def select(self, x, y, n_keep):
        cls = ensemble.RandomForestRegressor
        if self.classifier:
            cls = ensemble.RandomForestClassifier
        rf = cls(n_estimators=self.n,
                random_state=self.seed,
                n_jobs=-1)
        rf.fit(x.values, y.values)
        importances = rf.feature_importances_
        imps = sorted(zip(importances, x.columns),
                reverse=True)
        for i, x in enumerate(imps):
            imp, f = x
            logging.debug('%d\t%0.4f\t%s'%(i,imp, f))
        if self.thresh:
            imps = [t for t in imps if t[0] > self.thresh]
        return [t[1] for t in imps[:n_keep]]
#        sets = [[t[1] for t in imps[:i+1]] for i in range(len(imps))]
#        return sets
dataset/ETHPy150Open kvh/ramp/ramp/selectors.py/RandomForestSelector.select/original