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authorXiangrui Meng <meng@databricks.com>2015-03-05 11:50:09 -0800
committerXiangrui Meng <meng@databricks.com>2015-03-05 11:50:09 -0800
commit0bfacd5c5dd7d10a69bcbcbda630f0843d1cf285 (patch)
tree2b13352131bb3dbd88e4214c6c7728d26898d25e /python/run-tests
parentc9cfba0cebe3eb546e3e96f3e5b9b89a74c5b7de (diff)
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[SPARK-6090][MLLIB] add a basic BinaryClassificationMetrics to PySpark/MLlib
A simple wrapper around the Scala implementation. `DataFrame` is used for serialization/deserialization. Methods that return `RDD`s are not supported in this PR. davies If we recognize Scala's `Product`s in Py4J, we can easily add wrappers for Scala methods that returns `RDD[(Double, Double)]`. Is it easy to register serializer for `Product` in PySpark? Author: Xiangrui Meng <meng@databricks.com> Closes #4863 from mengxr/SPARK-6090 and squashes the following commits: 009a3a3 [Xiangrui Meng] provide schema dcddab5 [Xiangrui Meng] add a basic BinaryClassificationMetrics to PySpark/MLlib
Diffstat (limited to 'python/run-tests')
-rwxr-xr-xpython/run-tests1
1 files changed, 1 insertions, 0 deletions
diff --git a/python/run-tests b/python/run-tests
index a2c2f37a54..b7630c356c 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -75,6 +75,7 @@ function run_mllib_tests() {
echo "Run mllib tests ..."
run_test "pyspark/mllib/classification.py"
run_test "pyspark/mllib/clustering.py"
+ run_test "pyspark/mllib/evaluation.py"
run_test "pyspark/mllib/feature.py"
run_test "pyspark/mllib/linalg.py"
run_test "pyspark/mllib/rand.py"