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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index 9515ae9c6..ad627f9b8 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index df906e689..13a4babc0 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 3d52eae5f..5ad12b115 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": "2024-05-14T00:23:18.610599Z",
- "iopub.status.busy": "2024-05-14T00:23:18.610192Z",
- "iopub.status.idle": "2024-05-14T00:23:19.711241Z",
- "shell.execute_reply": "2024-05-14T00:23:19.710735Z"
+ "iopub.execute_input": "2024-05-14T00:40:31.101837Z",
+ "iopub.status.busy": "2024-05-14T00:40:31.101487Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.284083Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.283529Z"
},
"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.713562Z",
- "iopub.status.busy": "2024-05-14T00:23:19.713221Z",
- "iopub.status.idle": "2024-05-14T00:23:19.731184Z",
- "shell.execute_reply": "2024-05-14T00:23:19.730680Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.286694Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.286271Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.304842Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.304407Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.733187Z",
- "iopub.status.busy": "2024-05-14T00:23:19.732864Z",
- "iopub.status.idle": "2024-05-14T00:23:19.852041Z",
- "shell.execute_reply": "2024-05-14T00:23:19.851565Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.307114Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.306710Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.469863Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.469313Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.879672Z",
- "iopub.status.busy": "2024-05-14T00:23:19.879343Z",
- "iopub.status.idle": "2024-05-14T00:23:19.882515Z",
- "shell.execute_reply": "2024-05-14T00:23:19.882114Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.500841Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.500461Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.503971Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.503500Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.884444Z",
- "iopub.status.busy": "2024-05-14T00:23:19.884266Z",
- "iopub.status.idle": "2024-05-14T00:23:19.892435Z",
- "shell.execute_reply": "2024-05-14T00:23:19.892023Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.506045Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.505746Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.513771Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.513353Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.894349Z",
- "iopub.status.busy": "2024-05-14T00:23:19.894051Z",
- "iopub.status.idle": "2024-05-14T00:23:19.896402Z",
- "shell.execute_reply": "2024-05-14T00:23:19.895970Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.515852Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.515528Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.518200Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.517630Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.898410Z",
- "iopub.status.busy": "2024-05-14T00:23:19.898094Z",
- "iopub.status.idle": "2024-05-14T00:23:20.374029Z",
- "shell.execute_reply": "2024-05-14T00:23:20.373539Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.520144Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.519846Z",
+ "iopub.status.idle": "2024-05-14T00:40:33.032286Z",
+ "shell.execute_reply": "2024-05-14T00:40:33.031748Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:20.376186Z",
- "iopub.status.busy": "2024-05-14T00:23:20.375821Z",
- "iopub.status.idle": "2024-05-14T00:23:21.872135Z",
- "shell.execute_reply": "2024-05-14T00:23:21.871567Z"
+ "iopub.execute_input": "2024-05-14T00:40:33.034859Z",
+ "iopub.status.busy": "2024-05-14T00:40:33.034462Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.657987Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.657339Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:21.874737Z",
- "iopub.status.busy": "2024-05-14T00:23:21.874175Z",
- "iopub.status.idle": "2024-05-14T00:23:21.883739Z",
- "shell.execute_reply": "2024-05-14T00:23:21.883212Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.660488Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.659958Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.669866Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.669380Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:21.886295Z",
- "iopub.status.busy": "2024-05-14T00:23:21.885999Z",
- "iopub.status.idle": "2024-05-14T00:23:21.889735Z",
- "shell.execute_reply": "2024-05-14T00:23:21.889266Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.671951Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.671633Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.675799Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.675237Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:21.891693Z",
- "iopub.status.busy": "2024-05-14T00:23:21.891427Z",
- "iopub.status.idle": "2024-05-14T00:23:21.898351Z",
- "shell.execute_reply": "2024-05-14T00:23:21.897874Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.678016Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.677536Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.684194Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.683778Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:21.900124Z",
- "iopub.status.busy": "2024-05-14T00:23:21.899972Z",
- "iopub.status.idle": "2024-05-14T00:23:22.003958Z",
- "shell.execute_reply": "2024-05-14T00:23:22.003523Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.686230Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.685894Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.797033Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.796542Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:22.005768Z",
- "iopub.status.busy": "2024-05-14T00:23:22.005600Z",
- "iopub.status.idle": "2024-05-14T00:23:22.008117Z",
- "shell.execute_reply": "2024-05-14T00:23:22.007694Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.799220Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.798874Z",
+ "iopub.status.idle": "2024-05-14T00:40:34.801658Z",
+ "shell.execute_reply": "2024-05-14T00:40:34.801214Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:22.009880Z",
- "iopub.status.busy": "2024-05-14T00:23:22.009707Z",
- "iopub.status.idle": "2024-05-14T00:23:23.834866Z",
- "shell.execute_reply": "2024-05-14T00:23:23.834196Z"
+ "iopub.execute_input": "2024-05-14T00:40:34.803618Z",
+ "iopub.status.busy": "2024-05-14T00:40:34.803294Z",
+ "iopub.status.idle": "2024-05-14T00:40:36.794760Z",
+ "shell.execute_reply": "2024-05-14T00:40:36.794102Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:23.837643Z",
- "iopub.status.busy": "2024-05-14T00:23:23.837135Z",
- "iopub.status.idle": "2024-05-14T00:23:23.847494Z",
- "shell.execute_reply": "2024-05-14T00:23:23.847065Z"
+ "iopub.execute_input": "2024-05-14T00:40:36.797983Z",
+ "iopub.status.busy": "2024-05-14T00:40:36.797203Z",
+ "iopub.status.idle": "2024-05-14T00:40:36.808596Z",
+ "shell.execute_reply": "2024-05-14T00:40:36.808144Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:23.849242Z",
- "iopub.status.busy": "2024-05-14T00:23:23.849070Z",
- "iopub.status.idle": "2024-05-14T00:23:23.884706Z",
- "shell.execute_reply": "2024-05-14T00:23:23.884332Z"
+ "iopub.execute_input": "2024-05-14T00:40:36.810644Z",
+ "iopub.status.busy": "2024-05-14T00:40:36.810306Z",
+ "iopub.status.idle": "2024-05-14T00:40:36.866302Z",
+ "shell.execute_reply": "2024-05-14T00:40:36.865819Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index fefe7ea34..5bb4f91f4 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": "2024-05-14T00:23:26.493085Z",
- "iopub.status.busy": "2024-05-14T00:23:26.492914Z",
- "iopub.status.idle": "2024-05-14T00:23:29.408912Z",
- "shell.execute_reply": "2024-05-14T00:23:29.408356Z"
+ "iopub.execute_input": "2024-05-14T00:40:39.847129Z",
+ "iopub.status.busy": "2024-05-14T00:40:39.846969Z",
+ "iopub.status.idle": "2024-05-14T00:40:43.045474Z",
+ "shell.execute_reply": "2024-05-14T00:40:43.044843Z"
},
"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -160,10 +160,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:29.411260Z",
- "iopub.status.busy": "2024-05-14T00:23:29.411000Z",
- "iopub.status.idle": "2024-05-14T00:23:29.414116Z",
- "shell.execute_reply": "2024-05-14T00:23:29.413645Z"
+ "iopub.execute_input": "2024-05-14T00:40:43.047979Z",
+ "iopub.status.busy": "2024-05-14T00:40:43.047676Z",
+ "iopub.status.idle": "2024-05-14T00:40:43.051204Z",
+ "shell.execute_reply": "2024-05-14T00:40:43.050755Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:29.416127Z",
- "iopub.status.busy": "2024-05-14T00:23:29.415721Z",
- "iopub.status.idle": "2024-05-14T00:23:29.418706Z",
- "shell.execute_reply": "2024-05-14T00:23:29.418242Z"
+ "iopub.execute_input": "2024-05-14T00:40:43.052947Z",
+ "iopub.status.busy": "2024-05-14T00:40:43.052772Z",
+ "iopub.status.idle": "2024-05-14T00:40:43.055820Z",
+ "shell.execute_reply": "2024-05-14T00:40:43.055373Z"
},
"nbsphinx": "hidden"
},
@@ -219,10 +219,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:29.420399Z",
- "iopub.status.busy": "2024-05-14T00:23:29.420238Z",
- "iopub.status.idle": "2024-05-14T00:23:29.466159Z",
- "shell.execute_reply": "2024-05-14T00:23:29.465668Z"
+ "iopub.execute_input": "2024-05-14T00:40:43.057785Z",
+ "iopub.status.busy": "2024-05-14T00:40:43.057468Z",
+ "iopub.status.idle": "2024-05-14T00:40:43.088276Z",
+ "shell.execute_reply": "2024-05-14T00:40:43.087817Z"
}
},
"outputs": [
@@ -312,10 +312,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:29.467971Z",
- "iopub.status.busy": "2024-05-14T00:23:29.467810Z",
- "iopub.status.idle": "2024-05-14T00:23:29.471102Z",
- "shell.execute_reply": "2024-05-14T00:23:29.470666Z"
+ "iopub.execute_input": "2024-05-14T00:40:43.090297Z",
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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_payment_fee_charged', 'beneficiary_not_allowed', 'getting_spare_card', 'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin', 'card_about_to_expire', 'lost_or_stolen_phone', 'cancel_transfer', 'apple_pay_or_google_pay'}\n"
+ "Classes: {'supported_cards_and_currencies', 'change_pin', 'card_payment_fee_charged', 'visa_or_mastercard', 'lost_or_stolen_phone', 'getting_spare_card', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'cancel_transfer'}\n"
]
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@@ -453,17 +453,17 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 3307a13f4..281d44d64 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",
"\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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
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@@ -157,10 +157,10 @@
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@@ -208,10 +208,10 @@
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@@ -242,10 +242,10 @@
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@@ -329,10 +329,10 @@
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@@ -557,10 +557,10 @@
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@@ -582,10 +582,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/data_monitor.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/data_monitor.ipynb
index f44d20b53..4fa426610 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/data_monitor.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/data_monitor.ipynb
@@ -5,10 +5,10 @@
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@@ -97,7 +97,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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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 e78a0014e..4adbc5c3c 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -118,10 +118,10 @@
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@@ -252,10 +252,10 @@
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@@ -353,10 +353,10 @@
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@@ -445,10 +445,10 @@
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@@ -517,10 +517,10 @@
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@@ -569,10 +569,10 @@
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@@ -608,10 +608,10 @@
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@@ -642,10 +642,10 @@
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@@ -709,10 +709,10 @@
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@@ -821,10 +821,10 @@
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@@ -936,10 +936,10 @@
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@@ -1069,17 +1069,17 @@
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+ "shell.execute_reply": "2024-05-14T00:42:03.906986Z"
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{
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- "model_id": "6bacd3ceb65341bb8f3f72852ab58aee",
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@@ -1115,10 +1115,10 @@
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@@ -1236,10 +1236,10 @@
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@@ -1296,10 +1296,10 @@
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"metadata": {
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+ "shell.execute_reply": "2024-05-14T00:42:03.950229Z"
}
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@@ -1431,74 +1431,49 @@
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- "_view_module": "@jupyter-widgets/controls",
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- "layout": "IPY_MODEL_573c1425cb9f469f81325bc12f94da3e",
- "placeholder": "",
- "style": "IPY_MODEL_fe503839d5eb434f8366d9248116ae2f",
- "tabbable": null,
- "tooltip": null,
- "value": "Saving the dataset (1/1 shards): 100%"
+ "_view_name": "StyleView",
+ "background": null,
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
}
},
- "2dd539a07b2e4cd296d833f3b1d3cffb": {
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"model_module_version": "2.0.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
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"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "2.0.0",
- "_model_name": "FloatProgressModel",
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- "layout": "IPY_MODEL_aefb58776c6d48a1a58ac7f39ed7025f",
- "max": 132.0,
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- "style": "IPY_MODEL_fd451565bbf04d219ad89efc81a65f70",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
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+ "IPY_MODEL_8b19517214b442339faf88cd4cfd0aba"
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@@ -1551,54 +1526,90 @@
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- "_model_name": "HBoxModel",
