Python sklearn.base 模块，RegressorMixin() 实例源码

def _check_sklearn_model(model):
    if not (isinstance(model, BaseEstimator) and
            isinstance(model, RegressorMixin)):
        raise RuntimeError('Needs to supply an instance of a scikit-learn '
                           'compatible regression class.')

def _generate_bases_test(est, pd_est):
    def test(self):
        self.assertTrue(isinstance(pd_est, FrameMixin), pd_est)
        self.assertFalse(isinstance(est, FrameMixin))
        self.assertTrue(isinstance(pd_est, base.BaseEstimator))
        try:
            mixins = [
                base.ClassifierMixin,
                base.ClusterMixin,
                base.BiclusterMixin,
                base.TransformerMixin,
                base.DensityMixin,
                base.MetaEstimatorMixin,
                base.ClassifierMixin,
                base.RegressorMixin]
        except:
            if _sklearn_ver > 17:
                raise
            mixins = [
                base.ClassifierMixin,
                base.ClusterMixin,
                base.BiclusterMixin,
                base.TransformerMixin,
                base.MetaEstimatorMixin,
                base.ClassifierMixin,
                base.RegressorMixin]
        for mixin in mixins:
            self.assertEqual(
                isinstance(pd_est, mixin),
                isinstance(est, mixin),
                mixin)

    return test

def evaluate(self, point):
        """
        Fits model using the particular setting of hyperparameters and
        evaluates the model validation data.

        Parameters
        ----------
        * `point`: dict
            A mapping of parameter names to the corresponding values

        Returns
        -------
        * `score`: float
            Score (more is better!) for some specific point
        """
        X_train, y_train, X_test, y_test = (
            self.X_train, self.y_train, self.X_test, self.y_test)

        # apply transformation to model parameters, for example exp transformation
        point_mapped = {}
        for param, val in point.items():
            point_mapped[param] = self.space[param][1](val)

        model_instance = self.model(**point_mapped)

        if 'random_state' in model_instance.get_params():
            model_instance.set_params(random_state=self.random_state)

        min_obj_val = -5.0

        # Infeasible parameters are expected to raise an exception, thus the try
        # catch below, infeasible parameters yield assumed smallest objective.
        try:
            model_instance.fit(X_train, y_train)
            if isinstance(model_instance, RegressorMixin): # r^2 metric
                y_predicted = model_instance.predict(X_test)
                score = r2_score(y_test, y_predicted)
            elif isinstance(model_instance, ClassifierMixin): # log loss
                y_predicted = model_instance.predict_proba(X_test)
                score = -log_loss(y_test, y_predicted) # in the context of this function, the higher score is better
            # avoid any kind of singularitites, eg probability being zero, and thus breaking the log_loss
            if math.isnan(score):
                score = min_obj_val
            score = max(score, min_obj_val) # this is necessary to avoid -inf or NaN
        except BaseException as ex:
            score = min_obj_val # on error: return assumed smallest value of objective function

        return score

# this is necessary to generate table for README in the end