Use new train() with SparkContext and pio sbt plugin
diff --git a/.gitignore b/.gitignore
index 295cafd..ebc8c6e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
manifest.json
target/
pio.log
+/pio.sbt
diff --git a/build.sbt b/build.sbt
index 62d4b72..6e2892e 100644
--- a/build.sbt
+++ b/build.sbt
@@ -7,6 +7,6 @@
organization := "io.prediction"
libraryDependencies ++= Seq(
- "io.prediction" %% "core" % "0.8.6" % "provided",
+ "io.prediction" %% "core" % pioVersion.value % "provided",
"org.apache.spark" %% "spark-core" % "1.2.0" % "provided",
"org.apache.spark" %% "spark-mllib" % "1.2.0" % "provided")
diff --git a/project/pio.sbt b/project/pio.sbt
new file mode 100644
index 0000000..8346a96
--- /dev/null
+++ b/project/pio.sbt
@@ -0,0 +1 @@
+addSbtPlugin("io.prediction" % "pio-build" % "0.9.0")
diff --git a/src/main/scala/ALSAlgorithm.scala b/src/main/scala/ALSAlgorithm.scala
index 8521af2..0c0db22 100644
--- a/src/main/scala/ALSAlgorithm.scala
+++ b/src/main/scala/ALSAlgorithm.scala
@@ -24,7 +24,7 @@
@transient lazy val logger = Logger[this.type]
- def train(data: PreparedData): ALSModel = {
+ def train(sc: SparkContext, data: PreparedData): ALSModel = {
// MLLib ALS cannot handle empty training data.
require(!data.ratings.take(1).isEmpty,
s"RDD[Rating] in PreparedData cannot be empty." +
