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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 |
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| ################################################################################ |
| |
| # Simple program that creates an IndexToStringModelExample instance 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.lib.feature.stringindexer import IndexToStringModel |
| from pyflink.table import StreamTableEnvironment |
| |
| # create a new StreamExecutionEnvironment |
| env = StreamExecutionEnvironment.get_execution_environment() |
| |
| # create a StreamTableEnvironment |
| t_env = StreamTableEnvironment.create(env) |
| |
| # generate input data |
| predict_table = t_env.from_data_stream( |
| env.from_collection([ |
| (0, 3), |
| (1, 2), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.INT(), Types.INT()]) |
| )) |
| |
| # create an index-to-string model and initialize its parameters and model data |
| model_data_table = t_env.from_data_stream( |
| 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()))]) |
| )) |
| |
| model = IndexToStringModel() \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') \ |
| .set_model_data(model_data_table) |
| |
| # use the index-to-string model for feature engineering |
| output = model.transform(predict_table)[0] |
| |
| # extract and display the results |
| field_names = output.get_schema().get_field_names() |
| input_values = [None for _ in model.get_input_cols()] |
| output_values = [None for _ in model.get_input_cols()] |
| for result in t_env.to_data_stream(output).execute_and_collect(): |
| for i in range(len(model.get_input_cols())): |
| input_values[i] = result[field_names.index(model.get_input_cols()[i])] |
| output_values[i] = result[field_names.index(model.get_output_cols()[i])] |
| print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values)) |