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index b24936208..432ea29bb 100644
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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index 2c77571f6..df411c770 100644
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diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
index dc4a39bac..f279273ae 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index 7871636e6..88e05bb1b 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 0c56c3881..aa00ed6e9 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:24.117516Z",
- "iopub.status.busy": "2024-07-02T12:00:24.117048Z",
- "iopub.status.idle": "2024-07-02T12:00:25.333194Z",
- "shell.execute_reply": "2024-07-02T12:00:25.332647Z"
+ "iopub.execute_input": "2024-07-02T15:09:49.406100Z",
+ "iopub.status.busy": "2024-07-02T15:09:49.405638Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.626225Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.625679Z"
},
"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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.335570Z",
- "iopub.status.busy": "2024-07-02T12:00:25.335300Z",
- "iopub.status.idle": "2024-07-02T12:00:25.352966Z",
- "shell.execute_reply": "2024-07-02T12:00:25.352544Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.628776Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.628382Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.646656Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.646174Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.355177Z",
- "iopub.status.busy": "2024-07-02T12:00:25.354929Z",
- "iopub.status.idle": "2024-07-02T12:00:25.498882Z",
- "shell.execute_reply": "2024-07-02T12:00:25.498315Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.649040Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.648771Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.799686Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.799107Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.528732Z",
- "iopub.status.busy": "2024-07-02T12:00:25.528329Z",
- "iopub.status.idle": "2024-07-02T12:00:25.532259Z",
- "shell.execute_reply": "2024-07-02T12:00:25.531790Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.830515Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.830286Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.833956Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.833391Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.534236Z",
- "iopub.status.busy": "2024-07-02T12:00:25.534064Z",
- "iopub.status.idle": "2024-07-02T12:00:25.542721Z",
- "shell.execute_reply": "2024-07-02T12:00:25.542178Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.836142Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.835713Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.843960Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.843409Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.544841Z",
- "iopub.status.busy": "2024-07-02T12:00:25.544667Z",
- "iopub.status.idle": "2024-07-02T12:00:25.547142Z",
- "shell.execute_reply": "2024-07-02T12:00:25.546723Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.846292Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.845872Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.848589Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.848046Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.549121Z",
- "iopub.status.busy": "2024-07-02T12:00:25.548952Z",
- "iopub.status.idle": "2024-07-02T12:00:26.069775Z",
- "shell.execute_reply": "2024-07-02T12:00:26.069166Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.850511Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.850252Z",
+ "iopub.status.idle": "2024-07-02T15:09:51.372873Z",
+ "shell.execute_reply": "2024-07-02T15:09:51.372266Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:26.072294Z",
- "iopub.status.busy": "2024-07-02T12:00:26.072111Z",
- "iopub.status.idle": "2024-07-02T12:00:27.964122Z",
- "shell.execute_reply": "2024-07-02T12:00:27.963476Z"
+ "iopub.execute_input": "2024-07-02T15:09:51.375361Z",
+ "iopub.status.busy": "2024-07-02T15:09:51.375157Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.243284Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.242604Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.966793Z",
- "iopub.status.busy": "2024-07-02T12:00:27.966128Z",
- "iopub.status.idle": "2024-07-02T12:00:27.975803Z",
- "shell.execute_reply": "2024-07-02T12:00:27.975266Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.246075Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.245483Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.255700Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.255167Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.977956Z",
- "iopub.status.busy": "2024-07-02T12:00:27.977648Z",
- "iopub.status.idle": "2024-07-02T12:00:27.981829Z",
- "shell.execute_reply": "2024-07-02T12:00:27.981303Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.257868Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.257460Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.261706Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.261166Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.984025Z",
- "iopub.status.busy": "2024-07-02T12:00:27.983701Z",
- "iopub.status.idle": "2024-07-02T12:00:27.990825Z",
- "shell.execute_reply": "2024-07-02T12:00:27.990380Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.263822Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.263391Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.270955Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.270531Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.992803Z",
- "iopub.status.busy": "2024-07-02T12:00:27.992505Z",
- "iopub.status.idle": "2024-07-02T12:00:28.104238Z",
- "shell.execute_reply": "2024-07-02T12:00:28.103750Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.273195Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.272768Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.386175Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.385548Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:28.106465Z",
- "iopub.status.busy": "2024-07-02T12:00:28.106127Z",
- "iopub.status.idle": "2024-07-02T12:00:28.108811Z",
- "shell.execute_reply": "2024-07-02T12:00:28.108400Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.388505Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.388085Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.390961Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.390511Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:28.110759Z",
- "iopub.status.busy": "2024-07-02T12:00:28.110457Z",
- "iopub.status.idle": "2024-07-02T12:00:30.104044Z",
- "shell.execute_reply": "2024-07-02T12:00:30.103432Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.392859Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.392685Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.359879Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.359148Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:30.106906Z",
