def test_pipeline_builtin(self):
steps1 = [PipelineStep(name="transformer", learner=DummyTransformer)]
steps2 = [PipelineStep(name="estimator", learner=DummyRegressor)]
steps3 = [
PipelineStep(name="transformer", learner=DummyTransformer),
PipelineStep(name="estimator", learner=DummyRegressor),
]
for steps in (steps1, steps2, steps3):
self.assertTrue(test_learner(Pipeline, steps=steps))
def test_learner_builtin(self):
learner_args = []
learner_kwargs = {}
for learner in (MLPClassifier, MLPRegressor):
self.assertTrue(
test_learner(learner, *learner_args, **learner_kwargs))
def test_dummy_transformer_builtin(self):
self.assertTrue(test_learner(DummyTransformer))
def test_learner_builtin(self):
learner_kwargs = {"a": 1, "b": 2, "c": 3}
self.assertTrue(test_learner(Learner, **learner_kwargs))
def test_dummy_classifier_builtin(self):
learner_kwargs = ({'strategy': 'mean'}, {'strategy': 'random'})
for kwargs in learner_kwargs:
self.assertTrue(test_learner(DummyClassifier, **kwargs))
def test_learner_builtin(self):
learner_args = []
learner_kwargs = dict(kernel_size=1, padding=0, maxpool_size=1)
for learner in (CNNClassifier, CNNRegressor):
self.assertTrue(
test_learner(learner, *learner_args, **learner_kwargs))
def test_transformer_builtin(self):
for transformer in [MinMaxScaler, StandardScaler]:
self.assertTrue(test_learner(transformer))
def test_learner_builtin(self):
learner_args = []
learner_kwargs = {}
for learner in (LinearRegressor, LogisticRegression):
self.assertTrue(
test_learner(learner, *learner_args, **learner_kwargs))
def test_transformer_builtin(self):
for transformer in [FeatureDrop, VarianceThreshold]:
self.assertTrue(test_learner(transformer, columns=[]))