024-09-04T16:37:35.626375Z"
},
"scrolled": true
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@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:08:24.099999Z",
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+ "iopub.execute_input": "2024-09-04T16:37:35.628964Z",
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+ "shell.execute_reply": "2024-09-04T16:37:35.637354Z"
}
},
"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:08:24.111257Z",
- "iopub.status.busy": "2024-08-29T17:08:24.110943Z",
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- "shell.execute_reply": "2024-08-29T17:08:24.127947Z"
+ "iopub.execute_input": "2024-09-04T16:37:35.640087Z",
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+ "iopub.status.idle": "2024-09-04T16:37:35.656365Z",
+ "shell.execute_reply": "2024-09-04T16:37:35.655938Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index c70a728dc..cb169fc85 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:08:27.059101Z",
- "iopub.status.busy": "2024-08-29T17:08:27.058926Z",
- "iopub.status.idle": "2024-08-29T17:08:30.040828Z",
- "shell.execute_reply": "2024-08-29T17:08:30.040273Z"
+ "iopub.execute_input": "2024-09-04T16:37:38.303462Z",
+ "iopub.status.busy": "2024-09-04T16:37:38.303044Z",
+ "iopub.status.idle": "2024-09-04T16:37:41.244865Z",
+ "shell.execute_reply": "2024-09-04T16:37:41.244308Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:08:30.043597Z",
- "iopub.status.busy": "2024-08-29T17:08:30.043071Z",
- "iopub.status.idle": "2024-08-29T17:08:30.046795Z",
- "shell.execute_reply": "2024-08-29T17:08:30.046202Z"
+ "iopub.execute_input": "2024-09-04T16:37:41.247427Z",
+ "iopub.status.busy": "2024-09-04T16:37:41.247143Z",
+ "iopub.status.idle": "2024-09-04T16:37:41.250634Z",
+ "shell.execute_reply": "2024-09-04T16:37:41.250198Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:08:30.048992Z",
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- "iopub.status.idle": "2024-08-29T17:08:33.008906Z",
- "shell.execute_reply": "2024-08-29T17:08:33.008290Z"
+ "iopub.execute_input": "2024-09-04T16:37:41.252505Z",
+ "iopub.status.busy": "2024-09-04T16:37:41.252330Z",
+ "iopub.status.idle": "2024-09-04T16:37:49.839843Z",
+ "shell.execute_reply": "2024-09-04T16:37:49.839373Z"
}
},
"outputs": [
{
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- "model_id": "02166c864ca449ecb48ca6570e5c3978",
+ "model_id": "319eb3c359274c29ba693fd30f98b99d",
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"version_minor": 0
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@@ -176,7 +176,7 @@
{
"data": {
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+ "model_id": "1abdd5d51e71429c83f3161fb0f34a8a",
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"version_minor": 0
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@@ -190,7 +190,7 @@
{
"data": {
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- "model_id": "3a4a80e9a3f547d9b039302dbdd73447",
+ "model_id": "be064f9db0c241afa490cc2cddb76c07",
"version_major": 2,
"version_minor": 0
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@@ -204,7 +204,7 @@
{
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+ "model_id": "39b7a60b22d9490c85820d3c8bb7afc7",
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@@ -218,7 +218,7 @@
{
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+ "model_id": "33af8738fba94dfab36a8701ff30857a",
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@@ -260,10 +260,10 @@
"execution_count": 4,
"metadata": {
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- "iopub.status.idle": "2024-08-29T17:08:33.014717Z",
- "shell.execute_reply": "2024-08-29T17:08:33.014140Z"
+ "iopub.execute_input": "2024-09-04T16:37:49.842162Z",
+ "iopub.status.busy": "2024-09-04T16:37:49.841818Z",
+ "iopub.status.idle": "2024-09-04T16:37:49.846030Z",
+ "shell.execute_reply": "2024-09-04T16:37:49.845588Z"
}
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"outputs": [
@@ -288,17 +288,17 @@
"execution_count": 5,
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- "iopub.status.busy": "2024-08-29T17:08:33.016616Z",
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- "shell.execute_reply": "2024-08-29T17:08:44.567340Z"
+ "iopub.execute_input": "2024-09-04T16:37:49.848035Z",
+ "iopub.status.busy": "2024-09-04T16:37:49.847710Z",
+ "iopub.status.idle": "2024-09-04T16:38:01.327046Z",
+ "shell.execute_reply": "2024-09-04T16:38:01.326503Z"
}
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- "model_id": "944d3d9122cd4a2688bd85cf843c82c1",
+ "model_id": "4bb198dfcbae4b5aa14ac1168e1b6695",
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@@ -336,10 +336,10 @@
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- "shell.execute_reply": "2024-08-29T17:09:03.079706Z"
+ "iopub.execute_input": "2024-09-04T16:38:01.329677Z",
+ "iopub.status.busy": "2024-09-04T16:38:01.329284Z",
+ "iopub.status.idle": "2024-09-04T16:38:19.893278Z",
+ "shell.execute_reply": "2024-09-04T16:38:19.892634Z"
}
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@@ -372,10 +372,10 @@
"execution_count": 7,
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- "shell.execute_reply": "2024-08-29T17:09:03.088125Z"
+ "iopub.execute_input": "2024-09-04T16:38:19.896038Z",
+ "iopub.status.busy": "2024-09-04T16:38:19.895653Z",
+ "iopub.status.idle": "2024-09-04T16:38:19.901344Z",
+ "shell.execute_reply": "2024-09-04T16:38:19.900884Z"
}
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@@ -413,10 +413,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:09:03.091044Z",
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- "shell.execute_reply": "2024-08-29T17:09:03.094381Z"
+ "iopub.execute_input": "2024-09-04T16:38:19.903276Z",
+ "iopub.status.busy": "2024-09-04T16:38:19.902941Z",
+ "iopub.status.idle": "2024-09-04T16:38:19.907079Z",
+ "shell.execute_reply": "2024-09-04T16:38:19.906541Z"
},
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},
@@ -553,10 +553,10 @@
"execution_count": 9,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:09:03.097052Z",
- "iopub.status.busy": "2024-08-29T17:09:03.096741Z",
- "iopub.status.idle": "2024-08-29T17:09:03.105849Z",
- "shell.execute_reply": "2024-08-29T17:09:03.105377Z"
+ "iopub.execute_input": "2024-09-04T16:38:19.909304Z",
+ "iopub.status.busy": "2024-09-04T16:38:19.908877Z",
+ "iopub.status.idle": "2024-09-04T16:38:19.917613Z",
+ "shell.execute_reply": "2024-09-04T16:38:19.917139Z"
},
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@@ -681,10 +681,10 @@
"execution_count": 10,
"metadata": {
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- "iopub.status.idle": "2024-08-29T17:09:03.136081Z",
- "shell.execute_reply": "2024-08-29T17:09:03.135415Z"
+ "iopub.execute_input": "2024-09-04T16:38:19.919533Z",
+ "iopub.status.busy": "2024-09-04T16:38:19.919361Z",
+ "iopub.status.idle": "2024-09-04T16:38:19.945136Z",
+ "shell.execute_reply": "2024-09-04T16:38:19.944720Z"
}
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"outputs": [],
@@ -721,10 +721,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:09:03.138517Z",
- "iopub.status.busy": "2024-08-29T17:09:03.138340Z",
- "iopub.status.idle": "2024-08-29T17:09:37.733449Z",
- "shell.execute_reply": "2024-08-29T17:09:37.732851Z"
+ "iopub.execute_input": "2024-09-04T16:38:19.947137Z",
+ "iopub.status.busy": "2024-09-04T16:38:19.946804Z",
+ "iopub.status.idle": "2024-09-04T16:38:52.806683Z",
+ "shell.execute_reply": "2024-09-04T16:38:52.806036Z"
}
},
"outputs": [
@@ -740,21 +740,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.447\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.850\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.931\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.533\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
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- "model_id": "30683b6059984426ba19841fd4a774ec",
+ "model_id": "e0d04f00652048319b15a7c8d92b501f",
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@@ -775,7 +775,7 @@
{
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+ "model_id": "14a337641dd1417c8844000cc271b2b4",
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},
@@ -798,21 +798,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.921\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.716\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.694\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.764\n",
"Computing feature embeddings ...\n"
]
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{
"data": {
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- "model_id": "e3b91fd43b3e46c08a3c6d32e9efbe48",
+ "model_id": "c88ba26dbe76450d8c7a7ecda8dfca3e",
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@@ -833,7 +833,7 @@
{
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- "model_id": "245f60ab16d24a57b85ef53a5fb96484",
+ "model_id": "b8fa2ba849184558b5054c6e4a4e5dd8",
"version_major": 2,
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@@ -856,21 +856,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.072\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.906\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.779\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.489\n",
"Computing feature embeddings ...\n"
]
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- "model_id": "dd4fc8da52cc4c5cbd12d26954963ed1",
+ "model_id": "8a1c87dd1eaf47458965198d643def42",
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@@ -891,7 +891,7 @@
{
"data": {
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- "model_id": "3a83a86ee3254d7fbaa7f292d3461e66",
+ "model_id": "75f52856abb347d89314c3cc706909a4",
"version_major": 2,
"version_minor": 0
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@@ -970,10 +970,10 @@
"execution_count": 12,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:09:37.736051Z",
- "iopub.status.busy": "2024-08-29T17:09:37.735695Z",
- "iopub.status.idle": "2024-08-29T17:09:37.752765Z",
- "shell.execute_reply": "2024-08-29T17:09:37.752270Z"
+ "iopub.execute_input": "2024-09-04T16:38:52.809587Z",
+ "iopub.status.busy": "2024-09-04T16:38:52.808875Z",
+ "iopub.status.idle": "2024-09-04T16:38:52.825892Z",
+ "shell.execute_reply": "2024-09-04T16:38:52.825344Z"
}
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@@ -998,10 +998,10 @@
"execution_count": 13,
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@@ -1021,10 +1021,10 @@
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+ "shell.execute_reply": "2024-09-04T16:40:43.225892Z"
}
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@@ -1063,7 +1063,7 @@
{
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- "model_id": "edc1a5f44295433790bd440975c40ab2",
+ "model_id": "52daadd720d843c0afa302f1faa5901a",
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@@ -1109,10 +1109,10 @@
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@@ -1258,10 +1258,10 @@
"execution_count": 16,
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@@ -1365,10 +1365,10 @@
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@@ -1498,10 +1498,10 @@
"execution_count": 18,
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@@ -1547,10 +1547,10 @@
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@@ -1585,10 +1585,10 @@
"execution_count": 20,
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@@ -1755,10 +1755,10 @@
"execution_count": 21,
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@@ -1834,10 +1834,10 @@
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@@ -1874,10 +1874,10 @@
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@@ -2034,10 +2034,10 @@
"execution_count": 24,
"metadata": {
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@@ -2082,10 +2082,10 @@
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@@ -2167,10 +2167,10 @@
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+ "shell.execute_reply": "2024-09-04T16:40:45.541029Z"
}
},
"outputs": [
@@ -2195,47 +2195,47 @@
" \n",
" \n",
" | \n",
- " is_dark_issue | \n",
" dark_score | \n",
+ " is_dark_issue | \n",
"
\n",
" \n",
"
\n",
" \n",
" 34848 | \n",
- " True | \n",
" 0.203922 | \n",
+ " True | \n",
"
\n",
" \n",
" 50270 | \n",
- " True | \n",
" 0.204588 | \n",
+ " True | \n",
"
\n",
" \n",
" 3936 | \n",
- " True | \n",
" 0.213098 | \n",
+ " True | \n",
"
\n",
" \n",
" 733 | \n",
- " True | \n",
" 0.217686 | \n",
+ " True | \n",
"
\n",
" \n",
" 8094 | \n",
- " True | \n",
" 0.230118 | \n",
+ " True | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " is_dark_issue dark_score\n",
- "34848 True 0.203922\n",
- "50270 True 0.204588\n",
- "3936 True 0.213098\n",
- "733 True 0.217686\n",
- "8094 True 0.230118"
+ " dark_score is_dark_issue\n",
+ "34848 0.203922 True\n",
+ "50270 0.204588 True\n",
+ "3936 0.213098 True\n",
+ "733 0.217686 True\n",
+ "8094 0.230118 True"
]
},
"execution_count": 26,
@@ -2298,10 +2298,10 @@
"execution_count": 27,
"metadata": {
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+ "iopub.execute_input": "2024-09-04T16:40:45.543993Z",
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@@ -2338,10 +2338,10 @@
"execution_count": 28,
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"outputs": [
@@ -2383,10 +2383,10 @@
"execution_count": 29,
"metadata": {
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- "shell.execute_reply": "2024-08-29T17:11:32.447641Z"
+ "iopub.execute_input": "2024-09-04T16:40:45.755369Z",
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+ "shell.execute_reply": "2024-09-04T16:40:45.761919Z"
}
},
"outputs": [
@@ -2411,47 +2411,47 @@
" \n",
" \n",
" | \n",
- " low_information_score | \n",
" is_low_information_issue | \n",
+ " low_information_score | \n",
"
\n",
" \n",
" \n",
" \n",
" 53050 | \n",
- " 0.067975 | \n",
" True | \n",
+ " 0.067975 | \n",
"
\n",
" \n",
" 40875 | \n",
- " 0.089929 | \n",
" True | \n",
+ " 0.089929 | \n",
"
\n",
" \n",
" 9594 | \n",
- " 0.092601 | \n",
" True | \n",
+ " 0.092601 | \n",
"
\n",
" \n",
" 34825 | \n",
- " 0.107744 | \n",
" True | \n",
+ " 0.107744 | \n",
"
\n",
" \n",
" 37530 | \n",
- " 0.108516 | \n",
" True | \n",
+ " 0.108516 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " low_information_score is_low_information_issue\n",
- "53050 0.067975 True\n",
- "40875 0.089929 True\n",
- "9594 0.092601 True\n",
- "34825 0.107744 True\n",
- "37530 0.108516 True"
+ " is_low_information_issue low_information_score\n",
