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Bryan Cutler authored
## What changes were proposed in this pull request? Improve example outputs to better reflect the functionality that is being presented. This mostly consisted of modifying what was printed at the end of the example, such as calling show() with truncate=False, but sometimes required minor tweaks in the example data to get relevant output. Explicitly set parameters when they are used as part of the example. Fixed Java examples that failed to run because of using old-style MLlib Vectors or problem with schema. Synced examples between different APIs. ## How was this patch tested? Ran each example for Scala, Python, and Java and made sure output was legible on a terminal of width 100. Author: Bryan Cutler <cutlerb@gmail.com> Closes #14308 from BryanCutler/ml-examples-improve-output-SPARK-16260.
Bryan Cutler authored## What changes were proposed in this pull request? Improve example outputs to better reflect the functionality that is being presented. This mostly consisted of modifying what was printed at the end of the example, such as calling show() with truncate=False, but sometimes required minor tweaks in the example data to get relevant output. Explicitly set parameters when they are used as part of the example. Fixed Java examples that failed to run because of using old-style MLlib Vectors or problem with schema. Synced examples between different APIs. ## How was this patch tested? Ran each example for Scala, Python, and Java and made sure output was legible on a terminal of width 100. Author: Bryan Cutler <cutlerb@gmail.com> Closes #14308 from BryanCutler/ml-examples-improve-output-SPARK-16260.
pagerank.py 3.10 KiB
#
# 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
#
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#
"""
This is an example implementation of PageRank. For more conventional use,
Please refer to PageRank implementation provided by graphx
Example Usage:
bin/spark-submit examples/src/main/python/pagerank.py data/mllib/pagerank_data.txt 10
"""
from __future__ import print_function
import re
import sys
from operator import add
from pyspark.sql import SparkSession
def computeContribs(urls, rank):
"""Calculates URL contributions to the rank of other URLs."""
num_urls = len(urls)
for url in urls:
yield (url, rank / num_urls)
def parseNeighbors(urls):
"""Parses a urls pair string into urls pair."""
parts = re.split(r'\s+', urls)
return parts[0], parts[1]
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: pagerank <file> <iterations>", file=sys.stderr)
exit(-1)
print("WARN: This is a naive implementation of PageRank and is given as an example!\n" +
"Please refer to PageRank implementation provided by graphx",
file=sys.stderr)
# Initialize the spark context.
spark = SparkSession\
.builder\
.appName("PythonPageRank")\
.getOrCreate()
# Loads in input file. It should be in format of:
# URL neighbor URL
# URL neighbor URL
# URL neighbor URL
# ...
lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
# Loads all URLs from input file and initialize their neighbors.
links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
# Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
ranks = links.map(lambda url_neighbors: (url_neighbors[0], 1.0))
# Calculates and updates URL ranks continuously using PageRank algorithm.
for iteration in range(int(sys.argv[2])):
# Calculates URL contributions to the rank of other URLs.
contribs = links.join(ranks).flatMap(
lambda url_urls_rank: computeContribs(url_urls_rank[1][0], url_urls_rank[1][1]))
# Re-calculates URL ranks based on neighbor contributions.
ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
# Collects all URL ranks and dump them to console.
for (link, rank) in ranks.collect():
print("%s has rank: %s." % (link, rank))
spark.stop()