Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27707367
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -70,26 +71,28 @@ class MatrixFactorizationModel(
/** Predict the rating of one user for one product. */
def predict(user: Int, product: Int): Double = {
- val userVector = userFeatures.lookup(user).head
- val productVector = productFeatures.lookup(product).head
- blas.ddot(userVector.length, userVector, 1, productVector, 1)
+ val userVector = Vectors.dense(userFeatures.lookup(user).head)
+ val productVector = Vectors.dense(productFeatures.lookup(product).head)
+ BLAS.dot(userVector, productVector)
}
/**
- * 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.
- */
+ * 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{
+ val users = userFeatures.join(usersProducts).map {
case (user, (uFeatures, product)) => (product, (user, uFeatures))
}
users.join(productFeatures).map {
case (product, ((user, uFeatures), pFeatures)) =>
- Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1,
pFeatures, 1))
+ val userVector = Vectors.dense(uFeatures)
+ val productVector = Vectors.dense(pFeatures)
+ Rating(user, product, BLAS.dot(userVector, productVector))
--- End diff --
Same here,
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