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我正在學習hadoop，機器學習和火花。我已經下載了Cloudera 5.7快速啓動虛擬機。我還將https://github.com/apache/spark中的示例作爲zip文件下載並複製到Cloudera VM。我有一個挑戰，運行機器學習和https://github.com/apache/spark的任何示例。我嘗試運行簡單的字數統計範例，但失敗了。下面是我的步驟和錯誤，我得到在Cloudera VM 5.7上運行spark示例和
[[email protected]] CD /火花主/例子/ src目錄/主/蟒蛇/毫升 [[email protected]]火花提交word2vec_example.py
我嘗試運行的所有示例都失敗，出現以下錯誤。
回溯（最近通話最後一個）：從pyspark.sql 文件 「/home/cloudera/training/spark-master/examples/src/main/python/ml/word2vec_example.py」 23行，在 進口SparkSession
我搜索了文件pyspark.sql，但我只能找到下面的文件 cd/spark-master 找到。 -name pyspark.sql ./python/docs/pyspark.sql.rst
請告知我怎樣才能解決這些錯誤，使我可以爲了加快我的機器學習和大數據運行這個例子。
用於字計數例子中的代碼是下面
貓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()