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- "IPY_MODEL_2dd539a07b2e4cd296d833f3b1d3cffb",
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- ],
- "layout": "IPY_MODEL_c5af301f908149e6a450728a965282f4",
+ "_view_name": "HTMLView",
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+ "description_allow_html": false,
+ "layout": "IPY_MODEL_d4b08794e2e640399c8c44901c0eec7d",
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+ "style": "IPY_MODEL_5feb5f0a644e47db9b0fc1ec84b56a93",
"tabbable": null,
- "tooltip": null
+ "tooltip": null,
+ "value": "Saving the dataset (1/1 shards): 100%"
}
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- "772ac856f88f4006a9f8db08c09d7564": {
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"_model_module_version": "2.0.0",
- "_model_name": "HTMLModel",
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"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
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"description_allow_html": false,
- "layout": "IPY_MODEL_d2cb0c8e80d142f884f816861d4ed05b",
- "placeholder": "",
- "style": "IPY_MODEL_4c8531763bcf42b1b4f09a072b4b11d5",
+ "layout": "IPY_MODEL_ab7afb48f2144d6899236d507f94b52e",
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+ "orientation": "horizontal",
+ "style": "IPY_MODEL_412f365ad55140a2bf47513a408d529c",
"tabbable": null,
"tooltip": null,
- "value": " 132/132 [00:00<00:00, 15000.36 examples/s]"
+ "value": 132.0
+ }
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+ "412f365ad55140a2bf47513a408d529c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
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+ "_model_module_version": "2.0.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
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+ "description_width": ""
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},
- "aefb58776c6d48a1a58ac7f39ed7025f": {
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+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
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+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLStyleModel",
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+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
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@@ -1651,7 +1662,30 @@
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+ "model_name": "HTMLModel",
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+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_1f29133eda834502ae347e071e286501",
+ "placeholder": "",
+ "style": "IPY_MODEL_0bec9b80581f4acdbd3deae35302aa57",
+ "tabbable": null,
+ "tooltip": null,
+ "value": " 132/132 [00:00<00:00, 12069.41 examples/s]"
+ }
+ },
+ "ab7afb48f2144d6899236d507f94b52e": {
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@@ -1704,7 +1738,7 @@
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@@ -1756,40 +1790,6 @@
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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 4d0c83f3d..fb2049b33 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.status.busy": "2024-05-14T00:24:46.842864Z",
- "iopub.status.idle": "2024-05-14T00:24:47.910346Z",
- "shell.execute_reply": "2024-05-14T00:24:47.909807Z"
+ "iopub.execute_input": "2024-05-14T00:42:06.712421Z",
+ "iopub.status.busy": "2024-05-14T00:42:06.712049Z",
+ "iopub.status.idle": "2024-05-14T00:42:07.870397Z",
+ "shell.execute_reply": "2024-05-14T00:42:07.869755Z"
},
"nbsphinx": "hidden"
},
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
"execution_count": 2,
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:24:47.914608Z"
+ "iopub.execute_input": "2024-05-14T00:42:07.872978Z",
+ "iopub.status.busy": "2024-05-14T00:42:07.872558Z",
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+ "shell.execute_reply": "2024-05-14T00:42:07.875033Z"
}
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@@ -250,10 +250,10 @@
"execution_count": 3,
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:24:47.925693Z"
+ "iopub.execute_input": "2024-05-14T00:42:07.877581Z",
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},
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@@ -356,10 +356,10 @@
"execution_count": 4,
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- "shell.execute_reply": "2024-05-14T00:24:47.931822Z"
+ "iopub.execute_input": "2024-05-14T00:42:07.888280Z",
+ "iopub.status.busy": "2024-05-14T00:42:07.888106Z",
+ "iopub.status.idle": "2024-05-14T00:42:07.892858Z",
+ "shell.execute_reply": "2024-05-14T00:42:07.892446Z"
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@@ -448,10 +448,10 @@
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- "shell.execute_reply": "2024-05-14T00:24:48.102749Z"
+ "iopub.execute_input": "2024-05-14T00:42:07.894976Z",
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@@ -520,10 +520,10 @@
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- "shell.execute_reply": "2024-05-14T00:24:48.452030Z"
+ "iopub.execute_input": "2024-05-14T00:42:08.081511Z",
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@@ -559,10 +559,10 @@
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@@ -602,10 +602,10 @@
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- "shell.execute_reply": "2024-05-14T00:24:48.491886Z"
+ "iopub.execute_input": "2024-05-14T00:42:08.401410Z",
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+ "shell.execute_reply": "2024-05-14T00:42:08.435944Z"
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@@ -647,10 +647,10 @@
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@@ -711,10 +711,10 @@
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@@ -842,10 +842,10 @@
"execution_count": 11,
"metadata": {
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@@ -956,10 +956,10 @@
"execution_count": 12,
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@@ -1026,10 +1026,10 @@
"execution_count": 13,
"metadata": {
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@@ -1221,10 +1221,10 @@
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"metadata": {
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"outputs": [
@@ -1340,10 +1340,10 @@
"execution_count": 15,
"metadata": {
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"scrolled": true
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@@ -1468,10 +1468,10 @@
"execution_count": 16,
"metadata": {
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+ "shell.execute_reply": "2024-05-14T00:42:10.149280Z"
}
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"outputs": [
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 8307da712..0cd963290 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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},
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@@ -112,10 +112,10 @@
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+ "shell.execute_reply": "2024-05-14T00:42:15.684439Z"
}
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"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
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- "shell.execute_reply": "2024-05-14T00:24:56.907484Z"
+ "iopub.execute_input": "2024-05-14T00:42:15.687080Z",
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+ "shell.execute_reply": "2024-05-14T00:42:17.996158Z"
}
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{
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+ "model_id": "a2548ffb9489453e8bfa880c00bf86c7",
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@@ -176,7 +176,7 @@
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@@ -190,7 +190,7 @@
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@@ -204,7 +204,7 @@
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@@ -246,10 +246,10 @@
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+ "shell.execute_reply": "2024-05-14T00:42:18.001731Z"
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@@ -274,17 +274,17 @@
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@@ -358,10 +358,10 @@
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@@ -399,10 +399,10 @@
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@@ -539,10 +539,10 @@
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@@ -667,10 +667,10 @@
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+ "shell.execute_reply": "2024-05-14T00:42:47.786621Z"
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@@ -707,10 +707,10 @@
"execution_count": 11,
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- "shell.execute_reply": "2024-05-14T00:25:55.367981Z"
+ "iopub.execute_input": "2024-05-14T00:42:47.789439Z",
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+ "shell.execute_reply": "2024-05-14T00:43:19.929392Z"
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@@ -726,21 +726,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.450\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.682\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.368\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.554\n",
"Computing feature embeddings ...\n"
]
},
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@@ -761,7 +761,7 @@
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@@ -784,21 +784,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.500\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.781\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.256\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.431\n",
"Computing feature embeddings ...\n"
]
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@@ -819,7 +819,7 @@
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@@ -842,21 +842,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.458\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.837\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.307\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.660\n",
"Computing feature embeddings ...\n"
]
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@@ -877,7 +877,7 @@
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@@ -956,10 +956,10 @@
"execution_count": 12,
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- "iopub.status.idle": "2024-05-14T00:25:55.386677Z",
- "shell.execute_reply": "2024-05-14T00:25:55.386251Z"
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+ "shell.execute_reply": "2024-05-14T00:43:19.948214Z"
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@@ -984,10 +984,10 @@
"execution_count": 13,
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- "shell.execute_reply": "2024-05-14T00:25:55.811713Z"
+ "iopub.execute_input": "2024-05-14T00:43:19.951002Z",
+ "iopub.status.busy": "2024-05-14T00:43:19.950675Z",
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+ "shell.execute_reply": "2024-05-14T00:43:20.409365Z"
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@@ -1007,10 +1007,10 @@
"execution_count": 14,
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- "shell.execute_reply": "2024-05-14T00:29:18.968873Z"
+ "iopub.execute_input": "2024-05-14T00:43:20.412716Z",
+ "iopub.status.busy": "2024-05-14T00:43:20.412384Z",
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+ "shell.execute_reply": "2024-05-14T00:46:55.935863Z"
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@@ -1058,7 +1058,7 @@
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@@ -1097,10 +1097,10 @@
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@@ -1241,10 +1241,10 @@
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@@ -1348,10 +1348,10 @@
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@@ -1481,10 +1481,10 @@
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@@ -1568,10 +1568,10 @@
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@@ -1738,10 +1738,10 @@
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@@ -1857,10 +1857,10 @@
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@@ -2017,10 +2017,10 @@
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@@ -2178,47 +2178,47 @@
" \n",
" \n",
" | \n",
- " is_dark_issue | \n",
" dark_score | \n",
+ " is_dark_issue | \n",
"
\n",
" \n",
" \n",
" \n",
" | 34848 | \n",
- " True | \n",
" 0.203922 | \n",
+ " True | \n",
"
\n",
" \n",
" | 50270 | \n",
- " True | \n",
" 0.204588 | \n",
+ " True | \n",
"
\n",
" \n",
" | 3936 | \n",
- " True | \n",
" 0.213098 | \n",
+ " True | \n",
"
\n",
" \n",
" | 733 | \n",
- " True | \n",
" 0.217686 | \n",
+ " True | \n",
"
\n",
" \n",
" | 8094 | \n",
- " True | \n",
" 0.230118 | \n",
+ " True | \n",
"
\n",
" \n",
"\n",
""
],
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- " is_dark_issue dark_score\n",
- "34848 True 0.203922\n",
- "50270 True 0.204588\n",
- "3936 True 0.213098\n",
- "733 True 0.217686\n",
- "8094 True 0.230118"
+ " dark_score is_dark_issue\n",
+ "34848 0.203922 True\n",
+ "50270 0.204588 True\n",
+ "3936 0.213098 True\n",
+ "733 0.217686 True\n",
+ "8094 0.230118 True"
]
},
"execution_count": 26,
@@ -2281,10 +2281,10 @@
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@@ -2366,10 +2366,10 @@
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@@ -2455,10 +2455,10 @@
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+ "_view_name": "StyleView",
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@@ -2640,7 +2630,25 @@
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@@ -2693,71 +2701,7 @@
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@@ -2772,15 +2716,15 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index 9f1b083a9..49bec9166 100644
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"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -111,10 +111,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.481807Z",
- "iopub.status.busy": "2024-05-14T00:29:25.481564Z",
- "iopub.status.idle": "2024-05-14T00:29:25.498876Z",
- "shell.execute_reply": "2024-05-14T00:29:25.498455Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.339165Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.338649Z",
+ "iopub.status.idle": "2024-05-14T00:47:03.358598Z",
+ "shell.execute_reply": "2024-05-14T00:47:03.357978Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.500833Z",
- "iopub.status.busy": "2024-05-14T00:29:25.500505Z",
- "iopub.status.idle": "2024-05-14T00:29:25.525366Z",
- "shell.execute_reply": "2024-05-14T00:29:25.524870Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.361274Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.360857Z",
+ "iopub.status.idle": "2024-05-14T00:47:03.507345Z",
+ "shell.execute_reply": "2024-05-14T00:47:03.506802Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.527549Z",
- "iopub.status.busy": "2024-05-14T00:29:25.527142Z",
- "iopub.status.idle": "2024-05-14T00:29:25.530372Z",
- "shell.execute_reply": "2024-05-14T00:29:25.529989Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.509611Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.509178Z",
