diff --git a/ILP/butadien/butadiensynthesis.py b/ILP/butadien/butadiensynthesis.py index c2244c4..eab2fa8 100644 --- a/ILP/butadien/butadiensynthesis.py +++ b/ILP/butadien/butadiensynthesis.py @@ -87,70 +87,6 @@ HYPERGRAPH3 = { 2: (['p_{0,0}'], []), } -VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}'] -#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] -#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] -#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] -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)] - -#Kombiniert Molekül it Wahrscheinlichkeit für NMR1: -VERTICE1 = {} -for vertice, likelihood in zip(VERTICES, NMR1): - VERTICE1[vertice] = likelihood -#Kombiniert Molekül it Wahrscheinlichkeit für NMR2: -VERTICE2 = {} -for vertice, likelihood in zip(VERTICES, NMR2): - VERTICE2[vertice] = likelihood -#Kombiniert Molekül it Wahrscheinlichkeit für NMR3: -VERTICE3 = {} -for vertice, likelihood in zip(VERTICES, NMR3): - VERTICE3[vertice] = likelihood - -#Kantenwahrscheinlichkeiten für NMR1: -EDGE1 = {} -#Kantenwahrscheinlichkeiten für NMR2: -EDGE2 = {} -#Kantenwahrscheinlichkeiten für NMR3: -EDGE3 = {} -#Maximum der verschiedenen Kantenwahrscheinlichkeiten: -EDGEMAX = {} - -modes = ["Product", "Average"] -mode = modes[0] - -#Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden -#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte -for edge, (tails, heads) in HYPERGRAPH.items(): - if heads == [] or tails == []: - EDGE1[edge] = 0.0 - EDGE2[edge] = 0.0 - EDGE3[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) - EDGE3[edge] = round(np.prod([VERTICE3[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) - 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[23]) -#print(EDGEMAX[42]) - FIXED_FLOWS = { #213: 3, @@ -171,7 +107,6 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None): vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads) - print(vertices) #Every item created has to be consumed: ''' for v in vertices: @@ -228,7 +163,7 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None): #2 Butadien create first different molecule and it has to be created first: startmolecule = ["Butadien"] - model.addConstr(quicksum(b[e_id] for e_id, (tails, _) in hyperedges.items() if list(set(tails)) == startmolecule) == 1) + #model.addConstr(quicksum(b[e_id] for e_id, (tails, _) in hyperedges.items() if list(set(tails)) == startmolecule) == 1) #model.addConstr(b[4] + b[7] == 1) model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails == [] and heads == startmolecule)== 1) @@ -251,8 +186,8 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None): 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 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 edgelikelihoods(variable_dict, edgelikelihood, threshold = 0.5): + return {e_id: edgelikelihood[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}:") @@ -269,6 +204,73 @@ def print_solution(title, flow_solution, binary_solution, edge_likelihoods, hype print(f"Number of used hyperedges: {len(binary_solution)}") def main(): + VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}'] + #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] + #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] + #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] + 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'] + + #Chosable parameters + modes = ["Product", "Average"] + mode = modes[1] + normalize = True + + if normalize: + print("test") + NMR1 = [round(l/sum(NMR1), 2) for l in NMR1] + NMR2 = [round(l/sum(NMR2), 2) for l in NMR2] + NMR3 = [round(l/sum(NMR3), 2) for l in NMR3] + + + #Kombiniert Molekül it Wahrscheinlichkeit für NMR1: + VERTICE1 = {} + for vertice, likelihood in zip(VERTICES, NMR1): + VERTICE1[vertice] = likelihood + #Kombiniert Molekül it Wahrscheinlichkeit für NMR2: + VERTICE2 = {} + for vertice, likelihood in zip(VERTICES, NMR2): + VERTICE2[vertice] = likelihood + #Kombiniert Molekül it Wahrscheinlichkeit für NMR3: + VERTICE3 = {} + for vertice, likelihood in zip(VERTICES, NMR3): + VERTICE3[vertice] = likelihood + + #Kantenwahrscheinlichkeiten für NMR1: + EDGE1 = {} + #Kantenwahrscheinlichkeiten für NMR2: + EDGE2 = {} + #Kantenwahrscheinlichkeiten für NMR3: + EDGE3 = {} + #Maximum der verschiedenen Kantenwahrscheinlichkeiten: + EDGEMAX = {} + + #Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden + #Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte + for edge, (tails, heads) in HYPERGRAPH.items(): + if heads == [] or tails == []: + EDGE1[edge] = 0.0 + EDGE2[edge] = 0.0 + EDGE3[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) + EDGE3[edge] = round(np.prod([VERTICE3[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) + 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[23]) + #print(EDGEMAX[42]) + + model, x, b = build_model("HypergraphFlow", HYPERGRAPH, EDGEMAX, EDGE1, EDGE2, EDGE3) model.optimize() if model.status != GRB.Status.OPTIMAL: @@ -276,7 +278,7 @@ def main(): return optimal_solution = positive_entries(x) optimal_binary_solution = positive_entries(b) - optimal_edgelikelihoods = edgelikelihoods(b) + optimal_edgelikelihoods = edgelikelihoods(b, EDGEMAX) print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, optimal_edgelikelihoods, HYPERGRAPH) excluded_support = list(optimal_binary_solution.keys()) @@ -286,7 +288,7 @@ def main(): if second_model.status == GRB.Status.OPTIMAL: second_solution = positive_entries(x2) second_binary_solution = positive_entries(b2) - second_edgelikelihoods = edgelikelihoods(b2) + second_edgelikelihoods = edgelikelihoods(b2, EDGEMAX) print_solution("Second Best Solution", second_solution, second_binary_solution, second_edgelikelihoods, HYPERGRAPH) else: print("No optimal solution found for the second best model.")