- "iopub.status.busy": "2024-07-02T12:00:30.106328Z",
- "iopub.status.idle": "2024-07-02T12:00:30.117548Z",
- "shell.execute_reply": "2024-07-02T12:00:30.117099Z"
+ "iopub.execute_input": "2024-07-02T15:09:55.362970Z",
+ "iopub.status.busy": "2024-07-02T15:09:55.362388Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.374161Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.373705Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:30.119573Z",
- "iopub.status.busy": "2024-07-02T12:00:30.119249Z",
- "iopub.status.idle": "2024-07-02T12:00:30.150922Z",
- "shell.execute_reply": "2024-07-02T12:00:30.150454Z"
+ "iopub.execute_input": "2024-07-02T15:09:55.376352Z",
+ "iopub.status.busy": "2024-07-02T15:09:55.375903Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.432383Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.431845Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index d42308ae9..cac09ab25 100644
--- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
@@ -115,10 +115,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:34.059784Z",
- "iopub.status.busy": "2024-07-02T12:00:34.059279Z",
- "iopub.status.idle": "2024-07-02T12:00:36.809187Z",
- "shell.execute_reply": "2024-07-02T12:00:36.808623Z"
+ "iopub.execute_input": "2024-07-02T15:09:59.845378Z",
+ "iopub.status.busy": "2024-07-02T15:09:59.845205Z",
+ "iopub.status.idle": "2024-07-02T15:10:02.560189Z",
+ "shell.execute_reply": "2024-07-02T15:10:02.559618Z"
},
"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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:36.811854Z",
- "iopub.status.busy": "2024-07-02T12:00:36.811437Z",
- "iopub.status.idle": "2024-07-02T12:00:36.814737Z",
- "shell.execute_reply": "2024-07-02T12:00:36.814309Z"
+ "iopub.execute_input": "2024-07-02T15:10:02.562794Z",
+ "iopub.status.busy": "2024-07-02T15:10:02.562496Z",
+ "iopub.status.idle": "2024-07-02T15:10:02.565788Z",
+ "shell.execute_reply": "2024-07-02T15:10:02.565349Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:36.816857Z",
- "iopub.status.busy": "2024-07-02T12:00:36.816534Z",
- "iopub.status.idle": "2024-07-02T12:00:36.819520Z",
- "shell.execute_reply": "2024-07-02T12:00:36.819089Z"
+ "iopub.execute_input": "2024-07-02T15:10:02.567948Z",
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@@ -219,10 +219,10 @@
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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: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}\n"
+ "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}\n"
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@@ -453,17 +453,17 @@
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- "layout": "IPY_MODEL_982a96cc3c0b4c00938e722c374cd707",
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- "value": " 2.21k/2.21k [00:00<00:00, 389kB/s]"
+ "value": "tokenizer.json: 100%"
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@@ -3624,6 +3585,45 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 9db139a3f..a4fd4545f 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
@@ -78,10 +78,10 @@
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@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
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"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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -208,10 +208,10 @@
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@@ -617,10 +617,10 @@
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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 58bbdaa8a..0a658abc0 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 @@
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -1247,10 +1247,10 @@
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+ "_view_name": "ProgressView",
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+ "layout": "IPY_MODEL_e5651455523845919804bfd3f20d32fd",
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@@ -1516,30 +1526,49 @@
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@@ -1554,65 +1583,49 @@
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- "layout": "IPY_MODEL_8addd7af612b43d395a8dfcfeb6287ef",
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- "value": " 132/132 [00:00<00:00, 13162.98 examples/s]"
+ "value": "Saving the dataset (1/1 shards): 100%"
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@@ -1665,7 +1678,7 @@
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@@ -1718,7 +1731,30 @@
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+ "style": "IPY_MODEL_a43777fd323b46498d1b65ddfdcb03d7",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 132/132 [00:00<00:00, 13503.28 examples/s]"
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@@ -1770,42 +1806,6 @@
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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 61c4891f1..cf7301700 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
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- "shell.execute_reply": "2024-07-02T12:01:23.344925Z"
+ "iopub.execute_input": "2024-07-02T15:10:48.203913Z",
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+ "shell.execute_reply": "2024-07-02T15:10:49.370326Z"
},
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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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 @@
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@@ -821,10 +821,10 @@
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@@ -935,10 +935,10 @@
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@@ -1005,10 +1005,10 @@
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"metadata": {
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@@ -1200,10 +1200,10 @@
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"metadata": {
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"outputs": [
@@ -1319,10 +1319,10 @@
"execution_count": 15,
"metadata": {
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+ "shell.execute_reply": "2024-07-02T15:10:52.068314Z"
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@@ -1447,10 +1447,10 @@
"execution_count": 16,
"metadata": {
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+ "shell.execute_reply": "2024-07-02T15:10:52.079380Z"
}
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"outputs": [
@@ -1553,10 +1553,10 @@
"execution_count": 17,
"metadata": {
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+ "iopub.status.idle": "2024-07-02T15:10:52.097277Z",
+ "shell.execute_reply": "2024-07-02T15:10:52.096807Z"
},
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 3baceeb0b..2852ac72e 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": {
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- "iopub.status.idle": "2024-07-02T12:01:31.827318Z",
- "shell.execute_reply": "2024-07-02T12:01:31.826688Z"
+ "iopub.execute_input": "2024-07-02T15:10:54.880751Z",
+ "iopub.status.busy": "2024-07-02T15:10:54.880594Z",
+ "iopub.status.idle": "2024-07-02T15:10:57.696869Z",
+ "shell.execute_reply": "2024-07-02T15:10:57.696388Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:01:31.829957Z",
