[GitHub] [spark] hirakendu commented on pull request #33710: [SPARK-36481][ML] Expose LogisticRegression.setInitialModel, like KMeans et al do

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hirakendu commented on pull request #33710:
URL: https://github.com/apache/spark/pull/33710#issuecomment-913234281


   @srowen  Thanks a lot for making this change and starting to introduce this very important functionality.
   
   @zhengruifeng  While it may be good and convenient to have some alternative ways to load model from file, at minimum we need the ability to specify the initial weights as vector / matrix. And it's best to keep it consistent with the other parameters. For example, the [pyspark.ml.classification.LogisticRegression](http://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.classification.LogisticRegression.html) class has existing parameters `lowerBoundsOnCoefficients`, `upperBoundsOnCoefficients`, `lowerBoundsOnIntercepts`, `upperBoundsOnIntercepts`. In the same format, it would be best to introduce new parameters `initialCoefficients` and `initialIntercepts`.
   
   To use a model file, one can simply load it and pass the coefficients and intercepts:
   
   ```python
   ## Load previously saved model file.
   lr_model1 = LogisticRegressionModel.read().load("/path/to/lr_model1.bin")
   
   ## Pass it as initial state.
   lr_classifier2 = LogisticRegression(
       initialCoefficients = lr_model1.coefficients,
       initialIntercepts = lr_model1.intercept
     )
   lr_model2 = lr_classifier2.fit(train_df)
   ```


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