Add Naive Bayes to Python MLlib, and some API fixes

- Added a Python wrapper for Naive Bayes
- Updated the Scala Naive Bayes to match the style of our other
  algorithms better and in particular make it easier to call from Java
  (added builder pattern, removed default value in train method)
- Updated Python MLlib functions to not require a SparkContext; we can
  get that from the RDD the user gives
- Added a toString method in LabeledPoint
- Made the Python MLlib tests run as part of run-tests as well (before
  they could only be run individually through each file)

1 files changed, 10 insertions, 7 deletions

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index a3a68b29e0..e90b72893f 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -47,14 +47,15 @@ class LinearRegressionModel(LinearRegressionModelBase):
     """A linear regression model derived from a least-squares fit.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = LinearRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
     """
 
 class LinearRegressionWithSGD(object):
     @classmethod
-    def train(cls, sc, data, iterations=100, step=1.0,
+    def train(cls, data, iterations=100, step=1.0,
               mini_batch_fraction=1.0, initial_weights=None):
         """Train a linear regression model on the given data."""
+        sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
                         d._jrdd, iterations, step, mini_batch_fraction, i),
@@ -65,14 +66,15 @@ class LassoModel(LinearRegressionModelBase):
     l_1 penalty term.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = LassoWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
     """
-    
+
 class LassoWithSGD(object):
     @classmethod
-    def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
+    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
               mini_batch_fraction=1.0, initial_weights=None):
         """Train a Lasso regression model on the given data."""
+        sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
                         iterations, step, reg_param, mini_batch_fraction, i),
@@ -83,14 +85,15 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     l_2 penalty term.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = RidgeRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
     """
 
 class RidgeRegressionWithSGD(object):
     @classmethod
-    def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
+    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
               mini_batch_fraction=1.0, initial_weights=None):
         """Train a ridge regression model on the given data."""
+        sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
                         iterations, step, reg_param, mini_batch_fraction, i),