FIX: Fix processing of sparse data frames #2826
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david-cortes-intel
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ahuber21,
ethanglaser,
icfaust,
razdoburdin and
yuejiaointel
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david-cortes-intel
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Vika-F,
avolkov-intel,
homksei and
napetrov
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/intelci: run |
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/intelci: run |
icfaust
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Dec 7, 2025
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| def make_sparse_array(): | ||
| out = sp.random(50, 4, 0.5, format="csc", random_state=123) |
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To test that the format is converted as needed.
icfaust
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Dec 7, 2025
| assert not onedal.datatypes._data_conversion._convert_one_to_table.called | ||
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| # Note that some estimators that are implemented through daal4py do |
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Shouldn't these tests then be in daal4py?
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They'd be harder to find and to connect among each other if they are all spread throughout different places.
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/intelci: run |
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/azp run Nightly |
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Azure Pipelines successfully started running 1 pipeline(s). |
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CI failure is due to a bug in KMeans unrelated to this PR. |
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Description
Many estimators check for sparse inputs using scipy's
issparse, but sklearn's data validators also take sparse data frames as sparse and convert them to COO/CSC/CSR internally, whereas sklearnex assumes that data frames are always dense.This PR fixes the sparsity checks to consider also sparse data frames, and adds tests along the way to ensure that they are processed in the right format.
Checklist:
Completeness and readability
Testing