shahidki31 opened a new pull request #26038: [SPARK-29235][ML][Pyspark]Support avgMetrics in read/write of CrossValidatorModel URL: https://github.com/apache/spark/pull/26038 ### What changes were proposed in this pull request? Currently pyspark doesn't write/read `avgMetrics` in `CrossValidatorModel`, whereas scala supports it. ### Why are the changes needed? Test step to reproduce it: ``` dataset = spark.createDataFrame([(Vectors.dense([0.0]), 0.0), (Vectors.dense([0.4]), 1.0), (Vectors.dense([0.5]), 0.0), (Vectors.dense([0.6]), 1.0), (Vectors.dense([1.0]), 1.0)] * 10, ["features", "label"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,parallelism=2) cvModel = cv.fit(dataset) cvModel.write().save("/tmp/model") cvModel2 = CrossValidatorModel.read().load("/tmp/model") print(cvModel.avgMetrics) # prints non empty result as expected print(cvModel2.avgMetrics). # Bug: prints an empty result. ``` ### Does this PR introduce any user-facing change? No ### How was this patch tested? Manually tested Before patch: ``` >>> cvModel.write().save("/tmp/model_0") >>> cvModel2 = CrossValidatorModel.read().load("/tmp/model_0") >>> print(cvModel2.avgMetrics) [] ``` After patch: ``` >>> cvModel2 = CrossValidatorModel.read().load("/tmp/model_2") >>> print(cvModel2.avgMetrics[0]) 0.5 ```
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