How to cross validate RandomForest model? - apache-spark

Question

I want to evaluate a random forest being trained on some data. Is there any utility in Apache Spark to do the same or do I have to perform cross validation manually?

Answer 1

ML provides CrossValidator class which can be used to perform cross-validation and parameter search. Assuming your data is already preprocessed you can add cross-validation as follows:
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// [label: double, features: vector]
trainingData org.apache.spark.sql.DataFrame = ???
val nFolds: Int = ???
val numTrees: Int = ???
val metric: String = ???
val rf = new RandomForestClassifier()
.setLabelCol("label")
.setFeaturesCol("features")
.setNumTrees(numTrees)
val pipeline = new Pipeline().setStages(Array(rf))
val paramGrid = new ParamGridBuilder().build() // No parameter search
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
// "f1" (default), "weightedPrecision", "weightedRecall", "accuracy"
.setMetricName(metric)
val cv = new CrossValidator()
// ml.Pipeline with ml.classification.RandomForestClassifier
.setEstimator(pipeline)
// ml.evaluation.MulticlassClassificationEvaluator
.setEvaluator(evaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(nFolds)
val model = cv.fit(trainingData) // trainingData: DataFrame
Using PySpark:
from pyspark.ml import Pipeline
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
trainingData = ... # DataFrame[label: double, features: vector]
numFolds = ... # Integer
rf = RandomForestClassifier(labelCol="label", featuresCol="features")
evaluator = MulticlassClassificationEvaluator() # + other params as in Scala
pipeline = Pipeline(stages=[rf])
paramGrid = (ParamGridBuilder.
.addGrid(rf.numTrees, [3, 10])
.addGrid(...) # Add other parameters
.build())
crossval = CrossValidator(
estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=evaluator,
numFolds=numFolds)
model = crossval.fit(trainingData)

Answer 2

To build on zero323's great answer using Random Forest Classifier, here is a similar example for Random Forest Regressor:
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.regression.RandomForestRegressor // CHANGED
import org.apache.spark.ml.evaluation.RegressionEvaluator // CHANGED
import org.apache.spark.ml.feature.{VectorAssembler, VectorIndexer}
val numFolds = ??? // Integer
val data = ??? // DataFrame
// Training (80%) and test data (20%)
val Array(train, test) = data.randomSplit(Array(0.8,0.2))
val featuresCols = data.columns
val va = new VectorAssembler()
va.setInputCols(featuresCols)
va.setOutputCol("rawFeatures")
val vi = new VectorIndexer()
vi.setInputCol("rawFeatures")
vi.setOutputCol("features")
vi.setMaxCategories(5)
val regressor = new RandomForestRegressor()
regressor.setLabelCol("events")
val metric = "rmse"
val evaluator = new RegressionEvaluator()
.setLabelCol("events")
.setPredictionCol("prediction")
// "rmse" (default): root mean squared error
// "mse": mean squared error
// "r2": R2 metric
// "mae": mean absolute error
.setMetricName(metric)
val paramGrid = new ParamGridBuilder().build()
val cv = new CrossValidator()
.setEstimator(regressor)
.setEvaluator(evaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(numFolds)
val model = cv.fit(train) // train: DataFrame
val predictions = model.transform(test)
predictions.show
val rmse = evaluator.evaluate(predictions)
println(rmse)
Evaluator metric source:
https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.evaluation.RegressionEvaluator