How to use the mleap.sklearn.preprocessing.data.OneHotEncoder function in mleap

def one_hot_encoder_deserializer_test(self):

        labels = ['a', 'b', 'c']

        le = LabelEncoder(input_features=['label_feature'],
                          output_features='label_feature_le_encoded')

        oh_data = le.fit_transform(labels).reshape(3, 1)

        one_hot_encoder_tf = OneHotEncoder(sparse=False)
        one_hot_encoder_tf.mlinit(prior_tf = le,
                                  output_features='{}_one_hot_encoded'.format(le.output_features))
        one_hot_encoder_tf.fit(oh_data)

        one_hot_encoder_tf.serialize_to_bundle(self.tmp_dir, one_hot_encoder_tf.name)

        # Deserialize the OneHotEncoder
        node_name = "{}.node".format(one_hot_encoder_tf.name)
        one_hot_encoder_tf_ds = OneHotEncoder()
        one_hot_encoder_tf_ds.deserialize_from_bundle(self.tmp_dir, node_name)

        # Transform some sample data
        res_a = one_hot_encoder_tf.transform(oh_data)
        res_b = one_hot_encoder_tf_ds.transform(oh_data)

        self.assertEqual(res_a[0][0], res_b[0][0])

le = LabelEncoder(input_features=['label_feature'],
                          output_features='label_feature_le_encoded')

        oh_data = le.fit_transform(labels).reshape(3, 1)

        one_hot_encoder_tf = OneHotEncoder(sparse=False)
        one_hot_encoder_tf.mlinit(prior_tf = le,
                                  output_features='{}_one_hot_encoded'.format(le.output_features))
        one_hot_encoder_tf.fit(oh_data)

        one_hot_encoder_tf.serialize_to_bundle(self.tmp_dir, one_hot_encoder_tf.name)

        # Deserialize the OneHotEncoder
        node_name = "{}.node".format(one_hot_encoder_tf.name)
        one_hot_encoder_tf_ds = OneHotEncoder()
        one_hot_encoder_tf_ds.deserialize_from_bundle(self.tmp_dir, node_name)

        # Transform some sample data
        res_a = one_hot_encoder_tf.transform(oh_data)
        res_b = one_hot_encoder_tf_ds.transform(oh_data)

        self.assertEqual(res_a[0][0], res_b[0][0])
        self.assertEqual(res_a[1][0], res_b[1][0])
        self.assertEqual(res_a[2][0], res_b[2][0])

        # Test node.json
        with open("{}/{}.node/node.json".format(self.tmp_dir, one_hot_encoder_tf.name)) as json_data:
            node = json.load(json_data)

        self.assertEqual(one_hot_encoder_tf_ds.name, node['name'])
        self.assertEqual(one_hot_encoder_tf_ds.input_features[0], node['shape']['inputs'][0]['name'])

def one_hot_encoder_serializer_test(self):

        labels = ['a', 'b', 'c']

        le = LabelEncoder(input_features=['label_feature'],
                          output_features='label_feature_le_encoded')

        oh_data = le.fit_transform(labels).reshape(3, 1)

        one_hot_encoder_tf = OneHotEncoder(sparse=False)
        one_hot_encoder_tf.mlinit(prior_tf=le,
                                  output_features='{}_one_hot_encoded'.format(le.output_features))
        one_hot_encoder_tf.fit(oh_data)

        one_hot_encoder_tf.serialize_to_bundle(self.tmp_dir, one_hot_encoder_tf.name)

        # Test model.json
        with open("{}/{}.node/model.json".format(self.tmp_dir, one_hot_encoder_tf.name)) as json_data:
            model = json.load(json_data)

        self.assertEqual(one_hot_encoder_tf.op, model['op'])
        self.assertEqual(3, model['attributes']['size']['long'])
        self.assertEqual(True, model['attributes']['drop_last']['boolean'])