

def fit(self, X, y):
    'Run score function on (X, y) and get the appropriate features.\n\n        Parameters\n        ----------\n        X : array-like, shape = [n_samples, n_features]\n            The training input samples.\n\n        y : array-like, shape = [n_samples]\n            The target values (class labels in classification, real numbers in\n            regression).\n\n        Returns\n        -------\n        self : object\n            Returns self.\n        '
    (X, y) = check_X_y(X, y, ['csr', 'csc'])
    if (not callable(self.score_func)):
        raise TypeError(('The score function should be a callable, %s (%s) was passed.' % (y.score_func, type(self.score_func))))
    self._check_params(X, y)
    score_func_ret = self.score_func(X, y)
    if isinstance(score_func_ret, (list, tuple)):
        (self.scores_, self.pvalues_) = score_func_ret
        self.pvalues_ = np.asarray(self.pvalues_)
    else:
        self.scores_ = score_func_ret
        self.pvalues_ = None
    self.scores_ = np.asarray(self.scores_)
    return self

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py _BaseFilter.fit/VarMisuse@55/240 `self`->`y`