8000 MAINT Use memory views in _hashing_fast.pyx by OmarManzoor · Pull Request #25574 · scikit-learn/scikit-learn · GitHub
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MAINT Use memory views in _hashing_fast.pyx #25574

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MAINT Use memory views in _hashing_fast.pyx #25574

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2 changes: 1 addition & 1 deletion sklearn/feature_extraction/_hashing_fast.pyx
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Expand Up @@ -35,7 +35,7 @@ def transform(raw_X, Py_ssize_t n_features, dtype,
# and values arrays ourselves. Use a Py_ssize_t capacity for safety.
cdef Py_ssize_t capacity = 8192 # arbitrary
cdef cnp.int64_t size = 0
cdef cnp.ndarray values = np.empty(capacity, dtype=dtype)
cdef double[:] values = np.empty(capacity, dtype=np.float64)
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We likely need to keep this as cnp.ndarray because of how the dtype argument is passed in.

With this PR, _hashing_transform would always return a float64. Later, when the csr_matrix is formed, it will make a copy if self.dtype=np.float32:

scikit-learn/sklearn/feature_extraction/_hash.py

Lines 185 to 189 in 4f85597

X = sp.csr_matrix(
(values, indices, indptr),
dtype=self.dtype,
shape=(n_samples, self.n_features),
)

With the current code in main and dtype=np.float32, the values would be float32 and csr_matrix does not need to make a copy.

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I see. That means that dtype can only be float32 or float64? In one of the tests I observed that the dtype passed to the fast hash transform function was a string.

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Since we declare value as a double I think a fused type like floating would also not be totally suitable.

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@thomasjpfan thomasjpfan Feb 9, 2023 •
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I think the dtype can be anything. float32, float64, int32, etc. I do not think it's worth fusing everything because we can miss a dtype someone might be using.

It's okay for value to be double. When setting value[size] = value, it would do C-style casting.

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Sounds good. I am going to close this PR as no change needs to be made here.


for x in raw_X:
for f, v in x:
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