def inverse_transform(self, y: cudf.Series) -> cudf.Series:
''' Revert ordinal label to original label
Parameters
----------
y : cudf.Series, dtype=int32
Ordinal labels to be reverted
Returns
-------
reverted : cudf.Series
Reverted labels
'''
# check LabelEncoder is fitted
self._check_is_fitted()
# check input type is cudf.Series
if not isinstance(y, cudf.Series):
raise TypeError(
'Input of type {} is not cudf.Series'.format(type(y)))
# check if y's dtype is np.int32, otherwise convert it
y = _enforce_npint32(y)
# check if ord_label out of bound
ord_label = y.unique()
category_num = len(self._cats.keys())
for ordi in ord_label:
if ordi < 0 or ordi >= category_num:
raise ValueError(
'y contains previously unseen label {}'.format(ordi))
# convert ordinal label to string label
reverted = cudf.Series(self._cats.gather_strings(
y.data.mem.device_ctypes_pointer.value, len(y)))
return reverted
def inverse_transform(self, y: cudf.Series) -> cudf.Series:
"""
Revert ordinal label to original label
Parameters
----------
y : cudf.Series, dtype=int32
Ordinal labels to be reverted
Returns
-------
reverted : cudf.Series
Reverted labels
"""
# check LabelEncoder is fitted
self._check_is_fitted()
# check input type is cudf.Series
if not isinstance(y, cudf.Series):
raise TypeError(
'Input of type {} is not cudf.Series'.format(type(y)))
# check if ord_label out of bound
ord_label = y.unique()
category_num = len(self.classes_)
if self.handle_unknown == 'error':
for ordi in ord_label.values_host:
if ordi < 0 or ordi >= category_num:
raise ValueError(
'y contains previously unseen label {}'.format(ordi))
y = y.astype(self.dtype)
ran_idx = cudf.Series(cp.arange(len(self.classes_))).astype(self.dtype)
reverted = y._column.find_and_replace(ran_idx, self.classes_, False)
return cudf.Series(reverted)
def inverse_transform(self, y: cudf.Series) -> cudf.Series:
"""
Revert ordinal label to original label
Parameters
----------
y : cudf.Series, pandas.Series, cupy.ndarray or numpy.ndarray
dtype=int32
Ordinal labels to be reverted
Returns
-------
reverted : the same type as y
Reverted labels
"""
# check LabelEncoder is fitted
self._check_is_fitted()
# check input type is cudf.Series
y = self._to_cudf_series(y)
# check if ord_label out of bound
ord_label = y.unique()
category_num = len(self.classes_)
if self.handle_unknown == 'error':
for ordi in ord_label.values_host:
if ordi < 0 or ordi >= category_num:
raise ValueError(
'y contains previously unseen label {}'.format(ordi))
y = y.astype(self.dtype)
ran_idx = cudf.Series(cp.arange(len(self.classes_))).astype(self.dtype)
reverted = y._column.find_and_replace(ran_idx, self.classes_, False)
res = cudf.Series(reverted)
return res
def test_label_encode_dtype(ncats, cat_dtype):
s = Series([str(i % ncats) for i in range(ncats + 1)])
cats = s.unique().astype(s.dtype)
encoded_col = s.label_encoding(cats=cats)
np.testing.assert_equal(encoded_col.dtype, cat_dtype)
def _fit_unique(self, y: cudf.Series):
y_uniqs = y.unique()
self._cats_parts.append(y_uniqs.to_pandas())
