#
# 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

from pyspark import SparkContext
from pyspark.sql import SQLContext
# $example on$
from pyspark.ml.feature import Word2Vec
# $example off$

if __name__ == "__main__":
    sc = SparkContext(appName="Word2VecExample")
    sqlContext = SQLContext(sc)

    # $example on$
    # Input data: Each row is a bag of words from a sentence or document.
    documentDF = sqlContext.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)
    # $example off$

    sc.stop()