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author | Xiangrui Meng <meng@databricks.com> | 2015-11-06 14:51:03 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-11-06 14:51:03 -0800 |
commit | c447c9d54603890db7399fb80adc9fae40b71f64 (patch) | |
tree | 0f8a339ee0b28a00944bea96879600315ab3ef17 /data/mllib | |
parent | 3a652f691b220fada0286f8d0a562c5657973d4d (diff) | |
download | spark-c447c9d54603890db7399fb80adc9fae40b71f64.tar.gz spark-c447c9d54603890db7399fb80adc9fae40b71f64.tar.bz2 spark-c447c9d54603890db7399fb80adc9fae40b71f64.zip |
[SPARK-11217][ML] save/load for non-meta estimators and transformers
This PR implements the default save/load for non-meta estimators and transformers using the JSON serialization of param values. The saved metadata includes:
* class name
* uid
* timestamp
* paramMap
The save/load interface is similar to DataFrames. We use the current active context by default, which should be sufficient for most use cases.
~~~scala
instance.save("path")
instance.write.context(sqlContext).overwrite().save("path")
Instance.load("path")
~~~
The param handling is different from the design doc. We didn't save default and user-set params separately, and when we load it back, all parameters are user-set. This does cause issues. But it also cause other issues if we modify the default params.
TODOs:
* [x] Java test
* [ ] a follow-up PR to implement default save/load for all non-meta estimators and transformers
cc jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #9454 from mengxr/SPARK-11217.
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