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-rw-r--r--examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala4
-rw-r--r--examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala4
-rw-r--r--examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala4
3 files changed, 6 insertions, 6 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
index ec9de022c1..4001908c98 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
@@ -22,7 +22,7 @@ import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
-import org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetrics
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater}
@@ -96,7 +96,7 @@ object BinaryClassification {
Logger.getRootLogger.setLevel(Level.WARN)
- val examples = MLUtils.loadLibSVMData(sc, params.input).cache()
+ val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()
val splits = examples.randomSplit(Array(0.8, 0.2))
val training = splits(0).cache()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index 1723ca6931..658d370f86 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -22,7 +22,7 @@ import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
-import org.apache.spark.mllib.util.{MulticlassLabelParser, MLUtils}
+import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1Updater}
/**
@@ -82,7 +82,7 @@ object LinearRegression extends App {
Logger.getRootLogger.setLevel(Level.WARN)
- val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser).cache()
+ val examples = MLUtils.loadLibSVMFile(sc, params.input, multiclass = true).cache()
val splits = examples.randomSplit(Array(0.8, 0.2))
val training = splits(0).cache()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
index 25b6768b8d..537e68a099 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
@@ -75,8 +75,8 @@ object SparseNaiveBayes {
val minPartitions =
if (params.minPartitions > 0) params.minPartitions else sc.defaultMinPartitions
- val examples = MLUtils.loadLibSVMData(sc, params.input, MulticlassLabelParser,
- params.numFeatures, minPartitions)
+ val examples =
+ MLUtils.loadLibSVMFile(sc, params.input, multiclass = true, params.numFeatures, minPartitions)
// Cache examples because it will be used in both training and evaluation.
examples.cache()