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from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.regression import LinearRegression
from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit


    

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


    

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lr = LinearRegression(maxIter=10)


    

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# 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()


    

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# 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)


    

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


    

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