[LogisticRegression] Support standardization for dense vectors #565
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Signed-off-by: Jinfeng <[email protected]>
Signed-off-by: Jinfeng <[email protected]>
…-memory access bug in sum kernal
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| init_parameters["fit_intercept"] is True | ||
| and len(intercept_array) > 1 | ||
| ): | ||
| intercept_mean = sum(intercept_array) / len(intercept_array) |
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Is there a 'mean' method that can be called? Also, how does this not change the model output?
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Yeah, revised the code to use np.mean and cp.mean
| convert_to_sparse: bool = False, | ||
| ) -> LogisticRegression: | ||
| assert ( | ||
| standardization is False | ||
| ), "standardization=True is not supported due to testing with single-GPU LogisticRegression" |
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Can we use cuml standard scalar preprocessr?
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revised the code to remove the standardization option from argument list.
this test was for standardization=False only.
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Feb 17, 2024
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Sparse vectors will be densified to dense vectors if standardization is on.
The PR removes uvm in the test of nlp20news dataset, because it is found the uvm leads to a cuda memset error in vars calculation.