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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.tuning
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.internal.Logging
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.evaluation.Evaluator
import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.StructType
/**
* Params for [[TrainValidationSplit]] and [[TrainValidationSplitModel]].
*/
private[ml] trait TrainValidationSplitParams extends ValidatorParams {
/**
* Param for ratio between train and validation data. Must be between 0 and 1.
* Default: 0.75
* @group param
*/
val trainRatio: DoubleParam = new DoubleParam(this, "trainRatio",
"ratio between training set and validation set (>= 0 && <= 1)", ParamValidators.inRange(0, 1))
/** @group getParam */
def getTrainRatio: Double = $(trainRatio)
setDefault(trainRatio -> 0.75)
}
/**
* :: Experimental ::
* Validation for hyper-parameter tuning.
* Randomly splits the input dataset into train and validation sets,
* and uses evaluation metric on the validation set to select the best model.
* Similar to [[CrossValidator]], but only splits the set once.
*/
@Since("1.5.0")
@Experimental
class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: String)
extends Estimator[TrainValidationSplitModel]
with TrainValidationSplitParams with Logging {
@Since("1.5.0")
def this() = this(Identifiable.randomUID("tvs"))
/** @group setParam */
@Since("1.5.0")
def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
/** @group setParam */
@Since("1.5.0")
def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value)
/** @group setParam */
@Since("1.5.0")
def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
/** @group setParam */
@Since("1.5.0")
def setTrainRatio(value: Double): this.type = set(trainRatio, value)
@Since("1.5.0")
override def fit(dataset: DataFrame): TrainValidationSplitModel = {
val schema = dataset.schema
transformSchema(schema, logging = true)
val sqlCtx = dataset.sqlContext
val est = $(estimator)
val eval = $(evaluator)
val epm = $(estimatorParamMaps)
val numModels = epm.length
val metrics = new Array[Double](epm.length)
val Array(training, validation) =
dataset.rdd.randomSplit(Array($(trainRatio), 1 - $(trainRatio)))
val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
// multi-model training
logDebug(s"Train split with multiple sets of parameters.")
val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
trainingDataset.unpersist()
var i = 0
while (i < numModels) {
// TODO: duplicate evaluator to take extra params from input
val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
metrics(i) += metric
i += 1
}
validationDataset.unpersist()
logInfo(s"Train validation split metrics: ${metrics.toSeq}")
val (bestMetric, bestIndex) =
if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
else metrics.zipWithIndex.minBy(_._1)
logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
logInfo(s"Best train validation split metric: $bestMetric.")
val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
copyValues(new TrainValidationSplitModel(uid, bestModel, metrics).setParent(this))
}
@Since("1.5.0")
override def transformSchema(schema: StructType): StructType = transformSchemaImpl(schema)
@Since("1.5.0")
override def copy(extra: ParamMap): TrainValidationSplit = {
val copied = defaultCopy(extra).asInstanceOf[TrainValidationSplit]
if (copied.isDefined(estimator)) {
copied.setEstimator(copied.getEstimator.copy(extra))
}
if (copied.isDefined(evaluator)) {
copied.setEvaluator(copied.getEvaluator.copy(extra))
}
copied
}
}
/**
* :: Experimental ::
* Model from train validation split.
*
* @param uid Id.
* @param bestModel Estimator determined best model.
* @param validationMetrics Evaluated validation metrics.
*/
@Since("1.5.0")
@Experimental
class TrainValidationSplitModel private[ml] (
@Since("1.5.0") override val uid: String,
@Since("1.5.0") val bestModel: Model[_],
@Since("1.5.0") val validationMetrics: Array[Double])
extends Model[TrainValidationSplitModel] with TrainValidationSplitParams {
@Since("1.5.0")
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
bestModel.transform(dataset)
}
@Since("1.5.0")
override def transformSchema(schema: StructType): StructType = {
bestModel.transformSchema(schema)
}
@Since("1.5.0")
override def copy(extra: ParamMap): TrainValidationSplitModel = {
val copied = new TrainValidationSplitModel (
uid,
bestModel.copy(extra).asInstanceOf[Model[_]],
validationMetrics.clone())
copyValues(copied, extra)
}
}
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