mirror of
https://github.com/IBM/ai-privacy-toolkit.git
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57e38ea4fa
* General model wrappers and methods supporting multi-label classifiers * Support for pytorch multi-label binary classifier * New model output types + single implementation of score method that supports multiple output types. * Anonymization with pytorch multi-output binary model * Support for multi-label binary models in minimizer. * Support for multi-label logits/probabilities --------- Signed-off-by: abigailt <abigailt@il.ibm.com>
1458 lines
67 KiB
Python
1458 lines
67 KiB
Python
import pytest
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import numpy as np
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import pandas as pd
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import scipy
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from sklearn.compose import ColumnTransformer
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from sklearn.datasets import load_diabetes
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from sklearn.impute import SimpleImputer
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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
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from torch import nn, optim, sigmoid, where
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from torch.nn import functional
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from scipy.special import expit
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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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from apt.utils.datasets.datasets import PytorchData
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from apt.utils.models.pytorch_model import PyTorchClassifier
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from apt.minimization import GeneralizeToRepresentative
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from apt.utils.dataset_utils import get_iris_dataset_np, get_adult_dataset_pd, get_german_credit_dataset_pd
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from apt.utils.datasets import ArrayDataset
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from apt.utils.models import SklearnClassifier, SklearnRegressor, KerasClassifier, \
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CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL, \
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CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS, CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
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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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@pytest.fixture
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def cells():
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cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": None, "end": 170}}, "label": 0,
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'categories': {}, "representative": {"age": 26, "height": 149}},
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{"id": 2, "ranges": {"age": {"start": 39, "end": None}, "height": {"start": None, "end": 170}}, "label": 1,
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'categories': {}, "representative": {"age": 58, "height": 163}},
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{"id": 3, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": 171, "end": None}}, "label": 0,
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'categories': {}, "representative": {"age": 31, "height": 184}},
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{"id": 4, "ranges": {"age": {"start": 39, "end": None}, "height": {"start": 171, "end": None}}, "label": 1,
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'categories': {}, "representative": {"age": 45, "height": 176}}
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]
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features = ['age', 'height']
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x = np.array([[23, 165],
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[45, 158],
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[18, 190]])
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y = [1, 1, 0]
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return cells, features, x, y
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@pytest.fixture
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def cells_categorical():
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cells = [{'id': 1, 'label': 0, 'ranges': {'age': {'start': None, 'end': None}},
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'categories': {'sex': ['f', 'm']}, 'hist': [2, 0],
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'representative': {'age': 45, 'height': 149, 'sex': 'f'},
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'untouched': ['height']},
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{'id': 3, 'label': 1, 'ranges': {'age': {'start': None, 'end': None}},
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'categories': {'sex': ['f', 'm']}, 'hist': [0, 3],
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'representative': {'age': 23, 'height': 165, 'sex': 'f'},
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'untouched': ['height']},
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{'id': 4, 'label': 0, 'ranges': {'age': {'start': None, 'end': None}},
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'categories': {'sex': ['f', 'm']}, 'hist': [1, 0],
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'representative': {'age': 18, 'height': 190, 'sex': 'm'},
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'untouched': ['height']}
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]
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features = ['age', 'height', 'sex']
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x = [[23, 165, 'f'],
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[45, 158, 'f'],
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[56, 123, 'f'],
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[67, 154, 'm'],
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[45, 149, 'f'],
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[42, 166, 'm'],
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[73, 172, 'm'],
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[94, 168, 'f'],
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[69, 175, 'm'],
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[24, 181, 'm'],
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[18, 190, 'm']]
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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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return cells, features, x, y
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@pytest.fixture
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def data_two_features():
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x = np.array([[23, 165],
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[45, 158],
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[56, 123],
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[67, 154],
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[45, 149],
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[42, 166],
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[73, 172],
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[94, 168],
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[69, 175],
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[24, 181],
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[18, 190]])
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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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x1 = np.array([[33, 165],
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[43, 150],
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[71, 143],
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[92, 194],
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[13, 125],
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[22, 169]])
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features = ['age', 'height']
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return x, y, features, x1
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@pytest.fixture
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def data_three_features():
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features = ['age', 'height', 'weight']
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x = np.array([[23, 165, 70],
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[45, 158, 67],
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[56, 123, 65],
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[67, 154, 90],
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[45, 149, 67],
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[42, 166, 58],
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[73, 172, 68],
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[94, 168, 69],
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[69, 175, 80],
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[24, 181, 95],
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[18, 190, 102]])
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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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return x, y, features
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@pytest.fixture
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def data_four_features():
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features = ['age', 'height', 'sex', 'ola']
