def batch_run(params, layers, player_ordering=[3, 1]):
""" Batch run INDP optimization problem for all samples (currently 1-1000), given global parameters.
Format for params:
"NUM_ITERATIONS": For single timestep INDP, specifies how many iterations to run.
For InfoShare, specifies how many rounds of information sharing to perform.
"OUTPUT_DIR" : Directory to output results.
"MAGNITUDE" : Magnitude of earthquake. Used to look up failure scenarios.
"V" : How many resources used in a scenario. Used to look up failure scenarios, and specify v_r for indp.
"T" : Number of timesteps to optimize over. Used for td-INDP and InfoShare.
"WINDOW_LENGTH" : Slding time window length for td-INDP (for efficiency).
"N" : InfrastructureNetwork to use in indp.
"SIM_NUMBER" : What failure scenario simulation to use. Used to look up failure scenarios."""
if "N" not in params:
print("Initializing network...")
InterdepNet = initialize_network(
BASE_DIR="../data/INDP_7-20-2015/",
external_interdependency_dir="../data/INDP_4-12-2016",
sim_number=0,
magnitude=params["MAGNITUDE"],
v=params["V"])
print(InterdepNet)
else:
InterdepNet = params["N"]
for i in range(1, 2):
print("Running sample", i, "...")
add_failure_scenario(InterdepNet,
BASE_DIR="../data/INDP_7-20-2015/",
magnitude=params["MAGNITUDE"],
v=params["V"],
sim_number=i)
params["N"] = InterdepNet
params["SIM_NUMBER"] = i
if params["ALGORITHM"] == "INDP":
run_indp(params,
layers=layers,
controlled_layers=layers,
T=params["T"])
elif params["ALGORITHM"] == "INFO_SHARE":
run_info_share(params, layers=layers, T=params["T"])
elif params["ALGORITHM"] == "INRG":
run_inrg(params, layers=layers, player_ordering=player_ordering)
elif params["ALGORITHM"] == "BACKWARDS_INDUCTION":
gametree.run_backwards_induction(InterdepNet,
i,
players=layers,
player_ordering=player_ordering,
T=params["T"],
outdir=params["OUTPUT_DIR"])
def batch_run(params, fail_sce_param, player_ordering=[3, 1]):
'''
Batch run different methods for a given list of damage scenarios,
given global parameters.
Parameters
----------
params : dict
DESCRIPTION.
fail_sce_param : dict
DESCRIPTION.
player_ordering : list, optional
DESCRIPTION. The default is [3, 1].
Returns
-------
None. Writes to file
'''
# Set root directories
base_dir = fail_sce_param['BASE_DIR']
damage_dir = fail_sce_param['DAMAGE_DIR']
layers = params['L']
topology = None
shelby_data = True
ext_interdependency = None
if fail_sce_param['TYPE'] == 'Andres':
ext_interdependency = "../data/INDP_4-12-2016"
elif fail_sce_param['TYPE'] == 'WU':
if fail_sce_param['FILTER_SCE'] is not None:
list_high_dam = pd.read_csv(fail_sce_param['FILTER_SCE'])
elif fail_sce_param['TYPE'] == 'random':
pass
elif fail_sce_param['TYPE'] == 'synthetic':
shelby_data = False
topology = fail_sce_param['TOPO']
for m in fail_sce_param['MAGS']:
for i in fail_sce_param['SAMPLE_RANGE']:
try:
list_high_dam
if len(list_high_dam.loc[(list_high_dam.set == i) &
(list_high_dam.sce == m)].index) == 0:
continue
except NameError:
pass
print('\n---Running Magnitude ' + str(m) + ' sample ' + str(i) +
'...')
print("Initializing network...")
if shelby_data:
interdep_net, _, _ = indp.initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
sim_number=0,
magnitude=6,
sample=0,
v=params["V"],
shelby_data=shelby_data)
else:
interdep_net, no_resource, layers = indp.initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
magnitude=m,
sample=i,
shelby_data=shelby_data,
topology=topology)
params["V"] = no_resource
params["N"] = interdep_net
params["SIM_NUMBER"] = i
params["MAGNITUDE"] = m
if fail_sce_param['TYPE'] == 'WU':
indp.add_Wu_failure_scenario(interdep_net,
DAM_DIR=damage_dir,
noSet=i,
noSce=m)
elif fail_sce_param['TYPE'] == 'ANDRES':
indp.add_failure_scenario(interdep_net,
DAM_DIR=damage_dir,
magnitude=m,
v=params["V"],
sim_number=i)
elif fail_sce_param['TYPE'] == 'random':
indp.add_random_failure_scenario(interdep_net,
DAM_DIR=damage_dir,
sample=i)
elif fail_sce_param['TYPE'] == 'synthetic':
indp.add_synthetic_failure_scenario(interdep_net,
DAM_DIR=base_dir,
topology=topology,
config=m,
sample=i)
if params["ALGORITHM"] == "INDP":
indp.run_indp(params,
layers=layers,
controlled_layers=layers,
T=params["T"],
saveModel=False,
print_cmd_line=True)
elif params["ALGORITHM"] == "INFO_SHARE":
indp.run_info_share(params, layers=layers, T=params["T"])
elif params["ALGORITHM"] == "INRG":
indp.run_inrg(params,
layers=layers,
player_ordering=player_ordering)
elif params["ALGORITHM"] == "BACKWARDS_INDUCTION":
gametree.run_backwards_induction(
interdep_net,
i,
players=layers,
player_ordering=player_ordering,
T=params["T"],
outdir=params["OUTPUT_DIR"])
elif params["ALGORITHM"] == "JC":
dindp.run_judgment_call(params,
layers=layers,
T=params["T"],
save_jc_model=False,
print_cmd=True)
def batch_run(params, failSce_param, layers, player_ordering=[3, 1]):
""" Batch run INDP optimization problem for all samples (currently 1-1000), given global parameters.
