Wie validiere ich das RandomForest Modell?

Question

Ich möchte eine zufällige Gesamtstruktur auswerten, die an einigen Daten trainiert wird. Gibt es ein Dienstprogramm in Apache Spark, um dasselbe zu tun, oder muss ich die Kreuzvalidierung manuell durchführen?

Answer 1

ML bietet CrossValidator Klasse, die verwendet werden kann, um Kreuzvalidierung und Parametersuche durchzuführen. Vorausgesetzt, dass Ihre Daten bereits vorverarbeitet können Sie Kreuzvalidierung wie folgt hinzufügen:

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 

Mit 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]) 

crossval = CrossValidator(
    estimator=pipeline, 
    estimatorParamMaps=paramGrid, 
    evaluator=evaluator, 
    numFolds=numFolds) 

model = crossval.fit(trainingData) 

Answer 2

Um auf zero323 die große Antwort zu bauen mit Random Forest Classifier, hier ist ein ähnliches Beispiel für Random Wald 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 metrische Quelle: https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.evaluation.RegressionEvaluator