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Now ILP solver picks optimal combination of two different similarities and similarity can also be calcuated using the mean of similarity instead of the comparitive value
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kilian
2026-06-24 11:29:14 +02:00
parent 03a54636a0
commit 20824b6400
2 changed files with 95 additions and 38 deletions
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ILP/caffeinesynthesis.py
+67 -25
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@@ -33,6 +33,10 @@ VERTICES1 = {
8: ([0, 0], ['Caffeine']),
}
#25014CX3
#03
VERTICES2 = {
1: ([0], ['Xanthine']),
2: ([0], ['p_{0,0}']),
@@ -47,69 +51,107 @@ VERTICES2 = {
VERTICES = ['Xanthine', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'Caffeine']
NMRLIKELYHOODS = [0.0, 0.2, 0.7, 0.1, 0.1, 0.2, 0.7, 0.0]
NMRLIKELYHOODS1 = [0.75, 0.59, 0.36, 0.89, 0.59, 0.86, 0.78, 0.65]
NMRLIKELYHOODS2 = [0.56, 0.75, 0.45, 0.79, 0.75, 0.76, 0.84, 0.74]
NMRLIKELYHOODS3 = [0.54, 0.53, 0.7, 0.77, 0.53, 0.85, 0.94, 0.79]
NMRLIKELYHOODS1 = [0.54, 0.52, 0.43, 0.62, 0.52, 0.56, 0.48, 0.5]
NMRLIKELYHOODS2 = [0.52, 0.52, 0.52, 0.61, 0.52, 0.56, 0.61, 0.59]
NMRLIKELYHOODS3 = [0.5, 0.53, 0.59, 0.59, 0.53, 0.56, 0.68, 0.62]
FIXED_FLOWS = {
1: 1,
14: 1,
}
def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=None):
def build_model(name, hyperedges, vertices, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3, 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}
n = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb=0.0, ub=1.0, name="nmr")
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}
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")
c1 = model.addVars(vertices, vtype = GRB.BINARY, name = "n1_choice")
c2 = model.addVars(vertices, vtype = GRB.BINARY, name = "n2_choice")
c3 = model.addVars(vertices, vtype = GRB.BINARY, name = "n3_choice")
for v, nmr in zip(vertices, nmrlikelihoods):
n[v] = nmr
print(f'Vertice: {v}, Similarity: {n[v]}')
for v, nmr in zip(vertices, nmrlikelihoods1):
n1[v] = nmr
#print(f'Vertice: {v}, Similarity: {n1[v]}')
for v, nmr in zip(vertices, nmrlikelihoods2):
n2[v] = nmr
for v, nmr in zip(vertices, nmrlikelihoods3):
n3[v] = nmr
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
for v in vertices:
inflow = quicksum(x[e_id] for e_id, (_, heads) in hyperedges.items() if v in heads)
outflow = quicksum(x[e_id] for e_id, (tails, _) in hyperedges.items() if v in tails)
model.addConstr(inflow == outflow, name=f"flow_conservation_{v}")
model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
for e_id, value in FIXED_FLOWS.items():
model.addConstr(x[e_id] == value, name=f"fixed_flow_{e_id}")
model.addConstr(x[e_id] == value, name = f"fixed_flow_{e_id}")
for e_id in hyperedges:
model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name=f"unused_implies_zero_{e_id}")
model.addConstr(x[e_id] >= b[e_id], name=f"used_implies_positive_flow_{e_id}")
model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name = f"unused_implies_zero_{e_id}")
model.addConstr(x[e_id] >= b[e_id], name = f"used_implies_positive_flow_{e_id}")
reaction_path = {}
#for v_id in vertices:
#model.addConstr()
if excluded_support:
model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name="different_hyperedges",)
model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name = "different_hyperedges",)
#Multiplizier den node Wert mit infow + outflow
#model.setObjective(quicksum(n[v_id] for v_id in vertices), GRB.MINIMIZE) #Maximize Similarity of Nodes
model.ModelSense = GRB.MAXIMIZE
#Multiply with a list of binarys where only one value is one and the rest zero and not both the same position
'''
model.setObjectiveN(
quicksum(n[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
index=0,
priority=2,
name="maximize_nmr_similarity",
quicksum(n1[v_id] * c1[v_id] for v_id in vertices if v_id != [])
+ quicksum(n3[v_id] * c3[v_id] for v_id in vertices if v_id != []),
index = 0,
priority = 2,
name = "maximize_nmr_similarity",
)
'''
#Multiply node value with infow or outflow
'''
model.setObjectiveN(
quicksum(n1[t_id[0]] * c1[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != [])
+quicksum(n3[t_id[0]] * c3[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
index = 0,
priority = 2,
name = "maximize_nmr_similarity",
)
'''
#Hybrid of both above
model.setObjective(
quicksum(n1[t_id[0]] * c1[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != [])
+quicksum(n3[t_id[0]] * c3[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
sense = GRB.MAXIMIZE,
)
model.addConstr(gp.quicksum(c1[v_id] for v_id in vertices) == 1)
model.addConstr(gp.quicksum(c3[v_id] for v_id in vertices) == 1)
for v_id in vertices:
model.addConstr(c1[v_id] + c3[v_id] <= 1)
model.addConstr(gp.quicksum(x[e_id] for e_id, (_, t_id) in hyperedges.items()) == 5)
''''
model.setObjectiveN(
quicksum(-1 *b[e_id] for e_id in hyperedges),
index=1,
priority=1,
name="minimize_used_hyperedges",
)
'''
return model, x, b
def positive_entries(variable_dict, threshold=0.5):
def positive_entries(variable_dict, threshold = 0.5):
return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
def print_solution(title, flow_solution, binary_solution, hyperedges):
@@ -126,7 +168,7 @@ 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", HYPEREDGES, VERTICES, NMRLIKELYHOODS)
model, x, b = build_model("HypergraphFlow", HYPEREDGES, VERTICES, NMRLIKELYHOODS1, NMRLIKELYHOODS2, NMRLIKELYHOODS3)
model.optimize()
if model.status != GRB.Status.OPTIMAL:
print("No optimal solution found for the first model.")