+ "iopub.status.idle": "2024-05-14T00:47:03.512892Z",
+ "shell.execute_reply": "2024-05-14T00:47:03.512432Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.532130Z",
- "iopub.status.busy": "2024-05-14T00:29:25.531870Z",
- "iopub.status.idle": "2024-05-14T00:29:25.538911Z",
- "shell.execute_reply": "2024-05-14T00:29:25.538516Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.515101Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.514710Z",
+ "iopub.status.idle": "2024-05-14T00:47:03.522980Z",
+ "shell.execute_reply": "2024-05-14T00:47:03.522541Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.540952Z",
- "iopub.status.busy": "2024-05-14T00:29:25.540585Z",
- "iopub.status.idle": "2024-05-14T00:29:25.543057Z",
- "shell.execute_reply": "2024-05-14T00:29:25.542660Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.525157Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.524813Z",
+ "iopub.status.idle": "2024-05-14T00:47:03.527372Z",
+ "shell.execute_reply": "2024-05-14T00:47:03.526936Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:25.545018Z",
- "iopub.status.busy": "2024-05-14T00:29:25.544711Z",
- "iopub.status.idle": "2024-05-14T00:29:28.320627Z",
- "shell.execute_reply": "2024-05-14T00:29:28.320144Z"
+ "iopub.execute_input": "2024-05-14T00:47:03.529384Z",
+ "iopub.status.busy": "2024-05-14T00:47:03.529067Z",
+ "iopub.status.idle": "2024-05-14T00:47:06.530765Z",
+ "shell.execute_reply": "2024-05-14T00:47:06.530132Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:28.323057Z",
- "iopub.status.busy": "2024-05-14T00:29:28.322689Z",
- "iopub.status.idle": "2024-05-14T00:29:28.331873Z",
- "shell.execute_reply": "2024-05-14T00:29:28.331474Z"
+ "iopub.execute_input": "2024-05-14T00:47:06.533708Z",
+ "iopub.status.busy": "2024-05-14T00:47:06.533221Z",
+ "iopub.status.idle": "2024-05-14T00:47:06.542953Z",
+ "shell.execute_reply": "2024-05-14T00:47:06.542399Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:28.333789Z",
- "iopub.status.busy": "2024-05-14T00:29:28.333501Z",
- "iopub.status.idle": "2024-05-14T00:29:29.919492Z",
- "shell.execute_reply": "2024-05-14T00:29:29.918748Z"
+ "iopub.execute_input": "2024-05-14T00:47:06.545165Z",
+ "iopub.status.busy": "2024-05-14T00:47:06.544858Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.290246Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.289636Z"
}
},
"outputs": [
@@ -484,10 +484,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:29.923053Z",
- "iopub.status.busy": "2024-05-14T00:29:29.921803Z",
- "iopub.status.idle": "2024-05-14T00:29:29.944854Z",
- "shell.execute_reply": "2024-05-14T00:29:29.944404Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.293156Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.292411Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.316707Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.316208Z"
},
"scrolled": true
},
@@ -612,10 +612,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:29.948025Z",
- "iopub.status.busy": "2024-05-14T00:29:29.947190Z",
- "iopub.status.idle": "2024-05-14T00:29:29.957719Z",
- "shell.execute_reply": "2024-05-14T00:29:29.957275Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.320287Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.319361Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.330466Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.329974Z"
}
},
"outputs": [
@@ -719,10 +719,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:29.960867Z",
- "iopub.status.busy": "2024-05-14T00:29:29.960030Z",
- "iopub.status.idle": "2024-05-14T00:29:29.971719Z",
- "shell.execute_reply": "2024-05-14T00:29:29.971273Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.333935Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.333008Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.345678Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.345189Z"
}
},
"outputs": [
@@ -851,10 +851,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:29.975015Z",
- "iopub.status.busy": "2024-05-14T00:29:29.974139Z",
- "iopub.status.idle": "2024-05-14T00:29:29.984440Z",
- "shell.execute_reply": "2024-05-14T00:29:29.983966Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.349241Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.348271Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.359571Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.359082Z"
}
},
"outputs": [
@@ -968,10 +968,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:29.987606Z",
- "iopub.status.busy": "2024-05-14T00:29:29.986776Z",
- "iopub.status.idle": "2024-05-14T00:29:29.998733Z",
- "shell.execute_reply": "2024-05-14T00:29:29.998243Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.363086Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.362166Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.374608Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.374048Z"
}
},
"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:30.001963Z",
- "iopub.status.busy": "2024-05-14T00:29:30.001135Z",
- "iopub.status.idle": "2024-05-14T00:29:30.008040Z",
- "shell.execute_reply": "2024-05-14T00:29:30.007670Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.376748Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.376571Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.383588Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.383124Z"
}
},
"outputs": [
@@ -1169,10 +1169,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:30.009962Z",
- "iopub.status.busy": "2024-05-14T00:29:30.009654Z",
- "iopub.status.idle": "2024-05-14T00:29:30.016881Z",
- "shell.execute_reply": "2024-05-14T00:29:30.016360Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.385590Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.385261Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.391586Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.391134Z"
}
},
"outputs": [
@@ -1265,10 +1265,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:30.019090Z",
- "iopub.status.busy": "2024-05-14T00:29:30.018936Z",
- "iopub.status.idle": "2024-05-14T00:29:30.025776Z",
- "shell.execute_reply": "2024-05-14T00:29:30.025278Z"
+ "iopub.execute_input": "2024-05-14T00:47:08.393578Z",
+ "iopub.status.busy": "2024-05-14T00:47:08.393292Z",
+ "iopub.status.idle": "2024-05-14T00:47:08.399913Z",
+ "shell.execute_reply": "2024-05-14T00:47:08.399349Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index d29637c82..58c3343da 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:32.358444Z",
- "iopub.status.busy": "2024-05-14T00:29:32.358024Z",
- "iopub.status.idle": "2024-05-14T00:29:34.800772Z",
- "shell.execute_reply": "2024-05-14T00:29:34.800217Z"
+ "iopub.execute_input": "2024-05-14T00:47:10.887012Z",
+ "iopub.status.busy": "2024-05-14T00:47:10.886844Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.532958Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.532347Z"
},
"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.803226Z",
- "iopub.status.busy": "2024-05-14T00:29:34.802953Z",
- "iopub.status.idle": "2024-05-14T00:29:34.805993Z",
- "shell.execute_reply": "2024-05-14T00:29:34.805530Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.535886Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.535277Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.539132Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.538672Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.807715Z",
- "iopub.status.busy": "2024-05-14T00:29:34.807551Z",
- "iopub.status.idle": "2024-05-14T00:29:34.810288Z",
- "shell.execute_reply": "2024-05-14T00:29:34.809893Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.541082Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.540762Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.543750Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.543291Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.811974Z",
- "iopub.status.busy": "2024-05-14T00:29:34.811816Z",
- "iopub.status.idle": "2024-05-14T00:29:34.833934Z",
- "shell.execute_reply": "2024-05-14T00:29:34.833484Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.545854Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.545519Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.596990Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.596525Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.835745Z",
- "iopub.status.busy": "2024-05-14T00:29:34.835571Z",
- "iopub.status.idle": "2024-05-14T00:29:34.838894Z",
- "shell.execute_reply": "2024-05-14T00:29:34.838433Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.599026Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.598751Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.602352Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.601806Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'card_about_to_expire', 'supported_cards_and_currencies', 'visa_or_mastercard', 'card_payment_fee_charged', 'getting_spare_card', 'cancel_transfer', 'change_pin'}\n"
+ "Classes: {'supported_cards_and_currencies', 'card_about_to_expire', 'change_pin', 'lost_or_stolen_phone', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'cancel_transfer'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.840552Z",
- "iopub.status.busy": "2024-05-14T00:29:34.840392Z",
- "iopub.status.idle": "2024-05-14T00:29:34.843441Z",
- "shell.execute_reply": "2024-05-14T00:29:34.842990Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.604394Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.604054Z",
+ "iopub.status.idle": "2024-05-14T00:47:13.607281Z",
+ "shell.execute_reply": "2024-05-14T00:47:13.606820Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:34.845282Z",
- "iopub.status.busy": "2024-05-14T00:29:34.845117Z",
- "iopub.status.idle": "2024-05-14T00:29:38.259880Z",
- "shell.execute_reply": "2024-05-14T00:29:38.259258Z"
+ "iopub.execute_input": "2024-05-14T00:47:13.609394Z",
+ "iopub.status.busy": "2024-05-14T00:47:13.609073Z",
+ "iopub.status.idle": "2024-05-14T00:47:18.903478Z",
+ "shell.execute_reply": "2024-05-14T00:47:18.902940Z"
}
},
"outputs": [
@@ -424,10 +424,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:38.262616Z",
- "iopub.status.busy": "2024-05-14T00:29:38.262184Z",
- "iopub.status.idle": "2024-05-14T00:29:39.106044Z",
- "shell.execute_reply": "2024-05-14T00:29:39.105496Z"
+ "iopub.execute_input": "2024-05-14T00:47:18.906183Z",
+ "iopub.status.busy": "2024-05-14T00:47:18.905765Z",
+ "iopub.status.idle": "2024-05-14T00:47:19.794605Z",
+ "shell.execute_reply": "2024-05-14T00:47:19.794019Z"
},
"scrolled": true
},
@@ -459,10 +459,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:39.108758Z",
- "iopub.status.busy": "2024-05-14T00:29:39.108457Z",
- "iopub.status.idle": "2024-05-14T00:29:39.111006Z",
- "shell.execute_reply": "2024-05-14T00:29:39.110557Z"
+ "iopub.execute_input": "2024-05-14T00:47:19.797657Z",
+ "iopub.status.busy": "2024-05-14T00:47:19.797238Z",
+ "iopub.status.idle": "2024-05-14T00:47:19.800197Z",
+ "shell.execute_reply": "2024-05-14T00:47:19.799707Z"
}
},
"outputs": [],
@@ -482,10 +482,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:39.113243Z",
- "iopub.status.busy": "2024-05-14T00:29:39.112886Z",
- "iopub.status.idle": "2024-05-14T00:29:40.537543Z",
- "shell.execute_reply": "2024-05-14T00:29:40.536987Z"
+ "iopub.execute_input": "2024-05-14T00:47:19.802607Z",
+ "iopub.status.busy": "2024-05-14T00:47:19.802227Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.359793Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.359140Z"
},
"scrolled": true
},
@@ -538,10 +538,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.541390Z",
- "iopub.status.busy": "2024-05-14T00:29:40.540288Z",
- "iopub.status.idle": "2024-05-14T00:29:40.564173Z",
- "shell.execute_reply": "2024-05-14T00:29:40.563707Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.363491Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.362687Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.386521Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.386102Z"
},
"scrolled": true
},
@@ -666,10 +666,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.567494Z",
- "iopub.status.busy": "2024-05-14T00:29:40.566607Z",
- "iopub.status.idle": "2024-05-14T00:29:40.577384Z",
- "shell.execute_reply": "2024-05-14T00:29:40.576933Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.388677Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.388351Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.396698Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.396160Z"
},
"scrolled": true
},
@@ -779,10 +779,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.580587Z",
- "iopub.status.busy": "2024-05-14T00:29:40.579776Z",
- "iopub.status.idle": "2024-05-14T00:29:40.585861Z",
- "shell.execute_reply": "2024-05-14T00:29:40.585406Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.398562Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.398389Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.402692Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.402145Z"
}
},
"outputs": [
@@ -820,10 +820,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.588992Z",
- "iopub.status.busy": "2024-05-14T00:29:40.588110Z",
- "iopub.status.idle": "2024-05-14T00:29:40.595367Z",
- "shell.execute_reply": "2024-05-14T00:29:40.594999Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.404648Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.404475Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.410761Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.410221Z"
}
},
"outputs": [
@@ -940,10 +940,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.597269Z",
- "iopub.status.busy": "2024-05-14T00:29:40.596995Z",
- "iopub.status.idle": "2024-05-14T00:29:40.603867Z",
- "shell.execute_reply": "2024-05-14T00:29:40.603394Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.412509Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.412339Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.418806Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.418351Z"
}
},
"outputs": [
@@ -1026,10 +1026,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.605606Z",
- "iopub.status.busy": "2024-05-14T00:29:40.605447Z",
- "iopub.status.idle": "2024-05-14T00:29:40.611130Z",
- "shell.execute_reply": "2024-05-14T00:29:40.610725Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.420847Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.420516Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.426456Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.426010Z"
}
},
"outputs": [
@@ -1137,10 +1137,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.612853Z",
- "iopub.status.busy": "2024-05-14T00:29:40.612700Z",
- "iopub.status.idle": "2024-05-14T00:29:40.620619Z",
