def test_shallow_trainable_individual_operator_defaults(self):
op: Ops.TrainableIndividualOp = LogisticRegression()
params = op.get_params(deep=False)
filtered_params = self.remove_lale_params(params)
expected = LogisticRegression.get_defaults()
self.assertEqual(filtered_params, expected)
def test_deep_planned_individual_operator(self):
op: Ops.PlannedIndividualOp = LogisticRegression
params = op.get_params(deep=True)
filtered_params = self.remove_lale_params(params)
expected = LogisticRegression.get_defaults()
self.assertEqual(filtered_params, expected)
def test_shallow0_planned_individual_operator(self):
op: Ops.PlannedIndividualOp = LogisticRegression
params = op.get_params(deep=0)
self.assertNotIn("_lale_schemas", params)
expected = LogisticRegression.get_defaults()
self.assertEqual(params, expected)
def test_shallow_trainable_individual_operator_configured(self):
op: Ops.TrainableIndividualOp = LogisticRegression(
LogisticRegression.enum.solver.saga)
params = op.get_params(deep=False)
filtered_params = self.remove_lale_params(params)
expected = dict(LogisticRegression.get_defaults())
expected["solver"] = "saga"
self.assertEqual(filtered_params, expected)
def test_shallow_trained_individual_operator_defaults(self):
op1: Ops.TrainableIndividualOp = LogisticRegression()
iris = load_iris()
op: Ops.TrainedIndividualOp = op1.fit(iris.data, iris.target)
params = op.get_params(deep=False)
filtered_params = self.remove_lale_params(params)
expected = LogisticRegression.get_defaults()
self.assertEqual(filtered_params, expected)
def test_shallow_trainable_pipeline_default(self):
op: Ops.TrainablePipeline = PCA() >> LogisticRegression()
params = op.get_params(deep=False)
assert "steps" in params
assert "_lale_preds" in params
pca = params["steps"][0]
lr = params["steps"][1]
assert isinstance(pca, Ops.TrainableIndividualOp)
assert isinstance(lr, Ops.TrainableIndividualOp)
lr_params = lr.get_params()
lr_filtered_params = self.remove_lale_params(lr_params)
lr_expected = LogisticRegression.get_defaults()
self.assertEqual(lr_filtered_params, lr_expected)
def test_deep_planned_pipeline(self):
op: Ops.PlannedPipeline = PCA >> LogisticRegression
params = op.get_params(deep=True)
assert "steps" in params
assert "_lale_preds" in params
pca = params["steps"][0]
lr = params["steps"][1]
assert isinstance(pca, Ops.PlannedIndividualOp)
assert isinstance(lr, Ops.PlannedIndividualOp)
assert "LogisticRegression__fit_intercept" in params
lr_params = lr.get_params()
lr_filtered_params = self.remove_lale_params(lr_params)
lr_expected = LogisticRegression.get_defaults()
self.assertEqual(lr_filtered_params, lr_expected)
def test_shallow0_trainable_pipeline_configured(self):
op: Ops.TrainablePipeline = PCA() >> LogisticRegression(
LogisticRegression.enum.solver.saga)
params = op.get_params(deep=0)
assert "steps" in params
assert "_lale_preds" not in params
pca = params["steps"][0]
lr = params["steps"][1]
assert isinstance(pca, Ops.TrainableIndividualOp)
assert isinstance(lr, Ops.TrainableIndividualOp)
lr_params = lr.get_params()
lr_expected = dict(LogisticRegression.get_defaults())
lr_expected["solver"] = "saga"
self.assertEqual(lr_params, lr_expected)
def test_shallow_planned_pipeline(self):
op: Ops.PlannedPipeline = PCA >> LogisticRegression
params = op.get_params(deep=False)
assert "steps" in params
assert "preds" in params
assert "ordered" in params
assert params["ordered"] is True
pca = params["steps"][0]
lr = params["steps"][1]
assert isinstance(pca, Ops.PlannedIndividualOp)
assert isinstance(lr, Ops.PlannedIndividualOp)
lr_params = lr.get_params()
lr_filtered_params = self.remove_lale_params(lr_params)
lr_expected = LogisticRegression.get_defaults()
self.assertEqual(lr_filtered_params, lr_expected)
def test_shallow_planned_pipeline_with_trainable_configured(self):
op: Ops.PlannedPipeline = PCA >> LogisticRegression(
LogisticRegression.enum.solver.saga)
params = op.get_params(deep=False)
assert "steps" in params
assert "_lale_preds" in params
pca = params["steps"][0]
lr = params["steps"][1]
assert isinstance(pca, Ops.PlannedIndividualOp)
assert isinstance(lr, Ops.TrainableIndividualOp)
lr_params = lr.get_params()
lr_filtered_params = self.remove_lale_params(lr_params)
lr_expected = dict(LogisticRegression.get_defaults())
lr_expected["solver"] = "saga"
self.assertEqual(lr_filtered_params, lr_expected)
def test_solver(self):
default = LogisticRegression.get_defaults()["solver"]
self.assertEqual(default, "liblinear")
def test_multi_class(self):
default = LogisticRegression.get_defaults()["multi_class"]
self.assertEqual(default, "ovr")