- "iopub.status.busy": "2024-07-02T12:01:31.829648Z",
- "iopub.status.idle": "2024-07-02T12:01:31.833462Z",
- "shell.execute_reply": "2024-07-02T12:01:31.833002Z"
+ "iopub.execute_input": "2024-07-02T15:10:57.699412Z",
+ "iopub.status.busy": "2024-07-02T15:10:57.698969Z",
+ "iopub.status.idle": "2024-07-02T15:10:57.702504Z",
+ "shell.execute_reply": "2024-07-02T15:10:57.702065Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:01:31.835341Z",
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- "shell.execute_reply": "2024-07-02T12:01:42.989362Z"
+ "iopub.execute_input": "2024-07-02T15:10:57.704607Z",
+ "iopub.status.busy": "2024-07-02T15:10:57.704218Z",
+ "iopub.status.idle": "2024-07-02T15:11:08.972759Z",
+ "shell.execute_reply": "2024-07-02T15:11:08.972290Z"
}
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"outputs": [
{
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- "model_id": "d4c59b0bfa86424a8c95a71f890f5454",
+ "model_id": "76447603597c41e58c504ba366dedf8b",
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@@ -176,7 +176,7 @@
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+ "model_id": "74d7207adb634a9a9648063cd4ebf05d",
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@@ -190,7 +190,7 @@
{
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+ "model_id": "24554a44a66045a29398e71c18b39f2f",
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"version_minor": 0
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@@ -204,7 +204,7 @@
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+ "model_id": "52a2b90360f7460f9d5e8e206e5b7b47",
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@@ -218,7 +218,7 @@
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+ "model_id": "1eca5328aef44e1ca18c8c422f647377",
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@@ -232,7 +232,7 @@
{
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+ "model_id": "c8ad57476e81431f9ef31378a786d5e9",
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@@ -246,7 +246,7 @@
{
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+ "model_id": "4761c3ddf1a643e8bda01b752e44ad8b",
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@@ -260,7 +260,7 @@
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@@ -302,10 +302,10 @@
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- "shell.execute_reply": "2024-07-02T12:01:42.995062Z"
+ "iopub.execute_input": "2024-07-02T15:11:08.975154Z",
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+ "shell.execute_reply": "2024-07-02T15:11:08.978061Z"
}
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@@ -330,17 +330,17 @@
"execution_count": 5,
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+ "iopub.execute_input": "2024-07-02T15:11:08.980647Z",
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+ "shell.execute_reply": "2024-07-02T15:11:20.197917Z"
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+ "model_id": "ea88c13811944930a76ece93362f7e4c",
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@@ -378,10 +378,10 @@
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- "shell.execute_reply": "2024-07-02T12:02:13.013360Z"
+ "iopub.execute_input": "2024-07-02T15:11:20.201174Z",
+ "iopub.status.busy": "2024-07-02T15:11:20.200947Z",
+ "iopub.status.idle": "2024-07-02T15:11:38.612541Z",
+ "shell.execute_reply": "2024-07-02T15:11:38.611926Z"
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@@ -414,10 +414,10 @@
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+ "iopub.execute_input": "2024-07-02T15:11:38.615766Z",
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@@ -455,10 +455,10 @@
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+ "iopub.execute_input": "2024-07-02T15:11:38.623170Z",
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+ "shell.execute_reply": "2024-07-02T15:11:38.626551Z"
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@@ -595,10 +595,10 @@
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- "shell.execute_reply": "2024-07-02T12:02:13.037284Z"
+ "iopub.execute_input": "2024-07-02T15:11:38.628931Z",
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+ "shell.execute_reply": "2024-07-02T15:11:38.637111Z"
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@@ -723,10 +723,10 @@
"execution_count": 10,
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- "shell.execute_reply": "2024-07-02T12:02:13.065500Z"
+ "iopub.execute_input": "2024-07-02T15:11:38.639743Z",
+ "iopub.status.busy": "2024-07-02T15:11:38.639336Z",
+ "iopub.status.idle": "2024-07-02T15:11:38.665352Z",
+ "shell.execute_reply": "2024-07-02T15:11:38.664931Z"
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@@ -763,10 +763,10 @@
"execution_count": 11,
"metadata": {
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- "shell.execute_reply": "2024-07-02T12:02:45.177789Z"
+ "iopub.execute_input": "2024-07-02T15:11:38.667332Z",
+ "iopub.status.busy": "2024-07-02T15:11:38.667160Z",
+ "iopub.status.idle": "2024-07-02T15:12:10.330212Z",
+ "shell.execute_reply": "2024-07-02T15:12:10.329611Z"
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"outputs": [
@@ -782,21 +782,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414\n",
"Computing feature embeddings ...\n"
]
},
{
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@@ -817,7 +817,7 @@
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@@ -840,21 +840,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.642\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471\n",
"Computing feature embeddings ...\n"
]
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@@ -875,7 +875,7 @@
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+ "model_id": "da2c01112d1f4e749b0ca2c79b09927f",
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@@ -898,21 +898,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.668\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531\n",
"Computing feature embeddings ...\n"
]
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@@ -933,7 +933,7 @@
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+ "model_id": "a3115a3594ce4aa497f8a610abb0af9e",
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@@ -1012,10 +1012,10 @@
"execution_count": 12,
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- "iopub.status.busy": "2024-07-02T12:02:45.180584Z",
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- "shell.execute_reply": "2024-07-02T12:02:45.193957Z"
+ "iopub.execute_input": "2024-07-02T15:12:10.332761Z",
+ "iopub.status.busy": "2024-07-02T15:12:10.332362Z",
+ "iopub.status.idle": "2024-07-02T15:12:10.346556Z",
+ "shell.execute_reply": "2024-07-02T15:12:10.346082Z"
}
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@@ -1040,10 +1040,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.execute_input": "2024-07-02T12:02:45.196378Z",
- "iopub.status.busy": "2024-07-02T12:02:45.196060Z",
- "iopub.status.idle": "2024-07-02T12:02:45.659461Z",
- "shell.execute_reply": "2024-07-02T12:02:45.658926Z"
+ "iopub.execute_input": "2024-07-02T15:12:10.348951Z",
