[SPARK-18230][MLLib]Throw a better exception, if the user or product doesn't exist

mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala

@@ -78,8 +78,13 @@ class MatrixFactorizationModel @Since("0.8.0") (
   /** Predict the rating of one user for one product. */
   @Since("0.8.0")
   def predict(user: Int, product: Int): Double = {
-    val userVector = userFeatures.lookup(user).head
-    val productVector = productFeatures.lookup(product).head
+    val userFeatureSeq = userFeatures.lookup(user)
+    require(userFeatureSeq.nonEmpty, s"userId: $user not found in the model")
+    val productFeatureSeq = productFeatures.lookup(product)
+    require(productFeatureSeq.nonEmpty, s"productId: $product not found in the model")
+
+    val userVector = userFeatureSeq.head
+    val productVector = productFeatureSeq.head
     blas.ddot(rank, userVector, 1, productVector, 1)
   }
 
@@ -164,9 +169,12 @@ class MatrixFactorizationModel @Since("0.8.0") (
    *  recommended the product is.
    */
   @Since("1.1.0")
-  def recommendProducts(user: Int, num: Int): Array[Rating] =
-    MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
+  def recommendProducts(user: Int, num: Int): Array[Rating] = {
+    val userFeatureSeq = userFeatures.lookup(user)
+    require(userFeatureSeq.nonEmpty, s"userId: $user not found in the model")
+    MatrixFactorizationModel.recommend(userFeatureSeq.head, productFeatures, num)
       .map(t => Rating(user, t._1, t._2))
+  }
 
   /**
    * Recommends users to a product. That is, this returns users who are most likely to be
@@ -181,9 +189,12 @@ class MatrixFactorizationModel @Since("0.8.0") (
    *  recommended the user is.
    */
   @Since("1.1.0")
-  def recommendUsers(product: Int, num: Int): Array[Rating] =
-    MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
+  def recommendUsers(product: Int, num: Int): Array[Rating] = {
+    val productFeatureSeq = productFeatures.lookup(product)
+    require(productFeatureSeq.nonEmpty, s"productId: $product not found in the model")
+    MatrixFactorizationModel.recommend(productFeatureSeq.head, userFeatures, num)
       .map(t => Rating(t._1, product, t._2))
+  }
 
   protected override val formatVersion: String = "1.0"
 

mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala

@@ -72,6 +72,27 @@ class MatrixFactorizationModelSuite extends SparkFunSuite with MLlibTestSparkCon
     }
   }
 
+  test("invalid user and product") {
+    val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures)
+
+    intercept[IllegalArgumentException] {
+      // invalid user
+      model.predict(5, 2)
+    }
+    intercept[IllegalArgumentException] {
+      // invalid product
+      model.predict(0, 5)
+    }
+    intercept[IllegalArgumentException] {
+      // invalid user
+      model.recommendProducts(5, 2)
+    }
+    intercept[IllegalArgumentException] {
+      // invalid product
+      model.recommendUsers(5, 2)
+    }
+  }
+
   test("batch predict API recommendProductsForUsers") {
     val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures)
     val topK = 10