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From m...@apache.org
Subject spark git commit: Revert "[SPARK-4604][MLLIB] make MatrixFactorizationModel public"
Date Wed, 26 Nov 2014 06:32:47 GMT
Repository: spark
Updated Branches:
  refs/heads/branch-1.2 17a4b8e59 -> 69d021b0b


Revert "[SPARK-4604][MLLIB] make MatrixFactorizationModel public"

This reverts commit 2756d0de91d996f80c0b0883cad1d2fab336ed84.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/69d021b0
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/69d021b0
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/69d021b0

Branch: refs/heads/branch-1.2
Commit: 69d021b0becdffe225a1c8859d8c6adeb1a94f4a
Parents: 17a4b8e
Author: Xiangrui Meng <meng@databricks.com>
Authored: Tue Nov 25 22:29:56 2014 -0800
Committer: Xiangrui Meng <meng@databricks.com>
Committed: Tue Nov 25 22:29:56 2014 -0800

----------------------------------------------------------------------
 .../MatrixFactorizationModel.scala              | 28 ++--------
 .../MatrixFactorizationModelSuite.scala         | 56 --------------------
 2 files changed, 3 insertions(+), 81 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/69d021b0/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
----------------------------------------------------------------------
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 ed2f8b4..969e23b 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
@@ -21,45 +21,23 @@ import java.lang.{Integer => JavaInteger}
 
 import org.jblas.DoubleMatrix
 
-import org.apache.spark.Logging
+import org.apache.spark.SparkContext._
 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(
+class MatrixFactorizationModel private[mllib] (
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
-    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.")
-    }
-  }
-
+    val productFeatures: RDD[(Int, Array[Double])]) extends Serializable {
   /** Predict the rating of one user for one product. */
   def predict(user: Int, product: Int): Double = {
     val userVector = new DoubleMatrix(userFeatures.lookup(user).head)

http://git-wip-us.apache.org/repos/asf/spark/blob/69d021b0/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala
----------------------------------------------------------------------
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala
b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala
deleted file mode 100644
index b9caecc..0000000
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * 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)
-    }
-  }
-}


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