sklearn_wrapper.py 文件源码

def score_core(self, X, y=None, sample_weight=None, batchsize=16):
        # Type check
        X, y = self._check_X_y(X, y)
        # during GridSearch, which only assumes score(X, y) interface.
        if y is None:
            test = X
            if isinstance(test, numpy.ndarray):  # TODO: reivew
                print('score_core numpy.ndarray received...')
                test = chainer.datasets.TupleDataset(test)
        else:
            test = chainer.datasets.TupleDataset(X, y)
        # For Classifier
        # `accuracy` is calculated as score, using `forward_batch`
        # For regressor
        # `loss` is calculated as score, using `forward_batch`
        self.forward_batch(test, batchsize=batchsize, retain_inputs=False, calc_score=True)
        return self.total_score