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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index eb0f026fb..9a3aebce5 100644
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index 01b6ae344..fb0d88c54 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index c0c98bed8..ac1eecb0d 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 827fac13e..cc8956d28 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": "2024-06-25T15:57:29.273547Z",
- "iopub.status.busy": "2024-06-25T15:57:29.273057Z",
- "iopub.status.idle": "2024-06-25T15:57:30.518447Z",
- "shell.execute_reply": "2024-06-25T15:57:30.517893Z"
+ "iopub.execute_input": "2024-06-25T19:31:27.766466Z",
+ "iopub.status.busy": "2024-06-25T19:31:27.766073Z",
+ "iopub.status.idle": "2024-06-25T19:31:28.950995Z",
+ "shell.execute_reply": "2024-06-25T19:31:28.950453Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T15:57:30.520903Z",
- "iopub.status.busy": "2024-06-25T15:57:30.520603Z",
- "iopub.status.idle": "2024-06-25T15:57:30.539170Z",
- "shell.execute_reply": "2024-06-25T15:57:30.538541Z"
+ "iopub.execute_input": "2024-06-25T19:31:28.953618Z",
+ "iopub.status.busy": "2024-06-25T19:31:28.953345Z",
+ "iopub.status.idle": "2024-06-25T19:31:28.970797Z",
+ "shell.execute_reply": "2024-06-25T19:31:28.970252Z"
}
},
"outputs": [],
@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:30.541860Z",
- "iopub.status.busy": "2024-06-25T15:57:30.541524Z",
- "iopub.status.idle": "2024-06-25T15:57:30.657228Z",
- "shell.execute_reply": "2024-06-25T15:57:30.656649Z"
+ "iopub.execute_input": "2024-06-25T19:31:28.973223Z",
+ "iopub.status.busy": "2024-06-25T19:31:28.972835Z",
+ "iopub.status.idle": "2024-06-25T19:31:29.167625Z",
+ "shell.execute_reply": "2024-06-25T19:31:29.167053Z"
}
},
"outputs": [
@@ -305,10 +305,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:30.688369Z",
- "iopub.status.busy": "2024-06-25T15:57:30.687895Z",
- "iopub.status.idle": "2024-06-25T15:57:30.691893Z",
- "shell.execute_reply": "2024-06-25T15:57:30.691334Z"
+ "iopub.execute_input": "2024-06-25T19:31:29.197486Z",
+ "iopub.status.busy": "2024-06-25T19:31:29.197079Z",
+ "iopub.status.idle": "2024-06-25T19:31:29.200622Z",
+ "shell.execute_reply": "2024-06-25T19:31:29.200145Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:30.694075Z",
- "iopub.status.busy": "2024-06-25T15:57:30.693736Z",
- "iopub.status.idle": "2024-06-25T15:57:30.702125Z",
- "shell.execute_reply": "2024-06-25T15:57:30.701714Z"
+ "iopub.execute_input": "2024-06-25T19:31:29.202620Z",
+ "iopub.status.busy": "2024-06-25T19:31:29.202441Z",
+ "iopub.status.idle": "2024-06-25T19:31:29.210646Z",
+ "shell.execute_reply": "2024-06-25T19:31:29.210233Z"
}
},
"outputs": [],
@@ -384,10 +384,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:30.704216Z",
- "iopub.status.busy": "2024-06-25T15:57:30.703917Z",
- "iopub.status.idle": "2024-06-25T15:57:30.706562Z",
- "shell.execute_reply": "2024-06-25T15:57:30.706046Z"
+ "iopub.execute_input": "2024-06-25T19:31:29.212637Z",
+ "iopub.status.busy": "2024-06-25T19:31:29.212443Z",
+ "iopub.status.idle": "2024-06-25T19:31:29.214911Z",
+ "shell.execute_reply": "2024-06-25T19:31:29.214495Z"
}
},
"outputs": [],
@@ -409,10 +409,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:30.708564Z",
- "iopub.status.busy": "2024-06-25T15:57:30.708280Z",
- "iopub.status.idle": "2024-06-25T15:57:31.236208Z",
- "shell.execute_reply": "2024-06-25T15:57:31.235674Z"
+ "iopub.execute_input": "2024-06-25T19:31:29.216761Z",
+ "iopub.status.busy": "2024-06-25T19:31:29.216593Z",
+ "iopub.status.idle": "2024-06-25T19:31:29.731597Z",
+ "shell.execute_reply": "2024-06-25T19:31:29.730952Z"
}
},
"outputs": [],
@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:31.238847Z",
- "iopub.status.busy": "2024-06-25T15:57:31.238353Z",
- "iopub.status.idle": "2024-06-25T15:57:33.199688Z",
- "shell.execute_reply": "2024-06-25T15:57:33.199050Z"
+ "iopub.execute_input": "2024-06-25T19:31:29.733935Z",
+ "iopub.status.busy": "2024-06-25T19:31:29.733740Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.552423Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.551801Z"
}
},
"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.202577Z",
- "iopub.status.busy": "2024-06-25T15:57:33.201887Z",
- "iopub.status.idle": "2024-06-25T15:57:33.212398Z",
- "shell.execute_reply": "2024-06-25T15:57:33.211922Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.554814Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.554296Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.564323Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.563854Z"
}
},
"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.214665Z",
- "iopub.status.busy": "2024-06-25T15:57:33.214259Z",
- "iopub.status.idle": "2024-06-25T15:57:33.218309Z",
- "shell.execute_reply": "2024-06-25T15:57:33.217865Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.566389Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.566065Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.570002Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.569569Z"
}
},
"outputs": [],
@@ -633,10 +633,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.220272Z",
- "iopub.status.busy": "2024-06-25T15:57:33.219956Z",
- "iopub.status.idle": "2024-06-25T15:57:33.227426Z",
- "shell.execute_reply": "2024-06-25T15:57:33.226966Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.572029Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.571709Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.579030Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.578475Z"
}
},
"outputs": [],
@@ -658,10 +658,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.229497Z",
- "iopub.status.busy": "2024-06-25T15:57:33.229164Z",
- "iopub.status.idle": "2024-06-25T15:57:33.342561Z",
- "shell.execute_reply": "2024-06-25T15:57:33.341996Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.581187Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.580887Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.691824Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.691204Z"
}
},
"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.344781Z",
- "iopub.status.busy": "2024-06-25T15:57:33.344414Z",
- "iopub.status.idle": "2024-06-25T15:57:33.347398Z",
- "shell.execute_reply": "2024-06-25T15:57:33.346835Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.694170Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.693686Z",
+ "iopub.status.idle": "2024-06-25T19:31:31.696628Z",
+ "shell.execute_reply": "2024-06-25T19:31:31.696102Z"
}
},
"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:33.349504Z",
- "iopub.status.busy": "2024-06-25T15:57:33.349196Z",
- "iopub.status.idle": "2024-06-25T15:57:35.395970Z",
- "shell.execute_reply": "2024-06-25T15:57:35.395346Z"
+ "iopub.execute_input": "2024-06-25T19:31:31.698847Z",
+ "iopub.status.busy": "2024-06-25T19:31:31.698415Z",
+ "iopub.status.idle": "2024-06-25T19:31:33.679358Z",
+ "shell.execute_reply": "2024-06-25T19:31:33.678623Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:35.399024Z",
- "iopub.status.busy": "2024-06-25T15:57:35.398268Z",
- "iopub.status.idle": "2024-06-25T15:57:35.410293Z",
- "shell.execute_reply": "2024-06-25T15:57:35.409709Z"
+ "iopub.execute_input": "2024-06-25T19:31:33.682516Z",
+ "iopub.status.busy": "2024-06-25T19:31:33.681890Z",
+ "iopub.status.idle": "2024-06-25T19:31:33.693245Z",
+ "shell.execute_reply": "2024-06-25T19:31:33.692694Z"
}
},
"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:35.412649Z",
- "iopub.status.busy": "2024-06-25T15:57:35.412293Z",
- "iopub.status.idle": "2024-06-25T15:57:35.434146Z",
- "shell.execute_reply": "2024-06-25T15:57:35.433694Z"
+ "iopub.execute_input": "2024-06-25T19:31:33.695397Z",
+ "iopub.status.busy": "2024-06-25T19:31:33.695096Z",
+ "iopub.status.idle": "2024-06-25T19:31:33.841440Z",
+ "shell.execute_reply": "2024-06-25T19:31:33.840949Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb
index e6fcf34d6..a83013185 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": "2024-06-25T15:57:38.612010Z",
- "iopub.status.busy": "2024-06-25T15:57:38.611854Z",
- "iopub.status.idle": "2024-06-25T15:57:41.717638Z",
- "shell.execute_reply": "2024-06-25T15:57:41.716988Z"
+ "iopub.execute_input": "2024-06-25T19:31:37.218802Z",
+ "iopub.status.busy": "2024-06-25T19:31:37.218626Z",
+ "iopub.status.idle": "2024-06-25T19:31:40.132819Z",
+ "shell.execute_reply": "2024-06-25T19:31:40.132198Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T15:57:41.720617Z",
- "iopub.status.busy": "2024-06-25T15:57:41.720113Z",
- "iopub.status.idle": "2024-06-25T15:57:41.723540Z",
- "shell.execute_reply": "2024-06-25T15:57:41.723091Z"
+ "iopub.execute_input": "2024-06-25T19:31:40.135382Z",
+ "iopub.status.busy": "2024-06-25T19:31:40.135098Z",
+ "iopub.status.idle": "2024-06-25T19:31:40.138344Z",
+ "shell.execute_reply": "2024-06-25T19:31:40.137917Z"
}
},
"outputs": [],
@@ -185,10 +185,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:57:41.725767Z",
- "iopub.status.busy": "2024-06-25T15:57:41.725363Z",
- "iopub.status.idle": "2024-06-25T15:57:41.728588Z",
- "shell.execute_reply": "2024-06-25T15:57:41.728027Z"
+ "iopub.execute_input": "2024-06-25T19:31:40.140291Z",
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@@ -312,10 +312,10 @@
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@@ -330,10 +330,10 @@
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@@ -342,7 +342,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'cancel_transfer', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'supported_cards_and_currencies', 'change_pin'}\n"
+ "Classes: {'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'visa_or_mastercard'}\n"
]
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb
index 61356dbf7..411158583 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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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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@@ -435,10 +435,10 @@
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@@ -557,10 +557,10 @@
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@@ -582,10 +582,10 @@
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@@ -627,10 +627,10 @@
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@@ -727,10 +727,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 81aa80744..33af481ea 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
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@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
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"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -830,10 +830,10 @@
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@@ -923,7 +923,7 @@
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+ "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n",
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@@ -1695,16 +1713,16 @@
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],
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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 d08884c96..701b2fb18 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -647,10 +647,10 @@
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@@ -710,10 +710,10 @@
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@@ -846,10 +846,10 @@
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@@ -960,10 +960,10 @@
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@@ -1030,10 +1030,10 @@
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@@ -1225,10 +1225,10 @@
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@@ -1344,10 +1344,10 @@
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@@ -1472,10 +1472,10 @@
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@@ -1578,10 +1578,10 @@
"execution_count": 17,
"metadata": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
index 7a8ae4b02..a549a7040 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
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+ "shell.execute_reply": "2024-06-25T19:32:37.482729Z"
},
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@@ -112,10 +112,10 @@
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}
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@@ -152,10 +152,10 @@
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}
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@@ -172,7 +172,7 @@
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@@ -186,7 +186,7 @@
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@@ -200,7 +200,7 @@
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@@ -214,7 +214,7 @@
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@@ -228,7 +228,7 @@
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+ "model_id": "815effa183cf4ca4a7160696d4e9eb83",
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@@ -242,7 +242,7 @@
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@@ -256,7 +256,7 @@
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@@ -270,7 +270,7 @@
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@@ -312,10 +312,10 @@
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@@ -340,17 +340,17 @@
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@@ -388,10 +388,10 @@
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@@ -424,10 +424,10 @@
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@@ -465,10 +465,10 @@
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@@ -605,10 +605,10 @@
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@@ -733,10 +733,10 @@
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@@ -773,10 +773,10 @@
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"outputs": [
@@ -792,21 +792,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.152\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.704\n"
]
},
{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.996\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.525\n",
"Computing feature embeddings ...\n"
]
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@@ -827,7 +827,7 @@
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@@ -850,21 +850,21 @@
"name": "stdout",
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"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.210\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.714\n"
]
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{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.688\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.460\n",
"Computing feature embeddings ...\n"
]
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@@ -885,7 +885,7 @@
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@@ -908,21 +908,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.915\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.742\n"
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{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.775\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.468\n",
"Computing feature embeddings ...\n"
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@@ -943,7 +943,7 @@
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@@ -1022,10 +1022,10 @@
"execution_count": 12,
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+ "shell.execute_reply": "2024-06-25T19:33:54.943339Z"
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@@ -1050,10 +1050,10 @@
"execution_count": 13,
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- "shell.execute_reply": "2024-06-25T15:59:54.136006Z"
+ "iopub.execute_input": "2024-06-25T19:33:54.946038Z",
