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@sajadabvi
sajadabvi committed Oct 26, 2023
1 parent 27bad0c commit a08d1d3
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Showing 5 changed files with 779 additions and 22 deletions.
56 changes: 42 additions & 14 deletions gunfolds/scripts/lineal_stat_scan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,13 +29,13 @@
help="stop traversing after growing equivalence class to this size.", type=int)
parser.add_argument("-b", "--BATCH", default=1, help="slurm batch.", type=int)
parser.add_argument("-p", "--PNUM", default=PNUM, help="number of CPUs in machine.", type=int)
parser.add_argument("-n", "--NODE", default=5, help="number of nodes in graph", type=int)
parser.add_argument("-d", "--DEN", default=0.15, help="density of graph", type=str)
parser.add_argument("-g", "--GTYPE", default="f", help="true for ringmore graph, false for random graph", type=str)
parser.add_argument("-n", "--NODE", default=6, help="number of nodes in graph", type=int)
parser.add_argument("-d", "--DEN", default=0.25, help="density of graph", type=str)
parser.add_argument("-g", "--GTYPE", default="t", help="true for ringmore graph, false for random graph", type=str)
parser.add_argument("-t", "--TIMEOUT", default=120, help="timeout in hours", type=int)
parser.add_argument("-r", "--THRESHOLD", default=5, help="threshold for SVAR", type=int)
parser.add_argument("-s", "--SCC", default="t", help="true to use SCC structure, false to not", type=str)
parser.add_argument("-m", "--SCCMEMBERS", default="t", help="true for using g_estimate SCC members, false for using "
parser.add_argument("-s", "--SCC", default="f", help="true to use SCC structure, false to not", type=str)
parser.add_argument("-m", "--SCCMEMBERS", default="f", help="true for using g_estimate SCC members, false for using "
"GT SCC members", type=str)
parser.add_argument("-u", "--UNDERSAMPLING", default=2, help="sampling rate in generated data", type=int)
parser.add_argument("-x", "--MAXU", default=15, help="maximum number of undersampling to look for solution.", type=int)
Expand Down Expand Up @@ -413,14 +413,24 @@ def amap(f, a):
min_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_norm_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_val = 1000000
min_cost = 10000000
for answer in r_estimated:
curr_errors = gk.OCE(bfutils.undersample(bfutils.num2CG(answer[0][0], len(GT)),answer[0][1][0]), g_estimated)
curr_normed_errors = gk.OCE(bfutils.undersample(bfutils.num2CG(answer[0][0], len(GT)),answer[0][1][0]), g_estimated, normalized=True)
if (curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
curr_cost = answer[1]
if (4*curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_val = (curr_errors['total'][0] + curr_errors['total'][1])
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_GuOptVsGest = answer
elif (4*curr_errors['total'][0] + curr_errors['total'][1]) == min_val:
if curr_cost < min_cost:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_GuOptVsGest = answer

'''G1_opt - the solution of optimization problem (r_estimated from g_estimated) in causal time scale'''
G1_opt_WRT_GuOptVsGest = bfutils.num2CG(min_answer_WRT_GuOptVsGest[0][0], len(g_estimated))
Expand All @@ -445,15 +455,24 @@ def amap(f, a):
min_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_norm_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_val = 1000000
min_cost = 10000000
for answer in r_estimated:
curr_errors = gk.OCE(bfutils.undersample(bfutils.num2CG(answer[0][0], len(GT)),answer[0][1][0]), bfutils.undersample(GT, answer[0][1][0]))
curr_normed_errors = gk.OCE(bfutils.undersample(bfutils.num2CG(answer[0][0], len(GT)),answer[0][1][0]), bfutils.undersample(GT, answer[0][1][0]), normalized=True)

if (curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
curr_cost = answer[1]
if (4*curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_val = (curr_errors['total'][0] + curr_errors['total'][1])
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_GuOptVsGTu = answer
elif (4*curr_errors['total'][0] + curr_errors['total'][1]) == min_val:
if curr_cost < min_cost:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_GuOptVsGTu = answer

'''G1_opt - the solution of optimization problem (r_estimated from g_estimated) in causal time scale'''
G1_opt_WRT_GuOptVsGTu = bfutils.num2CG(min_answer_WRT_GuOptVsGTu[0][0], len(g_estimated))
Expand All @@ -477,15 +496,24 @@ def amap(f, a):
min_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_norm_err = {'directed': (0, 0), 'bidirected': (0, 0), 'total': (0, 0)}
min_val = 1000000
min_cost = 10000000
for answer in r_estimated:
curr_errors = gk.OCE(bfutils.num2CG(answer[0][0], len(GT)),GT)
curr_normed_errors = gk.OCE(bfutils.num2CG(answer[0][0], len(GT)), GT, normalized=True)

if (curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
curr_cost = answer[1]
if (4*curr_errors['total'][0] + curr_errors['total'][1]) < min_val:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_val = (curr_errors['total'][0] + curr_errors['total'][1])
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_G1OptVsGT = answer
elif (4*curr_errors['total'][0] + curr_errors['total'][1]) == min_val:
if curr_cost < min_cost:
min_err = curr_errors
min_norm_err = curr_normed_errors
min_cost = curr_cost
min_val = (4*curr_errors['total'][0] + curr_errors['total'][1])
min_answer_WRT_G1OptVsGT = answer

'''G1_opt - the solution of optimization problem (r_estimated from g_estimated) in causal time scale'''
G1_opt_WRT_G1OptVsGT = bfutils.num2CG(min_answer_WRT_G1OptVsGT[0][0], len(g_estimated))
Expand Down Expand Up @@ -515,7 +543,7 @@ def amap(f, a):
'dm': DD,
'bdm': BD,
'optim_cost': sorted_data[-1][1],
'min_cost_sol': sorted_data[-1],
'num_sols': len(r_estimated),
'GT': GT,
'GT_at_actual_U': GT_at_actual_U,
'threshold': EDGE_CUTOFF * k_threshold,
Expand Down
4 changes: 2 additions & 2 deletions gunfolds/scripts/lineal_stat_scan_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
parser = argparse.ArgumentParser(description='Run settings.')
parser.add_argument("-c", "--CAPSIZE", default=0,
help="stop traversing after growing equivalence class to this size.", type=int)
parser.add_argument("-b", "--BATCH", default=1, help="slurm batch.", type=int)
parser.add_argument("-b", "--BATCH", default=11, help="slurm batch.", type=int)
parser.add_argument("-p", "--PNUM", default=PNUM, help="number of CPUs in machine.", type=int)
parser.add_argument("-n", "--NODE", default=8, help="number of nodes in graph", type=int)
parser.add_argument("-d", "--DEN", default=0.14, help="density of graph", type=str)
Expand Down Expand Up @@ -505,7 +505,7 @@ def amap(f, a):
'dm': DD,
'bdm': BD,
'optim_cost': sorted_data[-1][1],
'min_cost_sol': sorted_data[-1],
'num_sols': len(r_estimated),
'GT': GT,
'GT_at_actual_U': GT_at_actual_U,
'threshold': EDGE_CUTOFF * k_threshold,
Expand Down
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