Format for params:
"NUM_ITERATIONS": For single timestep INDP, specifies how many iterations to run.
For InfoShare, specifies how many rounds of information sharing to perform.
"OUTPUT_DIR" : Directory to output results.
"MAGNITUDE" : Magnitude of earthquake. Used to look up failure scenarios.
"V" : How many resources used in a scenario. Used to look up failure scenarios, and specify v_r for indp.
"T" : Number of timesteps to optimize over. Used for td-INDP and InfoShare.
"WINDOW_LENGTH" : Slding time window length for td-INDP (for efficiency).
"N" : InfrastructureNetwork to use in indp.
"SIM_NUMBER" : What failure scenario simulation to use. Used to look up failure scenarios."""
# Set root directories
if failSce_param['type'] == 'Andres':
base_dir = "../data/INDP_7-20-2015/"
ext_interdependency = "../data/INDP_4-12-2016"
topology = None
shelby_data = True
elif failSce_param['type'] == 'WU':
if failSce_param['filtered_List'] != None:
listHD = pd.read_csv(failSce_param['filtered_List'])
base_dir = "../data/Extended_Shelby_County/"
ext_interdependency = None
topology = None
shelby_data = True
elif failSce_param['type'] == 'synthetic':
base_dir = failSce_param['Base_dir']
ext_interdependency = None
shelby_data = False
topology = failSce_param['topology']
for m in failSce_param['mags']:
for i in failSce_param['sample_range']:
if failSce_param['filtered_List'] == None or len(
listHD.loc[(listHD.set == i) & (listHD.sce == m)].index):
print '\n---Running Magnitude ' + ` m ` + ' sample ' + ` i ` + '...'
print("Initializing network...")
if not shelby_data:
InterdepNet, noResource, layers = initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
magnitude=m,
sample=i,
shelby_data=shelby_data,
topology=topology)
params["V"] = noResource
else:
InterdepNet, _, _ = initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
sim_number=0,
magnitude=6,
sample=0,
v=params["V"],
shelby_data=shelby_data)
params["N"] = InterdepNet
params["SIM_NUMBER"] = i
params["MAGNITUDE"] = m
if failSce_param['type'] == 'WU':
add_Wu_failure_scenario(
InterdepNet,
BASE_DIR="../data/Wu_Damage_scenarios/",
noSet=i,
noSce=m)
elif failSce_param['type'] == 'ANDRES':
add_failure_scenario(InterdepNet,
BASE_DIR="../data/INDP_7-20-2015/",
magnitude=m,
v=params["V"],
sim_number=i)
elif failSce_param['type'] == 'synthetic':
add_synthetic_failure_scenario(InterdepNet,
BASE_DIR=base_dir,
topology=topology,
config=m,
sample=i)
if params["ALGORITHM"] == "INDP":
run_indp(params,
layers=layers,
controlled_layers=layers,
T=params["T"],
saveModel=False,
print_cmd_line=False)
elif params["ALGORITHM"] == "INFO_SHARE":
run_info_share(params, layers=layers, T=params["T"])
elif params["ALGORITHM"] == "INRG":
run_inrg(params,
layers=layers,
player_ordering=player_ordering)
elif params["ALGORITHM"] == "BACKWARDS_INDUCTION":
gametree.run_backwards_induction(
InterdepNet,
i,
players=layers,
player_ordering=player_ordering,
T=params["T"],
outdir=params["OUTPUT_DIR"])
elif params["ALGORITHM"] == "JUDGMENT_CALL":
run_judgment_call(params,
layers=layers,
T=params["T"],
saveJCModel=False,
print_cmd=False)
def batch_run(params, fail_sce_param, player_ordering=[3, 1]):
'''
Batch run different methods for a given list of damage scenarios,
given global parameters.