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+ "iopub.status.idle": "2024-06-25T19:33:55.403627Z",
+ "shell.execute_reply": "2024-06-25T19:33:55.402981Z"
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@@ -1073,10 +1073,10 @@
"execution_count": 14,
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- "iopub.execute_input": "2024-06-25T15:59:54.139214Z",
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- "iopub.status.idle": "2024-06-25T16:01:33.037683Z",
- "shell.execute_reply": "2024-06-25T16:01:33.037091Z"
+ "iopub.execute_input": "2024-06-25T19:33:55.406220Z",
+ "iopub.status.busy": "2024-06-25T19:33:55.406041Z",
+ "iopub.status.idle": "2024-06-25T19:35:30.535430Z",
+ "shell.execute_reply": "2024-06-25T19:35:30.534808Z"
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@@ -1123,7 +1123,7 @@
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+ "model_id": "d65cb8246aa14189b49a0eeae6f3bad0",
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@@ -1162,10 +1162,10 @@
"execution_count": 15,
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- "shell.execute_reply": "2024-06-25T16:01:33.511469Z"
+ "iopub.execute_input": "2024-06-25T19:35:30.537781Z",
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+ "shell.execute_reply": "2024-06-25T19:35:30.983121Z"
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@@ -1311,10 +1311,10 @@
"execution_count": 16,
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+ "iopub.execute_input": "2024-06-25T19:35:30.986665Z",
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+ "shell.execute_reply": "2024-06-25T19:35:31.047866Z"
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@@ -1418,10 +1418,10 @@
"execution_count": 17,
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" \n",
" \n",
" | \n",
- " low_information_score | \n",
" is_low_information_issue | \n",
+ " low_information_score | \n",
"
\n",
" \n",
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\n",
" \n",
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- " 0.067975 | \n",
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index d018ad0f4..d470496b0 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
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- "shell.execute_reply": "2024-06-25T16:01:40.921892Z"
+ "iopub.execute_input": "2024-06-25T19:35:37.857546Z",
+ "iopub.status.busy": "2024-06-25T19:35:37.857301Z",
+ "iopub.status.idle": "2024-06-25T19:35:37.902804Z",
+ "shell.execute_reply": "2024-06-25T19:35:37.902282Z"
}
},
"outputs": [
@@ -264,10 +264,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:40.924681Z",
- "iopub.status.busy": "2024-06-25T16:01:40.924312Z",
- "iopub.status.idle": "2024-06-25T16:01:40.927988Z",
- "shell.execute_reply": "2024-06-25T16:01:40.927543Z"
+ "iopub.execute_input": "2024-06-25T19:35:37.904835Z",
+ "iopub.status.busy": "2024-06-25T19:35:37.904541Z",
+ "iopub.status.idle": "2024-06-25T19:35:37.907889Z",
+ "shell.execute_reply": "2024-06-25T19:35:37.907366Z"
}
},
"outputs": [],
@@ -288,10 +288,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:40.930108Z",
- "iopub.status.busy": "2024-06-25T16:01:40.929767Z",
- "iopub.status.idle": "2024-06-25T16:01:40.938088Z",
- "shell.execute_reply": "2024-06-25T16:01:40.937632Z"
+ "iopub.execute_input": "2024-06-25T19:35:37.909808Z",
+ "iopub.status.busy": "2024-06-25T19:35:37.909561Z",
+ "iopub.status.idle": "2024-06-25T19:35:37.917137Z",
+ "shell.execute_reply": "2024-06-25T19:35:37.916719Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:40.940711Z",
- "iopub.status.busy": "2024-06-25T16:01:40.940301Z",
- "iopub.status.idle": "2024-06-25T16:01:40.943183Z",
- "shell.execute_reply": "2024-06-25T16:01:40.942671Z"
+ "iopub.execute_input": "2024-06-25T19:35:37.919119Z",
+ "iopub.status.busy": "2024-06-25T19:35:37.918944Z",
+ "iopub.status.idle": "2024-06-25T19:35:37.921447Z",
+ "shell.execute_reply": "2024-06-25T19:35:37.921009Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:40.945475Z",
- "iopub.status.busy": "2024-06-25T16:01:40.945057Z",
- "iopub.status.idle": "2024-06-25T16:01:43.970784Z",
- "shell.execute_reply": "2024-06-25T16:01:43.970132Z"
+ "iopub.execute_input": "2024-06-25T19:35:37.923229Z",
+ "iopub.status.busy": "2024-06-25T19:35:37.923058Z",
+ "iopub.status.idle": "2024-06-25T19:35:40.863311Z",
+ "shell.execute_reply": "2024-06-25T19:35:40.862782Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:43.973696Z",
- "iopub.status.busy": "2024-06-25T16:01:43.973243Z",
- "iopub.status.idle": "2024-06-25T16:01:43.982712Z",
- "shell.execute_reply": "2024-06-25T16:01:43.982163Z"
+ "iopub.execute_input": "2024-06-25T19:35:40.866333Z",
+ "iopub.status.busy": "2024-06-25T19:35:40.865865Z",
+ "iopub.status.idle": "2024-06-25T19:35:40.875269Z",
+ "shell.execute_reply": "2024-06-25T19:35:40.874719Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:43.984936Z",
- "iopub.status.busy": "2024-06-25T16:01:43.984621Z",
- "iopub.status.idle": "2024-06-25T16:01:46.020904Z",
- "shell.execute_reply": "2024-06-25T16:01:46.020180Z"
+ "iopub.execute_input": "2024-06-25T19:35:40.877815Z",
+ "iopub.status.busy": "2024-06-25T19:35:40.877412Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.759452Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.758773Z"
}
},
"outputs": [
@@ -484,10 +484,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.023944Z",
- "iopub.status.busy": "2024-06-25T16:01:46.023252Z",
- "iopub.status.idle": "2024-06-25T16:01:46.044713Z",
- "shell.execute_reply": "2024-06-25T16:01:46.044132Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.761931Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.761498Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.780225Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.779777Z"
},
"scrolled": true
},
@@ -617,10 +617,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.046999Z",
- "iopub.status.busy": "2024-06-25T16:01:46.046551Z",
- "iopub.status.idle": "2024-06-25T16:01:46.054695Z",
- "shell.execute_reply": "2024-06-25T16:01:46.054238Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.782418Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.782011Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.789930Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.789485Z"
}
},
"outputs": [
@@ -724,10 +724,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.056749Z",
- "iopub.status.busy": "2024-06-25T16:01:46.056335Z",
- "iopub.status.idle": "2024-06-25T16:01:46.065313Z",
- "shell.execute_reply": "2024-06-25T16:01:46.064844Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.791897Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.791568Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.800098Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.799646Z"
}
},
"outputs": [
@@ -856,10 +856,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.067393Z",
- "iopub.status.busy": "2024-06-25T16:01:46.067010Z",
- "iopub.status.idle": "2024-06-25T16:01:46.075090Z",
- "shell.execute_reply": "2024-06-25T16:01:46.074640Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.802085Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.801908Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.809958Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.809510Z"
}
},
"outputs": [
@@ -973,10 +973,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.076959Z",
- "iopub.status.busy": "2024-06-25T16:01:46.076789Z",
- "iopub.status.idle": "2024-06-25T16:01:46.085272Z",
- "shell.execute_reply": "2024-06-25T16:01:46.084709Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.811794Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.811623Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.820374Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.819927Z"
}
},
"outputs": [
@@ -1087,10 +1087,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.087253Z",
- "iopub.status.busy": "2024-06-25T16:01:46.087082Z",
- "iopub.status.idle": "2024-06-25T16:01:46.094606Z",
- "shell.execute_reply": "2024-06-25T16:01:46.094154Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.822187Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.822017Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.829544Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.829102Z"
}
},
"outputs": [
@@ -1205,10 +1205,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.096448Z",
- "iopub.status.busy": "2024-06-25T16:01:46.096279Z",
- "iopub.status.idle": "2024-06-25T16:01:46.103680Z",
- "shell.execute_reply": "2024-06-25T16:01:46.103253Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.831790Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.831383Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.838578Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.838124Z"
}
},
"outputs": [
@@ -1308,10 +1308,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:46.105721Z",
- "iopub.status.busy": "2024-06-25T16:01:46.105550Z",
- "iopub.status.idle": "2024-06-25T16:01:46.113743Z",
- "shell.execute_reply": "2024-06-25T16:01:46.113307Z"
+ "iopub.execute_input": "2024-06-25T19:35:42.840523Z",
+ "iopub.status.busy": "2024-06-25T19:35:42.840354Z",
+ "iopub.status.idle": "2024-06-25T19:35:42.848877Z",
+ "shell.execute_reply": "2024-06-25T19:35:42.848311Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 570f8fa58..5e2df2074 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": "2024-06-25T16:01:49.080606Z",
- "iopub.status.busy": "2024-06-25T16:01:49.080431Z",
- "iopub.status.idle": "2024-06-25T16:01:51.868569Z",
- "shell.execute_reply": "2024-06-25T16:01:51.868008Z"
+ "iopub.execute_input": "2024-06-25T19:35:45.390789Z",
+ "iopub.status.busy": "2024-06-25T19:35:45.390619Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.008658Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.008097Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T16:01:51.871269Z",
- "iopub.status.busy": "2024-06-25T16:01:51.870744Z",
- "iopub.status.idle": "2024-06-25T16:01:51.874137Z",
- "shell.execute_reply": "2024-06-25T16:01:51.873685Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.011088Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.010783Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.014230Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.013782Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:51.876275Z",
- "iopub.status.busy": "2024-06-25T16:01:51.875837Z",
- "iopub.status.idle": "2024-06-25T16:01:51.878967Z",
- "shell.execute_reply": "2024-06-25T16:01:51.878524Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.016267Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.015916Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.019094Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.018529Z"
},
"nbsphinx": "hidden"
},
@@ -178,10 +178,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:51.881057Z",
- "iopub.status.busy": "2024-06-25T16:01:51.880603Z",
- "iopub.status.idle": "2024-06-25T16:01:51.910717Z",
- "shell.execute_reply": "2024-06-25T16:01:51.910165Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.021232Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.020813Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.073023Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.072456Z"
}
},
"outputs": [
@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:51.912918Z",
- "iopub.status.busy": "2024-06-25T16:01:51.912554Z",
- "iopub.status.idle": "2024-06-25T16:01:51.916418Z",
- "shell.execute_reply": "2024-06-25T16:01:51.915973Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.075330Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.074995Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.078963Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.078513Z"
}
},
"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'lost_or_stolen_phone', 'getting_spare_card', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'card_about_to_expire'}\n"
+ "Classes: {'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'getting_spare_card', 'cancel_transfer', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies'}\n"
]
}
],
@@ -307,10 +307,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:51.918533Z",
- "iopub.status.busy": "2024-06-25T16:01:51.918103Z",
- "iopub.status.idle": "2024-06-25T16:01:51.921336Z",
- "shell.execute_reply": "2024-06-25T16:01:51.920794Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.080913Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.080733Z",
+ "iopub.status.idle": "2024-06-25T19:35:48.083997Z",
+ "shell.execute_reply": "2024-06-25T19:35:48.083535Z"
}
},
"outputs": [
@@ -365,10 +365,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:51.923429Z",
- "iopub.status.busy": "2024-06-25T16:01:51.923050Z",
- "iopub.status.idle": "2024-06-25T16:01:55.870267Z",
- "shell.execute_reply": "2024-06-25T16:01:55.869625Z"
+ "iopub.execute_input": "2024-06-25T19:35:48.086043Z",
+ "iopub.status.busy": "2024-06-25T19:35:48.085869Z",
+ "iopub.status.idle": "2024-06-25T19:35:52.539336Z",
+ "shell.execute_reply": "2024-06-25T19:35:52.538772Z"
}
},
"outputs": [
@@ -424,10 +424,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:55.873234Z",
- "iopub.status.busy": "2024-06-25T16:01:55.872784Z",
- "iopub.status.idle": "2024-06-25T16:01:56.785446Z",
- "shell.execute_reply": "2024-06-25T16:01:56.784870Z"
+ "iopub.execute_input": "2024-06-25T19:35:52.541851Z",
+ "iopub.status.busy": "2024-06-25T19:35:52.541641Z",
+ "iopub.status.idle": "2024-06-25T19:35:53.417381Z",
+ "shell.execute_reply": "2024-06-25T19:35:53.416793Z"
},
"scrolled": true
},
@@ -459,10 +459,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:56.788374Z",
- "iopub.status.busy": "2024-06-25T16:01:56.788033Z",
- "iopub.status.idle": "2024-06-25T16:01:56.790866Z",
- "shell.execute_reply": "2024-06-25T16:01:56.790359Z"
+ "iopub.execute_input": "2024-06-25T19:35:53.420304Z",
+ "iopub.status.busy": "2024-06-25T19:35:53.419913Z",
+ "iopub.status.idle": "2024-06-25T19:35:53.422789Z",
+ "shell.execute_reply": "2024-06-25T19:35:53.422303Z"
}
},
"outputs": [],
@@ -482,10 +482,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:56.793259Z",
- "iopub.status.busy": "2024-06-25T16:01:56.792882Z",
- "iopub.status.idle": "2024-06-25T16:01:58.878619Z",
- "shell.execute_reply": "2024-06-25T16:01:58.877823Z"
+ "iopub.execute_input": "2024-06-25T19:35:53.425167Z",
+ "iopub.status.busy": "2024-06-25T19:35:53.424776Z",
+ "iopub.status.idle": "2024-06-25T19:35:55.333188Z",
+ "shell.execute_reply": "2024-06-25T19:35:55.332528Z"
},
"scrolled": true
},
@@ -537,10 +537,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:58.881919Z",
- "iopub.status.busy": "2024-06-25T16:01:58.881071Z",
- "iopub.status.idle": "2024-06-25T16:01:58.906603Z",
- "shell.execute_reply": "2024-06-25T16:01:58.906027Z"
+ "iopub.execute_input": "2024-06-25T19:35:55.336733Z",
+ "iopub.status.busy": "2024-06-25T19:35:55.336306Z",
+ "iopub.status.idle": "2024-06-25T19:35:55.363099Z",
+ "shell.execute_reply": "2024-06-25T19:35:55.362613Z"
},
"scrolled": true
},
@@ -670,10 +670,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:58.910119Z",
- "iopub.status.busy": "2024-06-25T16:01:58.909047Z",
- "iopub.status.idle": "2024-06-25T16:01:58.920232Z",
- "shell.execute_reply": "2024-06-25T16:01:58.919795Z"
+ "iopub.execute_input": "2024-06-25T19:35:55.366640Z",
+ "iopub.status.busy": "2024-06-25T19:35:55.365705Z",
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+ "shell.execute_reply": "2024-06-25T19:35:55.375622Z"
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@@ -783,10 +783,10 @@
"execution_count": 13,
