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Support additional use cases for data (#46)

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* Make ART black box classifier not apply preprocessing to data
* Add option to store predictions (in addition to x,y) in Dataset and Data classes
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abigailgold 2022-07-11 14:28:09 +03:00 • committed by GitHub
parent e25e58b253
commit 00f9c16863
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6 changed files with 139 additions and 62 deletions
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15
tests/test_model.py
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@ -77,7 +77,7 @@ def test_blackbox_classifier():
assert(pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
assert(score == 1.0)
def test_blackbox_classifier_no_test():
(x_train, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
@ -90,7 +90,7 @@ def test_blackbox_classifier_no_test():
assert(pred.shape[0] == x_train.shape[0])
score = model.score(train)
assert(0.0 <= score <= 1.0)
assert (score == 1.0)
def test_blackbox_classifier_no_train():
@ -103,7 +103,7 @@ def test_blackbox_classifier_no_train():
assert(pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
assert (score == 1.0)
def test_blackbox_classifier_no_test_y():
@ -117,7 +117,7 @@ def test_blackbox_classifier_no_test_y():
assert(pred.shape[0] == x_train.shape[0])
score = model.score(train)
assert(0.0 <= score <= 1.0)
assert (score == 1.0)
# since no test_y, BBC should use only test thus predict test should fail
unable_to_predict_test = False
@ -139,7 +139,7 @@ def test_blackbox_classifier_no_train_y():
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert (0.0 <= score <= 1.0)
assert (score == 1.0)
# since no train_y, BBC should use only test thus predict train should fail
unable_to_predict_train = False
@ -164,7 +164,7 @@ def test_blackbox_classifier_probabilities():
assert (pred < 1.0).all()
score = model.score(train)
assert (0.0 <= score <= 1.0)
assert (score == 1.0)
def test_blackbox_classifier_predict():
@ -172,6 +172,7 @@ def test_blackbox_classifier_predict():
return [0.23, 0.56, 0.21]
(x_train, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
y_train = np.array([[0.23, 0.56, 0.21] for i in range(105)])
train = ArrayDataset(x_train, y_train)
@ -182,7 +183,7 @@ def test_blackbox_classifier_predict():
assert (pred < 1.0).all()
score = model.score(train)
assert (0.0 <= score <= 1.0)
assert (score == 1.0)
def test_is_one_hot():
(_, y_train), (_, _) = dataset_utils.get_iris_dataset_np()