examples/src/main/python/ml/word2vec_example.py

#
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#    http://www.apache.org/licenses/LICENSE-2.0
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# $example on$
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 row in result.collect():
        text, vector = row
        print("Text: [%s] => \nVector: %s\n" % (", ".join(text), str(vector)))
    # $example off$

    spark.stop()