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[SPARK-19939] [ML] Add support for association rules in ML - #17280
[SPARK-19939] [ML] Add support for association rules in ML#17280hhbyyh wants to merge 18 commits into
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SparkQA
commented
Mar 13, 2017
Test build #74458 has finished for PR 17280 at commit
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I'll focus on #17130 first and resolve conflict here. |
SparkQA
commented
Mar 15, 2017
Test build #74610 has finished for PR 17280 at commit
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SparkQA
commented
Apr 11, 2017
Test build #75715 has finished for PR 17280 at commit
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I'll update this after FPGrowth examples and doc merged #17130, since there'll be some conflicts. update: Done merging the conflict |
-better code
SparkQA
commented
Jun 9, 2017
Test build #77829 has finished for PR 17280 at commit
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SparkQA
commented
Jun 13, 2017
Test build #78005 has started for PR 17280 at commit |
shaneknapp
commented
Jun 13, 2017
test this please |
SparkQA
commented
Jun 14, 2017
Test build #78010 has finished for PR 17280 at commit
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| | [r]| 3| | ||
| | [y]| 3| | ||
| |[y, x]| 3| | ||
| +------+----+ |
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this seems to change the result quite a bit, is this expected?
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or rather, why is https://github.com/apache/spark/pull/17280/files#diff-b6dbf16870bd2cca9b4140df8aebd681L189 changed?
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set a higher minSupport to avoid the 0.3333333... in the support column.
| override def load(path: String): FPGrowthModel = { | ||
| val metadata = DefaultParamsReader.loadMetadata(path, sc, className) | ||
| implicit val format = DefaultFormats | ||
| val numTrainingRecords = (metadata.metadata \ "numTrainingRecords").extract[Long] |
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Does this break backward compatibility for loading?
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Yes it does now. If saving numTrainingRecords to FPGrowthModel looks good, I can update the load logic.
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Since we're adding numTrainingRecords to FPGrowthModel and there isn't a proper default number, I suggest we break the backward model loading compatibility. Otherwise we need to fill numTrainingRecords with an incorrect value and will likely create a maintenance trap.
hhbyyh
commented
Jan 17, 2018
Thanks for taking a look @MLnick |
hhbyyh
commented
Mar 21, 2018
Please advice if this is a good feature to add. If not I'll close it. Thanks. |
SparkQA
commented
Jul 1, 2018
Test build #92514 has finished for PR 17280 at commit
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SparkQA
commented
Jul 30, 2018
Test build #93814 has finished for PR 17280 at commit
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SparkQA
commented
Aug 1, 2018
Test build #93842 has finished for PR 17280 at commit
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hhbyyh
commented
Aug 1, 2018
Updated to support backward model loading compatibility. |
SparkQA
commented
Aug 27, 2018
Test build #95316 has finished for PR 17280 at commit
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SparkQA
commented
Aug 28, 2018
Test build #95318 has finished for PR 17280 at commit
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We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable. |
gungor2
commented
Apr 20, 2021
Is there any plan to revive this PR? It would be great if we can the support of rules exposed. |
What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-19939
Adding another essential characteristic for the Association Rule in Spark ml.fpm.
Support is an indication of how frequently the itemset of an association rule appears in the database and suggests if the rules are generally applicable to the dateset. refer to wiki for more details.
Before adding support:
After adding support:
Thus to allow a better understanding for the generated association rules. This is a new feature and was not included in the original function parity PR.
How was this patch tested?
existing and new unit test