| # |
| # 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. |
| # |
| |
| # $example on$ |
| from pyspark.ml.feature import ElementwiseProduct |
| from pyspark.ml.linalg import Vectors |
| # $example off$ |
| from pyspark.sql import SparkSession |
| |
| if __name__ == "__main__": |
| spark = SparkSession\ |
| .builder\ |
| .appName("ElementwiseProductExample")\ |
| .getOrCreate() |
| |
| # $example on$ |
| # Create some vector data; also works for sparse vectors |
| data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)] |
| df = spark.createDataFrame(data, ["vector"]) |
| transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]), |
| inputCol="vector", outputCol="transformedVector") |
| # Batch transform the vectors to create new column: |
| transformer.transform(df).show() |
| # $example off$ |
| |
| spark.stop() |