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update samples from Release-53 as a part of 1.19.0 SDK stable release

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amlrelsa-ms
2020-12-07 18:55:07 +00:00
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contrib/fairness/fairlearn-azureml-mitigation.ipynb
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@@ -38,7 +38,7 @@
"## Introduction\n",
"This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
"\n",
"We will apply the [grid search algorithm](https://fairlearn.github.io/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
"We will apply the [grid search algorithm](https://fairlearn.github.io/master/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
"\n",
"### Setup\n",
"\n",
@@ -98,8 +98,11 @@
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_openml\n",
"data = fetch_openml(data_id=1590, as_frame=True)\n",
"from utilities import fetch_openml_with_retries\n",
"\n",
"data = fetch_openml_with_retries(data_id=1590)\n",
" \n",
"# Extract the items we want\n",
"X_raw = data.data\n",
"Y = (data.target == '>50K') * 1\n",
"\n",