I am learning hadoop, machine learning and spark. I have downloaded Cloudera 5.7 Quick Start VM. I have also downloaded the examples from https://github.com/apache/spark as a zip file and copied them to the Cloudera VM. I have a challenge running the machine learning and any examples from https://github.com/apache/spark. I tried running the simple word count example but failed. Below are my steps and the error i get
[cloudera@quickstart.cloudera] cd /spark-master/examples/src/main/python/ml [cloudera@quickstart.cloudera] spark-submit word2vec_example.py
All examples I try to run fail with the below error.
Traceback (most recent call last): File "/home/cloudera/training/spark-master/examples/src/main/python/ml/word2vec_example.py", line 23, in from pyspark.sql import SparkSession
I did a search for the file pyspark.sql but I could only find the below file cd /spark-master find . -name pyspark.sql ./python/docs/pyspark.sql.rst
Please advise on how i can resolve these errors so that i can run this example in order speed up my machine learning and big data.
the code for the word count example is below
cat word2vec_example.py
#
# 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.
#
from __future__ import print_function
# $example on$
from pyspark.ml.feature import Word2Vec
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("Word2VecExample")\
.getOrCreate()
# $example on$
# Input data: Each row is a bag of words from a sentence or document.
documentDF = spark.createDataFrame([
("Hi I heard about Spark".split(" "), ),
("I wish Java could use case classes".split(" "), ),
("Logistic regression models are neat".split(" "), )
], ["text"])
# Learn a mapping from words to Vectors.
word2Vec = Word2Vec(vectorSize=3, minCount=0, inputCol="text", outputCol="result")
model = word2Vec.fit(documentDF)
result = model.transform(documentDF)
for feature in result.select("result").take(3):
print(feature)
# $example off$
spark.stop()
