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Train just on qi (#15)
* QI updates * update code to support training ML on QI features * fix code so features that are not from QI should not be part of generalizations and add description * merging two branches, training on QI and on all data * adding tests and asserts
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olasaadi
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4 changed files with 373 additions and 135 deletions
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@ -12,8 +12,7 @@ from sklearn.preprocessing import OneHotEncoder, StandardScaler
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from apt.minimization import GeneralizeToRepresentative
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from sklearn.tree import DecisionTreeClassifier
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from apt.utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset
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from apt.utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset, get_german_credit_dataset
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@pytest.fixture
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def data():
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@ -43,11 +42,7 @@ def test_minimizer_params(data):
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gen = GeneralizeToRepresentative(base_est, features=features, cells=cells)
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gen.fit()
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transformed = gen.transform(X)
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expected_transformed = np.array([[26, 149],
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[58, 163],
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[31, 184]])
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assert(np.array_equal(expected_transformed, transformed))
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def test_minimizer_fit(data):
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features = ['age', 'height']
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@ -73,7 +68,8 @@ def test_minimizer_fit(data):
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {}, 'categories': {}, 'untouched': ['age', 'height']}
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expexted_generalizations = {'ranges': {}, 'categories': {}, 'untouched': ['height', 'age']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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@ -136,7 +132,8 @@ def test_minimizer_fit_pandas(data):
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {'age': []}, 'categories': {}, 'untouched': ['sex', 'height', 'ola']}
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expexted_generalizations = {'ranges': {'age': []}, 'categories': {}, 'untouched': ['ola', 'height', 'sex']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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@ -206,17 +203,113 @@ def test_minimizer_params_categorical(data):
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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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gen = GeneralizeToRepresentative(base_est, features=features, target_accuracy=0.5,
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categorical_features=categorical_features)
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categorical_features=categorical_features, cells=cells)
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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def test_minimizer_fit_QI(data):
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features = ['age', 'height', 'weight']
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X = np.array([[23, 165, 70],
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[45, 158, 67],
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[56, 123, 65],
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[67, 154, 90],
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[45, 149, 67],
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[42, 166, 58],
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[73, 172, 68],
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[94, 168, 69],
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[69, 175, 80],
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[24, 181, 95],
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[18, 190, 102]])
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print(X)
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y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
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QI = [0, 2]
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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base_est.fit(X, y)
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predictions = base_est.predict(X)
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gen = GeneralizeToRepresentative(base_est, features=features, target_accuracy=0.5, features_to_minimize=QI)
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {'age': []}, 'categories': {}, 'untouched': ['height', 'sex']}
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expexted_generalizations = {'ranges': {'age': [], 'weight': [67.5]}, 'categories': {}, 'untouched': ['height']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
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set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
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assert ((np.delete(transformed, QI, axis=1) == np.delete(X, QI, axis=1)).all())
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modified_features = [f for f in features if
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f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
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'ranges'].keys()]
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indexes = []
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for i in range(len(features)):
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if features[i] in modified_features:
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indexes.append(i)
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assert ((np.delete(transformed, indexes, axis=1) == np.delete(X, indexes, axis=1)).all())
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ncp = gen.ncp_
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if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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def test_minimizer_fit_pandas_QI(data):
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features = ['age', 'height', 'weight', 'sex', 'ola']
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X = [[23, 165, 65, 'f', 'aa'],
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[45, 158, 76, 'f', 'aa'],
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[56, 123, 78, 'f', 'bb'],
