Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75222563
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/MultinomialLogisticRegression.scala
---
@@ -0,0 +1,622 @@
+/*
+ * 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 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 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)
+
--- End diff --
ditto
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