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Data and Model wrappers (#26)
* Squashed commit of wrappers:
Wrapper minimizer
* apply dataset wrapper on minimizer
* apply changes on minimization notebook
* add black_box_access and unlimited_queries params
Dataset wrapper anonymizer
Add features_names to ArrayDataset
and allow providing features names in QI and Cat features not just indexes
update notebooks
categorical features and QI passed by indexes
dataset include feature names and is_pandas param
add pytorch Dataset
Remove redundant code.
Use data wrappers in model wrapper APIs.
add generic dataset components
Create initial version of wrappers for models
* Fix handling of categorical features
This commit is contained in:
abigailgold
GitHub
parent
d53818644e
commit
2b2dab6bef
17 changed files with 1340 additions and 752 deletions
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@ -5,14 +5,15 @@ from sklearn.compose import ColumnTransformer
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from sklearn.datasets import load_boston, load_diabetes
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from sklearn.impute import SimpleImputer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import OneHotEncoder, StandardScaler
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from sklearn.preprocessing import OneHotEncoder
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from apt.minimization import GeneralizeToRepresentative
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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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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from apt.utils.dataset_utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset, get_german_credit_dataset
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from apt.utils.datasets import ArrayDataset
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from apt.utils.models import SklearnClassifier, ModelOutputType, SklearnRegressor
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@pytest.fixture
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@ -38,11 +39,12 @@ def test_minimizer_params(data):
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y = [1, 1, 0]
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(X, y))
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gen = GeneralizeToRepresentative(base_est, features=features, cells=cells)
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gen = GeneralizeToRepresentative(model, cells=cells)
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gen.fit()
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transformed = gen.transform(X)
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transformed = gen.transform(dataset=ArrayDataset(X, features_names=features))
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def test_minimizer_fit(data):
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@ -58,15 +60,20 @@ def test_minimizer_fit(data):
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[69, 175],
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[24, 181],
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[18, 190]])
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y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(X, y))
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predictions = model.predict(X)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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gen = GeneralizeToRepresentative(base_est, features=features, target_accuracy=0.5)
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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gen = GeneralizeToRepresentative(model, target_accuracy=0.5)
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train_dataset = ArrayDataset(X, predictions, features_names=features)
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gen.fit(dataset=train_dataset)
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transformed = gen.transform(dataset=ArrayDataset(X))
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {}, 'categories': {}, 'untouched': ['height', 'age']}
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@ -103,7 +110,7 @@ def test_minimizer_fit_pandas(data):
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[69, 175, 'm', 'aa'],
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[24, 181, 'm', 'bb'],
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[18, 190, 'm', 'bb']]
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y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
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y = np.array([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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numeric_features = ["age", "height"]
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@ -121,16 +128,22 @@ def test_minimizer_fit_pandas(data):
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]
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)
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encoded = preprocessor.fit_transform(X)
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encoded = pd.DataFrame(encoded)
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(encoded, y))
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predictions = model.predict(encoded)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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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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gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
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categorical_features=categorical_features)
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gen.fit(X, predictions)
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transformed = gen.transform(X)
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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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transformed = gen.transform(dataset=ArrayDataset(X))
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gener = gen.generalizations_
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expexted_generalizations = {'ranges': {'age': []}, 'categories': {}, 'untouched': ['ola', 'height', 'sex']}
