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Support 1-hot encoded features in anonymization + fixes related to encoding in minimization (#86)
* Support 1-hot encoded features in anonymization (#72) * Fix anonymization adult notebook + new notebook to demonstrate anonymization on 1-hot encoded data * Minimizer: No default encoder, if none provided data is supplied to the model as is. Fix data type of representative values. Fix and add more tests. Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailgold
GitHub
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7 changed files with 670 additions and 255 deletions
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@ -1,5 +1,6 @@
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import pytest
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import numpy as np
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import pandas as pd
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from sklearn.compose import ColumnTransformer
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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@ -118,6 +119,74 @@ def test_regression():
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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_ndarray_one_hot():
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x_train = np.array([[23, 0, 1, 165],
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[45, 0, 1, 158],
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[56, 1, 0, 123],
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[67, 0, 1, 154],
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[45, 1, 0, 149],
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[42, 1, 0, 166],
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[73, 0, 1, 172],
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[94, 0, 1, 168],
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[69, 0, 1, 175],
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[24, 1, 0, 181],
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[18, 1, 0, 190]])
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y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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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, 1, 2]
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QI_slices = [[1, 2]]
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anonymizer = Anonymize(k, QI, train_only_QI=True, quasi_identifer_slices=QI_slices)
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anon = anonymizer.anonymize(ArrayDataset(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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anonymized_slice = anon[:, QI_slices[0]]
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assert ((np.sum(anonymized_slice, axis=1) == 1).all())
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assert ((np.max(anonymized_slice, axis=1) == 1).all())
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assert ((np.min(anonymized_slice, axis=1) == 0).all())
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def test_anonymize_pandas_one_hot():
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feature_names = ["age", "gender_M", "gender_F", "height"]
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x_train = np.array([[23, 0, 1, 165],
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[45, 0, 1, 158],
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[56, 1, 0, 123],
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[67, 0, 1, 154],
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[45, 1, 0, 149],
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[42, 1, 0, 166],
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[73, 0, 1, 172],
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[94, 0, 1, 168],
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[69, 0, 1, 175],
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[24, 1, 0, 181],
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[18, 1, 0, 190]])
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y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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x_train = pd.DataFrame(x_train, columns=feature_names)
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y_train = pd.Series(y_train)
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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 = ["age", "gender_M", "gender_F"]
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QI_slices = [["gender_M", "gender_F"]]
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anonymizer = Anonymize(k, QI, train_only_QI=True, quasi_identifer_slices=QI_slices)
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anon = anonymizer.anonymize(ArrayDataset(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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np.testing.assert_array_equal(anon.drop(QI, axis=1), x_train.drop(QI, axis=1))
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anonymized_slice = anon.loc[:, QI_slices[0]]
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assert ((np.sum(anonymized_slice, axis=1) == 1).all())
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assert ((np.max(anonymized_slice, axis=1) == 1).all())
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assert ((np.min(anonymized_slice, axis=1) == 0).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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@ -11,6 +11,8 @@ 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
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from torch import nn, optim
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import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Input
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@ -24,6 +26,9 @@ from apt.utils.models import SklearnClassifier, ModelOutputType, SklearnRegresso
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tf.compat.v1.disable_eager_execution()
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ACCURACY_DIFF = 0.05
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@pytest.fixture
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def diabetes_dataset():
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return load_diabetes()
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@ -286,7 +291,7 @@ def test_minimizer_fit(data_two_features):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimizer_ncp(data_two_features):
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@ -348,12 +353,15 @@ def test_minimizer_ncp_categorical(data_four_features):
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train_dataset = ArrayDataset(x, predictions, features_names=features)
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gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, generalize_using_transform=False)
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categorical_features=categorical_features,
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generalize_using_transform=False,
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encoder=preprocessor)
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gen1.fit(dataset=train_dataset)
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ncp1 = gen1.ncp.fit_score
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ncp2 = gen1.calculate_ncp(ad1)
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gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, categorical_features=categorical_features)
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gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, categorical_features=categorical_features,
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encoder=preprocessor)
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gen2.fit(dataset=train_dataset)
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ncp3 = gen2.ncp.fit_score
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gen2.transform(dataset=ad1)
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@ -414,7 +422,8 @@ def test_minimizer_fit_pandas(data_four_features):
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# Now we have a full prediction pipeline.