+ "53050 True 0.067975\n",
+ "40875 True 0.089929\n",
+ "9594 True 0.092601\n",
+ "34825 True 0.107744\n",
+ "37530 True 0.108516"
]
},
"execution_count": 29,
@@ -2472,10 +2472,10 @@
"execution_count": 30,
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- "shell.execute_reply": "2024-08-29T17:11:32.640466Z"
+ "iopub.execute_input": "2024-09-04T16:40:45.764526Z",
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"outputs": [
@@ -2515,10 +2515,10 @@
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@@ -2555,77 +2555,7 @@
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@@ -2678,23 +2608,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index 093f71f03..2b3e4b6b1 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
@@ -73,10 +73,10 @@
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@@ -86,7 +86,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:45.779533Z",
- "iopub.status.busy": "2024-08-29T17:11:45.779168Z",
- "iopub.status.idle": "2024-08-29T17:11:48.629019Z",
- "shell.execute_reply": "2024-08-29T17:11:48.628451Z"
+ "iopub.execute_input": "2024-09-04T16:40:58.594849Z",
+ "iopub.status.busy": "2024-09-04T16:40:58.594436Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.338933Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.338290Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:11:48.631743Z",
- "iopub.status.busy": "2024-08-29T17:11:48.631306Z",
- "iopub.status.idle": "2024-08-29T17:11:48.634659Z",
- "shell.execute_reply": "2024-08-29T17:11:48.634082Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.341714Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.341211Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.344547Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.344080Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:48.636768Z",
- "iopub.status.busy": "2024-08-29T17:11:48.636377Z",
- "iopub.status.idle": "2024-08-29T17:11:48.639545Z",
- "shell.execute_reply": "2024-08-29T17:11:48.638976Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.346577Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.346195Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.349208Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.348748Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:48.641703Z",
- "iopub.status.busy": "2024-08-29T17:11:48.641295Z",
- "iopub.status.idle": "2024-08-29T17:11:48.663938Z",
- "shell.execute_reply": "2024-08-29T17:11:48.663379Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.351352Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.350932Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.371702Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.371170Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:48.665947Z",
- "iopub.status.busy": "2024-08-29T17:11:48.665634Z",
- "iopub.status.idle": "2024-08-29T17:11:48.669380Z",
- "shell.execute_reply": "2024-08-29T17:11:48.668822Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.374126Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.373690Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.377781Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.377254Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'cancel_transfer', 'change_pin', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'getting_spare_card', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'lost_or_stolen_phone'}\n"
+ "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'change_pin', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'cancel_transfer', 'card_payment_fee_charged', 'card_about_to_expire'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:48.671469Z",
- "iopub.status.busy": "2024-08-29T17:11:48.671133Z",
- "iopub.status.idle": "2024-08-29T17:11:48.674361Z",
- "shell.execute_reply": "2024-08-29T17:11:48.673893Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.379956Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.379537Z",
+ "iopub.status.idle": "2024-09-04T16:41:01.382734Z",
+ "shell.execute_reply": "2024-09-04T16:41:01.382205Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:48.676512Z",
- "iopub.status.busy": "2024-08-29T17:11:48.676178Z",
- "iopub.status.idle": "2024-08-29T17:11:52.298515Z",
- "shell.execute_reply": "2024-08-29T17:11:52.297829Z"
+ "iopub.execute_input": "2024-09-04T16:41:01.384801Z",
+ "iopub.status.busy": "2024-09-04T16:41:01.384484Z",
+ "iopub.status.idle": "2024-09-04T16:41:05.425042Z",
+ "shell.execute_reply": "2024-09-04T16:41:05.424383Z"
}
},
"outputs": [
@@ -416,10 +416,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:52.301372Z",
- "iopub.status.busy": "2024-08-29T17:11:52.301006Z",
- "iopub.status.idle": "2024-08-29T17:11:53.205868Z",
- "shell.execute_reply": "2024-08-29T17:11:53.205269Z"
+ "iopub.execute_input": "2024-09-04T16:41:05.428052Z",
+ "iopub.status.busy": "2024-09-04T16:41:05.427629Z",
+ "iopub.status.idle": "2024-09-04T16:41:06.348646Z",
+ "shell.execute_reply": "2024-09-04T16:41:06.348110Z"
},
"scrolled": true
},
@@ -451,10 +451,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:53.208825Z",
- "iopub.status.busy": "2024-08-29T17:11:53.208214Z",
- "iopub.status.idle": "2024-08-29T17:11:53.211611Z",
- "shell.execute_reply": "2024-08-29T17:11:53.211104Z"
+ "iopub.execute_input": "2024-09-04T16:41:06.351379Z",
+ "iopub.status.busy": "2024-09-04T16:41:06.350975Z",
+ "iopub.status.idle": "2024-09-04T16:41:06.353832Z",
+ "shell.execute_reply": "2024-09-04T16:41:06.353336Z"
}
},
"outputs": [],
@@ -474,10 +474,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:53.214175Z",
- "iopub.status.busy": "2024-08-29T17:11:53.213781Z",
- "iopub.status.idle": "2024-08-29T17:11:55.217193Z",
- "shell.execute_reply": "2024-08-29T17:11:55.216477Z"
+ "iopub.execute_input": "2024-09-04T16:41:06.356200Z",
+ "iopub.status.busy": "2024-09-04T16:41:06.355817Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.326773Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.326129Z"
},
"scrolled": true
},
@@ -521,10 +521,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.221784Z",
- "iopub.status.busy": "2024-08-29T17:11:55.220564Z",
- "iopub.status.idle": "2024-08-29T17:11:55.247534Z",
- "shell.execute_reply": "2024-08-29T17:11:55.246994Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.329840Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.329196Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.352768Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.352270Z"
},
"scrolled": true
},
@@ -654,10 +654,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.251301Z",
- "iopub.status.busy": "2024-08-29T17:11:55.250349Z",
- "iopub.status.idle": "2024-08-29T17:11:55.259480Z",
- "shell.execute_reply": "2024-08-29T17:11:55.258979Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.355171Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.354780Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.364358Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.363857Z"
},
"scrolled": true
},
@@ -767,10 +767,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.261705Z",
- "iopub.status.busy": "2024-08-29T17:11:55.261259Z",
- "iopub.status.idle": "2024-08-29T17:11:55.265695Z",
- "shell.execute_reply": "2024-08-29T17:11:55.265143Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.366701Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.366397Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.370411Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.369835Z"
}
},
"outputs": [
@@ -808,10 +808,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.267789Z",
- "iopub.status.busy": "2024-08-29T17:11:55.267464Z",
- "iopub.status.idle": "2024-08-29T17:11:55.273881Z",
- "shell.execute_reply": "2024-08-29T17:11:55.273336Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.372360Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.372021Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.378227Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.377738Z"
}
},
"outputs": [
@@ -928,10 +928,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.276103Z",
- "iopub.status.busy": "2024-08-29T17:11:55.275716Z",
- "iopub.status.idle": "2024-08-29T17:11:55.282363Z",
- "shell.execute_reply": "2024-08-29T17:11:55.281836Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.380294Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.379963Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.386336Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.385784Z"
}
},
"outputs": [
@@ -1014,10 +1014,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.284483Z",
- "iopub.status.busy": "2024-08-29T17:11:55.284080Z",
- "iopub.status.idle": "2024-08-29T17:11:55.290321Z",
- "shell.execute_reply": "2024-08-29T17:11:55.289768Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.388135Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.387960Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.393752Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.393284Z"
}
},
"outputs": [
@@ -1125,10 +1125,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.292501Z",
- "iopub.status.busy": "2024-08-29T17:11:55.292181Z",
- "iopub.status.idle": "2024-08-29T17:11:55.300591Z",
- "shell.execute_reply": "2024-08-29T17:11:55.300041Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.395608Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.395432Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.403730Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.403294Z"
}
},
"outputs": [
@@ -1239,10 +1239,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.302719Z",
- "iopub.status.busy": "2024-08-29T17:11:55.302381Z",
- "iopub.status.idle": "2024-08-29T17:11:55.307865Z",
- "shell.execute_reply": "2024-08-29T17:11:55.307405Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.405680Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.405503Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.410937Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.410395Z"
}
},
"outputs": [
@@ -1310,10 +1310,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.309805Z",
- "iopub.status.busy": "2024-08-29T17:11:55.309470Z",
- "iopub.status.idle": "2024-08-29T17:11:55.314831Z",
- "shell.execute_reply": "2024-08-29T17:11:55.314389Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.413050Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.412728Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.417941Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.417454Z"
}
},
"outputs": [
@@ -1392,10 +1392,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.316839Z",
- "iopub.status.busy": "2024-08-29T17:11:55.316503Z",
- "iopub.status.idle": "2024-08-29T17:11:55.320158Z",
- "shell.execute_reply": "2024-08-29T17:11:55.319714Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.419859Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.419677Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.423027Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.422505Z"
}
},
"outputs": [
@@ -1449,10 +1449,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:55.322296Z",
- "iopub.status.busy": "2024-08-29T17:11:55.321948Z",
- "iopub.status.idle": "2024-08-29T17:11:55.327047Z",
- "shell.execute_reply": "2024-08-29T17:11:55.326621Z"
+ "iopub.execute_input": "2024-09-04T16:41:08.425011Z",
+ "iopub.status.busy": "2024-09-04T16:41:08.424833Z",
+ "iopub.status.idle": "2024-09-04T16:41:08.430112Z",
+ "shell.execute_reply": "2024-09-04T16:41:08.429634Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index 45e10659e..1a14ce756 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:58.718767Z",
- "iopub.status.busy": "2024-08-29T17:11:58.718583Z",
- "iopub.status.idle": "2024-08-29T17:11:59.146531Z",
- "shell.execute_reply": "2024-08-29T17:11:59.145994Z"
+ "iopub.execute_input": "2024-09-04T16:41:12.664036Z",
+ "iopub.status.busy": "2024-09-04T16:41:12.663550Z",
+ "iopub.status.idle": "2024-09-04T16:41:13.084540Z",
+ "shell.execute_reply": "2024-09-04T16:41:13.084042Z"
}
},
"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:59.149240Z",
- "iopub.status.busy": "2024-08-29T17:11:59.148744Z",
- "iopub.status.idle": "2024-08-29T17:11:59.280675Z",
- "shell.execute_reply": "2024-08-29T17:11:59.280097Z"
+ "iopub.execute_input": "2024-09-04T16:41:13.087150Z",
+ "iopub.status.busy": "2024-09-04T16:41:13.086739Z",
+ "iopub.status.idle": "2024-09-04T16:41:13.215203Z",
+ "shell.execute_reply": "2024-09-04T16:41:13.214722Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:59.282900Z",
- "iopub.status.busy": "2024-08-29T17:11:59.282658Z",
- "iopub.status.idle": "2024-08-29T17:11:59.305884Z",
- "shell.execute_reply": "2024-08-29T17:11:59.305336Z"
+ "iopub.execute_input": "2024-09-04T16:41:13.217480Z",
+ "iopub.status.busy": "2024-09-04T16:41:13.217063Z",
+ "iopub.status.idle": "2024-09-04T16:41:13.239737Z",
+ "shell.execute_reply": "2024-09-04T16:41:13.239204Z"
}
},
"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:11:59.308547Z",
- "iopub.status.busy": "2024-08-29T17:11:59.308330Z",
- "iopub.status.idle": "2024-08-29T17:12:02.115117Z",
- "shell.execute_reply": "2024-08-29T17:12:02.114510Z"
+ "iopub.execute_input": "2024-09-04T16:41:13.242377Z",
+ "iopub.status.busy": "2024-09-04T16:41:13.241951Z",
+ "iopub.status.idle": "2024-09-04T16:41:15.980442Z",
+ "shell.execute_reply": "2024-09-04T16:41:15.979864Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:02.117844Z",
- "iopub.status.busy": "2024-08-29T17:12:02.117264Z",
- "iopub.status.idle": "2024-08-29T17:12:10.972806Z",
- "shell.execute_reply": "2024-08-29T17:12:10.972199Z"
+ "iopub.execute_input": "2024-09-04T16:41:15.983269Z",
+ "iopub.status.busy": "2024-09-04T16:41:15.982680Z",
+ "iopub.status.idle": "2024-09-04T16:41:25.804093Z",
+ "shell.execute_reply": "2024-09-04T16:41:25.803484Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 6,
"metadata": {
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- "iopub.execute_input": "2024-08-29T17:12:10.975125Z",
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@@ -838,10 +838,10 @@
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@@ -1000,10 +1000,10 @@
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@@ -1082,10 +1082,10 @@
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@@ -1115,10 +1115,10 @@
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@@ -1146,10 +1146,10 @@
