def saveConstraints(dataTensor,variables,orderingNotImp,repeatDim,ind,num_nurses,direc,seed,tag):
r=set([v for v in range(len(variables)) if v not in repeatDim])
subsets=cf.split(r,(),repeatDim)
# direc='/home/mohit/CLUT/code/data'
with open(os.path.join(direc, "result_N"+str(num_nurses)+'_seed'+str(seed)+"_"+tag+".csv") ,"a") as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
if ind==0:
row=(['']*2)
for subset in subsets:
row.extend(subset)
row.extend(['']*5)
csvWriter.writerow(row)
else:
row=[]
for l in range(len(subsets)):
subset=subsets[l]
newset=subset[0]+subset[1]
# this value will be used to filter max constraints
maxPossible=1
for i in range(len(subset[1])):
maxPossible*=len(variables[subset[1][i]])
idTensor=cf.tensorIndicator(dataTensor,newset, variables)
sumSet = range(len(subset[0]),len(newset))
sumTensor_max,sumTensor_min=cf.tensorSum(idTensor,sumSet, np.array(variables)[list(newset)],0)
if sumTensor_min != 0 and sumTensor_min!=maxPossible:
row.extend([sumTensor_min])
else:
row.extend([''])
if sumTensor_max != maxPossible and sumTensor_max!=0:
row.extend([sumTensor_max])
else:
row.extend([''])
if len(set(subset[1]))==1 and len(set(orderingNotImp) & set(subset[1]))==0:
# print(subset)
minConsZero,maxConsZero,minConsNonZero,maxConsNonZero = cf.tensorConsZero(idTensor,sumSet, np.array(variables)[list(newset)])
if minConsZero!=0 and minConsZero!=maxPossible:
row.extend([minConsZero])
else:
row.extend([''])
if maxConsZero!=maxPossible and maxConsZero>0:
row.extend([maxConsZero])
else:
row.extend([''])
if minConsNonZero!=0 and minConsNonZero!=maxPossible:
row.extend([minConsNonZero])
else:
row.extend([''])
if maxConsNonZero!=maxPossible and maxConsNonZero>0:
row.extend([maxConsNonZero])
else:
row.extend([''])
else:
row.extend(['']*4)
row.extend([''])
csvWriter.writerow(row)
def findConstraints(dataTensor, variables, orderingNotImp, extraConstraints,
repeatDim):
r = set([v for v in range(len(variables)) if v not in repeatDim])
subsets = cf.split(r, (), repeatDim)
for l in range(len(subsets)):
subset = subsets[l]
newset = subset[0] + subset[1]
print(subset)
print(newset)
# this value will be used to filter max constraints
maxPossible = 1
for i in range(len(subset[1])):
maxPossible *= len(variables[subset[1][i]])
idTensor = cf.tensorIndicator(dataTensor, newset, variables)
sumSet = range(len(subset[0]), len(newset))
print(sumSet)
if subset[0] in extraConstraints:
sumTensor = cf.tensorSum(idTensor, sumSet,
np.array(variables)[list(newset)], 1)
print('M:', subset[0], ' S:', subset[1])
print(sumTensor)
else:
sumTensor_max, sumTensor_min = cf.tensorSum(
idTensor, sumSet,
np.array(variables)[list(newset)], 0)
if (sumTensor_min != 0 and sumTensor_min != maxPossible) or (
sumTensor_max != maxPossible and sumTensor_max != 0):
print('M:', subset[0], ' S:', subset[1])
if sumTensor_min != 0 and sumTensor_min != maxPossible:
print('min: ', sumTensor_min)
if sumTensor_max != maxPossible and sumTensor_max != 0:
print('max: ', sumTensor_max)
if len(set(
subset[1])) == 1 and len(set(orderingNotImp)
& set(subset[1])) == 0:
minConsZero, maxConsZero, minConsNonZero, maxConsNonZero = cf.tensorConsZero(
idTensor, sumSet,
np.array(variables)[list(newset)])
if (minConsZero != 0 and minConsZero != maxPossible
) or (maxConsZero != maxPossible and maxConsZero > 0) or (
minConsNonZero != 0 and minConsNonZero != maxPossible) or (
maxConsNonZero != maxPossible and maxConsNonZero > 0):
print('M:', subset[0], ' S:', subset[1])
if minConsZero != 0 and minConsZero != maxPossible:
print('min cons Zero: ', minConsZero)
if maxConsZero != maxPossible and maxConsZero > 0:
print('max cons Zero: ', maxConsZero)
if minConsNonZero != 0 and minConsNonZero != maxPossible:
print('min cons NonZero: ', minConsNonZero)
if maxConsNonZero != maxPossible and maxConsNonZero > 0:
print('max cons NonZero: ', maxConsNonZero)
def saveConstraintsForAll(dataTensor, variables, orderingNotImp, ind,
num_nurses, directory, tag):
repeatDim = ()
r = set([v for v in range(len(variables)) if v not in repeatDim])
subsets = cf.split(r, (), repeatDim)
with open(
os.path.join(directory + "/results",
"learnedBounds" + "_" + tag + ".csv"), "a") as my_csv:
csvWriter = csv.writer(my_csv, delimiter=',')
if ind == 0:
row = ([''] * 2)
for subset in subsets:
row.extend(subset)
row.extend([''] * 5)
csvWriter.writerow(row)
else:
row = []
for l in range(len(subsets)):
subset = subsets[l]
newset = subset[0] + subset[1]
# this value will be used to filter max constraints
maxPossible = 1
for i in range(len(subset[1])):
maxPossible *= len(variables[subset[1][i]])
idTensor = cf.tensorIndicator(dataTensor, newset, variables)
sumSet = range(len(subset[0]), len(newset))
sumTensor_max, sumTensor_min = cf.tensorSum(
idTensor, sumSet,
np.array(variables)[list(newset)], 0)
row.extend([sumTensor_min])
row.extend([sumTensor_max])
# if sumTensor_min==maxPossible:
# row.extend([0])
# else:
# row.extend([sumTensor_min])
# if sumTensor_max==maxPossible:
# row.extend([0])
# else:
# row.extend([sumTensor_max])
if len(set(subset[1])) == 1 and len(
set(orderingNotImp) & set(subset[1])) == 0:
minConsZero, maxConsZero, minConsNonZero, maxConsNonZero = cf.tensorConsZero(
idTensor, sumSet,
np.array(variables)[list(newset)])
row.extend([minConsZero])
row.extend([maxConsZero])
# if minConsZero==maxPossible:
# row.extend([0])
# else:
# row.extend([minConsZero])
# if maxConsZero==maxPossible:
# row.extend([0])
# else:
# row.extend([maxConsZero])
# row.extend([maxConsZero])
row.extend([minConsNonZero])
row.extend([maxConsNonZero])
# if minConsNonZero==maxPossible:
# row.extend([0])
# else:
# row.extend([minConsNonZero])
# if maxConsNonZero==maxPossible:
# row.extend([0])
# else:
# row.extend([maxConsNonZero])
# row.extend([maxConsNonZero])
else:
row.extend([''] * 4)
row.extend([''])
csvWriter.writerow(row)
