Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75236065
--- 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
+ }
--- End diff --
`instances.persist(StorageLevel.MEMORY_AND_DISK)` is the one cached; as a
result, you will touch the source twice which is not ideal. Why do you need to
use `MetadataUtils.getNumClasses`? I think we just just do the following,
```scala
val numClasses = histogram.keys.max - 1
```
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