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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. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| ################################################################################ |
| |
| # Simple program that trains a StringIndexer 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.lib.feature.stringindexer import StringIndexer |
| 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_table = t_env.from_data_stream( |
| env.from_collection([ |
| ('a', 1.), |
| ('b', 1.), |
| ('b', 2.), |
| ('c', 0.), |
| ('d', 2.), |
| ('a', 2.), |
| ('b', 2.), |
| ('b', -1.), |
| ('a', -1.), |
| ('c', -1.), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.STRING(), Types.DOUBLE()]) |
| )) |
| |
| predict_table = t_env.from_data_stream( |
| env.from_collection([ |
| ('a', 2.), |
| ('b', 1.), |
| ('c', 2.), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input_col1', 'input_col2'], |
| [Types.STRING(), Types.DOUBLE()]) |
| )) |
| |
| # create a string-indexer object and initialize its parameters |
| string_indexer = StringIndexer() \ |
| .set_string_order_type('alphabetAsc') \ |
| .set_input_cols('input_col1', 'input_col2') \ |
| .set_output_cols('output_col1', 'output_col2') |
| |
| # train the string-indexer model |
| model = string_indexer.fit(train_table) |
| |
| # use the string-indexer 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 string_indexer.get_input_cols()] |
| output_values = [None for _ in string_indexer.get_input_cols()] |
| for result in t_env.to_data_stream(output).execute_and_collect(): |
| for i in range(len(string_indexer.get_input_cols())): |
| input_values[i] = result[field_names.index(string_indexer.get_input_cols()[i])] |
| output_values[i] = result[field_names.index(string_indexer.get_output_cols()[i])] |
| print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values)) |