Added a method to enable bulk prediction

1 files changed, 23 insertions, 1 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index af43d89c70..bc13a66dbe 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -20,7 +20,9 @@ package org.apache.spark.mllib.recommendation
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 
+
 import org.jblas._
+import java.nio.{ByteOrder, ByteBuffer}
 
 /**
  * Model representing the result of matrix factorization.
@@ -44,6 +46,26 @@ class MatrixFactorizationModel(
     userVector.dot(productVector)
   }
 
-  // TODO: Figure out what good bulk prediction methods would look like.
+  /**
+    * Predict the rating of many users for many products.
+    * The output RDD has an element per each element in the input RDD (including all duplicates)
+    * unless a user or product is missing in the training set.
+    *
+    * @param usersProducts  RDD of (user, product) pairs.
+    * @return RDD of Ratings.
+    */
+  def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
+    val users = userFeatures.join(usersProducts).map{
+      case (user, (uFeatures, product)) => (product, (user, uFeatures))
+    }
+    users.join(productFeatures).map {
+      case (product, ((user, uFeatures), pFeatures)) =>
+        val userVector = new DoubleMatrix(uFeatures)
+        val productVector = new DoubleMatrix(pFeatures)
+        Rating(user, product, userVector.dot(productVector))
+    }
+  }
+
+  // TODO: Figure out what other good bulk prediction methods would look like.
   // Probably want a way to get the top users for a product or vice-versa.
 }