- "shell.execute_reply": "2024-05-14T00:29:40.620220Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.428611Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.428286Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.436846Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.436413Z"
}
},
"outputs": [
@@ -1251,10 +1251,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.622341Z",
- "iopub.status.busy": "2024-05-14T00:29:40.622171Z",
- "iopub.status.idle": "2024-05-14T00:29:40.627124Z",
- "shell.execute_reply": "2024-05-14T00:29:40.626631Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.438818Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.438508Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.443888Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.443339Z"
}
},
"outputs": [
@@ -1322,10 +1322,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.628927Z",
- "iopub.status.busy": "2024-05-14T00:29:40.628754Z",
- "iopub.status.idle": "2024-05-14T00:29:40.633803Z",
- "shell.execute_reply": "2024-05-14T00:29:40.633324Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.445839Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.445538Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.450694Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.450249Z"
}
},
"outputs": [
@@ -1404,10 +1404,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.635877Z",
- "iopub.status.busy": "2024-05-14T00:29:40.635670Z",
- "iopub.status.idle": "2024-05-14T00:29:40.639750Z",
- "shell.execute_reply": "2024-05-14T00:29:40.639287Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.452729Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.452398Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.455904Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.455395Z"
}
},
"outputs": [
@@ -1455,10 +1455,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:40.641738Z",
- "iopub.status.busy": "2024-05-14T00:29:40.641428Z",
- "iopub.status.idle": "2024-05-14T00:29:40.646873Z",
- "shell.execute_reply": "2024-05-14T00:29:40.646430Z"
+ "iopub.execute_input": "2024-05-14T00:47:21.458044Z",
+ "iopub.status.busy": "2024-05-14T00:47:21.457612Z",
+ "iopub.status.idle": "2024-05-14T00:47:21.462998Z",
+ "shell.execute_reply": "2024-05-14T00:47:21.462457Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 85cd751df..1f5cca0bd 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
@@ -70,10 +70,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:43.595401Z",
- "iopub.status.busy": "2024-05-14T00:29:43.594975Z",
- "iopub.status.idle": "2024-05-14T00:29:44.614729Z",
- "shell.execute_reply": "2024-05-14T00:29:44.614076Z"
+ "iopub.execute_input": "2024-05-14T00:47:24.668804Z",
+ "iopub.status.busy": "2024-05-14T00:47:24.668389Z",
+ "iopub.status.idle": "2024-05-14T00:47:25.757970Z",
+ "shell.execute_reply": "2024-05-14T00:47:25.757400Z"
},
"nbsphinx": "hidden"
},
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -110,10 +110,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:44.617329Z",
- "iopub.status.busy": "2024-05-14T00:29:44.617057Z",
- "iopub.status.idle": "2024-05-14T00:29:44.619971Z",
- "shell.execute_reply": "2024-05-14T00:29:44.619445Z"
+ "iopub.execute_input": "2024-05-14T00:47:25.760492Z",
+ "iopub.status.busy": "2024-05-14T00:47:25.760050Z",
+ "iopub.status.idle": "2024-05-14T00:47:25.762874Z",
+ "shell.execute_reply": "2024-05-14T00:47:25.762434Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:44.622007Z",
- "iopub.status.busy": "2024-05-14T00:29:44.621849Z",
- "iopub.status.idle": "2024-05-14T00:29:44.633443Z",
- "shell.execute_reply": "2024-05-14T00:29:44.632999Z"
+ "iopub.execute_input": "2024-05-14T00:47:25.764976Z",
+ "iopub.status.busy": "2024-05-14T00:47:25.764806Z",
+ "iopub.status.idle": "2024-05-14T00:47:25.776905Z",
+ "shell.execute_reply": "2024-05-14T00:47:25.776449Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:44.635548Z",
- "iopub.status.busy": "2024-05-14T00:29:44.635171Z",
- "iopub.status.idle": "2024-05-14T00:29:47.808427Z",
- "shell.execute_reply": "2024-05-14T00:29:47.807998Z"
+ "iopub.execute_input": "2024-05-14T00:47:25.778753Z",
+ "iopub.status.busy": "2024-05-14T00:47:25.778585Z",
+ "iopub.status.idle": "2024-05-14T00:47:30.362128Z",
+ "shell.execute_reply": "2024-05-14T00:47:30.361640Z"
},
"id": "dhTHOg8Pyv5G"
},
@@ -694,7 +694,13 @@
"\n",
"\n",
"🎯 Mnist_test_set 🎯\n",
- "\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"\n",
"Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n",
"\n",
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index 6c8e59712..60cc6371b 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
"id": "2a4efdde",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:49.819217Z",
- "iopub.status.busy": "2024-05-14T00:29:49.818851Z",
- "iopub.status.idle": "2024-05-14T00:29:50.822726Z",
- "shell.execute_reply": "2024-05-14T00:29:50.822210Z"
+ "iopub.execute_input": "2024-05-14T00:47:32.518930Z",
+ "iopub.status.busy": "2024-05-14T00:47:32.518533Z",
+ "iopub.status.idle": "2024-05-14T00:47:33.595585Z",
+ "shell.execute_reply": "2024-05-14T00:47:33.595037Z"
},
"nbsphinx": "hidden"
},
@@ -137,10 +137,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:50.825162Z",
- "iopub.status.busy": "2024-05-14T00:29:50.824772Z",
- "iopub.status.idle": "2024-05-14T00:29:50.827974Z",
- "shell.execute_reply": "2024-05-14T00:29:50.827567Z"
+ "iopub.execute_input": "2024-05-14T00:47:33.598254Z",
+ "iopub.status.busy": "2024-05-14T00:47:33.597883Z",
+ "iopub.status.idle": "2024-05-14T00:47:33.601018Z",
+ "shell.execute_reply": "2024-05-14T00:47:33.600598Z"
}
},
"outputs": [],
@@ -176,10 +176,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:50.829829Z",
- "iopub.status.busy": "2024-05-14T00:29:50.829543Z",
- "iopub.status.idle": "2024-05-14T00:29:53.515329Z",
- "shell.execute_reply": "2024-05-14T00:29:53.514659Z"
+ "iopub.execute_input": "2024-05-14T00:47:33.603011Z",
+ "iopub.status.busy": "2024-05-14T00:47:33.602716Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.560456Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.559722Z"
}
},
"outputs": [],
@@ -202,10 +202,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.518265Z",
- "iopub.status.busy": "2024-05-14T00:29:53.517604Z",
- "iopub.status.idle": "2024-05-14T00:29:53.544608Z",
- "shell.execute_reply": "2024-05-14T00:29:53.543962Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.563755Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.563017Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.597380Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.596792Z"
}
},
"outputs": [],
@@ -228,10 +228,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.547272Z",
- "iopub.status.busy": "2024-05-14T00:29:53.546838Z",
- "iopub.status.idle": "2024-05-14T00:29:53.572486Z",
- "shell.execute_reply": "2024-05-14T00:29:53.571967Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.600092Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.599787Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.629091Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.628505Z"
}
},
"outputs": [],
@@ -253,10 +253,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.574871Z",
- "iopub.status.busy": "2024-05-14T00:29:53.574478Z",
- "iopub.status.idle": "2024-05-14T00:29:53.577380Z",
- "shell.execute_reply": "2024-05-14T00:29:53.576849Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.631615Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.631372Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.634367Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.633893Z"
}
},
"outputs": [],
@@ -278,10 +278,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.579248Z",
- "iopub.status.busy": "2024-05-14T00:29:53.579016Z",
- "iopub.status.idle": "2024-05-14T00:29:53.581995Z",
- "shell.execute_reply": "2024-05-14T00:29:53.581602Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.636489Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.636065Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.638671Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.638198Z"
}
},
"outputs": [],
@@ -363,10 +363,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.584059Z",
- "iopub.status.busy": "2024-05-14T00:29:53.583768Z",
- "iopub.status.idle": "2024-05-14T00:29:53.608821Z",
- "shell.execute_reply": "2024-05-14T00:29:53.608323Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.640812Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.640411Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.662964Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.662413Z"
}
},
"outputs": [
@@ -380,7 +380,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c5df35a1f26e4b31a3b35d0e72c31b83",
+ "model_id": "39cc4257cfa64cb5a80deeebae9f01dd",
"version_major": 2,
"version_minor": 0
},
@@ -394,7 +394,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "cb687553e3634007834cd0c71afc8de9",
+ "model_id": "10c385ea3d1c4c0a8e9cb45e2c945514",
"version_major": 2,
"version_minor": 0
},
@@ -452,10 +452,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.612763Z",
- "iopub.status.busy": "2024-05-14T00:29:53.612482Z",
- "iopub.status.idle": "2024-05-14T00:29:53.618729Z",
- "shell.execute_reply": "2024-05-14T00:29:53.618293Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.669511Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.669094Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.675690Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.675277Z"
},
"nbsphinx": "hidden"
},
@@ -486,10 +486,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:29:53.620522Z",
- "iopub.status.busy": "2024-05-14T00:29:53.620229Z",
- "iopub.status.idle": "2024-05-14T00:29:53.623375Z",
- "shell.execute_reply": "2024-05-14T00:29:53.622958Z"
+ "iopub.execute_input": "2024-05-14T00:47:36.677682Z",
+ "iopub.status.busy": "2024-05-14T00:47:36.677363Z",
+ "iopub.status.idle": "2024-05-14T00:47:36.680695Z",
+ "shell.execute_reply": "2024-05-14T00:47:36.680273Z"
},
"nbsphinx": "hidden"
},
@@ -512,10 +512,10 @@
"id": "9092b8a0",
"metadata": {
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"name": "stdout",
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- "Finding underperforming_group issues ...\n",
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+ "text": [
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"Audit complete. 0 issues found in the dataset.\n"
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+ "/tmp/ipykernel_7933/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index 7ab6f3f93..94b325f45 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
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@@ -68,7 +68,7 @@
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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- "shell.execute_reply": "2024-05-14T00:30:04.606120Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.391223Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.390965Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.400451Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.400031Z"
},
"id": "0lonvOYvjruV"
},
@@ -1554,10 +1554,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.608402Z",
- "iopub.status.busy": "2024-05-14T00:30:04.608109Z",
- "iopub.status.idle": "2024-05-14T00:30:04.684618Z",
- "shell.execute_reply": "2024-05-14T00:30:04.684086Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.402588Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.402169Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.488090Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.487464Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1638,10 +1638,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.686863Z",
- "iopub.status.busy": "2024-05-14T00:30:04.686690Z",
- "iopub.status.idle": "2024-05-14T00:30:04.790196Z",
- "shell.execute_reply": "2024-05-14T00:30:04.789603Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.490345Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.490121Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.610862Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.610301Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1701,10 +1701,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.792285Z",
- "iopub.status.busy": "2024-05-14T00:30:04.792107Z",
- "iopub.status.idle": "2024-05-14T00:30:04.795920Z",
- "shell.execute_reply": "2024-05-14T00:30:04.795452Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.613149Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.612851Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.616809Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.616278Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1742,10 +1742,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.797710Z",
- "iopub.status.busy": "2024-05-14T00:30:04.797550Z",
- "iopub.status.idle": "2024-05-14T00:30:04.800889Z",
- "shell.execute_reply": "2024-05-14T00:30:04.800412Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.618898Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.618557Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.622294Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.621702Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1800,10 +1800,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.802786Z",
- "iopub.status.busy": "2024-05-14T00:30:04.802475Z",
- "iopub.status.idle": "2024-05-14T00:30:04.837412Z",
- "shell.execute_reply": "2024-05-14T00:30:04.836951Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.624382Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.624058Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.662864Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.662349Z"
},
"id": "ZpipUliyjruW"
},
@@ -1854,10 +1854,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.839170Z",
- "iopub.status.busy": "2024-05-14T00:30:04.839015Z",
- "iopub.status.idle": "2024-05-14T00:30:04.878557Z",
- "shell.execute_reply": "2024-05-14T00:30:04.878142Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.665084Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.664723Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.706969Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.706477Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1926,10 +1926,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.882725Z",
- "iopub.status.busy": "2024-05-14T00:30:04.880244Z",
- "iopub.status.idle": "2024-05-14T00:30:04.966944Z",
- "shell.execute_reply": "2024-05-14T00:30:04.966270Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.708993Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.708685Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.803692Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.802997Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1961,10 +1961,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:04.969327Z",
- "iopub.status.busy": "2024-05-14T00:30:04.968938Z",
