| ################################################################################ |
| # 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 typing import List |
| |
| from pyflink.common import Types |
| from pyflink.ml.tests.test_utils import PyFlinkMLTestCase, update_existing_params |
| |
| from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo, DenseVector |
| |
| from pyflink.ml.feature.robustscaler import RobustScaler, RobustScalerModel |
| from pyflink.table import Table |
| |
| |
| class RobustScalerTest(PyFlinkMLTestCase): |
| |
| def setUp(self): |
| super(RobustScalerTest, self).setUp() |
| self.train_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| (1, Vectors.dense(0.0, 0.0),), |
| (2, Vectors.dense(1.0, -1.0),), |
| (3, Vectors.dense(2.0, -2.0),), |
| (4, Vectors.dense(3.0, -3.0),), |
| (5, Vectors.dense(4.0, -4.0),), |
| (6, Vectors.dense(5.0, -5.0),), |
| (7, Vectors.dense(6.0, -6.0),), |
| (8, Vectors.dense(7.0, -7.0),), |
| (9, Vectors.dense(8.0, -8.0),), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['id', 'input'], |
| [Types.INT(), DenseVectorTypeInfo()]) |
| )) |
| |
| self.predict_table = self.t_env.from_data_stream( |
| self.env.from_collection([ |
| (Vectors.dense(3.0, -3.0),), |
| (Vectors.dense(6.0, -6.0),), |
| (Vectors.dense(99.0, -99.0),), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input'], |
| [DenseVectorTypeInfo()]) |
| )) |
| |
| self.expected_output = [ |
| Vectors.dense(0.75, -0.75), |
| Vectors.dense(1.5, -1.5), |
| Vectors.dense(24.75, -24.75)] |
| |
| def test_param(self): |
| robust_scaler = RobustScaler() |
| self.assertEqual("input", robust_scaler.input_col) |
| self.assertEqual("output", robust_scaler.output_col) |
| self.assertEqual(0.25, robust_scaler.lower) |
| self.assertEqual(0.75, robust_scaler.upper) |
| self.assertEqual(0.001, robust_scaler.relative_error) |
| self.assertFalse(robust_scaler.with_centering) |
| self.assertTrue(robust_scaler.with_scaling) |
| |
| robust_scaler\ |
| .set_input_col("test_input")\ |
| .set_output_col("test_output")\ |
| .set_lower(0.1)\ |
| .set_upper(0.9)\ |
| .set_relative_error(0.01)\ |
| .set_with_centering(True)\ |
| .set_with_scaling(False) |
| |
| self.assertEqual("test_input", robust_scaler.input_col) |
| self.assertEqual("test_output", robust_scaler.output_col) |
| self.assertEqual(0.1, robust_scaler.lower) |
| self.assertEqual(0.9, robust_scaler.upper) |
| self.assertEqual(0.01, robust_scaler.relative_error) |
| self.assertTrue(robust_scaler.with_centering) |
| self.assertFalse(robust_scaler.with_scaling) |
| |
| def test_output_schema(self): |
| robust_scaler = RobustScaler().set_output_col('test_output') |
| model = robust_scaler.fit(self.train_table) |
| output = model.transform(self.predict_table.alias('test_input'))[0] |
| self.assertEqual( |
| ['test_input', 'test_output'], |
| output.get_schema().get_field_names()) |
| |
| def test_fit_and_predict(self): |
| robust_scaler = RobustScaler() |
| model = robust_scaler.fit(self.train_table) |
| output = model.transform(self.predict_table)[0] |
| self.verify_output_result( |
| output, |
| robust_scaler.get_output_col(), |
| output.get_schema().get_field_names(), |
| self.expected_output) |
| |
| def test_get_model_data(self): |
| robust_scaler = RobustScaler() |
| model = robust_scaler.fit(self.train_table) |
| model_data = model.get_model_data()[0] |
| expected_field_names = ['medians', 'ranges'] |
| 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)) |
| self.assertListAlmostEqual( |
| [4.0, -4.0], model_rows[0][expected_field_names.index('medians')]) |
| self.assertListAlmostEqual( |
| [4.0, 4.0], model_rows[0][expected_field_names.index('ranges')]) |
| |
| def test_set_model_data(self): |
| robust_scaler = RobustScaler() |
| model_a = robust_scaler.fit(self.train_table) |
| model_data = model_a.get_model_data()[0] |
| |
| model_b = RobustScalerModel().set_model_data(model_data) |
| update_existing_params(model_b, model_a) |
| |
| output = model_b.transform(self.predict_table)[0] |
| self.verify_output_result( |
| output, |
| robust_scaler.get_output_col(), |
| output.get_schema().get_field_names(), |
| self.expected_output) |
| |
| def test_save_load_predict(self): |
| robust_scaler = RobustScaler() |
| reloaded_robust_scaler = self.save_and_reload(robust_scaler) |
| model = reloaded_robust_scaler.fit(self.train_table) |
| reloaded_model = self.save_and_reload(model) |
| output = reloaded_model.transform(self.predict_table)[0] |
| self.verify_output_result( |
| output, |
| robust_scaler.get_output_col(), |
| output.get_schema().get_field_names(), |
| self.expected_output) |
| |
| def verify_output_result( |
| self, output: Table, |
| output_col: str, |
| field_names: List[str], |
| expected_result: List[DenseVector]): |
| collected_results = [result for result in |
| self.t_env.to_data_stream(output).execute_and_collect()] |
| results = [] |
| for item in collected_results: |
| item.set_field_names(field_names) |
| results.append(item[output_col]) |
| results.sort(key=lambda x: x[0]) |
| self.assertEqual(expected_result, results) |
