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| author | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:29:48 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:31:43 -0800 |
| commit | 8395e8fbdf23bef286ec68a4bbadcc448b504c2c (patch) | |
| tree | 8bea8ca2bba38d861a8e428b9e295bb8782d8d85 /dev/run-tests | |
| parent | 42d02db86cd973cf31ceeede0c5a723238bbe746 (diff) | |
| download | spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.tar.gz spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.tar.bz2 spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.zip | |
[SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDD
Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.
~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~
marmbrus jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #3070 from mengxr/SPARK-3573 and squashes the following commits:
3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
(cherry picked from commit 1a9c6cddadebdc53d083ac3e0da276ce979b5d1f)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Diffstat (limited to 'dev/run-tests')
| -rwxr-xr-x | dev/run-tests | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/dev/run-tests b/dev/run-tests index 0e9eefa76a..de607e4344 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -180,7 +180,7 @@ CURRENT_BLOCK=$BLOCK_SPARK_UNIT_TESTS if [ -n "$_SQL_TESTS_ONLY" ]; then # This must be an array of individual arguments. Otherwise, having one long string #+ will be interpreted as a single test, which doesn't work. - SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test") + SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test" "mllib/test") else SBT_MAVEN_TEST_ARGS=("test") fi |