- "iopub.status.idle": "2024-05-14T00:30:05.041605Z",
- "shell.execute_reply": "2024-05-14T00:30:05.041081Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.806570Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.806156Z",
+ "iopub.status.idle": "2024-05-14T00:47:48.897233Z",
+ "shell.execute_reply": "2024-05-14T00:47:48.896620Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2021,10 +2021,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:05.043918Z",
- "iopub.status.busy": "2024-05-14T00:30:05.043540Z",
- "iopub.status.idle": "2024-05-14T00:30:05.244176Z",
- "shell.execute_reply": "2024-05-14T00:30:05.243788Z"
+ "iopub.execute_input": "2024-05-14T00:47:48.899559Z",
+ "iopub.status.busy": "2024-05-14T00:47:48.899321Z",
+ "iopub.status.idle": "2024-05-14T00:47:49.110362Z",
+ "shell.execute_reply": "2024-05-14T00:47:49.109740Z"
},
"id": "WETRL74tE_sU"
},
@@ -2059,10 +2059,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:05.246049Z",
- "iopub.status.busy": "2024-05-14T00:30:05.245874Z",
- "iopub.status.idle": "2024-05-14T00:30:05.396137Z",
- "shell.execute_reply": "2024-05-14T00:30:05.395598Z"
+ "iopub.execute_input": "2024-05-14T00:47:49.112702Z",
+ "iopub.status.busy": "2024-05-14T00:47:49.112266Z",
+ "iopub.status.idle": "2024-05-14T00:47:49.283495Z",
+ "shell.execute_reply": "2024-05-14T00:47:49.282873Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2224,10 +2224,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:05.398207Z",
- "iopub.status.busy": "2024-05-14T00:30:05.398001Z",
- "iopub.status.idle": "2024-05-14T00:30:05.403860Z",
- "shell.execute_reply": "2024-05-14T00:30:05.403469Z"
+ "iopub.execute_input": "2024-05-14T00:47:49.285779Z",
+ "iopub.status.busy": "2024-05-14T00:47:49.285584Z",
+ "iopub.status.idle": "2024-05-14T00:47:49.291905Z",
+ "shell.execute_reply": "2024-05-14T00:47:49.291450Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2281,10 +2281,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:05.405722Z",
- "iopub.status.busy": "2024-05-14T00:30:05.405431Z",
- "iopub.status.idle": "2024-05-14T00:30:05.607980Z",
- "shell.execute_reply": "2024-05-14T00:30:05.607440Z"
+ "iopub.execute_input": "2024-05-14T00:47:49.293958Z",
+ "iopub.status.busy": "2024-05-14T00:47:49.293546Z",
+ "iopub.status.idle": "2024-05-14T00:47:49.510385Z",
+ "shell.execute_reply": "2024-05-14T00:47:49.509786Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2331,10 +2331,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:05.609921Z",
- "iopub.status.busy": "2024-05-14T00:30:05.609754Z",
- "iopub.status.idle": "2024-05-14T00:30:06.598557Z",
- "shell.execute_reply": "2024-05-14T00:30:06.598106Z"
+ "iopub.execute_input": "2024-05-14T00:47:49.512499Z",
+ "iopub.status.busy": "2024-05-14T00:47:49.512318Z",
+ "iopub.status.idle": "2024-05-14T00:47:50.585488Z",
+ "shell.execute_reply": "2024-05-14T00:47:50.584978Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 0424c410b..42e3ce314 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:09.743226Z",
- "iopub.status.busy": "2024-05-14T00:30:09.742881Z",
- "iopub.status.idle": "2024-05-14T00:30:10.758021Z",
- "shell.execute_reply": "2024-05-14T00:30:10.757455Z"
+ "iopub.execute_input": "2024-05-14T00:47:53.768936Z",
+ "iopub.status.busy": "2024-05-14T00:47:53.768462Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.894973Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.894325Z"
},
"nbsphinx": "hidden"
},
@@ -101,7 +101,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.760252Z",
- "iopub.status.busy": "2024-05-14T00:30:10.760006Z",
- "iopub.status.idle": "2024-05-14T00:30:10.762939Z",
- "shell.execute_reply": "2024-05-14T00:30:10.762468Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.897634Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.897354Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.900515Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.899997Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.764914Z",
- "iopub.status.busy": "2024-05-14T00:30:10.764645Z",
- "iopub.status.idle": "2024-05-14T00:30:10.771718Z",
- "shell.execute_reply": "2024-05-14T00:30:10.771206Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.902711Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.902445Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.910075Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.909514Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.773640Z",
- "iopub.status.busy": "2024-05-14T00:30:10.773224Z",
- "iopub.status.idle": "2024-05-14T00:30:10.817256Z",
- "shell.execute_reply": "2024-05-14T00:30:10.816836Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.911922Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.911751Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.959480Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.958980Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.819124Z",
- "iopub.status.busy": "2024-05-14T00:30:10.818856Z",
- "iopub.status.idle": "2024-05-14T00:30:10.834503Z",
- "shell.execute_reply": "2024-05-14T00:30:10.833992Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.961897Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.961661Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.979366Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.978884Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.836405Z",
- "iopub.status.busy": "2024-05-14T00:30:10.836035Z",
- "iopub.status.idle": "2024-05-14T00:30:10.839580Z",
- "shell.execute_reply": "2024-05-14T00:30:10.839101Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.981488Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.981144Z",
+ "iopub.status.idle": "2024-05-14T00:47:54.985092Z",
+ "shell.execute_reply": "2024-05-14T00:47:54.984627Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.841497Z",
- "iopub.status.busy": "2024-05-14T00:30:10.841190Z",
- "iopub.status.idle": "2024-05-14T00:30:10.868892Z",
- "shell.execute_reply": "2024-05-14T00:30:10.868389Z"
+ "iopub.execute_input": "2024-05-14T00:47:54.987264Z",
+ "iopub.status.busy": "2024-05-14T00:47:54.986866Z",
+ "iopub.status.idle": "2024-05-14T00:47:55.016894Z",
+ "shell.execute_reply": "2024-05-14T00:47:55.016400Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.870847Z",
- "iopub.status.busy": "2024-05-14T00:30:10.870555Z",
- "iopub.status.idle": "2024-05-14T00:30:10.895077Z",
- "shell.execute_reply": "2024-05-14T00:30:10.894526Z"
+ "iopub.execute_input": "2024-05-14T00:47:55.019248Z",
+ "iopub.status.busy": "2024-05-14T00:47:55.019049Z",
+ "iopub.status.idle": "2024-05-14T00:47:55.045846Z",
+ "shell.execute_reply": "2024-05-14T00:47:55.045424Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:10.897249Z",
- "iopub.status.busy": "2024-05-14T00:30:10.896951Z",
- "iopub.status.idle": "2024-05-14T00:30:12.489224Z",
- "shell.execute_reply": "2024-05-14T00:30:12.488710Z"
+ "iopub.execute_input": "2024-05-14T00:47:55.047990Z",
+ "iopub.status.busy": "2024-05-14T00:47:55.047655Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.788586Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.788078Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.491817Z",
- "iopub.status.busy": "2024-05-14T00:30:12.491406Z",
- "iopub.status.idle": "2024-05-14T00:30:12.497509Z",
- "shell.execute_reply": "2024-05-14T00:30:12.497017Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.791058Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.790780Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.797585Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.797040Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.499387Z",
- "iopub.status.busy": "2024-05-14T00:30:12.499055Z",
- "iopub.status.idle": "2024-05-14T00:30:12.510445Z",
- "shell.execute_reply": "2024-05-14T00:30:12.510029Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.799541Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.799368Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.811528Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.811110Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.512418Z",
- "iopub.status.busy": "2024-05-14T00:30:12.512258Z",
- "iopub.status.idle": "2024-05-14T00:30:12.517959Z",
- "shell.execute_reply": "2024-05-14T00:30:12.517549Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.813504Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.813179Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.819535Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.819086Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.519961Z",
- "iopub.status.busy": "2024-05-14T00:30:12.519671Z",
- "iopub.status.idle": "2024-05-14T00:30:12.522073Z",
- "shell.execute_reply": "2024-05-14T00:30:12.521682Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.821531Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.821208Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.823869Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.823406Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
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- "iopub.status.busy": "2024-05-14T00:30:12.523595Z",
- "iopub.status.idle": "2024-05-14T00:30:12.526605Z",
- "shell.execute_reply": "2024-05-14T00:30:12.526145Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.825863Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.825554Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.828886Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.828381Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.528473Z",
- "iopub.status.busy": "2024-05-14T00:30:12.528190Z",
- "iopub.status.idle": "2024-05-14T00:30:12.530495Z",
- "shell.execute_reply": "2024-05-14T00:30:12.530088Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.830921Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.830594Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.833184Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.832747Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.532249Z",
- "iopub.status.busy": "2024-05-14T00:30:12.532094Z",
- "iopub.status.idle": "2024-05-14T00:30:12.536154Z",
- "shell.execute_reply": "2024-05-14T00:30:12.535727Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.835135Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.834815Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.838996Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.838536Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.538034Z",
- "iopub.status.busy": "2024-05-14T00:30:12.537758Z",
- "iopub.status.idle": "2024-05-14T00:30:12.564819Z",
- "shell.execute_reply": "2024-05-14T00:30:12.564367Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.841002Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.840681Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.869808Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.869224Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:12.566837Z",
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- "iopub.status.idle": "2024-05-14T00:30:12.570713Z",
- "shell.execute_reply": "2024-05-14T00:30:12.570168Z"
+ "iopub.execute_input": "2024-05-14T00:47:56.872208Z",
+ "iopub.status.busy": "2024-05-14T00:47:56.871783Z",
+ "iopub.status.idle": "2024-05-14T00:47:56.876523Z",
+ "shell.execute_reply": "2024-05-14T00:47:56.875988Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 5e4f0f93d..80a215b20 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:15.161102Z",
- "iopub.status.busy": "2024-05-14T00:30:15.160934Z",
- "iopub.status.idle": "2024-05-14T00:30:16.220393Z",
- "shell.execute_reply": "2024-05-14T00:30:16.219862Z"
+ "iopub.execute_input": "2024-05-14T00:47:59.521731Z",
+ "iopub.status.busy": "2024-05-14T00:47:59.521318Z",
+ "iopub.status.idle": "2024-05-14T00:48:00.672347Z",
+ "shell.execute_reply": "2024-05-14T00:48:00.671781Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -105,10 +105,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:16.222850Z",
- "iopub.status.busy": "2024-05-14T00:30:16.222431Z",
- "iopub.status.idle": "2024-05-14T00:30:16.408336Z",
- "shell.execute_reply": "2024-05-14T00:30:16.407790Z"
+ "iopub.execute_input": "2024-05-14T00:48:00.674961Z",
+ "iopub.status.busy": "2024-05-14T00:48:00.674524Z",
+ "iopub.status.idle": "2024-05-14T00:48:00.869872Z",
+ "shell.execute_reply": "2024-05-14T00:48:00.869370Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:16.410795Z",
- "iopub.status.busy": "2024-05-14T00:30:16.410399Z",
- "iopub.status.idle": "2024-05-14T00:30:16.422891Z",
- "shell.execute_reply": "2024-05-14T00:30:16.422321Z"
+ "iopub.execute_input": "2024-05-14T00:48:00.872651Z",
+ "iopub.status.busy": "2024-05-14T00:48:00.872189Z",
+ "iopub.status.idle": "2024-05-14T00:48:00.884965Z",
+ "shell.execute_reply": "2024-05-14T00:48:00.884485Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
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- "iopub.execute_input": "2024-05-14T00:30:16.424784Z",
- "iopub.status.busy": "2024-05-14T00:30:16.424502Z",
- "iopub.status.idle": "2024-05-14T00:30:18.886517Z",
- "shell.execute_reply": "2024-05-14T00:30:18.885962Z"
+ "iopub.execute_input": "2024-05-14T00:48:00.887103Z",
+ "iopub.status.busy": "2024-05-14T00:48:00.886764Z",
+ "iopub.status.idle": "2024-05-14T00:48:03.537991Z",
+ "shell.execute_reply": "2024-05-14T00:48:03.537453Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:18.888933Z",
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- "iopub.status.idle": "2024-05-14T00:30:20.143047Z",
- "shell.execute_reply": "2024-05-14T00:30:20.142466Z"
+ "iopub.execute_input": "2024-05-14T00:48:03.540003Z",
+ "iopub.status.busy": "2024-05-14T00:48:03.539826Z",
+ "iopub.status.idle": "2024-05-14T00:48:04.869797Z",
+ "shell.execute_reply": "2024-05-14T00:48:04.869242Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
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- "iopub.execute_input": "2024-05-14T00:30:20.145266Z",
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- "iopub.status.idle": "2024-05-14T00:30:20.148711Z",
- "shell.execute_reply": "2024-05-14T00:30:20.148235Z"
+ "iopub.execute_input": "2024-05-14T00:48:04.872083Z",
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+ "iopub.status.idle": "2024-05-14T00:48:04.875946Z",
+ "shell.execute_reply": "2024-05-14T00:48:04.875488Z"
}
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"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:20.150534Z",
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- "iopub.status.idle": "2024-05-14T00:30:21.755760Z",
- "shell.execute_reply": "2024-05-14T00:30:21.755199Z"
+ "iopub.execute_input": "2024-05-14T00:48:04.877931Z",
+ "iopub.status.busy": "2024-05-14T00:48:04.877625Z",
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+ "shell.execute_reply": "2024-05-14T00:48:06.657406Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:30:21.764730Z"
