def test_transform_schema_higher_order(self):
from lale.datasets.data_schemas import to_schema
inner = LogisticRegression
outer = IdentityWrapper(op=LogisticRegression)
input_schema = to_schema(self._digits['X'])
transformed_inner = inner.transform_schema(input_schema)
transformed_outer = outer.transform_schema(input_schema)
self.maxDiff = None
self.assertEqual(transformed_inner, transformed_outer)
def test_transform_schema_higher_order(self):
with EnableSchemaValidation():
from lale.datasets.data_schemas import to_schema
inner = LogisticRegression
outer = IdentityWrapper(op=LogisticRegression)
digits = self._digits
assert digits is not None
input_schema = to_schema(digits["X"])
transformed_inner = inner.transform_schema(input_schema)
transformed_outer = outer.transform_schema(input_schema)
self.maxDiff = None
self.assertEqual(transformed_inner, transformed_outer)
def test_transform_schema_higher_order(self):
from lale.datasets.data_schemas import to_schema
existing_flag = disable_data_schema_validation
set_disable_data_schema_validation(False)
inner = LogisticRegression
outer = IdentityWrapper(op=LogisticRegression)
input_schema = to_schema(self._digits["X"])
transformed_inner = inner.transform_schema(input_schema)
transformed_outer = outer.transform_schema(input_schema)
self.maxDiff = None
self.assertEqual(transformed_inner, transformed_outer)
set_disable_data_schema_validation(existing_flag)
def test_input_schema_fit(self):
self.maxDiff = None
self.assertEqual(
LogisticRegression.input_schema_fit(),
LogisticRegression.get_schema("input_fit"),
)
self.assertEqual(
(NMF >> LogisticRegression).input_schema_fit(), NMF.get_schema("input_fit")
)
self.assertEqual(
IdentityWrapper(op=LogisticRegression).input_schema_fit(),
LogisticRegression.get_schema("input_fit"),
)
actual = (TfidfVectorizer | NMF).input_schema_fit()
expected = {
"anyOf": [
{
"type": "object",
"required": ["X"],
"additionalProperties": False,
"properties": {
"X": {
"anyOf": [
{"type": "array", "items": {"type": "string"}},
{
"type": "array",
"items": {
"type": "array",
"minItems": 1,
"maxItems": 1,
"items": {"type": "string"},
},
},
]
},
"y": {},
},
},
{
"type": "object",
"required": ["X"],
"additionalProperties": False,
"properties": {
"X": {
"type": "array",
"items": {
"type": "array",
"items": {"type": "number", "minimum": 0.0},
},
},
"y": {},
},
},
]
}
self.assertEqual(actual, expected)
def test_input_schema_fit(self):
self.maxDiff = None
self.assertEqual(LogisticRegression.input_schema_fit(),
LogisticRegression.get_schema('input_fit'))
self.assertEqual((NMF >> LogisticRegression).input_schema_fit(),
NMF.get_schema('input_fit'))
self.assertEqual(
IdentityWrapper(op=LogisticRegression).input_schema_fit(),
LogisticRegression.get_schema('input_fit'))
actual = (TfidfVectorizer | NMF).input_schema_fit()
expected = {
'anyOf': [{
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'string'
}
}, {
'type': 'array',
'items': {
'type': 'array',
'minItems': 1,
'maxItems': 1,
'items': {
'type': 'string'
}
}
}]
},
'y': {}
}
}, {
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number',
'minimum': 0.0
}
}
},
'y': {}
}
}]
}
self.assertEqual(actual, expected)