Improve type annotations in sklearn.metrics._regression
#357
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Various sklearn metrics return floats or ndarrays based on the value of `multioutput` parameter. This commit adds overloads for the separate paths.
Various sklearn metrics return either a standard Python float, or a numpy flating point scalar type. E.g. ``` >>> import numpy as np >>> from sklearn.metrics import mean_absolute_error, median_absolute_error >>> a = np.array([1,2,3]) >>> b = np.array([4,5,6]) >>> type(mean_absolute_error(a,b)) float >>> type(median_absolute_error(a,b)) numpy.float64 ``` This commit fixes the type annotations for the following functions: - `mean_absolute_error` - `mean_absolute_percentage_error` - `mean_squared_error` - `r2_score` - `mean_tweedie_deviance` - `d2_pinball_score` - `d2_absolute_error_score`
The docs say float or ndarray but there is not ndarray return path.
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@microsoft-github-policy-service agree |
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@matkozak, thanks for the contribution! Can you please add some unit tests for these overloads similar to https://github.com/microsoft/python-type-stubs/blob/main/tests/sklearn/preprocessing_tests.py? |
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Improve type annotations in scikit-learn's regression metrics to more accurately represent the relationship between input parameters and return types. Specifically:
ndarraywhenmultioutput="raw_values"and floats for other multioutput options.floatfor functions that explicitly convert their result withfloat(result),Floattype alias for functions that return NumPy floating-point types (np.float64, etc.)d2_tweedie_scorereturn type which claims to return "float or ndarray of floats" in the docstring but has no code path that returns anndarray.