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IRELAND.py
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from gurobipy import *
import numpy as np
import sys
import time
import json
from pathlib import Path
import datetime
from collections import defaultdict
import multiprocessing as mp
from copy import copy
def load_data(datafolder,dataset):
X = np.genfromtxt(datafolder+'/'+dataset+'_X.csv',delimiter=',',dtype=int)
y = np.genfromtxt(datafolder+'/'+dataset+'_y.csv',delimiter=',',dtype=int)
N = X.shape[0]
P = X.shape[1]
N_cases = np.sum(y)
N_controls = N - N_cases
w = np.zeros(N,dtype=float)
w[np.where(y==1)[0]] = N_controls / (N_cases + N_controls)
w[np.where(y==0)[0]] = N_cases / (N_cases + N_controls)
return (X,y,w,N,P,N_controls)
def master(X,y,w,N,K,M,threads,timelimit):
global UBlist
t = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UBlist[0]).replace('.','p')+'/t.csv',delimiter=';',dtype=float)
for UB_ in UBlist[1:]:
t_ = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_).replace('.','p')+'/t.csv',delimiter=';',dtype=float)
if len(t_.shape) == 1:
t_ = np.expand_dims(t_,axis=0)
t = np.vstack((t,t_))
if len(t.shape) > 1:
K_ = t.shape[0]
else:
K_ = 1
t = np.expand_dims(t,axis=0)
samples = range(N)
clauses = range(K_)
m = Model(env=env)
q = m.addVars(clauses, vtype=GRB.BINARY, name='q')
yhat = m.addVars(samples, vtype=GRB.BINARY, name='yhat')
# yhat_n - sum_k[t_nk] <= 0
m.addConstrs(yhat[n] - quicksum(t[k,n]*q[k] for k in clauses) <= 0 for n in samples) # if y[n] == 1)
# - yhat_n + t_nk >= 0 for all k
m.addConstrs(-yhat[n] + t[k,n]*q[k] <= 0 for k in clauses for n in samples) # if y[n] == 0)
# sum_[k] q_k <= K
m.addConstr(quicksum(q[k] for k in clauses) <= K)
# - yhat_n >= 0
m.addConstrs(-yhat[n] <= 0 for n in samples) # if y[n] == 0)
# minimize sum_[n: y_n = 0] yhat[n] + sum_[n:y_n = 1] (1-yhat_n)
m.setObjective(quicksum(w[n] * yhat[n] for n in samples if y[n] == 0) + quicksum(w[n] * (1-yhat[n]) for n in samples if y[n]==1), GRB.MINIMIZE)
m.setParam('OutputFlag',0)
m.Params.Threads = threads
m.setParam(GRB.Param.TimeLimit,timelimit)
starttime = time.time()
m.optimize()
# Process results
if m.status == GRB.Status.OPTIMAL or m.status == GRB.Status.TIME_LIMIT and m.SolCount > 0:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/ModelStatus.txt','a') as f:
f.write('Master problem: ')
if m.status == GRB.Status.OPTIMAL:
f.write('OPTIMAL\n')
elif m.status == GRB.Status.TIME_LIMIT:
f.write('TIME_LIMIT\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/Runtimes.txt','a') as f:
f.write('Master problem: ')
f.write(str(time.time()-starttime)+'\n')
for var in m.getVars():
try:
test = var.X
except:
stop = 1
continue
break
# Get the optimal/best so far solution
qnew = np.empty((K_),dtype=int)
ynew = np.empty((N),dtype=int)
for var in m.getVars():
splitname = var.VarName.split('[')
if ',' in splitname[1]:
splitname = [splitname[0],splitname[1].split(',')[0],splitname[1].split(',')[1]]
splitname = [splitname[0],splitname[1],splitname[2].split(']')[0]]
else:
splitname = [splitname[0],splitname[1].split(']')[0]]
if splitname[0] == 'q':
qnew[int(splitname[1])] = var.X
elif splitname[0] == 'yhat':
ynew[int(splitname[1])] = var.X
np.savetxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/q.csv',qnew,delimiter=';',fmt='%i')
np.savetxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/y.csv',ynew,delimiter=';',fmt='%i')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/ObjectiveValue_master.csv','a') as f:
f.write(str(m.getObjective().getValue())+'\n')
obj = m.getObjective().getValue()
return
def masterUB(UB):
t = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UBlist[0]).replace('.','p')+'/t.csv',delimiter=';',dtype=float)