"metadata": {
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+ "shell.execute_reply": "2024-06-25T19:35:55.382206Z"
}
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"outputs": [
@@ -824,10 +824,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:01:58.928833Z",
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"outputs": [
@@ -944,10 +944,10 @@
"execution_count": 15,
"metadata": {
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"outputs": [
@@ -1030,10 +1030,10 @@
"execution_count": 16,
"metadata": {
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"outputs": [
@@ -1141,10 +1141,10 @@
"execution_count": 17,
"metadata": {
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@@ -1255,10 +1255,10 @@
"execution_count": 18,
"metadata": {
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"outputs": [
@@ -1326,10 +1326,10 @@
"execution_count": 19,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:01:58.971896Z",
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@@ -1408,10 +1408,10 @@
"execution_count": 20,
"metadata": {
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@@ -1459,10 +1459,10 @@
"execution_count": 21,
"metadata": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
index c5b93e0b1..073e233c2 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:02:03.278476Z",
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- "iopub.status.idle": "2024-06-25T16:02:03.738248Z",
- "shell.execute_reply": "2024-06-25T16:02:03.737620Z"
+ "iopub.execute_input": "2024-06-25T19:35:59.467250Z",
+ "iopub.status.busy": "2024-06-25T19:35:59.467073Z",
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+ "shell.execute_reply": "2024-06-25T19:35:59.885107Z"
}
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"outputs": [],
@@ -87,10 +87,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:03.740991Z",
- "iopub.status.busy": "2024-06-25T16:02:03.740456Z",
- "iopub.status.idle": "2024-06-25T16:02:03.870272Z",
- "shell.execute_reply": "2024-06-25T16:02:03.869699Z"
+ "iopub.execute_input": "2024-06-25T19:35:59.888637Z",
+ "iopub.status.busy": "2024-06-25T19:35:59.888151Z",
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+ "shell.execute_reply": "2024-06-25T19:36:00.014148Z"
}
},
"outputs": [
@@ -181,10 +181,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:03.872521Z",
- "iopub.status.busy": "2024-06-25T16:02:03.872274Z",
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- "shell.execute_reply": "2024-06-25T16:02:03.895004Z"
+ "iopub.execute_input": "2024-06-25T19:36:00.016873Z",
+ "iopub.status.busy": "2024-06-25T19:36:00.016623Z",
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+ "shell.execute_reply": "2024-06-25T19:36:00.039305Z"
}
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"outputs": [],
@@ -210,10 +210,10 @@
"execution_count": 4,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:06.877213Z"
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+ "shell.execute_reply": "2024-06-25T19:36:02.696318Z"
}
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"outputs": [
@@ -716,10 +716,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:06.880622Z",
- "iopub.status.busy": "2024-06-25T16:02:06.880154Z",
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- "shell.execute_reply": "2024-06-25T16:02:14.827773Z"
+ "iopub.execute_input": "2024-06-25T19:36:02.699546Z",
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+ "shell.execute_reply": "2024-06-25T19:36:11.209947Z"
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"outputs": [
@@ -820,10 +820,10 @@
"execution_count": 6,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:14.992007Z"
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"outputs": [],
@@ -854,10 +854,10 @@
"execution_count": 7,
"metadata": {
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"outputs": [
@@ -1016,10 +1016,10 @@
"execution_count": 8,
"metadata": {
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"outputs": [
@@ -1098,10 +1098,10 @@
"execution_count": 9,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:16.806435Z"
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"outputs": [],
@@ -1131,10 +1131,10 @@
"execution_count": 10,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:16.830060Z"
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"outputs": [],
@@ -1162,10 +1162,10 @@
"execution_count": 11,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:17.052990Z"
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+ "shell.execute_reply": "2024-06-25T19:36:13.369417Z"
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"outputs": [],
@@ -1205,10 +1205,10 @@
"execution_count": 12,
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:02:17.075526Z"
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+ "shell.execute_reply": "2024-06-25T19:36:13.390786Z"
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"outputs": [
@@ -1406,10 +1406,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:02:17.078246Z",
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- "shell.execute_reply": "2024-06-25T16:02:17.224353Z"
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+ "shell.execute_reply": "2024-06-25T19:36:13.561518Z"
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"outputs": [
@@ -1476,10 +1476,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:02:17.227348Z",
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- "shell.execute_reply": "2024-06-25T16:02:17.237274Z"
+ "iopub.execute_input": "2024-06-25T19:36:13.564551Z",
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+ "shell.execute_reply": "2024-06-25T19:36:13.573705Z"
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"outputs": [
@@ -1745,10 +1745,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:02:17.239946Z",
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- "shell.execute_reply": "2024-06-25T16:02:17.248766Z"
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+ "shell.execute_reply": "2024-06-25T19:36:13.584885Z"
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"outputs": [
@@ -1935,10 +1935,10 @@
"execution_count": 16,
"metadata": {
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"outputs": [],
@@ -1972,10 +1972,10 @@
"execution_count": 17,
"metadata": {
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"outputs": [],
@@ -1997,10 +1997,10 @@
"execution_count": 18,
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"outputs": [
@@ -2158,10 +2158,10 @@
"execution_count": 19,
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diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index 2b283a3e9..6a954c6b0 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": "2024-06-25T16:02:21.580164Z",
- "iopub.status.busy": "2024-06-25T16:02:21.579985Z",
- "iopub.status.idle": "2024-06-25T16:02:22.763255Z",
- "shell.execute_reply": "2024-06-25T16:02:22.762705Z"
+ "iopub.execute_input": "2024-06-25T19:36:17.536909Z",
+ "iopub.status.busy": "2024-06-25T19:36:17.536739Z",
+ "iopub.status.idle": "2024-06-25T19:36:18.659278Z",
+ "shell.execute_reply": "2024-06-25T19:36:18.658730Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T16:02:22.765949Z",
- "iopub.status.busy": "2024-06-25T16:02:22.765396Z",
- "iopub.status.idle": "2024-06-25T16:02:22.768318Z",
- "shell.execute_reply": "2024-06-25T16:02:22.767866Z"
+ "iopub.execute_input": "2024-06-25T19:36:18.661733Z",
+ "iopub.status.busy": "2024-06-25T19:36:18.661422Z",
+ "iopub.status.idle": "2024-06-25T19:36:18.664275Z",
+ "shell.execute_reply": "2024-06-25T19:36:18.663748Z"
},
"id": "_UvI80l42iyi"
},
@@ -203,10 +203,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:22.770655Z",
- "iopub.status.busy": "2024-06-25T16:02:22.770476Z",
- "iopub.status.idle": "2024-06-25T16:02:22.783781Z",
- "shell.execute_reply": "2024-06-25T16:02:22.783292Z"
+ "iopub.execute_input": "2024-06-25T19:36:18.666336Z",
+ "iopub.status.busy": "2024-06-25T19:36:18.666027Z",
+ "iopub.status.idle": "2024-06-25T19:36:18.678092Z",
+ "shell.execute_reply": "2024-06-25T19:36:18.677567Z"
},
"nbsphinx": "hidden"
},
@@ -285,10 +285,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:22.785989Z",
- "iopub.status.busy": "2024-06-25T16:02:22.785704Z",
- "iopub.status.idle": "2024-06-25T16:02:26.605815Z",
- "shell.execute_reply": "2024-06-25T16:02:26.605337Z"
+ "iopub.execute_input": "2024-06-25T19:36:18.680164Z",
+ "iopub.status.busy": "2024-06-25T19:36:18.679860Z",
+ "iopub.status.idle": "2024-06-25T19:36:28.874863Z",
+ "shell.execute_reply": "2024-06-25T19:36:28.874371Z"
},
"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 cf0213fe0..649612439 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": "2024-06-25T16:02:29.093911Z",
- "iopub.status.busy": "2024-06-25T16:02:29.093556Z",
- "iopub.status.idle": "2024-06-25T16:02:30.271993Z",
- "shell.execute_reply": "2024-06-25T16:02:30.271440Z"
+ "iopub.execute_input": "2024-06-25T19:36:31.054579Z",
+ "iopub.status.busy": "2024-06-25T19:36:31.054404Z",
+ "iopub.status.idle": "2024-06-25T19:36:32.183683Z",
+ "shell.execute_reply": "2024-06-25T19:36:32.183056Z"
},
"nbsphinx": "hidden"
},
@@ -137,10 +137,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:30.275199Z",
- "iopub.status.busy": "2024-06-25T16:02:30.274597Z",
- "iopub.status.idle": "2024-06-25T16:02:30.278598Z",
- "shell.execute_reply": "2024-06-25T16:02:30.278052Z"
+ "iopub.execute_input": "2024-06-25T19:36:32.186495Z",
+ "iopub.status.busy": "2024-06-25T19:36:32.186073Z",
+ "iopub.status.idle": "2024-06-25T19:36:32.189610Z",
+ "shell.execute_reply": "2024-06-25T19:36:32.189148Z"
}
},
"outputs": [],
@@ -176,10 +176,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:30.280929Z",
- "iopub.status.busy": "2024-06-25T16:02:30.280543Z",
- "iopub.status.idle": "2024-06-25T16:02:33.664200Z",
- "shell.execute_reply": "2024-06-25T16:02:33.663468Z"
+ "iopub.execute_input": "2024-06-25T19:36:32.191776Z",
+ "iopub.status.busy": "2024-06-25T19:36:32.191309Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.412500Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.411739Z"
}
},
"outputs": [],
@@ -202,10 +202,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.667568Z",
- "iopub.status.busy": "2024-06-25T16:02:33.666753Z",
- "iopub.status.idle": "2024-06-25T16:02:33.706553Z",
- "shell.execute_reply": "2024-06-25T16:02:33.705839Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.415868Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.414996Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.452492Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.451863Z"
}
},
"outputs": [],
@@ -228,10 +228,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.709552Z",
- "iopub.status.busy": "2024-06-25T16:02:33.709080Z",
- "iopub.status.idle": "2024-06-25T16:02:33.747295Z",
- "shell.execute_reply": "2024-06-25T16:02:33.746692Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.455265Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.454795Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.489174Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.488560Z"
}
},
"outputs": [],
@@ -253,10 +253,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.750054Z",
- "iopub.status.busy": "2024-06-25T16:02:33.749633Z",
- "iopub.status.idle": "2024-06-25T16:02:33.752782Z",
- "shell.execute_reply": "2024-06-25T16:02:33.752208Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.491931Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.491449Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.494631Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.494157Z"
}
},
"outputs": [],
@@ -278,10 +278,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.754793Z",
- "iopub.status.busy": "2024-06-25T16:02:33.754393Z",
- "iopub.status.idle": "2024-06-25T16:02:33.757184Z",
- "shell.execute_reply": "2024-06-25T16:02:33.756618Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.496822Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.496395Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.499017Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.498537Z"
}
},
"outputs": [],
@@ -363,10 +363,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.759344Z",
- "iopub.status.busy": "2024-06-25T16:02:33.759015Z",
- "iopub.status.idle": "2024-06-25T16:02:33.784607Z",
- "shell.execute_reply": "2024-06-25T16:02:33.783988Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.501249Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.500816Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.525422Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.524821Z"
}
},
"outputs": [
@@ -380,7 +380,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2c6a877036d2484099e04e3a1a4f477a",
+ "model_id": "d8af54b634f1457680edc574c7fcb110",
"version_major": 2,
"version_minor": 0
},
@@ -394,7 +394,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3dcecf61b1ea493891d9c418f6d478c9",
+ "model_id": "84b64175499142ae9cf770d1e88b80ac",
"version_major": 2,
"version_minor": 0
},
@@ -452,10 +452,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.791372Z",
- "iopub.status.busy": "2024-06-25T16:02:33.790898Z",
- "iopub.status.idle": "2024-06-25T16:02:33.798187Z",
- "shell.execute_reply": "2024-06-25T16:02:33.797608Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.532028Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.531847Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.538645Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.538198Z"
},
"nbsphinx": "hidden"
},
@@ -486,10 +486,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.800382Z",
- "iopub.status.busy": "2024-06-25T16:02:33.800203Z",
- "iopub.status.idle": "2024-06-25T16:02:33.803758Z",
- "shell.execute_reply": "2024-06-25T16:02:33.803335Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.540612Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.540437Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.543848Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.543410Z"
},
"nbsphinx": "hidden"
},
@@ -512,10 +512,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.805911Z",
- "iopub.status.busy": "2024-06-25T16:02:33.805578Z",
- "iopub.status.idle": "2024-06-25T16:02:33.811761Z",
- "shell.execute_reply": "2024-06-25T16:02:33.811312Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.545806Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.545508Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.551703Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.551260Z"
}
},
"outputs": [],
@@ -565,10 +565,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.813716Z",
- "iopub.status.busy": "2024-06-25T16:02:33.813419Z",
- "iopub.status.idle": "2024-06-25T16:02:33.850139Z",
- "shell.execute_reply": "2024-06-25T16:02:33.849563Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.553602Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.553415Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.589414Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.588805Z"
}
},
"outputs": [],
@@ -585,10 +585,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.852668Z",
- "iopub.status.busy": "2024-06-25T16:02:33.852353Z",
- "iopub.status.idle": "2024-06-25T16:02:33.887227Z",
- "shell.execute_reply": "2024-06-25T16:02:33.886643Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.592001Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.591752Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.628128Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.627508Z"
},
"nbsphinx": "hidden"
},
@@ -667,10 +667,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:33.889899Z",