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@@ -1189,10 +1189,10 @@
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@@ -1390,10 +1390,10 @@
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@@ -1460,10 +1460,10 @@
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@@ -1729,10 +1729,10 @@
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@@ -1919,10 +1919,10 @@
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@@ -1956,10 +1956,10 @@
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@@ -1981,10 +1981,10 @@
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@@ -2142,10 +2142,10 @@
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@@ -2178,10 +2178,10 @@
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@@ -2327,10 +2327,10 @@
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@@ -2397,10 +2397,10 @@
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@@ -2451,10 +2451,10 @@
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@@ -2733,10 +2733,10 @@
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@@ -3003,10 +3003,10 @@
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@@ -3031,10 +3031,10 @@
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@@ -3206,10 +3206,10 @@
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@@ -3241,10 +3241,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index f499d38de..327b4bd04 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
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"dependencies = [\"cleanlab\", \"requests\"]\n",
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@0620487f86634df0f530d3659a564db463d09b34\n",
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" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index ec1def7c4..6746ec983 100644
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diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
index 60df5097f..555c3d9d7 100644
--- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb
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"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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+ "iopub.execute_input": "2024-09-04T16:41:59.679604Z",
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@@ -312,10 +312,10 @@
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"outputs": [
@@ -418,10 +418,10 @@
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"outputs": [],
@@ -449,10 +449,10 @@
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@@ -488,10 +488,10 @@
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@@ -506,10 +506,10 @@
"id": "7ac47c3d-9e87-45b7-9064-bfa45578872e",
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"outputs": [
@@ -609,10 +609,10 @@
"id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b",
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"outputs": [
@@ -724,10 +724,10 @@
"id": "b68e0418-86cf-431f-9107-2dd0a310ca42",
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"outputs": [
@@ -931,10 +931,10 @@
"id": "0e9bd131-429f-48af-b4fc-ed8b907950b9",
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"outputs": [
@@ -968,10 +968,10 @@
"id": "e72320ec-7792-4347-b2fb-630f2519127c",
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"outputs": [
@@ -1005,10 +1005,10 @@
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"outputs": [
@@ -1205,10 +1205,10 @@
"id": "3c002665-c48b-4f04-91f7-ad112a49efc7",
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"outputs": [],
@@ -1234,10 +1234,10 @@
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"outputs": [
@@ -1711,10 +1711,10 @@
"id": "044c0eb1-299a-4851-b1bf-268d5bce56c1",
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@@ -1738,10 +1738,10 @@
"id": "c43df278-abfe-40e5-9d48-2df3efea9379",
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"outputs": [
@@ -1953,10 +1953,10 @@
"id": "77c7f776-54b3-45b5-9207-715d6d2e90c0",
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"outputs": [
@@ -2073,10 +2073,10 @@
"id": "7e218d04-0729-4f42-b264-51c73601ebe6",
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@@ -2090,10 +2090,10 @@
"id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b",
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"outputs": [],
@@ -2117,10 +2117,10 @@
"id": "5ce2d89f-e832-448d-bfac-9941da15c895",
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"outputs": [
@@ -2160,10 +2160,10 @@
"id": "9f437756-112e-4531-84fc-6ceadd0c9ef5",
"metadata": {
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"outputs": [],
@@ -2194,10 +2194,10 @@
"id": "707625f6",
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"outputs": [
@@ -2408,10 +2408,10 @@
"id": "25afe46c-a521-483c-b168-728c76d970dc",
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"outputs": [
@@ -2441,10 +2441,10 @@
"id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4",
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"outputs": [
@@ -2477,10 +2477,10 @@
"id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd",
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"outputs": [
@@ -2513,10 +2513,10 @@
"id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe",
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"outputs": [
@@ -2542,10 +2542,10 @@
"id": "08080458-0cd7-447d-80e6-384cb8d31eaf",
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@@ -2569,10 +2569,10 @@
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"outputs": [
@@ -3052,10 +3052,10 @@
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- "shell.execute_reply": "2024-08-29T17:12:47.825387Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.740343Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.739973Z",
+ "iopub.status.idle": "2024-09-04T16:42:02.752701Z",
+ "shell.execute_reply": "2024-09-04T16:42:02.752269Z"
}
},
"outputs": [
@@ -3111,10 +3111,10 @@
"id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:47.828396Z",
- "iopub.status.busy": "2024-08-29T17:12:47.827941Z",
- "iopub.status.idle": "2024-08-29T17:12:47.830752Z",
- "shell.execute_reply": "2024-08-29T17:12:47.830284Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.754991Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.754640Z",
+ "iopub.status.idle": "2024-09-04T16:42:02.757462Z",
+ "shell.execute_reply": "2024-09-04T16:42:02.757043Z"
}
},
"outputs": [],
@@ -3150,10 +3150,10 @@
"id": "941ab2a6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:47.832857Z",
- "iopub.status.busy": "2024-08-29T17:12:47.832409Z",
- "iopub.status.idle": "2024-08-29T17:12:47.842686Z",
- "shell.execute_reply": "2024-08-29T17:12:47.842124Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.759660Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.759312Z",
+ "iopub.status.idle": "2024-09-04T16:42:02.768158Z",
+ "shell.execute_reply": "2024-09-04T16:42:02.767743Z"
}
},
"outputs": [],
@@ -3261,10 +3261,10 @@
"id": "50666fb9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:47.844570Z",
- "iopub.status.busy": "2024-08-29T17:12:47.844400Z",
- "iopub.status.idle": "2024-08-29T17:12:47.852487Z",
- "shell.execute_reply": "2024-08-29T17:12:47.852017Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.770481Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.770127Z",
+ "iopub.status.idle": "2024-09-04T16:42:02.776682Z",
+ "shell.execute_reply": "2024-09-04T16:42:02.776278Z"
},
"nbsphinx": "hidden"
},
@@ -3346,10 +3346,10 @@
"id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:47.854472Z",
- "iopub.status.busy": "2024-08-29T17:12:47.854122Z",
- "iopub.status.idle": "2024-08-29T17:12:47.857490Z",
- "shell.execute_reply": "2024-08-29T17:12:47.857030Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.778893Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.778539Z",
+ "iopub.status.idle": "2024-09-04T16:42:02.781991Z",
+ "shell.execute_reply": "2024-09-04T16:42:02.781579Z"
}
},
"outputs": [],
@@ -3373,10 +3373,10 @@
"id": "ce1c0ada-88b1-4654-b43f-3c0b59002979",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:47.859416Z",
- "iopub.status.busy": "2024-08-29T17:12:47.859115Z",
- "iopub.status.idle": "2024-08-29T17:12:51.895185Z",
- "shell.execute_reply": "2024-08-29T17:12:51.894624Z"
+ "iopub.execute_input": "2024-09-04T16:42:02.784175Z",
+ "iopub.status.busy": "2024-09-04T16:42:02.783822Z",
+ "iopub.status.idle": "2024-09-04T16:42:06.744322Z",
+ "shell.execute_reply": "2024-09-04T16:42:06.743807Z"
}
},
"outputs": [
@@ -3419,10 +3419,10 @@
"id": "3f572acf-31c3-4874-9100-451796e35b06",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:51.898749Z",
- "iopub.status.busy": "2024-08-29T17:12:51.897837Z",
- "iopub.status.idle": "2024-08-29T17:12:51.901554Z",
- "shell.execute_reply": "2024-08-29T17:12:51.901116Z"
+ "iopub.execute_input": "2024-09-04T16:42:06.746831Z",
+ "iopub.status.busy": "2024-09-04T16:42:06.746621Z",
+ "iopub.status.idle": "2024-09-04T16:42:06.750865Z",
+ "shell.execute_reply": "2024-09-04T16:42:06.750427Z"
}
},
"outputs": [
@@ -3460,10 +3460,10 @@
"id": "6a025a88",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:51.903470Z",
- "iopub.status.busy": "2024-08-29T17:12:51.903230Z",
- "iopub.status.idle": "2024-08-29T17:12:51.906015Z",
- "shell.execute_reply": "2024-08-29T17:12:51.905545Z"
+ "iopub.execute_input": "2024-09-04T16:42:06.752867Z",
+ "iopub.status.busy": "2024-09-04T16:42:06.752550Z",
+ "iopub.status.idle": "2024-09-04T16:42:06.755427Z",
+ "shell.execute_reply": "2024-09-04T16:42:06.754905Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index 5b136c65b..ea452d54f 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:54.886949Z",
- "iopub.status.busy": "2024-08-29T17:12:54.886773Z",
- "iopub.status.idle": "2024-08-29T17:12:56.104468Z",
- "shell.execute_reply": "2024-08-29T17:12:56.103850Z"
+ "iopub.execute_input": "2024-09-04T16:42:09.669951Z",
+ "iopub.status.busy": "2024-09-04T16:42:09.669454Z",
+ "iopub.status.idle": "2024-09-04T16:42:10.857078Z",
+ "shell.execute_reply": "2024-09-04T16:42:10.856536Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:12:56.107011Z",
- "iopub.status.busy": "2024-08-29T17:12:56.106751Z",
- "iopub.status.idle": "2024-08-29T17:12:56.295335Z",
- "shell.execute_reply": "2024-08-29T17:12:56.294795Z"
+ "iopub.execute_input": "2024-09-04T16:42:10.859602Z",
+ "iopub.status.busy": "2024-09-04T16:42:10.859180Z",
+ "iopub.status.idle": "2024-09-04T16:42:11.036049Z",
+ "shell.execute_reply": "2024-09-04T16:42:11.035540Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:56.297915Z",
- "iopub.status.busy": "2024-08-29T17:12:56.297716Z",
- "iopub.status.idle": "2024-08-29T17:12:56.309783Z",
- "shell.execute_reply": "2024-08-29T17:12:56.309342Z"
+ "iopub.execute_input": "2024-09-04T16:42:11.038424Z",
+ "iopub.status.busy": "2024-09-04T16:42:11.038097Z",
+ "iopub.status.idle": "2024-09-04T16:42:11.049631Z",
+ "shell.execute_reply": "2024-09-04T16:42:11.049041Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:56.311871Z",
- "iopub.status.busy": "2024-08-29T17:12:56.311520Z",
- "iopub.status.idle": "2024-08-29T17:12:56.520091Z",
- "shell.execute_reply": "2024-08-29T17:12:56.519498Z"
+ "iopub.execute_input": "2024-09-04T16:42:11.051824Z",
+ "iopub.status.busy": "2024-09-04T16:42:11.051385Z",
+ "iopub.status.idle": "2024-09-04T16:42:11.259202Z",
+ "shell.execute_reply": "2024-09-04T16:42:11.258640Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:56.522281Z",
- "iopub.status.busy": "2024-08-29T17:12:56.522084Z",
- "iopub.status.idle": "2024-08-29T17:12:56.547806Z",
- "shell.execute_reply": "2024-08-29T17:12:56.547360Z"
+ "iopub.execute_input": "2024-09-04T16:42:11.261584Z",
+ "iopub.status.busy": "2024-09-04T16:42:11.261174Z",
+ "iopub.status.idle": "2024-09-04T16:42:11.287643Z",
+ "shell.execute_reply": "2024-09-04T16:42:11.287051Z"
}
},
"outputs": [],
@@ -428,10 +428,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:56.549864Z",
- "iopub.status.busy": "2024-08-29T17:12:56.549518Z",
- "iopub.status.idle": "2024-08-29T17:12:58.659802Z",
- "shell.execute_reply": "2024-08-29T17:12:58.659098Z"
+ "iopub.execute_input": "2024-09-04T16:42:11.289979Z",
+ "iopub.status.busy": "2024-09-04T16:42:11.289536Z",
+ "iopub.status.idle": "2024-09-04T16:42:13.340382Z",
+ "shell.execute_reply": "2024-09-04T16:42:13.339824Z"
}
},
"outputs": [
@@ -474,10 +474,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:58.662480Z",
- "iopub.status.busy": "2024-08-29T17:12:58.661939Z",
- "iopub.status.idle": "2024-08-29T17:12:58.679875Z",
- "shell.execute_reply": "2024-08-29T17:12:58.679307Z"
+ "iopub.execute_input": "2024-09-04T16:42:13.343035Z",
+ "iopub.status.busy": "2024-09-04T16:42:13.342535Z",
+ "iopub.status.idle": "2024-09-04T16:42:13.360303Z",
+ "shell.execute_reply": "2024-09-04T16:42:13.359860Z"
},
"scrolled": true
},
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:12:58.681955Z",
- "iopub.status.busy": "2024-08-29T17:12:58.681645Z",
- "iopub.status.idle": "2024-08-29T17:13:00.311192Z",
- "shell.execute_reply": "2024-08-29T17:13:00.310203Z"
+ "iopub.execute_input": "2024-09-04T16:42:13.362391Z",
+ "iopub.status.busy": "2024-09-04T16:42:13.362067Z",
+ "iopub.status.idle": "2024-09-04T16:42:14.929094Z",
+ "shell.execute_reply": "2024-09-04T16:42:14.928504Z"
},
"id": "AaHC5MRKjruT"
},
@@ -729,10 +729,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.313932Z",
- "iopub.status.busy": "2024-08-29T17:13:00.313214Z",
- "iopub.status.idle": "2024-08-29T17:13:00.327251Z",
- "shell.execute_reply": "2024-08-29T17:13:00.326773Z"
+ "iopub.execute_input": "2024-09-04T16:42:14.932157Z",
+ "iopub.status.busy": "2024-09-04T16:42:14.931170Z",
+ "iopub.status.idle": "2024-09-04T16:42:14.944484Z",
+ "shell.execute_reply": "2024-09-04T16:42:14.944022Z"
},
"id": "Wy27rvyhjruU"
},
@@ -781,10 +781,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.329451Z",
- "iopub.status.busy": "2024-08-29T17:13:00.328984Z",
- "iopub.status.idle": "2024-08-29T17:13:00.409424Z",
- "shell.execute_reply": "2024-08-29T17:13:00.408792Z"
+ "iopub.execute_input": "2024-09-04T16:42:14.946582Z",
+ "iopub.status.busy": "2024-09-04T16:42:14.946252Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.025582Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.024954Z"
},
"id": "Db8YHnyVjruU"
},
@@ -891,10 +891,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.411870Z",
- "iopub.status.busy": "2024-08-29T17:13:00.411399Z",
- "iopub.status.idle": "2024-08-29T17:13:00.625259Z",