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x = [[23, 165, 'f', 'aa'],
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[45, 158, 'f', 'aa'],
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[56, 123, 'f', 'bb'],
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[67, 154, 'm', 'aa'],
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[45, 149, 'f', 'bb'],
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[42, 166, 'm', 'bb'],
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[73, 172, 'm', 'bb'],
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[94, 168, 'f', 'aa'],
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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 = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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x1 = [[33, 165, 'f', 'aa'],
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[43, 150, 'm', 'aa'],
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[71, 143, 'f', 'aa'],
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[92, 194, 'm', 'aa'],
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[13, 125, 'f', 'aa'],
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[22, 169, 'f', 'bb']]
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return x, y, features, x1
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@pytest.fixture
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def data_five_features():
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features = ['age', 'height', 'weight', 'sex', 'ola']
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x = [[23, 165, 65, 'f', 'aa'],
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[45, 158, 76, 'f', 'aa'],
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[56, 123, 78, 'f', 'bb'],
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[67, 154, 87, 'm', 'aa'],
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[45, 149, 45, 'f', 'bb'],
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[42, 166, 76, 'm', 'bb'],
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[73, 172, 85, 'm', 'bb'],
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[94, 168, 92, 'f', 'aa'],
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[69, 175, 95, 'm', 'aa'],
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[24, 181, 49, 'm', 'bb'],
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[18, 190, 69, 'm', 'bb']]
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y = pd.Series([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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return x, y, features
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def compare_generalizations(gener, expected_generalizations):
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for key in expected_generalizations['ranges']:
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assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
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for key in expected_generalizations['categories']:
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assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
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== set([frozenset(sl) for sl in gener['categories'][key]]))
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assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
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if 'range_representatives' in expected_generalizations:
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for key in expected_generalizations['range_representatives']:
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assert (set(expected_generalizations['range_representatives'][key])
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== set(gener['range_representatives'][key]))
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if 'category_representatives' in expected_generalizations:
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for key in expected_generalizations['category_representatives']:
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assert (set(expected_generalizations['category_representatives'][key])
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== set(gener['category_representatives'][key]))
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def check_features(features, expected_generalizations, transformed, x, pandas=False):
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modified_features = [f for f in features if
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f in expected_generalizations['categories'].keys() or f in expected_generalizations[
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'ranges'].keys()]
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if pandas:
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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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if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
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assert (((transformed[modified_features]).equals(x[modified_features])) is False)
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else:
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indexes = []
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for i in range(len(features)):
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if features[i] in modified_features:
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indexes.append(i)
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if len(indexes) != transformed.shape[1]:
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assert (np.array_equal(np.delete(transformed, indexes, axis=1), np.delete(x, indexes, axis=1)))
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if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
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assert (not np.array_equal(transformed[:, indexes], x[:, indexes]))
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def check_ncp(ncp, expected_generalizations):
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if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
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assert (ncp > 0.0)
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def test_minimizer_params(cells):
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# Assume two features, age and height, and boolean label
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cells, features, x, y = cells
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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expected_generalizations = {'categories': {}, 'category_representatives': {},
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'range_representatives': {'age': [38, 0.5, 40], 'height': [170, 0.5, 172]},
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'ranges': {'age': [38, 39], 'height': [170, 171]}, 'untouched': []}
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gen = GeneralizeToRepresentative(model, cells=cells)
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gener = gen.generalizations
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compare_generalizations(gener, expected_generalizations)
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gen.fit()
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gen.transform(dataset=ArrayDataset(x, features_names=features))
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def create_encoder(numeric_features, categorical_features, x):
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numeric_transformer = Pipeline(
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steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
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)
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categorical_transformer = OneHotEncoder(handle_unknown="ignore")
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preprocessor = ColumnTransformer(
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transformers=[
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("num", numeric_transformer, numeric_features),
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("cat", categorical_transformer, categorical_features),
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]
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)
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encoded = preprocessor.fit_transform(x)
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if scipy.sparse.issparse(encoded):
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pd.DataFrame.sparse.from_spmatrix(encoded)
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else:
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encoded = pd.DataFrame(encoded)
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return preprocessor, encoded
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def test_minimizer_params_not_transform(cells):
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# Assume two features, age and height, and boolean label
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cells, features, x, y = cells
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samples = ArrayDataset(x, y, features)
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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gen = GeneralizeToRepresentative(model, cells=cells, generalize_using_transform=False)
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ncp = gen.calculate_ncp(samples)
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assert (ncp > 0.0)
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def test_minimizer_fit(data_two_features):
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x, y, features, _ = data_two_features
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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ad = ArrayDataset(x)
