[SPARK-8484] [ML] Added TrainValidationSplit for hyper-parameter tuning.
- [X] Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model. It should be similar to CrossValidator, but simpler and less expensive. - [X] Simplified replacement of https://github.com/apache/spark/pull/6996 Author: martinzapletal <zapletal-martin@email.cz> Closes #7337 from zapletal-martin/SPARK-8484-TrainValidationSplit and squashes the following commits: cafc949 [martinzapletal] Review comments https://github.com/apache/spark/pull/7337. 511b398 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8484-TrainValidationSplit f4fc9c4 [martinzapletal] SPARK-8484 Resolved feedback to https://github.com/apache/spark/pull/7337 00c4f5a [martinzapletal] SPARK-8484. Styling. d699506 [martinzapletal] SPARK-8484. Styling. 93ed2ee [martinzapletal] Styling. 3bc1853 [martinzapletal] SPARK-8484. Styling. 2aa6f43 [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model. 21662eb [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model.
This commit is contained in:
martinzapletal
Xiangrui Meng
parent
5307c9d3f7
commit
a721ee5270
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@ -32,38 +32,7 @@ import org.apache.spark.sql.types.StructType
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/**
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* Params for [[CrossValidator]] and [[CrossValidatorModel]].
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*/
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private[ml] trait CrossValidatorParams extends Params {
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/**
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* param for the estimator to be cross-validated
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* @group param
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*/
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val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
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/** @group getParam */
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def getEstimator: Estimator[_] = $(estimator)
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/**
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* param for estimator param maps
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* @group param
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*/
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val estimatorParamMaps: Param[Array[ParamMap]] =
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new Param(this, "estimatorParamMaps", "param maps for the estimator")
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/** @group getParam */
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def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
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/**
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* param for the evaluator used to select hyper-parameters that maximize the cross-validated
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* metric
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* @group param
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*/
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val evaluator: Param[Evaluator] = new Param(this, "evaluator",
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"evaluator used to select hyper-parameters that maximize the cross-validated metric")
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/** @group getParam */
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def getEvaluator: Evaluator = $(evaluator)
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private[ml] trait CrossValidatorParams extends ValidatorParams {
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/**
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* Param for number of folds for cross validation. Must be >= 2.
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* Default: 3
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@ -0,0 +1,168 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.ml.tuning
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import org.apache.spark.Logging
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import org.apache.spark.annotation.Experimental
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import org.apache.spark.ml.evaluation.Evaluator
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import org.apache.spark.ml.{Estimator, Model}
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import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
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import org.apache.spark.ml.util.Identifiable
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import org.apache.spark.sql.DataFrame
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import org.apache.spark.sql.types.StructType
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/**
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* Params for [[TrainValidationSplit]] and [[TrainValidationSplitModel]].
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*/
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private[ml] trait TrainValidationSplitParams extends ValidatorParams {
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/**
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* Param for ratio between train and validation data. Must be between 0 and 1.
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* Default: 0.75
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* @group param
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*/
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val trainRatio: DoubleParam = new DoubleParam(this, "trainRatio",
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"ratio between training set and validation set (>= 0 && <= 1)", ParamValidators.inRange(0, 1))
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/** @group getParam */
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def getTrainRatio: Double = $(trainRatio)
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setDefault(trainRatio -> 0.75)
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}
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/**
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* :: Experimental ::
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* Validation for hyper-parameter tuning.
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* Randomly splits the input dataset into train and validation sets,
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* and uses evaluation metric on the validation set to select the best model.
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* Similar to [[CrossValidator]], but only splits the set once.
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*/
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@Experimental
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class TrainValidationSplit(override val uid: String) extends Estimator[TrainValidationSplitModel]
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with TrainValidationSplitParams with Logging {
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def this() = this(Identifiable.randomUID("tvs"))
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/** @group setParam */
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def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
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/** @group setParam */
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def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value)
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/** @group setParam */
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def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
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/** @group setParam */
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def setTrainRatio(value: Double): this.type = set(trainRatio, value)
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override def fit(dataset: DataFrame): TrainValidationSplitModel = {
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val schema = dataset.schema
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transformSchema(schema, logging = true)
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val sqlCtx = dataset.sqlContext
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val est = $(estimator)
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val eval = $(evaluator)
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val epm = $(estimatorParamMaps)
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val numModels = epm.length
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val metrics = new Array[Double](epm.length)
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val Array(training, validation) =
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dataset.rdd.randomSplit(Array($(trainRatio), 1 - $(trainRatio)))
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val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
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val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
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// multi-model training
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logDebug(s"Train split with multiple sets of parameters.")
