From 03055f9946a42c74b6fc5b2d112b240f85510db7 Mon Sep 17 00:00:00 2001 From: kilian Date: Mon, 7 Sep 2026 12:51:05 +0200 Subject: [PATCH] np.prod, head tail zuordnungs korrektur --- ILP/butadien/butadiensynthesis.py | 69 ++++++++++++++++++------------- 1 file changed, 41 insertions(+), 28 deletions(-) diff --git a/ILP/butadien/butadiensynthesis.py b/ILP/butadien/butadiensynthesis.py index ebf65a1..9f8ce38 100644 --- a/ILP/butadien/butadiensynthesis.py +++ b/ILP/butadien/butadiensynthesis.py @@ -1,4 +1,4 @@ -import math +import numpy as np import gurobipy as gp from gurobipy import GRB, Model, quicksum @@ -91,15 +91,15 @@ VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', ' #Vergleich mit dem NMR von Ethylen und Hexatrien NMR1 = [0.32, 0.5, 0.87, 0.0, 0.11, 0.58, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0, 0.17, 0.0, 0.09] #Normalisiert -NMR1 = [0.11, 0.18, 0.31, 0.0, 0.04, 0.21, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.06, 0.0, 0.03] +#NMR1 = [0.11, 0.18, 0.31, 0.0, 0.04, 0.21, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.06, 0.0, 0.03] #Vergleich mit dem NMR von Ethylen und Octrien NMR2 = [0.33, 0.43, 0.6, 0.0, 0.13, 0.9, 0.07, 0.01, 0.0, 0.0, 0.01, 0.0, 0.0, 0.01, 0.1, 0.0, 0.0, 0.22, 0.0, 0.13] #Normalisiert -NMR2 = [0.11, 0.15, 0.21, 0.0, 0.04, 0.31, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.07, 0.0, 0.04] +#NMR2 = [0.11, 0.15, 0.21, 0.0, 0.04, 0.31, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.07, 0.0, 0.04] #Vergleich mit dem NMR von Ethylen und Benzol NMR3 = [0.0, 0.32, 0.0, 0.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.02, 0.0, 0.29, 0.95, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05] #Normalisiert -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, 0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03] +#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, 0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03] 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)] @@ -127,22 +127,30 @@ EDGEMAX = {} modes = ["Product", "Average"] mode = modes[0] + +#print(round(np.prod([1, 0.9]))) #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: +for edge, (tails, heads) in HYPERGRAPH.items(): + if heads == [] or tails == []: EDGE1[edge] = 0.0 EDGE2[edge] = 0.0 EDGE3[edge] = 0.0 - EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE2[edge]]) + elif mode == "Product": + #Produkt klappt nur, wenn normalisierte Likelihoods + #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) + EDGE3[edge] = round(np.prod([VERTICE3[head] for head in heads]),2) + elif mode == "Average": + print(tails) + 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) + EDGE3[edge] = round(sum(VERTICE3[head] for head in heads)/len(heads),2) + + EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE3[edge]]) + print(EDGEMAX[edge], HYPERGRAPH[edge]) +#print(EDGEMAX[4]) +#print(EDGEMAX[42]) FIXED_FLOWS = { @@ -180,8 +188,8 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike 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)) + for edge, (tails, heads) in hyperedges.items(): + count[mol] += x[edge] * (tails.count(mol) - heads.count(mol)) model.addConstr(count[mol] == 0) ''' @@ -209,7 +217,7 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike #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()), + quicksum(enmax[e_id] * b[e_id] for e_id in hyperedges), index = 0, priority = 2, name = "maximize_nmr_similarity", @@ -223,20 +231,20 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike ) #Excluding creation and destruction only three reactions for three nmr - model.addConstr(quicksum(b[e_id] for e_id, (heads, tails) in hyperedges.items() if tails != [] and heads != []) == 3) + model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails != [] and heads != []) == 3) #Restrict number of used edges to prevent using all available #model.addConstr(quicksum(x[e_id] for e_id in hyperedges) <= 16) #2 Butadien create first different molecule and it has to be created first: model.addConstr(b[4] + b[7] == 1) model.addConstr(b[213] == 1) - model.addConstr(b[41] == 1) + #model.addConstr(b[41] == 1) #No cyclic reaction pairs: - for e_id1, (heads1, tails1) in hyperedges.items(): - for e_id2, (heads2, tails2) in hyperedges.items(): + for e_id1, (tails1, heads1) in hyperedges.items(): + for e_id2, (tails2, heads2) in hyperedges.items(): if (heads1, tails1) == (tails2, heads2): #print(e_id1, e_id2) model.addConstr((b[e_id1] + b[e_id2]) <= 1) @@ -247,12 +255,16 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike 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): +def edgelikelihoods(variable_dict, threshold = 0.5): + return {e_id: EDGEMAX[e_id] for e_id, var in variable_dict.items() if var.X > threshold} + +def print_solution(title, flow_solution, binary_solution, edge_likelihoods, 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}") + likelihood = edge_likelihoods[e_id] + print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}, With likelihood = {likelihood}") print("\nBinary Variables:") for e_id in sorted(binary_solution): print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}") @@ -268,7 +280,8 @@ def main(): return optimal_solution = positive_entries(x) optimal_binary_solution = positive_entries(b) - print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPERGRAPH) + optimal_edgelikelihoods = edgelikelihoods(b) + print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, optimal_edgelikelihoods, HYPERGRAPH) excluded_support = list(optimal_binary_solution.keys()) second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPERGRAPH, EDGEMAX, EDGE1, EDGE2, EDGE3, excluded_support=excluded_support,) @@ -277,9 +290,9 @@ def main(): 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) + second_edgelikelihoods = edgelikelihoods(b2) + print_solution("Second Best Solution", second_solution, second_binary_solution, second_edgelikelihoods, HYPERGRAPH) else: print("No optimal solution found for the second best model.") - if __name__ == "__main__": main() \ No newline at end of file