| ################################################################################ |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # 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 creates a VectorAssembler 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.tokenizer import Tokenizer |
| from pyflink.table import StreamTableEnvironment |
| |
| env = StreamExecutionEnvironment.get_execution_environment() |
| |
| t_env = StreamTableEnvironment.create(env) |
| |
| # Generates input data. |
| input_data_table = t_env.from_data_stream( |
| env.from_collection([ |
| ('Test for tokenization.',), |
| ('Te,st. punct',), |
| ], |
| type_info=Types.ROW_NAMED( |
| ['input'], |
| [Types.STRING()]))) |
| |
| # Creates a Tokenizer object and initializes its parameters. |
| tokenizer = Tokenizer() \ |
| .set_input_col("input") \ |
| .set_output_col("output") |
| |
| # Uses the Tokenizer object for feature transformations. |
| output = tokenizer.transform(input_data_table)[0] |
| |
| # Extracts and displays 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(tokenizer.get_input_col())] |
| output_value = result[field_names.index(tokenizer.get_output_col())] |
| print('Input Values: ' + str(input_value) + '\tOutput Value: ' + str(output_value)) |