diff --git a/config_model.yml b/config_model.yml
index 9323bfcbb1994f857f436e6fe6cd1190a090b8c3..97953475d3d9b7395064cededd06a0f7cf643bb6 100644
--- a/config_model.yml
+++ b/config_model.yml
@@ -37,8 +37,8 @@ siting_params:
no_iterations: 2000
no_epochs: 500
initial_temp: 100.
- no_runs: 20
- algorithm: 'MIR' # 'SGH', 'RS'
+ no_runs: 100
+ algorithm: 'SGH' # 'SGH', 'RS'
GRED_DET:
set: False
no_runs: 10
@@ -62,7 +62,7 @@ siting_params:
no_years: 10
no_years_train: 5
no_years_test: 5
- no_experiments: 10
+ no_experiments: 50
no_runs_per_experiment: 10
criterion: 'max'
algorithm: 'custom' # 'k_fold'
diff --git a/src/jl/SitingHeuristics.jl b/src/jl/SitingHeuristics.jl
index af2a2e3e8b977ea2e269b8818042226408312574..adc1318a398cdd6911737ca7838c60c1b41d1d92 100644
--- a/src/jl/SitingHeuristics.jl
+++ b/src/jl/SitingHeuristics.jl
@@ -21,7 +21,7 @@ function myunpickle(filename)
end
-function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run, p, data_path)
+function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run, p)
index_dict = Dict([(convert(Int64, k), convert(Int64, index_dict[k])) for k in keys(index_dict)])
deployment_dict = Dict([(convert(Int64, k), convert(Int64, deployment_dict[k])) for k in keys(deployment_dict)])
@@ -34,8 +34,7 @@ function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run,
T_init = convert(Float64, T_init)
R = convert(Int64, R)
run = string(run)
- p = string(p)
- data_path = string(data_path)
+ p = convert(Float64, p)
legacy_index = Vector{Int64}(undef, 0)
W, L = size(D)
@@ -74,16 +73,17 @@ function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run,
elseif run == "SGH"
- x_sol, LB_sol, obj_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R), Array{Float64, 2}(undef, R, I)
-
- # Read LB_init from file
- LB_init = myunpickle(joinpath(data_path, "objective_vector.p"))
- # Read x_init from file
- x_init = myunpickle(joinpath(data_path, "solution_matrix.p"))
+ R_init = 10
+ x_init, LB_init = Array{Float64, 2}(undef, R_init, L), Array{Float64, 1}(undef, R_init)
+ for r = 1:R_init
+ x_init[r, :], LB_init[r] = stochastic_threshold_greedy_algorithm(D, c, n, p)
+ end
LB_init_best = argmax(LB_init)
x_init_best = x_init[LB_init_best, :]
+ println("Initial solution computed. Starting LS.")
+ x_sol, LB_sol, obj_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R), Array{Float64, 2}(undef, R, I)
for r = 1:R
x_sol[r, :], LB_sol[r], obj_sol[r, :] = simulated_annealing_local_search(D, c, n, N, I, E, x_init_best, T_init, legacy_index)
end
diff --git a/src/main.py b/src/main.py
index 397855515dddc72e7dfc80a734691afe1b052551..43061f4d2359630693153ca0f535bffb2480d7d2 100644
--- a/src/main.py
+++ b/src/main.py
@@ -26,18 +26,11 @@ def parse_args():
parser.add_argument('--run_RAND', type=bool, default=False)
parser.add_argument('--run_CROSS', type=bool, default=False)
parser.add_argument('--LS_init_algorithm', type=str, default=None)
- parser.add_argument('--init_sol_folder', type=str, default=None)
parsed_args = vars(parser.parse_args())
return parsed_args
-
-def single_true(iterable):
- i = iter(iterable)
- return any(i) and not any(i)
-
-
if __name__ == '__main__':
args = parse_args()
@@ -99,10 +92,6 @@ if __name__ == '__main__':
c = args['c']
- if not single_true([args['run_BB'], args['run_LS'], args['run_GRED_DET'], args['run_GRED_STO'],
- args['run_RAND'], args['run_CROSS']]):
- raise ValueError(' More than one run selected in the argparser.')
-
if siting_parameters['solution_method']['BB']['set']:
custom_log(' BB chosen to solve the IP.')
@@ -139,8 +128,6 @@ if __name__ == '__main__':
params = siting_parameters['solution_method']['LS']
jl_dict = generate_jl_input(deployment_dict, site_coordinates)
- path_to_sol = args['init_sol_folder'] + str(args['c']) + '_GRED_STGH_p' + str(args['p'])
- path_to_init_sol_folder = join(data_path, 'output', path_to_sol)
import julia
j = julia.Julia(compiled_modules=False)
@@ -152,12 +139,11 @@ if __name__ == '__main__':
criticality_data, c, params['neighborhood'],
params['no_iterations'], params['no_epochs'],
params['initial_temp'], params['no_runs'],
- args['LS_init_algorithm'],
- args['p'], path_to_init_sol_folder)
+ args['LS_init_algorithm'], args['p'])
end = time.time()
print(f"Average CPU time for c={c}: {round((end-start)/params['no_runs'], 1)} s")
- output_folder = init_folder(model_parameters, c, suffix=f"_LS_{args['LS_init_algorithm']}_bis")
+ output_folder = init_folder(model_parameters, c, suffix=f"_LS_{args['LS_init_algorithm']}")
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)