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Dateien nach "ILP/butadien" hochladen

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kilian
2026-09-04 12:38:57 +02:00
parent 6a4454bdc2
commit 742c32af2b
1 changed files with 66 additions and 59 deletions
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ILP/butadien/butadiensynthesis.py
+66 -59
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@@ -103,7 +103,46 @@ NMR3 = [0.0, 0.17, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.01, 0.0, 0.16, 0.51, 0.
VERTICESSMILES = ['C=CC=C', 'C=C', 'C=CC=CC=C', 'C1CCC(C=C)CC=1', 'C(CCC(C=C)CC=C)=C', 'C=CC=CC=CC=C', 'C1CCC(C=CC=C)CC=1', 'C=CC1C=CCCC1', 'C1CCCCC=1', 'C=CC1CC=CCC1C=C', 'C1CCC(C=C)C(C=C)C=1', 'C(C1CC(C=C)C=CC1)=C', 'C1C=CC=CC=1', 'C(CCC1C=CC=CC1)=C', 'C1C(C=CC=C)CCCC=1', 'C=CC(C=C)CCCC=C', 'C=CCCCCC=C', 'C=CC1C=CC(C=C)CC1', 'C1CC2CCCCC2CC=1', 'C1CC2C=CC=CC2CC=1']
NMRMAX = [max(n1,n2,n3) for n1,n2,n3 in zip(NMR1,NMR2,NMR3)]
#Implement Edgelikelihood before Model
#Kombiniert Molekül it Wahrscheinlichkeit für NMR1:
VERTICE1 = {}
for vertice, likelihood in zip(VERTICES, NMR1):
VERTICE1[vertice] = likelihood
#Kombiniert Molekül it Wahrscheinlichkeit für NMR2:
VERTICE2 = {}
for vertice, likelihood in zip(VERTICES, NMR2):
VERTICE2[vertice] = likelihood
#Kombiniert Molekül it Wahrscheinlichkeit für NMR3:
VERTICE3 = {}
for vertice, likelihood in zip(VERTICES, NMR3):
VERTICE3[vertice] = likelihood
#Kantenwahrscheinlichkeiten für NMR1:
EDGE1 = {}
#Kantenwahrscheinlichkeiten für NMR2:
EDGE2 = {}
#Kantenwahrscheinlichkeiten für NMR3:
EDGE3 = {}
#Maximum der verschiedenen Kantenwahrscheinlichkeiten:
EDGEMAX = {}
modes = ["Product", "Average"]
mode = modes[0]
#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte
for edge, (heads, tails) in HYPERGRAPH.items():
if heads != [] and tails != [] and mode == "Product":
#Produkt klappt nur, wenn normalisierte Likelihoods
EDGE1[edge] = math.prod(VERTICE1[head] for head in heads)
EDGE2[edge] = math.prod(VERTICE2[head] for head in heads)
EDGE2[edge] = math.prod(VERTICE2[head] for head in heads)
if heads != [] and tails != [] and mode == "Average":
EDGE1[edge] = quicksum(VERTICE1[head] for head in heads)/len(heads)
EDGE2[edge] = quicksum(VERTICE2[head] for head in heads)/len(heads)
EDGE3[edge] = quicksum(VERTICE3[head] for head in heads)/len(heads)
else:
EDGE1[edge] = 0.0
EDGE2[edge] = 0.0
EDGE3[edge] = 0.0
EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE2[edge]])
FIXED_FLOWS = {
@@ -115,64 +154,37 @@ FIXED_FLOWS = {
#42: 1,
}
def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3, excluded_support=None):
def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelikelihoods2, edgelikelihoods3, excluded_support=None):
model = Model(name)
x = {e_id: model.addVar(vtype=GRB.INTEGER, lb = 0, name = f"x_{e_id}") for e_id in hyperedges}
b = {e_id: model.addVar(vtype=GRB.BINARY, name = f"b_{e_id}") for e_id in hyperedges}
count = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, name = "count")
nmax = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmrmax")
n1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr1")
n2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr2")
n3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr3")
enmax = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmrmax")
en1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr1")
en2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr2")
en3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr3")
#Assigns every Molecule the likelihood compared to the three different reference spectra
for v, nmrmax, nmr1, nmr2, nmr3 in zip(vertices, nmrlikelihoodsmax, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3):
nmax[v] = nmrmax
n1[v] = nmr1
n2[v] = nmr2
n3[v] = nmr3
count[v] = 0
#Assigns the edges a likelihood based on the products
for e, (tails, heads) in hyperedges.items():
if heads != [] and tails != []: #Multiplication better?