- "shell.execute_reply": "2024-08-29T17:13:00.624717Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.028128Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.027669Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.238127Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.237611Z"
},
"id": "iJqAHuS2jruV"
},
@@ -931,10 +931,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.627503Z",
- "iopub.status.busy": "2024-08-29T17:13:00.627134Z",
- "iopub.status.idle": "2024-08-29T17:13:00.644883Z",
- "shell.execute_reply": "2024-08-29T17:13:00.644395Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.240189Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.240007Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.256890Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.256430Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1400,10 +1400,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.647235Z",
- "iopub.status.busy": "2024-08-29T17:13:00.646834Z",
- "iopub.status.idle": "2024-08-29T17:13:00.659081Z",
- "shell.execute_reply": "2024-08-29T17:13:00.658556Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.258893Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.258711Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.268357Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.267918Z"
},
"id": "0lonvOYvjruV"
},
@@ -1550,10 +1550,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.661251Z",
- "iopub.status.busy": "2024-08-29T17:13:00.660934Z",
- "iopub.status.idle": "2024-08-29T17:13:00.756825Z",
- "shell.execute_reply": "2024-08-29T17:13:00.756221Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.270535Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.270213Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.362596Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.361968Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1634,10 +1634,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.759739Z",
- "iopub.status.busy": "2024-08-29T17:13:00.759326Z",
- "iopub.status.idle": "2024-08-29T17:13:00.905850Z",
- "shell.execute_reply": "2024-08-29T17:13:00.905202Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.365198Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.364807Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.500380Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.499762Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1697,10 +1697,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.908205Z",
- "iopub.status.busy": "2024-08-29T17:13:00.908011Z",
- "iopub.status.idle": "2024-08-29T17:13:00.912072Z",
- "shell.execute_reply": "2024-08-29T17:13:00.911517Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.503164Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.502765Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.506446Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.505904Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1738,10 +1738,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.914030Z",
- "iopub.status.busy": "2024-08-29T17:13:00.913851Z",
- "iopub.status.idle": "2024-08-29T17:13:00.917359Z",
- "shell.execute_reply": "2024-08-29T17:13:00.916838Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.508587Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.508253Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.511968Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.511423Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1796,10 +1796,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.919315Z",
- "iopub.status.busy": "2024-08-29T17:13:00.918974Z",
- "iopub.status.idle": "2024-08-29T17:13:00.956318Z",
- "shell.execute_reply": "2024-08-29T17:13:00.955832Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.513964Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.513647Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.550349Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.549796Z"
},
"id": "ZpipUliyjruW"
},
@@ -1850,10 +1850,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:00.958581Z",
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- "shell.execute_reply": "2024-08-29T17:13:00.998783Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.552389Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.552071Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.593615Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.593022Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1922,10 +1922,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.001564Z",
- "iopub.status.busy": "2024-08-29T17:13:01.001211Z",
- "iopub.status.idle": "2024-08-29T17:13:01.105915Z",
- "shell.execute_reply": "2024-08-29T17:13:01.105289Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.595654Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.595316Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.693816Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.692973Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1957,10 +1957,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.108842Z",
- "iopub.status.busy": "2024-08-29T17:13:01.108455Z",
- "iopub.status.idle": "2024-08-29T17:13:01.219741Z",
- "shell.execute_reply": "2024-08-29T17:13:01.219092Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.696412Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.696064Z",
+ "iopub.status.idle": "2024-09-04T16:42:15.796781Z",
+ "shell.execute_reply": "2024-09-04T16:42:15.796134Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2017,10 +2017,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.222436Z",
- "iopub.status.busy": "2024-08-29T17:13:01.222026Z",
- "iopub.status.idle": "2024-08-29T17:13:01.435601Z",
- "shell.execute_reply": "2024-08-29T17:13:01.435091Z"
+ "iopub.execute_input": "2024-09-04T16:42:15.799073Z",
+ "iopub.status.busy": "2024-09-04T16:42:15.798843Z",
+ "iopub.status.idle": "2024-09-04T16:42:16.015067Z",
+ "shell.execute_reply": "2024-09-04T16:42:16.014457Z"
},
"id": "WETRL74tE_sU"
},
@@ -2055,10 +2055,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.437978Z",
- "iopub.status.busy": "2024-08-29T17:13:01.437610Z",
- "iopub.status.idle": "2024-08-29T17:13:01.655421Z",
- "shell.execute_reply": "2024-08-29T17:13:01.654757Z"
+ "iopub.execute_input": "2024-09-04T16:42:16.017515Z",
+ "iopub.status.busy": "2024-09-04T16:42:16.017082Z",
+ "iopub.status.idle": "2024-09-04T16:42:16.225862Z",
+ "shell.execute_reply": "2024-09-04T16:42:16.225198Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2220,10 +2220,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.657848Z",
- "iopub.status.busy": "2024-08-29T17:13:01.657478Z",
- "iopub.status.idle": "2024-08-29T17:13:01.663871Z",
- "shell.execute_reply": "2024-08-29T17:13:01.663321Z"
+ "iopub.execute_input": "2024-09-04T16:42:16.228308Z",
+ "iopub.status.busy": "2024-09-04T16:42:16.227913Z",
+ "iopub.status.idle": "2024-09-04T16:42:16.233968Z",
+ "shell.execute_reply": "2024-09-04T16:42:16.233513Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2277,10 +2277,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.665915Z",
- "iopub.status.busy": "2024-08-29T17:13:01.665595Z",
- "iopub.status.idle": "2024-08-29T17:13:01.887112Z",
- "shell.execute_reply": "2024-08-29T17:13:01.886583Z"
+ "iopub.execute_input": "2024-09-04T16:42:16.236018Z",
+ "iopub.status.busy": "2024-09-04T16:42:16.235680Z",
+ "iopub.status.idle": "2024-09-04T16:42:16.448694Z",
+ "shell.execute_reply": "2024-09-04T16:42:16.448152Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2327,10 +2327,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:01.889533Z",
- "iopub.status.busy": "2024-08-29T17:13:01.889116Z",
- "iopub.status.idle": "2024-08-29T17:13:02.961779Z",
- "shell.execute_reply": "2024-08-29T17:13:02.961228Z"
+ "iopub.execute_input": "2024-09-04T16:42:16.450793Z",
+ "iopub.status.busy": "2024-09-04T16:42:16.450474Z",
+ "iopub.status.idle": "2024-09-04T16:42:17.528667Z",
+ "shell.execute_reply": "2024-09-04T16:42:17.528043Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 21c5da844..c630f8f0d 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:07.379288Z",
- "iopub.status.busy": "2024-08-29T17:13:07.379129Z",
- "iopub.status.idle": "2024-08-29T17:13:08.557670Z",
- "shell.execute_reply": "2024-08-29T17:13:08.557107Z"
+ "iopub.execute_input": "2024-09-04T16:42:21.027161Z",
+ "iopub.status.busy": "2024-09-04T16:42:21.026989Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.153963Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.153417Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:13:08.560315Z",
- "iopub.status.busy": "2024-08-29T17:13:08.559785Z",
- "iopub.status.idle": "2024-08-29T17:13:08.563039Z",
- "shell.execute_reply": "2024-08-29T17:13:08.562482Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.156406Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.156139Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.159307Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.158851Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.565443Z",
- "iopub.status.busy": "2024-08-29T17:13:08.565021Z",
- "iopub.status.idle": "2024-08-29T17:13:08.572955Z",
- "shell.execute_reply": "2024-08-29T17:13:08.572477Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.161273Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.161083Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.168998Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.168531Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.574969Z",
- "iopub.status.busy": "2024-08-29T17:13:08.574627Z",
- "iopub.status.idle": "2024-08-29T17:13:08.621464Z",
- "shell.execute_reply": "2024-08-29T17:13:08.620954Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.170762Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.170587Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.217387Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.216879Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.623819Z",
- "iopub.status.busy": "2024-08-29T17:13:08.623616Z",
- "iopub.status.idle": "2024-08-29T17:13:08.641297Z",
- "shell.execute_reply": "2024-08-29T17:13:08.640824Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.219237Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.219063Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.235988Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.235545Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.643432Z",
- "iopub.status.busy": "2024-08-29T17:13:08.643093Z",
- "iopub.status.idle": "2024-08-29T17:13:08.646846Z",
- "shell.execute_reply": "2024-08-29T17:13:08.646360Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.238015Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.237684Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.241487Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.240915Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.648905Z",
- "iopub.status.busy": "2024-08-29T17:13:08.648571Z",
- "iopub.status.idle": "2024-08-29T17:13:08.662471Z",
- "shell.execute_reply": "2024-08-29T17:13:08.661979Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.243605Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.243290Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.256635Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.256173Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.664485Z",
- "iopub.status.busy": "2024-08-29T17:13:08.664146Z",
- "iopub.status.idle": "2024-08-29T17:13:08.690511Z",
- "shell.execute_reply": "2024-08-29T17:13:08.690055Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.258677Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.258352Z",
+ "iopub.status.idle": "2024-09-04T16:42:22.283974Z",
+ "shell.execute_reply": "2024-09-04T16:42:22.283385Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:08.692667Z",
- "iopub.status.busy": "2024-08-29T17:13:08.692336Z",
- "iopub.status.idle": "2024-08-29T17:13:10.692423Z",
- "shell.execute_reply": "2024-08-29T17:13:10.691861Z"
+ "iopub.execute_input": "2024-09-04T16:42:22.285867Z",
+ "iopub.status.busy": "2024-09-04T16:42:22.285695Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.216087Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.215532Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.694992Z",
- "iopub.status.busy": "2024-08-29T17:13:10.694558Z",
- "iopub.status.idle": "2024-08-29T17:13:10.701315Z",
- "shell.execute_reply": "2024-08-29T17:13:10.700750Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.218573Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.218136Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.224781Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.224222Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.703445Z",
- "iopub.status.busy": "2024-08-29T17:13:10.703082Z",
- "iopub.status.idle": "2024-08-29T17:13:10.716205Z",
- "shell.execute_reply": "2024-08-29T17:13:10.715753Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.226767Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.226468Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.239484Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.238946Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.718157Z",
- "iopub.status.busy": "2024-08-29T17:13:10.717838Z",
- "iopub.status.idle": "2024-08-29T17:13:10.724109Z",
- "shell.execute_reply": "2024-08-29T17:13:10.723562Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.241592Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.241186Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.247396Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.246863Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.726270Z",
- "iopub.status.busy": "2024-08-29T17:13:10.725863Z",
- "iopub.status.idle": "2024-08-29T17:13:10.728666Z",
- "shell.execute_reply": "2024-08-29T17:13:10.728121Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.249422Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.249105Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.251824Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.251348Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.730677Z",
- "iopub.status.busy": "2024-08-29T17:13:10.730279Z",
- "iopub.status.idle": "2024-08-29T17:13:10.733950Z",