Parameters
----------
params : dict
DESCRIPTION.
fail_sce_param : dict
DESCRIPTION.
player_ordering : list, optional
DESCRIPTION. The default is [3, 1].
Returns
-------
None. Writes to file
'''
# Set root directories
base_dir = fail_sce_param['BASE_DIR']
damage_dir = fail_sce_param['DAMAGE_DIR']
topology = None
infrastructure_data = None
ext_interdependency = None
if fail_sce_param['TYPE'] == 'Andres':
infrastructure_data = 'shelby_old'
ext_interdependency = "../data/INDP_4-12-2016"
elif fail_sce_param['TYPE'] == 'WU':
infrastructure_data = 'shelby_extended'
if fail_sce_param['FILTER_SCE'] is not None:
list_high_dam = pd.read_csv(fail_sce_param['FILTER_SCE'])
elif fail_sce_param['TYPE'] == 'random':
infrastructure_data = 'shelby_extended'
elif fail_sce_param['TYPE'] == 'synthetic':
topology = fail_sce_param['TOPO']
print('----Running for resources: ' + str(params['V']))
for m in fail_sce_param['MAGS']:
for i in fail_sce_param['SAMPLE_RANGE']:
try:
list_high_dam
if len(list_high_dam.loc[(list_high_dam.set == i)&\
(list_high_dam.sce == m)].index) == 0:
continue
except NameError:
pass
print('---Running Magnitude ' + str(m) + ' sample ' + str(i) +
'...')
print("Initializing network...")
if infrastructure_data:
params["N"], _, _ = indp.initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
sim_number=0,
magnitude=6,
sample=0,
v=params["V"],
infrastructure_data=infrastructure_data)
else:
params["N"], params["V"], params[
'L'] = indp.initialize_network(
BASE_DIR=base_dir,
external_interdependency_dir=ext_interdependency,
magnitude=m,
sample=i,
infrastructure_data=infrastructure_data,
topology=topology)
params["SIM_NUMBER"] = i
params["MAGNITUDE"] = m
# Check if the results exist
output_dir_full = ''
if params["ALGORITHM"] in ["INDP"]:
output_dir_full = params["OUTPUT_DIR"]+'_L'+str(len(params["L"]))+'_m'+\
str(params["MAGNITUDE"])+"_v"+str(params["V"])+'/actions_'+str(i)+'_.csv'
if os.path.exists(output_dir_full):
print('results are already there\n')
continue
if fail_sce_param['TYPE'] == 'WU':
indp.add_Wu_failure_scenario(params["N"],
DAM_DIR=damage_dir,
noSet=i,
noSce=m)
elif fail_sce_param['TYPE'] == 'ANDRES':
indp.add_failure_scenario(params["N"],
DAM_DIR=damage_dir,
magnitude=m,
v=params["V"],
sim_number=i)
elif fail_sce_param['TYPE'] == 'random':
indp.add_random_failure_scenario(params["N"],
DAM_DIR=damage_dir,
sample=i)
elif fail_sce_param['TYPE'] == 'synthetic':
indp.add_synthetic_failure_scenario(params["N"],
DAM_DIR=base_dir,
topology=topology,
config=m,
sample=i)
if params["ALGORITHM"] == "INDP":
indp.run_indp(params,
layers=params['L'],
controlled_layers=params['L'],
T=params["T"],
saveModel=False,
print_cmd_line=False,
co_location=True)
if params["ALGORITHM"] == "MH":
mh.run_mh(params,
validate=False,
T=params["T"],
layers=params['L'],
controlled_layers=params['L'],
saveModel=True,
print_cmd_line=False,
co_location=True)
elif params["ALGORITHM"] == "INFO_SHARE":
indp.run_info_share(params, layers=params['L'], T=params["T"])
elif params["ALGORITHM"] == "INRG":
indp.run_inrg(params,
layers=params['L'],
player_ordering=player_ordering)
elif params["ALGORITHM"] == "BACKWARDS_INDUCTION":
gametree.run_backwards_induction(
params["N"],
i,
players=params['L'],
player_ordering=player_ordering,
T=params["T"],
outdir=params["OUTPUT_DIR"])
elif params["ALGORITHM"] == "JC":
dindputils.run_judgment_call(params,
save_jc_model=True,
print_cmd=False)
elif params["ALGORITHM"] in ["NORMALGAME", "BAYESGAME"]:
gameutils.run_game(params,
save_results=True,
print_cmd=False,
save_model=False,
plot2D=False) #!!!
def batch_run(params, fail_sce_param):
"""
Batch run different methods for a given list of damage scenarios,
given global parameters.
Parameters
----------
params : dict
DESCRIPTION.
fail_sce_param : dict
DESCRIPTION.
