diff options
Diffstat (limited to 'python/pyspark/mllib/tests.py')
| -rw-r--r-- | python/pyspark/mllib/tests.py | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 9d1e5be637..6f3ec8ac94 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -39,6 +39,7 @@ except: class VectorTests(unittest.TestCase): + def test_serialize(self): sv = SparseVector(4, {1: 1, 3: 2}) dv = array([1., 2., 3., 4.]) @@ -81,6 +82,7 @@ class VectorTests(unittest.TestCase): class ListTests(PySparkTestCase): + """ Test MLlib algorithms on plain lists, to make sure they're passed through as NumPy arrays. @@ -128,7 +130,7 @@ class ListTests(PySparkTestCase): self.assertTrue(nb_model.predict(features[2]) <= 0) self.assertTrue(nb_model.predict(features[3]) > 0) - categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories + categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories dt_model = \ DecisionTree.trainClassifier(rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo) @@ -168,7 +170,7 @@ class ListTests(PySparkTestCase): self.assertTrue(rr_model.predict(features[2]) <= 0) self.assertTrue(rr_model.predict(features[3]) > 0) - categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories + categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories dt_model = \ DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) self.assertTrue(dt_model.predict(features[0]) <= 0) @@ -179,6 +181,7 @@ class ListTests(PySparkTestCase): @unittest.skipIf(not _have_scipy, "SciPy not installed") class SciPyTests(PySparkTestCase): + """ Test both vector operations and MLlib algorithms with SciPy sparse matrices, if SciPy is available. @@ -276,7 +279,7 @@ class SciPyTests(PySparkTestCase): self.assertTrue(nb_model.predict(features[2]) <= 0) self.assertTrue(nb_model.predict(features[3]) > 0) - categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories + categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories dt_model = DecisionTree.trainClassifier(rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo) self.assertTrue(dt_model.predict(features[0]) <= 0) @@ -315,7 +318,7 @@ class SciPyTests(PySparkTestCase): self.assertTrue(rr_model.predict(features[2]) <= 0) self.assertTrue(rr_model.predict(features[3]) > 0) - categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories + categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories dt_model = DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) self.assertTrue(dt_model.predict(features[0]) <= 0) self.assertTrue(dt_model.predict(features[1]) > 0) |