- "shell.execute_reply": "2024-08-29T17:13:10.733379Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.253838Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.253452Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.256894Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.256408Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.736069Z",
- "iopub.status.busy": "2024-08-29T17:13:10.735675Z",
- "iopub.status.idle": "2024-08-29T17:13:10.738431Z",
- "shell.execute_reply": "2024-08-29T17:13:10.737869Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.258990Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.258657Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.261106Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.260668Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.740315Z",
- "iopub.status.busy": "2024-08-29T17:13:10.740022Z",
- "iopub.status.idle": "2024-08-29T17:13:10.744394Z",
- "shell.execute_reply": "2024-08-29T17:13:10.743929Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.263123Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.262788Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.266861Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.266310Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.746411Z",
- "iopub.status.busy": "2024-08-29T17:13:10.746112Z",
- "iopub.status.idle": "2024-08-29T17:13:10.774547Z",
- "shell.execute_reply": "2024-08-29T17:13:10.773921Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.268975Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.268663Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.296639Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.296222Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:10.777035Z",
- "iopub.status.busy": "2024-08-29T17:13:10.776710Z",
- "iopub.status.idle": "2024-08-29T17:13:10.781535Z",
- "shell.execute_reply": "2024-08-29T17:13:10.780958Z"
+ "iopub.execute_input": "2024-09-04T16:42:24.298825Z",
+ "iopub.status.busy": "2024-09-04T16:42:24.298377Z",
+ "iopub.status.idle": "2024-09-04T16:42:24.302894Z",
+ "shell.execute_reply": "2024-09-04T16:42:24.302447Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 8fa07059e..71d46a48c 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:13.815853Z",
- "iopub.status.busy": "2024-08-29T17:13:13.815684Z",
- "iopub.status.idle": "2024-08-29T17:13:15.038986Z",
- "shell.execute_reply": "2024-08-29T17:13:15.038442Z"
+ "iopub.execute_input": "2024-09-04T16:42:27.239726Z",
+ "iopub.status.busy": "2024-09-04T16:42:27.239555Z",
+ "iopub.status.idle": "2024-09-04T16:42:28.412171Z",
+ "shell.execute_reply": "2024-09-04T16:42:28.411622Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:13:15.041340Z",
- "iopub.status.busy": "2024-08-29T17:13:15.041071Z",
- "iopub.status.idle": "2024-08-29T17:13:15.237062Z",
- "shell.execute_reply": "2024-08-29T17:13:15.236517Z"
+ "iopub.execute_input": "2024-09-04T16:42:28.414764Z",
+ "iopub.status.busy": "2024-09-04T16:42:28.414366Z",
+ "iopub.status.idle": "2024-09-04T16:42:28.605821Z",
+ "shell.execute_reply": "2024-09-04T16:42:28.605210Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:15.239727Z",
- "iopub.status.busy": "2024-08-29T17:13:15.239451Z",
- "iopub.status.idle": "2024-08-29T17:13:15.252949Z",
- "shell.execute_reply": "2024-08-29T17:13:15.252360Z"
+ "iopub.execute_input": "2024-09-04T16:42:28.608437Z",
+ "iopub.status.busy": "2024-09-04T16:42:28.608049Z",
+ "iopub.status.idle": "2024-09-04T16:42:28.621489Z",
+ "shell.execute_reply": "2024-09-04T16:42:28.620904Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:15.255134Z",
- "iopub.status.busy": "2024-08-29T17:13:15.254739Z",
- "iopub.status.idle": "2024-08-29T17:13:17.899597Z",
- "shell.execute_reply": "2024-08-29T17:13:17.899091Z"
+ "iopub.execute_input": "2024-09-04T16:42:28.623727Z",
+ "iopub.status.busy": "2024-09-04T16:42:28.623319Z",
+ "iopub.status.idle": "2024-09-04T16:42:31.199521Z",
+ "shell.execute_reply": "2024-09-04T16:42:31.198956Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:17.901950Z",
- "iopub.status.busy": "2024-08-29T17:13:17.901492Z",
- "iopub.status.idle": "2024-08-29T17:13:19.253074Z",
- "shell.execute_reply": "2024-08-29T17:13:19.252410Z"
+ "iopub.execute_input": "2024-09-04T16:42:31.202037Z",
+ "iopub.status.busy": "2024-09-04T16:42:31.201637Z",
+ "iopub.status.idle": "2024-09-04T16:42:32.534841Z",
+ "shell.execute_reply": "2024-09-04T16:42:32.534286Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:19.255731Z",
- "iopub.status.busy": "2024-08-29T17:13:19.255365Z",
- "iopub.status.idle": "2024-08-29T17:13:19.259482Z",
- "shell.execute_reply": "2024-08-29T17:13:19.259020Z"
+ "iopub.execute_input": "2024-09-04T16:42:32.537189Z",
+ "iopub.status.busy": "2024-09-04T16:42:32.536822Z",
+ "iopub.status.idle": "2024-09-04T16:42:32.540854Z",
+ "shell.execute_reply": "2024-09-04T16:42:32.540371Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:19.261405Z",
- "iopub.status.busy": "2024-08-29T17:13:19.261098Z",
- "iopub.status.idle": "2024-08-29T17:13:21.317331Z",
- "shell.execute_reply": "2024-08-29T17:13:21.316688Z"
+ "iopub.execute_input": "2024-09-04T16:42:32.542938Z",
+ "iopub.status.busy": "2024-09-04T16:42:32.542591Z",
+ "iopub.status.idle": "2024-09-04T16:42:34.557797Z",
+ "shell.execute_reply": "2024-09-04T16:42:34.557104Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:21.319921Z",
- "iopub.status.busy": "2024-08-29T17:13:21.319573Z",
- "iopub.status.idle": "2024-08-29T17:13:21.327758Z",
- "shell.execute_reply": "2024-08-29T17:13:21.327204Z"
+ "iopub.execute_input": "2024-09-04T16:42:34.560515Z",
+ "iopub.status.busy": "2024-09-04T16:42:34.560011Z",
+ "iopub.status.idle": "2024-09-04T16:42:34.567955Z",
+ "shell.execute_reply": "2024-09-04T16:42:34.567443Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:21.330061Z",
- "iopub.status.busy": "2024-08-29T17:13:21.329554Z",
- "iopub.status.idle": "2024-08-29T17:13:24.097931Z",
- "shell.execute_reply": "2024-08-29T17:13:24.097339Z"
+ "iopub.execute_input": "2024-09-04T16:42:34.570323Z",
+ "iopub.status.busy": "2024-09-04T16:42:34.569985Z",
+ "iopub.status.idle": "2024-09-04T16:42:37.280228Z",
+ "shell.execute_reply": "2024-09-04T16:42:37.279719Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:24.100261Z",
- "iopub.status.busy": "2024-08-29T17:13:24.099922Z",
- "iopub.status.idle": "2024-08-29T17:13:24.103149Z",
- "shell.execute_reply": "2024-08-29T17:13:24.102694Z"
+ "iopub.execute_input": "2024-09-04T16:42:37.282713Z",
+ "iopub.status.busy": "2024-09-04T16:42:37.282138Z",
+ "iopub.status.idle": "2024-09-04T16:42:37.285988Z",
+ "shell.execute_reply": "2024-09-04T16:42:37.285420Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:24.105303Z",
- "iopub.status.busy": "2024-08-29T17:13:24.104969Z",
- "iopub.status.idle": "2024-08-29T17:13:24.108822Z",
- "shell.execute_reply": "2024-08-29T17:13:24.108408Z"
+ "iopub.execute_input": "2024-09-04T16:42:37.287931Z",
+ "iopub.status.busy": "2024-09-04T16:42:37.287655Z",
+ "iopub.status.idle": "2024-09-04T16:42:37.291248Z",
+ "shell.execute_reply": "2024-09-04T16:42:37.290676Z"
}
},
"outputs": [],
@@ -769,10 +769,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:24.110940Z",
- "iopub.status.busy": "2024-08-29T17:13:24.110614Z",
- "iopub.status.idle": "2024-08-29T17:13:24.114216Z",
- "shell.execute_reply": "2024-08-29T17:13:24.113802Z"
+ "iopub.execute_input": "2024-09-04T16:42:37.293519Z",
+ "iopub.status.busy": "2024-09-04T16:42:37.293103Z",
+ "iopub.status.idle": "2024-09-04T16:42:37.296465Z",
+ "shell.execute_reply": "2024-09-04T16:42:37.296001Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 3087da8d1..656db3626 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:26.707579Z",
- "iopub.status.busy": "2024-08-29T17:13:26.707407Z",
- "iopub.status.idle": "2024-08-29T17:13:27.929509Z",
- "shell.execute_reply": "2024-08-29T17:13:27.928890Z"
+ "iopub.execute_input": "2024-09-04T16:42:39.738738Z",
+ "iopub.status.busy": "2024-09-04T16:42:39.738561Z",
+ "iopub.status.idle": "2024-09-04T16:42:40.919102Z",
+ "shell.execute_reply": "2024-09-04T16:42:40.918465Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:13:27.932306Z",
- "iopub.status.busy": "2024-08-29T17:13:27.931716Z",
- "iopub.status.idle": "2024-08-29T17:13:29.129230Z",
- "shell.execute_reply": "2024-08-29T17:13:29.128545Z"
+ "iopub.execute_input": "2024-09-04T16:42:40.921962Z",
+ "iopub.status.busy": "2024-09-04T16:42:40.921532Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.104525Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.103811Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.131841Z",
- "iopub.status.busy": "2024-08-29T17:13:29.131633Z",
- "iopub.status.idle": "2024-08-29T17:13:29.134883Z",
- "shell.execute_reply": "2024-08-29T17:13:29.134444Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.106929Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.106730Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.110188Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.109732Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.136741Z",
- "iopub.status.busy": "2024-08-29T17:13:29.136568Z",
- "iopub.status.idle": "2024-08-29T17:13:29.143057Z",
- "shell.execute_reply": "2024-08-29T17:13:29.142623Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.112113Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.111792Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.118685Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.118218Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.144959Z",
- "iopub.status.busy": "2024-08-29T17:13:29.144782Z",
- "iopub.status.idle": "2024-08-29T17:13:29.640180Z",
- "shell.execute_reply": "2024-08-29T17:13:29.639545Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.120591Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.120419Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.609650Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.609066Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.642961Z",
- "iopub.status.busy": "2024-08-29T17:13:29.642738Z",
- "iopub.status.idle": "2024-08-29T17:13:29.648359Z",
- "shell.execute_reply": "2024-08-29T17:13:29.647788Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.612557Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.612368Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.617513Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.616965Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.650165Z",
- "iopub.status.busy": "2024-08-29T17:13:29.649990Z",
- "iopub.status.idle": "2024-08-29T17:13:29.653811Z",
- "shell.execute_reply": "2024-08-29T17:13:29.653376Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.619427Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.619125Z",
+ "iopub.status.idle": "2024-09-04T16:42:44.622981Z",
+ "shell.execute_reply": "2024-09-04T16:42:44.622533Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:29.655941Z",
- "iopub.status.busy": "2024-08-29T17:13:29.655603Z",
- "iopub.status.idle": "2024-08-29T17:13:30.532765Z",
- "shell.execute_reply": "2024-08-29T17:13:30.532094Z"
+ "iopub.execute_input": "2024-09-04T16:42:44.624920Z",
+ "iopub.status.busy": "2024-09-04T16:42:44.624597Z",
+ "iopub.status.idle": "2024-09-04T16:42:45.487014Z",
+ "shell.execute_reply": "2024-09-04T16:42:45.486401Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:30.535273Z",
- "iopub.status.busy": "2024-08-29T17:13:30.534885Z",
- "iopub.status.idle": "2024-08-29T17:13:30.785223Z",
- "shell.execute_reply": "2024-08-29T17:13:30.784735Z"
+ "iopub.execute_input": "2024-09-04T16:42:45.489297Z",
+ "iopub.status.busy": "2024-09-04T16:42:45.489035Z",
+ "iopub.status.idle": "2024-09-04T16:42:45.702626Z",
+ "shell.execute_reply": "2024-09-04T16:42:45.702067Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:30.787457Z",
- "iopub.status.busy": "2024-08-29T17:13:30.787105Z",
- "iopub.status.idle": "2024-08-29T17:13:30.791415Z",
- "shell.execute_reply": "2024-08-29T17:13:30.790846Z"
+ "iopub.execute_input": "2024-09-04T16:42:45.704657Z",
+ "iopub.status.busy": "2024-09-04T16:42:45.704348Z",
+ "iopub.status.idle": "2024-09-04T16:42:45.708586Z",
+ "shell.execute_reply": "2024-09-04T16:42:45.708028Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:30.793442Z",
- "iopub.status.busy": "2024-08-29T17:13:30.793262Z",
- "iopub.status.idle": "2024-08-29T17:13:31.259811Z",
- "shell.execute_reply": "2024-08-29T17:13:31.259155Z"
+ "iopub.execute_input": "2024-09-04T16:42:45.710729Z",
+ "iopub.status.busy": "2024-09-04T16:42:45.710332Z",
+ "iopub.status.idle": "2024-09-04T16:42:46.157861Z",
+ "shell.execute_reply": "2024-09-04T16:42:46.157294Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:31.264390Z",
- "iopub.status.busy": "2024-08-29T17:13:31.263984Z",
- "iopub.status.idle": "2024-08-29T17:13:31.575388Z",
- "shell.execute_reply": "2024-08-29T17:13:31.574740Z"
+ "iopub.execute_input": "2024-09-04T16:42:46.161127Z",
+ "iopub.status.busy": "2024-09-04T16:42:46.160743Z",
+ "iopub.status.idle": "2024-09-04T16:42:46.494374Z",
+ "shell.execute_reply": "2024-09-04T16:42:46.493916Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:31.577837Z",
- "iopub.status.busy": "2024-08-29T17:13:31.577527Z",
- "iopub.status.idle": "2024-08-29T17:13:31.947615Z",
- "shell.execute_reply": "2024-08-29T17:13:31.947052Z"
+ "iopub.execute_input": "2024-09-04T16:42:46.496557Z",
+ "iopub.status.busy": "2024-09-04T16:42:46.496197Z",
+ "iopub.status.idle": "2024-09-04T16:42:46.858145Z",
+ "shell.execute_reply": "2024-09-04T16:42:46.857570Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:31.950390Z",
- "iopub.status.busy": "2024-08-29T17:13:31.950110Z",
- "iopub.status.idle": "2024-08-29T17:13:32.400281Z",
- "shell.execute_reply": "2024-08-29T17:13:32.399703Z"
+ "iopub.execute_input": "2024-09-04T16:42:46.861411Z",
+ "iopub.status.busy": "2024-09-04T16:42:46.861015Z",
+ "iopub.status.idle": "2024-09-04T16:42:47.297930Z",
+ "shell.execute_reply": "2024-09-04T16:42:47.297412Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:32.404955Z",
- "iopub.status.busy": "2024-08-29T17:13:32.404554Z",
- "iopub.status.idle": "2024-08-29T17:13:32.856691Z",
- "shell.execute_reply": "2024-08-29T17:13:32.856137Z"
+ "iopub.execute_input": "2024-09-04T16:42:47.302316Z",
+ "iopub.status.busy": "2024-09-04T16:42:47.301930Z",
+ "iopub.status.idle": "2024-09-04T16:42:47.746611Z",
+ "shell.execute_reply": "2024-09-04T16:42:47.746072Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:32.860308Z",
- "iopub.status.busy": "2024-08-29T17:13:32.859889Z",
