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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 SchemaRDD as a dataset for ML. Run with::
bin/spark-submit examples/src/main/python/mllib/dataset_example.py
"""
import os
import sys
import tempfile
import shutil
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.mllib.util import MLUtils
from pyspark.mllib.stat import Statistics
def summarize(dataset):
print "schema: %s" % dataset.schema().json()
labels = dataset.map(lambda r: r.label)
print "label average: %f" % labels.mean()
features = dataset.map(lambda r: r.features)
summary = Statistics.colStats(features)
print "features average: %r" % summary.mean()
if __name__ == "__main__":
if len(sys.argv) > 2:
print >> sys.stderr, "Usage: dataset_example.py <libsvm file>"
exit(-1)
sc = SparkContext(appName="DatasetExample")
sqlCtx = SQLContext(sc)
if len(sys.argv) == 2:
input = sys.argv[1]
else:
input = "data/mllib/sample_libsvm_data.txt"
points = MLUtils.loadLibSVMFile(sc, input)
dataset0 = sqlCtx.inferSchema(points).setName("dataset0").cache()
summarize(dataset0)
tempdir = tempfile.NamedTemporaryFile(delete=False).name
os.unlink(tempdir)
print "Save dataset as a Parquet file to %s." % tempdir
dataset0.saveAsParquetFile(tempdir)
print "Load it back and summarize it again."
dataset1 = sqlCtx.parquetFile(tempdir).setName("dataset1").cache()
summarize(dataset1)
shutil.rmtree(tempdir)
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