| ################################################################################ |
| # 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. |
| ################################################################################ |
| |
| from pyflink.common import Types, Row |
| |
| from pyflink.ml.feature.stringindexer import StringIndexer, StringIndexerModel |
| from pyflink.ml.tests.test_utils import PyFlinkMLTestCase, update_existing_params |
| |
| |
| class StringIndexerTest(PyFlinkMLTestCase): |
| def setUp(self): |
| super(StringIndexerTest, self).setUp() |
| self.train_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| ('a', 1.0), |
| ('b', 1.0), |
| ('b', 2.0), |
| ('c', 0.0), |
| ('d', 2.0), |
| ('a', 2.0), |
| ('b', 2.0), |
| ('b', -1.0), |
| ('a', -1.0), |
| ('c', -1.0), |
| ('d', None), |
| (None, 2.0), |
| (None, None), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.STRING(), Types.DOUBLE()]))) |
| |
| self.predict_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| ('a', 2.0), |
| ('b', 1.0), |
| ('e', 2.0), |
| ('f', None), |
| (None, None), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.STRING(), Types.DOUBLE()]))) |
| |
| self.expected_alphabetic_asc_predict_data = [ |
| Row(input_col1='a', input_col2=2.0, output_col1=0, output_col2=3), |
| Row(input_col1='b', input_col2=1.0, output_col1=1, output_col2=2), |
| Row(input_col1='e', input_col2=2.0, output_col1=4, output_col2=3), |
| Row(input_col1='f', input_col2=None, output_col1=4, output_col2=4), |
| Row(input_col1=None, input_col2=None, output_col1=4, output_col2=4), |
| ] |
| |
| def test_param(self): |
| string_indexer = StringIndexer() |
| |
| self.assertEqual('arbitrary', string_indexer.string_order_type) |
| self.assertEqual('error', string_indexer.handle_invalid) |
| self.assertEqual(2 ** 31 - 1, string_indexer.max_index_num) |
| |
| string_indexer.set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_max_index_num(100) \ |
| .set_handle_invalid('skip') |
| |
| self.assertEqual(('input_col1', 'input_col2'), string_indexer.input_cols) |
| self.assertEqual(('output_col1', 'output_col2'), string_indexer.output_cols) |
| self.assertEqual('alphabetAsc', string_indexer.string_order_type) |
| self.assertEqual(100, string_indexer.max_index_num) |
| self.assertEqual('skip', string_indexer.handle_invalid) |
| |
| def test_output_schema(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_handle_invalid('skip') |
| |
| output = string_indexer.fit(self.train_table).transform(self.predict_table)[0] |
| |
| self.assertEqual( |
| ['input_col1', 'input_col2', 'output_col1', 'output_col2'], |
| output.get_schema().get_field_names()) |
| |
| def test_string_order_type(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_handle_invalid('keep') |
| |
| string_indexer.set_string_order_type('alphabetAsc') |
| output = string_indexer.fit(self.train_table).transform(self.predict_table)[0] |
| |
| predicted_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| |
| predicted_results.sort(key=lambda x: (x[0] is None, x[0])) |
| |
| self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data) |
| |
| def test_max_index_num(self): |
| string_indexer = StringIndexer() \ |
| .set_max_index_num(3) \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_handle_invalid('keep') \ |
| .set_string_order_type("frequencyDesc") |
| |
| expected_predict_data = [ |
| Row(input_col1='a', input_col2=2.0, output_col1=1, output_col2=0), |
| Row(input_col1='b', input_col2=1.0, output_col1=0, output_col2=2), |
| Row(input_col1='e', input_col2=2.0, output_col1=2, output_col2=0), |
| Row(input_col1='f', input_col2=None, output_col1=2, output_col2=2), |
| Row(input_col1=None, input_col2=None, output_col1=2, output_col2=2), |
| ] |
| |
| output = string_indexer.fit(self.train_table).transform(self.predict_table)[0] |
| |
| predicted_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| |
| predicted_results.sort(key=lambda x: (x[0] is None, x[0])) |
| |
| self.assertEqual(predicted_results, expected_predict_data) |
| |
| def test_fit_and_predict(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_handle_invalid('keep') |
| |
| output = string_indexer.fit(self.train_table).transform(self.predict_table)[0] |
| |
| predicted_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| |
| predicted_results.sort(key=lambda x: (x[0] is None, x[0])) |
| |
| self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data) |
| |
| def test_get_model_data(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') |
| model = string_indexer.fit(self.train_table) |
| model_data = model.get_model_data()[0] |
| expected_field_names = ['stringArrays'] |
| self.assertEqual(expected_field_names, model_data.get_schema().get_field_names()) |
| |
| model_rows = [result for result in |
| self.t_env.to_data_stream(model_data).execute_and_collect()] |
| self.assertEqual(1, len(model_rows)) |
| string_arrays = model_rows[0][expected_field_names.index('stringArrays')] |
| self.assertListEqual(["a", "b", "c", "d"], string_arrays[0]) |
| self.assertListEqual(["-1.0", "0.0", "1.0", "2.0"], string_arrays[1]) |
| |
| def test_set_model_data(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_handle_invalid('keep') |
| model_a = string_indexer.fit(self.train_table) |
| model_data = model_a.get_model_data()[0] |
| |
| model_b = StringIndexerModel().set_model_data(model_data) |
| update_existing_params(model_b, model_a) |
| |
| output = model_b.transform(self.predict_table)[0] |
| |
| predicted_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| |
| predicted_results.sort(key=lambda x: (x[0] is None, x[0])) |
| |
| self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data) |
| |
| def test_save_load_and_predict(self): |
| string_indexer = StringIndexer() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_handle_invalid('keep') |
| reloaded_string_indexer = self.save_and_reload(string_indexer) |
| |
| model = reloaded_string_indexer.fit(self.train_table) |
| reloaded_model = self.save_and_reload(model) |
| |
| output = reloaded_model.transform(self.predict_table)[0] |
| predicted_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| predicted_results.sort(key=lambda x: (x[0] is None, x[0])) |
| self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data) |