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val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
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trainingDataset.unpersist()
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var i = 0
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while (i < numModels) {
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// TODO: duplicate evaluator to take extra params from input
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val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
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logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
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metrics(i) += metric
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i += 1
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}
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validationDataset.unpersist()
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logInfo(s"Train validation split metrics: ${metrics.toSeq}")
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val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
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logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
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logInfo(s"Best train validation split metric: $bestMetric.")
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val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
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copyValues(new TrainValidationSplitModel(uid, bestModel, metrics).setParent(this))
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}
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override def transformSchema(schema: StructType): StructType = {
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$(estimator).transformSchema(schema)
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}
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override def validateParams(): Unit = {
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super.validateParams()
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val est = $(estimator)
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for (paramMap <- $(estimatorParamMaps)) {
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est.copy(paramMap).validateParams()
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}
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}
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override def copy(extra: ParamMap): TrainValidationSplit = {
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val copied = defaultCopy(extra).asInstanceOf[TrainValidationSplit]
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if (copied.isDefined(estimator)) {
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copied.setEstimator(copied.getEstimator.copy(extra))
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}
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if (copied.isDefined(evaluator)) {
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copied.setEvaluator(copied.getEvaluator.copy(extra))
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}
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copied
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}
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}
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/**
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* :: Experimental ::
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* Model from train validation split.
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*
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* @param uid Id.
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* @param bestModel Estimator determined best model.
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* @param validationMetrics Evaluated validation metrics.
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*/
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@Experimental
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class TrainValidationSplitModel private[ml] (
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override val uid: String,
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val bestModel: Model[_],
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val validationMetrics: Array[Double])
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extends Model[TrainValidationSplitModel] with TrainValidationSplitParams {
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override def validateParams(): Unit = {
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bestModel.validateParams()
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}
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override def transform(dataset: DataFrame): DataFrame = {
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transformSchema(dataset.schema, logging = true)
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bestModel.transform(dataset)
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}
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override def transformSchema(schema: StructType): StructType = {
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bestModel.transformSchema(schema)
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}
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override def copy(extra: ParamMap): TrainValidationSplitModel = {
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val copied = new TrainValidationSplitModel (
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uid,
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bestModel.copy(extra).asInstanceOf[Model[_]],
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validationMetrics.clone())
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copyValues(copied, extra)
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}
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}
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@ -0,0 +1,60 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.ml.tuning
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import org.apache.spark.annotation.DeveloperApi
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import org.apache.spark.ml.Estimator
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import org.apache.spark.ml.evaluation.Evaluator
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import org.apache.spark.ml.param.{ParamMap, Param, Params}
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/**
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* :: DeveloperApi ::
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* Common params for [[TrainValidationSplitParams]] and [[CrossValidatorParams]].
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*/
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@DeveloperApi
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private[ml] trait ValidatorParams extends Params {
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/**
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* param for the estimator to be validated
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* @group param
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*/
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val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
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/** @group getParam */
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def getEstimator: Estimator[_] = $(estimator)
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/**
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* param for estimator param maps
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* @group param
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*/
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val estimatorParamMaps: Param[Array[ParamMap]] =
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new Param(this, "estimatorParamMaps", "param maps for the estimator")
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/** @group getParam */
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def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
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/**
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* param for the evaluator used to select hyper-parameters that maximize the validated metric
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* @group param
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*/
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val evaluator: Param[Evaluator] = new Param(this, "evaluator",
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"evaluator used to select hyper-parameters that maximize the validated metric")
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/** @group getParam */
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def getEvaluator: Evaluator = $(evaluator)
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}
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@ -0,0 +1,139 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.ml.tuning
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import org.apache.spark.SparkFunSuite
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import org.apache.spark.ml.{Estimator, Model}
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import org.apache.spark.ml.classification.LogisticRegression
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import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
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import org.apache.spark.ml.param.ParamMap
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import org.apache.spark.ml.param.shared.HasInputCol
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import org.apache.spark.ml.regression.LinearRegression
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import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
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import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
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import org.apache.spark.sql.DataFrame
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import org.apache.spark.sql.types.StructType
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class TrainValidationSplitSuite extends SparkFunSuite with MLlibTestSparkContext {
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test("train validation with logistic regression") {
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val dataset = sqlContext.createDataFrame(
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sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
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val lr = new LogisticRegression
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val lrParamMaps = new ParamGridBuilder()
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.addGrid(lr.regParam, Array(0.001, 1000.0))
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.addGrid(lr.maxIter, Array(0, 10))
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.build()
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val eval = new BinaryClassificationEvaluator
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val cv = new TrainValidationSplit()
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.setEstimator(lr)
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.setEstimatorParamMaps(lrParamMaps)
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.setEvaluator(eval)
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.setTrainRatio(0.5)
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val cvModel = cv.fit(dataset)
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val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression]
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assert(cv.getTrainRatio === 0.5)
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assert(parent.getRegParam === 0.001)
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assert(parent.getMaxIter === 10)
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assert(cvModel.validationMetrics.length === lrParamMaps.length)
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}
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test("train validation with linear regression") {
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val dataset = sqlContext.createDataFrame(
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sc.parallelize(LinearDataGenerator.generateLinearInput(
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6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
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val trainer = new LinearRegression
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val lrParamMaps = new ParamGridBuilder()
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.addGrid(trainer.regParam, Array(1000.0, 0.001))
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.addGrid(trainer.maxIter, Array(0, 10))
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.build()
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val eval = new RegressionEvaluator()
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val cv = new TrainValidationSplit()
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.setEstimator(trainer)
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.setEstimatorParamMaps(lrParamMaps)
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.setEvaluator(eval)
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.setTrainRatio(0.5)
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val cvModel = cv.fit(dataset)
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val parent = cvModel.bestModel.parent.asInstanceOf[LinearRegression]
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assert(parent.getRegParam === 0.001)
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assert(parent.getMaxIter === 10)
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assert(cvModel.validationMetrics.length === lrParamMaps.length)
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eval.setMetricName("r2")
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val cvModel2 = cv.fit(dataset)
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val parent2 = cvModel2.bestModel.parent.asInstanceOf[LinearRegression]
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assert(parent2.getRegParam === 0.001)
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assert(parent2.getMaxIter === 10)
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assert(cvModel2.validationMetrics.length === lrParamMaps.length)
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}
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test("validateParams should check estimatorParamMaps") {
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import TrainValidationSplitSuite._
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val est = new MyEstimator("est")
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val eval = new MyEvaluator
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val paramMaps = new ParamGridBuilder()
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.addGrid(est.inputCol, Array("input1", "input2"))
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.build()
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val cv = new TrainValidationSplit()
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.setEstimator(est)
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.setEstimatorParamMaps(paramMaps)
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.setEvaluator(eval)
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.setTrainRatio(0.5)
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cv.validateParams() // This should pass.
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val invalidParamMaps = paramMaps :+ ParamMap(est.inputCol -> "")
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cv.setEstimatorParamMaps(invalidParamMaps)
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intercept[IllegalArgumentException] {
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cv.validateParams()
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}
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}
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}
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object TrainValidationSplitSuite {
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abstract class MyModel extends Model[MyModel]
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class MyEstimator(override val uid: String) extends Estimator[MyModel] with HasInputCol {
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override def validateParams(): Unit = require($(inputCol).nonEmpty)
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override def fit(dataset: DataFrame): MyModel = {
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throw new UnsupportedOperationException
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}
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override def transformSchema(schema: StructType): StructType = {
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throw new UnsupportedOperationException
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}
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override def copy(extra: ParamMap): MyEstimator = defaultCopy(extra)
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}
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class MyEvaluator extends Evaluator {
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override def evaluate(dataset: DataFrame): Double = {
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throw new UnsupportedOperationException
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}
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override val uid: String = "eval"
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override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
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}
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}
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