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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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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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transformed = gen.transform(dataset=ad)
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gener = gen.generalizations
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expected_generalizations = {'ranges': {}, 'categories': {}, 'untouched': ['height', 'age']}
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compare_generalizations(gener, expected_generalizations)
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check_features(features, expected_generalizations, transformed, x)
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assert (np.equal(x, transformed).all())
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ncp = gen.ncp.transform_score
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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) <= ACCURACY_DIFF)
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def test_minimizer_ncp(data_two_features):
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x, y, features, x1 = data_two_features
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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ad = ArrayDataset(x)
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ad1 = ArrayDataset(x1, features_names=features)
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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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target_accuracy = 0.4
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train_dataset = ArrayDataset(x, predictions, features_names=features)
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gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
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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)
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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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ncp4 = gen2.ncp.transform_score
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gen2.transform(dataset=ad)
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ncp5 = gen2.ncp.transform_score
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gen2.transform(dataset=ad1)
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ncp6 = gen2.ncp.transform_score
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assert (ncp1 <= ncp3)
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assert (ncp2 != ncp3)
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assert (ncp3 != ncp4)
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assert (ncp4 != ncp5)
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assert (ncp6 == ncp4)
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def test_minimizer_ncp_categorical(data_four_features):
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x, y, features, x1 = data_four_features
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x = pd.DataFrame(x, columns=features)
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x1 = pd.DataFrame(x1, columns=features)
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numeric_features = ["age", "height"]
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categorical_features = ["sex", "ola"]
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preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(encoded, y))
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ad = ArrayDataset(x)
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ad1 = ArrayDataset(x1)
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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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target_accuracy = 0.4
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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,
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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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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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ncp4 = gen2.ncp.transform_score
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gen2.transform(dataset=ad)
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ncp5 = gen2.ncp.transform_score
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gen2.transform(dataset=ad1)
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ncp6 = gen2.ncp.transform_score
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assert (ncp1 <= ncp3)
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assert (ncp2 != ncp3)
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assert (ncp3 != ncp4)
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assert (ncp4 != ncp5)
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assert (ncp6 == ncp4)
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def test_minimizer_fit_not_transform(data_two_features):
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x, y, features, x1 = data_two_features
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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ad = ArrayDataset(x)
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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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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
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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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gener = gen.generalizations
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expected_generalizations = {'ranges': {'age': [], 'height': [157.0]}, 'categories': {}, 'untouched': []}
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compare_generalizations(gener, expected_generalizations)
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ncp = gen.ncp.fit_score
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check_ncp(ncp, expected_generalizations)
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def test_minimizer_fit_pandas(data_four_features):
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x, y, features, _ = data_four_features
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x = pd.DataFrame(x, columns=features)
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numeric_features = ["age", "height"]
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categorical_features = ["sex", "ola"]
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preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
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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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
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model.fit(ArrayDataset(encoded, y))
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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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# Append classifier to preprocessing pipeline.
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# Now we have a full prediction pipeline.
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target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features,
|
|
encoder=preprocessor)
|
|
train_dataset = ArrayDataset(x, predictions)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ArrayDataset(x))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': []}, 'categories': {},
|
|
'untouched': ['height', 'sex', 'ola']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
check_features(features, expected_generalizations, transformed, x, True)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimizer_params_categorical(cells_categorical):
|
|
# Assume three features, age, sex and height, and boolean label
|
|
cells, features, x, y = cells_categorical
|
|
|
|
x = pd.DataFrame(x, columns=features)
|
|
numeric_features = ["age", "height"]
|
|
categorical_features = ["sex"]
|
|
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(encoded, y))
|
|
predictions = model.predict(ArrayDataset(encoded))
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
# Append classifier to preprocessing pipeline.
|
|
# Now we have a full prediction pipeline.
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features, cells=cells,
|
|
encoder=preprocessor)
|
|
train_dataset = ArrayDataset(x, predictions)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ArrayDataset(x))
|
|
|
|
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimizer_fit_qi(data_three_features):
|
|
x, y, features = data_three_features
|
|
qi = ['age', 'weight']
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(x, y))
|
|
ad = ArrayDataset(x)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=qi)
|
|
train_dataset = ArrayDataset(x, predictions, features_names=features)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ad)
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'weight': [67.5]}, 'categories': {}, 'untouched': ['height']}
|
|
compare_generalizations(gener, expected_generalizations)
|
|
check_features(features, expected_generalizations, transformed, x)
|
|
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x, [0, 2], axis=1)).all())
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimizer_fit_pandas_qi(data_five_features):
|
|
x, y, features = data_five_features
|
|
x = pd.DataFrame(x, columns=features)
|
|
qi = ['age', 'weight', 'ola']
|
|
|
|
numeric_features = ["age", "height", "weight"]
|
|
categorical_features = ["sex", "ola"]
|
|
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
|
|
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(encoded, y))
|
|
predictions = model.predict(ArrayDataset(encoded))
