boselector.py 文件源码

def __init__(self, bootstrap_fraction, random_seed=None, feature_importance_metric=None, feature_importance_threshold=None, **kwargs):

        self.Cs = kwargs.get('Cs', 10)
        self.fit_intercept = kwargs.get('fit_intercept', True)
        self.cv = kwargs.get('cv', None)
        self.dual = kwargs.get('dual', False)
        self.scoring = kwargs.get('scoring', None)
        self.tol = kwargs.get('tol', 1e-4)
        self.max_iter = kwargs.get('max_iter', 100)
        self.class_weight = kwargs.get('class_weight', None)
        self.n_jobs = kwargs.get('n_jobs', 1)
        self.verbose = kwargs.get('verbose', 0)
        self.refit = kwargs.get('refit', True)
        self.intercept_scaling = kwargs.get('intercept_scaling', 1.0)
        self.multi_class = kwargs.get('multi_class', 'ovr')
        self.random_state = kwargs.get('random_state', None)

        # The following parameters are changed from default
        # since we want to induce sparsity in the final
        # feature set of Bolasso.
        # liblinear is needed to be working with 'L1' penalty.
        self.logit = LogisticRegressionCV(
            Cs=self.Cs,
            fit_intercept=self.fit_intercept,
            cv=self.cv,
            dual=self.dual,
            penalty='l1',
            scoring=self.scoring,
            solver='liblinear',
            tol=self.tol,
            max_iter=self.max_iter,
            class_weight=self.class_weight,
            n_jobs=self.n_jobs,
            verbose=self.verbose,
            refit=self.refit,
            intercept_scaling=self.intercept_scaling,
            multi_class=self.multi_class,
            random_state=self.random_state
        )

        super(Bolasso, self).__init__(bootstrap_fraction, self.logit, random_seed=random_seed, feature_importance_metric=feature_importance_metric, feature_importance_threshold=feature_importance_threshold)