a5a6e074ed792733fc2547e1544711d52659d3fd,python/tests/transformers/named_image_test.py,NamedImageTransformerBaseTestCase,test_featurization,#NamedImageTransformerBaseTestCase#,131

Before Change

        collected = transformed_df.collect()
        for row in collected:
            predictions = row[output_col]
            self.assertEqual(len(predictions), self.appModel.numOutputFeatures())
            // TODO: actually check the value of the output to see if they are reasonable
            // e.g. -- compare to just running with keras.

    def test_featurizer_in_pipeline(self):

After Change

        // Note: keras features may be multi-dimensional np arrays, but transformer features
        // will be 1-d vectors. Regardless, the dimensions should add up to the same.
        self.assertEqual(np.prod(self.kerasFeatures.shape), np.prod(features.shape))
        kerasReshaped = self.kerasFeatures.reshape(self.kerasFeatures.shape[0], -1)
        np.testing.assert_array_almost_equal(kerasReshaped, features, decimal=6)

    def test_featurizer_in_pipeline(self):
        

Instances