Initial version of data minimization

tests/test_minimizer.py

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+import pytest
+import numpy as np
+
+from sklearn.datasets import load_boston
+
+from apt.minimization import GeneralizeToRepresentative
+from sklearn.tree import DecisionTreeClassifier
+
+
+@pytest.fixture
+def data():
+    return load_boston(return_X_y=True)
+
+def test_minimizer_params(data):
+    # Assume two features, age and height, and boolean label
+    cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": None, "end": 170}}, "label": 0,
+              "representative": {"age": 26, "height": 149}},
+             {"id": 2, "ranges": {"age": {"start": 39, "end": None}, "height": {"start": None, "end": 170}}, "label": 1,
+              "representative": {"age": 58, "height": 163}},
+             {"id": 3, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": 171, "end": None}}, "label": 0,
+              "representative": {"age": 31, "height": 184}},
+             {"id": 4, "ranges": {"age": {"start": 39, "end": None}, "height": {"start": 171, "end": None}}, "label": 1,
+              "representative": {"age": 45, "height": 176}}
+             ]
+    features = ['age', 'height']
+    X = np.array([[23, 165],
+                     [45, 158],
+                     [18, 190]])
+    print(X.dtype)
+    y = [1,1,0]
+    base_est = DecisionTreeClassifier()
+    base_est.fit(X, y)
+
+    gen = GeneralizeToRepresentative(base_est, features=features, cells=cells)
+    gen.fit()
+    transformed = gen.transform(X)
+    print(transformed)
+
+def test_minimizer_fit(data):
+    features = ['age', 'height']
+    X = np.array([[23, 165],
+                  [45, 158],
+                  [56, 123],
+                  [67, 154],
+                  [45, 149],
+                  [42, 166],
+                  [73, 172],
+                  [94, 168],
+                  [69, 175],
+                  [24, 181],
+                  [18, 190]])
+    print(X)
+    y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
+    base_est = DecisionTreeClassifier()
+    base_est.fit(X, y)
+    predictions = base_est.predict(X)
+
+    gen = GeneralizeToRepresentative(base_est, features=features, target_accuracy=0.5)
+    gen.fit(X, predictions)
+    transformed = gen.transform(X)
+    print(X)
+    print(transformed)
+
+