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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 a FeatureHasher 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.featurehasher import FeatureHasher |
| 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_table = t_env.from_data_stream( |
| env.from_collection([ |
| (0, 'a', 1.0, True), |
| (1, 'c', 1.0, False), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['id', 'f0', 'f1', 'f2'], |
| [Types.INT(), Types.STRING(), Types.DOUBLE(), Types.BOOLEAN()]))) |
| |
| # create a feature hasher object and initialize its parameters |
| feature_hasher = FeatureHasher() \ |
| .set_input_cols('f0', 'f1', 'f2') \ |
| .set_categorical_cols('f0', 'f2') \ |
| .set_output_col('vec') \ |
| .set_num_features(1000) |
| |
| # use the feature hasher for feature engineering |
| output = feature_hasher.transform(input_data_table)[0] |
| |
| # extract and display the results |
| field_names = output.get_schema().get_field_names() |
| input_values = [None for _ in feature_hasher.get_input_cols()] |
| for result in t_env.to_data_stream(output).execute_and_collect(): |
| for i in range(len(feature_hasher.get_input_cols())): |
| input_values[i] = result[field_names.index(feature_hasher.get_input_cols()[i])] |
| output_value = result[field_names.index(feature_hasher.get_output_col())] |
| print('Input Values: ' + str(input_values) + '\tOutput Value: ' + str(output_value)) |