add unit test for LR

Author: mengxr

python/pyspark/ml/classification.py

@@ -26,6 +26,22 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                          HasRegParam):
     """
     Logistic regression.
+
+    >>> from pyspark.sql import Row
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> dataset = sqlCtx.inferSchema(sc.parallelize([ \
+            Row(label=1.0, features=Vectors.dense(1.0)), \
+            Row(label=0.0, features=Vectors.sparse(1, [], []))]))
+    >>> lr = LogisticRegression() \
+            .setMaxIter(5) \
+            .setRegParam(0.01)
+    >>> model = lr.fit(dataset)
+    >>> test0 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.dense(-1.0))]))
+    >>> print model.transform(test0).first().prediction
+    0.0
+    >>> test1 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]))
+    >>> print model.transform(test1).first().prediction
+    1.0
     """
 
     def __init__(self):
@@ -52,3 +68,21 @@ def __init__(self, java_model):
     @property
     def _java_class(self):
         return "org.apache.spark.ml.classification.LogisticRegressionModel"
+
+
+if __name__ == "__main__":
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.feature tests")
+    sqlCtx = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlCtx'] = sqlCtx
+    (failure_count, test_count) = doctest.testmod(
+        globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)

python/pyspark/ml/feature.py

@@ -73,7 +73,7 @@ def _java_class(self):
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext
-    from pyspark.sql import Row, SQLContext
+    from pyspark.sql import SQLContext
     globs = globals().copy()
     # The small batch size here ensures that we see multiple batches,
     # even in these small test examples: