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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
parent b47ba24906
commit d52fcd0041
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16 changed files with 91 additions and 92 deletions
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43
tests/test_model.py
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@ -27,10 +27,10 @@ def test_sklearn_classifier():
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
assert (0.0 <= score <= 1.0)
def test_sklearn_regressor():
@ -43,7 +43,7 @@ def test_sklearn_regressor():
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
model.score(test)
def test_keras_classifier():
@ -63,10 +63,10 @@ def test_keras_classifier():
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
assert (0.0 <= score <= 1.0)
def test_keras_regressor():
@ -88,7 +88,7 @@ def test_keras_regressor():
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
model.score(test)
def test_xgboost_classifier():
@ -99,10 +99,10 @@ def test_xgboost_classifier():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
assert (0.0 <= score <= 1.0)
model.fit(train)
@ -115,10 +115,10 @@ def test_blackbox_classifier():
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(score == 1.0)
assert (score == 1.0)
assert model.model_type is None
@ -131,7 +131,7 @@ def test_blackbox_classifier_predictions():
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
assert model.model_type is None
with pytest.raises(ValueError):
@ -146,10 +146,10 @@ def test_blackbox_classifier_predictions_y():
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(score == 1.0)
assert (score == 1.0)
assert model.model_type is None
@ -161,7 +161,7 @@ def test_blackbox_classifier_mismatch():
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
with pytest.raises(ValueError):
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
def test_blackbox_classifier_no_test():
@ -172,7 +172,7 @@ def test_blackbox_classifier_no_test():
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
assert (pred.shape[0] == x_train.shape[0])
score = model.score(train)
assert (score == 1.0)
@ -189,7 +189,7 @@ def test_blackbox_classifier_no_train():
data = Data(test=test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert (score == 1.0)
@ -207,7 +207,7 @@ def test_blackbox_classifier_no_test_y():
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
assert (pred.shape[0] == x_train.shape[0])
score = model.score(train)
assert (score == 1.0)
@ -216,11 +216,12 @@ def test_blackbox_classifier_no_test_y():
unable_to_predict_test = False
try:
model.predict(test)
except BaseException:
except BaseException:
unable_to_predict_test = True
assert unable_to_predict_test
def test_blackbox_classifier_no_train_y():
(x_train, _), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
@ -243,6 +244,7 @@ def test_blackbox_classifier_no_train_y():
assert unable_to_predict_train
def test_blackbox_classifier_probabilities():
(x_train, _), (_, _) = dataset_utils.get_iris_dataset_np()
y_train = np.array([[0.23, 0.56, 0.21] for i in range(105)])
@ -300,7 +302,7 @@ def test_is_one_hot():
(_, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
assert (not is_one_hot(y_train))
assert (not is_one_hot(y_train.reshape(-1,1)))
assert (not is_one_hot(y_train.reshape(-1, 1)))
assert (is_one_hot(to_categorical(y_train)))
@ -314,7 +316,7 @@ def test_get_nb_classes():
assert (nb_classes_test == 3)
# shape: (x,1) - not 1-hot
nb_classes_test = get_nb_classes(y_test.reshape(-1,1))
nb_classes_test = get_nb_classes(y_test.reshape(-1, 1))
assert (nb_classes_test == 3)
# shape: (x,3) - 1-hot
@ -326,4 +328,3 @@ def test_get_nb_classes():
y_test[y_test == 0] = 4
nb_classes = get_nb_classes(y_test)
assert (nb_classes == 5)