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| # to you under the Apache License, Version 2.0 (the |
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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, |
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| ################################################################################ |
| |
| # Simple program that trains a MinMaxScaler model and uses it for feature |
| # engineering. |
| # |
| # Before executing this program, please make sure you have followed Flink ML's |
| # quick start guideline to set up Flink ML and Flink environment. The guideline |
| # can be found at |
| # |
| # https://nightlies.apache.org/flink/flink-ml-docs-master/docs/try-flink-ml/quick-start/ |
| |
| from pyflink.common import Types |
| from pyflink.datastream import StreamExecutionEnvironment |
| from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo |
| from pyflink.ml.lib.feature.minmaxscaler import MinMaxScaler |
| from pyflink.table import StreamTableEnvironment |
| |
| # create a new StreamExecutionEnvironment |
| env = StreamExecutionEnvironment.get_execution_environment() |
| |
| # create a StreamTableEnvironment |
| t_env = StreamTableEnvironment.create(env) |
| |
| # generate input training and prediction data |
| train_data = t_env.from_data_stream( |
| env.from_collection([ |
| (Vectors.dense(0.0, 3.0),), |
| (Vectors.dense(2.1, 0.0),), |
| (Vectors.dense(4.1, 5.1),), |
| (Vectors.dense(6.1, 8.1),), |
| (Vectors.dense(200, 400),), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input'], |
| [DenseVectorTypeInfo()]) |
| )) |
| |
| predict_data = t_env.from_data_stream( |
| env.from_collection([ |
| (Vectors.dense(150.0, 90.0),), |
| (Vectors.dense(50.0, 40.0),), |
| (Vectors.dense(100.0, 50.0),), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input'], |
| [DenseVectorTypeInfo()]) |
| )) |
| |
| # create a min-max-scaler object and initialize its parameters |
| min_max_scaler = MinMaxScaler() |
| |
| # train the min-max-scaler model |
| model = min_max_scaler.fit(train_data) |
| |
| # use the min-max-scaler model for predictions |
| output = model.transform(predict_data)[0] |
| |
| # extract and display the results |
| field_names = output.get_schema().get_field_names() |
| for result in t_env.to_data_stream(output).execute_and_collect(): |
| input_value = result[field_names.index(min_max_scaler.get_input_col())] |
| output_value = result[field_names.index(min_max_scaler.get_output_col())] |
| print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value)) |