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
|
|
# Append classifier to preprocessing pipeline.
|
|
# Now we have a full prediction pipeline.
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features, features_to_minimize=qi,
|
|
encoder=preprocessor)
|
|
train_dataset = ArrayDataset(x, predictions)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ArrayDataset(x))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'weight': [47.0]}, 'categories': {'ola': [['bb', 'aa']]},
|
|
'untouched': ['height', 'sex']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
check_features(features, expected_generalizations, transformed, x, True)
|
|
np.testing.assert_array_equal(transformed.drop(qi, axis=1), x.drop(qi, axis=1))
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimize_ndarray_iris():
|
|
features = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
|
|
(x_train, y_train), _ = get_iris_dataset_np()
|
|
qi = ['sepal length (cm)', 'petal length (cm)']
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
|
|
model.fit(ArrayDataset(x_train, y_train))
|
|
predictions = model.predict(ArrayDataset(x_train))
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.3
|
|
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
|
|
expected_generalizations = {'ranges': {'sepal length (cm)': [], 'petal length (cm)': [2.449999988079071]},
|
|
'categories': {}, 'untouched': ['petal width (cm)', 'sepal width (cm)']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x_train, [0, 2], axis=1)).all())
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimize_pandas_adult():
|
|
(x_train, y_train), _ = get_adult_dataset_pd()
|
|
x_train = x_train.head(1000)
|
|
y_train = y_train.head(1000)
|
|
|
|
features = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
|
|
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country']
|
|
x_train = pd.DataFrame(x_train, columns=features)
|
|
|
|
categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
|
|
'hours-per-week', 'native-country']
|
|
|
|
qi = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
|
|
'native-country']
|
|
|
|
numeric_features = [f for f in features if f not in categorical_features]
|
|
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x_train)
|
|
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(encoded, y_train))
|
|
predictions = model.predict(ArrayDataset(encoded))
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features, features_to_minimize=qi,
|
|
encoder=preprocessor)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions, features_names=features))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'education-num': []}, 'categories': {
|
|
'workclass': [['Self-emp-not-inc', 'Private', 'Federal-gov', 'Self-emp-inc', '?', 'Local-gov', 'State-gov']],
|
|
'marital-status': [
|
|
['Divorced', 'Married-AF-spouse', 'Married-spouse-absent', 'Widowed', 'Separated', 'Married-civ-spouse',
|
|
'Never-married']], 'occupation': [
|
|
['Tech-support', 'Priv-house-serv', 'Machine-op-inspct', 'Other-service', 'Prof-specialty', 'Adm-clerical',
|
|
'Protective-serv', 'Handlers-cleaners', 'Transport-moving', 'Armed-Forces', '?', 'Sales',
|
|
'Farming-fishing', 'Exec-managerial', 'Craft-repair']],
|
|
'relationship': [['Not-in-family', 'Wife', 'Other-relative', 'Husband', 'Unmarried', 'Own-child']],
|
|
'race': [['Asian-Pac-Islander', 'White', 'Other', 'Black', 'Amer-Indian-Eskimo']], 'sex': [['Female', 'Male']],
|
|
'native-country': [
|
|
['Euro_1', 'LatinAmerica', 'BritishCommonwealth', 'SouthAmerica', 'UnitedStates', 'China', 'Euro_2',
|
|
'SE_Asia', 'Other', 'Unknown']]}, 'untouched': ['capital-loss', 'hours-per-week', 'capital-gain']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
np.testing.assert_array_equal(transformed.drop(qi, axis=1), x_train.drop(qi, axis=1))
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train, True)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_german_credit_pandas():
|
|
(x_train, y_train), _ = get_german_credit_dataset_pd()
|
|
features = ["Existing_checking_account", "Duration_in_month", "Credit_history", "Purpose", "Credit_amount",
|
|
"Savings_account", "Present_employment_since", "Installment_rate", "Personal_status_sex", "debtors",
|
|
"Present_residence", "Property", "Age", "Other_installment_plans", "Housing",
|
|
"Number_of_existing_credits", "Job", "N_people_being_liable_provide_maintenance", "Telephone",
|
|
"Foreign_worker"]
|
|
categorical_features = ["Existing_checking_account", "Credit_history", "Purpose", "Savings_account",
|
|