Returns
-------
None. Writes to file
"""
# Set root directories
base_dir = fail_sce_param['BASE_DIR']
damage_dir = fail_sce_param['DAMAGE_DIR']
topology = None
infrastructure_data = None
ext_interdependency = None
if fail_sce_param['TYPE'] == 'Andres':
infrastructure_data = 'shelby_old'
ext_interdependency = "../data/INDP_4-12-2016"
elif fail_sce_param['TYPE'] == 'WU':
infrastructure_data = 'shelby_extended'
if fail_sce_param['FILTER_SCE'] is not None:
list_high_dam = pd.read_csv(fail_sce_param['FILTER_SCE'])
elif fail_sce_param['TYPE'] == 'from_csv':
infrastructure_data = 'shelby_extended'
elif fail_sce_param['TYPE'] == 'synthetic':
topology = fail_sce_param['TOPO']
print('----Running for resources: ' + str(params['V']))
for m in fail_sce_param['MAGS']:
for i in fail_sce_param['SAMPLE_RANGE']:
params["SIM_NUMBER"] = i
params["MAGNITUDE"] = m
try:
list_high_dam
if len(list_high_dam.loc[(list_high_dam.set == i) & \
(list_high_dam.sce == m)].index) == 0:
continue
except NameError:
pass
# Check if the results exist
# !!! move it after initializing network for synthetic nets since L is identified there
output_dir_full = ''
if params["ALGORITHM"] in ["INDP"]:
out_dir_suffix_res = indp.get_resource_suffix(params)
output_dir_full = params["OUTPUT_DIR"] + '_L' + str(len(params["L"])) + '_m' + \
str(params["MAGNITUDE"]) + "_v" + out_dir_suffix_res + '/actions_' + str(i) + '_.csv'
if os.path.exists(output_dir_full):
print('results are already there\n')
continue
print('---Running Magnitude ' + str(m) + ' sample ' + str(i) +
'...')
if params['TIME_RESOURCE']:
print('Computing repair times...')
indp.time_resource_usage_curves(base_dir, damage_dir, i)
print("Initializing network...")
if infrastructure_data:
params["N"], _, _ = indp.initialize_network(
base_dir=base_dir,
external_interdependency_dir=ext_interdependency,
sim_number=0,
magnitude=m,
sample=i,
v=params["V"],
infrastructure_data=infrastructure_data,
extra_commodity=params["EXTRA_COMMODITY"])
else:
params["N"], params["V"], params[
'L'] = indp.initialize_network(
base_dir=base_dir,
external_interdependency_dir=ext_interdependency,
magnitude=m,
sample=i,
infrastructure_data=infrastructure_data,
topology=topology)
if params['DYNAMIC_PARAMS']:
print("Computing dislocation data...")
dyn_dmnd = dislocationutils.create_dynamic_param(
params, N=params["N"], T=params["NUM_ITERATIONS"])
params['DYNAMIC_PARAMS']['DEMAND_DATA'] = dyn_dmnd
if fail_sce_param['TYPE'] == 'WU':
indp.add_wu_failure_scenario(params["N"],
dam_dir=damage_dir,
no_set=i,
no_sce=m)
elif fail_sce_param['TYPE'] == 'ANDRES':
indp.add_failure_scenario(params["N"],
dam_dir=damage_dir,
magnitude=m,
v=params["V"],
sim_number=i)
elif fail_sce_param['TYPE'] == 'from_csv':
indp.add_from_csv_failure_scenario(params["N"],
sample=i,
dam_dir=damage_dir)
elif fail_sce_param['TYPE'] == 'synthetic':
indp.add_synthetic_failure_scenario(params["N"],
dam_dir=base_dir,
topology=topology,
config=m,
sample=i)
if params["ALGORITHM"] == "INDP":
indp.run_indp(params,
layers=params['L'],
controlled_layers=params['L'],
T=params["T"],
save_model=True,
print_cmd_line=False,
co_location=False)
if params["ALGORITHM"] == "MH":
mh.run_mh(params,
validate=False,
T=params["T"],
layers=params['L'],
controlled_layers=params['L'],
saveModel=True,
print_cmd_line=False,
co_location=True)
elif params["ALGORITHM"] == "INRG":
gameutils.run_inrg(params,
layers=params['L'],
player_ordering=player_ordering)
elif params["ALGORITHM"] == "BACKWARDS_INDUCTION":
gametree.run_backwards_induction(
params["N"],
i,
players=params['L'],
player_ordering=player_ordering,
T=params["T"],
outdir=params["OUTPUT_DIR"])
elif params["ALGORITHM"] == "JC":
dindputils.run_judgment_call(params,
save_jc_model=False,
print_cmd=False)
elif params["ALGORITHM"] in ["NORMALGAME", "BAYESGAME"]:
gameutils.run_game(params,
save_results=True,
print_cmd=False,
save_model=False,
plot2D=False) # !!!