Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75418682
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/MultinomialLogisticRegressionSuite.scala
---
@@ -0,0 +1,1016 @@
+/*
+ * 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.language.existentials
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.classification.LogisticRegressionSuite._
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+
+class MultinomialLogisticRegressionSuite
+ extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ @transient var dataset: Dataset[_] = _
+ @transient var multinomialDataset: DataFrame = _
+ private val eps: Double = 1e-5
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+
+ dataset = {
+ val nPoints = 100
+ val coefficients = Array(
+ -0.57997, 0.912083, -0.371077,
+ -0.16624, -0.84355, -0.048509)
+
+ val xMean = Array(5.843, 3.057)
+ val xVariance = Array(0.6856, 0.1899)
+
+ val testData = generateMultinomialLogisticInput(
+ coefficients, xMean, xVariance, addIntercept = true, nPoints, 42)
+
+ val df = spark.createDataFrame(sc.parallelize(testData, 4))
+ df.cache()
+ df
+ }
+
+ multinomialDataset = {
+ val nPoints = 10000
+ val coefficients = Array(
+ -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
+ -0.16624, -0.84355, -0.048509, -0.301789, 4.170682)
+
+ val xMean = Array(5.843, 3.057, 3.758, 1.199)
+ val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+
+ val testData = generateMultinomialLogisticInput(
+ coefficients, xMean, xVariance, addIntercept = true, nPoints, 42)
+
+ val df = spark.createDataFrame(sc.parallelize(testData, 4))
+ df.cache()
+ df
+ }
+ }
+
+ /**
+ * Enable the ignored test to export the dataset into CSV format,
+ * so we can validate the training accuracy compared with R's glmnet
package.
+ */
+ ignore("export test data into CSV format") {
+ val rdd = multinomialDataset.rdd.map { case Row(label: Double,
features: Vector) =>
+ label + "," + features.toArray.mkString(",")
+ }.repartition(1)
+
rdd.saveAsTextFile("target/tmp/MultinomialLogisticRegressionSuite/multinomialDataset")
+ }
+
+ test("params") {
+ ParamsSuite.checkParams(new MultinomialLogisticRegression)
+ val model = new MultinomialLogisticRegressionModel("mLogReg",
+ Matrices.dense(2, 1, Array(0.0, 0.0)), Vectors.dense(0.0, 0.0), 2)
+ ParamsSuite.checkParams(model)
+ }
+
+ test("multinomial logistic regression: default params") {
+ val mlr = new MultinomialLogisticRegression
+ assert(mlr.getLabelCol === "label")
+ assert(mlr.getFeaturesCol === "features")
+ assert(mlr.getPredictionCol === "prediction")
+ assert(mlr.getRawPredictionCol === "rawPrediction")
+ assert(mlr.getProbabilityCol === "probability")
+ assert(!mlr.isDefined(mlr.weightCol))
+ assert(!mlr.isDefined(mlr.thresholds))
+ assert(mlr.getFitIntercept)
+ assert(mlr.getStandardization)
+ val model = mlr.fit(dataset)
+ model.transform(dataset)
+ .select("label", "probability", "prediction", "rawPrediction")
+ .collect()
+ assert(model.getFeaturesCol === "features")
+ assert(model.getPredictionCol === "prediction")
+ assert(model.getRawPredictionCol === "rawPrediction")
+ assert(model.getProbabilityCol === "probability")
+ assert(model.intercepts !== Vectors.dense(0.0, 0.0))
+ assert(model.hasParent)
+ }
+
+ test("multinomial logistic regression with intercept without
regularization") {
+
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+
.setElasticNetParam(0.0).setRegParam(0.0).setStandardization(true).setMaxIter(100)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(0.0).setRegParam(0.0).setStandardization(false)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+
+ /*
+ Using the following R code to load the data and train the model
using glmnet package.
