Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/11601#discussion_r80518709
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala ---
@@ -0,0 +1,122 @@
+/*
+ * 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.util.{DefaultReadWriteTest}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.Row
+
+class ImputerSuite extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ test("Imputer for Double with default missing Value NaN") {
+ val df = spark.createDataFrame( Seq(
+ (0, 1.0, 1.0, 1.0),
+ (1, 1.0, 1.0, 1.0),
+ (2, 3.0, 3.0, 3.0),
+ (3, 4.0, 4.0, 4.0),
+ (4, Double.NaN, 2.25, 1.0)
+ )).toDF("id", "value", "expected_mean", "expected_median")
+ Seq("mean", "median").foreach { strategy =>
+ val imputer = new
Imputer().setInputCol("value").setOutputCol("out").setStrategy(strategy)
+ val model = imputer.fit(df)
+ model.transform(df).select("expected_" + strategy,
"out").collect().foreach {
+ case Row(exp: Double, out: Double) =>
+ assert(exp ~== out absTol 1e-5, s"Imputed values differ.
Expected: $exp, actual: $out")
+ }
+ }
+ }
+
+ test("Imputer should handle NaNs when computing surrogate value, if
missingValue is not NaN") {
+ val df = spark.createDataFrame( Seq(
+ (0, 1.0, 1.0, 1.0),
+ (1, 3.0, 3.0, 3.0),
+ (2, Double.NaN, Double.NaN, Double.NaN),
+ (3, -1.0, 2.0, 3.0)
+ )).toDF("id", "value", "expected_mean", "expected_median")
+ Seq("mean", "median").foreach { strategy =>
+ val imputer = new
Imputer().setInputCol("value").setOutputCol("out").setStrategy(strategy)
+ .setMissingValue(-1.0)
+ val model = imputer.fit(df)
+ model.transform(df).select("expected_" + strategy,
"out").collect().foreach {
+ case Row(exp: Double, out: Double) =>
+ assert((exp.isNaN && out.isNaN) || (exp ~== out absTol 1e-5),
+ s"Imputed values differ. Expected: $exp, actual: $out")
+ }
+ }
+ }
+
+ test("Imputer for Float with missing Value -1.0") {
+ val df = spark.createDataFrame( Seq(
+ (0, 1.0F, 1.0F, 1.0F),
+ (1, 3.0F, 3.0F, 3.0F),
+ (2, 10.0F, 10.0F, 10.0F),
+ (3, 10.0F, 10.0F, 10.0F),
+ (4, -1.0F, 6.0F, 3.0F)
+ )).toDF("id", "value", "expected_mean", "expected_median")
+
+ Seq("mean", "median").foreach { strategy =>
+ val imputer = new
Imputer().setInputCol("value").setOutputCol("out").setStrategy(strategy)
+ .setMissingValue(-1)
+ val model = imputer.fit(df)
+ val result = model.transform(df)
--- End diff --
this is never used.
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