def test_np_matrix():
"""
Confirm that input validation code does not return np.matrix
"""
X = np.arange(12).reshape(3, 4)
assert_false(isinstance(as_float_array(X), np.matrix))
assert_false(isinstance(as_float_array(np.matrix(X)), np.matrix))
assert_false(isinstance(as_float_array(sp.csc_matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csr(X), np.matrix))
assert_false(isinstance(atleast2d_or_csr(np.matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csr(sp.csc_matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csc(X), np.matrix))
assert_false(isinstance(atleast2d_or_csc(np.matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csc(sp.csr_matrix(X)), np.matrix))
assert_false(isinstance(safe_asarray(X), np.matrix))
assert_false(isinstance(safe_asarray(np.matrix(X)), np.matrix))
assert_false(isinstance(safe_asarray(sp.lil_matrix(X)), np.matrix))
assert_true(atleast2d_or_csr(X, copy=False) is X)
assert_false(atleast2d_or_csr(X, copy=True) is X)
assert_true(atleast2d_or_csc(X, copy=False) is X)
assert_false(atleast2d_or_csc(X, copy=True) is X)
def test_np_matrix():
"""
Confirm that input validation code does not return np.matrix
"""
X = np.arange(12).reshape(3, 4)
assert_false(isinstance(as_float_array(X), np.matrix))
assert_false(isinstance(as_float_array(np.matrix(X)), np.matrix))
assert_false(isinstance(as_float_array(sp.csc_matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csr(X), np.matrix))
assert_false(isinstance(atleast2d_or_csr(np.matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csr(sp.csc_matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csc(X), np.matrix))
assert_false(isinstance(atleast2d_or_csc(np.matrix(X)), np.matrix))
assert_false(isinstance(atleast2d_or_csc(sp.csr_matrix(X)), np.matrix))
assert_false(isinstance(safe_asarray(X), np.matrix))
assert_false(isinstance(safe_asarray(np.matrix(X)), np.matrix))
assert_false(isinstance(safe_asarray(sp.lil_matrix(X)), np.matrix))
assert_true(atleast2d_or_csr(X, copy=False) is X)
assert_false(atleast2d_or_csr(X, copy=True) is X)
assert_true(atleast2d_or_csc(X, copy=False) is X)
assert_false(atleast2d_or_csc(X, copy=True) is X)
def _transform_selected(X, transform, selected="all", copy=True):
"""Apply a transform function to portion of selected features
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
transform : callable
A callable transform(X) -> X_transformed
copy : boolean, optional
Copy X even if it could be avoided.
selected: "all" or array of indices or mask
Specify which features to apply the transform to.
Returns
-------
X : array or sparse matrix, shape=(n_samples, n_features_new)
"""
if selected == "all":
X = safe_asarray(X, copy=copy, force_all_finite=False)
return transform(X)
X = atleast2d_or_csc(X, copy=copy, force_all_finite=False)
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]])
X_not_sel = X[:, ind[not_sel]]
if sparse.issparse(X_sel) or sparse.issparse(X_not_sel):
return sparse.hstack((X_sel, X_not_sel)).tocsr()
else:
return np.hstack((X_sel, X_not_sel))
def test_atleast2d_or_sparse():
for typ in [sp.csr_matrix, sp.dok_matrix, sp.lil_matrix, sp.coo_matrix]:
X = typ(np.arange(9, dtype=float).reshape(3, 3))
Y = atleast2d_or_csr(X, copy=True)
assert_true(isinstance(Y, sp.csr_matrix))
Y.data[:] = 1
assert_array_equal(X.toarray().ravel(), np.arange(9))
Y = atleast2d_or_csc(X, copy=False)
Y.data[:] = 4
assert_true(np.all(X.data == 4)
if isinstance(X, sp.csc_matrix)
else np.all(X.toarray().ravel() == np.arange(9)))
Y = atleast2d_or_csr(X, dtype=np.float32)
assert_true(Y.dtype == np.float32)
def test_atleast2d_or_sparse():
for typ in [sp.csr_matrix, sp.dok_matrix, sp.lil_matrix, sp.coo_matrix]:
X = typ(np.arange(9, dtype=float).reshape(3, 3))
Y = atleast2d_or_csr(X, copy=True)
assert_true(isinstance(Y, sp.csr_matrix))
Y.data[:] = 1
assert_array_equal(X.toarray().ravel(), np.arange(9))
Y = atleast2d_or_csc(X, copy=False)
Y.data[:] = 4
assert_true(
np.all(X.data == 4) if isinstance(X, sp.csc_matrix) else np.all(
X.toarray().ravel() == np.arange(9)))
Y = atleast2d_or_csr(X, dtype=np.float32)
assert_true(Y.dtype == np.float32)
def fit(self, X, y=None):
X = atleast2d_or_csc(X)
self.n_input_feats = X.shape[1]
return self
