def _fit(self, table_data):
"""Fit the model to the table.
Args:
table_data (pandas.DataFrame):
Data to be learned.
"""
distributions = self._field_distributions
fields = self._metadata.get_fields()
transformers = {}
for field in table_data:
field_name = field.replace('.value', '')
if field_name in fields and fields.get(
field_name,
dict(),
).get('type') != 'categorical':
transformers[field] = GaussianCopulaTransformer(
distribution=distributions.get(field_name,
self._default_distribution))
self._ht = HyperTransformer(field_transformers=transformers)
table_data = self._ht.fit_transform(table_data)
super()._fit(table_data)
def _fit(self, table_data):
"""Fit the model to the table.
Args:
table_data (pandas.DataFrame):
Data to be learned.
"""
distributions = self._field_distributions
default = self._default_distribution
fields = self._metadata.get_fields()
transformers = {
field: GaussianCopulaTransformer(
distribution=distributions.get(field, default))
for field in table_data.columns
if fields.get(field, dict()).get('type') != 'categorical'
}
self._ht = HyperTransformer(transformers=transformers)
table_data = self._ht.fit_transform(table_data)
super()._fit(table_data)