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https://github.com/IBM/ai-privacy-toolkit.git
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fe676fa426
* keras wrapper + blackbox classifier wrapper (fix #7) * fix error in NCP calculation * Update notebooks * Fix #25 (incorrect attack_feature indexes for social feature in notebook) * Consistent naming of internal parameters
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
import pytest
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from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, BlackboxClassifier
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from apt.utils.datasets import ArrayDataset, Data
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from apt.utils import dataset_utils
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.ensemble import RandomForestClassifier
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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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def test_sklearn_classifier():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
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underlying_model = RandomForestClassifier()
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model = SklearnClassifier(underlying_model, ModelOutputType.CLASSIFIER_PROBABILITIES)
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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model.fit(train)
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pred = model.predict(test)
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assert(pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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assert(0.0 <= score <= 1.0)
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def test_sklearn_regressor():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset_np()
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underlying_model = DecisionTreeRegressor()
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model = SklearnRegressor(underlying_model)
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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model.fit(train)
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pred = model.predict(test)
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assert (pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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def test_keras_classifier():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
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underlying_model = Sequential()
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underlying_model.add(Input(shape=(4,)))
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underlying_model.add(Dense(100, activation="relu"))
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underlying_model.add(Dense(10, activation="relu"))
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underlying_model.add(Dense(3, activation='softmax'))
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underlying_model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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model = KerasClassifier(underlying_model, ModelOutputType.CLASSIFIER_PROBABILITIES)
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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model.fit(train)
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pred = model.predict(test)
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assert(pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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assert(0.0 <= score <= 1.0)
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def test_blackbox_classifier():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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data = Data(train, test)
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model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
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pred = model.predict(test)
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assert(pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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assert(0.0 <= score <= 1.0)
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