for UB_ in UBlist[1:]:
t_ = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_).replace('.','p')+'/t.csv',delimiter=';',dtype=float)
if len(t_.shape) == 1:
t_ = np.expand_dims(t_,axis=0)
t = np.vstack((t,t_))
if len(t.shape) > 1:
K_ = t.shape[0]
else:
K_ = 1
t = np.expand_dims(t,axis=0)
samples = range(N_)
clauses = range(K_)
m = Model(env=env)
q = m.addVars(clauses, vtype=GRB.BINARY, name='q')
yhat = m.addVars(samples, vtype=GRB.BINARY, name='yhat')
# yhat_n - sum_k[t_nk] <= 0
m.addConstrs(yhat[n] - quicksum(t[k,n]*q[k] for k in clauses) <= 0 for n in samples) # if y[n] == 1)
# - yhat_n + t_nk >= 0 for all k
m.addConstrs(-yhat[n] + t[k,n]*q[k] <= 0 for k in clauses for n in samples) # if y[n] == 0)
# sum_[k] q_k <= K
m.addConstr(quicksum(q[k] for k in clauses) <= K)
#sum_[n: y[n] ==0] t[n] <= UB
m.addConstr(quicksum(yhat[n] for n in samples if y_[n] == 0) <= UB*N_controls_)
# minimize sum_[n:y_n = 1] (1-yhat_n)
m.setObjective(quicksum(1-yhat[n] for n in samples if y_[n]==1), GRB.MINIMIZE)
m.setParam('OutputFlag',0)
m.Params.Threads = threads
m.setParam(GRB.Param.TimeLimit,timelimit)
starttime = time.time()
m.optimize()
# Process results
if m.status == GRB.Status.OPTIMAL or m.status == GRB.Status.TIME_LIMIT and m.SolCount > 0:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/ModelStatus.txt','a') as f:
f.write('Master problem: ')
if m.status == GRB.Status.OPTIMAL:
f.write('OPTIMAL\n')
elif m.status == GRB.Status.TIME_LIMIT:
f.write('TIME_LIMIT\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Runtimes.txt','a') as f:
f.write('Master problem: ')
f.write(str(time.time()-starttime)+'\n')
for var in m.getVars():
try:
test = var.X
except:
stop = 1
continue
break
# Get the optimal/best so far solution
qnew = np.empty((K_),dtype=int)
ynew = np.empty((N_),dtype=int)
for var in m.getVars():
splitname = var.VarName.split('[')
if ',' in splitname[1]:
splitname = [splitname[0],splitname[1].split(',')[0],splitname[1].split(',')[1]]
splitname = [splitname[0],splitname[1],splitname[2].split(']')[0]]
else:
splitname = [splitname[0],splitname[1].split(']')[0]]
if splitname[0] == 'q':
qnew[int(splitname[1])] = var.X
elif splitname[0] == 'yhat':
ynew[int(splitname[1])] = var.X
np.savetxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/q.csv',qnew,delimiter=';',fmt='%i')
np.savetxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/y.csv',ynew,delimiter=';',fmt='%i')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/ObjectiveValue_master.csv','a') as f:
f.write(str(m.getObjective().getValue())+'\n')
result = {}
result['obj'] = m.getObjective().getValue()
result['ynew'] = ynew
result['UB'] = UB
return result
def subproblem(a):
iter = a[0]
UB = a[1]
initializing = a[2]
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('Calling sub problem for UB '+str(UB)+'.\n')
controls = np.genfromtxt(runfolder+'/Controls.csv',delimiter=',',dtype=int)
selection = np.genfromtxt(runfolder+'/Nselect_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',delimiter=',',dtype=int)
w = np.genfromtxt(runfolder+'/Weights_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',delimiter=',',dtype=float)
Nselect = np.hstack((controls,selection))
X = X_[Nselect,:]
y = y_[Nselect]
N = X.shape[0]
P = P_
if initializing == 0:
s_prev = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UBlist[0]).replace('.','p')+'/s.csv',delimiter=';',dtype=float)
if len(s_prev.shape) == 1:
s_prev = np.expand_dims(s_prev,axis=0)
for UB_ in UBlist[1:]:
s_prev_ = np.genfromtxt(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_).replace('.','p')+'/s.csv',delimiter=';',dtype=float)
if len(s_prev_.shape) == 1:
s_prev_ = np.expand_dims(s_prev_,axis=0)
s_prev = np.vstack((s_prev,s_prev_))
