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#
# 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>
"""
from __future__ import print_function
import os
import sys
import tempfile
import shutil
from pyspark.sql import SparkSession
from pyspark.mllib.stat import Statistics
if __name__ == "__main__":
if len(sys.argv) > 2:
print("Usage: dataframe_example.py <libsvm file>", file=sys.stderr)
exit(-1)
spark = SparkSession\
.builder\
.appName("DataFrameExample")\
.getOrCreate()
if len(sys.argv) == 2:
input = sys.argv[1]
else:
input = "data/mllib/sample_libsvm_data.txt"
# Load input data
print("Loading LIBSVM file with UDT from " + input + ".")
df = spark.read.format("libsvm").load(input).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 = df.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()
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