sklearn_wrappers.py 文件源码

def __init__(self, data, N_i, N_c, *args, **kwargs):
        """ Fit a regularized logistic regression model to a Dataset object.

        By default, uses L1 regularization with the strength chosen from
        10 options spaced logarithmically between 1e-4 and 1e4 
        (the sklearn LogisticRegressionCV default) using
        min(10,data.n_subjects) folds of crossvalidation, but other
        options may be chosen by specifing arguments to the 
        LogisticRegressionCV constructor through *args and **kwargs.

        N_i, N_c: parameters defining allowed time windows. See the
        transform_X method.

        args, kwargs: passed to the LogisticRegressionCV constructor.

        """
        Wrapper.__init__(self,data,N_i,N_c)
        default_folds = min(10,data.n_subjects)
        default_classifier_arguments = {
            'cv': default_folds,
            'solver': 'liblinear',
            'penalty': 'l1', 
        }
        # Update with the arguments passed in by the user, clobbering
        # the default settings if alternate values are provided.
        default_classifier_arguments.update(kwargs)
        self.classifier = LogisticRegressionCV(
            *args, 
            **default_classifier_arguments
        )
        self.classifier.fit(self.fit_X,self.fit_y)