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apply changes after rebase with wrappers

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olasaadi 2022-03-10 13:49:05 +02:00
parent 6afb175d6f
commit b4eddabd37
4 changed files with 32 additions and 36 deletions
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4
apt/anonymization/anonymizer.py
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@ -5,7 +5,7 @@ from collections import Counter
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.preprocessing import OneHotEncoder
from apt.utils.datasets import BaseDataset, Data
from apt.utils.datasets import ArrayDataset
from typing import Union, Optional
@ -38,7 +38,7 @@ class Anonymize:
self.categorical_features = categorical_features
self.is_regression = is_regression
def anonymize(self, dataset: BaseDataset) -> Union[np.ndarray, pd.DataFrame]:
def anonymize(self, dataset: ArrayDataset) -> Union[np.ndarray, pd.DataFrame]:
"""
Method for performing model-guided anonymization.

22
apt/utils/dataset_utils.py
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@ -5,7 +5,7 @@ import ssl
from os import path, mkdir
from six.moves.urllib.request import urlretrieve
from apt.utils.datasets import BaseDataset, Data
from apt.utils.datasets import ArrayDataset, Data
def _load_iris(test_set_size: float = 0.3):
@ -16,8 +16,8 @@ def _load_iris(test_set_size: float = 0.3):
# Split training and test sets
x_train, x_test, y_train, y_test = model_selection.train_test_split(data, labels, test_size=test_set_size,
random_state=18, stratify=labels)
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
return dataset
@ -41,8 +41,8 @@ def _load_diabetes(test_set_size: float = 0.3):
x_train, x_test, y_train, y_test = model_selection.train_test_split(data, labels, test_size=test_set_size,
random_state=18)
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
return dataset
@ -104,8 +104,8 @@ def get_german_credit_dataset(test_set: float = 0.3):
x_test.reset_index(drop=True, inplace=True)
y_test.reset_index(drop=True, inplace=True)
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
return dataset
@ -166,8 +166,8 @@ def get_adult_dataset():
y_train = train.loc[:, 'label']
x_test = test.drop(['label'], axis=1)
y_test = test.loc[:, 'label']
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
return dataset
@ -330,7 +330,7 @@ def get_nursery_dataset(raw: bool = True, test_set: float = 0.2, transform_socia
x_train = x_train.astype(str)
x_test = x_test.astype(str)
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
return dataset

18
tests/test_anonymizer.py
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@ -7,7 +7,7 @@ from apt.anonymization import Anonymize
from apt.utils.dataset_utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from apt.utils.datasets import BaseDataset, Data
from apt.utils.datasets import ArrayDataset, Data
def test_anonymize_ndarray_iris():
dataset = get_iris_dataset()
@ -18,7 +18,7 @@ def test_anonymize_ndarray_iris():
k = 10
QI = [0, 2]
anonymizer = Anonymize(k, QI)
anon = anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), pred))
anon = anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), pred))
assert(len(np.unique(anon[:, QI], axis=0)) < len(np.unique(dataset.get_train_samples()[:, QI], axis=0)))
_, counts_elements = np.unique(anon[:, QI], return_counts=True)
assert (np.min(counts_elements) >= k)
@ -38,7 +38,7 @@ def test_anonymize_pandas_adult():
categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
'native-country']
anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
anon = anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), pred))
anon = anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), pred))
assert(anon.loc[:, QI].drop_duplicates().shape[0] < dataset.get_train_samples().loc[:, QI].drop_duplicates().shape[0])
assert (anon.loc[:, QI].value_counts().min() >= k)
@ -56,7 +56,7 @@ def test_anonymize_pandas_nursery():
QI = ["finance", "social", "health"]
categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
anon = anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), pred))
anon = anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), pred))
assert(anon.loc[:, QI].drop_duplicates().shape[0] < dataset.get_train_samples().loc[:, QI].drop_duplicates().shape[0])
assert (anon.loc[:, QI].value_counts().min() >= k)
@ -66,8 +66,8 @@ def test_anonymize_pandas_nursery():
def test_regression():
x_train, x_test, y_train, y_test = train_test_split(load_diabetes().data, load_diabetes().target, test_size=0.5, random_state=14)
train_dataset = BaseDataset(x_train, y_train)
test_dataset = BaseDataset(x_test, y_test)
train_dataset = ArrayDataset(x_train, y_train)
test_dataset = ArrayDataset(x_test, y_test)
dataset = Data(train_dataset, test_dataset)
model = DecisionTreeRegressor(random_state=10, min_samples_split=2)
model.fit(dataset.get_train_samples(), dataset.get_train_labels())
@ -75,7 +75,7 @@ def test_regression():
k = 10
QI = [0, 2, 5, 8]
anonymizer = Anonymize(k, QI, is_regression=True)
anon = anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), pred))
anon = anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), pred))
print('Base model accuracy (R2 score): ', model.score(dataset.get_test_samples(), dataset.get_test_labels()))
model.fit(anon, dataset.get_train_labels())
print('Base model accuracy (R2 score) after anonymization: ', model.score(dataset.get_test_samples(), dataset.get_test_labels()))
@ -95,7 +95,7 @@ def test_errors():
anonymizer = Anonymize(10, [0, 2])
dataset = get_iris_dataset()
with pytest.raises(ValueError):
anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), dataset.get_test_labels()))
anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), dataset.get_test_labels()))
dataset = get_adult_dataset()
with pytest.raises(ValueError):
anonymizer.anonymize(BaseDataset(dataset.get_train_samples(), dataset.get_train_labels()))
anonymizer.anonymize(ArrayDataset(dataset.get_train_samples(), dataset.get_train_labels()))

24
tests/test_model.py
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@ -9,28 +9,24 @@ from sklearn.ensemble import RandomForestClassifier
def test_sklearn_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
dataset = dataset_utils.get_iris_dataset()
underlying_model = RandomForestClassifier()
model = SklearnClassifier(underlying_model)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert(pred.shape[0] == x_test.shape[0])
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert(pred.shape[0] == dataset.get_test_samples().shape[0])
score = model.score(test)
score = model.score(dataset.test)
assert(0.0 <= score <= 1.0)
def test_sklearn_regressor():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
dataset = dataset_utils.get_diabetes_dataset()
underlying_model = DecisionTreeRegressor()
model = SklearnRegressor(underlying_model)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert (pred.shape[0] == x_test.shape[0])
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert (pred.shape[0] == dataset.get_test_samples().shape[0])
score = model.score(test)
score = model.score(dataset.test)
assert (0 <= score <= 1)