"Present_employment_since", "Personal_status_sex", "debtors", "Property",
|
|
"Other_installment_plans", "Housing", "Job"]
|
|
qi = ["Duration_in_month", "Credit_history", "Purpose", "debtors", "Property", "Other_installment_plans",
|
|
"Housing", "Job"]
|
|
|
|
numeric_features = [f for f in features if f not in categorical_features]
|
|
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x_train)
|
|
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(encoded, y_train))
|
|
predictions = model.predict(ArrayDataset(encoded))
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features, features_to_minimize=qi,
|
|
encoder=preprocessor)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'Duration_in_month': [31.5]},
|
|
'categories': {'Credit_history': [['A30', 'A32', 'A31', 'A34', 'A33']],
|
|
'Purpose': [
|
|
['A41', 'A46', 'A43', 'A40', 'A44', 'A410', 'A49', 'A45', 'A48',
|
|
'A42']],
|
|
'debtors': [['A101', 'A102', 'A103']],
|
|
'Property': [['A124', 'A121', 'A122', 'A123']],
|
|
'Other_installment_plans': [['A142', 'A141', 'A143']],
|
|
'Housing': [['A151', 'A152', 'A153']],
|
|
'Job': [['A172', 'A171', 'A174', 'A173']]},
|
|
'untouched': ['Installment_rate', 'Present_residence', 'Personal_status_sex',
|
|
'Foreign_worker', 'Telephone', 'Savings_account',
|
|
'Number_of_existing_credits', 'N_people_being_liable_provide_maintenance',
|
|
'Age', 'Existing_checking_account', 'Credit_amount',
|
|
'Present_employment_since']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
np.testing.assert_array_equal(transformed.drop(qi, axis=1), x_train.drop(qi, axis=1))
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train, True)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_regression(diabetes_dataset):
|
|
x_train, x_test, y_train, y_test = train_test_split(diabetes_dataset.data, diabetes_dataset.target, test_size=0.5,
|
|
random_state=14)
|
|
|
|
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
|
model = SklearnRegressor(base_est)
|
|
model.fit(ArrayDataset(x_train, y_train))
|
|
predictions = model.predict(ArrayDataset(x_train))
|
|
qi = ['age', 'bmi', 's2', 's5']
|
|
features = ['age', 'sex', 'bmi', 'bp',
|
|
's1', 's2', 's3', 's4', 's5', 's6']
|
|
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, 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(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
|
|
expected_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']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
assert ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_x_y():
|
|
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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(x, y))
|
|
ad = ArrayDataset(x)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=qi)
|
|
gen.fit(X=x, y=predictions)
|
|
transformed = gen.transform(x)
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'0': [], '2': [67.5]}, 'categories': {}, 'untouched': ['1']}
|
|
compare_generalizations(gener, expected_generalizations)
|
|
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x, [0, 2], axis=1)).all())
|
|
check_features(features, expected_generalizations, transformed, x)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_x_y_features_names():
|
|
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, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(x, y))
|
|
ad = ArrayDataset(x)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=qi)
|
|
gen.fit(X=x, y=predictions, features_names=features)
|
|
transformed = gen.transform(X=x, features_names=features)
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'weight': [67.5]}, 'categories': {}, 'untouched': ['height']}
|
|
compare_generalizations(gener, expected_generalizations)
|
|
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x, [0, 2], axis=1)).all())
|
|
check_features(features, expected_generalizations, transformed, x)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_BaseEstimator_classification(data_five_features):
|
|
x, y, features = data_five_features
|
|
x = pd.DataFrame(x, columns=features)
|
|
QI = ['age', 'weight', 'ola']
|
|
|
|
numeric_features = ["age", "height", "weight"]
|
|
categorical_features = ["sex", "ola"]
|
|
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
|
|
|
|
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.
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
|
|
categorical_features=categorical_features, features_to_minimize=QI,
|
|
encoder=preprocessor)
|
|
