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, 59 insertions, 6 deletions

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 70de332d34..03ff5a572e 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -15,6 +15,8 @@
 # limitations under the License.
 #
 
+import numpy
+
 from numpy import array, dot, shape
 from pyspark import SparkContext
 from pyspark.mllib._common import \
@@ -29,8 +31,8 @@ class LogisticRegressionModel(LinearModel):
     """A linear binary classification model derived from logistic regression.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
-    >>> lrm = LogisticRegressionWithSGD.train(sc, sc.parallelize(data))
-    >>> lrm.predict(array([1.0])) != None
+    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
+    >>> lrm.predict(array([1.0])) > 0
     True
     """
     def predict(self, x):
@@ -41,9 +43,10 @@ class LogisticRegressionModel(LinearModel):
 
 class LogisticRegressionWithSGD(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 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),
@@ -53,8 +56,8 @@ class SVMModel(LinearModel):
     """A support vector machine.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
-    >>> svm = SVMWithSGD.train(sc, sc.parallelize(data))
-    >>> svm.predict(array([1.0])) != None
+    >>> svm = SVMWithSGD.train(sc.parallelize(data))
+    >>> svm.predict(array([1.0])) > 0
     True
     """
     def predict(self, x):
@@ -64,14 +67,64 @@ class SVMModel(LinearModel):
 
 class SVMWithSGD(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 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)
 
+class NaiveBayesModel(object):
+    """
+    Model for Naive Bayes classifiers.
+
+    Contains two parameters:
+    - pi: vector of logs of class priors (dimension C)
+    - theta: matrix of logs of class conditional probabilities (CxD)
+
+    >>> data = array([0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 1.0, 0.0]).reshape(3,3)
+    >>> model = NaiveBayes.train(sc.parallelize(data))
+    >>> model.predict(array([0.0, 1.0]))
+    0
+    >>> model.predict(array([1.0, 0.0]))
+    1
+    """
+
+    def __init__(self, pi, theta):
+        self.pi = pi
+        self.theta = theta
+
+    def predict(self, x):
+        """Return the most likely class for a data vector x"""
+        return numpy.argmax(self.pi + dot(x, self.theta))
+
+class NaiveBayes(object):
+    @classmethod
+    def train(cls, data, lambda_=1.0):
+        """
+        Train a Naive Bayes model given an RDD of (label, features) vectors.
+
+        This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
+        handle all kinds of discrete data.  For example, by converting
+        documents into TF-IDF vectors, it can be used for document
+        classification.  By making every vector a 0-1 vector, it can also be
+        used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
+
+        @param data: RDD of NumPy vectors, one per element, where the first
+               coordinate is the label and the rest is the feature vector
+               (e.g. a count vector).
+        @param lambda_: The smoothing parameter
+        """
+        sc = data.context
+        dataBytes = _get_unmangled_double_vector_rdd(data)
+        ans = sc._jvm.PythonMLLibAPI().trainNaiveBayes(dataBytes._jrdd, lambda_)
+        return NaiveBayesModel(
+            _deserialize_double_vector(ans[0]),
+            _deserialize_double_matrix(ans[1]))
+
+
 def _test():
     import doctest
     globs = globals().copy()