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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/03/23 05:40:47 UTC
[GitHub] [spark] zhengruifeng commented on a change in pull request #27982:
[SPARK-31222][ML] Make ANOVATest Sparsity-Aware
zhengruifeng commented on a change in pull request #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982#discussion_r396222712
##########
File path: mllib/src/test/scala/org/apache/spark/ml/stat/ANOVATestSuite.scala
##########
@@ -144,22 +144,30 @@ class ANOVATestSuite
}
test("test DataFrame with sparse vector") {
- val df = spark.createDataFrame(Seq(
- (3, Vectors.sparse(6, Array((0, 6.0), (1, 7.0), (3, 7.0), (4, 6.0)))),
- (1, Vectors.sparse(6, Array((1, 9.0), (2, 6.0), (4, 5.0), (5, 9.0)))),
- (3, Vectors.sparse(6, Array((1, 9.0), (2, 3.0), (4, 5.0), (5, 5.0)))),
- (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0))),
- (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0))),
- (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0)))
- )).toDF("label", "features")
+ val data = Seq(
+ (3, Vectors.dense(Array(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0))),
+ (1, Vectors.dense(Array(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0))),
+ (3, Vectors.dense(Array(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0))),
+ (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0))),
+ (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0))),
+ (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0, 0.0))))
- val anovaResult = ANOVATest.test(df, "features", "label")
- val (pValues: Vector, fValues: Vector) =
- anovaResult.select("pValues", "fValues")
- .as[(Vector, Vector)].head()
- assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 0.45824059, 0.84633632,
- 0.15673368) relTol 1e-6)
- assert(fValues ~== Vectors.dense(0.375, 0.375, 1.5625, 1.02364865, 0.17647059,
- 3.66) relTol 1e-6)
+ val df1 = spark.createDataFrame(data.map(t => (t._1, t._2.toDense)))
+ .toDF("label", "features")
+ val df2 = spark.createDataFrame(data.map(t => (t._1, t._2.toSparse)))
+ .toDF("label", "features")
+ val df3 = spark.createDataFrame(data.map(t => (t._1, t._2.compressed)))
+ .toDF("label", "features")
+
+ Seq(df1, df2, df3).foreach { df =>
+ val anovaResult = ANOVATest.test(df, "features", "label")
+ val (pValues: Vector, fValues: Vector) =
+ anovaResult.select("pValues", "fValues")
+ .as[(Vector, Vector)].head()
+ assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 0.45824059, 0.84633632,
+ 0.15673368, Double.NaN) relTol 1e-6)
Review comment:
for column only containing zero values, sklearn will return nan:
```python
X = np.zeros([3,5])
y = [1,2,3]
f_classif(X, y)
/home/zrf/Applications/anaconda3/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:110: RuntimeWarning: invalid value encountered in true_divide
msw = sswn / float(dfwn)
Out[24]: (array([nan, nan, nan, nan, nan]), array([nan, nan, nan, nan, nan]))
=
```
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