Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75248492
--- 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 ) {
+ temp += math.exp(m(i))
+ }
+ }
+ temp
+ }
+
+ var i = 0
+ while (i < m.size) {
+ m.values(i) = math.exp(m.values(i)) / sum
+ i += 1
+ }
+ m
+ }
+
+ /**
+ * Predict label for the given feature vector.
+ * The behavior of this can be adjusted using [[thresholds]].
+ */
+ override protected def predict(features: Vector): Double = {
+ if (isDefined(thresholds)) {
+ val thresholds: Array[Double] = getThresholds
+ val scaledProbability: Array[Double] =
+ scores(features).toArray.zip(thresholds).map { case (p, t) =>
+ if (t == 0.0) Double.PositiveInfinity else p / t
+ }
--- End diff --
this is fairly slow. maybe
```scala
val probabilities = scores(features).toArray
var i = 0
while ( i < numClasses) {
if (thresholds(i) == 0.0) probabilities(i) = Double.PositiveInfinity
else probabilities(i) / = t
}
```
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