- "iopub.status.busy": "2024-06-25T16:02:33.889587Z",
- "iopub.status.idle": "2024-06-25T16:02:34.020226Z",
- "shell.execute_reply": "2024-06-25T16:02:34.019640Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.630864Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.630509Z",
+ "iopub.status.idle": "2024-06-25T19:36:35.751028Z",
+ "shell.execute_reply": "2024-06-25T19:36:35.750367Z"
}
},
"outputs": [
@@ -737,10 +737,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:34.022853Z",
- "iopub.status.busy": "2024-06-25T16:02:34.022310Z",
- "iopub.status.idle": "2024-06-25T16:02:37.124984Z",
- "shell.execute_reply": "2024-06-25T16:02:37.124295Z"
+ "iopub.execute_input": "2024-06-25T19:36:35.753981Z",
+ "iopub.status.busy": "2024-06-25T19:36:35.753115Z",
+ "iopub.status.idle": "2024-06-25T19:36:38.820276Z",
+ "shell.execute_reply": "2024-06-25T19:36:38.819614Z"
}
},
"outputs": [
@@ -826,10 +826,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:37.127549Z",
- "iopub.status.busy": "2024-06-25T16:02:37.127178Z",
- "iopub.status.idle": "2024-06-25T16:02:37.190571Z",
- "shell.execute_reply": "2024-06-25T16:02:37.189933Z"
+ "iopub.execute_input": "2024-06-25T19:36:38.822817Z",
+ "iopub.status.busy": "2024-06-25T19:36:38.822359Z",
+ "iopub.status.idle": "2024-06-25T19:36:38.881135Z",
+ "shell.execute_reply": "2024-06-25T19:36:38.880677Z"
}
},
"outputs": [
@@ -1285,10 +1285,10 @@
"id": "af3052ac",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:37.192895Z",
- "iopub.status.busy": "2024-06-25T16:02:37.192539Z",
- "iopub.status.idle": "2024-06-25T16:02:37.236637Z",
- "shell.execute_reply": "2024-06-25T16:02:37.235957Z"
+ "iopub.execute_input": "2024-06-25T19:36:38.883155Z",
+ "iopub.status.busy": "2024-06-25T19:36:38.882856Z",
+ "iopub.status.idle": "2024-06-25T19:36:38.922999Z",
+ "shell.execute_reply": "2024-06-25T19:36:38.922558Z"
}
},
"outputs": [
@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "89239277",
+ "id": "91d13c0b",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1327,7 +1327,7 @@
},
{
"cell_type": "markdown",
- "id": "ac0629ed",
+ "id": "838b0e29",
"metadata": {},
"source": [
"The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n",
@@ -1338,7 +1338,7 @@
},
{
"cell_type": "markdown",
- "id": "24692753",
+ "id": "72c82160",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by Datalab?\n",
@@ -1349,13 +1349,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "9a0a402a",
+ "id": "c8ef0e49",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:37.239147Z",
- "iopub.status.busy": "2024-06-25T16:02:37.238875Z",
- "iopub.status.idle": "2024-06-25T16:02:37.247577Z",
- "shell.execute_reply": "2024-06-25T16:02:37.246901Z"
+ "iopub.execute_input": "2024-06-25T19:36:38.925175Z",
+ "iopub.status.busy": "2024-06-25T19:36:38.924869Z",
+ "iopub.status.idle": "2024-06-25T19:36:38.933100Z",
+ "shell.execute_reply": "2024-06-25T19:36:38.932519Z"
}
},
"outputs": [],
@@ -1457,7 +1457,7 @@
},
{
"cell_type": "markdown",
- "id": "ea91a64c",
+ "id": "bfd8eea7",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1472,13 +1472,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "ced0d3fc",
+ "id": "7515c699",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:37.250161Z",
- "iopub.status.busy": "2024-06-25T16:02:37.249739Z",
- "iopub.status.idle": "2024-06-25T16:02:37.272485Z",
- "shell.execute_reply": "2024-06-25T16:02:37.271873Z"
+ "iopub.execute_input": "2024-06-25T19:36:38.935170Z",
+ "iopub.status.busy": "2024-06-25T19:36:38.934961Z",
+ "iopub.status.idle": "2024-06-25T19:36:38.958819Z",
+ "shell.execute_reply": "2024-06-25T19:36:38.958261Z"
}
},
"outputs": [
@@ -1495,7 +1495,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_7641/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ "/tmp/ipykernel_7655/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n"
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},
@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -95,10 +95,10 @@
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@@ -234,10 +234,10 @@
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@@ -340,10 +340,10 @@
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}
},
"outputs": [
@@ -393,10 +393,10 @@
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"metadata": {
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}
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"outputs": [],
@@ -428,10 +428,10 @@
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@@ -482,10 +482,10 @@
"execution_count": 7,
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@@ -615,10 +615,10 @@
"execution_count": 8,
"metadata": {
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@@ -737,10 +737,10 @@
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@@ -789,10 +789,10 @@
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- "shell.execute_reply": "2024-06-25T16:02:46.307209Z"
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@@ -899,10 +899,10 @@
"execution_count": 11,
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- "shell.execute_reply": "2024-06-25T16:02:46.521273Z"
+ "iopub.execute_input": "2024-06-25T19:36:47.094203Z",
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@@ -939,10 +939,10 @@
"execution_count": 12,
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@@ -1408,10 +1408,10 @@
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@@ -1558,10 +1558,10 @@
"execution_count": 14,
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- "shell.execute_reply": "2024-06-25T16:02:46.641381Z"
+ "iopub.execute_input": "2024-06-25T19:36:47.337943Z",
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@@ -1642,10 +1642,10 @@
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@@ -1705,10 +1705,10 @@
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@@ -1746,10 +1746,10 @@
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@@ -1804,10 +1804,10 @@
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@@ -1858,10 +1858,10 @@
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@@ -1930,10 +1930,10 @@
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@@ -1965,10 +1965,10 @@
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@@ -2025,10 +2025,10 @@
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@@ -2063,10 +2063,10 @@
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@@ -2228,10 +2228,10 @@
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@@ -2285,10 +2285,10 @@
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@@ -2335,10 +2335,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 26efb45d7..906c55fbe 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -88,10 +88,10 @@
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- "shell.execute_reply": "2024-06-25T16:02:54.304563Z"
+ "iopub.execute_input": "2024-06-25T19:36:52.983005Z",
+ "iopub.status.busy": "2024-06-25T19:36:52.982831Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.092198Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.091645Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T16:02:54.308106Z",
- "iopub.status.busy": "2024-06-25T16:02:54.307481Z",
- "iopub.status.idle": "2024-06-25T16:02:54.310824Z",
- "shell.execute_reply": "2024-06-25T16:02:54.310289Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.094787Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.094431Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.097617Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.097173Z"
}
},
"outputs": [],
@@ -263,10 +263,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.313010Z",
- "iopub.status.busy": "2024-06-25T16:02:54.312692Z",
- "iopub.status.idle": "2024-06-25T16:02:54.321327Z",
- "shell.execute_reply": "2024-06-25T16:02:54.320765Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.099720Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.099372Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.107610Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.107140Z"
},
"nbsphinx": "hidden"
},
@@ -350,10 +350,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.323419Z",
- "iopub.status.busy": "2024-06-25T16:02:54.323092Z",
- "iopub.status.idle": "2024-06-25T16:02:54.372385Z",
- "shell.execute_reply": "2024-06-25T16:02:54.371847Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.109674Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.109247Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.157412Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.156840Z"
}
},
"outputs": [],
@@ -379,10 +379,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.375070Z",
- "iopub.status.busy": "2024-06-25T16:02:54.374642Z",
- "iopub.status.idle": "2024-06-25T16:02:54.393435Z",
- "shell.execute_reply": "2024-06-25T16:02:54.392940Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.159654Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.159472Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.177229Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.176762Z"
}
},
"outputs": [
@@ -597,10 +597,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.395635Z",
- "iopub.status.busy": "2024-06-25T16:02:54.395296Z",
- "iopub.status.idle": "2024-06-25T16:02:54.399316Z",
- "shell.execute_reply": "2024-06-25T16:02:54.398875Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.179344Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.179010Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.182993Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.182561Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.401276Z",
- "iopub.status.busy": "2024-06-25T16:02:54.401014Z",
- "iopub.status.idle": "2024-06-25T16:02:54.416591Z",
- "shell.execute_reply": "2024-06-25T16:02:54.416179Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.185097Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.184777Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.198824Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.198358Z"
}
},
"outputs": [],
@@ -698,10 +698,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.418396Z",
- "iopub.status.busy": "2024-06-25T16:02:54.418219Z",
- "iopub.status.idle": "2024-06-25T16:02:54.445301Z",
- "shell.execute_reply": "2024-06-25T16:02:54.444811Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.200845Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.200664Z",
+ "iopub.status.idle": "2024-06-25T19:36:54.227151Z",
+ "shell.execute_reply": "2024-06-25T19:36:54.226585Z"
}
},
"outputs": [],
@@ -738,10 +738,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:54.447597Z",
- "iopub.status.busy": "2024-06-25T16:02:54.447422Z",
- "iopub.status.idle": "2024-06-25T16:02:56.432194Z",
- "shell.execute_reply": "2024-06-25T16:02:56.431615Z"
+ "iopub.execute_input": "2024-06-25T19:36:54.229370Z",
+ "iopub.status.busy": "2024-06-25T19:36:54.228984Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.088954Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.088321Z"
}
},
"outputs": [],
@@ -771,10 +771,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.435124Z",
- "iopub.status.busy": "2024-06-25T16:02:56.434375Z",
- "iopub.status.idle": "2024-06-25T16:02:56.441579Z",
- "shell.execute_reply": "2024-06-25T16:02:56.441127Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.091797Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.091365Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.098121Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.097667Z"
},
"scrolled": true
},
@@ -885,10 +885,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.443631Z",
- "iopub.status.busy": "2024-06-25T16:02:56.443317Z",
- "iopub.status.idle": "2024-06-25T16:02:56.455959Z",
- "shell.execute_reply": "2024-06-25T16:02:56.455509Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.100176Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.099747Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.112314Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.111779Z"
}
},
"outputs": [
@@ -1138,10 +1138,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.458164Z",
- "iopub.status.busy": "2024-06-25T16:02:56.457716Z",
- "iopub.status.idle": "2024-06-25T16:02:56.464156Z",
- "shell.execute_reply": "2024-06-25T16:02:56.463633Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.114307Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.113989Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.120308Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.119759Z"
},
"scrolled": true
},
@@ -1315,10 +1315,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.466323Z",
- "iopub.status.busy": "2024-06-25T16:02:56.466004Z",
- "iopub.status.idle": "2024-06-25T16:02:56.468521Z",
- "shell.execute_reply": "2024-06-25T16:02:56.468097Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.122321Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.122009Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.124766Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.124216Z"
}
},
"outputs": [],
@@ -1340,10 +1340,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.470402Z",
- "iopub.status.busy": "2024-06-25T16:02:56.470111Z",
- "iopub.status.idle": "2024-06-25T16:02:56.473462Z",
- "shell.execute_reply": "2024-06-25T16:02:56.472984Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.126666Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.126364Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.129930Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.129387Z"
},
"scrolled": true
},
@@ -1395,10 +1395,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.475444Z",
- "iopub.status.busy": "2024-06-25T16:02:56.475147Z",
- "iopub.status.idle": "2024-06-25T16:02:56.477810Z",
- "shell.execute_reply": "2024-06-25T16:02:56.477269Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.132039Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.131738Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.134411Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.133864Z"
}
},
"outputs": [],
@@ -1422,10 +1422,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.479686Z",
- "iopub.status.busy": "2024-06-25T16:02:56.479395Z",
- "iopub.status.idle": "2024-06-25T16:02:56.483532Z",
- "shell.execute_reply": "2024-06-25T16:02:56.482988Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.136459Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.136150Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.140438Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.139976Z"
}
},
"outputs": [
@@ -1480,10 +1480,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.485687Z",
- "iopub.status.busy": "2024-06-25T16:02:56.485297Z",
- "iopub.status.idle": "2024-06-25T16:02:56.514610Z",
- "shell.execute_reply": "2024-06-25T16:02:56.514006Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.142440Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.142121Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.170976Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.170425Z"
}
},
"outputs": [],
@@ -1526,10 +1526,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:02:56.516802Z",
- "iopub.status.busy": "2024-06-25T16:02:56.516469Z",
- "iopub.status.idle": "2024-06-25T16:02:56.521584Z",
- "shell.execute_reply": "2024-06-25T16:02:56.521158Z"
+ "iopub.execute_input": "2024-06-25T19:36:56.173162Z",
+ "iopub.status.busy": "2024-06-25T19:36:56.172858Z",
+ "iopub.status.idle": "2024-06-25T19:36:56.177426Z",
+ "shell.execute_reply": "2024-06-25T19:36:56.176864Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index df28f7e02..9e634f2f3 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": "2024-06-25T16:02:59.414501Z",
