path: root/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py

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"""
Gradient Boosted Tree Regressor Example.
"""
from __future__ import print_function

import sys

from pyspark import SparkContext, SQLContext
# $example on$
from pyspark.ml import Pipeline
from pyspark.ml.regression import GBTRegressor
from pyspark.ml.feature import VectorIndexer
from pyspark.ml.evaluation import RegressionEvaluator
# $example off$

if __name__ == "__main__":
    sc = SparkContext(appName="gradient_boosted_tree_regressor_example")
    sqlContext = SQLContext(sc)

    # $example on$
    # Load and parse the data file, converting it to a DataFrame.
    data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    # Automatically identify categorical features, and index them.
    # Set maxCategories so features with > 4 distinct values are treated as continuous.
    featureIndexer =\
        VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)

    # Split the data into training and test sets (30% held out for testing)
    (trainingData, testData) = data.randomSplit([0.7, 0.3])

    # Train a GBT model.
    gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10)

    # Chain indexer and GBT in a Pipeline
    pipeline = Pipeline(stages=[featureIndexer, gbt])

    # Train model.  This also runs the indexer.
    model = pipeline.fit(trainingData)

    # Make predictions.
    predictions = model.transform(testData)

    # Select example rows to display.
    predictions.select("prediction", "label", "features").show(5)

    # Select (prediction, true label) and compute test error
    evaluator = RegressionEvaluator(
        labelCol="label", predictionCol="prediction", metricName="rmse")
    rmse = evaluator.evaluate(predictions)
    print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)

    gbtModel = model.stages[1]
    print(gbtModel)  # summary only
    # $example off$

    sc.stop()