[jira] [Created] (SPARK-34045) OneVsRestModel.transform should not call setter of submodels

["zhengruifeng (Jira)" <ji...@apache.org>]

zhengruifeng created SPARK-34045:
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             Summary: OneVsRestModel.transform should not call setter of submodels
                 Key: SPARK-34045
                 URL: https://issues.apache.org/jira/browse/SPARK-34045
             Project: Spark
          Issue Type: Improvement
          Components: ML
    Affects Versions: 3.2.0
            Reporter: zhengruifeng


featuresCol of submodels maybe changed in transform:
{code:java}
 scala> val df = spark.read.format("libsvm").load("/d0/Dev/Opensource/spark/data/mllib/sample_multiclass_classification_data.txt")
21/01/08 09:52:01 WARN LibSVMFileFormat: 'numFeatures' option not specified, determining the number of features by going though the input. If you know the number in advance, please specify it via 'numFeatures' option to avoid the extra scan.
df: org.apache.spark.sql.DataFrame = [label: double, features: vector]

scala> val lr = new LogisticRegression().setMaxIter(1).setTol(1E-6).setFitIntercept(true)
lr: org.apache.spark.ml.classification.LogisticRegression = logreg_3003cb3321a1

scala> val ovr = new OneVsRest().setClassifier(lr)
ovr: org.apache.spark.ml.classification.OneVsRest = oneVsRest_b2ec3ec45dbf

scala> val ovrm = ovr.fit(df)
21/01/08 09:52:05 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
21/01/08 09:52:05 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
ovrm: org.apache.spark.ml.classification.OneVsRestModel = OneVsRestModel: uid=oneVsRest_b2ec3ec45dbf, classifier=logreg_3003cb3321a1, numClasses=3, numFeatures=4

scala> val df2 = df.withColumnRenamed("features", "features2")
df2: org.apache.spark.sql.DataFrame = [label: double, features2: vector]scala> ovrm.setFeaturesCol("features2")
res0: ovrm.type = OneVsRestModel: uid=oneVsRest_b2ec3ec45dbf, classifier=logreg_3003cb3321a1, numClasses=3, numFeatures=4


scala> ovrm.models.map(_.getFeaturesCol)
res1: Array[String] = Array(features, features, features)
scala> ovrm.transform(df2)
res2: org.apache.spark.sql.DataFrame = [label: double, features2: vector ... 2 more fields]
scala> ovrm.models.map(_.getFeaturesCol)
res3: Array[String] = Array(features2, features2, features2)
{code}



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