def transform(self, X):
"""Center and scale the data.
Can be called on sparse input, provided that ``RobustScaler`` has been
fitted to dense input and ``with_centering=False``.
Parameters
----------
X : {array-like, sparse matrix}
The data used to scale along the specified axis.
This implementation was copied and modified from Scikit-Learn.
See License information here:
https://github.com/scikit-learn/scikit-learn/blob/master/README.rst
"""
if self.with_centering:
check_is_fitted(self, "center_")
if self.with_scaling:
check_is_fitted(self, "scale_")
X = self._check_array(X, self.copy)
# if sparse.issparse(X):
# if self.with_scaling:
# inplace_column_scale(X, 1.0 / self.scale_)
# else:
if self.with_centering:
X -= self.center_
if self.with_scaling:
X /= self.scale_
return X
def transform(self, X, y=None):
"""Transform the columns in ``X`` according to ``self.categories_``.
Parameters
----------
X : pandas.DataFrame or dask.DataFrame
y : ignored
Returns
-------
X_trn : pandas.DataFrame or dask.DataFrame
Same type as the input. The columns in ``self.categories_`` will
be converted to categorical dtype.
"""
check_is_fitted(self, "categories_")
X = self._check_array(X).copy()
categories = self.categories_
for k, dtype in categories.items():
if _HAS_CTD:
if not isinstance(dtype, pd.api.types.CategoricalDtype):
dtype = pd.api.types.CategoricalDtype(*dtype)
X[k] = X[k].astype(dtype)
else:
cat, ordered = dtype
X[k] = X[k].astype("category").cat.set_categories(cat, ordered)
return X
def inverse_transform(
self, X: Union[ArrayLike,
DataFrameType]) -> Union[ArrayLike, DataFrameType]:
"""Scale back the data to the original representation
Parameters
----------
X : array-like
The data used to scale along the specified axis.
This implementation was copied and modified from Scikit-Learn.
See License information here:
https://github.com/scikit-learn/scikit-learn/blob/master/README.rst
"""
check_is_fitted(self, ["center_", "scale_"])
# if sparse.issparse(X):
# if self.with_scaling:
# inplace_column_scale(X, self.scale_)
# else:
if self.with_scaling:
X *= self.scale_
if self.with_centering:
X += self.center_
return X