train_dataset = ArrayDataset(x, predictions)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ArrayDataset(x))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'weight': [47.0]}, 'categories': {'ola': [['bb', 'aa']]},
|
|
'untouched': ['height', 'sex']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
np.testing.assert_array_equal(transformed.drop(QI, axis=1), x.drop(QI, axis=1))
|
|
check_features(features, expected_generalizations, transformed, x, True)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(preprocessor.transform(transformed), predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_BaseEstimator_regression(diabetes_dataset):
|
|
x_train, x_test, y_train, y_test = train_test_split(diabetes_dataset.data, diabetes_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']
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, 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))
|
|
gener = gen.generalizations
|
|
expected_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']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
assert ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
def test_minimizer_ndarray_one_hot():
|
|
x_train = np.array([[23, 0, 1, 165],
|
|
[45, 0, 1, 158],
|
|
[56, 1, 0, 123],
|
|
[67, 0, 1, 154],
|
|
[45, 1, 0, 149],
|
|
[42, 1, 0, 166],
|
|
[73, 0, 1, 172],
|
|
[94, 0, 1, 168],
|
|
[69, 0, 1, 175],
|
|
[24, 1, 0, 181],
|
|
[18, 1, 0, 190]])
|
|
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
features = ['0', '1', '2', '3']
|
|
QI = [0, 1, 2]
|
|
QI_slices = [[1, 2]]
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories': {}, 'category_representatives': {},
|
|
'range_representatives': {'0': [34.5]},
|
|
'ranges': {'0': [34.5]}, 'untouched': ['3', '1', '2']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_minimizer_ndarray_one_hot_single_value():
|
|
x_train = np.array([[23, 0, 1, 0, 165],
|
|
[45, 0, 1, 0, 158],
|
|
[56, 1, 0, 0, 123],
|
|
[67, 0, 1, 0, 154],
|
|
[45, 1, 0, 0, 149],
|
|
[42, 1, 0, 0, 166],
|
|
[73, 0, 1, 0, 172],
|
|
[94, 0, 1, 0, 168],
|
|
[69, 0, 1, 0, 175],
|
|
[24, 1, 0, 0, 181],
|
|
[18, 1, 0, 0, 190]])
|
|
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
features = ['0', '1', '2', '3', '4']
|
|
QI = [0, 1, 2, 3]
|
|
QI_slices = [[1, 2, 3]]
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories': {}, 'category_representatives': {},
|
|
'range_representatives': {'0': [34.5]}, 'ranges': {'0': [34.5]},
|
|
'untouched': ['4', '1', '2', '3']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_minimizer_ndarray_one_hot_gen():
|
|
x_train = np.array([[23, 0, 1, 165],
|
|
[45, 0, 1, 158],
|
|
[56, 1, 0, 123],
|
|
[67, 0, 1, 154],
|
|
[45, 1, 0, 149],
|
|
[42, 1, 0, 166],
|
|
[73, 0, 1, 172],
|
|
[94, 0, 1, 168],
|
|
[69, 0, 1, 175],
|
|
[24, 1, 0, 181],
|
|
[18, 1, 0, 190]])
|
|
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
features = ['0', '1', '2', '3']
|
|
QI = [0, 1, 2]
|
|
QI_slices = [[1, 2]]
|
|
target_accuracy = 0.2
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories': {'1': [[0, 1]], '2': [[0, 1]]},
|
|
'category_representatives': {'1': [0], '2': [1]},
|
|
'range_representatives': {'0': []}, 'ranges': {'0': []}, 'untouched': ['3']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_minimizer_ndarray_one_hot_multi():
|
|
x_train = np.array([[23, 0, 1, 0, 0, 1, 165],
|
|
[45, 0, 1, 0, 0, 1, 158],
|
|
[56, 1, 0, 0, 0, 1, 123],
|
|
[67, 0, 1, 1, 0, 0, 154],
|
|
[45, 1, 0, 1, 0, 0, 149],
|
|
[42, 1, 0, 1, 0, 0, 166],
|
|
[73, 0, 1, 0, 0, 1, 172],
|
|
[94, 0, 1, 0, 1, 0, 168],
|
|
[69, 0, 1, 0, 1, 0, 175],
|
|
[24, 1, 0, 0, 1, 0, 181],
|
|
[18, 1, 0, 0, 0, 1, 190]])
|
|
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
features = ['0', '1', '2', '3', '4', '5', '6']
|
|
QI = [0, 1, 2, 3, 4, 5]
|
|
QI_slices = [[1, 2], [3, 4, 5]]
|
|
target_accuracy = 0.2
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories':
|
|
{'1': [[0, 1]], '2': [[0, 1]], '3': [[0, 1]], '4': [[0, 1]], '5': [[0, 1]]},
|
|
'category_representatives': {'1': [0], '2': [1], '3': [0], '4': [1], '5': [0]},
|
|
'range_representatives': {'0': []}, 'ranges': {'0': []}, 'untouched': ['6']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
transformed_slice = transformed[:, QI_slices[1]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_minimizer_ndarray_one_hot_multi2():
|
|
x_train = np.array([[0, 0, 1],
|
|
[0, 0, 1],
|
|
[0, 1, 0],
|
|
[0, 1, 0],
|
|
[1, 0, 0],
|
|
[1, 0, 0]])
|
|
y_train = np.array([1, 1, 2, 2, 0, 0])
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
features = ['0', '1', '2']
|
|
QI = [0, 1, 2]
|
|
QI_slices = [[0, 1, 2]]