- "iopub.status.busy": "2024-06-25T16:02:59.414064Z",
- "iopub.status.idle": "2024-06-25T16:03:00.636346Z",
- "shell.execute_reply": "2024-06-25T16:03:00.635780Z"
+ "iopub.execute_input": "2024-06-25T19:36:58.919980Z",
+ "iopub.status.busy": "2024-06-25T19:36:58.919807Z",
+ "iopub.status.idle": "2024-06-25T19:37:00.071287Z",
+ "shell.execute_reply": "2024-06-25T19:37:00.070749Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T16:03:00.639002Z",
- "iopub.status.busy": "2024-06-25T16:03:00.638538Z",
- "iopub.status.idle": "2024-06-25T16:03:00.841947Z",
- "shell.execute_reply": "2024-06-25T16:03:00.841389Z"
+ "iopub.execute_input": "2024-06-25T19:37:00.073825Z",
+ "iopub.status.busy": "2024-06-25T19:37:00.073418Z",
+ "iopub.status.idle": "2024-06-25T19:37:00.265456Z",
+ "shell.execute_reply": "2024-06-25T19:37:00.264849Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:00.844664Z",
- "iopub.status.busy": "2024-06-25T16:03:00.844115Z",
- "iopub.status.idle": "2024-06-25T16:03:00.857495Z",
- "shell.execute_reply": "2024-06-25T16:03:00.856922Z"
+ "iopub.execute_input": "2024-06-25T19:37:00.268256Z",
+ "iopub.status.busy": "2024-06-25T19:37:00.267860Z",
+ "iopub.status.idle": "2024-06-25T19:37:00.281177Z",
+ "shell.execute_reply": "2024-06-25T19:37:00.280743Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:00.859967Z",
- "iopub.status.busy": "2024-06-25T16:03:00.859446Z",
- "iopub.status.idle": "2024-06-25T16:03:03.563815Z",
- "shell.execute_reply": "2024-06-25T16:03:03.563275Z"
+ "iopub.execute_input": "2024-06-25T19:37:00.283272Z",
+ "iopub.status.busy": "2024-06-25T19:37:00.282948Z",
+ "iopub.status.idle": "2024-06-25T19:37:02.915319Z",
+ "shell.execute_reply": "2024-06-25T19:37:02.914720Z"
}
},
"outputs": [
@@ -454,10 +454,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:03.566363Z",
- "iopub.status.busy": "2024-06-25T16:03:03.565825Z",
- "iopub.status.idle": "2024-06-25T16:03:04.931484Z",
- "shell.execute_reply": "2024-06-25T16:03:04.930842Z"
+ "iopub.execute_input": "2024-06-25T19:37:02.917655Z",
+ "iopub.status.busy": "2024-06-25T19:37:02.917303Z",
+ "iopub.status.idle": "2024-06-25T19:37:04.262113Z",
+ "shell.execute_reply": "2024-06-25T19:37:04.261389Z"
}
},
"outputs": [],
@@ -499,10 +499,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:04.934103Z",
- "iopub.status.busy": "2024-06-25T16:03:04.933906Z",
- "iopub.status.idle": "2024-06-25T16:03:04.937701Z",
- "shell.execute_reply": "2024-06-25T16:03:04.937197Z"
+ "iopub.execute_input": "2024-06-25T19:37:04.264665Z",
+ "iopub.status.busy": "2024-06-25T19:37:04.264273Z",
+ "iopub.status.idle": "2024-06-25T19:37:04.268776Z",
+ "shell.execute_reply": "2024-06-25T19:37:04.268171Z"
}
},
"outputs": [
@@ -544,10 +544,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:04.939781Z",
- "iopub.status.busy": "2024-06-25T16:03:04.939486Z",
- "iopub.status.idle": "2024-06-25T16:03:07.030228Z",
- "shell.execute_reply": "2024-06-25T16:03:07.029555Z"
+ "iopub.execute_input": "2024-06-25T19:37:04.271017Z",
+ "iopub.status.busy": "2024-06-25T19:37:04.270694Z",
+ "iopub.status.idle": "2024-06-25T19:37:06.209152Z",
+ "shell.execute_reply": "2024-06-25T19:37:06.208542Z"
}
},
"outputs": [
@@ -594,10 +594,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:07.032587Z",
- "iopub.status.busy": "2024-06-25T16:03:07.032175Z",
- "iopub.status.idle": "2024-06-25T16:03:07.040290Z",
- "shell.execute_reply": "2024-06-25T16:03:07.039729Z"
+ "iopub.execute_input": "2024-06-25T19:37:06.211688Z",
+ "iopub.status.busy": "2024-06-25T19:37:06.211198Z",
+ "iopub.status.idle": "2024-06-25T19:37:06.218564Z",
+ "shell.execute_reply": "2024-06-25T19:37:06.218036Z"
}
},
"outputs": [
@@ -633,10 +633,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:07.042262Z",
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- "iopub.status.idle": "2024-06-25T16:03:09.659875Z",
- "shell.execute_reply": "2024-06-25T16:03:09.659261Z"
+ "iopub.execute_input": "2024-06-25T19:37:06.220591Z",
+ "iopub.status.busy": "2024-06-25T19:37:06.220264Z",
+ "iopub.status.idle": "2024-06-25T19:37:08.793564Z",
+ "shell.execute_reply": "2024-06-25T19:37:08.792970Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:09.662397Z",
- "iopub.status.busy": "2024-06-25T16:03:09.661971Z",
- "iopub.status.idle": "2024-06-25T16:03:09.665641Z",
- "shell.execute_reply": "2024-06-25T16:03:09.665128Z"
+ "iopub.execute_input": "2024-06-25T19:37:08.795901Z",
+ "iopub.status.busy": "2024-06-25T19:37:08.795549Z",
+ "iopub.status.idle": "2024-06-25T19:37:08.798884Z",
+ "shell.execute_reply": "2024-06-25T19:37:08.798350Z"
}
},
"outputs": [
@@ -721,10 +721,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:09.667835Z",
- "iopub.status.busy": "2024-06-25T16:03:09.667530Z",
- "iopub.status.idle": "2024-06-25T16:03:09.671491Z",
- "shell.execute_reply": "2024-06-25T16:03:09.670936Z"
+ "iopub.execute_input": "2024-06-25T19:37:08.800984Z",
+ "iopub.status.busy": "2024-06-25T19:37:08.800677Z",
+ "iopub.status.idle": "2024-06-25T19:37:08.804151Z",
+ "shell.execute_reply": "2024-06-25T19:37:08.803635Z"
}
},
"outputs": [],
@@ -752,10 +752,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:09.673741Z",
- "iopub.status.busy": "2024-06-25T16:03:09.673312Z",
- "iopub.status.idle": "2024-06-25T16:03:09.676591Z",
- "shell.execute_reply": "2024-06-25T16:03:09.676142Z"
+ "iopub.execute_input": "2024-06-25T19:37:08.806163Z",
+ "iopub.status.busy": "2024-06-25T19:37:08.805988Z",
+ "iopub.status.idle": "2024-06-25T19:37:08.809167Z",
+ "shell.execute_reply": "2024-06-25T19:37:08.808609Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 7d2ad159a..aebe787bb 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": "2024-06-25T16:03:12.410569Z",
- "iopub.status.busy": "2024-06-25T16:03:12.410083Z",
- "iopub.status.idle": "2024-06-25T16:03:13.647607Z",
- "shell.execute_reply": "2024-06-25T16:03:13.647113Z"
+ "iopub.execute_input": "2024-06-25T19:37:11.308794Z",
+ "iopub.status.busy": "2024-06-25T19:37:11.308627Z",
+ "iopub.status.idle": "2024-06-25T19:37:12.452711Z",
+ "shell.execute_reply": "2024-06-25T19:37:12.452159Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": "2024-06-25T16:03:13.650138Z",
- "iopub.status.busy": "2024-06-25T16:03:13.649686Z",
- "iopub.status.idle": "2024-06-25T16:03:14.680510Z",
- "shell.execute_reply": "2024-06-25T16:03:14.679737Z"
+ "iopub.execute_input": "2024-06-25T19:37:12.455258Z",
+ "iopub.status.busy": "2024-06-25T19:37:12.454827Z",
+ "iopub.status.idle": "2024-06-25T19:37:14.890620Z",
+ "shell.execute_reply": "2024-06-25T19:37:14.889969Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:14.683282Z",
- "iopub.status.busy": "2024-06-25T16:03:14.683075Z",
- "iopub.status.idle": "2024-06-25T16:03:14.686559Z",
- "shell.execute_reply": "2024-06-25T16:03:14.686010Z"
+ "iopub.execute_input": "2024-06-25T19:37:14.893309Z",
+ "iopub.status.busy": "2024-06-25T19:37:14.892942Z",
+ "iopub.status.idle": "2024-06-25T19:37:14.896049Z",
+ "shell.execute_reply": "2024-06-25T19:37:14.895624Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:14.688758Z",
- "iopub.status.busy": "2024-06-25T16:03:14.688409Z",
- "iopub.status.idle": "2024-06-25T16:03:14.694466Z",
- "shell.execute_reply": "2024-06-25T16:03:14.694042Z"
+ "iopub.execute_input": "2024-06-25T19:37:14.898040Z",
+ "iopub.status.busy": "2024-06-25T19:37:14.897713Z",
+ "iopub.status.idle": "2024-06-25T19:37:14.903653Z",
+ "shell.execute_reply": "2024-06-25T19:37:14.903185Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:14.696653Z",
- "iopub.status.busy": "2024-06-25T16:03:14.696196Z",
- "iopub.status.idle": "2024-06-25T16:03:15.192910Z",
- "shell.execute_reply": "2024-06-25T16:03:15.192221Z"
+ "iopub.execute_input": "2024-06-25T19:37:14.905688Z",
+ "iopub.status.busy": "2024-06-25T19:37:14.905360Z",
+ "iopub.status.idle": "2024-06-25T19:37:15.391751Z",
+ "shell.execute_reply": "2024-06-25T19:37:15.391128Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:15.195650Z",
- "iopub.status.busy": "2024-06-25T16:03:15.195283Z",
- "iopub.status.idle": "2024-06-25T16:03:15.201692Z",
- "shell.execute_reply": "2024-06-25T16:03:15.200995Z"
+ "iopub.execute_input": "2024-06-25T19:37:15.394507Z",
+ "iopub.status.busy": "2024-06-25T19:37:15.394142Z",
+ "iopub.status.idle": "2024-06-25T19:37:15.399398Z",
+ "shell.execute_reply": "2024-06-25T19:37:15.398860Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:15.204599Z",
- "iopub.status.busy": "2024-06-25T16:03:15.204087Z",
- "iopub.status.idle": "2024-06-25T16:03:15.209021Z",
- "shell.execute_reply": "2024-06-25T16:03:15.208413Z"
+ "iopub.execute_input": "2024-06-25T19:37:15.401545Z",
+ "iopub.status.busy": "2024-06-25T19:37:15.401225Z",
+ "iopub.status.idle": "2024-06-25T19:37:15.404995Z",
+ "shell.execute_reply": "2024-06-25T19:37:15.404569Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:15.211428Z",
- "iopub.status.busy": "2024-06-25T16:03:15.210980Z",
- "iopub.status.idle": "2024-06-25T16:03:16.126313Z",
- "shell.execute_reply": "2024-06-25T16:03:16.125723Z"
+ "iopub.execute_input": "2024-06-25T19:37:15.407039Z",
+ "iopub.status.busy": "2024-06-25T19:37:15.406711Z",
+ "iopub.status.idle": "2024-06-25T19:37:16.295944Z",
+ "shell.execute_reply": "2024-06-25T19:37:16.295383Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:16.128644Z",
- "iopub.status.busy": "2024-06-25T16:03:16.128441Z",
- "iopub.status.idle": "2024-06-25T16:03:16.332017Z",
- "shell.execute_reply": "2024-06-25T16:03:16.331436Z"
+ "iopub.execute_input": "2024-06-25T19:37:16.298196Z",
+ "iopub.status.busy": "2024-06-25T19:37:16.297999Z",
+ "iopub.status.idle": "2024-06-25T19:37:16.525061Z",
+ "shell.execute_reply": "2024-06-25T19:37:16.524590Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:16.334321Z",
- "iopub.status.busy": "2024-06-25T16:03:16.333873Z",
- "iopub.status.idle": "2024-06-25T16:03:16.338153Z",
- "shell.execute_reply": "2024-06-25T16:03:16.337742Z"
+ "iopub.execute_input": "2024-06-25T19:37:16.527288Z",
+ "iopub.status.busy": "2024-06-25T19:37:16.526859Z",
+ "iopub.status.idle": "2024-06-25T19:37:16.531244Z",
+ "shell.execute_reply": "2024-06-25T19:37:16.530747Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:16.340198Z",
- "iopub.status.busy": "2024-06-25T16:03:16.339765Z",
- "iopub.status.idle": "2024-06-25T16:03:16.801350Z",
- "shell.execute_reply": "2024-06-25T16:03:16.800773Z"
+ "iopub.execute_input": "2024-06-25T19:37:16.533265Z",
+ "iopub.status.busy": "2024-06-25T19:37:16.533088Z",
+ "iopub.status.idle": "2024-06-25T19:37:16.979069Z",
+ "shell.execute_reply": "2024-06-25T19:37:16.978477Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:16.804336Z",
- "iopub.status.busy": "2024-06-25T16:03:16.803971Z",
- "iopub.status.idle": "2024-06-25T16:03:17.140729Z",
- "shell.execute_reply": "2024-06-25T16:03:17.140160Z"
+ "iopub.execute_input": "2024-06-25T19:37:16.981738Z",
+ "iopub.status.busy": "2024-06-25T19:37:16.981547Z",
+ "iopub.status.idle": "2024-06-25T19:37:17.310927Z",
+ "shell.execute_reply": "2024-06-25T19:37:17.310336Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:17.143514Z",
- "iopub.status.busy": "2024-06-25T16:03:17.143158Z",
- "iopub.status.idle": "2024-06-25T16:03:17.488592Z",
- "shell.execute_reply": "2024-06-25T16:03:17.487943Z"
+ "iopub.execute_input": "2024-06-25T19:37:17.313292Z",
+ "iopub.status.busy": "2024-06-25T19:37:17.312887Z",
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+ "shell.execute_reply": "2024-06-25T19:37:17.645269Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:17.491990Z",
- "iopub.status.busy": "2024-06-25T16:03:17.491768Z",
- "iopub.status.idle": "2024-06-25T16:03:17.937675Z",
- "shell.execute_reply": "2024-06-25T16:03:17.937078Z"
+ "iopub.execute_input": "2024-06-25T19:37:17.649071Z",
+ "iopub.status.busy": "2024-06-25T19:37:17.648711Z",
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+ "shell.execute_reply": "2024-06-25T19:37:18.055723Z"
}
},
"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:17.942126Z",
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- "shell.execute_reply": "2024-06-25T16:03:18.396513Z"
+ "iopub.execute_input": "2024-06-25T19:37:18.060462Z",
+ "iopub.status.busy": "2024-06-25T19:37:18.060093Z",
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+ "shell.execute_reply": "2024-06-25T19:37:18.505169Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:03:18.400047Z",
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- "shell.execute_reply": "2024-06-25T16:03:18.620579Z"
+ "iopub.execute_input": "2024-06-25T19:37:18.508548Z",
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+ "shell.execute_reply": "2024-06-25T19:37:18.697831Z"
}
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"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-06-25T16:03:18.822043Z"
+ "iopub.execute_input": "2024-06-25T19:37:18.700790Z",
+ "iopub.status.busy": "2024-06-25T19:37:18.700610Z",
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+ "shell.execute_reply": "2024-06-25T19:37:18.880186Z"
}
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"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
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+ "iopub.execute_input": "2024-06-25T19:37:18.882941Z",
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"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
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"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
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- "shell.execute_reply": "2024-06-25T16:03:19.938105Z"
+ "iopub.execute_input": "2024-06-25T19:37:19.793943Z",
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+ "shell.execute_reply": "2024-06-25T19:37:19.935101Z"
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"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
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"outputs": [],
@@ -1266,10 +1266,10 @@
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+ "shell.execute_reply": "2024-06-25T19:37:20.751385Z"
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"outputs": [
@@ -1351,10 +1351,10 @@
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"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 2dea51c7e..3359ecfd0 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-06-25T16:03:23.195602Z",
- "iopub.status.idle": "2024-06-25T16:03:26.091061Z",
- "shell.execute_reply": "2024-06-25T16:03:26.090423Z"
+ "iopub.execute_input": "2024-06-25T19:37:22.937714Z",
+ "iopub.status.busy": "2024-06-25T19:37:22.937546Z",
+ "iopub.status.idle": "2024-06-25T19:37:25.620183Z",
+ "shell.execute_reply": "2024-06-25T19:37:25.619593Z"
},
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\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": {
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- "iopub.status.idle": "2024-06-25T16:03:26.454459Z",
- "shell.execute_reply": "2024-06-25T16:03:26.453903Z"
+ "iopub.execute_input": "2024-06-25T19:37:25.622737Z",
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+ "shell.execute_reply": "2024-06-25T19:37:25.935452Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-06-25T16:03:26.460809Z",
- "shell.execute_reply": "2024-06-25T16:03:26.460238Z"
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},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
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- "shell.execute_reply": "2024-06-25T16:03:30.759210Z"
+ "iopub.execute_input": "2024-06-25T19:37:25.944514Z",