K_prev = s_prev.shape[0]
clauses = range(K_prev)
# Solve sub problem for selected samples
samples = range(N)
features = range(P)
m = Model(env=env)
s = m.addVars(features, vtype=GRB.BINARY, name='s')
t = m.addVars(samples, vtype=GRB.BINARY, name='t')
# sum_p[s_pk] <= M for k in K
m.addConstr(quicksum(s[p] for p in features) <= M)
m.addConstr(quicksum(s[p] for p in features) >= 1)
# sum_p[(1-X_np)*s_pk + P*t_nk <= P for k in K, for n in N1
m.addConstrs(quicksum((1-X[n,p])*s[p] for p in features) + P*t[n] <= P for n in samples if y[n] == 1)
# sum_p[(1-X_np)*s_pk + t_nk >= 1 for k in K, for n in N1
A = np.hstack((np.array((1-X)),np.eye(len(y))))
m.addMConstr(np.array(A),None,'>',0.99999999999*np.ones((len(y),)))
#sum_[n: y[n] ==0] t[n] <= UB
m.addConstr(quicksum(t[n] for n in samples if y[n] == 0) <= UB*N_controls_)
if initializing == 0:
#sum_[p: s_prev[k,p]==1] s[p] + sum_[p: s_prev[k,p]==0] 1-s[p] <= K_prev-1
m.addConstrs(quicksum(s[p] for p in features if s_prev[k,p] == 1) + quicksum(1-s[p] for p in features if s_prev[k,p] == 0) <= P-1 for k in clauses)
#m.setObjective(quicksum(-t[n] for n in samples if y[n] == 1) + (1/N**2) * quicksum(t[n] for n in samples if y[n] == 0), GRB.MINIMIZE)
m.setObjective(quicksum(w[n]*(1-t[n]) for n in samples if y[n] == 1), GRB.MINIMIZE)
m.setParam('OutputFlag',0)
m.Params.Threads = 4
m.setParam(GRB.Param.TimeLimit,timelimit)
starttime = time.time()
m.optimize()
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('Solcount: '+str(m.solcount)+'.\n')
f.write('Model status: '+str(m.status)+'.\n')
stop = 0
count = 1
if m.SolCount == 0:
while stop == 0:
m.optimize()
if m.SolCount > 0:
stop = 1
count += 1
if count > 5:
stop = 1
if not m.SolCount > 0:
stop = 0
X_controls = np.squeeze(X_[np.where(y_==0),:])
cols = 1
while stop == 0:
if np.squeeze(np.where(np.sum(X_controls[:,:cols],axis=1)>=cols)).shape[0]/X_controls.shape[0] <= 0.005:
stop = 1
else:
cols += 1
snew = np.zeros((P),dtype=int)
for c in range(cols):
snew[c] = 1
status = 'Plan B\n'
tnew = np.floor(np.matmul(X_,snew)/np.sum(snew)).astype(int)
t_cases = np.squeeze(tnew[np.where(y_==1)])
w = np.array(w_)
w_cases = np.squeeze(w[np.where(y_==1)])[0]
obj = w_cases*np.sum(np.ones((tnew.shape),dtype=int)-tnew)
runtime = str(time.time()-starttime)+'\n'
# Process results
if m.status == GRB.Status.OPTIMAL or m.status == GRB.Status.TIME_LIMIT and m.SolCount > 0:
if m.status == GRB.Status.OPTIMAL:
status = 'OPTIMAL\n'
elif m.status == GRB.Status.TIME_LIMIT:
status = 'TIME_LIMIT\n'
for var in m.getVars():
try:
test = var.X
except:
stop = 1
continue
break
obj = m.getObjective().getValue()
snew = np.empty((P),dtype=int)
for var in m.getVars():
splitname = var.VarName.split('[')
if ',' in splitname[1]:
splitname = [splitname[0],splitname[1].split(',')[0],splitname[1].split(',')[1]]
splitname = [splitname[0],splitname[1],splitname[2].split(']')[0]]
else:
splitname = [splitname[0],splitname[1].split(']')[0]]
if splitname[0] == 's':
snew[int(splitname[1])] = var.X
tnew = np.floor(np.matmul(X_,snew)/np.sum(snew)).astype(int)
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/t_temp.csv','a') as f:
f.write(';'.join(tnew.astype(str))+'\n')
else:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('Other status, namely: '+str(m.status)+', ')
f.write('or other solution count: '+str(m.solcount)+'\n')
result = {}
result['iter'] = iter
result['s'] = snew
result['t'] = tnew
result['runtime'] = runtime
result['status'] = status
result['obj'] = str(obj)+'\n'
result['UB'] = str(UB).replace('.','p')
result['UBfloat'] = UB
return result
def collect_results(result):
global results_collection
UB = result['UBfloat']
iter = result['iter']
del result['iter']
del result['UBfloat']
