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[SPARK-4604][MLLIB] make MatrixFactorizationModel public
User could construct an MF model directly. I added a note about the performance. Author: Xiangrui Meng <meng@databricks.com> 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 (cherry picked from commit b5fb141) Signed-off-by: Xiangrui Meng <meng@databricks.com>
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.../src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala
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/* | ||
* 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. | ||
*/ | ||
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package org.apache.spark.mllib.recommendation | ||
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import org.scalatest.FunSuite | ||
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import org.apache.spark.mllib.util.MLlibTestSparkContext | ||
import org.apache.spark.mllib.util.TestingUtils._ | ||
import org.apache.spark.rdd.RDD | ||
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class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext { | ||
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val rank = 2 | ||
var userFeatures: RDD[(Int, Array[Double])] = _ | ||
var prodFeatures: RDD[(Int, Array[Double])] = _ | ||
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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)))) | ||
} | ||
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test("constructor") { | ||
val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) | ||
assert(model.predict(0, 2) ~== 17.0 relTol 1e-14) | ||
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intercept[IllegalArgumentException] { | ||
new MatrixFactorizationModel(1, userFeatures, prodFeatures) | ||
} | ||
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val userFeatures1 = sc.parallelize(Seq((0, Array(1.0)), (1, Array(3.0)))) | ||
intercept[IllegalArgumentException] { | ||
new MatrixFactorizationModel(rank, userFeatures1, prodFeatures) | ||
} | ||
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val prodFeatures1 = sc.parallelize(Seq((2, Array(5.0)))) | ||
intercept[IllegalArgumentException] { | ||
new MatrixFactorizationModel(rank, userFeatures, prodFeatures1) | ||
} | ||
} | ||
} |