Sphinx setup, documentation for estimators.py, evaluators.py #53
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dsteinberg
approved these changes
Apr 6, 2023
| RegressionStatisticalResults | ||
| Linear coefficient statistics for this estimator. | ||
| """ | ||
| # TODO should we not check that dof_, t_ p_ are fitted as well? |
| class RegressionStatisticalResults(NamedTuple): | ||
| """Statistical results object for linear regressors. | ||
| TODO should this be private? Only used internally |
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yes probably, especially if this results object is never returned or accessed by a user
| This is a method of :class:`~sklearn.base.BaseEstimator`. | ||
| TODO make deep argument functional. | ||
| I believe this implementation could be replaced with the class's default implementation |
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| so that it's possible to update each component of a nested object. | ||
| TODO I believe this could be replaced with the base class's implementation | ||
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Agreed - it seems like this is not necessary anymore for newer versions of scikit learn
| # an sklearn Scorer takes an estimator, X and optional y, and returns a scalar score | ||
| # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer | ||
| Scorer = Callable[[Estimator, Optional[npt.ArrayLike]], Score] | ||
| ScoreEvaluation = Dict[str, List[Score]] |
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10/10 for effort - though I think we've lost this battle in python...
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| scores = defaultdict(list) | ||
| if self.groupby is not None: | ||
| # TODO: this makes X/y implicitly a dataframe. Is this intended? |
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Yeah this seems like a code smell to me - good catch. I think we should make a good fix for this... I suspect this happens throughout the code. IT may be fixed by assuming everything is a DataFrame? When would we not use a DataFrame?
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Apologies for the large pull request.
This contains:
Many type hints are updated/added as this has been relatively free given I have been going through the codebase.
I do not expect these hints to fully pass mypy, but should at least aid any future attempt to do so