[SPARK-10194] [MLlib] [PySpark] SGD algorithms need convergenceTol parameter in Python #8457
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Test build #41619 has finished for PR 8457 at commit
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| :param: miniBatchFraction Fraction of data on which SGD is run for each | ||
| :param stepSize: Step size for each iteration of gradient descent. | ||
| :param numIterations: Total number of iterations run. | ||
| :param miniBatchFraction: Fraction of data on which SGD is run for each |
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Not your fault, but if you make any additional changes can you add a "." at the end of this sentence?
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Also, the default values should be documented.
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LGTM overall The formal parameter changes to |
| @@ -212,7 +218,7 @@ private[python] class PythonMLLibAPI extends Serializable { | |||
| initialWeights: Vector, | |||
| regType: String, | |||
| intercept: Boolean, | |||
| validateData: Boolean): JList[Object] = { | |||
| validateData: Boolean, convergenceTol: Double): JList[Object] = { | |||
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I open SPARK-10560 to track the |
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Test build #42317 has finished for PR 8457 at commit
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LGTM. Merged into master. Thanks! |
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SPARK-3382 added a
convergenceTolparameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed).