zhengruifeng commented on a change in pull request #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component URL: https://github.com/apache/spark/pull/26124#discussion_r341438487
########## File path: mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala ########## @@ -0,0 +1,360 @@ +/* + * 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.classification + +import org.apache.hadoop.fs.Path + +import org.apache.spark.annotation.Since +import org.apache.spark.internal.Logging +import org.apache.spark.ml.linalg._ +import org.apache.spark.ml.param._ +import org.apache.spark.ml.regression._ +import org.apache.spark.ml.util._ +import org.apache.spark.ml.util.Instrumentation.instrumented +import org.apache.spark.mllib.linalg.{Vector => OldVector} +import org.apache.spark.mllib.linalg.VectorImplicits._ +import org.apache.spark.mllib.optimization.GradientDescent +import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Dataset, Row} +import org.apache.spark.sql.functions.col +import org.apache.spark.storage.StorageLevel + +/** + * Params for FMClassifier. + */ +private[classification] trait FMClassifierParams extends ProbabilisticClassifierParams + with FactorizationMachinesParams { +} + +/** + * Factorization Machines learning algorithm for classification. + * It supports normal gradient descent and AdamW solver. + * + * The implementation is based upon: + * <a href="https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf";> + * S. Rendle. "Factorization machines" 2010</a>. + * + * FM is able to estimate interactions even in problems with huge sparsity + * (like advertising and recommendation system). + * FM formula is: + * {{{ + * y = w_0 + \sum\limits^n_{i-1} w_i x_i + + * \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j + * }}} + * First two terms denote global bias and linear term (as same as linear regression), + * and last term denotes pairwise interactions term. {{{v_i}}} describes the i-th variable + * with k factors. + * + * FM classification model uses logistic loss which can be solved by gradient descent method, and + * regularization terms like L2 are usually added to the loss function to prevent overfitting. + * + * @note Multiclass labels are not currently supported. + */ +@Since("3.0.0") +class FMClassifier @Since("3.0.0") ( + @Since("3.0.0") override val uid: String) + extends ProbabilisticClassifier[Vector, FMClassifier, FMClassifierModel] + with FMClassifierParams with DefaultParamsWritable with Logging { + + import org.apache.spark.ml.regression.BaseFactorizationMachinesGradient.{LogisticLoss, parseLoss} + import org.apache.spark.ml.regression.FMRegressor.initCoefficients + + @Since("3.0.0") + def this() = this(Identifiable.randomUID("fmc")) + + /** + * Set the dimensionality of the factors. + * Default is 8. + * + * @group setParam + */ + @Since("3.0.0") + def setNumFactors(value: Int): this.type = set(numFactors, value) + setDefault(numFactors -> 8) + + /** + * Set whether to fit global bias term. + * Default is true. + * + * @group setParam + */ + @Since("3.0.0") + def setFitBias(value: Boolean): this.type = set(fitBias, value) + setDefault(fitBias -> true) + + /** + * Set whether to fit linear term. + * Default is true. + * + * @group setParam + */ + @Since("3.0.0") + def setFitLinear(value: Boolean): this.type = set(fitLinear, value) + setDefault(fitLinear -> true) + + /** + * Set the L2 regularization parameter. + * Default is 0.0. + * + * @group setParam + */ + @Since("3.0.0") + def setRegParam(value: Double): this.type = set(regParam, value) + setDefault(regParam -> 0.0) + + /** + * Set the mini-batch fraction parameter. + * Default is 1.0. + * + * @group setParam + */ + @Since("3.0.0") + def setMiniBatchFraction(value: Double): this.type = { + require(value > 0 && value <= 1.0, + s"Fraction for mini-batch SGD must be in range (0, 1] but got $value") + set(miniBatchFraction, value) + } + setDefault(miniBatchFraction -> 1.0) + + /** + * Set the standard deviation of initial coefficients. + * Default is 0.01. + * + * @group setParam + */ + @Since("3.0.0") + def setInitStd(value: Double): this.type = set(initStd, value) + setDefault(initStd -> 0.01) + + /** + * Set the maximum number of iterations. + * Default is 100. + * + * @group setParam + */ + @Since("3.0.0") + def setMaxIter(value: Int): this.type = set(maxIter, value) + setDefault(maxIter -> 100) + + /** + * Set the initial step size for the first step (like learning rate). + * Default is 1.0. + * + * @group setParam + */ + @Since("3.0.0") + def setStepSize(value: Double): this.type = set(stepSize, value) + setDefault(stepSize -> 1.0) + + /** + * Set the convergence tolerance of iterations. + * Default is 1E-6. + * + * @group setParam + */ + @Since("3.0.0") + def setTol(value: Double): this.type = set(tol, value) + setDefault(tol -> 1E-6) + + /** + * Set the solver algorithm used for optimization. + * Default is adamW. + * + * @group setParam + */ + @Since("3.0.0") + def setSolver(value: String): this.type = set(solver, value) + setDefault(solver -> AdamW) + + override protected[spark] def train(dataset: Dataset[_]): FMClassifierModel = { + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE + train(dataset, handlePersistence) + } + + protected[spark] def train( + dataset: Dataset[_], + handlePersistence: Boolean): FMClassifierModel = instrumented { instr => + val instances: RDD[OldLabeledPoint] = + dataset.select(col($(labelCol)), col($(featuresCol))).rdd.map { + case Row(label: Double, features: Vector) => + require(label == 0 || label == 1, s"FMClassifier was given" + + s" dataset with invalid label $label. Labels must be in {0,1}; note that" + + s" FMClassifier currently only supports binary classification.") + OldLabeledPoint(label, features) + } + + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) + + val numClasses = 2 + if (isDefined(thresholds)) { + require($(thresholds).length == numClasses, this.getClass.getSimpleName + + ".train() called with non-matching numClasses and thresholds.length." + + s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}") + } + + instr.logPipelineStage(this) + instr.logDataset(dataset) + instr.logParams(this, numFactors, fitBias, fitLinear, regParam, + miniBatchFraction, initStd, maxIter, stepSize, tol, solver) + instr.logNumClasses(numClasses) + + val numFeatures = instances.first().features.size + instr.logNumFeatures(numFeatures) + + val data = instances.map{ case OldLabeledPoint(label, features) => (label, features) } + + // initialize coefficients + val (initialCoefficients, coefficientsSize) = initCoefficients( + numFeatures, $(numFactors), $(fitBias), $(fitLinear), $(initStd)) + + // optimize coefficients with gradient descent + val gradient = parseLoss( + LogisticLoss, $(numFactors), $(fitBias), $(fitLinear), numFeatures) + + val updater = parseSolver($(solver), coefficientsSize) + + val optimizer = new GradientDescent(gradient, updater) + .setStepSize($(stepSize)) + .setNumIterations($(maxIter)) + .setRegParam($(regParam)) + .setMiniBatchFraction($(miniBatchFraction)) + .setConvergenceTol($(tol)) + val coefficients = optimizer.optimize(data, initialCoefficients) + + if (handlePersistence) instances.unpersist() + + val model = copyValues(new FMClassifierModel(uid, coefficients.asML, numFeatures, numClasses)) + model + } + + @Since("3.0.0") + override def copy(extra: ParamMap): FMClassifier = defaultCopy(extra) +} + +@Since("3.0.0") +object FMClassifier extends DefaultParamsReadable[FMClassifier] { + + @Since("3.0.0") + override def load(path: String): FMClassifier = super.load(path) +} + +/** + * Model produced by [[FMClassifier]] + */ +@Since("3.0.0") +class FMClassifierModel ( + @Since("3.0.0") override val uid: String, + @Since("3.0.0") val coefficients: Vector, + @Since("3.0.0") override val numFeatures: Int, + @Since("3.0.0") override val numClasses: Int) + extends ProbabilisticClassificationModel[Vector, FMClassifierModel] + with FMClassifierParams with MLWritable { + + import org.apache.spark.ml.regression.BaseFactorizationMachinesGradient.LogisticLoss + + /** + * Returns Factorization Machines coefficients + * coefficients concat from 2-way coefficients, 1-way coefficients, global bias + * index 0 ~ numFeatures*numFactors is 2-way coefficients, + * [i * numFactors + f] denotes i-th feature and f-th factor + * Following indices are 1-way coefficients and global bias. + */ + private val oldCoefficients: OldVector = coefficients Review comment: I guess we can mark it `lazy` and `transient`? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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