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author | hyukjinkwon <gurwls223@gmail.com> | 2016-06-10 18:29:26 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2016-06-10 18:29:26 -0700 |
commit | 99f3c82776fe5ea4f89a9965a288c7447585dc2c (patch) | |
tree | 1448dbcb50d8cad1f7635aa6e58b584e920ebded /dev/lint-r | |
parent | bba5d7999f7b3ae9d816ea552ba9378fea1615a6 (diff) | |
download | spark-99f3c82776fe5ea4f89a9965a288c7447585dc2c.tar.gz spark-99f3c82776fe5ea4f89a9965a288c7447585dc2c.tar.bz2 spark-99f3c82776fe5ea4f89a9965a288c7447585dc2c.zip |
[SPARK-14615][ML][FOLLOWUP] Fix Python examples to use the new ML Vector and Matrix APIs in the ML pipeline based algorithms
## What changes were proposed in this pull request?
This PR fixes Python examples to use the new ML Vector and Matrix APIs in the ML pipeline based algorithms.
I firstly executed this shell command, `grep -r "from pyspark.mllib" .` and then executed them all.
Some of tests in `ml` produced the error messages as below:
```
pyspark.sql.utils.IllegalArgumentException: u'requirement failed: Input type must be VectorUDT but got org.apache.spark.mllib.linalg.VectorUDTf71b0bce.'
```
So, I fixed them to use new ones just identically with some Python tests fixed in https://github.com/apache/spark/pull/12627
## How was this patch tested?
Manually tested for all the examples listed by `grep -r "from pyspark.mllib" .`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #13393 from HyukjinKwon/SPARK-14615.
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