- "iopub.status.idle": "2024-08-29T17:13:33.076853Z",
- "shell.execute_reply": "2024-08-29T17:13:33.076291Z"
+ "iopub.execute_input": "2024-09-04T16:42:47.748860Z",
+ "iopub.status.busy": "2024-09-04T16:42:47.748528Z",
+ "iopub.status.idle": "2024-09-04T16:42:47.960774Z",
+ "shell.execute_reply": "2024-09-04T16:42:47.960230Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:33.079045Z",
- "iopub.status.busy": "2024-08-29T17:13:33.078852Z",
- "iopub.status.idle": "2024-08-29T17:13:33.261899Z",
- "shell.execute_reply": "2024-08-29T17:13:33.261415Z"
+ "iopub.execute_input": "2024-09-04T16:42:47.962862Z",
+ "iopub.status.busy": "2024-09-04T16:42:47.962538Z",
+ "iopub.status.idle": "2024-09-04T16:42:48.161771Z",
+ "shell.execute_reply": "2024-09-04T16:42:48.161347Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:33.264222Z",
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- "iopub.status.idle": "2024-08-29T17:13:33.266872Z",
- "shell.execute_reply": "2024-08-29T17:13:33.266403Z"
+ "iopub.execute_input": "2024-09-04T16:42:48.163921Z",
+ "iopub.status.busy": "2024-09-04T16:42:48.163521Z",
+ "iopub.status.idle": "2024-09-04T16:42:48.166403Z",
+ "shell.execute_reply": "2024-09-04T16:42:48.165912Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:33.268750Z",
- "iopub.status.busy": "2024-08-29T17:13:33.268577Z",
- "iopub.status.idle": "2024-08-29T17:13:34.219660Z",
- "shell.execute_reply": "2024-08-29T17:13:34.219058Z"
+ "iopub.execute_input": "2024-09-04T16:42:48.168421Z",
+ "iopub.status.busy": "2024-09-04T16:42:48.168027Z",
+ "iopub.status.idle": "2024-09-04T16:42:49.184503Z",
+ "shell.execute_reply": "2024-09-04T16:42:49.183958Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:34.222039Z",
- "iopub.status.busy": "2024-08-29T17:13:34.221835Z",
- "iopub.status.idle": "2024-08-29T17:13:34.376874Z",
- "shell.execute_reply": "2024-08-29T17:13:34.376359Z"
+ "iopub.execute_input": "2024-09-04T16:42:49.187022Z",
+ "iopub.status.busy": "2024-09-04T16:42:49.186859Z",
+ "iopub.status.idle": "2024-09-04T16:42:49.405712Z",
+ "shell.execute_reply": "2024-09-04T16:42:49.405134Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:34.379144Z",
- "iopub.status.busy": "2024-08-29T17:13:34.378808Z",
- "iopub.status.idle": "2024-08-29T17:13:34.536905Z",
- "shell.execute_reply": "2024-08-29T17:13:34.536397Z"
+ "iopub.execute_input": "2024-09-04T16:42:49.407791Z",
+ "iopub.status.busy": "2024-09-04T16:42:49.407466Z",
+ "iopub.status.idle": "2024-09-04T16:42:49.596081Z",
+ "shell.execute_reply": "2024-09-04T16:42:49.595586Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:34.539100Z",
- "iopub.status.busy": "2024-08-29T17:13:34.538808Z",
- "iopub.status.idle": "2024-08-29T17:13:35.142058Z",
- "shell.execute_reply": "2024-08-29T17:13:35.141458Z"
+ "iopub.execute_input": "2024-09-04T16:42:49.598425Z",
+ "iopub.status.busy": "2024-09-04T16:42:49.598075Z",
+ "iopub.status.idle": "2024-09-04T16:42:50.231144Z",
+ "shell.execute_reply": "2024-09-04T16:42:50.230582Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:35.144434Z",
- "iopub.status.busy": "2024-08-29T17:13:35.144038Z",
- "iopub.status.idle": "2024-08-29T17:13:35.148054Z",
- "shell.execute_reply": "2024-08-29T17:13:35.147482Z"
+ "iopub.execute_input": "2024-09-04T16:42:50.233354Z",
+ "iopub.status.busy": "2024-09-04T16:42:50.232993Z",
+ "iopub.status.idle": "2024-09-04T16:42:50.236697Z",
+ "shell.execute_reply": "2024-09-04T16:42:50.236249Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 84d5ac94b..17b8edea7 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:37.518055Z",
- "iopub.status.busy": "2024-08-29T17:13:37.517881Z",
- "iopub.status.idle": "2024-08-29T17:13:40.358016Z",
- "shell.execute_reply": "2024-08-29T17:13:40.357398Z"
+ "iopub.execute_input": "2024-09-04T16:42:52.649194Z",
+ "iopub.status.busy": "2024-09-04T16:42:52.648683Z",
+ "iopub.status.idle": "2024-09-04T16:42:55.436647Z",
+ "shell.execute_reply": "2024-09-04T16:42:55.436105Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:13:40.360605Z",
- "iopub.status.busy": "2024-08-29T17:13:40.360296Z",
- "iopub.status.idle": "2024-08-29T17:13:40.692043Z",
- "shell.execute_reply": "2024-08-29T17:13:40.691484Z"
+ "iopub.execute_input": "2024-09-04T16:42:55.439281Z",
+ "iopub.status.busy": "2024-09-04T16:42:55.438854Z",
+ "iopub.status.idle": "2024-09-04T16:42:55.755725Z",
+ "shell.execute_reply": "2024-09-04T16:42:55.755177Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:40.694445Z",
- "iopub.status.busy": "2024-08-29T17:13:40.694127Z",
- "iopub.status.idle": "2024-08-29T17:13:40.698116Z",
- "shell.execute_reply": "2024-08-29T17:13:40.697695Z"
+ "iopub.execute_input": "2024-09-04T16:42:55.758406Z",
+ "iopub.status.busy": "2024-09-04T16:42:55.757955Z",
+ "iopub.status.idle": "2024-09-04T16:42:55.762158Z",
+ "shell.execute_reply": "2024-09-04T16:42:55.761743Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:40.700168Z",
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- "iopub.status.idle": "2024-08-29T17:13:45.580298Z",
- "shell.execute_reply": "2024-08-29T17:13:45.579739Z"
+ "iopub.execute_input": "2024-09-04T16:42:55.764223Z",
+ "iopub.status.busy": "2024-09-04T16:42:55.763908Z",
+ "iopub.status.idle": "2024-09-04T16:43:02.719949Z",
+ "shell.execute_reply": "2024-09-04T16:43:02.719382Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 1835008/170498071 [00:00<00:09, 18329994.51it/s]"
+ " 0%| | 32768/170498071 [00:00<09:55, 286476.18it/s]"
]
},
{
@@ -260,7 +260,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 0%| | 196608/170498071 [00:00<02:58, 952836.46it/s]"
]
},
{
@@ -268,7 +268,7 @@
"output_type": "stream",
"text": [
"\r",
- " 11%|█ | 18120704/170498071 [00:00<00:02, 68523893.70it/s]"
+ " 0%| | 819200/170498071 [00:00<00:56, 2976967.78it/s]"
]
},
{
@@ -276,7 +276,7 @@
"output_type": "stream",
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+ " 2%|▏ | 3276800/170498071 [00:00<00:16, 10184986.81it/s]"
]
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{
@@ -284,7 +284,7 @@
"output_type": "stream",
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"\r",
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+ " 6%|▌ | 9469952/170498071 [00:00<00:06, 26696954.83it/s]"
]
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{
@@ -292,7 +292,7 @@
"output_type": "stream",
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"\r",
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+ " 9%|▉ | 14942208/170498071 [00:00<00:04, 35418047.78it/s]"
]
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{
@@ -300,7 +300,7 @@
"output_type": "stream",
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"\r",
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+ " 12%|█▏ | 19791872/170498071 [00:00<00:03, 38238678.01it/s]"
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{
@@ -308,7 +308,7 @@
"output_type": "stream",
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"\r",
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+ " 15%|█▍ | 25264128/170498071 [00:00<00:03, 43164150.29it/s]"
]
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{
@@ -316,7 +316,7 @@
"output_type": "stream",
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"\r",
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+ " 18%|█▊ | 31064064/170498071 [00:00<00:03, 45456216.56it/s]"
]
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{
@@ -324,7 +324,7 @@
"output_type": "stream",
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+ " 21%|██▏ | 36503552/170498071 [00:01<00:02, 48033732.49it/s]"
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{
@@ -332,7 +332,7 @@
"output_type": "stream",
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+ " 25%|██▍ | 41943040/170498071 [00:01<00:02, 49560457.71it/s]"
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{
@@ -340,7 +340,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 28%|██▊ | 46956544/170498071 [00:01<00:02, 48811418.89it/s]"
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@@ -348,7 +348,7 @@
"output_type": "stream",
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+ " 31%|███ | 52002816/170498071 [00:01<00:02, 49276654.22it/s]"
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{
@@ -356,7 +356,7 @@
"output_type": "stream",
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"\r",
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+ " 34%|███▍ | 57704448/170498071 [00:01<00:02, 51159479.48it/s]"
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{
@@ -364,7 +364,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 37%|███▋ | 63078400/170498071 [00:01<00:02, 51312422.09it/s]"
]
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{
@@ -372,7 +372,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 40%|████ | 68222976/170498071 [00:01<00:02, 50084638.40it/s]"
]
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{
@@ -380,7 +380,7 @@
"output_type": "stream",
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"\r",
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+ " 43%|████▎ | 73957376/170498071 [00:01<00:01, 52172109.98it/s]"
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{
@@ -388,7 +388,7 @@
"output_type": "stream",
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+ " 46%|████▋ | 79200256/170498071 [00:01<00:01, 51059149.17it/s]"
]
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{
@@ -396,7 +396,143 @@
"output_type": "stream",
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+ " 49%|████▉ | 84344832/170498071 [00:02<00:01, 50541584.83it/s]"
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},
{
@@ -514,10 +650,10 @@
"id": "9b64e0aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:45.582739Z",
- "iopub.status.busy": "2024-08-29T17:13:45.582371Z",
- "iopub.status.idle": "2024-08-29T17:13:45.587064Z",
- "shell.execute_reply": "2024-08-29T17:13:45.586613Z"
+ "iopub.execute_input": "2024-09-04T16:43:02.722295Z",
+ "iopub.status.busy": "2024-09-04T16:43:02.721885Z",
+ "iopub.status.idle": "2024-09-04T16:43:02.726775Z",
+ "shell.execute_reply": "2024-09-04T16:43:02.726194Z"
},
"nbsphinx": "hidden"
},
@@ -568,10 +704,10 @@
"id": "a00aa3ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:13:45.589022Z",
- "iopub.status.busy": "2024-08-29T17:13:45.588754Z",
- "iopub.status.idle": "2024-08-29T17:13:46.101342Z",
- "shell.execute_reply": "2024-08-29T17:13:46.100720Z"
+ "iopub.execute_input": "2024-09-04T16:43:02.728763Z",
+ "iopub.status.busy": "2024-09-04T16:43:02.728492Z",
+ "iopub.status.idle": "2024-09-04T16:43:03.272195Z",
+ "shell.execute_reply": "2024-09-04T16:43:03.271652Z"
}
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index df55d12fa..3319fc622 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:14:19.347070Z",
- "iopub.status.busy": "2024-08-29T17:14:19.346882Z",
- "iopub.status.idle": "2024-08-29T17:14:20.597705Z",
- "shell.execute_reply": "2024-08-29T17:14:20.597132Z"
+ "iopub.execute_input": "2024-09-04T16:43:36.768501Z",
+ "iopub.status.busy": "2024-09-04T16:43:36.768020Z",
+ "iopub.status.idle": "2024-09-04T16:43:37.948956Z",
+ "shell.execute_reply": "2024-09-04T16:43:37.948387Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": {
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- "shell.execute_reply": "2024-08-29T17:14:20.617249Z"
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}
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@@ -164,10 +164,10 @@
"id": "284dc264",
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},
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@@ -198,10 +198,10 @@
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- "shell.execute_reply": "2024-08-29T17:14:20.697593Z"
+ "iopub.execute_input": "2024-09-04T16:43:37.975225Z",
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}
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"outputs": [
@@ -374,10 +374,10 @@
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},
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@@ -417,10 +417,10 @@
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@@ -456,10 +456,10 @@
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@@ -477,10 +477,10 @@
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- "shell.execute_reply": "2024-08-29T17:14:21.114917Z"
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@@ -527,10 +527,10 @@
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- "shell.execute_reply": "2024-08-29T17:14:21.119385Z"
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@@ -545,10 +545,10 @@
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- "shell.execute_reply": "2024-08-29T17:14:30.167432Z"
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@@ -572,10 +572,10 @@
"id": "f407bd69",
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- "iopub.status.idle": "2024-08-29T17:14:30.177989Z",
- "shell.execute_reply": "2024-08-29T17:14:30.177523Z"
+ "iopub.execute_input": "2024-09-04T16:43:47.578529Z",
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@@ -678,10 +678,10 @@
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@@ -696,10 +696,10 @@
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@@ -734,10 +734,10 @@
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@@ -756,10 +756,10 @@
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@@ -883,10 +883,10 @@
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@@ -921,10 +921,10 @@
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@@ -963,10 +963,10 @@
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@@ -1022,10 +1022,10 @@
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@@ -1041,10 +1041,10 @@
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@@ -1079,10 +1079,10 @@
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@@ -1189,10 +1189,10 @@
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@@ -1217,10 +1217,10 @@
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@@ -1264,10 +1264,10 @@
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@@ -1392,10 +1392,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 7a39f527b..260e00239 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
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@@ -79,10 +79,10 @@
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@@ -97,10 +97,10 @@
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@@ -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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -333,17 +333,17 @@
