Experimental setting: Same as the initial experiments 20170215a-NOOR with:
Experimenter: Jérôme Euzenat (INRIA)
Date: 2018-03-11
Lazy lavender hash: b86d0cb89108ca78d0ead507098e14683e4d39fb
Classpath: lib/lazylav/ll.jar:lib/slf4j/logback-classic-1.2.3.jar:lib/slf4j/logback-core-1.2.3.jar:.
Parameters: params.sh
Command line (script.sh):
JPATH=lib/lazylav/ll.jar:lib/slf4j/logback-classic-1.2.3.jar:lib/slf4j/logback-core-1.2.3.jar:.
OPT="-DnbAgents=4 -DnbIterations=10000 -DnbRuns=10 -DreportPrecRec"
# Dry test only for generating the runs
# bash scripts/runexp.sh -d 4-10000-nothing java -Dlog.level=INFO -cp ${JPATH} fr.inria.exmo.lazylavender.engine.Monitor ${OPT} -DrevisionModality=nothing -DsaveDir=input/expeRun -DsaveInit -DsaveParams -DsaveGames
LOADOPT="-DloadDir=input/expeRun -DloadEnv -DloadAgents -DreplayGames"
for mod in delete replace refine add addjoin refadd
do
bash scripts/runexp.sh -d 4-10000-${mod} java -Dlog.level=INFO -cp ${JPATH} fr.inria.exmo.lazylavender.engine.Monitor ${OPT} ${LOADOPT} -DrevisionModality=${mod} -DimmediateRatio=80
done
Class used: NOOEnvironment, AlignmentAdjustingAgent, AlignmentRevisionExperiment, ActionLogger, AverageLogger, Monitor.
Execution environment: Debian Linux virtual machine configured with four processors and 20GB of RAM running under a Dell PowerEdge T610 with 4*Intel Xeon Quad Core 1.9GHz E5-2420 processors, under Linux ProxMox 2 (Debian). - Java 1.8.0_151-b12 HotSpot
Note: This experiment has been set up to first generate the runs and then play them with different configuration. To strictly repeat it, it is necessary to reuse the same input and not generate a new one. Hence, the input is saved in the repository, the generating line above must be commented (including in Docker).
Input: Input required for reproducibility can be retrieved from: https://files.inria.fr/sakere/input/expeRun.zip Then unzip expeRun.zip -d input
4-10000-add-im80.tsv 4-10000-add-im80.txt 4-10000-addjoin-im80.tsv 4-10000-addjoin-im80.txt 4-10000-delete-im80.tsv 4-10000-delete-im80.txt 4-10000-refadd-im80.tsv 4-10000-refadd-im80.txt 4-10000-refine-im80.tsv 4-10000-refine-im80.txt 4-10000-replace-im80.tsv 4-10000-replace-im80.txt
op | success rate | network size | incoherence degree | semantic precision | semantic F-measure | semantic recall | maximum convergence |
---|---|---|---|---|---|---|---|
delete | 1.00 | 6 | 0.00 | 1.00 | 0.13 | 0.07 | 290 |
replace | 0.99 | 11 | 0.00 | 1.00 | 0.22 | 0.13 | 1224 |
refine | 0.99 | 19 | 0.00 | 1.00 | 0.35 | 0.21 | 1224 |
add | 0.98 | 25 | 0.00 | 1.00 | 0.41 | 0.26 | 2054 |
addjoin | 0.99 | 25 | 0.00 | 1.00 | 0.41 | 0.26 | 2029 |
refadd | 0.99 | 47 | 0.00 | 1.00 | 0.67 | 0.50 | 3376 |
Observations:
Key points:
Further experiments:
Remarks:
This file can be retrieved from URL https://sake.re/20180311-NOOR
It is possible to check out the repository by cloning https://felapton.inrialpes.fr/cakes/20180311-NOOR.git
This experiment has been transferred from its initial location at https://gforge.inria.fr (not available any more)
The original, unaltered associated zip file can be obtained from https://files.inria.fr/sakere/gforge/20180311-LOG.zip