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https://issues.apache.org/jira/browse/APEXMALHAR-2260?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16204844#comment-16204844
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Ananth commented on APEXMALHAR-2260:
------------------------------------
Integration with xgboost python package gives the following readings
The xgBoost ensemble of trees was generated for four depths ( and this resulted
in varying number of trees ). The readings are given for all four of these
modelling configurations
- 2012 Macbook Pro (2.6 GHz Intel Core i7 with 16GB RAM), No GPU was enabled
for either modelling or scoring
- The model was to perform iris data set recognition
- The source code for the modelling and the binary version of the model can be
located in the resources folder of the git project ( link in the second comment
)
- Readings in microseconds
Result
"github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3
( *60 trees* )":
*475.027 ±(99.9%) 5.441 us/op [Average]*
(min, avg, max) = (428.774, 475.027, 567.648), stdev = 23.037
CI (99.9%): [469.586, 480.468] (assumes normal distribution)
# Run complete. Total time: 00:08:28
Benchmark Mode Cnt Score
Error Units
XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3 avgt 200 475.027 ±
5.441 us/op
Result
"github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9
( *120 trees* )":
*479.907 ±(99.9%) 6.342 us/op [Average]*
(min, avg, max) = (427.637, 479.907, 576.946), stdev = 26.852
CI (99.9%): [473.565, 486.249] (assumes normal distribution)
# Run complete. Total time: 00:08:31
Benchmark Mode Cnt Score
Error Units
XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9 avgt 200 479.907 ±
6.342 us/op
Result
"github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27
( *300 trees* )":
*524.516 ±(99.9%) 13.392 us/op [Average]*
(min, avg, max) = (423.894, 524.516, 838.232), stdev = 56.701
CI (99.9%): [511.124, 537.908] (assumes normal distribution)
# Run complete. Total time: 00:08:30
Benchmark Mode Cnt Score
Error Units
XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27 avgt 200 524.516 ±
13.392 us/op
Result
"github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125
( *900 trees* )":
*519.460 ±(99.9%) 10.647 us/op [Average]*
(min, avg, max) = (458.625, 519.460, 693.956), stdev = 45.082
CI (99.9%): [508.812, 530.107] (assumes normal distribution)
# Run complete. Total time: 00:08:35
Benchmark Mode Cnt Score
Error Units
XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125 avgt 200 519.460
± 10.647 us/op
> Python execution for operator logic
> ------------------------------------
>
> Key: APEXMALHAR-2260
> URL: https://issues.apache.org/jira/browse/APEXMALHAR-2260
> Project: Apache Apex Malhar
> Issue Type: New Feature
> Reporter: Thomas Weise
> Assignee: Ananth
> Labels: roadmap
>
> Support execution of Python code in an operator.
> https://lists.apache.org/thread.html/9837b1dee8f909ed400c6030ce5c6a94a12f43183718019dd0bfd228@%3Cdev.apex.apache.org%3E
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