def __init__(self, pmml):
PMMLBaseRegressor.__init__(self, pmml)
# Setup a column transformer to deal with categorical variables
target = self.target_field.get('name')
fields = [field for name, field in self.fields.items() if name != target]
def encoder_for(field):
if field.get('optype') != 'categorical':
return 'passthrough'
encoder = OneHotEncoder()
encoder.categories_ = np.array([self.field_mapping[field.get('name')][1].categories])
encoder._legacy_mode = False
return encoder
transformer = ColumnTransformer(
transformers=[
(field.get('name'), encoder_for(field), [self.field_mapping[field.get('name')][0]])
for field in fields
if field.tag == 'DataField'
]
)
X = np.array([[0 for field in fields if field.tag == "DataField"]])
transformer._validate_transformers()
transformer._validate_column_callables(X)
transformer._validate_remainder(X)
transformer.transformers_ = transformer.transformers
transformer.sparse_output_ = False
transformer._feature_names_in = None
self.transformer = transformer
def __init__(self, pmml):
super().__init__(pmml)
# Setup a column transformer to deal with categorical variables
target = self.target_field.get('name')
fields = [
field for name, field in self.fields.items() if name != target
]
def encoder_for(field):
if field.get('optype') != 'categorical':
return 'passthrough'
encoder = OneHotEncoder()
encoder.categories_ = np.array(
[self.field_mapping[field.get('name')][1].categories])
encoder._legacy_mode = False
return encoder
transformer = ColumnTransformer(
transformers=[(field.get('name'), encoder_for(field),
[self.field_mapping[field.get('name')][0]])
for field in fields if field.tag == 'DataField'])
transformer.transformers_ = transformer.transformers
transformer.sparse_output_ = False
self.transformer = transformer