if not UB in results_collection.keys():
results_collection[UB] = {}
results_collection[UB][iter] = {}
results_collection[UB][iter] = result
return
def collect_results_masterUB(result):
global objectives
global ynew_dict
UB_ = result['UB']
ynew_dict[UB_] = result['ynew']
objectives[UB_] = result['obj']
return
def process_results(results_coll):
global UBlist_stillrunning
for UB_fl in UBlist_stillrunning:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_fl).replace('.','p')+'/Messages.txt','a') as f:
f.write('Step 1 writing results\n')
for k,v in results_coll[UB_fl].items():
UB = v['UB']
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_fl).replace('.','p')+'/Messages.txt','a') as f:
f.write('Step 2 writing results\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/ModelStatus.txt','a') as f:
f.write('Sub problem: ')
f.write(v['status'])
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Runtimes.txt','a') as f:
f.write('Sub problem: ')
f.write(v['runtime'])
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_fl).replace('.','p')+'/Messages.txt','a') as f:
f.write('Step 3 writing results\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/t.csv','a') as f:
f.write(';'.join(v['t'].astype(str))+'\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB_fl).replace('.','p')+'/Messages.txt','a') as f:
f.write('Step 4 writing results\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/s.csv','a') as f:
f.write(';'.join(v['s'].astype(str))+'\n')
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/ObjectiveValue_sub.csv','a') as f:
f.write(v['obj'])
def generate_firstPool(X,y,w,N,P,Nsub,UBlist):
global results_collection
cases = [n for n in range(N) if y[n] == 1]
controls = [n for n in range(N) if y[n] == 0]
np.savetxt(runfolder+'/Controls.csv',controls,delimiter=',',fmt='%i')
Nselect = np.random.choice(cases,Nsub).tolist()
np.savetxt(runfolder+'/Nselect_'+str(0.005).replace('.','p')+'_'+str(0)+'.csv',Nselect,delimiter=',',fmt='%i')
np.savetxt(runfolder+'/Weights_'+str(0.005).replace('.','p')+'_'+str(0)+'.csv',np.ones((Nsub+len(controls))).tolist(),delimiter=',',fmt='%s')
subproblem([0,0.005,1])
po = mp.Pool(processes = threads)
for UB_frac in UBlist:
Nselect = np.random.choice(cases,Nsub).tolist()
np.savetxt(runfolder+'/Nselect_'+str(UB_frac).replace('.','p')+'_'+str(iter)+'.csv',Nselect,delimiter=',',fmt='%i')
np.savetxt(runfolder+'/Weights_'+str(UB_frac).replace('.','p')+'_'+str(iter)+'.csv',np.ones((Nsub+len(controls))).tolist(),delimiter=',',fmt='%s')
po.apply_async(subproblem, args=([iter,UB_frac,1],), callback=collect_results)
po.close()
po.join()
starttime_ = time.time()
process_results(results_collection)
os.system('/bin/rm -r '+runfolder+'/Nselect_*')
os.system('/bin/rm -r '+runfolder+'/Weights_*')
return
def add_rules(X,y,w,N,P,Nsub):
global results_collection
po = mp.Pool(processes = threads)
for UB in UBlist_stillrunning:
ynew = ynew_dict[UB]
fn = [n for n in range(N) if y[n] == 1 and ynew[n] == 0]
controls = [n for n in range(N) if y[n] == 0]
cases = [n for n in range(N) if y[n] == 1]
if len(fn) > Nsub:
results_collection = defaultdict(dict)
fn_select = np.random.choice(fn,int(np.floor(Nsub))).tolist()
np.savetxt(runfolder+'/Nselect_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',fn_select,delimiter=',',fmt='%i')
np.savetxt(runfolder+'/Weights_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',np.ones((len(controls)+len(fn_select))),delimiter=',',fmt='%s')
po.apply_async(subproblem, args=([iter,UB,0],), callback=collect_results)
elif len(fn) > 0:
results_collection = defaultdict(dict)
np.savetxt(runfolder+'/Nselect_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',fn,delimiter=',',fmt='%i')