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features)
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categorical_features=categorical_features,
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encoder=preprocessor)
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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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@ -428,7 +437,7 @@ def test_minimizer_fit_pandas(data_four_features):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimizer_params_categorical(cells_categorical):
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@ -450,13 +459,14 @@ def test_minimizer_params_categorical(cells_categorical):
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# Now we have a full prediction pipeline.
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, cells=cells)
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categorical_features=categorical_features, cells=cells,
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encoder=preprocessor)
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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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rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimizer_fit_qi(data_three_features):
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@ -484,7 +494,7 @@ def test_minimizer_fit_qi(data_three_features):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimizer_fit_pandas_qi(data_five_features):
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@ -508,7 +518,8 @@ def test_minimizer_fit_pandas_qi(data_five_features):
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# Now we have a full prediction pipeline.
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, features_to_minimize=qi)
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categorical_features=categorical_features, features_to_minimize=qi,
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encoder=preprocessor)
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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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@ -523,7 +534,7 @@ def test_minimizer_fit_pandas_qi(data_five_features):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimize_ndarray_iris():
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@ -552,7 +563,7 @@ def test_minimize_ndarray_iris():
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_minimize_pandas_adult():
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@ -582,7 +593,8 @@ def test_minimize_pandas_adult():
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predictions = np.argmax(predictions, axis=1)
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target_accuracy = 0.7
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, features_to_minimize=qi)
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categorical_features=categorical_features, features_to_minimize=qi,
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encoder=preprocessor)
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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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@ -609,7 +621,7 @@ def test_minimize_pandas_adult():
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_german_credit_pandas():
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@ -637,7 +649,8 @@ def test_german_credit_pandas():
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predictions = np.argmax(predictions, axis=1)
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target_accuracy = 0.7
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, features_to_minimize=qi)
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categorical_features=categorical_features, features_to_minimize=qi,
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encoder=preprocessor)
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gen.fit(dataset=ArrayDataset(x_train, predictions))
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transformed = gen.transform(dataset=ArrayDataset(x_train))
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gener = gen.generalizations
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@ -666,7 +679,7 @@ def test_german_credit_pandas():
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_regression(diabetes_dataset):
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@ -726,7 +739,7 @@ def test_regression(diabetes_dataset):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_x_y():
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@ -766,7 +779,7 @@ def test_x_y():
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_x_y_features_names():
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@ -806,7 +819,7 @@ def test_x_y_features_names():
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_BaseEstimator_classification(data_five_features):
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@ -828,7 +841,8 @@ def test_BaseEstimator_classification(data_five_features):
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# Now we have a full prediction pipeline.
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features, features_to_minimize=QI)
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categorical_features=categorical_features, features_to_minimize=QI,
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encoder=preprocessor)
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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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@ -844,7 +858,7 @@ def test_BaseEstimator_classification(data_five_features):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(preprocessor.transform(transformed), predictions)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_BaseEstimator_regression(diabetes_dataset):
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@ -903,7 +917,7 @@ def test_BaseEstimator_regression(diabetes_dataset):
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check_ncp(ncp, expected_generalizations)
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rel_accuracy = model.score(transformed, predictions)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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def test_keras_model():
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@ -936,7 +950,39 @@ def test_keras_model():
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check_ncp(ncp, gener)
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rel_accuracy = model.score(ArrayDataset(transformed, predictions))
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
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class PytorchModel(nn.Module):
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def __init__(self, num_classes, num_features):
