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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.
*/
package org.apache.spark.mllib.feature
import org.apache.spark.SparkFunSuite
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.util.Utils
class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
// TODO: add more tests
test("Word2Vec") {
val sentence = "a b " * 100 + "a c " * 10
val localDoc = Seq(sentence, sentence)
val doc = sc.parallelize(localDoc)
.map(line => line.split(" ").toSeq)
val model = new Word2Vec().setVectorSize(10).setSeed(42L).fit(doc)
val syms = model.findSynonyms("a", 2)
assert(syms.length == 2)
assert(syms(0)._1 == "b")
assert(syms(1)._1 == "c")
// Test that model built using Word2Vec, i.e wordVectors and wordIndec
// and a Word2VecMap give the same values.
val word2VecMap = model.getVectors
val newModel = new Word2VecModel(word2VecMap)
assert(newModel.getVectors.mapValues(_.toSeq) === word2VecMap.mapValues(_.toSeq))
}
test("Word2Vec throws exception when vocabulary is empty") {
intercept[IllegalArgumentException] {
val sentence = "a b c"
val localDoc = Seq(sentence, sentence)
val doc = sc.parallelize(localDoc)
.map(line => line.split(" ").toSeq)
new Word2Vec().setMinCount(10).fit(doc)
}
}
test("Word2VecModel") {
val num = 2
val word2VecMap = Map(
("china", Array(0.50f, 0.50f, 0.50f, 0.50f)),
("japan", Array(0.40f, 0.50f, 0.50f, 0.50f)),
("taiwan", Array(0.60f, 0.50f, 0.50f, 0.50f)),
("korea", Array(0.45f, 0.60f, 0.60f, 0.60f))
)
val model = new Word2VecModel(word2VecMap)
val syms = model.findSynonyms("china", num)
assert(syms.length == num)
assert(syms(0)._1 == "taiwan")
assert(syms(1)._1 == "japan")
}
test("findSynonyms doesn't reject similar word vectors when called with a vector") {
val num = 2
val word2VecMap = Map(
("china", Array(0.50f, 0.50f, 0.50f, 0.50f)),
("japan", Array(0.40f, 0.50f, 0.50f, 0.50f)),
("taiwan", Array(0.60f, 0.50f, 0.50f, 0.50f)),
("korea", Array(0.45f, 0.60f, 0.60f, 0.60f))
)
val model = new Word2VecModel(word2VecMap)
val syms = model.findSynonyms(Vectors.dense(Array(0.52, 0.5, 0.5, 0.5)), num)
assert(syms.length == num)
assert(syms(0)._1 == "china")
assert(syms(1)._1 == "taiwan")
}
test("model load / save") {
val word2VecMap = Map(
("china", Array(0.50f, 0.50f, 0.50f, 0.50f)),
("japan", Array(0.40f, 0.50f, 0.50f, 0.50f)),
("taiwan", Array(0.60f, 0.50f, 0.50f, 0.50f)),
("korea", Array(0.45f, 0.60f, 0.60f, 0.60f))
)
val model = new Word2VecModel(word2VecMap)
val tempDir = Utils.createTempDir()
val path = tempDir.toURI.toString
try {
model.save(sc, path)
val sameModel = Word2VecModel.load(sc, path)
assert(sameModel.getVectors.mapValues(_.toSeq) === model.getVectors.mapValues(_.toSeq))
} finally {
Utils.deleteRecursively(tempDir)
}
}
test("big model load / save") {
// backupping old values
val oldBufferConfValue = spark.conf.get("spark.kryoserializer.buffer.max", "64m")
val oldBufferMaxConfValue = spark.conf.get("spark.kryoserializer.buffer", "64k")
// setting test values to trigger partitioning
spark.conf.set("spark.kryoserializer.buffer", "50b")
spark.conf.set("spark.kryoserializer.buffer.max", "50b")
// create a model bigger than 50 Bytes
val word2VecMap = Map((0 to 10).map(i => s"$i" -> Array.fill(10)(0.1f)): _*)
val model = new Word2VecModel(word2VecMap)
// est. size of this model, given the formula:
// (floatSize * vectorSize + 15) * numWords
// (4 * 10 + 15) * 10 = 550
// therefore it should generate multiple partitions
val tempDir = Utils.createTempDir()
val path = tempDir.toURI.toString
try {
model.save(sc, path)
val sameModel = Word2VecModel.load(sc, path)
assert(sameModel.getVectors.mapValues(_.toSeq) === model.getVectors.mapValues(_.toSeq))
}
catch {
case t: Throwable => fail("exception thrown persisting a model " +
"that spans over multiple partitions", t)
} finally {
Utils.deleteRecursively(tempDir)
spark.conf.set("spark.kryoserializer.buffer", oldBufferConfValue)
spark.conf.set("spark.kryoserializer.buffer.max", oldBufferMaxConfValue)
}
}
test("test similarity for word vectors with large values is not Infinity or NaN") {
val vecA = Array(-4.331467827487745E21, -5.26707742075006E21,
5.63551690626524E21, 2.833692188614257E21, -1.9688159903619345E21, -4.933950659913092E21,
-2.7401535502536787E21, -1.418671793782632E20).map(_.toFloat)
val vecB = Array(-3.9850175451103232E16, -3.4829783883841536E16,
9.421469251534848E15, 4.4069684466679808E16, 7.20936298872832E15, -4.2883302830374912E16,
-3.605579947835392E16, -2.8151294422155264E16).map(_.toFloat)
val vecC = Array(-1.9227381025734656E16, -3.907009342603264E16,
2.110207626838016E15, -4.8770066610651136E16, -1.9734964555743232E16, -3.2206001247617024E16,
2.7725358220443648E16, 3.1618718156980224E16).map(_.toFloat)
val wordMapIn = Map(
("A", vecA),
("B", vecB),
("C", vecC)
)
val model = new Word2VecModel(wordMapIn)
model.findSynonyms("A", 5).foreach { pair =>
assert(!(pair._2.isInfinite || pair._2.isNaN))
}
}
}
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