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| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
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| |
| # Simple program that creates a ElementwiseProduct 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.core.linalg import Vectors, DenseVectorTypeInfo |
| from pyflink.ml.lib.feature.elementwiseproduct import ElementwiseProduct |
| 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([ |
| (1, Vectors.dense(2.1, 3.1)), |
| (2, Vectors.dense(1.1, 3.3)) |
| ], |
| type_info=Types.ROW_NAMED( |
| ['id', 'vec'], |
| [Types.INT(), DenseVectorTypeInfo()]))) |
| |
| # create an elementwise product object and initialize its parameters |
| elementwise_product = ElementwiseProduct() \ |
| .set_input_col('vec') \ |
| .set_output_col('output_vec') \ |
| .set_scaling_vec(Vectors.dense(1.1, 1.1)) |
| |
| # use the elementwise product object for feature engineering |
| output = elementwise_product.transform(input_data_table)[0] |
| |
| # extract and display 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(elementwise_product.get_input_col())] |
| output_value = result[field_names.index(elementwise_product.get_output_col())] |
| print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value)) |