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@ -143,7 +156,7 @@ def test_minimizer_fit_pandas(data):
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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.drop(modified_features, axis=1)))
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np.testing.assert_array_equal(transformed.drop(modified_features, axis=1), X.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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@ -179,7 +192,7 @@ def test_minimizer_params_categorical(data):
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[24, 181, 'm'],
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[18, 190, 'm']]
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y = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
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y = np.array([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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numeric_features = ["age", "height"]
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numeric_transformer = Pipeline(
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@ -196,16 +209,21 @@ def test_minimizer_params_categorical(data):
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]
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)
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encoded = preprocessor.fit_transform(X)
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encoded = pd.DataFrame(encoded)
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(encoded, y))
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predictions = model.predict(encoded)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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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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gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
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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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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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transformed = gen.transform(dataset=ArrayDataset(X))
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def test_minimizer_fit_QI(data):
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@ -222,16 +240,20 @@ def test_minimizer_fit_QI(data):
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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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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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QI = ['age', 'weight']
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(X, y))
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predictions = model.predict(X)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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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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gen = GeneralizeToRepresentative(model, target_accuracy=0.5, features_to_minimize=QI)
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train_dataset = ArrayDataset(X, predictions, features_names=features)
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gen.fit(dataset=train_dataset)
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transformed = gen.transform(dataset=ArrayDataset(X))
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gener = gen.generalizations_
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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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@ -240,7 +262,7 @@ def test_minimizer_fit_QI(data):
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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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assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], 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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@ -269,7 +291,7 @@ def test_minimizer_fit_pandas_QI(data):
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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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y = pd.Series([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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@ -288,16 +310,22 @@ def test_minimizer_fit_pandas_QI(data):
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]
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)
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encoded = preprocessor.fit_transform(X)
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encoded = pd.DataFrame(encoded)
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(encoded, y))
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predictions = model.predict(encoded)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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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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gen = GeneralizeToRepresentative(model, 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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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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transformed = gen.transform(dataset=ArrayDataset(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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@ -308,12 +336,13 @@ def test_minimizer_fit_pandas_QI(data):
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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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# assert (transformed.drop(QI, axis=1).equals(X.drop(QI, axis=1)))
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np.testing.assert_array_equal(transformed.drop(QI, axis=1), 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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assert (transformed.drop(modified_features, axis=1).equals(X.drop(modified_features, axis=1)))
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# assert (transformed.drop(modified_features, axis=1).equals(X.drop(modified_features, axis=1)))
