SparkML Random Forest Classification Script with Cross-Validation and Parameter Sweep

SparkML_RandomForest_Classification.py

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## Title: Spark MLlib Random Forest Classification Script, with Cross-Validation and Parameter Sweep
## Language: PySpark
## Author: Colby T. Ford, Ph.D.
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from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.mllib.evaluation import BinaryClassificationMetrics
#from mmlspark import ComputeModelStatistics

# Create an initial RandomForest model.
rf = RandomForestClassifier(labelCol="label", featuresCol="features")

# Evaluate model
rfevaluator = BinaryClassificationEvaluator()

# Create ParamGrid for Cross Validation
rfparamGrid = (ParamGridBuilder()
             .addGrid(rf.maxDepth, [2, 5, 10, 20, 30])
             .addGrid(rf.maxBins, [10, 20, 40, 80, 100])
             .addGrid(rf.numTrees, [5, 20, 50, 100, 500])
             .build())

# Create 5-fold CrossValidator
rfcv = CrossValidator(estimator = rf,
                      estimatorParamMaps = rfparamGrid,
                      evaluator = rfevaluator,
                      numFolds = 5)

# Run cross validations.
rfcvModel = rfcv.fit(train)
print(rfcvModel)

# Use test set here so we can measure the accuracy of our model on new data
rfpredictions = rfcvModel.transform(test)

# cvModel uses the best model found from the Cross Validation
# Evaluate best model
print('Accuracy:', rfevaluator.evaluate(rfpredictions))
print('AUC:', BinaryClassificationMetrics(rfpredictions['label','prediction'].rdd).areaUnderROC)
#ComputeModelStatistics().transform(rfpredictions)