Repository: spark Updated Branches: refs/heads/branch-1.6 696fc33b8 -> 159d631f5
[SPARK-10560][PYSPARK][MLLIB][DOCS] Make StreamingLogisticRegressionWithSGD Python API equal to Scala one This is to bring the API documentation of StreamingLogisticReressionWithSGD and StreamingLinearRegressionWithSGC in line with the Scala versions. -Fixed the algorithm descriptions -Added default values to parameter descriptions -Changed StreamingLogisticRegressionWithSGD regParam to default to 0, as in the Scala version Author: Bryan Cutler <bjcut...@us.ibm.com> Closes #9141 from BryanCutler/StreamingLogisticRegressionWithSGD-python-api-sync. (cherry picked from commit 105745645b12afbbc2a350518cb5853a88944183) Signed-off-by: Joseph K. Bradley <jos...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/159d631f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/159d631f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/159d631f Branch: refs/heads/branch-1.6 Commit: 159d631f5d0a0a7209e0a2831d774ecf0d2fa95f Parents: 696fc33 Author: Bryan Cutler <bjcut...@us.ibm.com> Authored: Mon Nov 23 17:11:51 2015 -0800 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Mon Nov 23 17:11:59 2015 -0800 ---------------------------------------------------------------------- python/pyspark/mllib/classification.py | 37 +++++++++++++++++++---------- python/pyspark/mllib/regression.py | 32 ++++++++++++++++--------- 2 files changed, 46 insertions(+), 23 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/159d631f/python/pyspark/mllib/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index aab4015..9e6f17e 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -652,21 +652,34 @@ class NaiveBayes(object): @inherit_doc class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm): """ - Run LogisticRegression with SGD on a batch of data. - - The weights obtained at the end of training a stream are used as initial - weights for the next batch. - - :param stepSize: Step size for each iteration of gradient descent. - :param numIterations: Number of iterations run for each batch of data. - :param miniBatchFraction: Fraction of data on which SGD is run for each - iteration. - :param regParam: L2 Regularization parameter. - :param convergenceTol: A condition which decides iteration termination. + Train or predict a logistic regression model on streaming data. Training uses + Stochastic Gradient Descent to update the model based on each new batch of + incoming data from a DStream. + + Each batch of data is assumed to be an RDD of LabeledPoints. + The number of data points per batch can vary, but the number + of features must be constant. An initial weight + vector must be provided. + + :param stepSize: + Step size for each iteration of gradient descent. + (default: 0.1) + :param numIterations: + Number of iterations run for each batch of data. + (default: 50) + :param miniBatchFraction: + Fraction of each batch of data to use for updates. + (default: 1.0) + :param regParam: + L2 Regularization parameter. + (default: 0.0) + :param convergenceTol: + Value used to determine when to terminate iterations. + (default: 0.001) .. versionadded:: 1.5.0 """ - def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01, + def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.0, convergenceTol=0.001): self.stepSize = stepSize self.numIterations = numIterations http://git-wip-us.apache.org/repos/asf/spark/blob/159d631f/python/pyspark/mllib/regression.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 6f00d1d..13b3397 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -734,17 +734,27 @@ class StreamingLinearAlgorithm(object): @inherit_doc class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm): """ - Run LinearRegression with SGD on a batch of data. - - The problem minimized is (1 / n_samples) * (y - weights'X)**2. - After training on a batch of data, the weights obtained at the end of - training are used as initial weights for the next batch. - - :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 - iteration. - :param convergenceTol: A condition which decides iteration termination. + Train or predict a linear regression model on streaming data. Training uses + Stochastic Gradient Descent to update the model based on each new batch of + incoming data from a DStream (see `LinearRegressionWithSGD` for model equation). + + Each batch of data is assumed to be an RDD of LabeledPoints. + The number of data points per batch can vary, but the number + of features must be constant. An initial weight + vector must be provided. + + :param stepSize: + Step size for each iteration of gradient descent. + (default: 0.1) + :param numIterations: + Number of iterations run for each batch of data. + (default: 50) + :param miniBatchFraction: + Fraction of each batch of data to use for updates. + (default: 1.0) + :param convergenceTol: + Value used to determine when to terminate iterations. + (default: 0.001) .. versionadded:: 1.5.0 """ --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