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[67, 154, 87, 'm', 'aa'],
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[45, 149, 45, 'f', 'bb'],
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[42, 166, 76, 'm', 'bb'],
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[73, 172, 85, 'm', 'bb'],
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[94, 168, 92, 'f', 'aa'],
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[69, 175, 95, 'm', 'aa'],
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[24, 181, 49, 'm', 'bb'],
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[18, 190, 69, 'm', 'bb']]
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y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
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X = pd.DataFrame(X, columns=features)
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QI = ['age', 'weight', 'ola']
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numeric_features = ["age", "height", "weight"]
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numeric_transformer = Pipeline(
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steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
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)
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categorical_features = ["sex", "ola"]
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categorical_transformer = OneHotEncoder(handle_unknown="ignore")
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preprocessor = ColumnTransformer(
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transformers=[
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("num", numeric_transformer, numeric_features),
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("cat", categorical_transformer, categorical_features),
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]
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)
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encoded = preprocessor.fit_transform(X)
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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base_est.fit(encoded, y)
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predictions = base_est.predict(encoded)
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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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gen = GeneralizeToRepresentative(base_est, features=features, target_accuracy=0.5,
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categorical_features=categorical_features, features_to_minimize=QI)
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {'age': [], 'weight': [47.0]}, 'categories': {'ola': [['bb', 'aa']]},
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'untouched': ['height', 'sex']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
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set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
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assert (transformed.drop(QI, axis=1).equals(X.drop(QI, axis=1)))
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modified_features = [f for f in features if
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f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
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'ranges'].keys()]
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@ -230,24 +323,27 @@ def test_minimizer_params_categorical(data):
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def test_minimize_ndarray_iris():
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features = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
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(x_train, y_train), _ = get_iris_dataset()
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QI = [0, 2]
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model = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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model.fit(x_train, y_train)
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pred = model.predict(x_train)
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gen = GeneralizeToRepresentative(model, target_accuracy=0.7, features=features)
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gen = GeneralizeToRepresentative(model, target_accuracy=0.3, features=features, features_to_minimize=QI)
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gen.fit(x_train, pred)
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transformed = gen.transform(x_train)
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gener = gen.generalizations_
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expexted_generalizations = {
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'ranges': {'sepal length (cm)': [5.0], 'sepal width (cm)': [], 'petal length (cm)': [4.950000047683716],
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'petal width (cm)': [0.800000011920929, 1.699999988079071]}, 'categories': {}, 'untouched': []}
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expexted_generalizations = {'ranges': {'sepal length (cm)': [], 'petal length (cm)': [2.449999988079071]},
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'categories': {}, 'untouched': ['petal width (cm)', 'sepal width (cm)']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
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set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
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assert ((np.delete(transformed, QI, axis=1) == np.delete(x_train, QI, axis=1)).all())
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modified_features = [f for f in features if
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f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
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'ranges'].keys()]
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@ -262,70 +358,19 @@ def test_minimize_ndarray_iris():
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assert (((transformed[indexes]) != (x_train[indexes])).any())
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def test_minimize_pandas_nursery():
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(x_train, y_train), _ = get_nursery_dataset()
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x_train = x_train.astype(str)
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x_train.reset_index(inplace=True, drop=True)
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y_train.reset_index(inplace=True, drop=True)
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QI = ["finance", "social", "health"]
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features = ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health"]
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categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
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numeric_features = [f for f in features if f not in categorical_features]
