GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/9798
[SPARK-6787] [ML] add read/write to estimators under ml.feature (1)
Add read/write support to the following estimators under spark.ml:
* CountVectorizer
* IDF
* MinMaxScaler
* StandardScaler (a little awkward because we store some params in
spark.mllib model)
* StringIndexer
Added some necessary method for read/write. Maybe we should add
`private[ml] trait DefaultParamsReadable` and `DefaultParamsWritable` to save
some boilerplate code, though we still need to override `load` for Java
compatibility.
@jkbradley
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mengxr/spark SPARK-6787
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/9798.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #9798
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commit 7286bbb56f8c58ff9428ab07cbeb4f7441ad147a
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T01:42:31Z
add read/write to StringIndexer
commit 60b6a101c0ddb90257d507b86c51d4f24e2b9e5b
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T06:06:02Z
add read/write to IDF
commit 6df87ce47ede182881ded43a5ee4a1955eda57c1
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T06:35:29Z
add read/write to MinMaxScaler
commit 06fedbdf8f572f39680d3d2bc20759362dfe4874
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T07:00:11Z
add read/write to StandardScaler pending test
commit 750f4b76ee346b0fbf7de93ba1536afbf1646419
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T07:03:00Z
Merge remote-tracking branch 'apache/master' into read-write-string-indexer
commit 7b62e6ca96a4ef8ff73cc5de6df504aedd04cff7
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T07:09:05Z
add tests for StandardScaler read/write
commit 21e514f855ed5aa1579f10407b79a90c870d86ee
Author: Xiangrui Meng <[email protected]>
Date: 2015-11-18T07:28:37Z
add read/write to CountVectorizer
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