models.py 文件源码

def test(self, X, y, verbose=True):
        # if we don't need 3d inputs...
        if type(self.model.input_shape) is tuple:
            X = np.array(X)
            if len(self.model.input_shape) == 2:
                X = X.reshape((X.shape[0], -1))
        else:
            raise LanguageClassifierException('Mult-input models are not supported yet')

        if verbose:
            print("Getting predictions on the test set")
        predictions = self.predict(X)

        if len(predictions) != len(y):
            raise LanguageClassifierException("Non comparable arrays")

        if self.binary:
            acc = (predictions == y).mean()
            prec = np.sum(np.bitwise_and(predictions, y)) * 1.0 / np.sum(predictions)
            recall = np.sum(np.bitwise_and(predictions, y)) * 1.0 / np.sum(y)
            if verbose:
                print("Test set accuracy of {0:.3f}%".format(acc * 100.0))
                print("Test set error of {0:.3f}%".format((1 - acc) * 100.0))
                print("Precision for class=1: {0:.3f}".format(prec))
                print("Recall for class=1: {0:.3f}".format(recall))

            return (acc, prec, recall)
        else:
            # TODO: Obtain more metrics for the multiclass problem
            acc = (predictions == y).mean()
            if verbose:
                print("Test set accuracy of {0:.3f}%".format(acc * 100.0))
                print("Test set error of {0:.3f}%".format((1 - acc) * 100.0))
            return acc