+ "iopub.status.busy": "2024-07-02T15:12:10.348618Z",
+ "iopub.status.idle": "2024-07-02T15:12:10.823258Z",
+ "shell.execute_reply": "2024-07-02T15:12:10.822713Z"
}
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"outputs": [],
@@ -1063,10 +1063,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-07-02T12:02:45.661921Z",
- "iopub.status.busy": "2024-07-02T12:02:45.661522Z",
- "iopub.status.idle": "2024-07-02T12:04:21.084670Z",
- "shell.execute_reply": "2024-07-02T12:04:21.084011Z"
+ "iopub.execute_input": "2024-07-02T15:12:10.825656Z",
+ "iopub.status.busy": "2024-07-02T15:12:10.825310Z",
+ "iopub.status.idle": "2024-07-02T15:13:46.428675Z",
+ "shell.execute_reply": "2024-07-02T15:13:46.428018Z"
}
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@@ -1105,7 +1105,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "683ea97790a64507b71e617e6bb1960f",
+ "model_id": "b66bf1f268f64f16b0ab04fbfef16cb7",
"version_major": 2,
"version_minor": 0
},
@@ -1144,10 +1144,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:21.087384Z",
- "iopub.status.busy": "2024-07-02T12:04:21.086898Z",
- "iopub.status.idle": "2024-07-02T12:04:21.530187Z",
- "shell.execute_reply": "2024-07-02T12:04:21.529650Z"
+ "iopub.execute_input": "2024-07-02T15:13:46.431322Z",
+ "iopub.status.busy": "2024-07-02T15:13:46.430773Z",
+ "iopub.status.idle": "2024-07-02T15:13:46.883257Z",
+ "shell.execute_reply": "2024-07-02T15:13:46.882712Z"
}
},
"outputs": [
@@ -1293,10 +1293,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:21.532970Z",
- "iopub.status.busy": "2024-07-02T12:04:21.532489Z",
- "iopub.status.idle": "2024-07-02T12:04:21.594306Z",
- "shell.execute_reply": "2024-07-02T12:04:21.593726Z"
+ "iopub.execute_input": "2024-07-02T15:13:46.885977Z",
+ "iopub.status.busy": "2024-07-02T15:13:46.885501Z",
+ "iopub.status.idle": "2024-07-02T15:13:46.948513Z",
+ "shell.execute_reply": "2024-07-02T15:13:46.947996Z"
}
},
"outputs": [
@@ -1400,10 +1400,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:21.597613Z",
- "iopub.status.busy": "2024-07-02T12:04:21.597278Z",
- "iopub.status.idle": "2024-07-02T12:04:21.605873Z",
- "shell.execute_reply": "2024-07-02T12:04:21.605434Z"
+ "iopub.execute_input": "2024-07-02T15:13:46.950792Z",
+ "iopub.status.busy": "2024-07-02T15:13:46.950469Z",
+ "iopub.status.idle": "2024-07-02T15:13:46.958869Z",
+ "shell.execute_reply": "2024-07-02T15:13:46.958422Z"
}
},
"outputs": [
@@ -1533,10 +1533,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:21.607881Z",
- "iopub.status.busy": "2024-07-02T12:04:21.607595Z",
- "iopub.status.idle": "2024-07-02T12:04:21.612387Z",
- "shell.execute_reply": "2024-07-02T12:04:21.611934Z"
+ "iopub.execute_input": "2024-07-02T15:13:46.960882Z",
+ "iopub.status.busy": "2024-07-02T15:13:46.960564Z",
+ "iopub.status.idle": "2024-07-02T15:13:46.965390Z",
+ "shell.execute_reply": "2024-07-02T15:13:46.964852Z"
},
"nbsphinx": "hidden"
},
@@ -1582,10 +1582,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:21.614443Z",
- "iopub.status.busy": "2024-07-02T12:04:21.614030Z",
- "iopub.status.idle": "2024-07-02T12:04:22.120240Z",
- "shell.execute_reply": "2024-07-02T12:04:22.119680Z"
+ "iopub.execute_input": "2024-07-02T15:13:46.967456Z",
+ "iopub.status.busy": "2024-07-02T15:13:46.967155Z",
+ "iopub.status.idle": "2024-07-02T15:13:47.465450Z",
+ "shell.execute_reply": "2024-07-02T15:13:47.464898Z"
}
},
"outputs": [
@@ -1620,10 +1620,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.122526Z",
- "iopub.status.busy": "2024-07-02T12:04:22.122160Z",
- "iopub.status.idle": "2024-07-02T12:04:22.130544Z",
- "shell.execute_reply": "2024-07-02T12:04:22.130091Z"
+ "iopub.execute_input": "2024-07-02T15:13:47.467701Z",
+ "iopub.status.busy": "2024-07-02T15:13:47.467369Z",
+ "iopub.status.idle": "2024-07-02T15:13:47.475692Z",
+ "shell.execute_reply": "2024-07-02T15:13:47.475239Z"
}
},
"outputs": [
@@ -1790,10 +1790,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.132648Z",
- "iopub.status.busy": "2024-07-02T12:04:22.132322Z",
- "iopub.status.idle": "2024-07-02T12:04:22.139582Z",
- "shell.execute_reply": "2024-07-02T12:04:22.139132Z"
+ "iopub.execute_input": "2024-07-02T15:13:47.477736Z",
+ "iopub.status.busy": "2024-07-02T15:13:47.477444Z",
+ "iopub.status.idle": "2024-07-02T15:13:47.484538Z",
+ "shell.execute_reply": "2024-07-02T15:13:47.483995Z"
},
"nbsphinx": "hidden"
},
@@ -1869,10 +1869,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.141499Z",
- "iopub.status.busy": "2024-07-02T12:04:22.141182Z",
- "iopub.status.idle": "2024-07-02T12:04:22.871798Z",
- "shell.execute_reply": "2024-07-02T12:04:22.871228Z"
+ "iopub.execute_input": "2024-07-02T15:13:47.486504Z",
+ "iopub.status.busy": "2024-07-02T15:13:47.486124Z",
+ "iopub.status.idle": "2024-07-02T15:13:48.236887Z",
+ "shell.execute_reply": "2024-07-02T15:13:48.236330Z"
}
},
"outputs": [
@@ -1909,10 +1909,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.874107Z",
- "iopub.status.busy": "2024-07-02T12:04:22.873751Z",
- "iopub.status.idle": "2024-07-02T12:04:22.889160Z",
- "shell.execute_reply": "2024-07-02T12:04:22.888693Z"
+ "iopub.execute_input": "2024-07-02T15:13:48.238951Z",
+ "iopub.status.busy": "2024-07-02T15:13:48.238743Z",
+ "iopub.status.idle": "2024-07-02T15:13:48.254003Z",
+ "shell.execute_reply": "2024-07-02T15:13:48.253445Z"
}
},
"outputs": [
@@ -2069,10 +2069,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.891280Z",
- "iopub.status.busy": "2024-07-02T12:04:22.890945Z",
- "iopub.status.idle": "2024-07-02T12:04:22.896314Z",
- "shell.execute_reply": "2024-07-02T12:04:22.895869Z"
+ "iopub.execute_input": "2024-07-02T15:13:48.256077Z",
+ "iopub.status.busy": "2024-07-02T15:13:48.255753Z",
+ "iopub.status.idle": "2024-07-02T15:13:48.261132Z",
+ "shell.execute_reply": "2024-07-02T15:13:48.260713Z"
},
"nbsphinx": "hidden"
},
@@ -2117,10 +2117,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:22.898366Z",
- "iopub.status.busy": "2024-07-02T12:04:22.898042Z",
- "iopub.status.idle": "2024-07-02T12:04:23.354782Z",
- "shell.execute_reply": "2024-07-02T12:04:23.354256Z"
+ "iopub.execute_input": "2024-07-02T15:13:48.263200Z",
+ "iopub.status.busy": "2024-07-02T15:13:48.262806Z",
+ "iopub.status.idle": "2024-07-02T15:13:48.721823Z",
+ "shell.execute_reply": "2024-07-02T15:13:48.721244Z"
}
},
"outputs": [
@@ -2202,10 +2202,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:04:23.357430Z",
- "iopub.status.busy": "2024-07-02T12:04:23.357055Z",
- "iopub.status.idle": "2024-07-02T12:04:23.366373Z",
- "shell.execute_reply": "2024-07-02T12:04:23.365890Z"
+ "iopub.execute_input": "2024-07-02T15:13:48.724484Z",
+ "iopub.status.busy": "2024-07-02T15:13:48.724285Z",
+ "iopub.status.idle": "2024-07-02T15:13:48.733522Z",
+ "shell.execute_reply": "2024-07-02T15:13:48.732818Z"
}
},
"outputs": [
@@ -2230,47 +2230,47 @@
" \n",
" \n",
" \n",
" \n",
" \n",
- " is_dark_issue \n",
" dark_score \n",
+ " is_dark_issue \n",
"
