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27 | 27 | )
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28 | 28 |
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29 | 29 |
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30 |
| -class LowessRegression(BaseEstimator, RegressorMixin): |
| 30 | +class LowessRegression(RegressorMixin, BaseEstimator): |
31 | 31 | """`LowessRegression` estimator: LOWESS (Locally Weighted Scatterplot Smoothing) is a type of
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32 | 32 | [local regression](https://en.wikipedia.org/wiki/Local_regression).
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33 | 33 |
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@@ -155,7 +155,7 @@ def predict(self, X):
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155 | 155 | return results
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156 | 156 |
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157 | 157 |
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158 |
| -class ProbWeightRegression(BaseEstimator, RegressorMixin): |
| 158 | +class ProbWeightRegression(RegressorMixin, BaseEstimator): |
159 | 159 | """`ProbWeightRegression` assumes that all input signals in `X` need to be reweighted with weights that sum up to
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160 | 160 | one in order to predict `y`.
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161 | 161 |
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@@ -276,7 +276,7 @@ def coefs_(self):
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276 | 276 | return self.coef_
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277 | 277 |
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278 | 278 |
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279 |
| -class DeadZoneRegressor(BaseEstimator, RegressorMixin): |
| 279 | +class DeadZoneRegressor(RegressorMixin, BaseEstimator): |
280 | 280 | r"""The `DeadZoneRegressor` estimator implements a regression model that incorporates a _dead zone effect_ for
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281 | 281 | improving the robustness of regression predictions.
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282 | 282 |
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@@ -480,7 +480,7 @@ def allowed_effects(self):
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480 | 480 | return self._ALLOWED_EFFECTS
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481 | 481 |
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482 | 482 |
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483 |
| -class _FairClassifier(BaseEstimator, LinearClassifierMixin): |
| 483 | +class _FairClassifier(LinearClassifierMixin, BaseEstimator): |
484 | 484 | """Base class for fair classifiers that address sensitive attribute fairness.
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485 | 485 |
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486 | 486 | This base class provides a foundation for fair classifiers that aim to mitigate bias and discrimination by taking
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@@ -682,7 +682,7 @@ def _more_tags(self):
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682 | 682 | return {"poor_score": True}
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683 | 683 |
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684 | 684 |
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685 |
| -class DemographicParityClassifier(BaseEstimator, LinearClassifierMixin): |
| 685 | +class DemographicParityClassifier(LinearClassifierMixin, BaseEstimator): |
686 | 686 | r"""`DemographicParityClassifier` is a logistic regression classifier which can be constrained on demographic
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687 | 687 | parity (p% score).
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688 | 688 |
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@@ -800,7 +800,7 @@ def constraints(self, y_hat, y_true, sensitive, n_obs):
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800 | 800 | return []
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801 | 801 |
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802 | 802 |
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803 |
| -class EqualOpportunityClassifier(BaseEstimator, LinearClassifierMixin): |
| 803 | +class EqualOpportunityClassifier(LinearClassifierMixin, BaseEstimator): |
804 | 804 | r"""`EqualOpportunityClassifier` is a logistic regression classifier which can be constrained on equal opportunity
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805 | 805 | score.
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806 | 806 |
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@@ -914,7 +914,7 @@ def constraints(self, y_hat, y_true, sensitive, n_obs):
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914 | 914 | return []
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915 | 915 |
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916 | 916 |
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917 |
| -class BaseScipyMinimizeRegressor(BaseEstimator, RegressorMixin, ABC): |
| 917 | +class BaseScipyMinimizeRegressor(RegressorMixin, BaseEstimator, ABC): |
918 | 918 | """Abstract base class for regressors relying on Scipy's
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919 | 919 | [minimize method](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) to minimize a
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920 | 920 | (custom) loss function.
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