+ > library("glmnet")
+ > data <- read.csv("path", header=FALSE)
+ > label = as.factor(data$V1)
+ > features = as.matrix(data.frame(data$V2, data$V3, data$V4,
data$V5))
+ > coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0, lambda = 0))
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -2.24493379
+ V2 0.25096771
+ V3 -0.03915938
+ V4 0.14766639
+ V5 0.36810817
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.3778931
+ V2 -0.3327489
+ V3 0.8893666
+ V4 -0.2306948
+ V5 -0.4442330
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 1.86704066
+ V2 0.08178121
+ V3 -0.85020722
+ V4 0.08302840
+ V5 0.07612480
+ */
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.2509677, -0.0391594, 0.1476664, 0.3681082,
+ -0.3327489, 0.8893666, -0.2306948, -0.4442330,
+ 0.0817812, -0.8502072, 0.0830284, 0.0761248), isTransposed = true)
+ val interceptsR = Vectors.dense(-2.2449338, 0.3778931, 1.8670407)
+
+ assert(model1.coefficients ~== coefficientsR relTol 0.05)
+ assert(model1.intercepts ~== interceptsR relTol 0.05)
+ assert(model2.coefficients ~== coefficientsR relTol 0.05)
+ assert(model2.intercepts ~== interceptsR relTol 0.05)
+ }
+
+ test("multinomial logistic regression without intercept without
regularization") {
+
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.0).setRegParam(0.0).setStandardization(true)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.0).setRegParam(0.0).setStandardization(false)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+
+ /*
+ Using the following R code to load the data and train the model
using glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0, lambda = 0,
+ intercept=F))
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.06992464
+ V3 -0.36562784
+ V4 0.12142680
+ V5 0.32052211
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.3036269
+ V3 0.9449630
+ V4 -0.2271038
+ V5 -0.4364839
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.2337022
+ V3 -0.5793351
+ V4 0.1056770
+ V5 0.1159618
+ */
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.0699246, -0.3656278, 0.1214268, 0.3205221,
+ -0.3036269, 0.9449630, -0.2271038, -0.4364839,
+ 0.2337022, -0.5793351, 0.1056770, 0.1159618), isTransposed = true)
+
+ assert(model1.coefficients ~== coefficientsR relTol 0.05)
+ assert(model2.coefficients ~== coefficientsR relTol 0.05)
+ assert(model1.intercepts.toArray === Array.fill(3)(0.0))
+ assert(model2.intercepts.toArray === Array.fill(3)(0.0))
+ }
+
+ test("multinomial logistic regression with intercept with L1
regularization") {
+
+ // use tighter constraints because OWL-QN solver takes longer to
converge
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(1.0).setRegParam(0.05).setStandardization(true)
+ .setMaxIter(300).setTol(1e-10)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(1.0).setRegParam(0.05).setStandardization(false)
+ .setMaxIter(300).setTol(1e-10)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label,
family="multinomial", alpha = 1,
+ lambda = 0.05, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 1, lambda = 0.05,
+ standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.68988825
+ V2 .
+ V3 .
+ V4 .
+ V5 0.09404023
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.2303499
+ V2 -0.1232443
+ V3 0.3258380
+ V4 -0.1564688
+ V5 -0.2053965
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.9202381
+ V2 .
+ V3 -0.4803856
+ V4 .
+ V5 .
+
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.44893320
+ V2 .
+ V3 .
+ V4 0.01933812
+ V5 0.03666044
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.7376760
+ V2 -0.0577182
+ V3 .
+ V4 -0.2081718
+ V5 -0.1304592
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.2887428
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+ */
+
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0, 0.09404023,
+ -0.1232443, 0.3258380, -0.1564688, -0.2053965,
+ 0.0, -0.4803856, 0.0, 0.0), isTransposed = true)
+ val interceptsRStd = Vectors.dense(-0.68988825, -0.2303499, 0.9202381)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.01933812, 0.03666044,
+ -0.0577182, 0.0, -0.2081718, -0.1304592,
+ 0.0, 0.0, 0.0, 0.0), isTransposed = true)
+ val interceptsR = Vectors.dense(-0.44893320, 0.7376760, -0.2887428)
+
+ assert(model1.coefficients ~== coefficientsRStd absTol 0.02)
+ assert(model1.intercepts ~== interceptsRStd relTol 0.1)
+ assert(model2.coefficients ~== coefficientsR absTol 0.02)
+ assert(model2.intercepts ~== interceptsR relTol 0.1)
+ }
+
+ test("multinomial logistic regression without intercept with L1
regularization") {
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(1.0).setRegParam(0.05).setStandardization(true)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(1.0).setRegParam(0.05).setStandardization(false)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label, family="multinomial",
alpha = 1,
+ lambda = 0.05, intercept=F, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 1, lambda = 0.05,
+ intercept=F, standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 0.01525105
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.1502410
+ V3 0.5134658
+ V4 -0.1601146
+ V5 -0.2500232
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.003301875
+ V3 .
+ V4 .
+ V5 .
