Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/1775#discussion_r15795801
--- Diff: python/pyspark/mllib/classification.py ---
@@ -73,11 +73,36 @@ def predict(self, x):
class LogisticRegressionWithSGD(object):
@classmethod
- def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None):
- """Train a logistic regression model on the given data."""
+ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
+ initialWeights=None, regParam=1.0, regType=None,
intercept=False):
+ """
+ Train a logistic regression model on the given data.
+
+ @param data: The training data.
+ @param iterations: The number of iterations (default: 100).
+ @param step: The step parameter used in SGD
+ (default: 1.0).
+ @param miniBatchFraction: Fraction of data to be used for each SGD
+ iteration.
+ @param initialWeights: The initial weights (default: None).
+ @param regParam: The regularizer parameter (default: 1.0).
+ @param regType: The type of regularizer used for training
+ our model.
+ Allowed values: "l1" for using L1Updater,
+ "l2" for using
+ SquaredL2Updater,
+ "none" for no
regularizer.
+ (default: "none")
+ @param intercept: Boolean parameter which indicates the use
+ or not of the augmented representation
for
+ training data (i.e. whether bias features
+ are activated or not).
+ """
sc = data.context
+ if regType is None:
--- End diff --
As per above comment, you can just pass `regType` straight through if you
then wrap the null in `Option` on the Scala/Java side.
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