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52 lines
1.8 KiB
52 lines
1.8 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from typing import Callable, Optional
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import numpy as np
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import sklearn.base
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from sklearn.base import BaseEstimator
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from sklearn.utils.multiclass import unique_labels
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class SingleClassFix(BaseEstimator):
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"""
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Some sklearn classifiers, such as logistic regression, have issues with datasets
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that contain a single class. This meta-classifier fixes the issue. If the
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training data contains a single class, this meta-classifier always returns that
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class as a prediction. Otherwise, it fits the provided base classifier,
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and returns its predictions instead.
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"""
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def __init__(
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self,
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base_clf: BaseEstimator,
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clone_fn: Callable = sklearn.base.clone,
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):
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self.base_clf = base_clf
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self.clf_: Optional[BaseEstimator] = None
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self.constant_ = None
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self.classes_ = None
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self.clone_fn = clone_fn
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def fit(self, x: np.ndarray, y: np.ndarray) -> None:
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classes = unique_labels(y)
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if len(classes) == 1:
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assert classes[0] is not None
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self.clf_ = None
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self.constant_ = classes[0]
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self.classes_ = classes
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else:
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self.clf_ = self.clone_fn(self.base_clf)
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assert self.clf_ is not None
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self.clf_.fit(x, y)
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self.constant_ = None
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self.classes_ = self.clf_.classes_
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def predict(self, x: np.ndarray) -> np.ndarray:
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if self.constant_ is not None:
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return np.full(x.shape[0], self.constant_)
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else:
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assert self.clf_ is not None
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return self.clf_.predict(x)
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