def fit_transform(self, X, y=None):
res = _transform_selected(X, self._fit_transform, self.categorical_features, copy=True)
if self.drop_last:
res = res[:,:-1]
if self.sparse:
return res.todense()
return res
def transform(self, X):
"""Discretizes the data.
Parameters
----------
X : numeric array-like, shape (n_samples, n_features)
Data to be discretized.
Returns
-------
Xt : numeric array-like, shape (n_samples, n_features)
Data in the binned space.
"""
X = self._check_X_post_fit(X)
return _transform_selected(X,
self._transform,
self.transformed_features_,
copy=True,
retain_order=True)
def _check_transform_selected(X, X_expected, sel):
for M in (X, sparse.csr_matrix(X)):
Xtr = _transform_selected(M, Binarizer().transform, sel)
assert_array_equal(toarray(Xtr), X_expected)
def _check_transform_selected(X, X_expected, sel):
for M in (X, sparse.csr_matrix(X)):
Xtr = _transform_selected(M, Binarizer().transform, sel)
assert_array_equal(toarray(Xtr), X_expected)