+ "iopub.execute_input": "2024-05-14T00:48:06.660414Z",
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+ "shell.execute_reply": "2024-05-14T00:48:06.667303Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
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- "iopub.execute_input": "2024-05-14T00:30:21.766991Z",
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- "shell.execute_reply": "2024-05-14T00:30:24.192581Z"
+ "iopub.execute_input": "2024-05-14T00:48:06.670243Z",
+ "iopub.status.busy": "2024-05-14T00:48:06.669748Z",
+ "iopub.status.idle": "2024-05-14T00:48:09.235120Z",
+ "shell.execute_reply": "2024-05-14T00:48:09.234520Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:24.195065Z",
- "iopub.status.busy": "2024-05-14T00:30:24.194898Z",
- "iopub.status.idle": "2024-05-14T00:30:24.198051Z",
- "shell.execute_reply": "2024-05-14T00:30:24.197508Z"
+ "iopub.execute_input": "2024-05-14T00:48:09.237311Z",
+ "iopub.status.busy": "2024-05-14T00:48:09.237118Z",
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+ "shell.execute_reply": "2024-05-14T00:48:09.240334Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:24.200080Z",
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- "iopub.status.idle": "2024-05-14T00:30:24.202939Z",
- "shell.execute_reply": "2024-05-14T00:30:24.202510Z"
+ "iopub.execute_input": "2024-05-14T00:48:09.243053Z",
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+ "shell.execute_reply": "2024-05-14T00:48:09.246137Z"
}
},
"outputs": [],
@@ -752,10 +752,10 @@
"id": "d1a2c008",
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:30:24.206878Z"
+ "iopub.execute_input": "2024-05-14T00:48:09.248531Z",
+ "iopub.status.busy": "2024-05-14T00:48:09.248355Z",
+ "iopub.status.idle": "2024-05-14T00:48:09.251652Z",
+ "shell.execute_reply": "2024-05-14T00:48:09.251084Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index dd788427e..f4ea1a9a8 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:26.514925Z",
- "iopub.status.busy": "2024-05-14T00:30:26.514608Z",
- "iopub.status.idle": "2024-05-14T00:30:27.588176Z",
- "shell.execute_reply": "2024-05-14T00:30:27.587672Z"
+ "iopub.execute_input": "2024-05-14T00:48:11.630215Z",
+ "iopub.status.busy": "2024-05-14T00:48:11.630047Z",
+ "iopub.status.idle": "2024-05-14T00:48:12.796203Z",
+ "shell.execute_reply": "2024-05-14T00:48:12.795600Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -109,10 +109,10 @@
"id": "c90449c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:27.590603Z",
- "iopub.status.busy": "2024-05-14T00:30:27.590200Z",
- "iopub.status.idle": "2024-05-14T00:30:28.577286Z",
- "shell.execute_reply": "2024-05-14T00:30:28.576589Z"
+ "iopub.execute_input": "2024-05-14T00:48:12.798760Z",
+ "iopub.status.busy": "2024-05-14T00:48:12.798471Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.443550Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.442887Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:28.580025Z",
- "iopub.status.busy": "2024-05-14T00:30:28.579663Z",
- "iopub.status.idle": "2024-05-14T00:30:28.582658Z",
- "shell.execute_reply": "2024-05-14T00:30:28.582250Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.446308Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.445936Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.449315Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.448752Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:28.584503Z",
- "iopub.status.busy": "2024-05-14T00:30:28.584203Z",
- "iopub.status.idle": "2024-05-14T00:30:28.590435Z",
- "shell.execute_reply": "2024-05-14T00:30:28.590027Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.451657Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.451248Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.458220Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.457645Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:28.592311Z",
- "iopub.status.busy": "2024-05-14T00:30:28.592028Z",
- "iopub.status.idle": "2024-05-14T00:30:29.049353Z",
- "shell.execute_reply": "2024-05-14T00:30:29.048804Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.460497Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.460093Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.952821Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.952251Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:29.051622Z",
- "iopub.status.busy": "2024-05-14T00:30:29.051194Z",
- "iopub.status.idle": "2024-05-14T00:30:29.056221Z",
- "shell.execute_reply": "2024-05-14T00:30:29.055729Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.955614Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.955180Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.960388Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.959973Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:29.058145Z",
- "iopub.status.busy": "2024-05-14T00:30:29.057757Z",
- "iopub.status.idle": "2024-05-14T00:30:29.061393Z",
- "shell.execute_reply": "2024-05-14T00:30:29.060884Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.962469Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.962150Z",
+ "iopub.status.idle": "2024-05-14T00:48:14.965814Z",
+ "shell.execute_reply": "2024-05-14T00:48:14.965365Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:29.063428Z",
- "iopub.status.busy": "2024-05-14T00:30:29.063112Z",
- "iopub.status.idle": "2024-05-14T00:30:29.889102Z",
- "shell.execute_reply": "2024-05-14T00:30:29.888575Z"
+ "iopub.execute_input": "2024-05-14T00:48:14.967628Z",
+ "iopub.status.busy": "2024-05-14T00:48:14.967452Z",
+ "iopub.status.idle": "2024-05-14T00:48:15.951968Z",
+ "shell.execute_reply": "2024-05-14T00:48:15.951436Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:29.891463Z",
- "iopub.status.busy": "2024-05-14T00:30:29.891128Z",
- "iopub.status.idle": "2024-05-14T00:30:30.102628Z",
- "shell.execute_reply": "2024-05-14T00:30:30.102189Z"
+ "iopub.execute_input": "2024-05-14T00:48:15.954302Z",
+ "iopub.status.busy": "2024-05-14T00:48:15.953888Z",
+ "iopub.status.idle": "2024-05-14T00:48:16.168879Z",
+ "shell.execute_reply": "2024-05-14T00:48:16.168385Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:30.104536Z",
- "iopub.status.busy": "2024-05-14T00:30:30.104225Z",
- "iopub.status.idle": "2024-05-14T00:30:30.108337Z",
- "shell.execute_reply": "2024-05-14T00:30:30.107901Z"
+ "iopub.execute_input": "2024-05-14T00:48:16.171101Z",
+ "iopub.status.busy": "2024-05-14T00:48:16.170735Z",
+ "iopub.status.idle": "2024-05-14T00:48:16.175174Z",
+ "shell.execute_reply": "2024-05-14T00:48:16.174731Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:30.110159Z",
- "iopub.status.busy": "2024-05-14T00:30:30.109865Z",
- "iopub.status.idle": "2024-05-14T00:30:30.530968Z",
- "shell.execute_reply": "2024-05-14T00:30:30.530380Z"
+ "iopub.execute_input": "2024-05-14T00:48:16.177171Z",
+ "iopub.status.busy": "2024-05-14T00:48:16.176831Z",
+ "iopub.status.idle": "2024-05-14T00:48:16.630605Z",
+ "shell.execute_reply": "2024-05-14T00:48:16.630036Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:30.533567Z",
- "iopub.status.busy": "2024-05-14T00:30:30.533369Z",
- "iopub.status.idle": "2024-05-14T00:30:30.818759Z",
- "shell.execute_reply": "2024-05-14T00:30:30.818207Z"
+ "iopub.execute_input": "2024-05-14T00:48:16.633983Z",
+ "iopub.status.busy": "2024-05-14T00:48:16.633428Z",
+ "iopub.status.idle": "2024-05-14T00:48:16.964672Z",
+ "shell.execute_reply": "2024-05-14T00:48:16.964126Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:30.820931Z",
- "iopub.status.busy": "2024-05-14T00:30:30.820604Z",
- "iopub.status.idle": "2024-05-14T00:30:31.163204Z",
- "shell.execute_reply": "2024-05-14T00:30:31.162603Z"
+ "iopub.execute_input": "2024-05-14T00:48:16.967301Z",
+ "iopub.status.busy": "2024-05-14T00:48:16.967124Z",
+ "iopub.status.idle": "2024-05-14T00:48:17.301244Z",
+ "shell.execute_reply": "2024-05-14T00:48:17.300646Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:31.166495Z",
- "iopub.status.busy": "2024-05-14T00:30:31.166080Z",
- "iopub.status.idle": "2024-05-14T00:30:31.553528Z",
- "shell.execute_reply": "2024-05-14T00:30:31.553030Z"
+ "iopub.execute_input": "2024-05-14T00:48:17.304074Z",
+ "iopub.status.busy": "2024-05-14T00:48:17.303706Z",
+ "iopub.status.idle": "2024-05-14T00:48:17.741356Z",
+ "shell.execute_reply": "2024-05-14T00:48:17.740817Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:31.557352Z",
- "iopub.status.busy": "2024-05-14T00:30:31.557174Z",
- "iopub.status.idle": "2024-05-14T00:30:31.953091Z",
- "shell.execute_reply": "2024-05-14T00:30:31.952575Z"
+ "iopub.execute_input": "2024-05-14T00:48:17.745629Z",
+ "iopub.status.busy": "2024-05-14T00:48:17.745279Z",
+ "iopub.status.idle": "2024-05-14T00:48:18.170445Z",
+ "shell.execute_reply": "2024-05-14T00:48:18.169817Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:31.955925Z",
- "iopub.status.busy": "2024-05-14T00:30:31.955567Z",
- "iopub.status.idle": "2024-05-14T00:30:32.138364Z",
- "shell.execute_reply": "2024-05-14T00:30:32.137848Z"
+ "iopub.execute_input": "2024-05-14T00:48:18.173410Z",
+ "iopub.status.busy": "2024-05-14T00:48:18.173046Z",
+ "iopub.status.idle": "2024-05-14T00:48:18.364635Z",
+ "shell.execute_reply": "2024-05-14T00:48:18.364070Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:32.140343Z",
- "iopub.status.busy": "2024-05-14T00:30:32.140181Z",
- "iopub.status.idle": "2024-05-14T00:30:32.331309Z",
- "shell.execute_reply": "2024-05-14T00:30:32.330780Z"
+ "iopub.execute_input": "2024-05-14T00:48:18.366859Z",
+ "iopub.status.busy": "2024-05-14T00:48:18.366680Z",
+ "iopub.status.idle": "2024-05-14T00:48:18.547293Z",
+ "shell.execute_reply": "2024-05-14T00:48:18.546754Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:32.333726Z",
- "iopub.status.busy": "2024-05-14T00:30:32.333287Z",
- "iopub.status.idle": "2024-05-14T00:30:32.336183Z",
- "shell.execute_reply": "2024-05-14T00:30:32.335674Z"
+ "iopub.execute_input": "2024-05-14T00:48:18.549700Z",
+ "iopub.status.busy": "2024-05-14T00:48:18.549399Z",
+ "iopub.status.idle": "2024-05-14T00:48:18.552704Z",
+ "shell.execute_reply": "2024-05-14T00:48:18.552304Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:32.338231Z",
- "iopub.status.busy": "2024-05-14T00:30:32.337849Z",
- "iopub.status.idle": "2024-05-14T00:30:33.291412Z",
- "shell.execute_reply": "2024-05-14T00:30:33.290877Z"
+ "iopub.execute_input": "2024-05-14T00:48:18.554589Z",
+ "iopub.status.busy": "2024-05-14T00:48:18.554272Z",
+ "iopub.status.idle": "2024-05-14T00:48:19.536056Z",
+ "shell.execute_reply": "2024-05-14T00:48:19.535488Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:33.293696Z",
- "iopub.status.busy": "2024-05-14T00:30:33.293380Z",
- "iopub.status.idle": "2024-05-14T00:30:33.506131Z",
- "shell.execute_reply": "2024-05-14T00:30:33.505603Z"
+ "iopub.execute_input": "2024-05-14T00:48:19.538918Z",
+ "iopub.status.busy": "2024-05-14T00:48:19.538569Z",
+ "iopub.status.idle": "2024-05-14T00:48:19.684086Z",
+ "shell.execute_reply": "2024-05-14T00:48:19.683512Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:33.508210Z",
- "iopub.status.busy": "2024-05-14T00:30:33.507853Z",
- "iopub.status.idle": "2024-05-14T00:30:33.705778Z",
- "shell.execute_reply": "2024-05-14T00:30:33.705386Z"
+ "iopub.execute_input": "2024-05-14T00:48:19.686319Z",
+ "iopub.status.busy": "2024-05-14T00:48:19.685897Z",
+ "iopub.status.idle": "2024-05-14T00:48:19.911142Z",
+ "shell.execute_reply": "2024-05-14T00:48:19.910636Z"
}
},
"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:33.707589Z",
- "iopub.status.busy": "2024-05-14T00:30:33.707308Z",
- "iopub.status.idle": "2024-05-14T00:30:34.337223Z",
- "shell.execute_reply": "2024-05-14T00:30:34.336758Z"
+ "iopub.execute_input": "2024-05-14T00:48:19.913433Z",
+ "iopub.status.busy": "2024-05-14T00:48:19.913083Z",
+ "iopub.status.idle": "2024-05-14T00:48:20.669831Z",
+ "shell.execute_reply": "2024-05-14T00:48:20.669246Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:34.339305Z",
- "iopub.status.busy": "2024-05-14T00:30:34.339031Z",
- "iopub.status.idle": "2024-05-14T00:30:34.342257Z",
- "shell.execute_reply": "2024-05-14T00:30:34.341863Z"
+ "iopub.execute_input": "2024-05-14T00:48:20.671822Z",
+ "iopub.status.busy": "2024-05-14T00:48:20.671646Z",
+ "iopub.status.idle": "2024-05-14T00:48:20.675112Z",
+ "shell.execute_reply": "2024-05-14T00:48:20.674701Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 2d114c996..31d559525 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:36.515190Z",
- "iopub.status.busy": "2024-05-14T00:30:36.515036Z",
- "iopub.status.idle": "2024-05-14T00:30:39.005284Z",
- "shell.execute_reply": "2024-05-14T00:30:39.004750Z"
+ "iopub.execute_input": "2024-05-14T00:48:22.909157Z",
+ "iopub.status.busy": "2024-05-14T00:48:22.908980Z",
+ "iopub.status.idle": "2024-05-14T00:48:25.646991Z",
+ "shell.execute_reply": "2024-05-14T00:48:25.646432Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"id": "4396f544",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:39.007833Z",
- "iopub.status.busy": "2024-05-14T00:30:39.007402Z",
- "iopub.status.idle": "2024-05-14T00:30:39.305131Z",
- "shell.execute_reply": "2024-05-14T00:30:39.304546Z"
+ "iopub.execute_input": "2024-05-14T00:48:25.649720Z",
+ "iopub.status.busy": "2024-05-14T00:48:25.649153Z",
+ "iopub.status.idle": "2024-05-14T00:48:25.965735Z",
+ "shell.execute_reply": "2024-05-14T00:48:25.965179Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:39.307891Z",
- "iopub.status.busy": "2024-05-14T00:30:39.307410Z",
- "iopub.status.idle": "2024-05-14T00:30:39.311415Z",
- "shell.execute_reply": "2024-05-14T00:30:39.311016Z"
+ "iopub.execute_input": "2024-05-14T00:48:25.968120Z",
+ "iopub.status.busy": "2024-05-14T00:48:25.967815Z",
+ "iopub.status.idle": "2024-05-14T00:48:25.971995Z",
+ "shell.execute_reply": "2024-05-14T00:48:25.971476Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:30:39.313471Z",
- "iopub.status.busy": "2024-05-14T00:30:39.313151Z",
- "iopub.status.idle": "2024-05-14T00:30:43.446738Z",
- "shell.execute_reply": "2024-05-14T00:30:43.446201Z"
+ "iopub.execute_input": "2024-05-14T00:48:25.974171Z",
+ "iopub.status.busy": "2024-05-14T00:48:25.973840Z",
+ "iopub.status.idle": "2024-05-14T00:48:30.855582Z",
+ "shell.execute_reply": "2024-05-14T00:48:30.855076Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 1802240/170498071 [00:00<00:09, 17835695.83it/s]"
+ " 1%| | 1966080/170498071 [00:00<00:08, 19636333.61it/s]"
]
},
{
@@ -260,7 +260,7 @@
"output_type": "stream",
"text": [
"\r",
- " 8%|▊ | 13402112/170498071 [00:00<00:02, 75290705.66it/s]"
+ " 6%|▌ | 9535488/170498071 [00:00<00:03, 52186410.80it/s]"
]
},
{
@@ -268,7 +268,7 @@
"output_type": "stream",
"text": [
"\r",
- " 15%|█▍ | 24838144/170498071 [00:00<00:01, 93081579.34it/s]"
+ " 11%|█ | 18743296/170498071 [00:00<00:02, 70240260.20it/s]"
