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"""
This example demonstrates applying TrainValidationSplit to split data
and perform model selection.
Run with:

  bin/spark-submit examples/src/main/python/ml/train_validation_split.py
"""
# $example on$
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.regression import LinearRegression
from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
# $example off$
from pyspark.sql import SparkSession

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

    # $example on$
    # Prepare training and test data.
    data = spark.read.format("libsvm")\
        .load("data/mllib/sample_linear_regression_data.txt")
    train, test = data.randomSplit([0.9, 0.1], seed=12345)

    lr = LinearRegression(maxIter=10)

    # We use a ParamGridBuilder to construct a grid of parameters to search over.
    # TrainValidationSplit will try all combinations of values and determine best model using
    # the evaluator.
    paramGrid = ParamGridBuilder()\
        .addGrid(lr.regParam, [0.1, 0.01]) \
        .addGrid(lr.fitIntercept, [False, True])\
        .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0])\
        .build()

    # In this case the estimator is simply the linear regression.
    # A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
    tvs = TrainValidationSplit(estimator=lr,
                               estimatorParamMaps=paramGrid,
                               evaluator=RegressionEvaluator(),
                               # 80% of the data will be used for training, 20% for validation.
                               trainRatio=0.8)

    # Run TrainValidationSplit, and choose the best set of parameters.
    model = tvs.fit(train)

    # Make predictions on test data. model is the model with combination of parameters
    # that performed best.
    model.transform(test)\
        .select("features", "label", "prediction")\
        .show()

    # $example off$
    spark.stop()
