This is a customized/optimized version of the original Trinity (https://github.com/fredfeng/Trinity), with some additional features that support efficiency and research purpose.
# testing a benchmark with its target skeleton only
python ./test_morpheus_benchmark.py --benchmark 5 --dsl test_min --skeletons test_min
# testing a benchmark with shared skeleton list
python ./test_morpheus_benchmark.py --benchmark 5 --dsl test_min --skeletons ngram3_nojoinusage: test_morpheus_benchmark.py [-h] [-b BENCHMARK] [-d {test_min}] [-s {test_min,test_ks0,ngram3_nojoin}] [--record]
optional arguments:
-h, --help show this help message and exit
-b BENCHMARK, --benchmark BENCHMARK
morpheus benchmark id, default: 5
-d {test_min}, --dsl {test_min}
DSL definition to use, default: test_min
-s {test_min,test_ks0,ngram3_nojoin}, --skeletons {test_min,test_ks0,ngram3_nojoin}
skeleton list to use, default: test_min
--record whether to enable recorder or not, default: False- For a list of columns in the parameter, sort it ASC, and apply the semantics (negative: exclude, positive: include) according to the order.
spread/groupcomponent are much slower than others.- Test all morpheus benchmarks to make sure they can actually be solved.
- Sketch level CDCL is not yet ready.
- Automatically synthesize the abstract interpreter based on the provided language specs.
- To speed up parameter enumeration and improve efficacy, consider letting newly generated columns stay in front (not at the end).
If you find our work and this tool useful in your research, please consider citing:
@article{vldb19_trinity,
author = {Ruben Martins and
Jia Chen and
Yanju Chen and
Yu Feng and
Isil Dillig},
title = {Trinity: An Extensible Synthesis Framework for Data Science},
journal = {{PVLDB}},
volume = {12},
number = {12},
pages = {1914--1917},
year = {2019},
url = {http://www.vldb.org/pvldb/vol12/p1914-martins.pdf},
timestamp = {Fri, 30 Aug 2019 13:15:08 +0200},
biburl = {https://dblp.org/rec/bib/journals/pvldb/MartinsCCFD19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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