classify.py 文件源码

def cross_validate(self, X, y):

        print "fitting {} to the training set".format(self.name)
        if self.param_grid is not None:
            param_sets = list(ParameterGrid(self.param_grid))
            n_param_sets = len(param_sets)
            param_scores = []
            for j, param_set in enumerate(param_sets):

                print "--------------"
                print "training the classifier..."
                print "parameter set:"
                for k, v in param_set.iteritems():
                    print "{}:{}".format(k, v)

                param_score = self.evaluate(X, y, param_set=param_set)
                param_scores.append(param_score)
                p = np.argmax(np.array(param_scores))
                self.best_param_set = param_sets[p]
                print "best parameter set", self.best_param_set
                print "best score:", param_scores[p]
        else:
            score = self.evaluate(X, y)