#

# 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 Tokenizer, RegexTokenizer

from pyspark.sql.functions import col, udf

from pyspark.sql.types import IntegerType

# $example off$

from pyspark.sql import SparkSession

if __name__ == "__main__":

spark = SparkSession\

.builder\

.appName("TokenizerExample")\

.getOrCreate()

# $example on$

sentenceDataFrame = spark.createDataFrame([

(0, "Hi I heard about Spark"),

(1, "I wish Java could use case classes"),

(2, "Logistic,regression,models,are,neat")

], ["id", "sentence"])

tokenizer = Tokenizer(inputCol="sentence", outputCol="words")

regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")

# alternatively, pattern="\\w+", gaps(False)

countTokens = udf(lambda words: len(words), IntegerType())

tokenized = tokenizer.transform(sentenceDataFrame)

tokenized.select("sentence", "words")\

.withColumn("tokens", countTokens(col("words"))).show(truncate=False)

regexTokenized = regexTokenizer.transform(sentenceDataFrame)

regexTokenized.select("sentence", "words") \

.withColumn("tokens", countTokens(col("words"))).show(truncate=False)

# $example off$

spark.stop()