1 files changed, 15 insertions, 10 deletions
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index a0117c5713..4bc6351bdf 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -108,7 +108,8 @@ class LinearRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=np.array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=np.array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -135,12 +136,13 @@ class LinearRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...    miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2",
     ...    intercept=True, validateData=True)
     >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
@@ -238,7 +240,7 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -265,12 +267,13 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
     ...     validateData=True)
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
@@ -321,7 +324,8 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -348,12 +352,13 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
     ...     validateData=True)
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
@@ -396,7 +401,7 @@ def _test():
     from pyspark import SparkContext
     import pyspark.mllib.regression
     globs = pyspark.mllib.regression.__dict__.copy()
-    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
+    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count: