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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2019/10/20 22:47:23 UTC
[GitHub] [spark] srowen commented on a change in pull request #26124:
[SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component
srowen 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_r336802104
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File path: mllib/src/main/scala/org/apache/spark/ml/regression/FactorizationMachines.scala
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@@ -0,0 +1,839 @@
+/*
+ * 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.regression
+
+import scala.util.Random
+
+import breeze.linalg.{axpy => brzAxpy, norm => brzNorm, Vector => BV}
+import breeze.numerics.{sqrt => brzSqrt}
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.linalg.BLAS._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
+import org.apache.spark.mllib.{linalg => OldLinalg}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.mllib.optimization.{Gradient, GradientDescent, Updater}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.mllib.util.MLUtils
+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 Factorization Machines
+ */
+private[regression] trait FactorizationMachinesParams
+ extends PredictorParams
+ with HasMaxIter with HasStepSize with HasTol with HasSolver with HasLoss {
+
+ import FactorizationMachines._
+
+ /**
+ * Param for dimensionality of the factors (>= 0)
+ * @group param
+ */
+ @Since("3.0.0")
+ final val numFactors: IntParam = new IntParam(this, "numFactors",
+ "dimensionality of the factorization")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getNumFactors: Int = $(numFactors)
+
+ /**
+ * Param for whether to fit global bias term
+ * @group param
+ */
+ @Since("3.0.0")
+ final val fitBias: BooleanParam = new BooleanParam(this, "fitBias",
+ "whether to fit global bias term")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getFitBias: Boolean = $(fitBias)
+
+ /**
+ * Param for whether to fit linear term (aka 1-way term)
+ * @group param
+ */
+ @Since("3.0.0")
+ final val fitLinear: BooleanParam = new BooleanParam(this, "fitLinear",
+ "whether to fit linear term (aka 1-way term)")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getFitLinear: Boolean = $(fitLinear)
+
+ /**
+ * Param for L2 regularization parameter (>= 0)
+ * @group param
+ */
+ @Since("3.0.0")
+ final val regParam: DoubleParam = new DoubleParam(this, "regParam",
+ "regularization for L2")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getRegParam: Double = $(regParam)
+
+ /**
+ * Param for mini-batch fraction, must be in range (0, 1]
+ * @group param
+ */
+ @Since("3.0.0")
+ final val miniBatchFraction: DoubleParam = new DoubleParam(this, "miniBatchFraction",
+ "mini-batch fraction")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getMiniBatchFraction: Double = $(miniBatchFraction)
+
+ /**
+ * Param for standard deviation of initial coefficients
+ * @group param
+ */
+ @Since("3.0.0")
+ final val initStd: DoubleParam = new DoubleParam(this, "initStd",
+ "standard deviation of initial coefficients")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getInitStd: Double = $(initStd)
+
+ /**
+ * The solver algorithm for optimization.
+ * Supported options: "gd", "adamW".
+ * Default: "adamW"
+ *
+ * @group param
+ */
+ @Since("3.0.0")
+ final override val solver: Param[String] = new Param[String](this, "solver",
+ "The solver algorithm for optimization. Supported options: " +
+ s"${supportedSolvers.mkString(", ")}. (Default adamW)",
+ ParamValidators.inArray[String](supportedSolvers))
+
+ /**
+ * The loss function to be optimized.
+ * Supported options: "logisticLoss" and "squaredError".
+ * Default: "logisticLoss"
+ *
+ * @group param
+ */
+ @Since("3.0.0")
+ final override val loss: Param[String] = new Param[String](this, "loss", "The loss function to" +
+ s" be optimized. Supported options: ${supportedLosses.mkString(", ")}. (Default logisticLoss)",
+ ParamValidators.inArray[String](supportedLosses))
+
+ /**
+ * Param for whether to print information per iteration step
+ *
+ * @group param
+ */
+ @Since("3.0.0")
+ final val verbose: BooleanParam = new BooleanParam(this, "verbose",
+ "whether to print information per iteration step")
+
+ /** @group getParam */
+ @Since("3.0.0")
+ final def getVerbose: Boolean = $(verbose)
+
+}
+
+/**
+ * Factorization Machines
+ */
+@Since("3.0.0")
+class FactorizationMachines @Since("3.0.0") (
+ @Since("3.0.0") override val uid: String)
+ extends Predictor[Vector, FactorizationMachines, FactorizationMachinesModel]
+ with FactorizationMachinesParams with DefaultParamsWritable with Logging {
+
+ import FactorizationMachines._
+
+ @Since("3.0.0")
+ def this() = this(Identifiable.randomUID("fm"))
+
+ /**
+ * 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)
+
+ /**
+ * Sets the value of param [[loss]].
+ * - "logisticLoss" is used to classification, label must be in {0, 1}.
+ * - "squaredError" is used to regression.
+ * Default is logisticLoss.
+ *
+ * @group setParam
+ */
+ @Since("3.0.0")
+ def setLoss(value: String): this.type = set(loss, value)
+ setDefault(loss -> LogisticLoss)
+
+ /**
+ * Set the verbose.
+ * Factorization Machines may produce a gradient explosion, so output some log use logInfo.
+ * logInfo will output
+ * - iter_num: current iterate epoch
+ * - iter_step_size: step size(aka learning rate)
+ * - weights_norm2: weight's L2 norm
+ * - cur_tol: current tolerance in GradientDescent
+ * - grad_norm1: gradient's L1 norm
+ * - first_weight: first parameter in weights
+ * - first_gradient: first parameter's gradient
+ * Default is false.
+ *
+ * @group setParam
+ */
+ @Since("3.0.0")
+ def setVerbose(value: Boolean): this.type = set(verbose, value)
+ setDefault(verbose -> false)
+
+ override protected[spark] def train(dataset: Dataset[_]): FactorizationMachinesModel = {
+ val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
+ train(dataset, handlePersistence)
+ }
+
+ protected[spark] def train(
+ dataset: Dataset[_],
+ handlePersistence: Boolean): FactorizationMachinesModel = instrumented { instr =>
+ val instances: RDD[OldLabeledPoint] =
+ dataset.select(col($(labelCol)), col($(featuresCol))).rdd.map {
+ case Row(label: Double, features: Vector) =>
+ OldLabeledPoint(label, features)
+ }
+
+ if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
+
+ instr.logPipelineStage(this)
+ instr.logDataset(dataset)
+ instr.logParams(this, numFactors, fitBias, fitLinear, regParam,
+ miniBatchFraction, initStd, maxIter, stepSize, tol, solver, loss)
+
+ val numFeatures = instances.first().features.size
+ instr.logNumFeatures(numFeatures)
+
+ // initialize coefficients
+ val coefficientsSize = $(numFactors) * numFeatures +
+ (if ($(fitLinear)) numFeatures else 0) +
+ (if ($(fitBias)) 1 else 0)
+ val initialCoefficients =
+ Vectors.dense(Array.fill($(numFactors) * numFeatures)(Random.nextGaussian() * $(initStd)) ++
+ (if ($(fitLinear)) Array.fill(numFeatures)(0.0) else Array.empty[Double]) ++
+ (if ($(fitBias)) Array.fill(1)(0.0) else Array.empty[Double]))
+
+ val data = instances.map{ case OldLabeledPoint(label, features) => (label, features) }
Review comment:
Nit: space before brace
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