|
|
target_accuracy = 0.2
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories': {'0': [[0, 1]], '1': [[0, 1]], '2': [[0, 1]]},
|
|
'category_representatives': {'0': [0], '1': [0], '2': [1]}, 'range_representatives': {},
|
|
'ranges': {}, 'untouched': []}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_anonymize_pandas_one_hot():
|
|
features = ["age", "gender_M", "gender_F", "height"]
|
|
x_train = np.array([[23, 0, 1, 165],
|
|
[45, 0, 1, 158],
|
|
[56, 1, 0, 123],
|
|
[67, 0, 1, 154],
|
|
[45, 1, 0, 149],
|
|
[42, 1, 0, 166],
|
|
[73, 0, 1, 172],
|
|
[94, 0, 1, 168],
|
|
[69, 0, 1, 175],
|
|
[24, 1, 0, 181],
|
|
[18, 1, 0, 190]])
|
|
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
x_train = pd.DataFrame(x_train, columns=features)
|
|
y_train = pd.Series(y_train)
|
|
|
|
model = DecisionTreeClassifier()
|
|
model.fit(x_train, y_train)
|
|
predictions = model.predict(x_train)
|
|
|
|
QI = ["age", "gender_M", "gender_F"]
|
|
QI_slices = [["gender_M", "gender_F"]]
|
|
target_accuracy = 0.7
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
|
|
features_to_minimize=QI)
|
|
gen.fit(dataset=ArrayDataset(x_train, predictions))
|
|
transformed = gen.transform(dataset=ArrayDataset(x_train))
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'categories': {}, 'category_representatives': {},
|
|
'range_representatives': {'age': [34.5]},
|
|
'ranges': {'age': [34.5]}, 'untouched': ['height', 'gender_M', 'gender_F']}
|
|
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
check_features(features, expected_generalizations, transformed, x_train, True)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, expected_generalizations)
|
|
|
|
rel_accuracy = model.score(transformed, predictions)
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
transformed_slice = transformed.loc[:, QI_slices[0]]
|
|
assert ((np.sum(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.max(transformed_slice, axis=1) == 1).all())
|
|
assert ((np.min(transformed_slice, axis=1) == 0).all())
|
|
|
|
|
|
def test_keras_model():
|
|
(x, y), (x_test, y_test) = get_iris_dataset_np()
|
|
|
|
base_est = Sequential()
|
|
base_est.add(Input(shape=(4,)))
|
|
base_est.add(Dense(10, activation="relu"))
|
|
base_est.add(Dense(3, activation='softmax'))
|
|
|
|
base_est.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
|
|
|
|
model = KerasClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(x, y))
|
|
ad = ArrayDataset(x_test)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
|
|
test_dataset = ArrayDataset(x_test, predictions)
|
|
|
|
gen.fit(dataset=test_dataset)
|
|
transformed = gen.transform(dataset=ad)
|
|
gener = gen.generalizations
|
|
|
|
features = ['0', '1', '2', '3']
|
|
check_features(features, gener, transformed, x)
|
|
ncp = gen.ncp.transform_score
|
|
check_ncp(ncp, gener)
|
|
|
|
rel_accuracy = model.score(ArrayDataset(transformed, predictions))
|
|
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
|
|
|
|
|
|
class PytorchModel(nn.Module):
|
|
|
|
def __init__(self, num_classes, num_features):
|
|
super(PytorchModel, self).__init__()
|
|
|
|
self.fc1 = nn.Sequential(
|
|
nn.Linear(num_features, 1024),
|
|
nn.Tanh(), )
|
|
|
|
self.fc2 = nn.Sequential(
|
|
nn.Linear(1024, 512),
|
|
nn.Tanh(), )
|
|
|
|
self.fc3 = nn.Sequential(
|
|
nn.Linear(512, 256),
|
|
nn.Tanh(), )
|
|
|
|
self.fc4 = nn.Sequential(
|
|
nn.Linear(256, 128),
|
|
nn.Tanh(),
|
|
)
|
|
|
|
self.classifier = nn.Linear(128, num_classes)
|
|
|
|
def forward(self, x):
|
|
out = self.fc1(x)
|
|
out = self.fc2(out)
|
|
out = self.fc3(out)
|
|
out = self.fc4(out)
|
|
return self.classifier(out)
|
|
|
|
|
|
def test_minimizer_pytorch(data_three_features):
|
|
x, y, features = data_three_features
|
|
x = x.astype(np.float32)
|
|
qi = ['age', 'weight']
|
|
|
|
from apt.utils.datasets.datasets import PytorchData
|
|
from apt.utils.models.pytorch_model import PyTorchClassifier
|
|
|
|
base_est = PytorchModel(2, 3)
|
|
criterion = nn.CrossEntropyLoss()
|
|
optimizer = optim.Adam(base_est.parameters(), lr=0.01)
|
|
|
|
model = PyTorchClassifier(model=base_est,
|
|
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
|
|
loss=criterion,
|
|
optimizer=optimizer,
|
|
input_shape=(3,),
|
|
nb_classes=2)
|
|
model.fit(PytorchData(x, y), save_entire_model=False, nb_epochs=10)
|
|
|
|
ad = ArrayDataset(x)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
target_accuracy = 0.5
|
|
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, features_to_minimize=qi)
|
|
train_dataset = ArrayDataset(x, predictions, features_names=features)
