SparkML Logistic Regression Classification Script with Cross-Validation and Parameter Sweep

SparkML_LogisticRegression_Classification.py

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## Title: Spark MLlib Logistic Regression 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 LogisticRegression
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.mllib.evaluation import BinaryClassificationMetrics
#from mmlspark import ComputeModelStatistics

# Create initial LogisticRegression model
lr = LogisticRegression(labelCol="label", featuresCol="features", maxIter=10)

# Create ParamGrid for Cross Validation
lrparamGrid = (ParamGridBuilder()
             .addGrid(lr.regParam, [0.01, 0.1, 0.5, 1.0, 2.0])
             .addGrid(lr.elasticNetParam, [0.0, 0.25, 0.5, 0.75, 1.0])
             .addGrid(lr.maxIter, [1, 5, 10, 20, 50])
             .build())

# Evaluate model
lrevaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction")

# Create 5-fold CrossValidator
lrcv = CrossValidator(estimator = lr,
                    estimatorParamMaps = lrparamGrid,
                    evaluator = lrevaluator,
                    numFolds = 5)

# Run cross validations
lrcvModel = lrcv.fit(train)
print(lrcvModel)

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

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