def test_transform_schema_Concat_irisDf(self):
with EnableSchemaValidation():
from lale.datasets.data_schemas import to_schema
data_X, data_y = self._irisDf["X"], self._irisDf["y"]
s_in_X, s_in_y = to_schema(data_X), to_schema(data_y)
def check(s_actual, n_expected, s_expected):
assert s_actual["items"]["minItems"] == n_expected, str(
s_actual)
assert s_actual["items"]["maxItems"] == n_expected, str(
s_actual)
assert s_actual["items"]["items"] == s_expected, str(s_actual)
s_out_X = ConcatFeatures.transform_schema({"items": [s_in_X]})
check(s_out_X, 4, {"type": "number"})
s_out_y = ConcatFeatures.transform_schema({"items": [s_in_y]})
check(s_out_y, 1, {"description": "target", "type": "integer"})
s_out_XX = ConcatFeatures.transform_schema(
{"items": [s_in_X, s_in_X]})
check(s_out_XX, 8, {"type": "number"})
s_out_yy = ConcatFeatures.transform_schema(
{"items": [s_in_y, s_in_y]})
check(s_out_yy, 2, {"type": "integer"})
s_out_Xy = ConcatFeatures.transform_schema(
{"items": [s_in_X, s_in_y]})
check(s_out_Xy, 5, {"type": "number"})
s_out_XXX = ConcatFeatures.transform_schema(
{"items": [s_in_X, s_in_X, s_in_X]})
check(s_out_XXX, 12, {"type": "number"})
def test_transform_schema_Concat_irisArr(self):
from lale.datasets.data_schemas import to_schema
existing_flag = disable_data_schema_validation
set_disable_data_schema_validation(False)
data_X, data_y = self._irisArr["X"], self._irisArr["y"]
s_in_X, s_in_y = to_schema(data_X), to_schema(data_y)
def check(s_actual, n_expected, s_expected):
assert s_actual["items"]["minItems"] == n_expected, str(s_actual)
assert s_actual["items"]["maxItems"] == n_expected, str(s_actual)
assert s_actual["items"]["items"] == s_expected, str(s_actual)
s_out_X = ConcatFeatures.transform_schema({"items": [s_in_X]})
check(s_out_X, 4, {"type": "number"})
s_out_y = ConcatFeatures.transform_schema({"items": [s_in_y]})
check(s_out_y, 1, {"type": "integer"})
s_out_XX = ConcatFeatures.transform_schema({"items": [s_in_X, s_in_X]})
check(s_out_XX, 8, {"type": "number"})
s_out_yy = ConcatFeatures.transform_schema({"items": [s_in_y, s_in_y]})
check(s_out_yy, 2, {"type": "integer"})
s_out_Xy = ConcatFeatures.transform_schema({"items": [s_in_X, s_in_y]})
check(s_out_Xy, 5, {"type": "number"})
s_out_XXX = ConcatFeatures.transform_schema(
{"items": [s_in_X, s_in_X, s_in_X]})
check(s_out_XXX, 12, {"type": "number"})
set_disable_data_schema_validation(existing_flag)
def test_transform_schema_Concat_irisDf(self):
from lale.datasets.data_schemas import to_schema
data_X, data_y = self._irisDf['X'], self._irisDf['y']
s_in_X, s_in_y = to_schema(data_X), to_schema(data_y)
def check(s_actual, n_expected, s_expected):
assert s_actual['items']['minItems'] == n_expected, str(s_actual)
assert s_actual['items']['maxItems'] == n_expected, str(s_actual)
assert s_actual['items']['items'] == s_expected, str(s_actual)
s_out_X = ConcatFeatures.transform_schema({'items': [s_in_X]})
check(s_out_X, 4, {'type': 'number'})
s_out_y = ConcatFeatures.transform_schema({'items': [s_in_y]})
check(s_out_y, 1, {'description': 'target', 'type': 'integer'})
s_out_XX = ConcatFeatures.transform_schema({'items': [s_in_X, s_in_X]})
check(s_out_XX, 8, {'type': 'number'})
s_out_yy = ConcatFeatures.transform_schema({'items': [s_in_y, s_in_y]})
check(s_out_yy, 2, {'type': 'integer'})
s_out_Xy = ConcatFeatures.transform_schema({'items': [s_in_X, s_in_y]})
check(s_out_Xy, 5, {'type': 'number'})
s_out_XXX = ConcatFeatures.transform_schema({
'items': [s_in_X, s_in_X, s_in_X]})
check(s_out_XXX, 12, {'type': 'number'})