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+ "shell.execute_reply": "2024-06-25T19:37:33.409701Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
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"\r",
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+ " 0%| | 32768/170498071 [00:00<10:33, 269061.34it/s]"
]
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+ " 0%| | 229376/170498071 [00:00<02:43, 1044330.69it/s]"
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@@ -268,7 +268,7 @@
"output_type": "stream",
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+ " 1%| | 884736/170498071 [00:00<00:56, 2986468.56it/s]"
]
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@@ -276,7 +276,7 @@
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+ " 2%|▏ | 3506176/170498071 [00:00<00:15, 10508236.75it/s]"
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@@ -284,7 +284,7 @@
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+ " 5%|▌ | 8552448/170498071 [00:00<00:06, 23273913.94it/s]"
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@@ -292,7 +292,7 @@
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@@ -300,7 +300,7 @@
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+ " 10%|█ | 17661952/170498071 [00:00<00:04, 34683944.45it/s]"
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@@ -308,7 +308,7 @@
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+ " 13%|█▎ | 22478848/170498071 [00:00<00:03, 38419021.56it/s]"
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+ " 16%|█▌ | 27590656/170498071 [00:00<00:03, 42218756.72it/s]"
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@@ -324,7 +324,7 @@
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+ " 19%|█▉ | 32145408/170498071 [00:01<00:03, 42979378.57it/s]"
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@@ -332,7 +332,7 @@
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+ " 22%|██▏ | 37552128/170498071 [00:01<00:02, 46248329.44it/s]"
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@@ -340,7 +340,7 @@
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@@ -348,7 +348,7 @@
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+ " 28%|██▊ | 47546368/170498071 [00:01<00:02, 47987784.85it/s]"
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@@ -356,7 +356,7 @@
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+ " 31%|███ | 52396032/170498071 [00:01<00:02, 47660760.18it/s]"
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@@ -364,7 +364,7 @@
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+ " 34%|███▎ | 57212928/170498071 [00:01<00:02, 46221672.73it/s]"
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+ " 37%|███▋ | 62259200/170498071 [00:01<00:02, 47420663.64it/s]"
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@@ -380,7 +380,175 @@
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{
@@ -498,10 +666,10 @@
"id": "9b64e0aa",
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+ "iopub.execute_input": "2024-06-25T19:37:33.412458Z",
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},
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@@ -552,10 +720,10 @@
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@@ -588,10 +756,10 @@
"id": "41e5cb6b",
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@@ -629,10 +797,10 @@
"id": "1cf25354",
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@@ -655,17 +823,17 @@
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+ "iopub.execute_input": "2024-06-25T19:37:34.474255Z",
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}
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"outputs": [
{
"data": {
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- "model_id": "28d0d7b5bab24921acc304c33b071421",
+ "model_id": "3a6eebd9a9694b07864d194c78cdb317",
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@@ -724,10 +892,10 @@
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@@ -771,10 +939,10 @@
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@@ -810,10 +978,10 @@
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@@ -863,10 +1031,10 @@
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@@ -914,10 +1082,10 @@
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@@ -973,10 +1141,10 @@
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@@ -1037,10 +1205,10 @@
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@@ -1071,10 +1239,10 @@
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@@ -1088,10 +1256,10 @@
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@@ -1113,10 +1281,10 @@
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- "shell.execute_reply": "2024-06-25T16:04:02.128479Z"
+ "iopub.execute_input": "2024-06-25T19:38:03.187201Z",
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+ "shell.execute_reply": "2024-06-25T19:38:03.194449Z"
},
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@@ -1161,23 +1329,31 @@
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+ "model_name": "HBoxModel",
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- "_model_name": "ProgressStyleModel",
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+ "_view_module": "@jupyter-widgets/controls",
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- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
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@@ -1230,54 +1406,49 @@
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"_view_module_version": "2.0.0",
- "_view_name": "HTMLView",
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- "layout": "IPY_MODEL_68089751fa2f44b5bcf3262b28eca5c9",
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- "style": "IPY_MODEL_d37fdb352b8d4b6daf0eb44b1f04489f",
+ "layout": "IPY_MODEL_be482c428a914ad9bc5accbfcda59810",
+ "max": 102469840.0,
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+ "orientation": "horizontal",
+ "style": "IPY_MODEL_9117500d199d44088b40d306a6001f86",
"tabbable": null,
"tooltip": null,
- "value": " 102M/102M [00:01<00:00, 71.4MB/s]"
+ "value": 102469840.0
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},
- "60ca8b96f6b14eb9bc39d63462385b39": {
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@@ -1330,7 +1501,7 @@
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@@ -1383,74 +1554,7 @@
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- "layout": "IPY_MODEL_10a4e89474e54662b7165ff29385bc67",
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- "c08dc6f6ce09426d98ab2220c32db7d0": {
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@@ -1503,7 +1607,71 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 88f740b68..1e51dfcdc 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -102,10 +102,10 @@
"id": "2e1af7d8",
"metadata": {
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- "iopub.status.idle": "2024-06-25T16:04:07.522429Z",
- "shell.execute_reply": "2024-06-25T16:04:07.521859Z"
+ "iopub.execute_input": "2024-06-25T19:38:07.555838Z",
+ "iopub.status.busy": "2024-06-25T19:38:07.555668Z",
+ "iopub.status.idle": "2024-06-25T19:38:08.722369Z",
+ "shell.execute_reply": "2024-06-25T19:38:08.721811Z"
},
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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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -142,10 +142,10 @@
"id": "4fb10b8f",
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- "iopub.status.idle": "2024-06-25T16:04:07.543090Z",
- "shell.execute_reply": "2024-06-25T16:04:07.542435Z"
+ "iopub.execute_input": "2024-06-25T19:38:08.724901Z",
+ "iopub.status.busy": "2024-06-25T19:38:08.724626Z",
+ "iopub.status.idle": "2024-06-25T19:38:08.741782Z",
+ "shell.execute_reply": "2024-06-25T19:38:08.741233Z"
}
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@@ -164,10 +164,10 @@
"id": "284dc264",
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- "iopub.status.idle": "2024-06-25T16:04:07.548804Z",
- "shell.execute_reply": "2024-06-25T16:04:07.548231Z"
+ "iopub.execute_input": "2024-06-25T19:38:08.744094Z",
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},
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@@ -198,10 +198,10 @@
"id": "0f7450db",
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- "iopub.status.idle": "2024-06-25T16:04:07.587145Z",
- "shell.execute_reply": "2024-06-25T16:04:07.586567Z"
+ "iopub.execute_input": "2024-06-25T19:38:08.748783Z",
+ "iopub.status.busy": "2024-06-25T19:38:08.748471Z",
+ "iopub.status.idle": "2024-06-25T19:38:09.023742Z",
+ "shell.execute_reply": "2024-06-25T19:38:09.023127Z"
}
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@@ -374,10 +374,10 @@
"id": "55513fed",
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- "iopub.status.idle": "2024-06-25T16:04:07.779592Z",
- "shell.execute_reply": "2024-06-25T16:04:07.778963Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.025867Z",
+ "iopub.status.busy": "2024-06-25T19:38:09.025685Z",
+ "iopub.status.idle": "2024-06-25T19:38:09.204489Z",
+ "shell.execute_reply": "2024-06-25T19:38:09.203970Z"
},
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@@ -417,10 +417,10 @@
"id": "df5a0f59",
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- "iopub.status.idle": "2024-06-25T16:04:08.032776Z",
- "shell.execute_reply": "2024-06-25T16:04:08.032112Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.206625Z",
+ "iopub.status.busy": "2024-06-25T19:38:09.206444Z",
+ "iopub.status.idle": "2024-06-25T19:38:09.445281Z",
+ "shell.execute_reply": "2024-06-25T19:38:09.444670Z"
}
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@@ -456,10 +456,10 @@
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- "shell.execute_reply": "2024-06-25T16:04:08.039224Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.447540Z",
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+ "shell.execute_reply": "2024-06-25T19:38:09.451044Z"
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@@ -477,10 +477,10 @@
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"metadata": {
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- "iopub.execute_input": "2024-06-25T16:04:08.042148Z",
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- "iopub.status.idle": "2024-06-25T16:04:08.049678Z",
- "shell.execute_reply": "2024-06-25T16:04:08.049039Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.453555Z",
+ "iopub.status.busy": "2024-06-25T19:38:09.453375Z",
+ "iopub.status.idle": "2024-06-25T19:38:09.460592Z",
+ "shell.execute_reply": "2024-06-25T19:38:09.460157Z"
}
},
"outputs": [],
@@ -527,10 +527,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:08.052267Z",
- "iopub.status.busy": "2024-06-25T16:04:08.051893Z",
- "iopub.status.idle": "2024-06-25T16:04:08.054873Z",
- "shell.execute_reply": "2024-06-25T16:04:08.054306Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.462899Z",
+ "iopub.status.busy": "2024-06-25T19:38:09.462366Z",
+ "iopub.status.idle": "2024-06-25T19:38:09.465304Z",
+ "shell.execute_reply": "2024-06-25T19:38:09.464836Z"
}
},
"outputs": [],
@@ -545,10 +545,10 @@
"id": "7e1b7860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:08.057067Z",
- "iopub.status.busy": "2024-06-25T16:04:08.056616Z",
- "iopub.status.idle": "2024-06-25T16:04:16.878679Z",
- "shell.execute_reply": "2024-06-25T16:04:16.878144Z"
+ "iopub.execute_input": "2024-06-25T19:38:09.467150Z",
+ "iopub.status.busy": "2024-06-25T19:38:09.466976Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.068771Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.068131Z"
}
},
"outputs": [],
@@ -572,10 +572,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.881611Z",
- "iopub.status.busy": "2024-06-25T16:04:16.880992Z",
- "iopub.status.idle": "2024-06-25T16:04:16.888328Z",
- "shell.execute_reply": "2024-06-25T16:04:16.887857Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.071591Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.071196Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.078371Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.077824Z"
}
},
"outputs": [
@@ -678,10 +678,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.890380Z",
- "iopub.status.busy": "2024-06-25T16:04:16.890056Z",
- "iopub.status.idle": "2024-06-25T16:04:16.893550Z",
- "shell.execute_reply": "2024-06-25T16:04:16.893134Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.080333Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.080152Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.083810Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.083366Z"
}
},
"outputs": [],
@@ -696,10 +696,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.895458Z",
- "iopub.status.busy": "2024-06-25T16:04:16.895142Z",
- "iopub.status.idle": "2024-06-25T16:04:16.898097Z",
- "shell.execute_reply": "2024-06-25T16:04:16.897586Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.085821Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.085497Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.088621Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.088109Z"
}
},
"outputs": [
@@ -734,10 +734,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.899989Z",
- "iopub.status.busy": "2024-06-25T16:04:16.899818Z",
- "iopub.status.idle": "2024-06-25T16:04:16.902910Z",
- "shell.execute_reply": "2024-06-25T16:04:16.902462Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.090576Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.090262Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.093389Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.092821Z"
}
},
"outputs": [],
@@ -756,10 +756,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.904872Z",
- "iopub.status.busy": "2024-06-25T16:04:16.904469Z",
- "iopub.status.idle": "2024-06-25T16:04:16.912279Z",
- "shell.execute_reply": "2024-06-25T16:04:16.911760Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.095470Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.095154Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.103228Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.102775Z"
}
},
"outputs": [
@@ -883,10 +883,10 @@
"id": "9131d82d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.914347Z",
- "iopub.status.busy": "2024-06-25T16:04:16.914011Z",
- "iopub.status.idle": "2024-06-25T16:04:16.916453Z",
- "shell.execute_reply": "2024-06-25T16:04:16.916028Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.105003Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.104832Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.107625Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.107128Z"
},
"nbsphinx": "hidden"
},
@@ -921,10 +921,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:16.918455Z",
- "iopub.status.busy": "2024-06-25T16:04:16.918103Z",
- "iopub.status.idle": "2024-06-25T16:04:17.041099Z",
- "shell.execute_reply": "2024-06-25T16:04:17.040461Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.109671Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.109367Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.236233Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.235732Z"
}
},
"outputs": [
@@ -963,10 +963,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.043794Z",
- "iopub.status.busy": "2024-06-25T16:04:17.043242Z",
- "iopub.status.idle": "2024-06-25T16:04:17.148399Z",
- "shell.execute_reply": "2024-06-25T16:04:17.147842Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.238299Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.237942Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.347132Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.346641Z"
}
},
"outputs": [
@@ -1022,10 +1022,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.150850Z",
