| ################################################################################ |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| ################################################################################ |
| import os |
| |
| from pyflink.common import Types |
| |
| from pyflink.ml.core.linalg import Vectors |
| from pyflink.ml.lib.feature.featurehasher import FeatureHasher |
| from pyflink.ml.tests.test_utils import PyFlinkMLTestCase |
| |
| |
| class FeatureHasherTest(PyFlinkMLTestCase): |
| def setUp(self): |
| super(FeatureHasherTest, self).setUp() |
| self.input_data_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| (0, 'a', 1.0, True), |
| (1, 'c', 1.0, False) |
| ], |
| type_info=Types.ROW_NAMED( |
| ['id', 'f0', 'f1', 'f2'], |
| [Types.INT(), Types.STRING(), Types.DOUBLE(), Types.BOOLEAN()]))) |
| |
| self.expected_output_data_1 = Vectors.sparse(1000, [607, 635, 913], [1.0, 1.0, 1.0]) |
| self.expected_output_data_2 = Vectors.sparse(1000, [242, 869, 913], [1.0, 1.0, 1.0]) |
| |
| def test_param(self): |
| feature_hasher = FeatureHasher() |
| |
| self.assertEqual('output', feature_hasher.output_col) |
| self.assertEqual(262144, feature_hasher.num_features) |
| |
| feature_hasher.set_input_cols('f0', 'f1', 'f2') \ |
| .set_categorical_cols('f0', 'f2') \ |
| .set_output_col('vec') \ |
| .set_num_features(1000) |
| |
| self.assertEqual(('f0', 'f1', 'f2'), feature_hasher.input_cols) |
| self.assertEqual(('f0', 'f2'), feature_hasher.categorical_cols) |
| self.assertEqual(1000, feature_hasher.num_features) |
| self.assertEqual('vec', feature_hasher.output_col) |
| |
| def test_save_load_transform(self): |
| feature_hasher = FeatureHasher() \ |
| .set_input_cols('f0', 'f1', 'f2') \ |
| .set_categorical_cols('f0', 'f2') \ |
| .set_output_col('vec') \ |
| .set_num_features(1000) |
| |
| path = os.path.join(self.temp_dir, 'test_save_load_transform_feature_hasher') |
| feature_hasher.save(path) |
| feature_hasher = FeatureHasher.load(self.t_env, path) |
| |
| output_table = feature_hasher.transform(self.input_data_table)[0] |
| actual_outputs = [(result[0], result[4]) for result in |
| self.t_env.to_data_stream(output_table).execute_and_collect()] |
| self.assertEqual(2, len(actual_outputs)) |
| for actual_output in actual_outputs: |
| if actual_output[0] == 0: |
| self.assertEqual(self.expected_output_data_1, actual_output[1]) |
| else: |
| self.assertEqual(self.expected_output_data_2, actual_output[1]) |