Update some Python MLlib parameters to use camelCase, and tweak docs

We've used camel case in other Spark methods so it felt reasonable to
keep using it here and make the code match Scala/Java as much as
possible. Note that parameter names matter in Python because it allows
passing optional parameters by name.

1 files changed, 7 insertions, 7 deletions

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 03ff5a572e..19b90dfd6e 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -44,13 +44,13 @@ class LogisticRegressionModel(LinearModel):
 class LogisticRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a logistic regression model on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
-                        iterations, step, mini_batch_fraction, i),
-                LogisticRegressionModel, data, initial_weights)
+                        iterations, step, miniBatchFraction, i),
+                LogisticRegressionModel, data, initialWeights)
 
 class SVMModel(LinearModel):
     """A support vector machine.
@@ -67,14 +67,14 @@ class SVMModel(LinearModel):
 
 class SVMWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a support vector machine on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
-                        iterations, step, reg_param, mini_batch_fraction, i),
-                SVMModel, data, initial_weights)
+                        iterations, step, regParam, miniBatchFraction, i),
+                SVMModel, data, initialWeights)
 
 class NaiveBayesModel(object):
     """