Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6665#discussion_r31871953
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala ---
@@ -0,0 +1,126 @@
+/*
+ * 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.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector,
Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+class StandardScalerSuite extends FunSuite with MLlibTestSparkContext{
+
+ @transient var data: Array[Vector] = _
+ @transient var dataFrame: DataFrame = _
+ @transient var resWithStd: Array[Vector] = _
+ @transient var resWithMean: Array[Vector] = _
+ @transient var resWithBoth: Array[Vector] = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+
+ data = Array(
+ Vectors.dense(-2.0, 2.3, 0.0),
+ Vectors.dense(0.0, -1.0, -3.0),
+ Vectors.dense(0.0, -5.1, 0.0),
+ Vectors.dense(3.8, 0.0, 1.9),
+ Vectors.dense(1.7, -0.6, 0.0),
+ Vectors.dense(0.0, 1.9, 0.0)
+ )
+ resWithMean = Array(
+ Vectors.dense(-2.583333333333, 2.716666666667, 0.183333333333),
+ Vectors.dense(-0.583333333333, -0.583333333333, -2.816666666667),
+ Vectors.dense(-0.583333333333, -4.683333333333, 0.183333333333),
+ Vectors.dense(3.216666666667, 0.416666666667, 2.083333333333),
+ Vectors.dense(1.116666666667, -0.183333333333, 0.183333333333),
+ Vectors.dense(-0.583333333333, 2.316666666667, 0.183333333333)
+ )
+ resWithStd = Array(
+ Vectors.dense(-1.018281014244, 0.866496453808, 0.0),
+ Vectors.dense(0.0, -0.376737588612, -1.90436210586),
+ Vectors.dense(0, -1.921361701921, 0),
+ Vectors.dense(1.934733927063, 0, 1.20609600038),
+ Vectors.dense(0.865538862107, -0.226042553167, 0.0),
+ Vectors.dense(0, 0.715801418363, 0)
+ )
+ resWithBoth = Array(
+ Vectors.dense(-1.315279643398, 1.0234704490628, 0.116377684247),
+ Vectors.dense(-0.296998629154, -0.2197635933570, -1.787984421610),
+ Vectors.dense(-0.296998629154, -1.7643877066665, 0.116377684247),
+ Vectors.dense(1.637735297909, 0.1569739952550, 1.322473684622),
+ Vectors.dense(0.568540232953, -0.0690685579122, 0.116377684247),
+ Vectors.dense(-0.296998629154, 0.8727754136179, 0.116377684247)
+ )
+
+ dataFrame =
sqlContext.createDataFrame(data.map(Tuple1(_))).toDF("features")
+ }
+
+ def collectResult(result: DataFrame): Array[Vector] = {
+ result.select("standarded_features").collect().map {
+ case Row(features: Vector) => features
+ }
+ }
+
+ def assertValues(lhs: Array[Vector], rhs: Array[Vector]): Unit = {
+ assert((lhs, rhs).zipped.forall { (vector1, vector2) =>
+ vector1 ~== vector2 absTol 1E-5
+ }, "The vector value is not correct after standardization.")
+ }
+
+ test("Standardization with default parameter") {
+ val standardscaler0 = new StandardScaler()
+ .setInputCol("features")
+ .setOutputCol("standarded_features")
+ .fit(dataFrame)
+
+ val res = collectResult(standardscaler0.transform(dataFrame))
+ assertValues(res, resWithStd)
+ }
+
+ test("Standardization with setter") {
+ val standardscaler1 = new StandardScaler()
+ .setInputCol("features")
+ .setOutputCol("standarded_features")
+ .setWithMean(true)
+ .setWithStd(true)
+ .fit(dataFrame)
+
+ val standardscaler2 = new StandardScaler()
+ .setInputCol("features")
+ .setOutputCol("standarded_features")
+ .setWithMean(true)
+ .setWithStd(false)
+ .fit(dataFrame)
+
+ val standardscaler3 = new StandardScaler()
+ .setInputCol("features")
+ .setOutputCol("standarded_features")
+ .setWithMean(false)
+ .setWithStd(false)
+ .fit(dataFrame)
+
+ val res1 = collectResult(standardscaler1.transform(dataFrame))
--- End diff --
`collect` doesn't guarantee the ordering (if there exists multiple
partitions). So a better approach would be put the expected result side by side
in the same row, and then compare them. See
https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala#L48
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