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numeric_transformer = Pipeline(
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steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
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)
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categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
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preprocessor = ColumnTransformer(
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transformers=[
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("num", numeric_transformer, numeric_features),
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("cat", categorical_transformer, categorical_features),
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]
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)
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encoded = preprocessor.fit_transform(x_train)
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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base_est.fit(encoded, y_train)
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predictions = base_est.predict(encoded)
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gen = GeneralizeToRepresentative(base_est, target_accuracy=0.8, features=features,
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categorical_features=categorical_features)
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gen.fit(x_train, predictions)
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transformed = gen.transform(x_train)
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {}, 'categories': {'parents': [['great_pret', 'pretentious', 'usual']],
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'has_nurs': [['critical', 'less_proper', 'proper'],
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['very_crit'], ['improper']], 'form': [
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['foster', 'completed', 'complete', 'incomplete']], 'housing': [['convenient', 'less_conv', 'critical']],
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'finance': [['convenient', 'inconv']],
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'social': [['problematic', 'nonprob', 'slightly_prob']],
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'health': [['priority'], ['recommended'], ['not_recom']],
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'children': [['2', '3', '4', '1']]}, 'untouched': []}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
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set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
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modified_features = [f for f in features if
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f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
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'ranges'].keys()]
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assert (transformed.drop(modified_features, axis=1).equals(x_train.drop(modified_features, axis=1)))
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ncp = gen.ncp_
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if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
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def test_minimize_pandas_adult():
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(x_train, y_train), _ = get_adult_dataset()
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x_train = x_train.head(5000)
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y_train = y_train.head(5000)
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x_train = x_train.head(1000)
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y_train = y_train.head(1000)
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features = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
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'capital-gain', 'capital-loss', 'hours-per-week', 'native-country']
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categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
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'native-country']
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'hours-per-week', 'native-country']
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QI = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
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'native-country']
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numeric_features = [f for f in features if f not in categorical_features]
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numeric_transformer = Pipeline(
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@ -344,33 +389,101 @@ def test_minimize_pandas_adult():
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base_est.fit(encoded, y_train)
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predictions = base_est.predict(encoded)
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gen = GeneralizeToRepresentative(base_est, target_accuracy=0.8, features=features,
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categorical_features=categorical_features)
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gen = GeneralizeToRepresentative(base_est, target_accuracy=0.7, features=features,
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categorical_features=categorical_features, features_to_minimize=QI)
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gen.fit(x_train, predictions)
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transformed = gen.transform(x_train)
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gener = gen.generalizations_
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expexted_generalizations = {
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'ranges': {'age': [20.0], 'education-num': [11.5, 12.5], 'capital-gain': [5095.5, 7139.5], 'capital-loss': [],
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'hours-per-week': []}, 'categories': {'workclass': [
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['Private', 'Without-pay', 'Self-emp-not-inc', '?', 'Federal-gov', 'Self-emp-inc', 'State-gov',
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'Local-gov']], 'marital-status': [
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['Married-civ-spouse', 'Never-married', 'Widowed', 'Married-AF-spouse', 'Separated',
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'Married-spouse-absent'], ['Divorced']], 'occupation': [
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['Transport-moving', 'Priv-house-serv', '?', 'Armed-Forces', 'Prof-specialty', 'Farming-fishing',
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'Exec-managerial', 'Machine-op-inspct', 'Other-service', 'Sales', 'Protective-serv', 'Handlers-cleaners',
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'Tech-support', 'Craft-repair', 'Adm-clerical']], 'relationship': [
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['Not-in-family', 'Own-child', 'Wife', 'Other-relative', 'Husband', 'Unmarried']], 'race': [
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['Other', 'Asian-Pac-Islander', 'Black', 'White', 'Amer-Indian-Eskimo']], 'sex': [['Male', 'Female']],
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'native-country': [
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['LatinAmerica', 'Other', 'UnitedStates', 'SouthAmerica',
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'BritishCommonwealth', 'Euro_2', 'Unknown', 'China',
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'Euro_1', 'SE_Asia']]}, 'untouched': []}
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expexted_generalizations = {'ranges': {'age': [], 'education-num': []}, 'categories': {
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'workclass': [['Self-emp-not-inc', 'Private', 'Federal-gov', 'Self-emp-inc', '?', 'Local-gov', 'State-gov']],
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'marital-status': [
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['Divorced', 'Married-AF-spouse', 'Married-spouse-absent', 'Widowed', 'Separated', 'Married-civ-spouse',
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'Never-married']], 'occupation': [
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['Tech-support', 'Priv-house-serv', 'Machine-op-inspct', 'Other-service', 'Prof-specialty', 'Adm-clerical',
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'Protective-serv', 'Handlers-cleaners', 'Transport-moving', 'Armed-Forces', '?', 'Sales',
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'Farming-fishing', 'Exec-managerial', 'Craft-repair']],
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'relationship': [['Not-in-family', 'Wife', 'Other-relative', 'Husband', 'Unmarried', 'Own-child']],
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'race': [['Asian-Pac-Islander', 'White', 'Other', 'Black', 'Amer-Indian-Eskimo']], 'sex': [['Female', 'Male']],
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'native-country': [
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['Euro_1', 'LatinAmerica', 'BritishCommonwealth', 'SouthAmerica', 'UnitedStates', 'China', 'Euro_2',
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'SE_Asia', 'Other', 'Unknown']]}, 'untouched': ['capital-loss', 'hours-per-week', 'capital-gain']}
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for key in expexted_generalizations['ranges']:
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assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expexted_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
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set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
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assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
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modified_features = [f for f in features if
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f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
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'ranges'].keys()]
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assert (transformed.drop(modified_features, axis=1).equals(x_train.drop(modified_features, axis=1)))
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ncp = gen.ncp_
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if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
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def test_german_credit_pandas():
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(x_train, y_train), (x_test, y_test) = get_german_credit_dataset()
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features = ["Existing_checking_account", "Duration_in_month", "Credit_history", "Purpose", "Credit_amount",
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"Savings_account", "Present_employment_since", "Installment_rate", "Personal_status_sex", "debtors",
|
||||
"Present_residence", "Property", "Age", "Other_installment_plans", "Housing",
|
||||
"Number_of_existing_credits", "Job", "N_people_being_liable_provide_maintenance", "Telephone",
|
||||
"Foreign_worker"]
|
||||
categorical_features = ["Existing_checking_account", "Credit_history", "Purpose", "Savings_account",
|
||||
"Present_employment_since", "Personal_status_sex", "debtors", "Property",
|
||||
"Other_installment_plans", "Housing", "Job"]
|
||||
QI = ["Duration_in_month", "Credit_history", "Purpose", "debtors", "Property", "Other_installment_plans",
|
||||
"Housing", "Job"]
|
||||
|
||||
|
||||
numeric_features = [f for f in features if f not in categorical_features]
|
||||
numeric_transformer = Pipeline(
|
||||
steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
|
||||
)
|
||||
categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
|
||||
preprocessor = ColumnTransformer(
|
||||
transformers=[
|
||||
("num", numeric_transformer, numeric_features),
|
||||
("cat", categorical_transformer, categorical_features),
|
||||
]
|
||||
)
|
||||
encoded = preprocessor.fit_transform(x_train)
|
||||
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
||||
min_samples_leaf=1)
|
||||
base_est.fit(encoded, y_train)
|
||||
predictions = base_est.predict(encoded)
|
||||
|
||||
gen = GeneralizeToRepresentative(base_est, target_accuracy=0.7, features=features,
|
||||
categorical_features=categorical_features, features_to_minimize=QI)
|
||||
gen.fit(x_train, predictions)
|
||||
transformed = gen.transform(x_train)
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {'Duration_in_month': [31.5]},
|
||||
'categories': {'Credit_history': [['A30', 'A32', 'A31', 'A34', 'A33']], 'Purpose': [
|
||||
['A41', 'A46', 'A43', 'A40', 'A44', 'A410', 'A49', 'A45', 'A48', 'A42']],
|
||||
'debtors': [['A101', 'A102', 'A103']],
|
||||
'Property': [['A124', 'A121', 'A122', 'A123']],
|
||||
'Other_installment_plans': [['A142', 'A141', 'A143']],
|
||||
'Housing': [['A151', 'A152', 'A153']],
|
||||
'Job': [['A172', 'A171', 'A174', 'A173']]},
|
||||
'untouched': ['Installment_rate', 'Present_residence', 'Personal_status_sex',
|
||||
'Foreign_worker', 'Telephone', 'Savings_account',
|
||||
'Number_of_existing_credits', 'N_people_being_liable_provide_maintenance',
|
||||
'Age', 'Existing_checking_account', 'Credit_amount',
|
||||
'Present_employment_since']}
|
||||
|
||||
for key in expexted_generalizations['ranges']:
|
||||
assert (set(expexted_generalizations['ranges'][key]) == set(gener['ranges'][key]))
|
||||
for key in expexted_generalizations['categories']:
|
||||
assert (set([frozenset(sl) for sl in expexted_generalizations['categories'][key]]) ==
|
||||
set([frozenset(sl) for sl in gener['categories'][key]]))
|
||||
assert (set(expexted_generalizations['untouched']) == set(gener['untouched']))
|
||||
assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
|
||||
|
||||
modified_features = [f for f in features if
|
||||
f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
|
||||
'ranges'].keys()]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue