Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7388#discussion_r37095206
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala ---
@@ -0,0 +1,137 @@
+/*
+ * 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.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class CountVectorizerSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ test("params") {
+ ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
+ }
+
+ test("CountVectorizerModel common cases") {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a b c d".split("\\s+").toSeq,
+ Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
+ (1, "a b b c d a".split("\\s+").toSeq,
+ Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
+ (2, "a".split("\\s+").toSeq, Vectors.sparse(4, Seq((0, 1.0)))),
+ (3, "".split("\\s+").toSeq, Vectors.sparse(4, Seq())), // empty
string
+ (4, "a notInDict d".split("\\s+").toSeq,
+ 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")
+ val output = cv.transform(df).collect()
+ output.foreach { p =>
+ val features = p.getAs[Vector]("features")
+ val expected = p.getAs[Vector]("expected")
+ assert(features ~== expected absTol 1e-14)
+ }
+ }
+
+ test("CountVectorizer common cases") {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a b c d e".split("\\s+").toSeq,
+ Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4,
1.0)))),
+ (1, "a a a a a a".split("\\s+").toSeq, Vectors.sparse(5, Seq((0,
6.0)))),
+ (2, "c".split("\\s+").toSeq, Vectors.sparse(5, Seq((2, 1.0)))),
+ (3, "b b b b b".split("\\s+").toSeq, Vectors.sparse(5, Seq((1,
5.0)))))
+ ).toDF("id", "words", "expected")
+ val cv = new CountVectorizer()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .fit(df)
+ assert(cv.vocabulary.deep == Array("a", "b", "c", "d", "e").deep)
+
+ val output = cv.transform(df).collect()
+ output.foreach { p =>
+ val features = p.getAs[Vector]("features")
+ val expected = p.getAs[Vector]("expected")
+ assert(features ~== expected absTol 1e-14)
+ }
+ }
+
+ test("CountVectorizer vocabSize and minTokenCount") {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a a a a a".split("\\s+").toSeq, Vectors.sparse(3, Seq((0,
5.0)))),
+ (1, "b b b b".split("\\s+").toSeq, Vectors.sparse(3, Seq((1, 4.0)))),
+ (2, "c c c".split("\\s+").toSeq, Vectors.sparse(3, Seq((2, 3.0)))),
+ (3, "d d".split("\\s+").toSeq, Vectors.sparse(3, Seq())))
+ ).toDF("id", "words", "expected")
+ val cvModel = new CountVectorizer()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setVocabSize(3) // limit vocab size to 3
+ .fit(df)
+ assert(cvModel.vocabulary.deep == Array("a", "b", "c").deep)
+
+ val cvModel2 = new CountVectorizer()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setMinTokenCount(3) // ignore terms with count less than 3
+ .fit(df)
+ assert(cvModel2.vocabulary.deep == Array("a", "b", "c").deep)
+
+ val output = cvModel2.transform(df).collect()
+ output.foreach { p =>
+ val features = p.getAs[Vector]("features")
+ val expected = p.getAs[Vector]("expected")
+ assert(features ~== expected absTol 1e-14)
+ }
+ }
+
+ test("CountVectorizer throws exception when vocab is empty") {
+ intercept[IllegalArgumentException] {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a a b b c c".split("\\s+").toSeq),
+ (1, "aa bb cc".split("\\s+").toSeq))
+ ).toDF("id", "words")
+ val cvModel = new CountVectorizer()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setVocabSize(3) // limit vocab size to 3
+ .setMinTokenCount(3)
+ .fit(df)
+ }
+ }
+
+ test("CountVectorizerModel with minTermFreq") {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a a a b b c c c d ".split(" ").toSeq, Vectors.sparse(4, Seq((0,
3.0), (2, 3.0)))),
+ (1, "c c c c c c".split(" ").toSeq, Vectors.sparse(4, Seq((2,
6.0)))),
+ (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq())),
+ (3, "e e e e e".split(" ").toSeq, Vectors.sparse(4, Seq())))
+ ).toDF("id", "words", "expected")
+ val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setMinTermFreq(3)
+ val output = cv.transform(df).collect()
+ output.foreach { p =>
+ val features = p.getAs[Vector]("features")
--- End diff --
Maybe simpler to do `cv.transform(df).select("features",
"expected").collect()` then `foreach { case Row(features: Vector, expected:
Vector) => ...`.
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