|
|
gen.fit(dataset=train_dataset)
|
|
transformed = gen.transform(dataset=ad)
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [], 'weight': []}, 'categories': {}, 'untouched': ['height']}
|
|
compare_generalizations(gener, expected_generalizations)
|
|
check_features(features, expected_generalizations, transformed, x)
|
|
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x, [0, 2], axis=1)).all())
|
|
ncp = gen.ncp.transform_score
|
|
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) <= 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=CLASSIFIER_SINGLE_OUTPUT_CLASS_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_minimizer_pytorch_multi_label_binary():
|
|
class multi_label_binary_model(nn.Module):
|
|
def __init__(self, num_labels, num_features):
|
|
super(multi_label_binary_model, self).__init__()
|
|
|
|
self.fc1 = nn.Sequential(
|
|
nn.Linear(num_features, 256),
|
|
nn.Tanh(), )
|
|
|
|
self.classifier1 = nn.Linear(256, num_labels)
|
|
|
|
def forward(self, x):
|
|
return self.classifier1(self.fc1(x))
|
|
# missing sigmoid on each output
|
|
|
|
class FocalLoss(nn.Module):
|
|
def __init__(self, gamma=2, alpha=0.5):
|
|
super(FocalLoss, self).__init__()
|
|
self.gamma = gamma
|
|
self.alpha = alpha
|
|
|
|
def forward(self, input, target):
|
|
bce_loss = functional.binary_cross_entropy_with_logits(input, target, reduction='none')
|
|
|
|
p = sigmoid(input)
|
|
p = where(target >= 0.5, p, 1 - p)
|
|
|
|
modulating_factor = (1 - p) ** self.gamma
|
|
alpha = self.alpha * target + (1 - self.alpha) * (1 - target)
|
|
focal_loss = alpha * modulating_factor * bce_loss
|
|
|
|
return focal_loss.mean()
|
|
|
|
(x_train, y_train), _ = get_iris_dataset_np()
|
|
features = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
|
|
qi = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
|
|
|
|
# make multi-label binary
|
|
y_train = np.column_stack((y_train, y_train, y_train))
|
|
y_train[y_train > 1] = 1
|
|
x_train = x_train.astype(np.float32)
|
|
y_train = y_train.astype(np.float32)
|
|
|
|
orig_model = multi_label_binary_model(3, 4)
|
|
criterion = FocalLoss()
|
|
optimizer = optim.RMSprop(orig_model.parameters(), lr=0.01)
|
|
|
|
model = PyTorchClassifier(model=orig_model,
|
|
output_type=CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
|
|
loss=criterion,
|
|
optimizer=optimizer,
|
|
input_shape=(24,),
|
|
nb_classes=3)
|
|
model.fit(PytorchData(x_train, y_train), save_entire_model=False, nb_epochs=10)
|
|
predictions = model.predict(PytorchData(x_train, y_train))
|
|
predictions = expit(predictions)
|
|
predictions[predictions < 0.5] = 0
|
|
predictions[predictions >= 0.5] = 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():
|
|
cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}}, "label": 0,
|
|
'categories': {'gender': ['male']}, "representative": {"age": 26, "height": 149}},
|
|
{"id": 2, "ranges": {"age": {"start": 39, "end": None}}, "label": 1,
|
|
'categories': {'gender': ['female']}, "representative": {"age": 58, "height": 163}},
|
|
{"id": 3, "ranges": {"age": {"start": None, "end": 38}}, "label": 0,
|
|
'categories': {'gender': ['male']}, "representative": {"age": 31, "height": 184}},
|
|
{"id": 4, "ranges": {"age": {"start": 39, "end": None}}, "label": 1,
|
|
'categories': {'gender': ['male', 'female']}, "representative": {"age": 45, "height": 176}}
|
|
]
|
|
gen = GeneralizeToRepresentative(cells=cells)
|
|
gen._calculate_generalizations()
|
|
gener = gen.generalizations
|
|
expected_generalizations = {'ranges': {'age': [38, 39]}, 'categories': {}, 'untouched': ['gender']}
|
|
compare_generalizations(gener, expected_generalizations)
|
|
|
|
|
|
def test_errors():
|
|
features = ['age', 'height']
|
|
X = np.array([[23, 165],
|
|
[45, 158],
|
|
[56, 123],
|
|
[67, 154],
|
|
[45, 149],
|
|
[42, 166],
|
|
[73, 172],
|
|
[94, 168],
|
|
[69, 175],
|
|
[24, 181],
|
|
[18, 190]])
|
|
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
|
|
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
|
|
min_samples_leaf=1)
|
|
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
|
|
model.fit(ArrayDataset(X, y))
|
|
ad = ArrayDataset(X)
|
|
predictions = model.predict(ad)
|
|
if predictions.shape[1] > 1:
|
|
predictions = np.argmax(predictions, axis=1)
|
|
gen = GeneralizeToRepresentative(model, generalize_using_transform=False)
|
|
train_dataset = ArrayDataset(X, predictions, features_names=features)
|
|
gen.fit(dataset=train_dataset)
|
|
with pytest.raises(ValueError):
|
|
gen.transform(X)
|