| ################################################################################ |
| # 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 |
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| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
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| # |
| # 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 IndexToStringModel |
| from pyflink.ml.tests.test_utils import PyFlinkMLTestCase |
| |
| |
| class IndexToStringModelTest(PyFlinkMLTestCase): |
| def setUp(self): |
| super(IndexToStringModelTest, self).setUp() |
| self.model_data_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| ([['a', 'b', 'c', 'd'], [-1., 0., 1., 2.]],), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['stringArrays'], |
| [Types.OBJECT_ARRAY(Types.OBJECT_ARRAY(Types.STRING()))]) |
| )) |
| |
| self.predict_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| (0, 3), |
| (1, 2), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.INT(), Types.INT()]) |
| )) |
| |
| self.expected_prediction = [ |
| Row(input_col1=0, input_col2=3, output_col1='a', output_col2='2.0'), |
| Row(input_col1=1, input_col2=2, output_col1='b', output_col2='1.0'), |
| ] |
| |
| def test_output_schema(self): |
| model = IndexToStringModel() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_model_data(self.model_data_table) |
| |
| output = model.transform(self.predict_table)[0] |
| |
| self.assertEqual( |
| ['input_col1', 'input_col2', 'output_col1', 'output_col2'], |
| output.get_schema().get_field_names()) |
| |
| def test_fit_and_predict(self): |
| model = IndexToStringModel() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_model_data(self.model_data_table) |
| |
| output = 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]) |
| |
| self.assertEqual(predicted_results, self.expected_prediction) |
| |
| def test_get_model_data(self): |
| model = IndexToStringModel() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_model_data(self.model_data_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_save_load_and_predict(self): |
| model = IndexToStringModel() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_model_data(self.model_data_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]) |
| |
| self.assertEqual(predicted_results, self.expected_prediction) |