Preserving dtype for numpy.float32 in Least Angle Regression #20155
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…odel'coef_path should be tested or nor
sklearn/linear_model/_least_angle.py
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| for input_array in (X, y, Xy, Gram): | ||
| if input_array is not None: | ||
| return_dtype = input_array.dtype | ||
| break |
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We can pass training data x and target y to Least Angle Regressor. Test cases in this PR are that both are numpy.flaot32 or numpy.float64, because it is unclear which type is appropriate when x's type is different from y's type. This is the same with #13243 .
Maybe the following would feel less arbitrary?
dtypes = set(a.dtype for a in (X, y, Xy, Gram) if a is not None)
if len(dtypes) == 1:
# use the precision level of input data if it is consistent
return_dtype = next(iter(dtypes))
else:
# fallback to double precision otherwise
return_dtype = np.float64I am not so sure. Maybe giving the priority to X and Gram over y and Xy would make more sense.
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I which case, I think the tests should be extended to test the case where X.dtype is f32 but y.dtype is f64.
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I don't think this case could happen because when the data is validated, X and y dtypes are matched
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The suggestion is better. I have incorporated the suggestion. Thanks
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Regarding a case where calling fitmethod with x.dtype=np.float32 and y.dtypes=np.float64, trained coef_.dtype is np.float32. It may not be desirable. The mismatched case would be addressed later PR.
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I guess it's the expected outcome. In all estimators where both dtypes are supported, the dtype of y is always matched to the dtype of X.
I think this PR needs a change log because it does change the behavior of least angle regression. @takoika Please add an entry to the change log at |
Reference Issues/PRs
This PR is part of #11000 .
What does this implement/fix? Explain your changes.
This PR makes trained coefficients of Least Angle Regression to be numpy.float32 if input data is numpy.float32.
Conventionally when you pass numpy.float32 to Least Angle Regression, trained coefficients turn to be numpy.float64. This is inconsistent.
Any other comments?
We can pass training data x and target y to Least Angle Regressor. Test cases in this PR are that both are numpy.flaot32 or numpy.float64, because it is unclear which type is appropriate when x's type is different from y's type. This is the same with #13243 .
Further test cases may be required because this PR does not cover argument variations.
I used #13303 and #13243 as references to make this.