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np.testing.assert_array_equal(transformed.drop(modified_features, axis=1), X.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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@ -322,16 +351,19 @@ def test_minimizer_fit_pandas_QI(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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(x_train, y_train), (x_test, y_test) = get_iris_dataset()
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QI = ['sepal length (cm)', 'petal length (cm)']
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(x_train, y_train))
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predictions = model.predict(x_train)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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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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gen = GeneralizeToRepresentative(model, target_accuracy=0.3, features_to_minimize=QI)
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# gen.fit(dataset=ArrayDataset(x_train, predictions))
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transformed = gen.fit_transform(dataset=ArrayDataset(x_train, predictions, features_names=features))
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gener = gen.generalizations_
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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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@ -342,7 +374,7 @@ def test_minimize_ndarray_iris():
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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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assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x_train, [0, 2], 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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@ -359,12 +391,13 @@ def test_minimize_ndarray_iris():
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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, y_train), (x_test, y_test) = get_adult_dataset()
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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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x_train = pd.DataFrame(x_train, columns=features)
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categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
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'hours-per-week', 'native-country']
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@ -384,15 +417,19 @@ def test_minimize_pandas_adult():
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]
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)
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encoded = preprocessor.fit_transform(x_train)
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encoded = pd.DataFrame(encoded)
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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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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
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model.fit(ArrayDataset(encoded, y_train))
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predictions = model.predict(encoded)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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gen = GeneralizeToRepresentative(base_est, target_accuracy=0.7, features=features,
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gen = GeneralizeToRepresentative(model, target_accuracy=0.7,
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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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gen.fit(dataset=ArrayDataset(x_train, predictions, features_names=features))
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transformed = gen.transform(dataset=ArrayDataset(x_train))
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gener = gen.generalizations_
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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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@ -414,12 +451,14 @@ def test_minimize_pandas_adult():
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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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# assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
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np.testing.assert_array_equal(transformed.drop(QI, axis=1), 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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# assert (transformed.drop(modified_features, axis=1).equals(x_train.drop(modified_features, axis=1)))
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np.testing.assert_array_equal(transformed.drop(modified_features, axis=1), x_train.drop(modified_features, axis=1))
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ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
|
|
@ -451,15 +490,19 @@ def test_german_credit_pandas():
|
|||
]
|
||||
)
|
||||
encoded = preprocessor.fit_transform(x_train)
|
||||
encoded = pd.DataFrame(encoded)
|
||||
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)
|
||||
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
|
||||
model.fit(ArrayDataset(encoded, y_train))
|
||||
predictions = model.predict(encoded)
|
||||
if predictions.shape[1] > 1:
|
||||
predictions = np.argmax(predictions, axis=1)
|
||||
|
||||
gen = GeneralizeToRepresentative(base_est, target_accuracy=0.7, features=features,
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.7,
|
||||
categorical_features=categorical_features, features_to_minimize=QI)
|
||||
gen.fit(x_train, predictions)
|
||||
transformed = gen.transform(x_train)
|
||||
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
||||
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {'Duration_in_month': [31.5]},
|
||||
'categories': {'Credit_history': [['A30', 'A32', 'A31', 'A34', 'A33']], 'Purpose': [
|
||||
|
|
@ -481,12 +524,14 @@ def test_german_credit_pandas():
|
|||
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)))
|
||||
# assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
|
||||
np.testing.assert_array_equal(transformed.drop(QI, axis=1), 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()]
|
||||
assert (transformed.drop(modified_features, axis=1).equals(x_train.drop(modified_features, axis=1)))
|
||||
# assert (transformed.drop(modified_features, axis=1).equals(x_train.drop(modified_features, axis=1)))
|
||||
np.testing.assert_array_equal(transformed.drop(modified_features, axis=1), x_train.drop(modified_features, axis=1))
|
||||
ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
|
|
@ -497,17 +542,258 @@ def test_regression():
|
|||
dataset = load_diabetes()
|
||||
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
|
||||
|
||||
model = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
||||
model.fit(x_train, y_train)
|
||||
pred = model.predict(x_train)
|
||||
QI = [0, 2, 5, 8]
|
||||
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
||||
model = SklearnRegressor(base_est)
|
||||
model.fit(ArrayDataset(x_train, y_train))
|
||||
predictions = model.predict(x_train)
|
||||
QI = ['age', 'bmi', 's2', 's5']
|
||||
features = ['age', 'sex', 'bmi', 'bp',
|
||||
's1', 's2', 's3', 's4', 's5', 's6']
|
||||
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.7, features=features, is_regression=True,
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.7, is_regression=True,
|
||||
features_to_minimize=QI)
|
||||
gen.fit(x_train, pred)
|
||||
transformed = gen.transform(x_train)
|
||||
gen.fit(dataset=ArrayDataset(x_train, predictions, features_names=features))
|
||||
transformed = gen.transform(dataset=ArrayDataset(x_train, features_names=features))
|
||||
print('Base model accuracy (R2 score): ', model.score(ArrayDataset(x_test, y_test)))
|
||||
model.fit(ArrayDataset(transformed, y_train))
|
||||
print('Base model accuracy (R2 score) after anonymization: ', model.score(ArrayDataset(x_test, y_test)))
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {
|
||||
'age': [-0.07816532626748085, -0.07090024650096893, -0.05637009255588055, -0.05092128552496433,
|
||||
-0.04728874587453902, -0.04547247663140297, -0.04183994047343731, -0.027309784665703773,
|
||||
-0.023677248042076826, -0.020044708624482155, -0.01641217083670199, -0.001882016600575298,
|
||||
0.0017505218856967986, 0.0035667913616634905, 0.007199329789727926, 0.010831868276000023,
|
||||
0.02354575227946043, 0.030810829252004623, 0.03262709779664874, 0.03444336913526058,
|
||||
0.03625963814556599, 0.03807590529322624, 0.03807590715587139, 0.047157252207398415,
|
||||
0.06168740428984165, 0.0635036751627922, 0.06895248219370842, 0.07258502021431923, 0.07621755823493004,
|
||||
0.1034616008400917],
|
||||
'bmi': [-0.07626373693346977, -0.060635464265942574, -0.056863121688365936, -0.05578530766069889,
|
||||
-0.054168591275811195, -0.042312657460570335, -0.0374625027179718, -0.03422906715422869,
|
||||
-0.033690162003040314, -0.03261234890669584, -0.02614547684788704, -0.025067666545510292,
|
||||
-0.022373135201632977, -0.016984074376523495, -0.01375063881278038, -0.007822672137990594,
|
||||
-0.004589236050378531, 0.008344509289599955, 0.015889193629845977, 0.016967005096375942,
|
||||
0.024511689320206642, 0.0272062208969146, 0.030978563241660595, 0.032595280557870865,
|
||||
0.033673093654215336, 0.04391230642795563, 0.04552902653813362, 0.05469042807817459,
|
||||
0.06977979838848114, 0.07301323488354683, 0.09349166229367256],
|
||||
's2': [-0.1044962927699089, -0.08649025857448578, -0.07740895450115204, -0.07114598527550697,
|
||||
-0.06378699466586113, -0.05971606448292732, -0.04437179118394852, -0.0398311372846365,
|
||||
-0.03137612994760275, -0.022138250060379505, -0.018067320343106985, -0.017910746857523918,
|
||||
-0.017910745926201344, -0.01618842873722315, -0.007576846517622471, -0.007263698382303119,
|
||||
-0.0010007291566580534, 0.0010347360512241721, 0.006514834007248282, 0.00933317095041275,
|
||||
0.012464655097573996, 0.019197346206055954, 0.020919663831591606, 0.02217225730419159,
|
||||
0.032036433927714825, 0.036420512944459915, 0.04080459102988243, 0.04127431474626064,
|
||||
0.04268348217010498, 0.04424922354519367, 0.04424922540783882, 0.056462014093995094, 0.05928034894168377,
|
||||
0.061315815430134535, 0.06272498145699501, 0.06460387445986271]}, 'categories': {},
|
||||
'untouched': ['s5', 's3', 'bp', 's1', 'sex', 's6', 's4']}
|
||||
|
||||
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 ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
|
||||
|
||||
modified_features = [f for f in features if
|
||||
f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
|
||||
'ranges'].keys()]
|
||||
indexes = []
|
||||
for i in range(len(features)):
|
||||
if features[i] in modified_features:
|
||||
indexes.append(i)
|
||||
assert ((np.delete(transformed, indexes, axis=1) == np.delete(x_train, indexes, axis=1)).all())
|
||||
ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
assert (((transformed[indexes]) != (x_train[indexes])).any())
|
||||
|
||||
|
||||
def test_X_y(data):
|
||||
features = [0, 1, 2]
|
||||
X = np.array([[23, 165, 70],
|
||||
[45, 158, 67],
|
||||
[56, 123, 65],
|
||||
[67, 154, 90],
|
||||
[45, 149, 67],
|
||||
[42, 166, 58],
|
||||
[73, 172, 68],
|
||||
[94, 168, 69],
|
||||
[69, 175, 80],
|
||||
[24, 181, 95],
|
||||
[18, 190, 102]])
|
||||
print(X)
|
||||
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
||||
QI = [0, 2]
|
||||
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
||||
min_samples_leaf=1)
|
||||
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
|
||||
model.fit(ArrayDataset(X, y))
|
||||
predictions = model.predict(X)
|
||||
if predictions.shape[1] > 1:
|
||||
predictions = np.argmax(predictions, axis=1)
|
||||
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.5, features_to_minimize=QI)
|
||||
gen.fit(X=X, y=predictions)
|
||||
transformed = gen.transform(X)
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {'0': [], '2': [67.5]}, 'categories': {}, 'untouched': ['1']}
|
||||
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 ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], axis=1)).all())
|
||||
modified_features = [f for f in features if
|
||||
str(f) in expexted_generalizations['categories'].keys() or str(f) in expexted_generalizations[
|
||||