+
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 0.1943624
+ V4 -0.1902577
+ V5 -0.1028789
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+ */
+
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0, 0.01525105,
+ -0.1502410, 0.5134658, -0.1601146, -0.2500232,
+ 0.003301875, 0.0, 0.0, 0.0), isTransposed = true)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0, 0.0,
+ 0.0, 0.1943624, -0.1902577, -0.1028789,
+ 0.0, 0.0, 0.0, 0.0), isTransposed = true)
+
+ assert(model1.coefficients ~== coefficientsRStd absTol 0.01)
+ assert(model2.coefficients ~== coefficientsR absTol 0.01)
+ assert(model1.intercepts.toArray === Array.fill(3)(0.0))
+ assert(model2.intercepts.toArray === Array.fill(3)(0.0))
+ }
+
+ test("multinomial logistic regression with intercept with L2
regularization") {
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(0.0).setRegParam(0.1).setStandardization(true)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(0.0).setRegParam(0.1).setStandardization(false)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label, family="multinomial",
alpha = 0,
+ lambda = 0.1, intercept=T, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0,
+ lambda = 0.1, intercept=T, standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -1.70040424
+ V2 0.17576070
+ V3 0.01527894
+ V4 0.10216108
+ V5 0.26099531
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.2438590
+ V2 -0.2238875
+ V3 0.5967610
+ V4 -0.1555496
+ V5 -0.3010479
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 1.45654525
+ V2 0.04812679
+ V3 -0.61203992
+ V4 0.05338850
+ V5 0.04005258
+
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -1.65488543
+ V2 0.15715048
+ V3 0.01992903
+ V4 0.12428858
+ V5 0.22130317
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 1.1297533
+ V2 -0.1974768
+ V3 0.2776373
+ V4 -0.1869445
+ V5 -0.2510320
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.52513212
+ V2 0.04032627
+ V3 -0.29756637
+ V4 0.06265594
+ V5 0.02972883
+ */
+
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.17576070, 0.01527894, 0.10216108, 0.26099531,
+ -0.2238875, 0.5967610, -0.1555496, -0.3010479,
+ 0.04812679, -0.61203992, 0.05338850, 0.04005258), isTransposed =
true)
+ val interceptsRStd = Vectors.dense(-1.70040424, 0.2438590, 1.45654525)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.15715048, 0.01992903, 0.12428858, 0.22130317,
+ -0.1974768, 0.2776373, -0.1869445, -0.2510320,
+ 0.04032627, -0.29756637, 0.06265594, 0.02972883), isTransposed =
true)
+ val interceptsR = Vectors.dense(-1.65488543, 1.1297533, 0.52513212)
+
+ assert(model1.coefficients ~== coefficientsRStd relTol 0.05)
+ assert(model1.intercepts ~== interceptsRStd relTol 0.05)
+ assert(model2.coefficients ~== coefficientsR relTol 0.05)
+ assert(model2.intercepts ~== interceptsR relTol 0.05)
+ }
+
+ test("multinomial logistic regression without intercept with L2
regularization") {
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.0).setRegParam(0.1).setStandardization(true)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.0).setRegParam(0.1).setStandardization(false)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label, family="multinomial",
alpha = 0,
+ lambda = 0.1, intercept=F, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0,
+ lambda = 0.1, intercept=F, standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.03904171
+ V3 -0.23354322
+ V4 0.08288096
+ V5 0.22706393
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.2061848
+ V3 0.6341398
+ V4 -0.1530059
+ V5 -0.2958455
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.16714312
+ V3 -0.40059658
+ V4 0.07012496
+ V5 0.06878158
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.005704542
+ V3 -0.144466409
+ V4 0.092080736
+ V5 0.182927657
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.08469036
+ V3 0.38996748
+ V4 -0.16468436
+ V5 -0.22522976
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.09039490
+ V3 -0.24550107
+ V4 0.07260362
+ V5 0.04230210
+ */
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.03904171, -0.23354322, 0.08288096, 0.2270639,
+ -0.2061848, 0.6341398, -0.1530059, -0.2958455,
+ 0.16714312, -0.40059658, 0.07012496, 0.06878158), isTransposed =
true)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ -0.005704542, -0.144466409, 0.092080736, 0.182927657,
+ -0.08469036, 0.38996748, -0.16468436, -0.22522976,
+ 0.0903949, -0.24550107, 0.07260362, 0.0423021), isTransposed = true)
+
+ assert(model1.coefficients ~== coefficientsRStd absTol 0.01)
+ assert(model1.intercepts.toArray === Array.fill(3)(0.0))
+ assert(model2.coefficients ~== coefficientsR absTol 0.01)
+ assert(model2.intercepts.toArray === Array.fill(3)(0.0))
+ }
+
+ test("multinomial logistic regression with intercept with elasticnet
regularization") {
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(0.5).setRegParam(0.1).setStandardization(true)
+ .setMaxIter(300).setTol(1e-10)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(true)
+ .setElasticNetParam(0.5).setRegParam(0.1).setStandardization(false)
+ .setMaxIter(300).setTol(1e-10)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label, family="multinomial",
alpha = 0.5,
+ lambda = 0.1, intercept=T, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0.5,
+ lambda = 0.1, intercept=T, standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.5521819483
+ V2 0.0003092611
+ V3 .
