class ValidationStep:
def __init__(self, params):
# kfold is default validation technique
self.validation_type = params.get("validation_type", "kfold")
if self.validation_type == "kfold":
self.validator = KFoldValidator(params)
else:
raise Exception("Other validation types are not implemented yet!")
"""
elif self.validation_type == "split":
self.validator = SplitValidator(params, data)
elif self.validation_type == "with_dataset":
self.validator = WithDatasetValidator(params, data)
else:
msg = "Unknown validation type: {0}".format(self.validation_type)
raise ValidationStepException(msg)
"""
def get_split(self, k):
return self.validator.get_split(k)
def split(self):
return self.validator.split()
def get_n_splits(self):
return self.validator.get_n_splits()
def test_create_with_target_as_labels(self):
data = {
"train": {
"X": pd.DataFrame(np.array([[0, 0], [0, 1], [1, 0], [1, 1]])),
"y": pd.DataFrame(np.array(["a", "b", "a", "b"])),
}
}
params = {"shuffle": True, "stratify": True, "k_folds": 2}
vl = KFoldValidator(params, data)
self.assertEqual(params["k_folds"], vl.get_n_splits())
for train, validation in vl.split():
X_train, y_train = train.get("X"), train.get("y")
X_validation, y_validation = validation.get("X"), validation.get("y")
self.assertEqual(X_train.shape[0], 2)
self.assertEqual(y_train.shape[0], 2)
self.assertEqual(X_validation.shape[0], 2)
self.assertEqual(y_validation.shape[0], 2)
def test_missing_target_values(self):
# rows with missing target will be distributed equaly among folds
data = {
"train": {
"X": pd.DataFrame(
np.array([[1, 0], [2, 1], [3, 0], [4, 1], [5, 1], [6, 1]])
),
"y": pd.DataFrame(np.array(["a", "b", "a", "b", np.nan, np.nan])),
}
}
params = {"shuffle": True, "stratify": True, "k_folds": 2}
vl = KFoldValidator(params, data)
self.assertEqual(params["k_folds"], vl.get_n_splits())
for train, validation in vl.split():
X_train, y_train = train.get("X"), train.get("y")
X_validation, y_validation = validation.get("X"), validation.get("y")
self.assertEqual(X_train.shape[0], 3)
self.assertEqual(y_train.shape[0], 3)
self.assertEqual(X_validation.shape[0], 3)
self.assertEqual(y_validation.shape[0], 3)