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@@ -1252,33 +1303,38 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 97%[==================> ] 15.85M 3.94MB/s eta 4s \r",
+ "pred_probs.npz 100%[===================>] 16.26M 4.04MB/s in 5.1s \r\n",
"\r\n",
- "2024-08-29 17:17:27 (32.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-09-04 16:46:54 (3.21 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -187,10 +371,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:27.944256Z",
- "iopub.status.busy": "2024-08-29T17:17:27.943870Z",
- "iopub.status.idle": "2024-08-29T17:17:29.261156Z",
- "shell.execute_reply": "2024-08-29T17:17:29.260619Z"
+ "iopub.execute_input": "2024-09-04T16:46:55.009754Z",
+ "iopub.status.busy": "2024-09-04T16:46:55.009373Z",
+ "iopub.status.idle": "2024-09-04T16:46:56.250887Z",
+ "shell.execute_reply": "2024-09-04T16:46:56.250411Z"
},
"nbsphinx": "hidden"
},
@@ -201,7 +385,7 @@
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -227,10 +411,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:29.263848Z",
- "iopub.status.busy": "2024-08-29T17:17:29.263399Z",
- "iopub.status.idle": "2024-08-29T17:17:29.266823Z",
- "shell.execute_reply": "2024-08-29T17:17:29.266231Z"
+ "iopub.execute_input": "2024-09-04T16:46:56.253361Z",
+ "iopub.status.busy": "2024-09-04T16:46:56.252910Z",
+ "iopub.status.idle": "2024-09-04T16:46:56.256144Z",
+ "shell.execute_reply": "2024-09-04T16:46:56.255716Z"
}
},
"outputs": [],
@@ -280,10 +464,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:29.269094Z",
- "iopub.status.busy": "2024-08-29T17:17:29.268683Z",
- "iopub.status.idle": "2024-08-29T17:17:29.272062Z",
- "shell.execute_reply": "2024-08-29T17:17:29.271604Z"
+ "iopub.execute_input": "2024-09-04T16:46:56.258147Z",
+ "iopub.status.busy": "2024-09-04T16:46:56.257877Z",
+ "iopub.status.idle": "2024-09-04T16:46:56.260950Z",
+ "shell.execute_reply": "2024-09-04T16:46:56.260396Z"
},
"nbsphinx": "hidden"
},
@@ -301,10 +485,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:29.274632Z",
- "iopub.status.busy": "2024-08-29T17:17:29.274212Z",
- "iopub.status.idle": "2024-08-29T17:17:38.249516Z",
- "shell.execute_reply": "2024-08-29T17:17:38.248858Z"
+ "iopub.execute_input": "2024-09-04T16:46:56.263038Z",
+ "iopub.status.busy": "2024-09-04T16:46:56.262707Z",
+ "iopub.status.idle": "2024-09-04T16:47:05.313089Z",
+ "shell.execute_reply": "2024-09-04T16:47:05.312548Z"
}
},
"outputs": [],
@@ -378,10 +562,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:38.252121Z",
- "iopub.status.busy": "2024-08-29T17:17:38.251901Z",
- "iopub.status.idle": "2024-08-29T17:17:38.258509Z",
- "shell.execute_reply": "2024-08-29T17:17:38.257889Z"
+ "iopub.execute_input": "2024-09-04T16:47:05.315553Z",
+ "iopub.status.busy": "2024-09-04T16:47:05.315222Z",
+ "iopub.status.idle": "2024-09-04T16:47:05.320683Z",
+ "shell.execute_reply": "2024-09-04T16:47:05.320235Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +605,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:38.260575Z",
- "iopub.status.busy": "2024-08-29T17:17:38.260400Z",
- "iopub.status.idle": "2024-08-29T17:17:38.611890Z",
- "shell.execute_reply": "2024-08-29T17:17:38.611365Z"
+ "iopub.execute_input": "2024-09-04T16:47:05.322601Z",
+ "iopub.status.busy": "2024-09-04T16:47:05.322338Z",
+ "iopub.status.idle": "2024-09-04T16:47:05.667169Z",
+ "shell.execute_reply": "2024-09-04T16:47:05.666537Z"
}
},
"outputs": [],
@@ -461,10 +645,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:38.614285Z",
- "iopub.status.busy": "2024-08-29T17:17:38.614078Z",
- "iopub.status.idle": "2024-08-29T17:17:38.618338Z",
- "shell.execute_reply": "2024-08-29T17:17:38.617761Z"
+ "iopub.execute_input": "2024-09-04T16:47:05.669724Z",
+ "iopub.status.busy": "2024-09-04T16:47:05.669531Z",
+ "iopub.status.idle": "2024-09-04T16:47:05.673815Z",
+ "shell.execute_reply": "2024-09-04T16:47:05.673272Z"
}
},
"outputs": [
@@ -536,10 +720,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:38.620291Z",
- "iopub.status.busy": "2024-08-29T17:17:38.620118Z",
- "iopub.status.idle": "2024-08-29T17:17:41.255603Z",
- "shell.execute_reply": "2024-08-29T17:17:41.254897Z"
+ "iopub.execute_input": "2024-09-04T16:47:05.675875Z",
+ "iopub.status.busy": "2024-09-04T16:47:05.675554Z",
+ "iopub.status.idle": "2024-09-04T16:47:08.252819Z",
+ "shell.execute_reply": "2024-09-04T16:47:08.252090Z"
}
},
"outputs": [],
@@ -561,10 +745,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:41.258858Z",
- "iopub.status.busy": "2024-08-29T17:17:41.257988Z",
- "iopub.status.idle": "2024-08-29T17:17:41.262360Z",
- "shell.execute_reply": "2024-08-29T17:17:41.261875Z"
+ "iopub.execute_input": "2024-09-04T16:47:08.255982Z",
+ "iopub.status.busy": "2024-09-04T16:47:08.255203Z",
+ "iopub.status.idle": "2024-09-04T16:47:08.259378Z",
+ "shell.execute_reply": "2024-09-04T16:47:08.258831Z"
}
},
"outputs": [
@@ -600,10 +784,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:41.264483Z",
- "iopub.status.busy": "2024-08-29T17:17:41.264152Z",
- "iopub.status.idle": "2024-08-29T17:17:41.269679Z",
- "shell.execute_reply": "2024-08-29T17:17:41.269239Z"
+ "iopub.execute_input": "2024-09-04T16:47:08.261330Z",
+ "iopub.status.busy": "2024-09-04T16:47:08.261019Z",
+ "iopub.status.idle": "2024-09-04T16:47:08.266724Z",
+ "shell.execute_reply": "2024-09-04T16:47:08.266178Z"
}
},
"outputs": [
@@ -781,10 +965,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:41.271827Z",
- "iopub.status.busy": "2024-08-29T17:17:41.271498Z",
- "iopub.status.idle": "2024-08-29T17:17:41.298247Z",
- "shell.execute_reply": "2024-08-29T17:17:41.297765Z"
+ "iopub.execute_input": "2024-09-04T16:47:08.268756Z",
+ "iopub.status.busy": "2024-09-04T16:47:08.268443Z",
+ "iopub.status.idle": "2024-09-04T16:47:08.295398Z",
+ "shell.execute_reply": "2024-09-04T16:47:08.294831Z"
}
},
"outputs": [
@@ -886,10 +1070,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:41.300294Z",
- "iopub.status.busy": "2024-08-29T17:17:41.299959Z",
- "iopub.status.idle": "2024-08-29T17:17:41.304530Z",
- "shell.execute_reply": "2024-08-29T17:17:41.304060Z"
+ "iopub.execute_input": "2024-09-04T16:47:08.297388Z",
+ "iopub.status.busy": "2024-09-04T16:47:08.297074Z",
+ "iopub.status.idle": "2024-09-04T16:47:08.301299Z",
+ "shell.execute_reply": "2024-09-04T16:47:08.300740Z"
}
},
"outputs": [
@@ -963,10 +1147,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:41.306677Z",
- "iopub.status.busy": "2024-08-29T17:17:41.306340Z",
- "iopub.status.idle": "2024-08-29T17:17:42.766771Z",
- "shell.execute_reply": "2024-08-29T17:17:42.766235Z"
+ "iopub.execute_input": "2024-09-04T16:47:08.303333Z",
+ "iopub.status.busy": "2024-09-04T16:47:08.303011Z",
+ "iopub.status.idle": "2024-09-04T16:47:09.680188Z",
+ "shell.execute_reply": "2024-09-04T16:47:09.679587Z"
}
},
"outputs": [
@@ -1138,10 +1322,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:17:42.768887Z",
- "iopub.status.busy": "2024-08-29T17:17:42.768697Z",
- "iopub.status.idle": "2024-08-29T17:17:42.772738Z",
- "shell.execute_reply": "2024-08-29T17:17:42.772278Z"
+ "iopub.execute_input": "2024-09-04T16:47:09.682437Z",
+ "iopub.status.busy": "2024-09-04T16:47:09.682099Z",
+ "iopub.status.idle": "2024-09-04T16:47:09.686196Z",
+ "shell.execute_reply": "2024-09-04T16:47:09.685639Z"
},
"nbsphinx": "hidden"
},
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index 99dbaee83..d519c9ea5 100644
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diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree
index 2ab423af6..26d9caaf0 100644
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diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
index 12f0e7b01..80d54524c 100644
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diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 60960831a..37b7ed16b 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 fa4d0d45b..1fc055ceb 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 09e24f818..13e63362e 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 30f50c344..54820cc7b 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 d79def3da..eb6616780 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 9866d6b3d..449c74cbe 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 ca5d88906..fa225d07e 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 1db5b3c47..fc0b409cf 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb
index af832f9bc..97a5be3f5 100644
--- a/master/_sources/tutorials/improving_ml_performance.ipynb
+++ b/master/_sources/tutorials/improving_ml_performance.ipynb
@@ -67,7 +67,7 @@
"dependencies = [\"cleanlab\", \"xgboost\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 a536064e2..c95ddfd64 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 6e67b294a..ecb0c1720 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 e069830bb..bca21f41b 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 7a04f092e..0085c1132 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 e29763bf2..f9f6facf0 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 c8868b60d..3ae99dd1e 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 d1d864a9c..3fcae221a 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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 cbf859261..4366a6e20 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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index d254216ad..9ac49aa44 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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"Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [70, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [72, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [62, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [63, "module-cleanlab.multilabel_classification.filter"], [66, "filter"], [75, "filter"], [79, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "models": [[59, "models"]], "keras": [[60, "module-cleanlab.models.keras"]], "multiannotator": [[61, "module-cleanlab.multiannotator"]], "multilabel_classification": [[64, "multilabel-classification"]], "rank": [[65, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.rank"], [77, "module-cleanlab.segmentation.rank"], [81, "module-cleanlab.token_classification.rank"]], "object_detection": [[67, "object-detection"]], "summary": [[69, "summary"], [78, "module-cleanlab.segmentation.summary"], [82, "module-cleanlab.token_classification.summary"]], "regression.learn": [[73, "module-cleanlab.regression.learn"]], "regression.rank": [[74, "module-cleanlab.regression.rank"]], "segmentation": [[76, "segmentation"]], "token_classification": [[80, "token-classification"]], "cleanlab open-source documentation": [[83, "cleanlab-open-source-documentation"]], "Quickstart": [[83, "quickstart"]], "1. Install cleanlab": [[83, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[83, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[83, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[83, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[83, "improve-your-data-via-many-other-techniques"]], "Contributing": [[83, "contributing"]], "Easy Mode": [[83, "easy-mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[84, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[84, "function-and-class-name-changes"]], "Module name changes": [[84, "module-name-changes"]], "New modules": [[84, "new-modules"]], "Removed modules": [[84, "removed-modules"]], "Common argument and variable name changes": [[84, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[85, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[86, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[86, "1.-Install-required-dependencies"], [87, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[86, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[86, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[86, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[86, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[86, "Spending-too-much-time-on-data-quality?"], [87, "Spending-too-much-time-on-data-quality?"], [90, "Spending-too-much-time-on-data-quality?"], [93, "Spending-too-much-time-on-data-quality?"], [94, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [99, "Spending-too-much-time-on-data-quality?"], [102, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [105, "spending-too-much-time-on-data-quality"], [106, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[87, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[87, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[87, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[87, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[88, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[88, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[88, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[88, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[88, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[88, "5.-Use-cleanlab-to-find-label-issues"], [93, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[89, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[89, "Install-and-import-required-dependencies"]], "Create and load the data": [[89, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[89, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[89, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[89, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[89, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[89, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[89, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[90, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[90, "1.-Install-and-import-required-dependencies"], [91, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[90, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[90, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[90, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[90, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[90, "Get-additional-information"]], "Near duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[91, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module 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\ No newline at end of file
