Open chenyifanthu opened 1 year ago
params-sim-run.json
should be generated in the following steps.
python -m circa.experiment generate
to generate the simulation data.python -m circa.experiment tune
to explore parameter combinations.
--max-workers 16
before tune
to enable multi-processing. 16
can be replaced with other positive integers, e.g., the number of CPU cores.--model-params params-sim-tune.json
stated in README.md
is also optional, which is used to filtered out some parameter combinations.Rscript img/draw.sim.R
to produce summaries under img/output
.
img/output/best-sim-tuning.tex
is in need now.python -m circa.experiment params > params-sim-run.json
to create a parameter template.params-sim-run.json
(generated in the Step 4) according to img/output/best-sim-tuning.tex
(generated in the Step 3).
Method
in img/output/best-sim-tuning.tex
, choose the row with the highest AC.5
. The related parameters can be found in the name
column.name
of the SPOT instance with the highest AC.5
is Empty-SPOT\_p0.01
. Hence, in params-sim-run.json
, only 0.01
in the spot.risk
list is reserved and other options (0.001
and 0.0001
) are removed.graph
parameters are neglected in the simulation study. As for a real-world dataset, the leading component in name
before the first dash (-
) is for the graph. For example, PC\_gauss\_a0.5
means,
pc_gauss
is used,0.5
for alpha
, andmax_conds_dim
is unlimited with null
in the json file.Our experiment results can be found in figshare.
In circa.zip
, you can find all four param-*.json
we used.
How to generate file params-sim-run.json?