- "iopub.status.busy": "2024-06-25T16:04:17.150479Z",
- "iopub.status.idle": "2024-06-25T16:04:17.645229Z",
- "shell.execute_reply": "2024-06-25T16:04:17.644591Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.349402Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.349044Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.839672Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.839073Z"
}
},
"outputs": [],
@@ -1041,10 +1041,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.647808Z",
- "iopub.status.busy": "2024-06-25T16:04:17.647578Z",
- "iopub.status.idle": "2024-06-25T16:04:17.732922Z",
- "shell.execute_reply": "2024-06-25T16:04:17.732288Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.841931Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.841755Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.912662Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.912091Z"
}
},
"outputs": [
@@ -1079,10 +1079,10 @@
"id": "dbab6fb3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.735171Z",
- "iopub.status.busy": "2024-06-25T16:04:17.734985Z",
- "iopub.status.idle": "2024-06-25T16:04:17.744244Z",
- "shell.execute_reply": "2024-06-25T16:04:17.743814Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.915065Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.914579Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.923159Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.922730Z"
}
},
"outputs": [
@@ -1189,10 +1189,10 @@
"id": "5b39b8b5",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.746326Z",
- "iopub.status.busy": "2024-06-25T16:04:17.746024Z",
- "iopub.status.idle": "2024-06-25T16:04:17.748768Z",
- "shell.execute_reply": "2024-06-25T16:04:17.748295Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.925120Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.924947Z",
+ "iopub.status.idle": "2024-06-25T19:38:18.927502Z",
+ "shell.execute_reply": "2024-06-25T19:38:18.927067Z"
},
"nbsphinx": "hidden"
},
@@ -1217,10 +1217,10 @@
"id": "df06525b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:17.750556Z",
- "iopub.status.busy": "2024-06-25T16:04:17.750380Z",
- "iopub.status.idle": "2024-06-25T16:04:23.180932Z",
- "shell.execute_reply": "2024-06-25T16:04:23.180316Z"
+ "iopub.execute_input": "2024-06-25T19:38:18.929453Z",
+ "iopub.status.busy": "2024-06-25T19:38:18.929127Z",
+ "iopub.status.idle": "2024-06-25T19:38:24.397527Z",
+ "shell.execute_reply": "2024-06-25T19:38:24.396937Z"
}
},
"outputs": [
@@ -1264,10 +1264,10 @@
"id": "05282559",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:23.183436Z",
- "iopub.status.busy": "2024-06-25T16:04:23.182980Z",
- "iopub.status.idle": "2024-06-25T16:04:23.191635Z",
- "shell.execute_reply": "2024-06-25T16:04:23.191098Z"
+ "iopub.execute_input": "2024-06-25T19:38:24.400077Z",
+ "iopub.status.busy": "2024-06-25T19:38:24.399563Z",
+ "iopub.status.idle": "2024-06-25T19:38:24.408142Z",
+ "shell.execute_reply": "2024-06-25T19:38:24.407603Z"
}
},
"outputs": [
@@ -1376,10 +1376,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:23.193732Z",
- "iopub.status.busy": "2024-06-25T16:04:23.193357Z",
- "iopub.status.idle": "2024-06-25T16:04:23.257489Z",
- "shell.execute_reply": "2024-06-25T16:04:23.256902Z"
+ "iopub.execute_input": "2024-06-25T19:38:24.410281Z",
+ "iopub.status.busy": "2024-06-25T19:38:24.409820Z",
+ "iopub.status.idle": "2024-06-25T19:38:24.473861Z",
+ "shell.execute_reply": "2024-06-25T19:38:24.473281Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index cbb5060c6..253b92cf5 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:26.297786Z",
- "iopub.status.busy": "2024-06-25T16:04:26.297315Z",
- "iopub.status.idle": "2024-06-25T16:04:27.887896Z",
- "shell.execute_reply": "2024-06-25T16:04:27.887195Z"
+ "iopub.execute_input": "2024-06-25T19:38:27.445776Z",
+ "iopub.status.busy": "2024-06-25T19:38:27.445616Z",
+ "iopub.status.idle": "2024-06-25T19:38:29.357688Z",
+ "shell.execute_reply": "2024-06-25T19:38:29.356961Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:04:27.890914Z",
- "iopub.status.busy": "2024-06-25T16:04:27.890427Z",
- "iopub.status.idle": "2024-06-25T16:05:25.256867Z",
- "shell.execute_reply": "2024-06-25T16:05:25.256120Z"
+ "iopub.execute_input": "2024-06-25T19:38:29.360485Z",
+ "iopub.status.busy": "2024-06-25T19:38:29.360106Z",
+ "iopub.status.idle": "2024-06-25T19:39:24.167594Z",
+ "shell.execute_reply": "2024-06-25T19:39:24.166933Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:25.259565Z",
- "iopub.status.busy": "2024-06-25T16:05:25.259365Z",
- "iopub.status.idle": "2024-06-25T16:05:26.415816Z",
- "shell.execute_reply": "2024-06-25T16:05:26.415246Z"
+ "iopub.execute_input": "2024-06-25T19:39:24.170328Z",
+ "iopub.status.busy": "2024-06-25T19:39:24.169968Z",
+ "iopub.status.idle": "2024-06-25T19:39:25.274825Z",
+ "shell.execute_reply": "2024-06-25T19:39:25.274283Z"
},
"nbsphinx": "hidden"
},
@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -137,10 +137,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:26.418735Z",
- "iopub.status.busy": "2024-06-25T16:05:26.418225Z",
- "iopub.status.idle": "2024-06-25T16:05:26.421695Z",
- "shell.execute_reply": "2024-06-25T16:05:26.421227Z"
+ "iopub.execute_input": "2024-06-25T19:39:25.277334Z",
+ "iopub.status.busy": "2024-06-25T19:39:25.276961Z",
+ "iopub.status.idle": "2024-06-25T19:39:25.280274Z",
+ "shell.execute_reply": "2024-06-25T19:39:25.279814Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:26.423574Z",
- "iopub.status.busy": "2024-06-25T16:05:26.423393Z",
- "iopub.status.idle": "2024-06-25T16:05:26.427320Z",
- "shell.execute_reply": "2024-06-25T16:05:26.426786Z"
+ "iopub.execute_input": "2024-06-25T19:39:25.282318Z",
+ "iopub.status.busy": "2024-06-25T19:39:25.282060Z",
+ "iopub.status.idle": "2024-06-25T19:39:25.285902Z",
+ "shell.execute_reply": "2024-06-25T19:39:25.285458Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:26.429441Z",
- "iopub.status.busy": "2024-06-25T16:05:26.429045Z",
- "iopub.status.idle": "2024-06-25T16:05:26.432756Z",
- "shell.execute_reply": "2024-06-25T16:05:26.432275Z"
+ "iopub.execute_input": "2024-06-25T19:39:25.287764Z",
+ "iopub.status.busy": "2024-06-25T19:39:25.287595Z",
+ "iopub.status.idle": "2024-06-25T19:39:25.291186Z",
+ "shell.execute_reply": "2024-06-25T19:39:25.290735Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:26.434942Z",
- "iopub.status.busy": "2024-06-25T16:05:26.434398Z",
- "iopub.status.idle": "2024-06-25T16:05:26.437440Z",
- "shell.execute_reply": "2024-06-25T16:05:26.437002Z"
+ "iopub.execute_input": "2024-06-25T19:39:25.293004Z",
+ "iopub.status.busy": "2024-06-25T19:39:25.292834Z",
+ "iopub.status.idle": "2024-06-25T19:39:25.296491Z",
+ "shell.execute_reply": "2024-06-25T19:39:25.296049Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:05:26.439523Z",
- "iopub.status.busy": "2024-06-25T16:05:26.439086Z",
- "iopub.status.idle": "2024-06-25T16:06:01.882759Z",
- "shell.execute_reply": "2024-06-25T16:06:01.882179Z"
+ "iopub.execute_input": "2024-06-25T19:39:25.298372Z",
+ "iopub.status.busy": "2024-06-25T19:39:25.298196Z",
+ "iopub.status.idle": "2024-06-25T19:39:58.536591Z",
+ "shell.execute_reply": "2024-06-25T19:39:58.535983Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2af2403d62f9417892013c46d8d0308c",
+ "model_id": "944591b9a0384c6388bc6a076330ac62",
"version_major": 2,
"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "96760fcea90d4e7e9092c7269a9c7289",
+ "model_id": "456e1a39f8a0484d84df60d119f7d9b3",
"version_major": 2,
"version_minor": 0
},
@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:06:01.885484Z",
- "iopub.status.busy": "2024-06-25T16:06:01.885229Z",
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+ "style": "IPY_MODEL_a61e682d08cd4b079011fff3976213c6",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "number of examples processed for estimating thresholds: 100%"
}
},
- "fc6a14816890451bbcbaa406260ec37a": {
+ "f0c67deadadc41a681e33253811fe3c3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
@@ -2477,6 +2430,53 @@
"visibility": null,
"width": null
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+ "f1b83d31de91416b8454f54a7486c222": {
+ "model_module": "@jupyter-widgets/controls",
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+ "model_name": "HTMLModel",
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+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "2.0.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_allow_html": false,
+ "layout": "IPY_MODEL_2a9e65f5f4ec40d496f43ad1adfd040a",
+ "placeholder": "",
+ "style": "IPY_MODEL_13d8b2f426d2467cbc47898751b6f6dd",
+ "tabbable": null,
+ "tooltip": null,
+ "value": "images processed using softmin: 100%"
+ }
+ },
+ "f91d1545f3254e83bb88ef07ebe6e9fe": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
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+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "2.0.0",
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+ "IPY_MODEL_38cf33a9b7b9481cb9a58609d734b80f",
+ "IPY_MODEL_2184a8202604452c862d764c590163a7"
+ ],
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diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index d39bc02fe..c6bf67460 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
"id": "ae8a08e0",
"metadata": {
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- "iopub.execute_input": "2024-06-25T16:07:06.547023Z",
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- "iopub.status.idle": "2024-06-25T16:07:07.828490Z",
- "shell.execute_reply": "2024-06-25T16:07:07.827856Z"
+ "iopub.execute_input": "2024-06-25T19:41:02.971504Z",
+ "iopub.status.busy": "2024-06-25T19:41:02.971078Z",
+ "iopub.status.idle": "2024-06-25T19:41:04.919925Z",
+ "shell.execute_reply": "2024-06-25T19:41:04.919315Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-25 16:07:06-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-06-25 19:41:02-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,23 +94,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "169.150.236.97, 2400:52e0:1a00::894:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.236.97|:443... connected.\r\n"
+ "169.150.249.162, 2400:52e0:1a01::984:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|169.150.249.162|:443... connected.\r\n",
+ "HTTP request sent, awaiting response... 200 OK\r\n",
+ "Length: 982975 (960K) [application/zip]\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "HTTP request sent, awaiting response... "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "200 OK\r\n",
- "Length: 982975 (960K) [application/zip]\r\n",
"Saving to: ‘conll2003.zip’\r\n",
"\r\n",
"\r",
@@ -124,7 +117,7 @@
"\r",
"conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n",
"\r\n",
- "2024-06-25 16:07:07 (7.72 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-06-25 19:41:03 (8.03 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -144,9 +137,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-06-25 16:07:07-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.66.57, 52.217.229.33, 54.231.194.233, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.66.57|:443... connected.\r\n",
+ "--2024-06-25 19:41:03-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.196.49, 52.216.88.99, 3.5.9.136, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.196.49|:443... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "connected.\r\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"HTTP request sent, awaiting response... "
]
},
@@ -167,9 +173,25 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n",
+ "pred_probs.npz 1%[ ] 296.53K 1.27MB/s "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 30%[=====> ] 4.94M 10.8MB/s "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ "pred_probs.npz 100%[===================>] 16.26M 25.4MB/s in 0.6s \r\n",
"\r\n",
- "2024-06-25 16:07:07 (122 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-06-25 19:41:04 (25.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -186,10 +208,10 @@
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},
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@@ -200,7 +222,7 @@
"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@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@e604611b9bbdc89f91103c8112289faf56854619\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -226,10 +248,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:09.124877Z"
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- "shell.execute_reply": "2024-06-25T16:07:09.130888Z"
+ "iopub.execute_input": "2024-06-25T19:41:06.205901Z",
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+ "iopub.status.idle": "2024-06-25T19:41:06.208636Z",
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},
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@@ -300,10 +322,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:18.048398Z"
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}
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@@ -377,10 +399,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:18.056598Z"
+ "iopub.execute_input": "2024-06-25T19:41:15.085860Z",
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+ "iopub.status.idle": "2024-06-25T19:41:15.091166Z",
+ "shell.execute_reply": "2024-06-25T19:41:15.090711Z"
},
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},
@@ -420,10 +442,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:18.413940Z"
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@@ -460,10 +482,10 @@
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- "iopub.status.idle": "2024-06-25T16:07:18.421011Z",
- "shell.execute_reply": "2024-06-25T16:07:18.420474Z"
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@@ -535,10 +557,10 @@
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@@ -560,10 +582,10 @@
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- "iopub.status.idle": "2024-06-25T16:07:21.093137Z",
- "shell.execute_reply": "2024-06-25T16:07:21.092590Z"
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@@ -599,10 +621,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:21.099684Z"
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@@ -780,10 +802,10 @@
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- "shell.execute_reply": "2024-06-25T16:07:21.128341Z"
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@@ -885,10 +907,10 @@
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@@ -1137,10 +1159,10 @@
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@ffdbe77dc641fc9d59d1c6c4f22c78550cc7da49\n",
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diff --git a/master/cleanlab/benchmarking/index.html b/master/cleanlab/benchmarking/index.html
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= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. 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Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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"module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. 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\ No newline at end of file
diff --git a/master/tutorials/clean_learning/index.html b/master/tutorials/clean_learning/index.html
index 7336e32e7..c75404ad6 100644
--- a/master/tutorials/clean_learning/index.html
+++ b/master/tutorials/clean_learning/index.html
@@ -383,6 +383,15 @@
>
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
2. Load and format the text dataset
@@ -2126,7 +2135,7 @@ Easy Mode which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data
issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!
diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb
index 7a8ae4b02..a549a7040 100644
--- a/master/tutorials/datalab/image.ipynb
+++ b/master/tutorials/datalab/image.ipynb
@@ -71,10 +71,10 @@
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+ "iopub.status.busy": "2024-06-25T19:33:22.816239Z",
+ "iopub.status.idle": "2024-06-25T19:33:22.820245Z",
+ "shell.execute_reply": "2024-06-25T19:33:22.819819Z"
},
"nbsphinx": "hidden"
},
@@ -605,10 +605,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:19.431922Z",
- "iopub.status.busy": "2024-06-25T15:59:19.431665Z",
- "iopub.status.idle": "2024-06-25T15:59:19.440497Z",
- "shell.execute_reply": "2024-06-25T15:59:19.440049Z"
+ "iopub.execute_input": "2024-06-25T19:33:22.822196Z",
+ "iopub.status.busy": "2024-06-25T19:33:22.822022Z",
+ "iopub.status.idle": "2024-06-25T19:33:22.831142Z",
+ "shell.execute_reply": "2024-06-25T19:33:22.830697Z"
},
"nbsphinx": "hidden"
},
@@ -733,10 +733,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:19.442408Z",
- "iopub.status.busy": "2024-06-25T15:59:19.442147Z",
- "iopub.status.idle": "2024-06-25T15:59:19.471256Z",
- "shell.execute_reply": "2024-06-25T15:59:19.470777Z"
+ "iopub.execute_input": "2024-06-25T19:33:22.833164Z",
+ "iopub.status.busy": "2024-06-25T19:33:22.832846Z",
+ "iopub.status.idle": "2024-06-25T19:33:22.860883Z",
+ "shell.execute_reply": "2024-06-25T19:33:22.860460Z"
}
},
"outputs": [],
@@ -773,10 +773,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:19.473647Z",
- "iopub.status.busy": "2024-06-25T15:59:19.473224Z",
- "iopub.status.idle": "2024-06-25T15:59:53.630889Z",
- "shell.execute_reply": "2024-06-25T15:59:53.630274Z"
+ "iopub.execute_input": "2024-06-25T19:33:22.862805Z",
+ "iopub.status.busy": "2024-06-25T19:33:22.862633Z",
+ "iopub.status.idle": "2024-06-25T19:33:54.927685Z",
+ "shell.execute_reply": "2024-06-25T19:33:54.927065Z"
}
},
"outputs": [
@@ -792,21 +792,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.152\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.704\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.996\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.525\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "32d607be832e4a87b4d29b25908be79d",
+ "model_id": "b56125fc059b47e3b228dc3ed3b629c0",
"version_major": 2,
"version_minor": 0
},
@@ -827,7 +827,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "87333c993d264d4481aba459cf6e8ff5",
+ "model_id": "5c565e132b5a46d398435caf4df461d4",
"version_major": 2,
"version_minor": 0
},
@@ -850,21 +850,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.210\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.714\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.688\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.460\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "59321af79ae240afbb2d2bb44da084e7",
+ "model_id": "63e4117109d44d79bcece5146781039a",
"version_major": 2,
"version_minor": 0
},
@@ -885,7 +885,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "77ae27eb0a8144f99eb62c3768dbe735",
+ "model_id": "09c8fb8f5f2945a4948b758b41efb311",
"version_major": 2,
"version_minor": 0
},
@@ -908,21 +908,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.915\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.742\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.775\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.468\n",
"Computing feature embeddings ...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "fd6e7deac3fc4c7d91dd1737f449dd14",
+ "model_id": "85c627e125a94180abe254acf928a1fc",
"version_major": 2,
"version_minor": 0
},
@@ -943,7 +943,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "59ff8b60607a478c8df614b2f177106c",
+ "model_id": "85af3d53abef4aa8a6046017943dc826",
"version_major": 2,
"version_minor": 0
},
@@ -1022,10 +1022,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:53.633305Z",
- "iopub.status.busy": "2024-06-25T15:59:53.633008Z",
- "iopub.status.idle": "2024-06-25T15:59:53.648132Z",
- "shell.execute_reply": "2024-06-25T15:59:53.647680Z"
+ "iopub.execute_input": "2024-06-25T19:33:54.930258Z",
+ "iopub.status.busy": "2024-06-25T19:33:54.929870Z",
+ "iopub.status.idle": "2024-06-25T19:33:54.943872Z",
+ "shell.execute_reply": "2024-06-25T19:33:54.943339Z"
}
},
"outputs": [],
@@ -1050,10 +1050,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:53.650392Z",
- "iopub.status.busy": "2024-06-25T15:59:53.650090Z",
- "iopub.status.idle": "2024-06-25T15:59:54.136653Z",
- "shell.execute_reply": "2024-06-25T15:59:54.136006Z"
+ "iopub.execute_input": "2024-06-25T19:33:54.946038Z",
+ "iopub.status.busy": "2024-06-25T19:33:54.945618Z",
+ "iopub.status.idle": "2024-06-25T19:33:55.403627Z",
+ "shell.execute_reply": "2024-06-25T19:33:55.402981Z"
}
},
"outputs": [],
@@ -1073,10 +1073,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T15:59:54.139214Z",
- "iopub.status.busy": "2024-06-25T15:59:54.139017Z",
- "iopub.status.idle": "2024-06-25T16:01:33.037683Z",
- "shell.execute_reply": "2024-06-25T16:01:33.037091Z"
+ "iopub.execute_input": "2024-06-25T19:33:55.406220Z",
+ "iopub.status.busy": "2024-06-25T19:33:55.406041Z",
+ "iopub.status.idle": "2024-06-25T19:35:30.535430Z",
+ "shell.execute_reply": "2024-06-25T19:35:30.534808Z"
}
},
"outputs": [
@@ -1123,7 +1123,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "87e54fc2a5a049c6a320ecf5e3f96b0f",
+ "model_id": "d65cb8246aa14189b49a0eeae6f3bad0",
"version_major": 2,
"version_minor": 0
},
@@ -1162,10 +1162,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:33.040118Z",
- "iopub.status.busy": "2024-06-25T16:01:33.039683Z",
- "iopub.status.idle": "2024-06-25T16:01:33.512027Z",
- "shell.execute_reply": "2024-06-25T16:01:33.511469Z"
+ "iopub.execute_input": "2024-06-25T19:35:30.537781Z",
+ "iopub.status.busy": "2024-06-25T19:35:30.537412Z",
+ "iopub.status.idle": "2024-06-25T19:35:30.983712Z",
+ "shell.execute_reply": "2024-06-25T19:35:30.983121Z"
}
},
"outputs": [
@@ -1311,10 +1311,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:33.514282Z",
- "iopub.status.busy": "2024-06-25T16:01:33.513977Z",
- "iopub.status.idle": "2024-06-25T16:01:33.577771Z",
- "shell.execute_reply": "2024-06-25T16:01:33.577182Z"
+ "iopub.execute_input": "2024-06-25T19:35:30.986665Z",
+ "iopub.status.busy": "2024-06-25T19:35:30.986208Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.048426Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.047866Z"
}
},
"outputs": [
@@ -1418,10 +1418,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:33.580053Z",
- "iopub.status.busy": "2024-06-25T16:01:33.579708Z",
- "iopub.status.idle": "2024-06-25T16:01:33.588598Z",
- "shell.execute_reply": "2024-06-25T16:01:33.588129Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.050749Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.050363Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.060546Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.060026Z"
}
},
"outputs": [
@@ -1551,10 +1551,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:33.590752Z",
- "iopub.status.busy": "2024-06-25T16:01:33.590420Z",
- "iopub.status.idle": "2024-06-25T16:01:33.594920Z",
- "shell.execute_reply": "2024-06-25T16:01:33.594492Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.062742Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.062469Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.068251Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.067801Z"
},
"nbsphinx": "hidden"
},
@@ -1600,10 +1600,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:33.596980Z",
- "iopub.status.busy": "2024-06-25T16:01:33.596660Z",
- "iopub.status.idle": "2024-06-25T16:01:34.381403Z",
- "shell.execute_reply": "2024-06-25T16:01:34.380842Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.070241Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.069928Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.828844Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.828271Z"
}
},
"outputs": [
@@ -1638,10 +1638,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.383808Z",
- "iopub.status.busy": "2024-06-25T16:01:34.383372Z",
- "iopub.status.idle": "2024-06-25T16:01:34.392034Z",
- "shell.execute_reply": "2024-06-25T16:01:34.391496Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.831222Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.830895Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.839311Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.838857Z"
}
},
"outputs": [
@@ -1808,10 +1808,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.394025Z",
- "iopub.status.busy": "2024-06-25T16:01:34.393851Z",
- "iopub.status.idle": "2024-06-25T16:01:34.401149Z",
- "shell.execute_reply": "2024-06-25T16:01:34.400699Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.841482Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.841163Z",
+ "iopub.status.idle": "2024-06-25T19:35:31.848176Z",
+ "shell.execute_reply": "2024-06-25T19:35:31.847749Z"
},
"nbsphinx": "hidden"
},
@@ -1887,10 +1887,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.403215Z",
- "iopub.status.busy": "2024-06-25T16:01:34.402791Z",
- "iopub.status.idle": "2024-06-25T16:01:34.861899Z",
- "shell.execute_reply": "2024-06-25T16:01:34.861408Z"
+ "iopub.execute_input": "2024-06-25T19:35:31.850195Z",
+ "iopub.status.busy": "2024-06-25T19:35:31.849881Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.292692Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.292043Z"
}
},
"outputs": [
@@ -1927,10 +1927,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.864180Z",
- "iopub.status.busy": "2024-06-25T16:01:34.863943Z",
- "iopub.status.idle": "2024-06-25T16:01:34.881498Z",
- "shell.execute_reply": "2024-06-25T16:01:34.880946Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.295116Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.294759Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.310913Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.310451Z"
}
},
"outputs": [
@@ -2087,10 +2087,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.883593Z",
- "iopub.status.busy": "2024-06-25T16:01:34.883414Z",
- "iopub.status.idle": "2024-06-25T16:01:34.889203Z",
- "shell.execute_reply": "2024-06-25T16:01:34.888658Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.313089Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.312752Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.318396Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.317860Z"
},
"nbsphinx": "hidden"
},
@@ -2135,10 +2135,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:34.891291Z",
- "iopub.status.busy": "2024-06-25T16:01:34.890980Z",
- "iopub.status.idle": "2024-06-25T16:01:35.381738Z",
- "shell.execute_reply": "2024-06-25T16:01:35.381176Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.320370Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.320196Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.779377Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.778856Z"
}
},
"outputs": [
@@ -2220,10 +2220,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:35.384320Z",
- "iopub.status.busy": "2024-06-25T16:01:35.384117Z",
- "iopub.status.idle": "2024-06-25T16:01:35.393515Z",
- "shell.execute_reply": "2024-06-25T16:01:35.392948Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.782553Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.782090Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.791666Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.790923Z"
}
},
"outputs": [
@@ -2351,10 +2351,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:35.396099Z",
- "iopub.status.busy": "2024-06-25T16:01:35.395894Z",
- "iopub.status.idle": "2024-06-25T16:01:35.402126Z",
- "shell.execute_reply": "2024-06-25T16:01:35.401548Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.794003Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.793805Z",
+ "iopub.status.idle": "2024-06-25T19:35:32.799849Z",
+ "shell.execute_reply": "2024-06-25T19:35:32.799106Z"
},
"nbsphinx": "hidden"
},
@@ -2391,10 +2391,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:35.404738Z",
- "iopub.status.busy": "2024-06-25T16:01:35.404204Z",
- "iopub.status.idle": "2024-06-25T16:01:35.612273Z",
- "shell.execute_reply": "2024-06-25T16:01:35.611806Z"
+ "iopub.execute_input": "2024-06-25T19:35:32.802393Z",
+ "iopub.status.busy": "2024-06-25T19:35:32.802198Z",
+ "iopub.status.idle": "2024-06-25T19:35:33.003653Z",
+ "shell.execute_reply": "2024-06-25T19:35:33.003206Z"
}
},
"outputs": [
@@ -2436,10 +2436,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-25T16:01:35.614504Z",
- "iopub.status.busy": "2024-06-25T16:01:35.614159Z",
- "iopub.status.idle": "2024-06-25T16:01:35.622328Z",
- "shell.execute_reply": "2024-06-25T16:01:35.621874Z"
+ "iopub.execute_input": "2024-06-25T19:35:33.005778Z",
+ "iopub.status.busy": "2024-06-25T19:35:33.005613Z",
+ "iopub.status.idle": "2024-06-25T19:35:33.013113Z",
+ "shell.execute_reply": "2024-06-25T19:35:33.012647Z"
}
},
"outputs": [
@@ -2464,47 +2464,47 @@
" \n",
" \n",
" | \n",
- " low_information_score | \n",
" is_low_information_issue | \n",
+ " low_information_score | \n",
"
\n",
" \n",
" \n",
" \n",
" 53050 | \n",
- " 0.067975 | \n",
" True | \n",
+ " 0.067975 | \n",
"
\n",
" \n",
" 40875 | \n",
- " 0.089929 | \n",
" True | \n",
+ " 0.089929 | \n",
"
\n",
" \n",
" 9594 | \n",
- " 0.092601 | \n",
" True | \n",
+ " 0.092601 | \n",
"
\n",
" \n",
" 34825 | \n",
- " 0.107744 | \n",
" True | \n",
+ " 0.107744 | \n",
"
\n",
" \n",
" 37530 | \n",
- " 0.108516 | \n",
" True | \n",
+ " 0.108516 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " low_information_score is_low_information_issue\n",
- "53050 0.067975 True\n",
- "40875 0.089929 True\n",
- "9594 0.092601 True\n",
- "34825 0.107744 True\n",
- "37530 0.108516 True"
+ " is_low_information_issue low_information_score\n",
+ "53050 True 0.067975\n",
+ "40875 True 0.089929\n",
+ "9594 True 0.092601\n",
+ "34825 True 0.107744\n",
+ "37530 True 0.108516"
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diff --git a/master/tutorials/datalab/index.html b/master/tutorials/datalab/index.html
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>
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2. Load and format the text dataset
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6. (Optional) Visualize the Results
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]
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@@ -3567,10 +3567,10 @@
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@@ -3609,10 +3609,10 @@
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@@ -3648,10 +3648,10 @@
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@@ -3703,10 +3703,10 @@
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diff --git a/master/tutorials/dataset_health.html b/master/tutorials/dataset_health.html
index 33b3f9431..613b97d45 100644
--- a/master/tutorials/dataset_health.html
+++ b/master/tutorials/dataset_health.html
@@ -384,6 +384,15 @@
>
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
How can I find label issues in big datasets with limited memory?
-
+
@@ -1660,7 +1669,7 @@
How to handle near-duplicate data identified by Datalab?
@@ -1702,7 +1711,7 @@ Can’t find an answer to your question?. Our developers may also provide personalized assistance in our Slack Community.
Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai
diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb
index cf0213fe0..649612439 100644
--- a/master/tutorials/faq.ipynb
+++ b/master/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
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@@ -202,10 +202,10 @@
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@@ -228,10 +228,10 @@
"id": "90c10e18",
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@@ -253,10 +253,10 @@
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@@ -363,10 +363,10 @@
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@@ -380,7 +380,7 @@
{
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+ "model_id": "d8af54b634f1457680edc574c7fcb110",
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@@ -394,7 +394,7 @@
{
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@@ -452,10 +452,10 @@
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@@ -486,10 +486,10 @@
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@@ -512,10 +512,10 @@
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@@ -565,10 +565,10 @@
"id": "b0a01109",
"metadata": {
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@@ -585,10 +585,10 @@
"id": "8b1da032",
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@@ -667,10 +667,10 @@
"id": "4c9e9030",
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@@ -737,10 +737,10 @@
"id": "8751619e",
"metadata": {
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@@ -826,10 +826,10 @@
"id": "623df36d",
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@@ -1285,10 +1285,10 @@
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@@ -1319,7 +1319,7 @@
},
{
"cell_type": "markdown",
- "id": "89239277",
+ "id": "91d13c0b",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1327,7 +1327,7 @@
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@@ -2309,7 +2256,60 @@
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"model_name": "HTMLModel",
@@ -2324,15 +2324,15 @@
"_view_name": "HTMLView",
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- "layout": "IPY_MODEL_6458034c095740819698e38d034b409a",
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diff --git a/master/tutorials/indepth_overview.html b/master/tutorials/indepth_overview.html
index 66e257593..a350bf26f 100644
--- a/master/tutorials/indepth_overview.html
+++ b/master/tutorials/indepth_overview.html
@@ -384,6 +384,15 @@
>
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
+
+ v2.6.6
+
+
2. Pre-process the Cifar10 dataset