hahadsg 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_r344534187
########## File path: mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala ########## @@ -0,0 +1,326 @@ +/* + * 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.{FactorizationMachines, FactorizationMachinesParams} +import org.apache.spark.ml.regression.FactorizationMachines._ +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.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 FactorizationMachines with FMClassifierParams with DefaultParamsWritable with Logging { + + @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 = 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 def train(dataset: Dataset[_]): FMClassifierModel = instrumented { instr => + val data: RDD[(Double, OldVector)] = + 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.") + (label, features) + } + data.persist(StorageLevel.MEMORY_AND_DISK) Review comment: > ```scala > val instances = extractInstances(dataset) > if (handlePersistence) data.persist(StorageLevel.MEMORY_AND_DISK) > ``` @zhengruifeng `extractInstances` function return `RDD[Instance]`, but I need `RDD[(Double, OldVector)]` in train stage. Should I still get instances? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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