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update samples from Release-102 as a part of SDK release
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‎configuration.ipynb‎

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@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
106-
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
106+
"print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎contrib/fairness/fairlearn-azureml-mitigation.ipynb‎

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@@ -46,9 +46,10 @@
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"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
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"This notebook also requires the following packages:\n",
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"* `azureml-contrib-fairness`\n",
49-
"* `fairlearn==0.4.6` (v0.5.0 will work with minor modifications)\n",
49+
"* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
5050
"* `joblib`\n",
5151
"* `liac-arff`\n",
52+
"* `raiwidgets==0.4.0`\n",
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"\n",
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"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
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]
@@ -85,7 +86,7 @@
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"outputs": [],
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"source": [
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"from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
88-
"from fairlearn.widget import FairlearnDashboard\n",
89+
"from raiwidgets import FairnessDashboard\n",
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"\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.impute import SimpleImputer\n",
@@ -256,9 +257,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
259-
"FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
260-
" y_true=y_test,\n",
261-
" y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
260+
"FairnessDashboard(sensitive_features=A_test,\n",
261+
" y_true=y_test,\n",
262+
" y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
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]
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},
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{
@@ -311,8 +312,8 @@
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"sweep.fit(X_train, y_train,\n",
312313
" sensitive_features=A_train.sex)\n",
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"\n",
314-
"# For Fairlearn v0.5.0, need sweep.predictors_\n",
315-
"predictors = sweep._predictors"
315+
"# For Fairlearn pre-v0.5.0, need sweep._predictors\n",
316+
"predictors = sweep.predictors_"
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]
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},
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{
@@ -329,16 +330,14 @@
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"outputs": [],
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"source": [
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"errors, disparities = [], []\n",
332-
"for m in predictors:\n",
333-
" classifier = lambda X: m.predict(X)\n",
334-
" \n",
333+
"for predictor in predictors:\n",
335334
" error = ErrorRate()\n",
336335
" error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
337336
" disparity = DemographicParity()\n",
338337
" disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
339338
" \n",
340-
" errors.append(error.gamma(classifier)[0])\n",
341-
" disparities.append(disparity.gamma(classifier).max())\n",
339+
" errors.append(error.gamma(predictor.predict)[0])\n",
340+
" disparities.append(disparity.gamma(predictor.predict).max())\n",
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" \n",
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"all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
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"\n",
@@ -387,10 +386,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
390-
"FairlearnDashboard(sensitive_features=A_test, \n",
391-
" sensitive_feature_names=['Sex', 'Race'],\n",
392-
" y_true=y_test.tolist(),\n",
393-
" y_pred=predictions_dominant)"
389+
"FairnessDashboard(sensitive_features=A_test, \n",
390+
" y_true=y_test.tolist(),\n",
391+
" y_pred=predictions_dominant)"
394392
]
395393
},
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{
@@ -409,7 +407,7 @@
409407
"<a id=\"AzureUpload\"></a>\n",
410408
"## Uploading a Fairness Dashboard to Azure\n",
411409
"\n",
412-
"Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
410+
"Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
413411
"1. Register the dominant models\n",
414412
"1. Precompute all the required metrics\n",
415413
"1. Upload to Azure\n",

‎contrib/fairness/fairlearn-azureml-mitigation.yml‎

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Original file line numberDiff line numberDiff line change
@@ -3,6 +3,7 @@ dependencies:
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- pip:
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- azureml-sdk
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- azureml-contrib-fairness
6-
- fairlearn==0.4.6
6+
- fairlearn>=0.6.2
77
- joblib
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- liac-arff
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- raiwidgets==0.4.0

‎contrib/fairness/upload-fairness-dashboard.ipynb‎

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@@ -30,7 +30,7 @@
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"1. [Training Models](#TrainingModels)\n",
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"1. [Logging in to AzureML](#LoginAzureML)\n",
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"1. [Registering the Models](#RegisterModels)\n",
33-
"1. [Using the Fairlearn Dashboard](#LocalDashboard)\n",
33+
"1. [Using the Fairness Dashboard](#LocalDashboard)\n",
3434
"1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
3535
" 1. Computing Fairness Metrics\n",
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" 1. Uploading to Azure\n",
@@ -48,9 +48,10 @@
4848
"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
4949
"This notebook also requires the following packages:\n",
5050
"* `azureml-contrib-fairness`\n",
51-
"* `fairlearn==0.4.6` (should also work with v0.5.0)\n",
51+
"* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
5252
"* `joblib`\n",
5353
"* `liac-arff`\n",
54+
"* `raiwidgets==0.4.0`\n",
5455
"\n",
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"Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
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]
@@ -388,12 +389,11 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from fairlearn.widget import FairlearnDashboard\n",
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"from raiwidgets import FairnessDashboard\n",
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"\n",
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"FairlearnDashboard(sensitive_features=A_test, \n",
394-
" sensitive_feature_names=['Sex', 'Race'],\n",
395-
" y_true=y_test.tolist(),\n",
396-
" y_pred=ys_pred)"
394+
"FairnessDashboard(sensitive_features=A_test, \n",
395+
" y_true=y_test.tolist(),\n",
396+
" y_pred=ys_pred)"
397397
]
398398
},
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{
@@ -403,7 +403,7 @@
403403
"<a id=\"AzureUpload\"></a>\n",
404404
"## Uploading a Fairness Dashboard to Azure\n",
405405
"\n",
406-
"Uploading a fairness dashboard to Azure is a two stage process. The `FairlearnDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
406+
"Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
407407
"1. Precompute all the required metrics\n",
408408
"1. Upload to Azure\n",
409409
"\n",

‎contrib/fairness/upload-fairness-dashboard.yml‎

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@@ -3,6 +3,7 @@ dependencies:
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- pip:
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- azureml-sdk
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- azureml-contrib-fairness
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- fairlearn==0.4.6
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- fairlearn>=0.6.2
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- joblib
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- liac-arff
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- raiwidgets==0.4.0

‎how-to-use-azureml/automated-machine-learning/automl_env.yml‎

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.30.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_win32_requirements.txt [--no-deps]
28+
- -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_win32_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/automl_env_linux.yml‎

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.30.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_linux_requirements.txt [--no-deps]
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_linux_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/automl_env_mac.yml‎

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.30.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_darwin_requirements.txt [--no-deps]
29+
- -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_darwin_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
108+
"print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
109109
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
110110
]
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},

‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
96+
"print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
9797
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

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