\n", - " | Age | \n", - "Gender | \n", - "Location | \n", - "Annual_Spending | \n", - "Number_of_Transactions | \n", - "Last_Purchase_Date | \n", - "| | \n", - "is_null_issue | \n", - "null_score | \n", + "Age | \n", + "Gender | \n", + "Location | \n", + "Annual_Spending | \n", + "Number_of_Transactions | \n", + "Last_Purchase_Date | \n", + "| | \n", + "is_null_issue | \n", + "null_score | \n", "|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8 | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "nan | \n", - "NaT | \n", - "\n", - " | True | \n", - "0.000000 | \n", - "||||||||||
1 | \n", - "nan | \n", - "Female | \n", - "Rural | \n", - "6421.160000 | \n", - "5.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
9 | \n", - "nan | \n", - "Male | \n", - "Rural | \n", - "4655.820000 | \n", - "1.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
14 | \n", - "nan | \n", - "Male | \n", - "Rural | \n", - "6790.460000 | \n", - "3.000000 | \n", - "NaT | \n", - "\n", - " | False | \n", - "0.666667 | \n", - "||||||||||
13 | \n", - "nan | \n", - "Male | \n", - "Urban | \n", - "9167.470000 | \n", - "4.000000 | \n", - "2024-01-02 00:00:00 | \n", - "\n", - " | False | \n", - "0.833333 | \n", - "||||||||||
15 | \n", - "nan | \n", - "Other | \n", - "Rural | \n", - "5327.960000 | \n", - "8.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "0.833333 | \n", - "||||||||||
0 | \n", - "56.000000 | \n", - "Other | \n", - "Rural | \n", - "4099.620000 | \n", - "3.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
2 | \n", - "46.000000 | \n", - "Male | \n", - "Suburban | \n", - "5436.550000 | \n", - "3.000000 | \n", - "2024-02-26 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
3 | \n", - "32.000000 | \n", - "Female | \n", - "Rural | \n", - "4046.660000 | \n", - "3.000000 | \n", - "2024-03-23 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
4 | \n", - "60.000000 | \n", - "Female | \n", - "Suburban | \n", - "3467.670000 | \n", - "6.000000 | \n", - "2024-03-01 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
5 | \n", - "25.000000 | \n", - "Female | \n", - "Suburban | \n", - "4757.370000 | \n", - "4.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
6 | \n", - "38.000000 | \n", - "Female | \n", - "Rural | \n", - "4199.530000 | \n", - "6.000000 | \n", - "2024-01-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
7 | \n", - "56.000000 | \n", - "Male | \n", - "Suburban | \n", - "4991.710000 | \n", - "6.000000 | \n", - "2024-04-03 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
10 | \n", - "40.000000 | \n", - "Female | \n", - "Rural | \n", - "5584.020000 | \n", - "7.000000 | \n", - "2024-03-29 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
11 | \n", - "28.000000 | \n", - "Female | \n", - "Urban | \n", - "3102.320000 | \n", - "2.000000 | \n", - "2024-04-07 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", - "||||||||||
12 | \n", - "28.000000 | \n", - "Male | \n", - "Rural | \n", - "6637.990000 | \n", - "11.000000 | \n", - "2024-04-08 00:00:00 | \n", - "\n", - " | False | \n", - "1.000000 | \n", + "8 | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "nan | \n", + "NaT | \n", + "\n", + " | True | \n", + "0.000000 | \n", + "
1 | \n", + "nan | \n", + "Female | \n", + "Rural | \n", + "6421.160000 | \n", + "5.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
9 | \n", + "nan | \n", + "Male | \n", + "Rural | \n", + "4655.820000 | \n", + "1.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
14 | \n", + "nan | \n", + "Male | \n", + "Rural | \n", + "6790.460000 | \n", + "3.000000 | \n", + "NaT | \n", + "\n", + " | False | \n", + "0.666667 | \n", + "||||||||||
13 | \n", + "nan | \n", + "Male | \n", + "Urban | \n", + "9167.470000 | \n", + "4.000000 | \n", + "2024-01-02 00:00:00 | \n", + "\n", + " | False | \n", + "0.833333 | \n", + "||||||||||
15 | \n", + "nan | \n", + "Other | \n", + "Rural | \n", + "5327.960000 | \n", + "8.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "0.833333 | \n", + "||||||||||
0 | \n", + "56.000000 | \n", + "Other | \n", + "Rural | \n", + "4099.620000 | \n", + "3.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
2 | \n", + "46.000000 | \n", + "Male | \n", + "Suburban | \n", + "5436.550000 | \n", + "3.000000 | \n", + "2024-02-26 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
3 | \n", + "32.000000 | \n", + "Female | \n", + "Rural | \n", + "4046.660000 | \n", + "3.000000 | \n", + "2024-03-23 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
4 | \n", + "60.000000 | \n", + "Female | \n", + "Suburban | \n", + "3467.670000 | \n", + "6.000000 | \n", + "2024-03-01 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
5 | \n", + "25.000000 | \n", + "Female | \n", + "Suburban | \n", + "4757.370000 | \n", + "4.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
6 | \n", + "38.000000 | \n", + "Female | \n", + "Rural | \n", + "4199.530000 | \n", + "6.000000 | \n", + "2024-01-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
7 | \n", + "56.000000 | \n", + "Male | \n", + "Suburban | \n", + "4991.710000 | \n", + "6.000000 | \n", + "2024-04-03 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
10 | \n", + "40.000000 | \n", + "Female | \n", + "Rural | \n", + "5584.020000 | \n", + "7.000000 | \n", + "2024-03-29 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
11 | \n", + "28.000000 | \n", + "Female | \n", + "Urban | \n", + "3102.320000 | \n", + "2.000000 | \n", + "2024-04-07 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", + "||||||||||
12 | \n", + "28.000000 | \n", + "Male | \n", + "Rural | \n", + "6637.990000 | \n", + "11.000000 | \n", + "2024-04-08 00:00:00 | \n", + "\n", + " | False | \n", + "1.000000 | \n", "
from cleanlab.typing import DatasetLike, LabelLike
-[docs]def remove_noise_from_class(noise_matrix, class_without_noise) -> np.ndarray:
+[docs]def remove_noise_from_class(noise_matrix: np.ndarray, class_without_noise: int) -> np.ndarray:
"""A helper function in the setting of PU learning.
Sets all P(label=class_without_noise|true_label=any_other_class) = 0
in noise_matrix for pulearning setting, where we have
@@ -668,17 +668,16 @@ Source code for cleanlab.internal.util
x = np.copy(noise_matrix)
# Set P( labels = cwn | y != cwn) = 0 (no noise)
- x[cwn, [i for i in range(K) if i != cwn]] = 0.0
+ class_arange = np.arange(K)
+ x[cwn, class_arange[class_arange != cwn]] = 0.0
# Normalize columns by increasing diagonal terms
# Ensures noise_matrix is a valid probability matrix
- for i in range(K):
- x[i][i] = 1 - float(np.sum(x[:, i]) - x[i][i])
-
+ np.fill_diagonal(x, 1 - (np.sum(x, axis=0) - np.diag(x)))
return x
-[docs]def clip_noise_rates(noise_matrix) -> np.ndarray:
+[docs]def clip_noise_rates(noise_matrix: np.ndarray) -> np.ndarray:
"""Clip all noise rates to proper range [0,1), but
do not modify the diagonal terms because they are not
noise rates.
@@ -693,19 +692,11 @@ Source code for cleanlab.internal.util
Diagonal terms are not noise rates, but are consistency P(label=k|true_label=k)
Assumes columns of noise_matrix sum to 1"""
- def clip_noise_rate_range(noise_rate) -> float:
- """Clip noise rate P(label=k'|true_label=k) or P(true_label=k|label=k')
- into proper range [0,1)"""
- return min(max(noise_rate, 0.0), 0.9999)
-
- # Vectorize clip_noise_rate_range for efficiency with np.ndarrays.
