0.12.0-incubating release

Closes #6
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,202 @@
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diff --git a/README.md b/README.md
index 35df67d..fdcc6ef 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,11 @@
 
 ## Versions
 
+### v0.12.0-incubating
+- Bump version number to track PredictionIO version
+- Sets default build targets according to PredictionIO
+- Fix compilation issue with Scala 2.11
+
 ### v0.11.0-incubating
 
 - Update to build with PredictionIO 0.11.0-incubating
diff --git a/build.sbt b/build.sbt
index 36da38f..b71ab8c 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,8 +1,7 @@
-name := "template-java-ecom-recommender"
+name := "template-java-parallel-ecommercerecommendation"
 
+scalaVersion := "2.11.8"
 libraryDependencies ++= Seq(
-  "org.apache.predictionio" %% "apache-predictionio-core" % "0.11.0-incubating" % "provided",
-  "org.apache.spark"        %% "spark-core" % "1.3.0" % "provided",
-  "org.apache.spark"        %% "spark-mllib" % "1.3.0" % "provided",
-  "com.google.guava"         % "guava" % "12.0",
+  "org.apache.predictionio" %% "apache-predictionio-core" % "0.12.0-incubating" % "provided",
+  "org.apache.spark"        %% "spark-mllib" % "2.1.1"    % "provided",
   "org.jblas"                % "jblas" % "1.2.4")
diff --git a/data/import_eventserver.py b/data/import_eventserver.py
index 277e064..a40672b 100644
--- a/data/import_eventserver.py
+++ b/data/import_eventserver.py
@@ -1,5 +1,5 @@
 """
-Import sample data for similar product engine
+Import sample data for E-Commerce Recommendation Engine Template
 """
 
 import predictionio
@@ -11,13 +11,13 @@
 def import_events(client):
   random.seed(SEED)
   count = 0
-  print client.get_status()
-  print "Importing data..."
+  print(client.get_status())
+  print("Importing data...")
 
   # generate 10 users, with user ids u1,u2,....,u10
   user_ids = ["u%s" % i for i in range(1, 11)]
   for user_id in user_ids:
-    print "Set user", user_id
+    print("Set user", user_id)
     client.create_event(
       event="$set",
       entity_type="user",
@@ -30,7 +30,7 @@
   categories = ["c%s" % i for i in range(1, 7)]
   item_ids = ["i%s" % i for i in range(1, 51)]
   for item_id in item_ids:
-    print "Set itemEntityId", item_id
+    print("Set itemEntityId", item_id)
     client.create_event(
       event="$set",
       entity_type="item",
@@ -44,7 +44,7 @@
   # each user randomly viewed 10 items
   for user_id in user_ids:
     for viewed_item in random.sample(item_ids, 10):
-      print "User", user_id ,"views itemEntityId", viewed_item
+      print("User", user_id ,"views itemEntityId", viewed_item)
       client.create_event(
         event="view",
         entity_type="user",
@@ -57,7 +57,7 @@
   # each user randomly bought 3 items
   for user_id in user_ids:
     for item in random.sample(item_ids, 3):
-      print "User", user_id ,"buys itemEntityId", item
+      print("User", user_id ,"buys itemEntityId", item)
       client.create_event(
         event="buy",
         entity_type="user",
@@ -68,16 +68,16 @@
       count += 1
 
 
-  print "%s events are imported." % count
+  print("%s events are imported." % count)
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser(
-    description="Import sample data for similar product engine")
+    description="Import sample data for e-commerce recommendation engine")
   parser.add_argument('--access_key', default='invald_access_key')
   parser.add_argument('--url', default="http://localhost:7070")
 
   args = parser.parse_args()
-  print args
+  print(args)
 
   client = predictionio.EventClient(
     access_key=args.access_key,
@@ -85,4 +85,3 @@
     threads=5,
     qsize=500)
   import_events(client)
-
diff --git a/data/send_query.py b/data/send_query.py
index 1e4c4bb..3b2c2a4 100644
--- a/data/send_query.py
+++ b/data/send_query.py
@@ -4,4 +4,4 @@
 
 import predictionio
 engine_client = predictionio.EngineClient(url="http://localhost:8000")
-print engine_client.send_query({"userEntityId": "u1", "number": 4})
+print(engine_client.send_query({"userEntityId": "u1", "number": 4}))
diff --git a/project/assembly.sbt b/project/assembly.sbt
index e17409e..15a88b0 100644
--- a/project/assembly.sbt
+++ b/project/assembly.sbt
@@ -1 +1 @@
-addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.4")
+addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.5")
diff --git a/project/build.properties b/project/build.properties
index 64317fd..c091b86 100644
--- a/project/build.properties
+++ b/project/build.properties
@@ -1 +1 @@
-sbt.version=0.13.15
+sbt.version=0.13.16
diff --git a/src/main/java/org/example/recommendation/DataSource.java b/src/main/java/org/example/recommendation/DataSource.java
index 90ac975..75d7038 100644
--- a/src/main/java/org/example/recommendation/DataSource.java
+++ b/src/main/java/org/example/recommendation/DataSource.java
@@ -145,6 +145,6 @@
 
         Tuple3<TrainingData, EmptyParams, RDD<Tuple2<Query, Set<String>>>> setData = new Tuple3<>(new TrainingData(all.getUsers(), all.getItems(), trainingAndTestingViews[0], trainingAndTestingBuys[0]), new EmptyParams(), queryActual);
 
-        return JavaConversions.asScalaIterable(Collections.singletonList(setData)).toSeq();
+        return JavaConversions.iterableAsScalaIterable(Collections.singletonList(setData)).toSeq();
     }
 }
diff --git a/src/main/java/org/example/recommendation/evaluation/PrecisionMetric.java b/src/main/java/org/example/recommendation/evaluation/PrecisionMetric.java
index e412fd5..dc21929 100644
--- a/src/main/java/org/example/recommendation/evaluation/PrecisionMetric.java
+++ b/src/main/java/org/example/recommendation/evaluation/PrecisionMetric.java
@@ -34,7 +34,7 @@
 
     @Override
     public Double calculate(SparkContext sc, Seq<Tuple2<EmptyParams, RDD<Tuple3<Query, PredictedResult, Set<String>>>>> qpas) {
-        List<Tuple2<EmptyParams, RDD<Tuple3<Query, PredictedResult, Set<String>>>>> sets = JavaConversions.asJavaList(qpas);
+        List<Tuple2<EmptyParams, RDD<Tuple3<Query, PredictedResult, Set<String>>>>> sets = JavaConversions.seqAsJavaList(qpas);
         List<Double> allSetResults = new ArrayList<>();
 
         for (Tuple2<EmptyParams, RDD<Tuple3<Query, PredictedResult, Set<String>>>> set : sets) {