127 lines
4.9 KiB
Python
127 lines
4.9 KiB
Python
import gurobipy as gp
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from gurobipy import GRB, Model, quicksum
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HYPEREDGES = {
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1: ([], ['Xanthine']),
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2: (['Xanthine'], ['1-Methylxanthine']),
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3: (['Xanthine'], ['3-Methylxanthine']),
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4: (['Xanthine'], ['7-Methylxanthine']),
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5: (['1-Methylxanthine'], ['Theophylline']),
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6: (['1-Methylxanthine'], ['Paraxanthine']),
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7: (['3-Methylxanthine'], ['Theophylline']),
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8: (['3-Methylxanthine'], ['Theobromine']),
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9: (['7-Methylxanthine'], ['Paraxanthine']),
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10: (['7-Methylxanthine'], ['Theobromine']),
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11: (['Theophylline'], ['Caffeine']),
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12: (['Paraxanthine'], ['Caffeine']),
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13: (['Theobromine'], ['Caffeine']),
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14: (['Caffeine'], []),
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}
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VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine']
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#Change to only have one likelihood, where with manual order association
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NMRLIKELYHOODS = [0.0, 0.49, 0.43, 0.6, 0.5, 0.51, 0.67, 0.0]
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#Results for comparison with 7-Methylxanthine bei shift von 2.5
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NMRLIKELYHOODS1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51]
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#Results for comparison with 3,7-Methylxanthine bei shift von 2.5
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NMRLIKELYHOODS2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59]
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#Results for comparison with 3,7-Methylxanthine with Methyl C of spectrum 2 bei shift von 2.5
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NMRLIKELYHOODS3 = [0.48, 0.5, 0.51, 0.59, 0.5, 0.51, 0.67, 0.61]
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FIXED_FLOWS = {
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1: 1,
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14: 1,
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}
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def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=None):
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model = Model(name)
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x = {e_id: model.addVar(vtype=GRB.INTEGER, lb = 0, name = f"x_{e_id}") for e_id in hyperedges}
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b = {e_id: model.addVar(vtype=GRB.BINARY, name = f"b_{e_id}") for e_id in hyperedges}
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n = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr")
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for v, nmr in zip(vertices, nmrlikelihoods):
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n[v] = nmr
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vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
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for v in vertices:
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inflow = quicksum(x[e_id] for e_id, (_, heads) in hyperedges.items() if v in heads)
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outflow = quicksum(x[e_id] for e_id, (tails, _) in hyperedges.items() if v in tails)
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model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
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for e_id, value in FIXED_FLOWS.items():
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model.addConstr(x[e_id] == value, name = f"fixed_flow_{e_id}")
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for e_id in hyperedges:
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model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name = f"unused_implies_zero_{e_id}")
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model.addConstr(x[e_id] >= b[e_id], name = f"used_implies_positive_flow_{e_id}")
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reaction_path = {}
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if excluded_support:
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model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name = "different_hyperedges",)
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#Multiplizier den node Wert mit infow + outflow
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model.ModelSense = GRB.MAXIMIZE
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#Multiply node value with infow or outflow
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model.setObjectiveN(
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quicksum(n[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
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index = 0,
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priority = 2,
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name = "maximize_nmr_similarity",
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)
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model.setObjectiveN(
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quicksum(-1 * x[e_id] for e_id in hyperedges),
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index=1,
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priority=1,
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name="minimize_used_hyperedges",
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)
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return model, x, b
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def positive_entries(variable_dict, threshold = 0.5):
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return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
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def print_solution(title, flow_solution, binary_solution, hyperedges):
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print(f"\n{title}:")
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for e_id in sorted(flow_solution):
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flow = flow_solution[e_id]
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tails, heads = hyperedges[e_id]
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print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}")
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print("\nBinary Variables:")
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for e_id in sorted(binary_solution):
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print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}")
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print(f"\nTotal flow: {sum(flow_solution.values())}")
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print(f"Number of used hyperedges: {len(binary_solution)}")
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def main():
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model, x, b = build_model("HypergraphFlow", HYPEREDGES, VERTICES, NMRLIKELYHOODS)
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model.optimize()
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if model.status != GRB.Status.OPTIMAL:
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print("No optimal solution found for the first model.")
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return
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optimal_solution = positive_entries(x)
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optimal_binary_solution = positive_entries(b)
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print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPEREDGES)
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""" excluded_support = list(optimal_binary_solution.keys())
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second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPEREDGES, excluded_support=excluded_support,)
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second_model.optimize()
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if second_model.status == GRB.Status.OPTIMAL:
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second_solution = positive_entries(x2)
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second_binary_solution = positive_entries(b2)
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print_solution("Second Best Solution", second_solution, second_binary_solution, HYPEREDGES, VERTICES)
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else:
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print("No optimal solution found for the second best model.")
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"""
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if __name__ == "__main__":
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main() |