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"""
Pipeline Example.
"""

# $example on$
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
# $example off$
from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("PipelineExample")\
        .getOrCreate()

    # $example on$
    # Prepare training documents from a list of (id, text, label) tuples.
    training = spark.createDataFrame([
        (0L, "a b c d e spark", 1.0),
        (1L, "b d", 0.0),
        (2L, "spark f g h", 1.0),
        (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])

    # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
    tokenizer = Tokenizer(inputCol="text", outputCol="words")
    hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
    lr = LogisticRegression(maxIter=10, regParam=0.01)
    pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])

    # Fit the pipeline to training documents.
    model = pipeline.fit(training)

    # Prepare test documents, which are unlabeled (id, text) tuples.
    test = spark.createDataFrame([
        (4L, "spark i j k"),
        (5L, "l m n"),
        (6L, "mapreduce spark"),
        (7L, "apache hadoop")], ["id", "text"])

    # Make predictions on test documents and print columns of interest.
    prediction = model.transform(test)
    selected = prediction.select("id", "text", "prediction")
    for row in selected.collect():
        print(row)
    # $example off$

    spark.stop()