MFpipe.py 文件源码

def selected_features(self):
        """Get the number of times a feature was selected
        """
        if len(self.best_estimator_):
            # Get selected features from the best estimator :
            iterator = product(range(self._rep), range(self._nfolds))
            fselected = []
            featrange = np.arange(self._nfeat)[np.newaxis, ...]
            for k, i in iterator:
                estimator = self.best_estimator_[k][i].get_params()['features']
                fselected.extend(list(estimator.transform(featrange).ravel().astype(int)))
            # Get the count for each feature :
            bins = np.bincount(np.array(fselected))
            selectedBins = np.zeros((self._nfeat,), dtype=int)
            selectedBins[np.arange(len(bins))] = bins
            # Put everything in a Dataframe :
            resum = pd.DataFrame({'Name':self._name, 'Count':selectedBins,
                                 'Percent':100*selectedBins/selectedBins.sum()}, columns=['Name', 'Count', 'Percent'])
            return resum
        else:
            print('You must run the fit() method before')