Fix PEP8 violations in Python mllib.

Author: Reynold Xin <rxin@apache.org>

Closes #871 from rxin/mllib-pep8 and squashes the following commits:

848416f [Reynold Xin] Fixed a typo in the previous cleanup (c -> sc).
a8db4cd [Reynold Xin] Fix PEP8 violations in Python mllib.

1 files changed, 14 insertions, 12 deletions

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 6772e4337e..1c0c536c4f 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -29,6 +29,7 @@ from pyspark.mllib.linalg import SparseVector
 from pyspark.mllib.regression import LabeledPoint, LinearModel
 from math import exp, log
 
+
 class LogisticRegressionModel(LinearModel):
     """A linear binary classification model derived from logistic regression.
 
@@ -68,14 +69,14 @@ class LogisticRegressionModel(LinearModel):
 
 class LogisticRegressionWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0,
-              miniBatchFraction=1.0, initialWeights=None):
+    def train(cls, data, iterations=100, step=1.0, 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, miniBatchFraction, i),
-                LogisticRegressionModel, data, initialWeights)
+        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
+            d._jrdd, iterations, step, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
+                                         initialWeights)
+
 
 class SVMModel(LinearModel):
     """A support vector machine.
@@ -106,16 +107,17 @@ class SVMModel(LinearModel):
         margin = _dot(x, self._coeff) + self._intercept
         return 1 if margin >= 0 else 0
 
+
 class SVMWithSGD(object):
     @classmethod
     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, regParam, miniBatchFraction, i),
-                SVMModel, data, initialWeights)
+        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
+            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)
+
 
 class NaiveBayesModel(object):
     """
@@ -156,6 +158,7 @@ class NaiveBayesModel(object):
         """Return the most likely class for a data vector x"""
         return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
 
+
 class NaiveBayes(object):
     @classmethod
     def train(cls, data, lambda_=1.0):
@@ -186,8 +189,7 @@ def _test():
     import doctest
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs,
-            optionflags=doctest.ELLIPSIS)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         exit(-1)