+ V4 .
+ V5 0.0913818490
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.27531989
+ V2 -0.09790029
+ V3 0.28502034
+ V4 -0.12416487
+ V5 -0.16513373
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.8275018
+ V2 .
+ V3 -0.4044859
+ V4 .
+ V5 .
+
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.39876213
+ V2 .
+ V3 .
+ V4 0.02547520
+ V5 0.03893991
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ 0.61089869
+ V2 -0.04224269
+ V3 .
+ V4 -0.18923970
+ V5 -0.09104249
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ -0.2121366
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+ */
+
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.0003092611, 0.0, 0.0, 0.091381849,
+ -0.09790029, 0.28502034, -0.12416487, -0.16513373,
+ 0.0, -0.4044859, 0.0, 0.0), isTransposed = true)
+ val interceptsRStd = Vectors.dense(-0.5521819483, -0.27531989,
0.8275018)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0254752, 0.03893991,
+ -0.04224269, 0.0, -0.1892397, -0.09104249,
+ 0.0, 0.0, 0.0, 0.0), isTransposed = true)
+ val interceptsR = Vectors.dense(-0.39876213, 0.61089869, -0.2121366)
+
+ assert(model1.coefficients ~== coefficientsRStd absTol 0.01)
+ assert(model1.intercepts ~== interceptsRStd absTol 0.01)
+ assert(model2.coefficients ~== coefficientsR absTol 0.01)
+ assert(model2.intercepts ~== interceptsR absTol 0.01)
+ }
+ test("multinomial logistic regression without intercept with elasticnet
regularization") {
+ val trainer1 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.5).setRegParam(0.1).setStandardization(true)
+ .setMaxIter(300).setTol(1e-10)
+ val trainer2 = (new
MultinomialLogisticRegression).setFitIntercept(false)
+ .setElasticNetParam(0.5).setRegParam(0.1).setStandardization(false)
+ .setMaxIter(300).setTol(1e-10)
+
+ val model1 = trainer1.fit(multinomialDataset)
+ val model2 = trainer2.fit(multinomialDataset)
+ /*
+ Use the following R code to load the data and train the model using
glmnet package.
+ library("glmnet")
+ data <- read.csv("path", header=FALSE)
+ label = as.factor(data$V1)
+ features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+ coefficientsStd = coef(glmnet(features, label, family="multinomial",
alpha = 0.5,
+ lambda = 0.1, intercept=F, standardization=T))
+ coefficients = coef(glmnet(features, label, family="multinomial",
alpha = 0.5,
+ lambda = 0.1, intercept=F, standardization=F))
+ > coefficientsStd
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 0.03543706
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 -0.1187387
+ V3 0.4025482
+ V4 -0.1270969
+ V5 -0.1918386
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 0.00774365
+ V3 .
+ V4 .
+ V5 .
+
+ > coefficients
+ $`0`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+
+ $`1`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 0.14666497
+ V4 -0.16570638
+ V5 -0.05982875
+
+ $`2`
+ 5 x 1 sparse Matrix of class "dgCMatrix"
+ s0
+ .
+ V2 .
+ V3 .
+ V4 .
+ V5 .
