Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/19715#discussion_r150404292
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala
---
@@ -146,4 +146,172 @@ class QuantileDiscretizerSuite
val model = discretizer.fit(df)
assert(model.hasParent)
}
+
+ test("Multiple Columns: Test observed number of buckets and their sizes
match expected values") {
+ val spark = this.spark
+ import spark.implicits._
+
+ val datasetSize = 100000
+ val numBuckets = 5
+ val data1 = Array.range(1, 100001, 1).map(_.toDouble)
+ val data2 = Array.range(1, 200000, 2).map(_.toDouble)
+ val data = (0 until 100000).map { idx =>
+ (data1(idx), data2(idx))
+ }
+ val df: DataFrame = data.toSeq.toDF("input1", "input2")
+
+ val discretizer = new QuantileDiscretizer()
+ .setInputCols(Array("input1", "input2"))
+ .setOutputCols(Array("result1", "result2"))
+ .setNumBuckets(numBuckets)
+ assert(discretizer.isQuantileDiscretizeMultipleColumns())
+ val result = discretizer.fit(df).transform(df)
+
+ val relativeError = discretizer.getRelativeError
+ val isGoodBucket = udf {
+ (size: Int) => math.abs( size - (datasetSize / numBuckets)) <=
(relativeError * datasetSize)
+ }
+
+ for (i <- 1 to 2) {
+ val observedNumBuckets = result.select("result" + i).distinct.count
+ assert(observedNumBuckets === numBuckets,
+ "Observed number of buckets does not equal expected number of
buckets.")
+
+ val numGoodBuckets = result.groupBy("result" +
i).count.filter(isGoodBucket($"count")).count
+ assert(numGoodBuckets === numBuckets,
+ "Bucket sizes are not within expected relative error tolerance.")
+ }
+ }
+
+ test("Multiple Columns: Test on data with high proportion of duplicated
values") {
+ val spark = this.spark
+ import spark.implicits._
+
+ val numBuckets = 5
+ val expectedNumBucket = 3
+ val data1 = Array(1.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 2.0, 2.0, 2.0,
1.0, 3.0)
+ val data2 = Array(1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 3.0,
1.0, 2.0)
+ val data = (0 until data1.length).map { idx =>
+ (data1(idx), data2(idx))
+ }
+ val df: DataFrame = data.toSeq.toDF("input1", "input2")
+ val discretizer = new QuantileDiscretizer()
+ .setInputCols(Array("input1", "input2"))
+ .setOutputCols(Array("result1", "result2"))
+ .setNumBuckets(numBuckets)
+ assert(discretizer.isQuantileDiscretizeMultipleColumns())
+ val result = discretizer.fit(df).transform(df)
+ for (i <- 1 to 2) {
+ val observedNumBuckets = result.select("result" + i).distinct.count
+ assert(observedNumBuckets == expectedNumBucket,
+ s"Observed number of buckets are not correct." +
+ s" Expected $expectedNumBucket but found ($observedNumBuckets")
+ }
+ }
+
+ test("Multiple Columns: Test transform on data with NaN value") {
+ val spark = this.spark
+ import spark.implicits._
+
+ val numBuckets = 3
+ val validData1 = Array(-0.9, -0.5, -0.3, 0.0, 0.2, 0.5, 0.9,
Double.NaN, Double.NaN, Double.NaN)
+ val expectedKeep1 = Array(0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0,
3.0)
+ val validData2 = Array(0.2, -0.1, 0.3, 0.0, 0.1, 0.3, 0.5, 0.8,
Double.NaN, Double.NaN)
+ val expectedKeep2 = Array(1.0, 0.0, 2.0, 0.0, 1.0, 2.0, 2.0, 2.0, 3.0,
3.0)
+
+ val data = (0 until validData1.length).map { idx =>
+ (validData1(idx), validData2(idx), expectedKeep1(idx),
expectedKeep2(idx))
+ }
+ val dataFrame: DataFrame = data.toSeq.toDF("input1", "input2",
"expected1", "expected2")
+
+ val discretizer = new QuantileDiscretizer()
+ .setInputCols(Array("input1", "input2"))
+ .setOutputCols(Array("result1", "result2"))
+ .setNumBuckets(numBuckets)
+ assert(discretizer.isQuantileDiscretizeMultipleColumns())
+
+ withClue("QuantileDiscretizer with handleInvalid=error should throw
exception for NaN values") {
+ intercept[SparkException] {
+ discretizer.fit(dataFrame).transform(dataFrame).collect()
+ }
+ }
+
+ discretizer.setHandleInvalid("keep")
+ discretizer.fit(dataFrame).transform(dataFrame).
+ select("result1", "expected1", "result2", "expected2")
+ .collect().foreach {
+ case Row(r1: Double, e1: Double, r2: Double, e2: Double) =>
+ assert(r1 === e1,
+ s"The result value is not correct after bucketing. Expected $e1
but found $r1")
+ assert(r2 === e2,
+ s"The result value is not correct after bucketing. Expected $e2
but found $r2")
+ }
+
+ discretizer.setHandleInvalid("skip")
+ val result = discretizer.fit(dataFrame).transform(dataFrame)
+ for (i <- 1 to 2) {
+ val skipResults1: Array[Double] = result.select("result" +
i).as[Double].collect()
+ assert(skipResults1.length === 7)
+ assert(skipResults1.forall(_ !== 4.0))
+ }
+ }
+
+ test("Multiple Columns: Test numBucketsArray") {
+ val spark = this.spark
+ import spark.implicits._
+
+ val datasetSize = 20
+ val numBucketsArray: Array[Int] = Array(2, 5, 10)
+ val data1 = Array.range(1, 21, 1).map(_.toDouble)
+ val expected1 = Array (0.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 4.0, 4.0,
5.0,
+ 5.0, 5.0, 6.0, 6.0, 7.0, 8.0, 8.0, 9.0, 9.0, 9.0)
--- End diff --
Is this correct? I tried to apply the same data on current
`QuantileDiscretizer`:
```scala
val data1 = Array.range(1, 21, 1).map(_.toDouble)
val df = data1.toSeq.toDF
val discretizer = new
QuantileDiscretizer().setInputCol("value").setOutputCol("result").setNumBuckets(2)
discretizer.fit(df).transform(df).show
```
```
+-----+------+
|value|result|
+-----+------+
| 1.0| 0.0|
| 2.0| 0.0|
| 3.0| 0.0|
| 4.0| 0.0|
| 5.0| 0.0|
| 6.0| 0.0|
| 7.0| 0.0|
| 8.0| 0.0|
| 9.0| 0.0|
| 10.0| 1.0|
| 11.0| 1.0|
| 12.0| 1.0|
| 13.0| 1.0|
| 14.0| 1.0|
| 15.0| 1.0|
| 16.0| 1.0|
| 17.0| 1.0|
| 18.0| 1.0|
| 19.0| 1.0|
| 20.0| 1.0|
+-----+------+
```
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