'ranges'].keys()]
|
||||
indexes = []
|
||||
for i in range(len(features)):
|
||||
if features[i] in modified_features:
|
||||
indexes.append(i)
|
||||
assert ((np.delete(transformed, indexes, axis=1) == np.delete(X, indexes, axis=1)).all())
|
||||
ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
assert (((transformed[indexes]) != (X[indexes])).any())
|
||||
|
||||
|
||||
def test_X_y_features_names(data):
|
||||
features = ['age', 'height', 'weight']
|
||||
X = np.array([[23, 165, 70],
|
||||
[45, 158, 67],
|
||||
[56, 123, 65],
|
||||
[67, 154, 90],
|
||||
[45, 149, 67],
|
||||
[42, 166, 58],
|
||||
[73, 172, 68],
|
||||
[94, 168, 69],
|
||||
[69, 175, 80],
|
||||
[24, 181, 95],
|
||||
[18, 190, 102]])
|
||||
print(X)
|
||||
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
||||
QI = ['age', 'weight']
|
||||
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
||||
min_samples_leaf=1)
|
||||
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_VECTOR)
|
||||
model.fit(ArrayDataset(X, y))
|
||||
predictions = model.predict(X)
|
||||
if predictions.shape[1] > 1:
|
||||
predictions = np.argmax(predictions, axis=1)
|
||||
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.5, features_to_minimize=QI)
|
||||
gen.fit(X=X, y=predictions, features_names=features)
|
||||
transformed = gen.transform(X=X, features_names=features)
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {'age': [], 'weight': [67.5]}, 'categories': {}, 'untouched': ['height']}
|
||||
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 ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], axis=1)).all())
|
||||
modified_features = [f for f in features if
|
||||
f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
|
||||
'ranges'].keys()]
|
||||
indexes = []
|
||||
for i in range(len(features)):
|
||||
if features[i] in modified_features:
|
||||
indexes.append(i)
|
||||
assert ((np.delete(transformed, indexes, axis=1) == np.delete(X, indexes, axis=1)).all())
|
||||
ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
assert (((transformed[indexes]) != (X[indexes])).any())
|
||||
|
||||
|
||||
def test_BaseEstimator_classification(data):
|
||||
features = ['age', 'height', 'weight', 'sex', 'ola']
|
||||
X = [[23, 165, 65, 'f', 'aa'],
|
||||
[45, 158, 76, 'f', 'aa'],
|
||||
[56, 123, 78, 'f', 'bb'],
|
||||
[67, 154, 87, 'm', 'aa'],
|
||||
[45, 149, 45, 'f', 'bb'],
|
||||
[42, 166, 76, 'm', 'bb'],
|
||||
[73, 172, 85, 'm', 'bb'],
|
||||
[94, 168, 92, 'f', 'aa'],
|
||||
[69, 175, 95, 'm', 'aa'],
|
||||
[24, 181, 49, 'm', 'bb'],
|
||||
[18, 190, 69, 'm', 'bb']]
|
||||
|
||||
y = pd.Series([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
||||
X = pd.DataFrame(X, columns=features)
|
||||
QI = ['age', 'weight', 'ola']
|
||||
|
||||
numeric_features = ["age", "height", "weight"]
|
||||
numeric_transformer = Pipeline(
|
||||
steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
|
||||
)
|
||||
|
||||
categorical_features = ["sex", "ola"]
|
||||
categorical_transformer = OneHotEncoder(handle_unknown="ignore")
|
||||
|
||||
preprocessor = ColumnTransformer(
|
||||
transformers=[
|
||||
("num", numeric_transformer, numeric_features),
|
||||
("cat", categorical_transformer, categorical_features),
|
||||
]
|
||||
)
|
||||
encoded = preprocessor.fit_transform(X)
|
||||
encoded = pd.DataFrame(encoded)
|
||||
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
||||
min_samples_leaf=1)
|
||||
model = base_est
|
||||
model.fit(encoded, y)
|
||||
predictions = model.predict(encoded)
|
||||
|
||||
# Append classifier to preprocessing pipeline.
|
||||
# Now we have a full prediction pipeline.
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
|
||||
categorical_features=categorical_features, features_to_minimize=QI)
|
||||
train_dataset = ArrayDataset(X, predictions)
|
||||
gen.fit(dataset=train_dataset)
|
||||
transformed = gen.transform(dataset=ArrayDataset(X))
|
||||
gener = gen.generalizations_
|
||||
expexted_generalizations = {'ranges': {'age': [], 'weight': [47.0]}, 'categories': {'ola': [['bb', 'aa']]},
|
||||
'untouched': ['height', 'sex']}
|
||||
|
||||
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.drop(QI, axis=1)))
|
||||
np.testing.assert_array_equal(transformed.drop(QI, axis=1), X.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()]
|
||||
# assert (transformed.drop(modified_features, axis=1).equals(X.drop(modified_features, axis=1)))
|
||||
np.testing.assert_array_equal(transformed.drop(modified_features, axis=1), X.drop(modified_features, axis=1))
|
||||
ncp = gen.ncp_
|
||||
if len(expexted_generalizations['ranges'].keys()) > 0 or len(expexted_generalizations['categories'].keys()) > 0:
|
||||
assert (ncp > 0)
|
||||
assert (((transformed[modified_features]).equals(X[modified_features])) == False)
|
||||
|
||||
|
||||
def test_BaseEstimator_regression():
|
||||
dataset = load_diabetes()
|
||||
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
|
||||
|
||||
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
||||
model = base_est
|
||||
model.fit(x_train, y_train)
|
||||
predictions = model.predict(x_train)
|
||||
QI = ['age', 'bmi', 's2', 's5']
|
||||
features = ['age', 'sex', 'bmi', 'bp',
|
||||
's1', 's2', 's3', 's4', 's5', 's6']
|
||||
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=0.7, is_regression=True,
|
||||
features_to_minimize=QI)
|
||||
gen.fit(dataset=ArrayDataset(x_train, predictions, features_names=features))
|
||||
transformed = gen.transform(dataset=ArrayDataset(x_train, features_names=features))
|
||||
print('Base model accuracy (R2 score): ', model.score(x_test, y_test))
|
||||
model.fit(transformed, y_train)
|
||||
print('Base model accuracy (R2 score) after minimization: ', model.score(x_test, y_test))
|
||||
|
|
@ -546,7 +832,7 @@ def test_regression():
|
|||
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 ((np.delete(transformed, QI, axis=1) == np.delete(x_train, QI, axis=1)).all())
|
||||
assert ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
|
||||
|
||||
modified_features = [f for f in features if
|
||||
f in expexted_generalizations['categories'].keys() or f in expexted_generalizations[
|
||||
|
|
|
|||
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Add table
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Reference in a new issue