]
},
{
@@ -276,7 +276,7 @@
"output_type": "stream",
"text": [
"\r",
- " 21%|██ | 35880960/170498071 [00:00<00:01, 99841825.01it/s]"
+ " 16%|█▌ | 26836992/170498071 [00:00<00:01, 74424708.69it/s]"
]
},
{
@@ -284,7 +284,7 @@
"output_type": "stream",
"text": [
"\r",
- " 28%|██▊ | 47087616/170498071 [00:00<00:01, 104157651.24it/s]"
+ " 21%|██ | 34996224/170498071 [00:00<00:01, 76985741.60it/s]"
]
},
{
@@ -292,7 +292,7 @@
"output_type": "stream",
"text": [
"\r",
- " 34%|███▍ | 58458112/170498071 [00:00<00:01, 107346279.74it/s]"
+ " 26%|██▌ | 43515904/170498071 [00:00<00:01, 79750500.34it/s]"
]
},
{
@@ -300,7 +300,7 @@
"output_type": "stream",
"text": [
"\r",
- " 41%|████ | 69763072/170498071 [00:00<00:00, 109181538.59it/s]"
+ " 30%|███ | 51511296/170498071 [00:00<00:01, 78603227.95it/s]"
]
},
{
@@ -308,7 +308,7 @@
"output_type": "stream",
"text": [
"\r",
- " 48%|████▊ | 81068032/170498071 [00:00<00:00, 110396556.62it/s]"
+ " 35%|███▌ | 60063744/170498071 [00:00<00:01, 80767314.33it/s]"
]
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{
@@ -316,7 +316,7 @@
"output_type": "stream",
"text": [
"\r",
- " 54%|█████▍ | 92110848/170498071 [00:00<00:00, 110246857.05it/s]"
+ " 40%|███▉ | 68157440/170498071 [00:00<00:01, 77131614.58it/s]"
]
},
{
@@ -324,7 +324,7 @@
"output_type": "stream",
"text": [
"\r",
- " 61%|██████ | 103415808/170498071 [00:01<00:00, 111097064.56it/s]"
+ " 45%|████▌ | 76808192/170498071 [00:01<00:01, 79824429.13it/s]"
]
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{
@@ -332,7 +332,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 50%|████▉ | 84836352/170498071 [00:01<00:01, 76860701.06it/s]"
]
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{
@@ -340,7 +340,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 55%|█████▍ | 93585408/170498071 [00:01<00:00, 79835179.51it/s]"
]
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{
@@ -348,7 +348,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 60%|█████▉ | 101613568/170498071 [00:01<00:00, 76906181.22it/s]"
]
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{
@@ -356,7 +356,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 65%|██████▍ | 110166016/170498071 [00:01<00:00, 79345231.08it/s]"
]
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{
@@ -364,7 +364,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 69%|██████▉ | 118161408/170498071 [00:01<00:00, 75979423.00it/s]"
]
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{
@@ -372,7 +372,47 @@
"output_type": "stream",
"text": [
"\r",
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+ " 74%|███████▍ | 126812160/170498071 [00:01<00:00, 78724104.78it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 80%|███████▉ | 135790592/170498071 [00:01<00:00, 81661619.37it/s]"
+ ]
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 85%|████████▍ | 144670720/170498071 [00:01<00:00, 83697369.41it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 90%|█████████ | 153518080/170498071 [00:01<00:00, 85046936.90it/s]"
+ ]
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+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 95%|█████████▌| 162201600/170498071 [00:02<00:00, 85524137.53it/s]"
+ ]
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 9e0d634ea..9b56723d8 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
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@@ -116,7 +116,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -142,10 +142,10 @@
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@@ -164,10 +164,10 @@
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@@ -198,10 +198,10 @@
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@@ -374,10 +374,10 @@
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@@ -417,10 +417,10 @@
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@@ -456,10 +456,10 @@
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@@ -477,10 +477,10 @@
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@@ -572,10 +572,10 @@
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@@ -678,10 +678,10 @@
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@@ -696,10 +696,10 @@
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@@ -734,10 +734,10 @@
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@@ -756,10 +756,10 @@
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@@ -883,10 +883,10 @@
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@@ -921,10 +921,10 @@
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@@ -1079,10 +1079,10 @@
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@@ -1189,10 +1189,10 @@
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@@ -1217,10 +1217,10 @@
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@@ -1264,10 +1264,10 @@
"id": "05282559",
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@@ -1376,10 +1376,10 @@
"id": "95531cda",
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diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 93470797a..402c98ea6 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
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@@ -79,10 +79,10 @@
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@@ -97,10 +97,10 @@
"id": "439b0305",
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@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -137,10 +137,10 @@
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@@ -203,10 +203,10 @@
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@@ -247,10 +247,10 @@
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@@ -290,10 +290,10 @@
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@@ -333,17 +333,17 @@
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@@ -446,10 +446,10 @@
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@@ -519,17 +519,17 @@
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@@ -769,10 +769,10 @@
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@@ -786,10 +786,10 @@
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@@ -858,17 +858,17 @@
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@@ -915,10 +915,10 @@
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@@ -991,10 +991,10 @@
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@@ -1038,7 +1038,25 @@
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@@ -1203,30 +1180,31 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 862f61963..260d37526 100644
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 100%[===================>] 16.26M 47.1MB/s in 0.3s \r\n",
"\r\n",
- "2024-05-14 00:33:45 (90.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-05-14 00:51:52 (47.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -187,10 +194,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:45.428008Z",
- "iopub.status.busy": "2024-05-14T00:33:45.427694Z",
- "iopub.status.idle": "2024-05-14T00:33:46.551542Z",
- "shell.execute_reply": "2024-05-14T00:33:46.551017Z"
+ "iopub.execute_input": "2024-05-14T00:51:52.312022Z",
+ "iopub.status.busy": "2024-05-14T00:51:52.311690Z",
+ "iopub.status.idle": "2024-05-14T00:51:53.534584Z",
+ "shell.execute_reply": "2024-05-14T00:51:53.533978Z"
},
"nbsphinx": "hidden"
},
@@ -201,7 +208,7 @@
"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -227,10 +234,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:46.553875Z",
- "iopub.status.busy": "2024-05-14T00:33:46.553543Z",
- "iopub.status.idle": "2024-05-14T00:33:46.556745Z",
- "shell.execute_reply": "2024-05-14T00:33:46.556307Z"
+ "iopub.execute_input": "2024-05-14T00:51:53.537112Z",
+ "iopub.status.busy": "2024-05-14T00:51:53.536824Z",
+ "iopub.status.idle": "2024-05-14T00:51:53.540373Z",
+ "shell.execute_reply": "2024-05-14T00:51:53.539935Z"
}
},
"outputs": [],
@@ -280,10 +287,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:46.558723Z",
- "iopub.status.busy": "2024-05-14T00:33:46.558384Z",
- "iopub.status.idle": "2024-05-14T00:33:46.561130Z",
- "shell.execute_reply": "2024-05-14T00:33:46.560741Z"
+ "iopub.execute_input": "2024-05-14T00:51:53.542442Z",
+ "iopub.status.busy": "2024-05-14T00:51:53.542115Z",
+ "iopub.status.idle": "2024-05-14T00:51:53.545118Z",
+ "shell.execute_reply": "2024-05-14T00:51:53.544668Z"
},
"nbsphinx": "hidden"
},
@@ -301,10 +308,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:46.562983Z",
- "iopub.status.busy": "2024-05-14T00:33:46.562692Z",
- "iopub.status.idle": "2024-05-14T00:33:55.144888Z",
- "shell.execute_reply": "2024-05-14T00:33:55.144348Z"
+ "iopub.execute_input": "2024-05-14T00:51:53.547110Z",
+ "iopub.status.busy": "2024-05-14T00:51:53.546779Z",
+ "iopub.status.idle": "2024-05-14T00:52:02.449862Z",
+ "shell.execute_reply": "2024-05-14T00:52:02.449267Z"
}
},
"outputs": [],
@@ -378,10 +385,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:55.147130Z",
- "iopub.status.busy": "2024-05-14T00:33:55.146830Z",
- "iopub.status.idle": "2024-05-14T00:33:55.152093Z",
- "shell.execute_reply": "2024-05-14T00:33:55.151682Z"
+ "iopub.execute_input": "2024-05-14T00:52:02.452213Z",
+ "iopub.status.busy": "2024-05-14T00:52:02.452032Z",
+ "iopub.status.idle": "2024-05-14T00:52:02.457544Z",
+ "shell.execute_reply": "2024-05-14T00:52:02.457087Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +428,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:55.153843Z",
- "iopub.status.busy": "2024-05-14T00:33:55.153548Z",
- "iopub.status.idle": "2024-05-14T00:33:55.463494Z",
- "shell.execute_reply": "2024-05-14T00:33:55.462948Z"
+ "iopub.execute_input": "2024-05-14T00:52:02.459343Z",
+ "iopub.status.busy": "2024-05-14T00:52:02.459176Z",
+ "iopub.status.idle": "2024-05-14T00:52:02.802219Z",
+ "shell.execute_reply": "2024-05-14T00:52:02.801666Z"
}
},
"outputs": [],
@@ -461,10 +468,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:55.465688Z",
- "iopub.status.busy": "2024-05-14T00:33:55.465501Z",
- "iopub.status.idle": "2024-05-14T00:33:55.469519Z",
- "shell.execute_reply": "2024-05-14T00:33:55.469038Z"
+ "iopub.execute_input": "2024-05-14T00:52:02.804757Z",
+ "iopub.status.busy": "2024-05-14T00:52:02.804305Z",
+ "iopub.status.idle": "2024-05-14T00:52:02.808728Z",
+ "shell.execute_reply": "2024-05-14T00:52:02.808214Z"
}
},
"outputs": [
@@ -536,10 +543,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:55.471313Z",
- "iopub.status.busy": "2024-05-14T00:33:55.471159Z",
- "iopub.status.idle": "2024-05-14T00:33:57.619298Z",
- "shell.execute_reply": "2024-05-14T00:33:57.618665Z"
+ "iopub.execute_input": "2024-05-14T00:52:02.810948Z",
+ "iopub.status.busy": "2024-05-14T00:52:02.810595Z",
+ "iopub.status.idle": "2024-05-14T00:52:05.112968Z",
+ "shell.execute_reply": "2024-05-14T00:52:05.112199Z"
}
},
"outputs": [],
@@ -561,10 +568,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:57.622199Z",
- "iopub.status.busy": "2024-05-14T00:33:57.621506Z",
- "iopub.status.idle": "2024-05-14T00:33:57.625037Z",
- "shell.execute_reply": "2024-05-14T00:33:57.624564Z"
+ "iopub.execute_input": "2024-05-14T00:52:05.116212Z",
+ "iopub.status.busy": "2024-05-14T00:52:05.115458Z",
+ "iopub.status.idle": "2024-05-14T00:52:05.119688Z",
+ "shell.execute_reply": "2024-05-14T00:52:05.119205Z"
}
},
"outputs": [
@@ -600,10 +607,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:57.626979Z",
- "iopub.status.busy": "2024-05-14T00:33:57.626654Z",
- "iopub.status.idle": "2024-05-14T00:33:57.631631Z",
- "shell.execute_reply": "2024-05-14T00:33:57.631107Z"
+ "iopub.execute_input": "2024-05-14T00:52:05.121811Z",
+ "iopub.status.busy": "2024-05-14T00:52:05.121484Z",
+ "iopub.status.idle": "2024-05-14T00:52:05.126701Z",
+ "shell.execute_reply": "2024-05-14T00:52:05.126154Z"
}
},
"outputs": [
@@ -781,10 +788,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:57.633638Z",
- "iopub.status.busy": "2024-05-14T00:33:57.633346Z",
- "iopub.status.idle": "2024-05-14T00:33:57.658162Z",
- "shell.execute_reply": "2024-05-14T00:33:57.657745Z"
+ "iopub.execute_input": "2024-05-14T00:52:05.128880Z",
+ "iopub.status.busy": "2024-05-14T00:52:05.128576Z",
+ "iopub.status.idle": "2024-05-14T00:52:05.155624Z",
+ "shell.execute_reply": "2024-05-14T00:52:05.155052Z"
}
},
"outputs": [
@@ -886,10 +893,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:57.660033Z",
- "iopub.status.busy": "2024-05-14T00:33:57.659681Z",
- "iopub.status.idle": "2024-05-14T00:33:57.663715Z",
- "shell.execute_reply": "2024-05-14T00:33:57.663208Z"
+ "iopub.execute_input": "2024-05-14T00:52:05.157761Z",
+ "iopub.status.busy": "2024-05-14T00:52:05.157442Z",
+ "iopub.status.idle": "2024-05-14T00:52:05.161893Z",
+ "shell.execute_reply": "2024-05-14T00:52:05.161380Z"
}
},
"outputs": [
@@ -963,10 +970,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:57.665707Z",
- "iopub.status.busy": "2024-05-14T00:33:57.665308Z",
- "iopub.status.idle": "2024-05-14T00:33:58.937286Z",
- "shell.execute_reply": "2024-05-14T00:33:58.936789Z"
+ "iopub.execute_input": "2024-05-14T00:52:05.163899Z",
+ "iopub.status.busy": "2024-05-14T00:52:05.163602Z",
+ "iopub.status.idle": "2024-05-14T00:52:06.520077Z",
+ "shell.execute_reply": "2024-05-14T00:52:06.519559Z"
}
},
"outputs": [
@@ -1138,10 +1145,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:33:58.939169Z",
- "iopub.status.busy": "2024-05-14T00:33:58.938992Z",
- "iopub.status.idle": "2024-05-14T00:33:58.942806Z",
- "shell.execute_reply": "2024-05-14T00:33:58.942387Z"
+ "iopub.execute_input": "2024-05-14T00:52:06.522277Z",
+ "iopub.status.busy": "2024-05-14T00:52:06.521968Z",
+ "iopub.status.idle": "2024-05-14T00:52:06.525990Z",
+ "shell.execute_reply": "2024-05-14T00:52:06.525539Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree
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index 3e33a83dc..667590501 100644
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diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree
index 4bede780f..027de2d9d 100644
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diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree
index a5ae4b508..299019376 100644
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diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
index 2ca972811..09e0f52b9 100644
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diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 7ac65f44b..184dded21 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 bd55e36a4..bc7457bb4 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 44ce4d49c..0b4f84955 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/data_monitor.ipynb b/master/_sources/tutorials/datalab/data_monitor.ipynb
index 74e5b1d1d..604d426ea 100644
--- a/master/_sources/tutorials/datalab/data_monitor.ipynb
+++ b/master/_sources/tutorials/datalab/data_monitor.ipynb
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 2c0aca788..a31c877dc 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 a5d8cc25b..8a0664673 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 f1ec49125..7d02dddb6 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 541919016..75cbce614 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 0a82260cf..8994fa9bd 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 0370529b6..78ed3b4d0 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 1b24ef33f..b81903d97 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 9440bce41..cabb67a03 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 d80fb34c8..00aad810e 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 4658dd2bd..c252b1e16 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 24409517c..bc95253c4 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 e600e7eaa..5d41151c8 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 754968220..f07ca1b0d 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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\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 9edc03c84..5b2fdd0de 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Load and format the text dataset": [[83, "2.-Load-and-format-the-text-dataset"], [91, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[83, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[83, "4.-Train-a-more-robust-model-from-noisy-labels"], [101, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[84, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[84, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[84, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[84, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[84, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[84, "5.-Use-cleanlab-to-find-label-issues"], [90, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[85, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[85, "1.-Install-and-import-required-dependencies"], [87, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [96, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[85, "2.-Create-and-load-the-data-(can-skip-these-details)"], [87, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[85, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"], [87, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[85, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [87, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[85, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[85, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "7. Finding outliers in new data": [[85, "7.-Finding-outliers-in-new-data"]], "8. Looking for both label issues and outliers": [[85, "8.-Looking-for-both-label-issues-and-outliers"]], "Datalab: Advanced workflows to audit your data": [[86, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[86, "Install-and-import-required-dependencies"]], "Create and load the data": [[86, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[86, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[86, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[86, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[86, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[86, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[86, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[87, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "5. Learn more about the issues in your dataset": [[87, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[87, "Get-additional-information"]], "Near duplicate issues": [[87, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[88, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "Label issues": [[88, "Label-issues"], [90, "Label-issues"], [91, "Label-issues"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[88, "Outlier-issues"], [90, "Outlier-issues"], [91, "Outlier-issues"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "Datalab Tutorials": [[89, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[90, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[90, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[91, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[91, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Understanding Dataset-level Labeling Issues": [[92, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "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?": [[93, "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?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "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.": [[94, "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": [[94, "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": [[94, "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!": [[94, "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": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "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)": [[94, "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:": [[94, "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": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "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": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[97, "3.-Use-cleanlab-to-find-label-issues"], [98, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[97, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[97, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[97, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[97, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[97, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[98, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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Install cleanlab": [[79, "install-cleanlab"]], "2. Find common issues in your data": [[79, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[79, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[79, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[82, "1.-Install-required-dependencies"], [83, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [101, "1.-Install-required-dependencies"]], "2. Load and process the data": [[82, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [101, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[82, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [90, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[82, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[82, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[83, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[83, "2.-Load-and-format-the-text-dataset"], [91, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[83, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[83, "4.-Train-a-more-robust-model-from-noisy-labels"], [101, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[84, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[84, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[84, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[84, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[84, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[84, "5.-Use-cleanlab-to-find-label-issues"], [90, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[85, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[85, "1.-Install-and-import-required-dependencies"], [87, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [96, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[85, "2.-Create-and-load-the-data-(can-skip-these-details)"], [87, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. 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Looking for both label issues and outliers": [[85, "8.-Looking-for-both-label-issues-and-outliers"]], "Datalab: Advanced workflows to audit your data": [[86, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[86, "Install-and-import-required-dependencies"]], "Create and load the data": [[86, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[86, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[86, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[86, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[86, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[86, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[86, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[87, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "5. Learn more about the issues in your dataset": [[87, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[87, "Get-additional-information"]], "Near duplicate issues": [[87, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[88, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. 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Construct K nearest neighbours graph": [[90, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[91, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[91, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Understanding Dataset-level Labeling Issues": [[92, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "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?": [[93, "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?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "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.": [[94, "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": [[94, "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": [[94, "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!": [[94, "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": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "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)": [[94, "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:": [[94, "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": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "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": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. 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Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"cleanlab.internal.multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[50, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[52, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[53, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[55, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[56, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[57, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[58, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[60, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[61, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[62, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[62, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[63, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[64, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[65, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[66, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[66, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[67, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[68, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[69, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[69, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[69, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[70, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[70, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[71, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[71, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[72, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[73, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[74, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[75, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[75, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[76, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[77, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[78, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[78, "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 3d52eae5f..5ad12b115 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:18.610599Z",
- "iopub.status.busy": "2024-05-14T00:23:18.610192Z",
- "iopub.status.idle": "2024-05-14T00:23:19.711241Z",
- "shell.execute_reply": "2024-05-14T00:23:19.710735Z"
+ "iopub.execute_input": "2024-05-14T00:40:31.101837Z",
+ "iopub.status.busy": "2024-05-14T00:40:31.101487Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.284083Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.283529Z"
},
"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@fda34132759156efea8625a7abca5e473b2b5c6e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@ab05f86dd4e3fa67c4c5086f33af36757790c7ba\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-14T00:23:19.713562Z",
- "iopub.status.busy": "2024-05-14T00:23:19.713221Z",
- "iopub.status.idle": "2024-05-14T00:23:19.731184Z",
- "shell.execute_reply": "2024-05-14T00:23:19.730680Z"
+ "iopub.execute_input": "2024-05-14T00:40:32.286694Z",
+ "iopub.status.busy": "2024-05-14T00:40:32.286271Z",
+ "iopub.status.idle": "2024-05-14T00:40:32.304842Z",
+ "shell.execute_reply": "2024-05-14T00:40:32.304407Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
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"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
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"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
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"outputs": [],
@@ -384,10 +384,10 @@
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"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
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"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
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"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
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"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
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"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
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"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
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"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
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"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
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"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:23:23.847065Z"
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"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
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- "shell.execute_reply": "2024-05-14T00:23:23.884332Z"
+ "iopub.execute_input": "2024-05-14T00:40:36.810644Z",
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+ "shell.execute_reply": "2024-05-14T00:40:36.865819Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 358eda2d7..d288ab1a8 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -792,7 +792,7 @@ 2. Load and format the text dataset