np.savetxt(runfolder+'/Weights_'+str(UB).replace('.','p')+'_'+str(iter)+'.csv',np.ones((len(controls)+len(fn))),delimiter=',',fmt='%s')
po.apply_async(subproblem, args=([iter,UB,0],), callback=collect_results)
po.close()
po.join()
os.system('/bin/rm -r '+runfolder+'/Nselect_*')
os.system('/bin/rm -r '+runfolder+'/Weights_*')
process_results(results_collection)
return
if __name__ == "__main__":
overall_starttime = time.time()
homefolder = os.path.expanduser("~")
dataset = sys.argv[1]
datafolder = sys.argv[2]
resultfolder = sys.argv[3]
runfilesfolder = sys.argv[4]
threads = int(sys.argv[5])
timelimit = int(sys.argv[6])
Nsub_ = int(sys.argv[7])
K = int(sys.argv[8])
M = int(sys.argv[9])
runfolder = runfilesfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)
if not os.path.isdir(runfilesfolder+'/'+dataset):
os.system('mkdir '+runfilesfolder+'/'+dataset)
if not os.path.isdir(runfolder):
os.system('mkdir '+runfolder)
global UBlist
UBlist = [0.005,0.01,0.02,0.03,0.04,0.05]
env = Env(empty=True)
env.setParam('OutputFlag', 0)
env.start()
global X_
global y_
global w_
global N_
global P_
starttime_ = time.time()
(X_,y_,w_,N_,P_,N_controls_) = load_data(datafolder,dataset)
if not os.path.isdir(resultfolder):
os.system('mkdir '+resultfolder)
if not os.path.isdir(resultfolder+'/'+dataset):
os.system('mkdir '+resultfolder+'/'+dataset)
if not os.path.isdir(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)):
os.system('mkdir '+resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_))
for UB in UBlist:
if not os.path.isdir(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')):
os.system('mkdir '+resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p'))
global results_collection
results_collection = defaultdict(dict)
UBlist_stillrunning = UBlist[:]
starttime_genpool = time.time()
generate_firstPool(X_,y_,w_,N_,P_,Nsub_,sizeFirstPool,UBlist)
results_collection = defaultdict(dict)
stop = 0
objectives = {}
count_noImprovement = {}
global ynew_dict
for UB in UBlist:
count_noImprovement[UB] = 0
objectives[UB] = N_
i = 0
threshold = {}
for UB in UBlist[::-1]:
threshold[UBlist[i]] = UB
i += 1
while stop == 0:
ynew_dict = {}
objectives_prev = {}
for k,v in objectives.items():
objectives_prev[k] = v
objectives = {}
starttime_ = time.time()
masterUB(0.005)
po = mp.Pool(processes = threads)
for UB in UBlist_stillrunning:
po.apply_async(masterUB, args=(UB,), callback=collect_results_masterUB)
po.close()
po.join()
for UB in UBlist_stillrunning:
if objectives[UB] < threshold[UB]:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('Objective is below the threshold of '+str(threshold[UB])+'.\n')
UBlist_stillrunning.remove(UB)
elif objectives[UB] > objectives_prev[UB]:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('The objective value has increased, which should not happen.\n')
UBlist_stillrunning.remove(UB)
else:
if (objectives_prev[UB] - objectives[UB])/objectives_prev[UB] < 0.0001:
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/UB'+str(UB).replace('.','p')+'/Messages.txt','a') as f:
f.write('Objective improvement small: '+str(np.round((objectives_prev[UB] - objectives[UB])/objectives_prev[UB],6))+'.\n')
count_noImprovement[UB] += 1
else:
count_noImprovement[UB] = 0
if count_noImprovement[UB] > 1:
UBlist_stillrunning.remove(UB)
if len(UBlist_stillrunning) == 0:
stop = 1
else:
add_rules(X_,y_,w_,N_,P_,Nsub_,addToPool)
results_collection = defaultdict(dict)
master(X_,y_,w_,N_,K,M,threads,timelimit)
with open(resultfolder+'/'+dataset+'/K'+str(K)+'_M'+str(M)+'_time'+str(timelimit)+'_threads'+str(threads)+'_Nsub'+str(Nsub_)+'/Runtimes.txt','a') as f:
f.write('Overall: '+str(time.time()-overall_starttime))
os.system("'"+dataset+"' >> Instances_done.txt")