#Produkt klappt nur, wenn normalisierte Likelihoods
en1[e] = math.prod(n1[head] for head in heads)
en2[e] = math.prod(n2[head] for head in heads)
en3[e] = math.prod(n3[head] for head in heads)
enmax[e] = max([en1[e], en2[e], en3[e]])
else:
enmax[e] = 0.0
en1[e] = 0.0
en2[e] = 0.0
en3[e] = 0.0
'''
if heads != [] and tails != []:
enmax[e] = quicksum(nmax[head] for head in heads)/len(heads)
en1[e] = quicksum(n1[head] for head in heads)/len(heads)
en2[e] = quicksum(n2[head] for head in heads)/len(heads)
en3[e] = quicksum(n3[head] for head in heads)/len(heads)
else:
enmax[e] = 0.0
en1[e] = 0.0
en2[e] = 0.0
en3[e] = 0.0
'''
enmax = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmrmax")
en1 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr1")
en2 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr2")
en3 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr3")
for edge, elmax, el1, el2, el3 in zip(hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelikelihoods2, edgelikelihoods3):
enmax[edge] = elmax
en1[edge] = el1
en2[edge] = el2
en3[edge] = el3
print(enmax[2])
print(enmax[3])
print(enmax[17])
print(enmax[41])
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
#Every item created has to be consumed:
'''
for v in vertices:
count[v] = 0
count = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, name = "count")
for mol in vertices:
for edge, (heads, tails) in hyperedges.items():
count[mol] += x[edge] * (heads.count(mol) - tails.count(mol))
model.addConstr(count[mol] == 0)
'''
for v in vertices:
inflow = quicksum(x[e_id] * heads.count(v) for e_id, (_, heads) in hyperedges.items())
outflow = quicksum(x[e_id] * tails.count(v) for e_id, (tails, _) in hyperedges.items())
@@ -195,6 +207,7 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
model.ModelSense = GRB.MAXIMIZE
#Multiply edgelikelihood with the edge use boolean
#Adapt to have
model.setObjectiveN(
quicksum(enmax[e_id] * b[e_id] for e_id, (_, _) in hyperedges.items()),
index = 0,
@@ -219,13 +232,7 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
model.addConstr(b[213] == 1)
model.addConstr(b[41] == 1)
#Every item created has to be consumed:
'''
for mol in vertices:
for edge, (heads, tails) in hyperedges.items():
count[mol] += x[edge] * (heads.count(mol) - tails.count(mol))
model.addConstr(count[mol] == 0)
'''
#No cyclic reaction pairs:
for e_id1, (heads1, tails1) in hyperedges.items():
@@ -254,7 +261,7 @@ def print_solution(title, flow_solution, binary_solution, hyperedges):
print(f"Number of used hyperedges: {len(binary_solution)}")
def main():
model, x, b = build_model("HypergraphFlow", HYPERGRAPH, VERTICES, NMRMAX, NMR1, NMR2, NMR3)
model, x, b = build_model("HypergraphFlow", HYPERGRAPH, EDGEMAX, EDGE1, EDGE2, EDGE3)
model.optimize()
if model.status != GRB.Status.OPTIMAL:
print("No optimal solution found for the first model.")
@@ -264,7 +271,7 @@ def main():
print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPERGRAPH)
excluded_support = list(optimal_binary_solution.keys())
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPERGRAPH, VERTICES, NMRMAX, NMR1, NMR2, NMR3, excluded_support=excluded_support,)
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPERGRAPH, EDGEMAX, EDGE1, EDGE2, EDGE3, excluded_support=excluded_support,)
second_model.optimize()
if second_model.status == GRB.Status.OPTIMAL: