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"""Helpers for preprocessing"""
import numpy as np
from scipy import sparse
from ..utils import check_array
from ..utils.validation import FLOAT_DTYPES
from ..externals import six
def _transform_selected(X, transform, dtype, selected="all", copy=True,
retain_order=False):
"""Apply a transform function to portion of selected features.
Returns an array Xt, where the non-selected features appear on the right
side (largest column indices) of Xt.
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
Dense array or sparse matrix.
transform : callable
A callable transform(X) -> X_transformed
dtype : number type
Desired dtype of output.
copy : boolean, default=True
Copy X even if it could be avoided.
selected : "all" or array of indices or mask
Specify which features to apply the transform to.
retain_order : boolean, default=False
If True, the non-selected features will not be displaced to the right
side of the transformed array. The number of features in Xt must
match the number of features in X. Furthermore, X and Xt cannot be
sparse.
Returns
-------
Xt : array or sparse matrix, shape=(n_samples, n_features_new)
"""
X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
if sparse.issparse(X) and retain_order:
raise ValueError("The retain_order option can only be set to True "
"for dense matrices.")
if isinstance(selected, six.string_types) and selected == "all":
return transform(X)
if len(selected) == 0:
return X
n_features = X.shape[1]
ind = np.arange(n_features)
sel = np.zeros(n_features, dtype=bool)
sel[np.asarray(selected)] = True
not_sel = np.logical_not(sel)
n_selected = np.sum(sel)
if n_selected == 0:
# No features selected.
return X
elif n_selected == n_features:
# All features selected.
return transform(X)
else:
X_sel = transform(X[:, ind[sel]])
# The columns of X which are not transformed need
# to be casted to the desire dtype before concatenation.
# Otherwise, the stacking will cast to the higher-precision dtype.
X_not_sel = X[:, ind[not_sel]].astype(dtype)
if retain_order:
if X_sel.shape[1] + X_not_sel.shape[1] != n_features:
raise ValueError("The retain_order option can only be set to True "
"if the dimensions of the input array match the "
"dimensions of the transformed array.")
# Fancy indexing not supported for sparse matrices
X[:, ind[sel]] = X_sel
return X
if sparse.issparse(X_sel) or sparse.issparse(X_not_sel):
return sparse.hstack((X_sel, X_not_sel))
else:
return np.hstack((X_sel, X_not_sel))
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