Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/13796#discussion_r75247834 --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/MultinomialLogisticRegression.scala --- @@ -0,0 +1,611 @@ +/* + * 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 scala.collection.mutable + +import breeze.linalg.{DenseVector => BDV} +import breeze.optimize.{CachedDiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} +import org.apache.hadoop.fs.Path + +import org.apache.spark.SparkException +import org.apache.spark.annotation.{Experimental, Since} +import org.apache.spark.internal.Logging +import org.apache.spark.ml.feature.Instance +import org.apache.spark.ml.linalg._ +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util._ +import org.apache.spark.mllib.linalg.VectorImplicits._ +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Dataset, Row} +import org.apache.spark.sql.functions.{col, lit} +import org.apache.spark.sql.types.DoubleType +import org.apache.spark.storage.StorageLevel + +/** + * Params for multinomial logistic (softmax) regression. + */ +private[classification] trait MultinomialLogisticRegressionParams + extends ProbabilisticClassifierParams with HasRegParam with HasElasticNetParam with HasMaxIter + with HasFitIntercept with HasTol with HasStandardization with HasWeightCol { + + /** + * Set thresholds in multiclass (or binary) classification to adjust the probability of + * predicting each class. Array must have length equal to the number of classes, with values >= 0. + * The class with largest value p/t is predicted, where p is the original probability of that + * class and t is the class' threshold. + * + * @group setParam + */ + def setThresholds(value: Array[Double]): this.type = { + set(thresholds, value) + } + + /** + * Get thresholds for binary or multiclass classification. + * + * @group getParam + */ + override def getThresholds: Array[Double] = { + $(thresholds) + } +} + +/** + * :: Experimental :: + * Multinomial Logistic (softmax) regression. + */ +@Since("2.1.0") +@Experimental +class MultinomialLogisticRegression @Since("2.1.0") ( + @Since("2.1.0") override val uid: String) + extends ProbabilisticClassifier[Vector, + MultinomialLogisticRegression, MultinomialLogisticRegressionModel] + with MultinomialLogisticRegressionParams with DefaultParamsWritable with Logging { + + @Since("2.1.0") + def this() = this(Identifiable.randomUID("mlogreg")) + + /** + * Set the regularization parameter. + * Default is 0.0. + * + * @group setParam + */ + @Since("2.1.0") + def setRegParam(value: Double): this.type = set(regParam, value) + setDefault(regParam -> 0.0) + + /** + * Set the ElasticNet mixing parameter. + * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. + * For 0 < alpha < 1, the penalty is a combination of L1 and L2. + * Default is 0.0 which is an L2 penalty. + * + * @group setParam + */ + @Since("2.1.0") + def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value) + setDefault(elasticNetParam -> 0.0) + + /** + * Set the maximum number of iterations. + * Default is 100. + * + * @group setParam + */ + @Since("2.1.0") + def setMaxIter(value: Int): this.type = set(maxIter, value) + setDefault(maxIter -> 100) + + /** + * Set the convergence tolerance of iterations. + * Smaller value will lead to higher accuracy with the cost of more iterations. + * Default is 1E-6. + * + * @group setParam + */ + @Since("2.1.0") + def setTol(value: Double): this.type = set(tol, value) + setDefault(tol -> 1E-6) + + /** + * Whether to fit an intercept term. + * Default is true. + * + * @group setParam + */ + @Since("2.1.0") + def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + setDefault(fitIntercept -> true) + + /** + * Whether to standardize the training features before fitting the model. + * The coefficients of models will be always returned on the original scale, + * so it will be transparent for users. Note that with/without standardization, + * the models should always converge to the same solution when no regularization + * is applied. In R's GLMNET package, the default behavior is true as well. + * Default is true. + * + * @group setParam + */ + @Since("2.1.0") + def setStandardization(value: Boolean): this.type = set(standardization, value) + setDefault(standardization -> true) + + /** + * Sets the value of param [[weightCol]]. + * If this is not set or empty, we treat all instance weights as 1.0. + * Default is not set, so all instances have weight one. + * + * @group setParam + */ + @Since("2.1.0") + def setWeightCol(value: String): this.type = set(weightCol, value) + + @Since("2.1.0") + override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value) + + override protected[spark] def train(dataset: Dataset[_]): MultinomialLogisticRegressionModel = { + val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol)) + val