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Basic statistics allow computation on sparse data and add test #2095
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Basic statistics allow computation on sparse data and add test #2095
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if weighted: | ||
weights = gen.uniform(low=-0.5, high=1.0, size=row_count) | ||
weights = weights.astype(dtype=dtype) | ||
basicstat = BasicStatistics(result_options=["mean", "max", "sum"]) |
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According to onedal tests, need to exclude "max" at it contains bugs:
https://github.com/intel/scikit-learn-intelex/blob/main/onedal/basic_statistics/tests/test_basic_statistics.py#L273
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According to onedal tests, need to exclude "max" at it contains bugs: https://github.com/intel/scikit-learn-intelex/blob/main/onedal/basic_statistics/tests/test_basic_statistics.py#L273
@olegkkruglov please message out a link to the ticket associated with this error (just to make sure it wasn't lost)
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I'm not sure if I have it. This skip was added not by me.
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I observed that issue as well, temporarily removed the "max" in tests for this PR.
Please add this to run_to_run_stability sparse stability testing. |
@@ -178,6 +181,53 @@ def test_multiple_options_on_random_data( | |||
assert_allclose(gtr_sum, res_sum, atol=tol) | |||
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@pytest.mark.parametrize("queue", get_queues()) |
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please use _get_dataframes_and_queues
instead
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Sparse data can't work with dataframes
Description
Add a comprehensive description of proposed changes
List associated issue number(s) if exist(s): #6 (for example)
Documentation PR (if needed): #1340 (for example)
Benchmarks PR (if needed): IntelPython/scikit-learn_bench#155 (for example)
Checklist to comply with before moving PR from draft:
PR completeness and readability
Testing
Performance