| # |
| # 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. |
| # |
| |
| """ |
| An example of how to use DataFrame for ML. Run with:: |
| bin/spark-submit examples/src/main/python/ml/dataframe_example.py <input_path> |
| """ |
| import os |
| import sys |
| import tempfile |
| import shutil |
| |
| from pyspark.sql import SparkSession |
| from pyspark.mllib.stat import Statistics |
| from pyspark.mllib.util import MLUtils |
| |
| if __name__ == "__main__": |
| if len(sys.argv) > 2: |
| print("Usage: dataframe_example.py <libsvm file>", file=sys.stderr) |
| sys.exit(-1) |
| elif len(sys.argv) == 2: |
| input_path = sys.argv[1] |
| else: |
| input_path = "data/mllib/sample_libsvm_data.txt" |
| |
| spark = SparkSession \ |
| .builder \ |
| .appName("DataFrameExample") \ |
| .getOrCreate() |
| |
| # Load an input file |
| print("Loading LIBSVM file with UDT from " + input_path + ".") |
| df = spark.read.format("libsvm").load(input_path).cache() |
| print("Schema from LIBSVM:") |
| df.printSchema() |
| print("Loaded training data as a DataFrame with " + |
| str(df.count()) + " records.") |
| |
| # Show statistical summary of labels. |
| labelSummary = df.describe("label") |
| labelSummary.show() |
| |
| # Convert features column to an RDD of vectors. |
| features = MLUtils.convertVectorColumnsFromML(df, "features") \ |
| .select("features").rdd.map(lambda r: r.features) |
| summary = Statistics.colStats(features) |
| print("Selected features column with average values:\n" + |
| str(summary.mean())) |
| |
| # Save the records in a parquet file. |
| tempdir = tempfile.NamedTemporaryFile(delete=False).name |
| os.unlink(tempdir) |
| print("Saving to " + tempdir + " as Parquet file.") |
| df.write.parquet(tempdir) |
| |
| # Load the records back. |
| print("Loading Parquet file with UDT from " + tempdir) |
| newDF = spark.read.parquet(tempdir) |
| print("Schema from Parquet:") |
| newDF.printSchema() |
| shutil.rmtree(tempdir) |
| |
| spark.stop() |