instances: RDD[Instance] = + dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map { + case Row(label: Double, weight: Double, features: Vector) => + Instance(label, weight, features) + } + + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) + + val instr = Instrumentation.create(this, instances) + instr.logParams(regParam, elasticNetParam, standardization, thresholds, + maxIter, tol, fitIntercept) + + val (summarizer, labelSummarizer) = { + val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer), + instance: Instance) => + (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight)) + + val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer), + c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) => + (c1._1.merge(c2._1), c1._2.merge(c2._2)) + + instances.treeAggregate( + new MultivariateOnlineSummarizer, new MultiClassSummarizer)(seqOp, combOp) + } + + val histogram = labelSummarizer.histogram + val numInvalid = labelSummarizer.countInvalid + val numFeatures = summarizer.mean.size + val numFeaturesPlusIntercept = if (getFitIntercept) numFeatures + 1 else numFeatures + + val numClasses = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { + case Some(n: Int) => + require(n >= histogram.length, s"Specified number of classes $n was " + + s"less than the number of unique labels ${histogram.length}") + n + case None => histogram.length + } + + instr.logNumClasses(numClasses) + instr.logNumFeatures(numFeatures) + + val (coefficients, intercepts, objectiveHistory) = { + if (numInvalid != 0) { + val msg = s"Classification labels should be in {0 to ${numClasses - 1} " + + s"Found $numInvalid invalid labels." + logError(msg) + throw new SparkException(msg) + } + + val labelIsConstant = histogram.count(_ != 0) == 1 + + if ($(fitIntercept) && labelIsConstant) { + // we want to produce a model that will always predict the constant label + (Matrices.sparse(numClasses, numFeatures, Array.fill(numFeatures + 1)(0), Array(), Array()), + Vectors.sparse(numClasses, Seq((numClasses - 1, Double.PositiveInfinity))), + Array.empty[Double]) + } else { + if (!$(fitIntercept) && labelIsConstant) { + logWarning(s"All labels belong to a single class and fitIntercept=false. It's" + + s"a dangerous ground, so the algorithm may not converge.") + } + + val featuresStd = summarizer.variance.toArray.map(math.sqrt) + + val regParamL1 = $(elasticNetParam) * $(regParam) + val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam) + + val bcFeaturesStd = instances.context.broadcast(featuresStd) + val costFun = new LogisticCostFun(instances, numClasses, $(fitIntercept), + $(standardization), bcFeaturesStd, regParamL2, multinomial = true) + + val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) { + new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol)) + } else { + val standardizationParam = $(standardization) + def regParamL1Fun = (index: Int) => { + // Remove the L1 penalization on the intercept + val isIntercept = $(fitIntercept) && ((index + 1) % numFeaturesPlusIntercept == 0) + if (isIntercept) { + 0.0 + } else { + if (standardizationParam) { + regParamL1 + } else { + val featureIndex = if ($(fitIntercept)) { + index % numFeaturesPlusIntercept + } else { + index % numFeatures + } + // If `standardization` is false, we still standardize the data + // to improve the rate of convergence; as a result, we have to + // perform this reverse standardization by penalizing each component + // differently to get effectively the same objective function when + // the training dataset is not standardized. + if (featuresStd(featureIndex) != 0.0) { + regParamL1 / featuresStd(featureIndex) + } else { + 0.0 + } + } + } + } + new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol)) + } + + val initialCoefficientsWithIntercept = Vectors.zeros(numClasses * numFeaturesPlusIntercept) + + if ($(fitIntercept)) { + /* + For multinomial logistic regression, when we initialize the coefficients as zeros, + it will converge faster if we initialize the intercepts such that + it follows the distribution of the labels. + {{{ + P(0) = \exp(b_0) / (\sum_{k=1}^K \exp(b_k)) + ... + P(K) = \exp(b_K) / (\sum_{k=1}^K \exp(b_k)) + }}} + The solution to this is not identifiable, so choose the solution with minimum + L2 penalty (i.e. subtract the mean). Hence, + {{{ + b_k = \log{count_k / count_0} + b_k' = b_k - \frac{1}{K} \sum b_k + }}} + */ + val referenceCoef = histogram.indices.map { i => + if (histogram(i) > 0) { + math.log(histogram(i) / (histogram(0) + 1)) // add 1 for smoothing + } else { + 0.0 + } + } + val referenceMean = referenceCoef.sum / referenceCoef.length + histogram.indices.foreach { i => + initialCoefficientsWithIntercept.toArray(i * numFeaturesPlusIntercept + numFeatures) = + referenceCoef(i) - referenceMean + } + } + val states = optimizer.iterations(new CachedDiffFunction(costFun), + initialCoefficientsWithIntercept.asBreeze.toDenseVector) + + /* + Note that in Multinomial Logistic Regression, the objective history + (loss + regularization) is log-likelihood which is invariant under feature + standardization. As a result, the objective history from optimizer is the same as the + one in the original space. + */ + val arrayBuilder = mutable.ArrayBuilder.make[Double] + var state: optimizer.State = null + while (states.hasNext) { + state = states.next() + arrayBuilder += state.adjustedValue + } + + if (state == null) { + val msg = s"${optimizer.getClass.getName} failed." + logError(msg) + throw new SparkException(msg) + } + bcFeaturesStd.destroy(blocking = false) + + /* + The coefficients are trained in the scaled space; we're converting them back to + the original space. + Note that the intercept in scaled space and original space is the same; + as a result, no scaling is needed. + */ + var interceptSum = 0.0 + var coefSum = 0.0 + val rawCoefficients = Vectors.fromBreeze(state.x) + val (coefMatrix, interceptVector) = rawCoefficients match { + case dv: DenseVector => + val coefArray = Array.tabulate(numClasses * numFeatures) { i => + val flatIndex = if ($(fitIntercept)) i + i / numFeatures else i + val featureIndex = i % numFeatures + val unscaledCoef = if (featuresStd(featureIndex) != 0.0) { + dv(flatIndex) / featuresStd(featureIndex) + } else { + 0.0 + } + coefSum += unscaledCoef + unscaledCoef + } + val interceptVector = if ($(fitIntercept)) { + Vectors.dense(Array.tabulate(numClasses) { i => + val coefIndex = (i + 1) * numFeaturesPlusIntercept - 1 + val intercept = dv(coefIndex) + interceptSum += intercept + intercept + }) + } else { + Vectors.sparse(numClasses, Seq()) + } + (new DenseMatrix(numClasses, numFeatures, coefArray, isTransposed = true), + interceptVector) + case sv: SparseVector => + throw new IllegalArgumentException("SparseVector is not supported for coefficients") + } + + /* + When no regularization is applied, the coefficients lack identifiability because + we do not use a pivot class. We can add any constant value to the coefficients and + get the same likelihood. So here, we choose the mean centered coefficients for + reproducibility. This method follows the approach in glmnet, described here: + + Friedman, et al. "Regularization Paths for Generalized Linear Models via + Coordinate Descent," https://core.ac.uk/download/files/153/6287975.pdf + */ + if ($(regParam) == 0.0) { + val coefficientMean = coefSum / (numClasses * numFeatures) + coefMatrix.update(_ - coefficientMean) + } + /* + The intercepts are never regularized, so we always center the mean. + */ + val interceptMean = interceptSum / numClasses + interceptVector match { + case dv: DenseVector => (0 until dv.size).foreach { i => dv.toArray(i) -= interceptMean } + case sv: SparseVector => + (0 until sv.numNonzeros).foreach { i => sv.values(i) -= interceptMean } + } + (coefMatrix, interceptVector, arrayBuilder.result()) + } + } + if (handlePersistence) instances.unpersist() + + val model = copyValues( + new MultinomialLogisticRegressionModel(uid, coefficients, intercepts, numClasses)) + instr.logSuccess(model) + model + } + + @Since("2.1.0") + override def copy(extra: ParamMap): MultinomialLogisticRegression = defaultCopy(extra) +} + +@Since("2.1.0") +object MultinomialLogisticRegression extends DefaultParamsReadable[MultinomialLogisticRegression] { + + @Since("2.1.0") + override def load(path: String): MultinomialLogisticRegression = super.load(path) +} + +/** + * :: Experimental :: + * Model produced by [[MultinomialLogisticRegression]]. + */ +@Since("2.1.0") +@Experimental +class MultinomialLogisticRegressionModel private[spark] ( + @Since("2.1.0") override val uid: String, + @Since("2.1.0") val coefficients: Matrix, + @Since("2.1.0") val intercepts: Vector, + @Since("2.1.0") val numClasses: Int) + extends ProbabilisticClassificationModel[Vector, MultinomialLogisticRegressionModel] + with MultinomialLogisticRegressionParams with MLWritable { + + @Since("2.1.0") + override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value) + + @Since("2.1.0") + override def getThresholds: Array[Double] = super.getThresholds + + @Since("2.1.0") + override val numFeatures: Int = coefficients.numCols + + /** Margin (rawPrediction) for each class label. */ + private val margins: Vector => Vector = (features) => { + val m = intercepts.toDense.copy + BLAS.gemv(1.0, coefficients, features, 1.0, m) + m + } + + /** Score (probability) for each class label. */ + private val scores: Vector => Vector = (features) => { + val m = margins(features).toDense --- End diff -- val m = margins(features).toArray
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