+ */
+ val coefficientsRStd = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0, 0.03543706,
+ -0.1187387, 0.4025482, -0.1270969, -0.1918386,
+ 0.0, 0.0, 0.0, 0.00774365), isTransposed = true)
+
+ val coefficientsR = new DenseMatrix(3, 4, Array(
+ 0.0, 0.0, 0.0, 0.0,
+ 0.0, 0.14666497, -0.16570638, -0.05982875,
+ 0.0, 0.0, 0.0, 0.0), isTransposed = true)
+
+ assert(model1.coefficients ~== coefficientsRStd absTol 0.01)
+ assert(model1.intercepts.toArray === Array.fill(3)(0.0))
+ assert(model2.coefficients ~== coefficientsR absTol 0.01)
+ assert(model2.intercepts.toArray === Array.fill(3)(0.0))
+ }
+
+ test("prediction") {
+ val model = new MultinomialLogisticRegressionModel("mLogReg",
+ Matrices.dense(3, 2, Array(0.0, 0.0, 0.0, 1.0, 2.0, 3.0)),
+ Vectors.dense(0.0, 0.0, 0.0), 3)
+ val overFlowData = spark.createDataFrame(Seq(
+ LabeledPoint(1.0, Vectors.dense(0.0, 1000.0)),
+ LabeledPoint(1.0, Vectors.dense(0.0, -1.0))
+ ))
+ val results = model.transform(overFlowData).select("rawPrediction",
"probability").collect()
+
+ // probabilities are correct when margins have to be adjusted
+ val raw1 = results(0).getAs[Vector](0)
+ val prob1 = results(0).getAs[Vector](1)
+ assert(raw1 === Vectors.dense(1000.0, 2000.0, 3000.0))
+ assert(prob1 ~== Vectors.dense(0.0, 0.0, 1.0) absTol eps)
+
+ // probabilities are correct when margins don't have to be adjusted
+ val raw2 = results(1).getAs[Vector](0)
+ val prob2 = results(1).getAs[Vector](1)
+ assert(raw2 === Vectors.dense(-1.0, -2.0, -3.0))
+ assert(prob2 ~== Vectors.dense(0.66524096, 0.24472847, 0.09003057)
relTol eps)
+ }
+
+ test("multinomial logistic regression: Predictor, Classifier methods") {
+ val mlr = new MultinomialLogisticRegression
+
+ val model = mlr.fit(dataset)
+ assert(model.numClasses === 3)
+ val numFeatures =
dataset.select("features").first().getAs[Vector](0).size
+ assert(model.numFeatures === numFeatures)
+
+ val results = model.transform(dataset)
+ // check that raw prediction is coefficients dot features + intercept
+ results.select("rawPrediction", "features").collect().foreach {
+ case Row(raw: Vector, features: Vector) =>
+ assert(raw.size === 3)
+ val margins = Array.tabulate(3) { k =>
+ var margin = 0.0
+ features.foreachActive { (index, value) =>
+ margin += value * model.coefficients(k, index)
+ }
+ margin += model.intercepts(k)
+ margin
+ }
+ assert(raw ~== Vectors.dense(margins) relTol eps)
+ }
+
+ // Compare rawPrediction with probability
+ results.select("rawPrediction", "probability").collect().foreach {
+ case Row(raw: Vector, prob: Vector) =>
+ assert(raw.size === 3)
+ assert(prob.size === 3)
+ val max = raw.toArray.max
+ val subtract = if (max > 0) max else 0.0
+ val sum = raw.toArray.map(x => math.exp(x - subtract)).sum
+ val probFromRaw0 = math.exp(raw(0) - subtract) / sum
+ val probFromRaw1 = math.exp(raw(1) - subtract) / sum
+ assert(prob(0) ~== probFromRaw0 relTol eps)
+ assert(prob(1) ~== probFromRaw1 relTol eps)
+ assert(prob(2) ~== 1.0 - probFromRaw1 - probFromRaw0 relTol eps)
+ }
+
+ // Compare prediction with probability
+ results.select("prediction", "probability").collect().foreach {
+ case Row(pred: Double, prob: Vector) =>
+ val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2
+ assert(pred == predFromProb)
+ }
+ }
+
+ test("multinomial logistic regression coefficients should be centered") {
+ val mlr = new MultinomialLogisticRegression().setMaxIter(1)
+ val model = mlr.fit(dataset)
+ assert(model.intercepts.toArray.sum ~== 0.0 absTol 1e-6)
+ assert(model.coefficients.toArray.sum ~== 0.0 absTol 1e-6)
+ }
+
+ test("numClasses specified in metadata/inferred") {
+ val mlr = new MultinomialLogisticRegression().setMaxIter(1)
+
+ // specify more classes than unique label values
+ val labelMeta =
NominalAttribute.defaultAttr.withName("label").withNumValues(4).toMetadata()
+ val df = dataset.select(dataset("label").as("label", labelMeta),
dataset("features"))
+ val model1 = mlr.fit(df)
+ assert(model1.numClasses === 4)
+ assert(model1.intercepts.size === 4)
+
+ // specify two classes when there are really three
+ val labelMeta1 =
NominalAttribute.defaultAttr.withName("label").withNumValues(2).toMetadata()
+ val df1 = dataset.select(dataset("label").as("label", labelMeta1),
dataset("features"))
+ val thrown = intercept[IllegalArgumentException] {
+ mlr.fit(df1)
+ }
+ assert(thrown.getMessage.contains(
+ "less than the number of unique labels"))
--- End diff --
move it up to the same line
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