ENH Introduce dtype preservation semantics in DistanceMetric objects.#27006
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jjerphan merged 10 commits intoscikit-learn:mainfrom Aug 10, 2023
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ENH Introduce dtype preservation semantics in DistanceMetric objects.#27006jjerphan merged 10 commits intoscikit-learn:mainfrom
DistanceMetric objects.#27006jjerphan merged 10 commits intoscikit-learn:mainfrom
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Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
…into dm_float32
This reverts commit ee4faf4.
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I've reintroduced the dtype-dependent signatures. Do you think this warrants a separate changelog entry considering |
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I don't think this is necessary since |
OmarManzoor
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Aug 10, 2023
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LGTM. Thanks @Micky774
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I merged |
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…s. (scikit-learn#27006) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
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…s. (scikit-learn#27006) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
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Reference Issues/PRs
What does this implement/fix? Explain your changes.
Preserves dtype when computing distances, under the assumption that the precision of the input data is an implication of preferred precision of output data. Note that accumulation still largely occurs using
float64_twith some exceptions.Any other comments?
Current benchmarks (generated here) suggest that there is no regression in the dense case (
dist), and a 10-25% speedup in the sparse case (dist_csr).Benchmark Plots
Memory profiling indicates a reduction of memory usage in this script from
763MiBto382MiB.cc: @jjerphan @OmarManzoor @thomasjpfan