在机器学习中，如何根据给定的数据集，为算法模型拟合参数，使得模型达到最优的效果，这一过程称为“调参”（tuning）。
Spark的Mllib提供了CrossValidator和TrainValidationSplit两种方法，来帮助实现模型的调优。
一般使用上述的两种方法需要进行如下设置，
-
setEstimator方法指定需要调参的算法algorithm或是工作流Pipeline（Pipeline也是一种Estimator）； -
setEstimatorParamMaps方法指定“参数网格”（使用new ParamGridBuilder().addGrid(xxx,xxx).build()），作为备选的参数组合； -
setEvaluator指定评价方法，用来衡量训练好的模型在验证集上的表现。
交叉验证CrossValidator
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.sql.Row
// Prepare training data from a list of (id, text, label) tuples.
val training = spark.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0),
(4L, "b spark who", 1.0),
(5L, "g d a y", 0.0),
(6L, "spark fly", 1.0),
(7L, "was mapreduce", 0.0),
(8L, "e spark program", 1.0),
(9L, "a e c l", 0.0),
(10L, "spark compile", 1.0),
(11L, "hadoop software", 0.0)
)).toDF("id", "text", "label")
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
val paramGrid = new ParamGridBuilder()
.addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
.addGrid(lr.regParam, Array(0.1, 0.01))
.build()
// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
// This will allow us to jointly choose parameters for all Pipeline stages.
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
// is areaUnderROC.
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(2) // Use 3+ in practice
// Run cross-validation, and choose the best set of parameters.
val cvModel = cv.fit(training)
// Prepare test documents, which are unlabeled (id, text) tuples.
val test = spark.createDataFrame(Seq(
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")
)).toDF("id", "text")
// Make predictions on test documents. cvModel uses the best model found (lrModel).
cvModel.transform(test)
.select("id", "text", "probability", "prediction")
.collect()
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}输出
(4, spark i j k) --> prob=[0.12566260711357374,0.8743373928864263], prediction=1.0
(5, l m n) --> prob=[0.995215441016286,0.004784558983714038], prediction=0.0
(6, mapreduce spark) --> prob=[0.3069689523262643,0.6930310476737357], prediction=1.0
(7, apache hadoop) --> prob=[0.8040279442401395,0.1959720557598605], prediction=0.0prob中的两个数值分别表示预测结果为0或1的概率，默认的阈值为0.5。CrossValidator会对每一种参数组合评估多次，计算成本会比较高，因此不适合规模比较大的数据集。
TrainValidationSplit
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, RegressionEvaluator}
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
import org.apache.spark.sql.{Row, SparkSession}
// Prepare training and test data.
val data = spark.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0),
(4L, "b spark who", 1.0),
(5L, "g d a y", 0.0),
(6L, "spark fly", 1.0),
(7L, "was mapreduce", 0.0),
(8L, "e spark program", 1.0),
(9L, "a e c l", 0.0),
(10L, "spark compile", 1.0),
(11L, "hadoop software", 0.0)
)).toDF("id", "text", "label")
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// TrainValidationSplit will try all combinations of values and determine best model using
// the evaluator.
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, Array(0.1, 0.01))
.addGrid(lr.fitIntercept)
.addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
.build()
// In this case the estimator is a Pipeline
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
val trainValidationSplit = new TrainValidationSplit()
.setEstimator(pipeline)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
// 80% of the data will be used for training and the remaining 20% for validation.
.setTrainRatio(0.8)
// Run train validation split, and choose the best set of parameters.
val model = trainValidationSplit.fit(data)
val test = spark.createDataFrame(Seq(
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")
)).toDF("id", "text")
// Make predictions on test data. model is the model with combination of parameters
// that performed best.
model.transform(test)
.select("id", "text", "probability", "prediction")
.collect()
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}输出
(4, spark i j k) --> prob=[0.26612878920913,0.73387121079087], prediction=1.0
(5, l m n) --> prob=[0.9209302389399868,0.0790697610600131], prediction=0.0
(6, mapreduce spark) --> prob=[0.4429343598469927,0.5570656401530073], prediction=1.0
(7, apache hadoop) --> prob=[0.8583692828862762,0.14163071711372377], prediction=0.0TrainValidationSplit将输入数据按照setTrainRatio()的参数切分为训练集和验证集，根据模型在验证集上的表现，找出所有的参数组合中表现最好的一组，作为模型的最优参数。相较于CrossValidator对每一种参数组合都会评估多次，TrainValidationSplit只需对每一种参数组合评估一次，因此计算成本更低。但是当数据集的规模不是很大时，可能会因为数据分布的不均匀，影响模型选择的结果。