def transform(self, X):
"""Transform X separately by each transformer, merge results
into a dictionary.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data to be transformed.
Returns
-------
Xt : dict
Dictionary with the step names as keys and transformed
data as values.
"""
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_transform_one)(trans, weight, X)
for name, trans, weight in self._iter())
if not Xs:
# All transformers are None
return {}
Xt = self._update_transformed_dict(Xs)
return Xt
def fit_transform(self, X, y=None, **fit_params):
"""Fit all transformers using X, transform the data and
concatenate results.
Parameters
----------
X : array-like, sparse matrix or dataframe,
shape (n_samples, n_features)
Input data to be transformed.
Returns
-------
X_t : array-like, sparse matrix or dataframe,
shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
self._validate_transformers()
result = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_transform_one)(trans, weight, X, y, **fit_params)
for _, trans, weight in self._iter())
if not result:
# All transformers are None
return np.zeros((X.shape[0], 0))
Xs, transformers = zip(*result)
if self.keep_original:
Xs = list(itertools.chain([X], Xs))
self._update_transformer_list(transformers)
if any(sparse.issparse(f) for f in Xs):
Xs = sparse.hstack(Xs).tocsr()
elif all(isinstance(f, (pd.DataFrame, pd.Series)) for f in Xs):
if self.ignore_index:
Xs = [f.reset_index(drop=True) for f in Xs]
Xs = pd.concat(Xs, axis=1, copy=self.copy)
else:
Xs = np.hstack(Xs)
return Xs
def transform(self, X):
"""Transform X separately by each transformer, concatenate
results.
Parameters
----------
X : array-like, sparse matrix or dataframe,
shape (n_samples, n_features)
Input data to be transformed.
Returns
-------
X_t : array-like, sparse matrix or dataframe,
shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_transform_one)(trans, X, None, weight)
for _, trans, weight in self._iter())
if not Xs:
# All transformers are None
return np.zeros((X.shape[0], 0))
if self.keep_original:
Xs = list(itertools.chain([X], Xs))
if any(sparse.issparse(f) for f in Xs):
Xs = sparse.hstack(Xs).tocsr()
elif all(isinstance(f, (pd.DataFrame, pd.Series)) for f in Xs):
if self.ignore_index:
Xs = [f.reset_index(drop=True) for f in Xs]
Xs = pd.concat(Xs, axis=1, copy=self.copy)
else:
Xs = np.hstack(Xs)
return Xs
def fit_transform(self, X, y=None, **fit_params):
"""Fit all transformers using X, transform the data and
merge results into a dictionary.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data to be transformed.
y : iterable, default=None
Training targets.
**fit_params : dict, optional
Parameters to pass to the fit method.
Returns
-------
Xt : dict
Dictionary with the step names as keys and transformed
data as values.
"""
self._validate_transformers()
result = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_transform_one)(trans, weight, X, y, **fit_params)
for _, trans, weight in self._iter())
if not result:
# All transformers are None
return {}
Xs, transformers = zip(*result)
self._update_transformer_list(transformers)
Xt = self._update_transformed_dict(Xs)
return Xt
