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cb9278ddb5
* support DecisionTreeRegressor * support regression models * Update membership_inference_dp_diabetes_reg.ipynb
101 lines
4 KiB
Python
101 lines
4 KiB
Python
import pytest
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import numpy as np
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.preprocessing import OneHotEncoder
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from apt.anonymization import Anonymize
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from apt.utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset
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from sklearn.datasets import load_diabetes
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from sklearn.model_selection import train_test_split
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def test_anonymize_ndarray_iris():
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(x_train, y_train), _ = get_iris_dataset()
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model = DecisionTreeClassifier()
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model.fit(x_train, y_train)
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pred = model.predict(x_train)
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k = 10
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QI = [0, 2]
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anonymizer = Anonymize(k, QI)
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anon = anonymizer.anonymize(x_train, pred)
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assert(len(np.unique(anon[:, QI], axis=0)) < len(np.unique(x_train[:, QI], axis=0)))
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_, counts_elements = np.unique(anon[:, QI], return_counts=True)
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assert (np.min(counts_elements) >= k)
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assert ((np.delete(anon, QI, axis=1) == np.delete(x_train, QI, axis=1)).all())
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def test_anonymize_pandas_adult():
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(x_train, y_train), _ = get_adult_dataset()
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encoded = OneHotEncoder().fit_transform(x_train)
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model = DecisionTreeClassifier()
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model.fit(encoded, y_train)
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pred = model.predict(encoded)
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k = 100
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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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categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
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'native-country']
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anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
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anon = anonymizer.anonymize(x_train, pred)
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assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])
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assert (anon.loc[:, QI].value_counts().min() >= k)
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assert (anon.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
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def test_anonymize_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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encoded = OneHotEncoder().fit_transform(x_train)
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model = DecisionTreeClassifier()
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model.fit(encoded, y_train)
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pred = model.predict(encoded)
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k = 100
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QI = ["finance", "social", "health"]
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categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
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anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
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anon = anonymizer.anonymize(x_train, pred)
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assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])
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assert (anon.loc[:, QI].value_counts().min() >= k)
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assert (anon.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
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def test_regression():
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dataset = load_diabetes()
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x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
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model = DecisionTreeRegressor(random_state=10, min_samples_split=2)
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model.fit(x_train, y_train)
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pred = model.predict(x_train)
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k = 10
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QI = [0, 2, 5, 8]
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anonymizer = Anonymize(k, QI, is_regression=True)
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anon = anonymizer.anonymize(x_train, pred)
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print('Base model accuracy (R2 score): ', model.score(x_test, y_test))
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model.fit(anon, y_train)
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print('Base model accuracy (R2 score) after anonymization: ', model.score(x_test, y_test))
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assert(len(np.unique(anon[:, QI], axis=0)) < len(np.unique(x_train[:, QI], axis=0)))
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_, counts_elements = np.unique(anon[:, QI], return_counts=True)
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assert (np.min(counts_elements) >= k)
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assert ((np.delete(anon, QI, axis=1) == np.delete(x_train, QI, axis=1)).all())
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def test_errors():
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with pytest.raises(ValueError):
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Anonymize(1, [0, 2])
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with pytest.raises(ValueError):
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Anonymize(2, [])
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with pytest.raises(ValueError):
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Anonymize(2, None)
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anonymizer = Anonymize(10, [0, 2])
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(x_train, y_train), (x_test, y_test) = get_iris_dataset()
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with pytest.raises(ValueError):
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anonymizer.anonymize(x_train, y_test)
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(x_train, y_train), _ = get_adult_dataset()
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with pytest.raises(ValueError):
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anonymizer.anonymize(x_train, y_train)
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