- vectorized_clip = np.vectorize(clip_noise_rate_range)
-
# Preserve because diagonal entries are not noise rates.
diagonal = np.diagonal(noise_matrix)
# Clip all noise rates (efficiently).
- noise_matrix = vectorized_clip(noise_matrix)
+ noise_matrix = np.clip(noise_matrix, 0, 0.9999)
# Put unmodified diagonal back.
np.fill_diagonal(noise_matrix, diagonal)
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 9699fd59f..46b37d704 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 af77ff1a5..ede05464f 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 b012c3b83..8111dc9de 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 40b596a7c..59d67f1f0 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 6d03ae333..b77f310a5 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 ac39104cf..9b208f9fe 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 3e5552460..469ab488a 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 7af5e7f6e..c59730731 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 e036f973f..6ca13ceb0 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 56543bad0..8f5165de5 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 348a544a8..5d2685b6c 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 2d80f1068..1f90e9ca9 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 b6dbc6271..97e4513a6 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 fe223cf83..bb22c545c 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 f5ced067f..d2b0bbb42 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 f02e0094c..d8a611d23 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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\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 47f457c5d..ed2af2ad8 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module 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"correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module 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"cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, 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"cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, 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"multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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(in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index 0c56c3881..aa00ed6e9 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:24.117516Z",
- "iopub.status.busy": "2024-07-02T12:00:24.117048Z",
- "iopub.status.idle": "2024-07-02T12:00:25.333194Z",
- "shell.execute_reply": "2024-07-02T12:00:25.332647Z"
+ "iopub.execute_input": "2024-07-02T15:09:49.406100Z",
+ "iopub.status.busy": "2024-07-02T15:09:49.405638Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.626225Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.625679Z"
},
"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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.335570Z",
- "iopub.status.busy": "2024-07-02T12:00:25.335300Z",
- "iopub.status.idle": "2024-07-02T12:00:25.352966Z",
- "shell.execute_reply": "2024-07-02T12:00:25.352544Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.628776Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.628382Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.646656Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.646174Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.355177Z",
- "iopub.status.busy": "2024-07-02T12:00:25.354929Z",
- "iopub.status.idle": "2024-07-02T12:00:25.498882Z",
- "shell.execute_reply": "2024-07-02T12:00:25.498315Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.649040Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.648771Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.799686Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.799107Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.528732Z",
- "iopub.status.busy": "2024-07-02T12:00:25.528329Z",
- "iopub.status.idle": "2024-07-02T12:00:25.532259Z",
- "shell.execute_reply": "2024-07-02T12:00:25.531790Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.830515Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.830286Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.833956Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.833391Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.534236Z",
- "iopub.status.busy": "2024-07-02T12:00:25.534064Z",
- "iopub.status.idle": "2024-07-02T12:00:25.542721Z",
- "shell.execute_reply": "2024-07-02T12:00:25.542178Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.836142Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.835713Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.843960Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.843409Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.544841Z",
- "iopub.status.busy": "2024-07-02T12:00:25.544667Z",
- "iopub.status.idle": "2024-07-02T12:00:25.547142Z",
- "shell.execute_reply": "2024-07-02T12:00:25.546723Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.846292Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.845872Z",
+ "iopub.status.idle": "2024-07-02T15:09:50.848589Z",
+ "shell.execute_reply": "2024-07-02T15:09:50.848046Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:25.549121Z",
- "iopub.status.busy": "2024-07-02T12:00:25.548952Z",
- "iopub.status.idle": "2024-07-02T12:00:26.069775Z",
- "shell.execute_reply": "2024-07-02T12:00:26.069166Z"
+ "iopub.execute_input": "2024-07-02T15:09:50.850511Z",
+ "iopub.status.busy": "2024-07-02T15:09:50.850252Z",
+ "iopub.status.idle": "2024-07-02T15:09:51.372873Z",
+ "shell.execute_reply": "2024-07-02T15:09:51.372266Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:26.072294Z",
- "iopub.status.busy": "2024-07-02T12:00:26.072111Z",
- "iopub.status.idle": "2024-07-02T12:00:27.964122Z",
- "shell.execute_reply": "2024-07-02T12:00:27.963476Z"
+ "iopub.execute_input": "2024-07-02T15:09:51.375361Z",
+ "iopub.status.busy": "2024-07-02T15:09:51.375157Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.243284Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.242604Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.966793Z",
- "iopub.status.busy": "2024-07-02T12:00:27.966128Z",
- "iopub.status.idle": "2024-07-02T12:00:27.975803Z",
- "shell.execute_reply": "2024-07-02T12:00:27.975266Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.246075Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.245483Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.255700Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.255167Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.977956Z",
- "iopub.status.busy": "2024-07-02T12:00:27.977648Z",
- "iopub.status.idle": "2024-07-02T12:00:27.981829Z",
- "shell.execute_reply": "2024-07-02T12:00:27.981303Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.257868Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.257460Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.261706Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.261166Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.984025Z",
- "iopub.status.busy": "2024-07-02T12:00:27.983701Z",
- "iopub.status.idle": "2024-07-02T12:00:27.990825Z",
- "shell.execute_reply": "2024-07-02T12:00:27.990380Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.263822Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.263391Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.270955Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.270531Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:27.992803Z",
- "iopub.status.busy": "2024-07-02T12:00:27.992505Z",
- "iopub.status.idle": "2024-07-02T12:00:28.104238Z",
- "shell.execute_reply": "2024-07-02T12:00:28.103750Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.273195Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.272768Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.386175Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.385548Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:28.106465Z",
- "iopub.status.busy": "2024-07-02T12:00:28.106127Z",
- "iopub.status.idle": "2024-07-02T12:00:28.108811Z",
- "shell.execute_reply": "2024-07-02T12:00:28.108400Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.388505Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.388085Z",
+ "iopub.status.idle": "2024-07-02T15:09:53.390961Z",
+ "shell.execute_reply": "2024-07-02T15:09:53.390511Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:28.110759Z",
- "iopub.status.busy": "2024-07-02T12:00:28.110457Z",
- "iopub.status.idle": "2024-07-02T12:00:30.104044Z",
- "shell.execute_reply": "2024-07-02T12:00:30.103432Z"
+ "iopub.execute_input": "2024-07-02T15:09:53.392859Z",
+ "iopub.status.busy": "2024-07-02T15:09:53.392685Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.359879Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.359148Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:30.106906Z",
- "iopub.status.busy": "2024-07-02T12:00:30.106328Z",
- "iopub.status.idle": "2024-07-02T12:00:30.117548Z",
- "shell.execute_reply": "2024-07-02T12:00:30.117099Z"
+ "iopub.execute_input": "2024-07-02T15:09:55.362970Z",
+ "iopub.status.busy": "2024-07-02T15:09:55.362388Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.374161Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.373705Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T12:00:30.119573Z",
- "iopub.status.busy": "2024-07-02T12:00:30.119249Z",
- "iopub.status.idle": "2024-07-02T12:00:30.150922Z",
- "shell.execute_reply": "2024-07-02T12:00:30.150454Z"
+ "iopub.execute_input": "2024-07-02T15:09:55.376352Z",
+ "iopub.status.busy": "2024-07-02T15:09:55.375903Z",
+ "iopub.status.idle": "2024-07-02T15:09:55.432383Z",
+ "shell.execute_reply": "2024-07-02T15:09:55.431845Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index c0155c6ba..9c18d0c40 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@ 2. Load and format the text dataset
This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'lost_or_stolen_phone', 'getting_spare_card', 'change_pin', 'cancel_transfer', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'apple_pay_or_google_pay'}
+Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}
Let’s print the first example in the train set.
