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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.ml.feature
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.mllib.feature.{Word2VecModel => OldWord2VecModel}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._
import org.apache.spark.sql.{Row, SQLContext}
class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
test("params") {
ParamsSuite.checkParams(new Word2Vec)
val model = new Word2VecModel("w2v", new OldWord2VecModel(Map("a" -> Array(0.0f))))
ParamsSuite.checkParams(model)
}
test("Word2Vec") {
val sqlContext = this.sqlContext
import sqlContext.implicits._
val sentence = "a b " * 100 + "a c " * 10
val numOfWords = sentence.split(" ").size
val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
val codes = Map(
"a" -> Array(-0.2811822295188904, -0.6356269121170044, -0.3020961284637451),
"b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342),
"c" -> Array(-0.08456747233867645, 0.5137411952018738, 0.11731560528278351)
)
val expected = doc.map { sentence =>
Vectors.dense(sentence.map(codes.apply).reduce((word1, word2) =>
word1.zip(word2).map { case (v1, v2) => v1 + v2 }
).map(_ / numOfWords))
}
val docDF = doc.zip(expected).toDF("text", "expected")
val model = new Word2Vec()
.setVectorSize(3)
.setInputCol("text")
.setOutputCol("result")
.setSeed(42L)
.fit(docDF)
// copied model must have the same parent.
MLTestingUtils.checkCopy(model)
// These expectations are just magic values, characterizing the current
// behavior. The test needs to be updated to be more general, see SPARK-11502
val magicExp = Vectors.dense(0.30153007534417237, -0.6833061711354689, 0.5116530778733167)
model.transform(docDF).select("result", "expected").collect().foreach {
case Row(vector1: Vector, vector2: Vector) =>
assert(vector1 ~== magicExp absTol 1E-5, "Transformed vector is different with expected.")
}
}
test("getVectors") {
val sqlContext = this.sqlContext
import sqlContext.implicits._
val sentence = "a b " * 100 + "a c " * 10
val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
val codes = Map(
"a" -> Array(-0.2811822295188904, -0.6356269121170044, -0.3020961284637451),
"b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342),
"c" -> Array(-0.08456747233867645, 0.5137411952018738, 0.11731560528278351)
)
val expectedVectors = codes.toSeq.sortBy(_._1).map { case (w, v) => Vectors.dense(v) }
val docDF = doc.zip(doc).toDF("text", "alsotext")
val model = new Word2Vec()
.setVectorSize(3)
.setInputCol("text")
.setOutputCol("result")
.setSeed(42L)
.fit(docDF)
val realVectors = model.getVectors.sort("word").select("vector").map {
case Row(v: Vector) => v
}.collect()
// These expectations are just magic values, characterizing the current
// behavior. The test needs to be updated to be more general, see SPARK-11502
val magicExpected = Seq(
Vectors.dense(0.3326166272163391, -0.5603077411651611, -0.2309209555387497),
Vectors.dense(0.32463887333869934, -0.9306551218032837, 1.393115520477295),
Vectors.dense(-0.27150997519493103, 0.4372006058692932, -0.13465698063373566)
)
realVectors.zip(magicExpected).foreach {
case (real, expected) =>
assert(real ~== expected absTol 1E-5, "Actual vector is different from expected.")
}
}
test("findSynonyms") {
val sqlContext = this.sqlContext
import sqlContext.implicits._
val sentence = "a b " * 100 + "a c " * 10
val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
val docDF = doc.zip(doc).toDF("text", "alsotext")
val model = new Word2Vec()
.setVectorSize(3)
.setInputCol("text")
.setOutputCol("result")
.setSeed(42L)
.fit(docDF)
val expectedSimilarity = Array(0.18032623242822343, -0.5717976464798823)
val (synonyms, similarity) = model.findSynonyms("a", 2).map {
case Row(w: String, sim: Double) => (w, sim)
}.collect().unzip
assert(synonyms.toArray === Array("b", "c"))
expectedSimilarity.zip(similarity).map {
case (expected, actual) => assert(math.abs((expected - actual) / expected) < 1E-5)
}
}
test("window size") {
val sqlContext = this.sqlContext
import sqlContext.implicits._
val sentence = "a q s t q s t b b b s t m s t m q " * 100 + "a c " * 10
val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
val docDF = doc.zip(doc).toDF("text", "alsotext")
val model = new Word2Vec()
.setVectorSize(3)
.setWindowSize(2)
.setInputCol("text")
.setOutputCol("result")
.setSeed(42L)
.fit(docDF)
val (synonyms, similarity) = model.findSynonyms("a", 6).map {
case Row(w: String, sim: Double) => (w, sim)
}.collect().unzip
// Increase the window size
val biggerModel = new Word2Vec()
.setVectorSize(3)
.setInputCol("text")
.setOutputCol("result")
.setSeed(42L)
.setWindowSize(10)
.fit(docDF)
val (synonymsLarger, similarityLarger) = model.findSynonyms("a", 6).map {
case Row(w: String, sim: Double) => (w, sim)
}.collect().unzip
// The similarity score should be very different with the larger window
assert(math.abs(similarity(5) - similarityLarger(5) / similarity(5)) > 1E-5)
}
test("Word2Vec read/write") {
val t = new Word2Vec()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setMaxIter(2)
.setMinCount(8)
.setNumPartitions(1)
.setSeed(42L)
.setStepSize(0.01)
.setVectorSize(100)
testDefaultReadWrite(t)
}
test("Word2VecModel read/write") {
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 oldModel = new OldWord2VecModel(word2VecMap)
val instance = new Word2VecModel("myWord2VecModel", oldModel)
val newInstance = testDefaultReadWrite(instance)
assert(newInstance.getVectors.collect() === instance.getVectors.collect())
}
}
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