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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
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
class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext
with DefaultReadWriteTest {
test("params") {
ParamsSuite.checkParams(new CountVectorizer)
ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
}
private def split(s: String): Seq[String] = s.split("\\s+")
test("CountVectorizerModel common cases") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a b c d"),
Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
(1, split("a b b c d a"),
Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
(2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
(3, split(""), Vectors.sparse(4, Seq())), // empty string
(4, split("a notInDict d"),
Vectors.sparse(4, Seq((0, 1.0), (3, 1.0)))) // with words not in vocabulary
)).toDF("id", "words", "expected")
val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
.setInputCol("words")
.setOutputCol("features")
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizer common cases") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a b c d e"),
Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0)))),
(1, split("a a a a a a"), Vectors.sparse(5, Seq((0, 6.0)))),
(2, split("c"), Vectors.sparse(5, Seq((2, 1.0)))),
(3, split("b b b b b"), Vectors.sparse(5, Seq((1, 5.0)))))
).toDF("id", "words", "expected")
val cv = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.fit(df)
assert(cv.vocabulary === Array("a", "b", "c", "d", "e"))
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizer vocabSize and minDF") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a b c d"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
(1, split("a b c"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
(2, split("a b"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
(3, split("a"), Vectors.sparse(3, Seq((0, 1.0)))))
).toDF("id", "words", "expected")
val cvModel = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setVocabSize(3) // limit vocab size to 3
.fit(df)
assert(cvModel.vocabulary === Array("a", "b", "c"))
// minDF: ignore terms with count less than 3
val cvModel2 = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setMinDF(3)
.fit(df)
assert(cvModel2.vocabulary === Array("a", "b"))
cvModel2.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
// minDF: ignore terms with freq < 0.75
val cvModel3 = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setMinDF(3.0 / df.count())
.fit(df)
assert(cvModel3.vocabulary === Array("a", "b"))
cvModel3.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizer throws exception when vocab is empty") {
intercept[IllegalArgumentException] {
val df = sqlContext.createDataFrame(Seq(
(0, split("a a b b c c")),
(1, split("aa bb cc")))
).toDF("id", "words")
val cvModel = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setVocabSize(3) // limit vocab size to 3
.setMinDF(3)
.fit(df)
}
}
test("CountVectorizerModel with minTF count") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
(1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
(2, split("a"), Vectors.sparse(4, Seq())),
(3, split("e e e e e"), Vectors.sparse(4, Seq())))
).toDF("id", "words", "expected")
// minTF: count
val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
.setInputCol("words")
.setOutputCol("features")
.setMinTF(3)
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizerModel with minTF freq") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
(1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
(2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
(3, split("e e e e e"), Vectors.sparse(4, Seq())))
).toDF("id", "words", "expected")
// minTF: set frequency
val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
.setInputCol("words")
.setOutputCol("features")
.setMinTF(0.3)
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizerModel with binary") {
val df = sqlContext.createDataFrame(Seq(
(0, split("a a a b b c"), Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0)))),
(1, split("c c c"), Vectors.sparse(4, Seq((2, 1.0)))),
(2, split("a"), Vectors.sparse(4, Seq((0, 1.0))))
)).toDF("id", "words", "expected")
val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
.setInputCol("words")
.setOutputCol("features")
.setBinary(true)
cv.transform(df).select("features", "expected").collect().foreach {
case Row(features: Vector, expected: Vector) =>
assert(features ~== expected absTol 1e-14)
}
}
test("CountVectorizer read/write") {
val t = new CountVectorizer()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setMinDF(0.5)
.setMinTF(3.0)
.setVocabSize(10)
testDefaultReadWrite(t)
}
test("CountVectorizerModel read/write") {
val instance = new CountVectorizerModel("myCountVectorizerModel", Array("a", "b", "c"))
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setMinTF(3.0)
val newInstance = testDefaultReadWrite(instance)
assert(newInstance.vocabulary === instance.vocabulary)
}
}
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