@@ -880,43 +880,43 @@ 2. Load and format the text dataset
-
+
-
+
Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
Training on fold: 1 ... -epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.801 -epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.468 +epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.690 +epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.414 Computing feature embeddings ...
Training on fold: 2 ... -epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.793 -epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.570 +epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.642 +epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.471 Computing feature embeddings ...
Training on fold: 3 ... -epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.822 -epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.476 +epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.668 +epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.531 Computing feature embeddings ...
This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}
+Classes: {'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card'}
Let’s view the i-th example in the dataset:
diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 8395c410d..5204560ef 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:36.240740Z", - "iopub.status.busy": "2024-07-02T12:04:36.240404Z", - "iopub.status.idle": "2024-07-02T12:04:38.828958Z", - "shell.execute_reply": "2024-07-02T12:04:38.828416Z" + "iopub.execute_input": "2024-07-02T15:14:01.500489Z", + "iopub.status.busy": "2024-07-02T15:14:01.500322Z", + "iopub.status.idle": "2024-07-02T15:14:04.113035Z", + "shell.execute_reply": "2024-07-02T15:14:04.112481Z" }, "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@46226527e9d4c8f7ccdf91ff5dac4d6ee27e022b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.831414Z", - "iopub.status.busy": "2024-07-02T12:04:38.831139Z", - "iopub.status.idle": "2024-07-02T12:04:38.834207Z", - "shell.execute_reply": "2024-07-02T12:04:38.833787Z" + "iopub.execute_input": "2024-07-02T15:14:04.115579Z", + "iopub.status.busy": "2024-07-02T15:14:04.115125Z", + "iopub.status.idle": "2024-07-02T15:14:04.118367Z", + "shell.execute_reply": "2024-07-02T15:14:04.117915Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.836176Z", - "iopub.status.busy": "2024-07-02T12:04:38.835855Z", - "iopub.status.idle": "2024-07-02T12:04:38.838727Z", - "shell.execute_reply": "2024-07-02T12:04:38.838306Z" + "iopub.execute_input": "2024-07-02T15:14:04.120314Z", + "iopub.status.busy": "2024-07-02T15:14:04.119999Z", + "iopub.status.idle": "2024-07-02T15:14:04.123081Z", + "shell.execute_reply": "2024-07-02T15:14:04.122619Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.840549Z", - "iopub.status.busy": "2024-07-02T12:04:38.840377Z", - "iopub.status.idle": "2024-07-02T12:04:38.923955Z", - "shell.execute_reply": "2024-07-02T12:04:38.923459Z" + "iopub.execute_input": "2024-07-02T15:14:04.125041Z", + "iopub.status.busy": "2024-07-02T15:14:04.124728Z", + "iopub.status.idle": "2024-07-02T15:14:04.163294Z", + "shell.execute_reply": "2024-07-02T15:14:04.162806Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.926011Z", - "iopub.status.busy": "2024-07-02T12:04:38.925614Z", - "iopub.status.idle": "2024-07-02T12:04:38.929422Z", - "shell.execute_reply": "2024-07-02T12:04:38.928857Z" + "iopub.execute_input": "2024-07-02T15:14:04.165499Z", + "iopub.status.busy": "2024-07-02T15:14:04.165073Z", + "iopub.status.idle": "2024-07-02T15:14:04.168687Z", + "shell.execute_reply": "2024-07-02T15:14:04.168240Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'card_payment_fee_charged', 'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'getting_spare_card'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.931544Z", - "iopub.status.busy": "2024-07-02T12:04:38.931095Z", - "iopub.status.idle": "2024-07-02T12:04:38.934251Z", - "shell.execute_reply": "2024-07-02T12:04:38.933726Z" + "iopub.execute_input": "2024-07-02T15:14:04.170669Z", + "iopub.status.busy": "2024-07-02T15:14:04.170357Z", + "iopub.status.idle": "2024-07-02T15:14:04.173526Z", + "shell.execute_reply": "2024-07-02T15:14:04.172982Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:38.936534Z", - "iopub.status.busy": "2024-07-02T12:04:38.936327Z", - "iopub.status.idle": "2024-07-02T12:04:42.537806Z", - "shell.execute_reply": "2024-07-02T12:04:42.537162Z" + "iopub.execute_input": "2024-07-02T15:14:04.175608Z", + "iopub.status.busy": "2024-07-02T15:14:04.175312Z", + "iopub.status.idle": "2024-07-02T15:14:07.867281Z", + "shell.execute_reply": "2024-07-02T15:14:07.866722Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:42.540458Z", - "iopub.status.busy": "2024-07-02T12:04:42.540268Z", - "iopub.status.idle": "2024-07-02T12:04:43.423626Z", - "shell.execute_reply": "2024-07-02T12:04:43.423064Z" + "iopub.execute_input": "2024-07-02T15:14:07.870054Z", + "iopub.status.busy": "2024-07-02T15:14:07.869647Z", + "iopub.status.idle": "2024-07-02T15:14:08.750932Z", + "shell.execute_reply": "2024-07-02T15:14:08.750350Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.426912Z", - "iopub.status.busy": "2024-07-02T12:04:43.426508Z", - "iopub.status.idle": "2024-07-02T12:04:43.429416Z", - "shell.execute_reply": "2024-07-02T12:04:43.428926Z" + "iopub.execute_input": "2024-07-02T15:14:08.753892Z", + "iopub.status.busy": "2024-07-02T15:14:08.753472Z", + "iopub.status.idle": "2024-07-02T15:14:08.756403Z", + "shell.execute_reply": "2024