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Dateien nach "ILP/Kaffee" hochladen

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kilian
2026-09-16 10:20:34 +02:00
parent 1435b14397
commit c91b1b0ca9
1 changed files with 65 additions and 44 deletions
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ILP/Kaffee/caffeinesynthesis.py
+65 -44
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@@ -1,7 +1,8 @@
import numpy as np
import gurobipy as gp
from gurobipy import GRB, Model, quicksum
HYPEREDGES = {
HYPERGRAPH = {
1: ([], ['Xanthine']),
2: (['Xanthine'], ['1-Methylxanthine']),
3: (['Xanthine'], ['3-Methylxanthine']),
@@ -18,38 +19,22 @@ HYPEREDGES = {
14: (['Caffeine'], []),
}
VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine']
#Change to only have one likelihood, where with manual order association
NMRLIKELYHOODS = [0.0, 0.49, 0.43, 0.6, 0.5, 0.51, 0.67, 0.0]
#Results for comparison with 7-Methylxanthine bei shift von 2.5
NMRLIKELYHOODS1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51]
#Results for comparison with 3,7-Methylxanthine bei shift von 2.5
NMRLIKELYHOODS2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59]
#Results for comparison with 3,7-Methylxanthine with Methyl C of spectrum 2 bei shift von 2.5
NMRLIKELYHOODS3 = [0.48, 0.5, 0.51, 0.59, 0.5, 0.51, 0.67, 0.61]
FIXED_FLOWS = {
1: 1,
14: 1,
#1: 1,
#14: 1,
}
def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=None):
def build_model(name, hyperedges, vertices, ele, el1, el2, 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")
for v, nmr in zip(vertices, nmrlikelihoods):
n[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)
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())
model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
for e_id, value in FIXED_FLOWS.items():
@@ -64,25 +49,11 @@ def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=Non
if excluded_support:
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.ModelSense = GRB.MAXIMIZE
#Excluding creation and destruction only three reactions for three nmr
model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails != [] and heads != []) == 3)
#Multiply node value with infow or outflow
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",
)
model.setObjectiveN(
quicksum(-1 * x[e_id] for e_id in hyperedges),
index=1,
priority=1,
name="minimize_used_hyperedges",
)
model.setObjective(quicksum(1000 * ele[e_id] * b[e_id] - x[e_id] for e_id in hyperedges),GRB.MAXIMIZE)
return model, x, b
@@ -103,25 +74,75 @@ 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)
VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine']
#Results for comparison with 7-Methylxanthine bei shift von 2.5
NMR1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51]
#Results for comparison with 3,7-Methylxanthine bei shift von 2.5
NMR2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59]
#Change to only have one likelihood, where with manual order association
NMRE = [0.0, 0.49, 0.43, 0.6, 0.5, 0.51, 0.67, 0.0]
#Chosable parameters
modes = ["Product", "Average"]
mode = modes[0]
normalize = False
#Kombiniert Molekül mit Wahrscheinlichkeit für NMR1:
VERTICE1 = {}
for vertice, likelihood in zip(VERTICES, NMR1):
VERTICE1[vertice] = likelihood
#Kombiniert Molekül mit Wahrscheinlichkeit für NMR2:
VERTICE2 = {}
for vertice, likelihood in zip(VERTICES, NMR2):
VERTICE2[vertice] = likelihood
#Kombiniert Molekül mit Wahrscheinlichkeit für NMR Empiric:
VERTICEE = {}
for vertice, likelihood in zip(VERTICES, NMRE):
VERTICEE[vertice] = likelihood
#Kantenwahrscheinlichkeiten für NMR1:
EDGE1 = {}
#Kantenwahrscheinlichkeiten für NMR2:
EDGE2 = {}
#Maximum der verschiedenen Kantenwahrscheinlichkeiten:
EDGEE = {}
for edge, (tails, heads) in HYPERGRAPH.items():
if heads == [] or tails == []:
EDGE1[edge] = 0.0
EDGE2[edge] = 0.0
EDGEE[edge] = 0.0
elif mode == "Product":
#print([VERTICE1[head] for head in heads])
EDGE1[edge] = round(np.prod([VERTICE1[head] for head in heads]),2)
EDGE2[edge] = round(np.prod([VERTICE2[head] for head in heads]),2)
EDGEE[edge] = round(np.prod([VERTICEE[head] for head in heads]),2)
elif mode == "Average":
EDGE1[edge] = round(sum(VERTICE1[head] for head in heads)/len(heads),2)
EDGE2[edge] = round(sum(VERTICE2[head] for head in heads)/len(heads),2)
EDGEE[edge] = round(sum(VERTICEE[head] for head in heads)/len(heads),2)
model, x, b = build_model("HypergraphFlow", HYPERGRAPH, VERTICES, EDGEE, EDGE1, EDGE2)
model.optimize()
if model.status != GRB.Status.OPTIMAL:
print("No optimal solution found for the first model.")
return
optimal_solution = positive_entries(x)
optimal_binary_solution = positive_entries(b)
print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPEREDGES)
print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPERGRAPH)
""" excluded_support = list(optimal_binary_solution.keys())
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPEREDGES, excluded_support=excluded_support,)
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPERGRAPH, excluded_support=excluded_support,)
second_model.optimize()
if second_model.status == GRB.Status.OPTIMAL:
second_solution = positive_entries(x2)
second_binary_solution = positive_entries(b2)
print_solution("Second Best Solution", second_solution, second_binary_solution, HYPEREDGES, VERTICES)
print_solution("Second Best Solution", second_solution, second_binary_solution, HYPERGRAPH, VERTICES)
else:
print("No optimal solution found for the second best model.")
"""
"""
if __name__ == "__main__":
main()