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Check for mismatch between model output type and actual output

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abigailt 2022-07-19 08:43:19 +03:00 • committed by abigailgold
parent bc7ab0cc7f
commit 1cc73b3da1
5 changed files with 75 additions and 39 deletions
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24
tests/test_model.py
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@ -72,7 +72,7 @@ def test_blackbox_classifier():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
@ -81,13 +81,24 @@ def test_blackbox_classifier():
assert model.model_type is None
def test_blackbox_classifier_mismatch():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
with pytest.raises(ValueError):
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
def test_blackbox_classifier_no_test():
(x_train, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
@ -100,7 +111,7 @@ def test_blackbox_classifier_no_train():
test = ArrayDataset(x_test, y_test)
data = Data(test=test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
@ -114,7 +125,7 @@ def test_blackbox_classifier_no_test_y():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
@ -136,7 +147,7 @@ def test_blackbox_classifier_no_train_y():
train = ArrayDataset(x_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SCALAR)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -171,7 +182,7 @@ def test_blackbox_classifier_probabilities():
def test_blackbox_classifier_predict():
def predict(x):
return [0.23, 0.56, 0.21]
return np.array([[0.23, 0.56, 0.21] for i in range(x.shape[0])])
(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)])
@ -187,6 +198,7 @@ def test_blackbox_classifier_predict():
score = model.score(train)
assert (score == 1.0)
def test_is_one_hot():
(_, y_train), (_, _) = dataset_utils.get_iris_dataset_np()