-07-02T15:14:08.755906Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:43.431781Z", - "iopub.status.busy": "2024-07-02T12:04:43.431407Z", - "iopub.status.idle": "2024-07-02T12:04:45.304891Z", - "shell.execute_reply": "2024-07-02T12:04:45.304275Z" + "iopub.execute_input": "2024-07-02T15:14:08.759587Z", + "iopub.status.busy": "2024-07-02T15:14:08.758650Z", + "iopub.status.idle": "2024-07-02T15:14:10.695173Z", + "shell.execute_reply": "2024-07-02T15:14:10.694552Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.309001Z", - "iopub.status.busy": "2024-07-02T12:04:45.307874Z", - "iopub.status.idle": "2024-07-02T12:04:45.333199Z", - "shell.execute_reply": "2024-07-02T12:04:45.332708Z" + "iopub.execute_input": "2024-07-02T15:14:10.699111Z", + "iopub.status.busy": "2024-07-02T15:14:10.697727Z", + "iopub.status.idle": "2024-07-02T15:14:10.723548Z", + "shell.execute_reply": "2024-07-02T15:14:10.723039Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.336771Z", - "iopub.status.busy": "2024-07-02T12:04:45.335844Z", - "iopub.status.idle": "2024-07-02T12:04:45.346004Z", - "shell.execute_reply": "2024-07-02T12:04:45.345452Z" + "iopub.execute_input": "2024-07-02T15:14:10.727082Z", + "iopub.status.busy": "2024-07-02T15:14:10.726140Z", + "iopub.status.idle": "2024-07-02T15:14:10.737117Z", + "shell.execute_reply": "2024-07-02T15:14:10.736707Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.348315Z", - "iopub.status.busy": "2024-07-02T12:04:45.347931Z", - "iopub.status.idle": "2024-07-02T12:04:45.352195Z", - "shell.execute_reply": "2024-07-02T12:04:45.351669Z" + "iopub.execute_input": "2024-07-02T15:14:10.739972Z", + "iopub.status.busy": "2024-07-02T15:14:10.739233Z", + "iopub.status.idle": "2024-07-02T15:14:10.744512Z", + "shell.execute_reply": "2024-07-02T15:14:10.744100Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.354318Z", - "iopub.status.busy": "2024-07-02T12:04:45.354009Z", - "iopub.status.idle": "2024-07-02T12:04:45.360212Z", - "shell.execute_reply": "2024-07-02T12:04:45.359737Z" + "iopub.execute_input": "2024-07-02T15:14:10.746541Z", + "iopub.status.busy": "2024-07-02T15:14:10.746363Z", + "iopub.status.idle": "2024-07-02T15:14:10.752732Z", + "shell.execute_reply": "2024-07-02T15:14:10.752214Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.362212Z", - "iopub.status.busy": "2024-07-02T12:04:45.361899Z", - "iopub.status.idle": "2024-07-02T12:04:45.368332Z", - "shell.execute_reply": "2024-07-02T12:04:45.367912Z" + "iopub.execute_input": "2024-07-02T15:14:10.754855Z", + "iopub.status.busy": "2024-07-02T15:14:10.754542Z", + "iopub.status.idle": "2024-07-02T15:14:10.760876Z", + "shell.execute_reply": "2024-07-02T15:14:10.760354Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.370347Z", - "iopub.status.busy": "2024-07-02T12:04:45.370035Z", - "iopub.status.idle": "2024-07-02T12:04:45.375916Z", - "shell.execute_reply": "2024-07-02T12:04:45.375352Z" + "iopub.execute_input": "2024-07-02T15:14:10.762917Z", + "iopub.status.busy": "2024-07-02T15:14:10.762536Z", + "iopub.status.idle": "2024-07-02T15:14:10.768287Z", + "shell.execute_reply": "2024-07-02T15:14:10.767766Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.377933Z", - "iopub.status.busy": "2024-07-02T12:04:45.377533Z", - "iopub.status.idle": "2024-07-02T12:04:45.386285Z", - "shell.execute_reply": "2024-07-02T12:04:45.385744Z" + "iopub.execute_input": "2024-07-02T15:14:10.770234Z", + "iopub.status.busy": "2024-07-02T15:14:10.769934Z", + "iopub.status.idle": "2024-07-02T15:14:10.778237Z", + "shell.execute_reply": "2024-07-02T15:14:10.777705Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.388235Z", - "iopub.status.busy": "2024-07-02T12:04:45.387909Z", - "iopub.status.idle": "2024-07-02T12:04:45.393341Z", - "shell.execute_reply": "2024-07-02T12:04:45.392791Z" + "iopub.execute_input": "2024-07-02T15:14:10.780199Z", + "iopub.status.busy": "2024-07-02T15:14:10.779892Z", + "iopub.status.idle": "2024-07-02T15:14:10.785104Z", + "shell.execute_reply": "2024-07-02T15:14:10.784582Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.395404Z", - "iopub.status.busy": "2024-07-02T12:04:45.395057Z", - "iopub.status.idle": "2024-07-02T12:04:45.400341Z", - "shell.execute_reply": "2024-07-02T12:04:45.399863Z" + "iopub.execute_input": "2024-07-02T15:14:10.787024Z", + "iopub.status.busy": "2024-07-02T15:14:10.786715Z", + "iopub.status.idle": "2024-07-02T15:14:10.791931Z", + "shell.execute_reply": "2024-07-02T15:14:10.791409Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.402359Z", - "iopub.status.busy": "2024-07-02T12:04:45.402038Z", - "iopub.status.idle": "2024-07-02T12:04:45.405437Z", - "shell.execute_reply": "2024-07-02T12:04:45.405020Z" + "iopub.execute_input": "2024-07-02T15:14:10.793948Z", + "iopub.status.busy": "2024-07-02T15:14:10.793644Z", + "iopub.status.idle": "2024-07-02T15:14:10.797169Z", + "shell.execute_reply": "2024-07-02T15:14:10.796651Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-07-02T12:04:45.407623Z", - "iopub.status.busy": "2024-07-02T12:04:45.407307Z", - "iopub.status.idle": "2024-07-02T12:04:45.412091Z", - "shell.execute_reply": "2024-07-02T12:04:45.411668Z" + "iopub.execute_input": "2024-07-02T15:14:10.799179Z", + "iopub.status.busy": "2024-07-02T15:14:10.798916Z", + "iopub.status.idle": "2024-07-02T15:14:10.804228Z", + "shell.execute_reply": "2024-07-02T15:14:10.803755Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index cef347d5d..dee8c6eb4 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -833,7 +833,7 @@