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Formatting (#68)

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Fix most flake/lint errors and ignore a few others

Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailgold 2022-12-25 15:13:57 +02:00 • committed by GitHub
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16 changed files with 91 additions and 92 deletions
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76
tests/test_minimizer.py
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@ -1,7 +1,6 @@
import pytest
import numpy as np
import pandas as pd
from numpy.testing import assert_almost_equal
from sklearn.compose import ColumnTransformer
@ -17,9 +16,8 @@ from tensorflow.keras.layers import Dense, Input
from apt.minimization import GeneralizeToRepresentative
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from apt.utils.dataset_utils import get_iris_dataset_np, get_adult_dataset_pd, get_german_credit_dataset_pd
from apt.utils.datasets import ArrayDataset, Data
from apt.utils.models import SklearnClassifier, ModelOutputType, SklearnRegressor, KerasClassifier, \
BlackboxClassifierPredictions
from apt.utils.datasets import ArrayDataset
from apt.utils.models import SklearnClassifier, ModelOutputType, SklearnRegressor, KerasClassifier
@pytest.fixture
@ -50,7 +48,7 @@ def test_minimizer_params(data):
gen = GeneralizeToRepresentative(model, cells=cells)
gen.fit()
transformed = gen.transform(dataset=ArrayDataset(X, features_names=features))
gen.transform(dataset=ArrayDataset(X, features_names=features))
def test_minimizer_fit(data):
@ -87,8 +85,8 @@ def test_minimizer_fit(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
modified_features = [f for f in features if
f in expected_generalizations['categories'].keys() or f in expected_generalizations[
@ -162,8 +160,8 @@ def test_minimizer_fit_pandas(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
modified_features = [f for f in features if
f in expected_generalizations['categories'].keys() or f in expected_generalizations[
@ -172,7 +170,7 @@ def test_minimizer_fit_pandas(data):
ncp = gen.ncp
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (ncp > 0)
assert (((transformed[modified_features]).equals(X[modified_features])) == False)
assert (((transformed[modified_features]).equals(X[modified_features])) is False)
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
@ -279,8 +277,8 @@ def test_minimizer_fit_QI(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], axis=1)).all())
modified_features = [f for f in features if
@ -357,8 +355,8 @@ def test_minimizer_fit_pandas_QI(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
# assert (transformed.drop(QI, axis=1).equals(X.drop(QI, axis=1)))
np.testing.assert_array_equal(transformed.drop(QI, axis=1), X.drop(QI, axis=1))
@ -370,7 +368,7 @@ def test_minimizer_fit_pandas_QI(data):
ncp = gen.ncp
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (ncp > 0)
assert (((transformed[modified_features]).equals(X[modified_features])) == False)
assert (((transformed[modified_features]).equals(X[modified_features])) is False)
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
@ -398,8 +396,8 @@ def test_minimize_ndarray_iris():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(x_train, [0, 2], axis=1)).all())
@ -478,8 +476,8 @@ def test_minimize_pandas_adult():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
# assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
np.testing.assert_array_equal(transformed.drop(QI, axis=1), x_train.drop(QI, axis=1))
@ -492,7 +490,7 @@ def test_minimize_pandas_adult():
ncp = gen.ncp
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (ncp > 0)
assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
assert (((transformed[modified_features]).equals(x_train[modified_features])) is False)
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
@ -538,8 +536,10 @@ def test_german_credit_pandas():
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']],
'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']],
@ -554,8 +554,8 @@ def test_german_credit_pandas():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
# assert (transformed.drop(QI, axis=1).equals(x_train.drop(QI, axis=1)))
np.testing.assert_array_equal(transformed.drop(QI, axis=1), x_train.drop(QI, axis=1))
@ -568,7 +568,7 @@ def test_german_credit_pandas():
ncp = gen.ncp
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (ncp > 0)
assert (((transformed[modified_features]).equals(x_train[modified_features])) == False)
assert (((transformed[modified_features]).equals(x_train[modified_features])) is False)
rel_accuracy = model.score(ArrayDataset(preprocessor.transform(transformed), predictions))
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
@ -626,8 +626,8 @@ def test_regression():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
@ -681,8 +681,8 @@ def test_X_y(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], axis=1)).all())
modified_features = [f for f in features if
@ -735,8 +735,8 @@ def test_X_y_features_names(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2], axis=1) == np.delete(X, [0, 2], axis=1)).all())
modified_features = [f for f in features if
@ -811,8 +811,8 @@ def test_BaseEstimator_classification(data):
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
# assert (transformed.drop(QI, axis=1).equals(X.drop(QI, axis=1)))
np.testing.assert_array_equal(transformed.drop(QI, axis=1), X.drop(QI, axis=1))
@ -824,7 +824,7 @@ def test_BaseEstimator_classification(data):
ncp = gen.ncp
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (ncp > 0)
assert (((transformed[modified_features]).equals(X[modified_features])) == False)
assert (((transformed[modified_features]).equals(X[modified_features])) is False)
rel_accuracy = model.score(preprocessor.transform(transformed), predictions)
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
@ -881,8 +881,8 @@ def test_BaseEstimator_regression():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
assert ((np.delete(transformed, [0, 2, 5, 8], axis=1) == np.delete(x_train, [0, 2, 5, 8], axis=1)).all())
@ -961,6 +961,6 @@ def test_untouched():
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]]) ==
set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))