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Fetch and normalize the Fashion-MNIST dataset": [[91, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[91, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[91, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[91, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[91, "7.-Use-cleanlab-to-find-issues"]], "View report": [[91, "View-report"]], "Label issues": [[91, "Label-issues"], [93, "Label-issues"], [94, "Label-issues"]], "View most likely examples with label errors": [[91, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[91, "Outlier-issues"], [93, "Outlier-issues"], [94, "Outlier-issues"]], "View most severe outliers": [[91, "View-most-severe-outliers"]], "View sets of near duplicate images": [[91, "View-sets-of-near-duplicate-images"]], "Dark images": [[91, "Dark-images"]], "View top examples of dark images": [[91, "View-top-examples-of-dark-images"]], "Low information images": [[91, "Low-information-images"]], "Datalab Tutorials": [[92, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[93, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[93, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[93, "Near-duplicate-issues"], [94, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[94, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[94, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[94, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[94, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[95, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[95, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[95, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[95, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[95, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[95, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[95, "Explanation:"]], "Data Valuation": [[95, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[95, "1.-Load-and-Prepare-the-Dataset"], [95, "id2"], [95, "id5"]], "2. Vectorize the Text Data": [[95, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[95, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[95, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[95, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[95, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[95, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [95, "id3"]], "3. (Optional) Cluster the Data": [[95, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[95, "4.-Identify-Underperforming-Groups-with-Datalab"], [95, "id4"]], "5. (Optional) Visualize the Results": [[95, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[95, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[95, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[95, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[95, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[95, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[95, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[95, "1.-Load-the-Dataset"], [95, "id8"]], "2: Encode Categorical Values": [[95, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[95, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[95, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[95, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[95, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[95, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[95, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[95, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[95, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[95, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[95, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[95, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[96, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[96, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[96, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[96, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[97, "FAQ"]], "What data can cleanlab detect issues in?": [[97, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[97, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[97, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[97, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[97, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[97, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[97, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[97, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[97, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[97, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[97, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[97, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[97, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[97, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[98, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[98, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[98, "1.-Install-dependencies"]], "2. Preprocess the data": [[98, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[98, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[98, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[98, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[98, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[98, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[98, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[98, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[98, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[98, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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"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 1596ab50a..36baab6a0 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": "2024-08-29T17:07:20.536553Z",
- "iopub.status.busy": "2024-08-29T17:07:20.536073Z",
- "iopub.status.idle": "2024-08-29T17:07:21.786062Z",
- "shell.execute_reply": "2024-08-29T17:07:21.785506Z"
+ "iopub.execute_input": "2024-09-04T16:36:33.494350Z",
+ "iopub.status.busy": "2024-09-04T16:36:33.493852Z",
+ "iopub.status.idle": "2024-09-04T16:36:34.726026Z",
+ "shell.execute_reply": "2024-09-04T16:36:34.725399Z"
},
"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@0620487f86634df0f530d3659a564db463d09b34\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@d6fdc9f1c48140a209e3e9d1228fe6c945b2c575\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": "2024-08-29T17:07:21.788700Z",
- "iopub.status.busy": "2024-08-29T17:07:21.788279Z",
- "iopub.status.idle": "2024-08-29T17:07:21.806586Z",
- "shell.execute_reply": "2024-08-29T17:07:21.806009Z"
+ "iopub.execute_input": "2024-09-04T16:36:34.729286Z",
+ "iopub.status.busy": "2024-09-04T16:36:34.728744Z",
+ "iopub.status.idle": "2024-09-04T16:36:34.747897Z",
+ "shell.execute_reply": "2024-09-04T16:36:34.747378Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.808803Z",
- "iopub.status.busy": "2024-08-29T17:07:21.808416Z",
- "iopub.status.idle": "2024-08-29T17:07:21.929772Z",
- "shell.execute_reply": "2024-08-29T17:07:21.929178Z"
+ "iopub.execute_input": "2024-09-04T16:36:34.750373Z",
+ "iopub.status.busy": "2024-09-04T16:36:34.749905Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.046021Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.045440Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.960601Z",
- "iopub.status.busy": "2024-08-29T17:07:21.960225Z",
- "iopub.status.idle": "2024-08-29T17:07:21.963941Z",
- "shell.execute_reply": "2024-08-29T17:07:21.963468Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.076604Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.076192Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.079864Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.079398Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.965925Z",
- "iopub.status.busy": "2024-08-29T17:07:21.965589Z",
- "iopub.status.idle": "2024-08-29T17:07:21.973807Z",
- "shell.execute_reply": "2024-08-29T17:07:21.973371Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.081865Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.081597Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.090286Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.089725Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.975916Z",
- "iopub.status.busy": "2024-08-29T17:07:21.975570Z",
- "iopub.status.idle": "2024-08-29T17:07:21.978067Z",
- "shell.execute_reply": "2024-08-29T17:07:21.977625Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.092459Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.092118Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.094778Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.094312Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:21.980150Z",
- "iopub.status.busy": "2024-08-29T17:07:21.979829Z",
- "iopub.status.idle": "2024-08-29T17:07:22.497459Z",
- "shell.execute_reply": "2024-08-29T17:07:22.496833Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.096769Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.096372Z",
+ "iopub.status.idle": "2024-09-04T16:36:35.623436Z",
+ "shell.execute_reply": "2024-09-04T16:36:35.622805Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:22.499970Z",
- "iopub.status.busy": "2024-08-29T17:07:22.499787Z",
- "iopub.status.idle": "2024-08-29T17:07:24.411694Z",
- "shell.execute_reply": "2024-08-29T17:07:24.411025Z"
+ "iopub.execute_input": "2024-09-04T16:36:35.625916Z",
+ "iopub.status.busy": "2024-09-04T16:36:35.625730Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.510736Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.510124Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.414319Z",
- "iopub.status.busy": "2024-08-29T17:07:24.413697Z",
- "iopub.status.idle": "2024-08-29T17:07:24.424172Z",
- "shell.execute_reply": "2024-08-29T17:07:24.423712Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.513479Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.512707Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.522816Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.522356Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.426313Z",
- "iopub.status.busy": "2024-08-29T17:07:24.425865Z",
- "iopub.status.idle": "2024-08-29T17:07:24.429917Z",
- "shell.execute_reply": "2024-08-29T17:07:24.429479Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.524923Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.524600Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.528591Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.528155Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.432009Z",
- "iopub.status.busy": "2024-08-29T17:07:24.431607Z",
- "iopub.status.idle": "2024-08-29T17:07:24.439758Z",
- "shell.execute_reply": "2024-08-29T17:07:24.439330Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.530787Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.530452Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.538844Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.538421Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.441674Z",
- "iopub.status.busy": "2024-08-29T17:07:24.441407Z",
- "iopub.status.idle": "2024-08-29T17:07:24.553494Z",
- "shell.execute_reply": "2024-08-29T17:07:24.553024Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.540782Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.540515Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.658571Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.658063Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.555730Z",
- "iopub.status.busy": "2024-08-29T17:07:24.555391Z",
- "iopub.status.idle": "2024-08-29T17:07:24.558029Z",
- "shell.execute_reply": "2024-08-29T17:07:24.557585Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.660665Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.660392Z",
+ "iopub.status.idle": "2024-09-04T16:36:37.663359Z",
+ "shell.execute_reply": "2024-09-04T16:36:37.662802Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:24.560040Z",
- "iopub.status.busy": "2024-08-29T17:07:24.559706Z",
- "iopub.status.idle": "2024-08-29T17:07:26.688998Z",
- "shell.execute_reply": "2024-08-29T17:07:26.688364Z"
+ "iopub.execute_input": "2024-09-04T16:36:37.665583Z",
+ "iopub.status.busy": "2024-09-04T16:36:37.665415Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.737444Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.736766Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:26.692110Z",
- "iopub.status.busy": "2024-08-29T17:07:26.691312Z",
- "iopub.status.idle": "2024-08-29T17:07:26.702522Z",
- "shell.execute_reply": "2024-08-29T17:07:26.702054Z"
+ "iopub.execute_input": "2024-09-04T16:36:39.740370Z",
+ "iopub.status.busy": "2024-09-04T16:36:39.739756Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.751066Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.750597Z"
}
},
"outputs": [
@@ -786,10 +786,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-08-29T17:07:26.704548Z",
- "iopub.status.busy": "2024-08-29T17:07:26.704208Z",
- "iopub.status.idle": "2024-08-29T17:07:26.733958Z",
- "shell.execute_reply": "2024-08-29T17:07:26.733535Z"
+ "iopub.execute_input": "2024-09-04T16:36:39.753075Z",
+ "iopub.status.busy": "2024-09-04T16:36:39.752736Z",
+ "iopub.status.idle": "2024-09-04T16:36:39.921485Z",
+ "shell.execute_reply": "2024-09-04T16:36:39.920960Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index ddca5a9d1..0a081872f 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 2. Load and format the text dataset
@@ -2042,35 +2042,35 @@ Low information images
+
|
- Age |
- Gender |
- Location |
- Annual_Spending |
- Number_of_Transactions |
- Last_Purchase_Date |
- | |
- is_null_issue |
- null_score |
+ Age |
+ Gender |
+ Location |
+ Annual_Spending |
+ Number_of_Transactions |
+ Last_Purchase_Date |
+ | |
+ is_null_issue |
+ null_score |
- 8 |
- nan |
- nan |
- nan |
- nan |
- nan |
- NaT |
- |
- True |
- 0.000000 |
-
-
- 1 |
- nan |
- Female |
- Rural |
- 6421.160000 |
- 5.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 9 |
- nan |
- Male |
- Rural |
- 4655.820000 |
- 1.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 14 |
- nan |
- Male |
- Rural |
- 6790.460000 |
- 3.000000 |
- NaT |
- |
- False |
- 0.666667 |
-
-
- 13 |
- nan |
- Male |
- Urban |
- 9167.470000 |
- 4.000000 |
- 2024-01-02 00:00:00 |
- |
- False |
- 0.833333 |
-
-
- 15 |
- nan |
- Other |
- Rural |
- 5327.960000 |
- 8.000000 |
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@@ -3503,16 +3503,16 @@ 1. Load the Dataset