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Consistent one-hot-encoding (#38)
* Reuse code between generalize and transform methods * Option to get encoder from user * Consistent encoding for decision tree and generalizations (separate from target model encoding)
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abigailgold
GitHub
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commit
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2 changed files with 153 additions and 128 deletions
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@ -74,8 +74,8 @@ def test_minimizer_fit(data):
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predictions = model.predict(ad)
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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=0.5)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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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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@ -102,6 +102,9 @@ def test_minimizer_fit(data):
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_minimizer_fit_pandas(data):
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features = ['age', 'height', 'sex', 'ola']
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@ -145,7 +148,8 @@ def test_minimizer_fit_pandas(data):
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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
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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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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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@ -169,6 +173,9 @@ def test_minimizer_fit_pandas(data):
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(X[modified_features])) == False)
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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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def test_minimizer_params_categorical(data):
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# Assume three features, age, sex and height, and boolean label
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@ -226,12 +233,16 @@ def test_minimizer_params_categorical(data):
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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(model, target_accuracy=0.5,
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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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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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def test_minimizer_fit_QI(data):
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features = ['age', 'height', 'weight']
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@ -257,8 +268,8 @@ def test_minimizer_fit_QI(data):
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predictions = model.predict(ad)
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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=0.5, features_to_minimize=QI)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, 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=ad)
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@ -284,6 +295,9 @@ def test_minimizer_fit_QI(data):
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_minimizer_fit_pandas_QI(data):
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features = ['age', 'height', 'weight', 'sex', 'ola']
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@ -329,7 +343,8 @@ def test_minimizer_fit_pandas_QI(data):
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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
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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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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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@ -356,6 +371,9 @@ def test_minimizer_fit_pandas_QI(data):
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(X[modified_features])) == False)
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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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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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@ -368,8 +386,8 @@ def test_minimize_ndarray_iris():
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predictions = model.predict(ArrayDataset(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_to_minimize=QI)
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target_accuracy = 0.3
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, 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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@ -397,6 +415,9 @@ def test_minimize_ndarray_iris():
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assert (ncp > 0)
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assert (((transformed[indexes]) != (x_train[indexes])).any())
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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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def test_minimize_pandas_adult():
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(x_train, y_train), (x_test, y_test) = get_adult_dataset_pd()
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@ -433,8 +454,8 @@ def test_minimize_pandas_adult():
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predictions = model.predict(ArrayDataset(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(model, target_accuracy=0.7,
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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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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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@ -472,6 +493,9 @@ def test_minimize_pandas_adult():
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
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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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def test_german_credit_pandas():
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(x_train, y_train), (x_test, y_test) = get_german_credit_dataset_pd()
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@ -506,8 +530,8 @@ def test_german_credit_pandas():
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predictions = model.predict(ArrayDataset(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(model, target_accuracy=0.7,
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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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gen.fit(dataset=ArrayDataset(x_train, predictions))
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transformed = gen.transform(dataset=ArrayDataset(x_train))
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@ -545,6 +569,9 @@ def test_german_credit_pandas():
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
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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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def test_regression():
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dataset = load_diabetes()
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@ -558,7 +585,8 @@ def test_regression():
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features = ['age', 'sex', 'bmi', 'bp',
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's1', 's2', 's3', 's4', 's5', 's6']
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gen = GeneralizeToRepresentative(model, target_accuracy=0.7, is_regression=True,
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target_accuracy = 0.7
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, is_regression=True,
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features_to_minimize=QI)
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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, features_names=features))
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@ -615,6 +643,9 @@ def test_regression():
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assert (ncp > 0)
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assert (((transformed[indexes]) != (x_train[indexes])).any())
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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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def test_X_y(data):
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features = [0, 1, 2]
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@ -640,8 +671,8 @@ def test_X_y(data):
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predictions = model.predict(ad)
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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=0.5, features_to_minimize=QI)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=QI)
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gen.fit(X=X, y=predictions)
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transformed = gen.transform(X)
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gener = gen.generalizations
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@ -666,6 +697,9 @@ def test_X_y(data):
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_X_y_features_names(data):
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features = ['age', 'height', 'weight']
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@ -691,8 +725,8 @@ def test_X_y_features_names(data):
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predictions = model.predict(ad)
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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=0.5, features_to_minimize=QI)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=QI)
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gen.fit(X=X, y=predictions, features_names=features)
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transformed = gen.transform(X=X, features_names=features)
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gener = gen.generalizations
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@ -717,6 +751,9 @@ def test_X_y_features_names(data):
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_BaseEstimator_classification(data):
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features = ['age', 'height', 'weight', 'sex', 'ola']
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@ -760,7 +797,8 @@ def test_BaseEstimator_classification(data):
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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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gen = GeneralizeToRepresentative(model, target_accuracy=0.5,
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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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train_dataset = ArrayDataset(X, predictions)
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gen.fit(dataset=train_dataset)
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@ -787,6 +825,9 @@ def test_BaseEstimator_classification(data):
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assert (ncp > 0)
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assert (((transformed[modified_features]).equals(X[modified_features])) == False)
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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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def test_BaseEstimator_regression():
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dataset = load_diabetes()
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@ -799,8 +840,8 @@ def test_BaseEstimator_regression():
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QI = ['age', 'bmi', 's2', 's5']
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features = ['age', 'sex', 'bmi', 'bp',
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's1', 's2', 's3', 's4', 's5', 's6']
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gen = GeneralizeToRepresentative(model, target_accuracy=0.7, is_regression=True,
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target_accuracy = 0.7
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, is_regression=True,
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features_to_minimize=QI)
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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, features_names=features))
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@ -857,6 +898,9 @@ def test_BaseEstimator_regression():
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assert (ncp > 0)
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assert (((transformed[indexes]) != (x_train[indexes])).any())
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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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def test_keras_model():
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(X, y), (x_test, y_test) = get_iris_dataset_np()
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@ -874,8 +918,8 @@ def test_keras_model():
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predictions = model.predict(ad)
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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=0.5)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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test_dataset = ArrayDataset(x_test, predictions)
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gen.fit(dataset=test_dataset)
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@ -895,6 +939,9 @@ def test_keras_model():
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_blackbox_model():
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(X, y), (x_test, y_test) = get_iris_dataset_np()
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@ -907,8 +954,8 @@ def test_blackbox_model():
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predictions = model.predict(ad)
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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=0.5)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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train_dataset = ArrayDataset(x_test, predictions)
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gen.fit(dataset=train_dataset)
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@ -939,6 +986,9 @@ def test_blackbox_model():
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assert (ncp > 0)
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assert (((transformed[indexes]) != (X[indexes])).any())
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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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def test_untouched():
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cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}}, "label": 0,
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