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| # or more contributor license agreements. See the NOTICE file |
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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. |
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| ################################################################################ |
| |
| # Simple program that creates a Bucketizer 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.bucketizer import Bucketizer |
| 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 |
| input_data = t_env.from_data_stream( |
| env.from_collection([ |
| (-0.5, 0.0, 1.0, 0.0), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['f1', 'f2', 'f3', 'f4'], |
| [Types.DOUBLE(), Types.DOUBLE(), Types.DOUBLE(), Types.DOUBLE()]) |
| )) |
| |
| # create a bucketizer object and initialize its parameters |
| splits_array = [ |
| [-0.5, 0.0, 0.5], |
| [-1.0, 0.0, 2.0], |
| [float('-inf'), 10.0, float('inf')], |
| [float('-inf'), 1.5, float('inf')], |
| ] |
| |
| bucketizer = Bucketizer() \ |
| .set_input_cols('f1', 'f2', 'f3', 'f4') \ |
| .set_output_cols('o1', 'o2', 'o3', 'o4') \ |
| .set_splits_array(splits_array) |
| |
| # use the bucketizer model for feature engineering |
| output = bucketizer.transform(input_data)[0] |
| |
| # extract and display the results |
| field_names = output.get_schema().get_field_names() |
| input_values = [None for _ in bucketizer.get_input_cols()] |
| output_values = [None for _ in bucketizer.get_input_cols()] |
| for result in t_env.to_data_stream(output).execute_and_collect(): |
| for i in range(len(bucketizer.get_input_cols())): |
| input_values[i] = result[field_names.index(bucketizer.get_input_cols()[i])] |
| output_values[i] = result[field_names.index(bucketizer.get_output_cols()[i])] |
| print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values)) |