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super(PytorchModel, self).__init__()
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self.fc1 = nn.Sequential(
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nn.Linear(num_features, 1024),
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nn.Tanh(), )
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self.fc2 = nn.Sequential(
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nn.Linear(1024, 512),
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nn.Tanh(), )
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self.fc3 = nn.Sequential(
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nn.Linear(512, 256),
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nn.Tanh(), )
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self.fc4 = nn.Sequential(
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nn.Linear(256, 128),
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nn.Tanh(),
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)
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self.classifier = nn.Linear(128, num_classes)
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def forward(self, x):
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out = self.fc1(x)
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out = self.fc2(out)
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out = self.fc3(out)
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out = self.fc4(out)
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return self.classifier(out)
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def test_minimizer_pytorch(data_three_features):
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@ -944,49 +990,17 @@ def test_minimizer_pytorch(data_three_features):
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x = x.astype(np.float32)
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qi = ['age', 'weight']
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from torch import nn, optim
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from apt.utils.datasets.datasets import PytorchData
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from apt.utils.models.pytorch_model import PyTorchClassifier
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class pytorch_model(nn.Module):
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def __init__(self, num_classes, num_features):
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super(pytorch_model, self).__init__()
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self.fc1 = nn.Sequential(
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nn.Linear(num_features, 1024),
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nn.Tanh(), )
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self.fc2 = nn.Sequential(
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nn.Linear(1024, 512),
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nn.Tanh(), )
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self.fc3 = nn.Sequential(
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nn.Linear(512, 256),
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nn.Tanh(), )
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self.fc4 = nn.Sequential(
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nn.Linear(256, 128),
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nn.Tanh(),
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)
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self.classifier = nn.Linear(128, num_classes)
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def forward(self, x):
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out = self.fc1(x)
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out = self.fc2(out)
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out = self.fc3(out)
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out = self.fc4(out)
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return self.classifier(out)
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base_est = pytorch_model(2, 3)
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base_est = PytorchModel(2, 3)
|
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criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.Adam(base_est.parameters(), lr=0.01)
|
||||
|
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model = PyTorchClassifier(model=base_est, output_type=ModelOutputType.CLASSIFIER_LOGITS, loss=criterion,
|
||||
optimizer=optimizer, input_shape=(3,),
|
||||
nb_classes=2)
|
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model.fit(PytorchData(x.astype(np.float32), y), save_entire_model=False, nb_epochs=10)
|
||||
model.fit(PytorchData(x, y), save_entire_model=False, nb_epochs=10)
|
||||
|
||||
ad = ArrayDataset(x)
|
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predictions = model.predict(ad)
|
||||
|
|
@ -1006,7 +1020,41 @@ def test_minimizer_pytorch(data_three_features):
|
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check_ncp(ncp, expected_generalizations)
|
||||
|
||||
rel_accuracy = model.score(ArrayDataset(transformed.astype(np.float32), predictions))
|
||||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
||||
|
||||
|
||||
def test_minimizer_pytorch_iris():
|
||||
features = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
|
||||
(x_train, y_train), _ = get_iris_dataset_np()
|
||||
x_train = x_train.astype(np.float32)
|
||||
qi = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
|
||||
|
||||
from apt.utils.datasets.datasets import PytorchData
|
||||
from apt.utils.models.pytorch_model import PyTorchClassifier
|
||||
|
||||
base_est = PytorchModel(3, 4)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.Adam(base_est.parameters(), lr=0.01)
|
||||
|
||||
model = PyTorchClassifier(model=base_est, output_type=ModelOutputType.CLASSIFIER_LOGITS, loss=criterion,
|
||||
optimizer=optimizer, input_shape=(4,),
|
||||
nb_classes=3)
|
||||
model.fit(PytorchData(x_train, y_train), save_entire_model=False, nb_epochs=10)
|
||||
|
||||
predictions = model.predict(ArrayDataset(x_train))
|
||||
if predictions.shape[1] > 1:
|
||||
predictions = np.argmax(predictions, axis=1)
|
||||
target_accuracy = 0.99
|
||||
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=qi)
|
||||
transformed = gen.fit_transform(dataset=ArrayDataset(x_train, predictions, features_names=features))
|
||||
gener = gen.generalizations
|
||||
|
||||
check_features(features, gener, transformed, x_train)
|
||||
ncp = gen.ncp.transform_score
|
||||
check_ncp(ncp, gener)
|
||||
|
||||
rel_accuracy = model.score(ArrayDataset(transformed.astype(np.float32), predictions))
|
||||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
||||
|
||||
|
||||
def test_untouched():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue