def _maybe_add_intercept(self, X):
from dask_glm.utils import add_intercept
if self.fit_intercept:
return add_intercept(X)
else:
return X
def test_add_intercept_dask():
X = da.from_array(np.zeros((4, 4)), chunks=(2, 4))
result = utils.add_intercept(X)
expected = da.from_array(np.array([
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
], dtype=X.dtype), chunks=2)
assert_eq(result, expected)
def test_add_intercept():
X = np.zeros((4, 4))
result = utils.add_intercept(X)
expected = np.array([
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
], dtype=X.dtype)
assert_eq(result, expected)
def test_add_intercept_sparse():
X = sparse.COO(np.zeros((4, 4)))
result = utils.add_intercept(X)
expected = sparse.COO(np.array([
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
], dtype=X.dtype))
assert (result == expected).all()
def _check_array(self, X):
if self.fit_intercept:
X = add_intercept(X)
return check_array(X)
def _check_array(self, X):
if self.fit_intercept:
X = add_intercept(X)
return check_array(X, accept_unknown_chunks=True)
