[SPARK-1594][MLLIB] Cleaning up MLlib APIs and guide

Final pass before the v1.0 release.

* Remove `VectorRDDs`
* Move `BinaryClassificationMetrics` from `evaluation.binary` to `evaluation`
* Change default value of `addIntercept` to false and allow to add intercept in Ridge and Lasso.
* Clean `DecisionTree` package doc and test suite.
* Mark model constructors `private[spark]`
* Rename `loadLibSVMData` to `loadLibSVMFile` and hide `LabelParser` from users.
* Add `saveAsLibSVMFile`.
* Add `appendBias` to `MLUtils`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #524 from mengxr/mllib-cleaning and squashes the following commits:

295dc8b [Xiangrui Meng] update loadLibSVMFile doc
1977ac1 [Xiangrui Meng] fix doc of appendBias
649fcf0 [Xiangrui Meng] rename loadLibSVMData to loadLibSVMFile; hide LabelParser from user APIs
54b812c [Xiangrui Meng] add appendBias
a71e7d0 [Xiangrui Meng] add saveAsLibSVMFile
d976295 [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
b7e5cec [Xiangrui Meng] remove some experimental annotations and make model constructors private[mllib]
9b02b93 [Xiangrui Meng] minor code style update
a593ddc [Xiangrui Meng] fix python tests
fc28c18 [Xiangrui Meng] mark more classes experimental
f6cbbff [Xiangrui Meng] fix Java tests
0af70b0 [Xiangrui Meng] minor
6e139ef [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
94e6dce [Xiangrui Meng] move BinaryLabelCounter and BinaryConfusionMatrixImpl to evaluation.binary
df34907 [Xiangrui Meng] clean DecisionTreeSuite to use LocalSparkContext
c81807f [Xiangrui Meng] set the default value of AddIntercept to false
03389c0 [Xiangrui Meng] allow to add intercept in Ridge and Lasso
c66c56f [Xiangrui Meng] move tree md to package object doc
a2695df [Xiangrui Meng] update guide for BinaryClassificationMetrics
9194f4c [Xiangrui Meng] move BinaryClassificationMetrics one level up
1c1a0e3 [Xiangrui Meng] remove VectorRDDs because it only contains one function that is not necessary for us to maintain

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()