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ENH: Allow add_intercept for unknown dims #45

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ENH: Allow add_intercept for unknown dims #45

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12 changes: 12 additions & 0 deletions dask_glm/tests/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,18 @@ def test_add_intercept_dask():
assert_eq(result, expected)


def test_add_intercept_unknown():
dd = pytest.importorskip('dask.dataframe')
X = dd.from_array(da.from_array(np.zeros((4, 4)), chunks=(2, 4))).values
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_sparse():
sparse = pytest.importorskip('sparse')
from sparse.utils import assert_eq
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14 changes: 7 additions & 7 deletions dask_glm/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,16 +117,16 @@ def add_intercept(X):
return np.concatenate([X, np.ones((X.shape[0], 1))], axis=1)


def _add_intercept_block(x):
o = np.ones((len(x), 1), dtype=x.dtype)
return np.concatenate([x, o], axis=1)


@dispatch(da.Array)
def add_intercept(X):
if np.isnan(np.sum(X.shape)):
raise NotImplementedError("Can not add intercept to array with "
"unknown chunk shape")
j, k = X.chunks
o = da.ones((X.shape[0], 1), chunks=(j, 1))
# TODO: Needed this `.rechunk` for the solver to work
# Is this OK / correct?
X_i = da.concatenate([X, o], axis=1).rechunk((j, (k[0] + 1,)))
k2 = (__builtins__['sum'](k) + 1,)
X_i = X.map_blocks(_add_intercept_block, dtype=X.dtype, chunks=(j, k2))
return X_i


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