[SPARK-4604][MLLIB] make MatrixFactorizationModel public

User could construct an MF model directly. I added a note about the performance.

Author: Xiangrui Meng <[email protected]>

Closes #3459 from mengxr/SPARK-4604 and squashes the following commits:

f64bcd3 [Xiangrui Meng] organize imports
ed08214 [Xiangrui Meng] check preconditions and unit tests
a624c12 [Xiangrui Meng] make MatrixFactorizationModel public

Author: mengxr

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

@@ -21,23 +21,45 @@ import java.lang.{Integer => JavaInteger}
 
 import org.jblas.DoubleMatrix
 
-import org.apache.spark.SparkContext._
+import org.apache.spark.Logging
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
 
 /**
  * Model representing the result of matrix factorization.
  *
+ * Note: If you create the model directly using constructor, please be aware that fast prediction
+ * requires cached user/product features and their associated partitioners.
+ *
  * @param rank Rank for the features in this model.
  * @param userFeatures RDD of tuples where each tuple represents the userId and
  *                     the features computed for this user.
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
  */
-class MatrixFactorizationModel private[mllib] (
+class MatrixFactorizationModel(
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
-    val productFeatures: RDD[(Int, Array[Double])]) extends Serializable {
+    val productFeatures: RDD[(Int, Array[Double])]) extends Serializable with Logging {
+
+  require(rank > 0)
+  validateFeatures("User", userFeatures)
+  validateFeatures("Product", productFeatures)
+
+  /** Validates factors and warns users if there are performance concerns. */
+  private def validateFeatures(name: String, features: RDD[(Int, Array[Double])]): Unit = {
+    require(features.first()._2.size == rank,
+      s"$name feature dimension does not match the rank $rank.")
+    if (features.partitioner.isEmpty) {
+      logWarning(s"$name factor does not have a partitioner. "
+        + "Prediction on individual records could be slow.")
+    }
+    if (features.getStorageLevel == StorageLevel.NONE) {
+      logWarning(s"$name factor is not cached. Prediction could be slow.")
+    }
+  }
+
   /** Predict the rating of one user for one product. */
   def predict(user: Int, product: Int): Double = {
     val userVector = new DoubleMatrix(userFeatures.lookup(user).head)

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

@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.recommendation
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.rdd.RDD
+
+class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext {
+
+  val rank = 2
+  var userFeatures: RDD[(Int, Array[Double])] = _
+  var prodFeatures: RDD[(Int, Array[Double])] = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    userFeatures = sc.parallelize(Seq((0, Array(1.0, 2.0)), (1, Array(3.0, 4.0))))
+    prodFeatures = sc.parallelize(Seq((2, Array(5.0, 6.0))))
+  }
+
+  test("constructor") {
+    val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures)
+    assert(model.predict(0, 2) ~== 17.0 relTol 1e-14)
+
+    intercept[IllegalArgumentException] {
+      new MatrixFactorizationModel(1, userFeatures, prodFeatures)
+    }
+
+    val userFeatures1 = sc.parallelize(Seq((0, Array(1.0)), (1, Array(3.0))))
+    intercept[IllegalArgumentException] {
+      new MatrixFactorizationModel(rank, userFeatures1, prodFeatures)
+    }
+
+    val prodFeatures1 = sc.parallelize(Seq((2, Array(5.0))))
+    intercept[IllegalArgumentException] {
+      new MatrixFactorizationModel(rank, userFeatures, prodFeatures1)
+    }
+  }
+}