148 lines
6.0 KiB
Python
148 lines
6.0 KiB
Python
import numpy as np
|
|
import gurobipy as gp
|
|
from gurobipy import GRB, Model, quicksum
|
|
|
|
HYPERGRAPH = {
|
|
1: ([], ['Xanthine']),
|
|
2: (['Xanthine'], ['1-Methylxanthine']),
|
|
3: (['Xanthine'], ['3-Methylxanthine']),
|
|
4: (['Xanthine'], ['7-Methylxanthine']),
|
|
5: (['1-Methylxanthine'], ['Theophylline']),
|
|
6: (['1-Methylxanthine'], ['Paraxanthine']),
|
|
7: (['3-Methylxanthine'], ['Theophylline']),
|
|
8: (['3-Methylxanthine'], ['Theobromine']),
|
|
9: (['7-Methylxanthine'], ['Paraxanthine']),
|
|
10: (['7-Methylxanthine'], ['Theobromine']),
|
|
11: (['Theophylline'], ['Caffeine']),
|
|
12: (['Paraxanthine'], ['Caffeine']),
|
|
13: (['Theobromine'], ['Caffeine']),
|
|
14: (['Caffeine'], []),
|
|
}
|
|
|
|
FIXED_FLOWS = {
|
|
#1: 1,
|
|
#14: 1,
|
|
}
|
|
|
|
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}
|
|
|
|
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
|
|
|
|
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())
|
|
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}")
|
|
|
|
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}")
|
|
|
|
reaction_path = {}
|
|
|
|
|
|
if excluded_support:
|
|
model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name = "different_hyperedges",)
|
|
|
|
#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)
|
|
|
|
model.setObjective(quicksum(1000 * ele[e_id] * b[e_id] - x[e_id] for e_id in hyperedges),GRB.MAXIMIZE)
|
|
|
|
return model, x, b
|
|
|
|
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):
|
|
print(f"\n{title}:")
|
|
for e_id in sorted(flow_solution):
|
|
flow = flow_solution[e_id]
|
|
tails, heads = hyperedges[e_id]
|
|
print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}")
|
|
print("\nBinary Variables:")
|
|
for e_id in sorted(binary_solution):
|
|
print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}")
|
|
|
|
print(f"\nTotal flow: {sum(flow_solution.values())}")
|
|
print(f"Number of used hyperedges: {len(binary_solution)}")
|
|
|
|
def main():
|
|
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, HYPERGRAPH)
|
|
|
|
""" excluded_support = list(optimal_binary_solution.keys())
|
|
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, HYPERGRAPH, VERTICES)
|
|
else:
|
|
print("No optimal solution found for the second best model.")
|
|
"""
|
|
if __name__ == "__main__":
|
|
main() |