Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75247923
--- 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
+ val maxMarginIndex = m.argmax
+ val maxMargin = m(maxMarginIndex)
+
+ // adjust margins for overflow
+ val sum = {
+ var temp = 0.0
+ if (maxMargin > 0) {
+ for (i <- 0 until numClasses) {
+ m.toArray(i) -= maxMargin
+ temp += math.exp(m(i))
+ }
+ } else {
+ for (i <- 0 until numClasses ) {
--- End diff --
extra space after numClasses
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
