add aif360 demo for future refs

demo/aif360.py

@@ -0,0 +1,74 @@
+from lale.lib.aif360 import fetch_creditg_df
+all_X, all_y, fairness_info = fetch_creditg_df()
+
+import pandas as pd
+pd.options.display.max_columns = None
+pd.concat([all_y, all_X], axis=1)
+
+import lale.pretty_print
+lale.pretty_print.ipython_display(fairness_info)
+
+from lale.lib.aif360 import fair_stratified_train_test_split
+train_X, test_X, train_y, test_y = fair_stratified_train_test_split(
+    all_X, all_y, **fairness_info, test_size=0.33, random_state=42)
+
+from lale.lib.aif360 import disparate_impact
+disparate_impact_scorer = disparate_impact(**fairness_info)
+print("disparate impact of training data {:.2f}, test data {:.2f}".format(
+    disparate_impact_scorer.scoring(X=train_X, y_pred=train_y),
+    disparate_impact_scorer.scoring(X=test_X, y_pred=test_y)))
+
+from lale.lib.lale import Project
+from sklearn.preprocessing import OneHotEncoder
+from lale.lib.lale import ConcatFeatures
+from sklearn.linear_model import LogisticRegression as LR
+from sklearn.tree import DecisionTreeClassifier as Tree
+from sklearn.neighbors import KNeighborsClassifier as KNN
+
+import lale
+lale.wrap_imported_operators()
+prep_to_numbers = (
+    (Project(columns={"type": "string"}) >> OneHotEncoder(handle_unknown="ignore"))
+    & Project(columns={"type": "number"})
+    ) >> ConcatFeatures
+planned_orig = prep_to_numbers >> (LR | Tree | KNN)
+planned_orig.visualize()
+
+from lale.lib.lale import Hyperopt
+best_estimator = planned_orig.auto_configure(
+    train_X, train_y, optimizer=Hyperopt, cv=3, max_evals=10)
+best_estimator.visualize()
+
+best_estimator.pretty_print(ipython_display=True, show_imports=False)
+
+import sklearn.metrics
+accuracy_scorer = sklearn.metrics.make_scorer(sklearn.metrics.accuracy_score)
+print(f'accuracy {accuracy_scorer(best_estimator, test_X, test_y):.1%}')
+
+print(f'disparate impact {disparate_impact_scorer(best_estimator, test_X, test_y):.2f}')
+
+from lale.lib.aif360 import DisparateImpactRemover
+lale.pretty_print.ipython_display(
+    DisparateImpactRemover.hyperparam_schema('repair_level'))
+
+di_remover = DisparateImpactRemover(
+    **fairness_info, preparation=prep_to_numbers)
+planned_fairer = di_remover >> (LR | Tree | KNN)
+planned_fairer.visualize()
+
+from lale.lib.aif360 import accuracy_and_disparate_impact
+combined_scorer = accuracy_and_disparate_impact(**fairness_info)
+
+from lale.lib.aif360 import FairStratifiedKFold
+fair_cv = FairStratifiedKFold(**fairness_info, n_splits=3)
+
+trained_fairer = planned_fairer.auto_configure(
+    train_X, train_y, optimizer=Hyperopt, cv=fair_cv,
+    max_evals=10, scoring=combined_scorer, best_score=1.0)
+
+print(f'accuracy {accuracy_scorer(trained_fairer, test_X, test_y):.1%}')
+print(f'disparate impact {disparate_impact_scorer(trained_fairer, test_X, test_y):.2f}')
+trained_fairer.visualize()
+
+trained_fairer.pretty_print(ipython_display=True, show_imports=False)
+