Dateien nach "ILP/butadien" hochladen

ILP/butadien/butadiensynthesis.py

@@ -1,3 +1,4 @@
+import math
 import gurobipy as gp
 from gurobipy import GRB, Model, quicksum
 
@@ -65,6 +66,27 @@ HYPERGRAPH = {
 	 211: (['Butadien', 'p_{0,11}'], ['p_{0,18}']),
 	 213: ([], ['Butadien']),
 }
+
+HYPERGRAPH2 = {
+    2: (['p_{0,0}'], []),
+    3: (['p_{0,1}'], []),
+    4: (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
+    6: (['p_{0,2}'], []),
+    7: (['Butadien', 'Butadien'], ['p_{0,2}']),
+    17: (['p_{0,4}'], []),
+    23: (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
+    41: (['p_{0,11}'], []),
+    42: (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
+    213: ([], ['Butadien']),
+}
+
+HYPERGRAPH3 = {
+    0: (['Butadien'], []),
+    213: ([], ['Butadien']),
+    4: (['Butadien', 'Butadien'], ['p_{0,0}', 'Butadien']), 
+    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]
@@ -75,8 +97,11 @@ 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.
 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)]
 
+#Implement Edgelikelihood before Model
+
+
 FIXED_FLOWS = {
-    213: 3,
+    #213: 3,
     #2: 3,
     #4: 1,
     #23: 1,
@@ -89,6 +114,7 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
 
     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}
+    count = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, name = "count")
     nmax = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmrmax")
     n1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr1")
     n2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr2")
@@ -104,10 +130,16 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
         n1[v] = nmr1
         n2[v] = nmr2
         n3[v] = nmr3
+        count[v] = 0
 
     #Assigns the edges a likelihood based on the products
     for e, (tails, heads) in hyperedges.items():
-        if heads != [] and tails != []:
+        en1[e] = math.prod(n1[head] for head in heads)
+        en2[e] = math.prod(n2[head] for head in heads)
+        en3[e] = math.prod(n3[head] for head in heads)
+        enmax[e] = max([en1[e], en2[e], en3[e]])
+        '''
+        if heads != [] and tails != []: #Multiplication better?
             enmax[e] = quicksum(nmax[head] for head in heads)/len(heads)
             en1[e] = quicksum(n1[head] for head in heads)/len(heads)
             en2[e] = quicksum(n2[head] for head in heads)/len(heads)
@@ -117,15 +149,15 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
             en1[e] = 0.0
             en2[e] = 0.0
             en3[e] = 0.0 
-
+        '''
-    print(enmax[123])
+    #print(enmax[123])
-    print(enmax[42])
+    #print(enmax[42])
 
     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)
+        inflow = quicksum(x[e_id] * heads.count(v) 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)
+        outflow = quicksum(x[e_id] * tails.count(v) for e_id, (tails, _) in hyperedges.items() if v in tails)
         model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
 
     for e_id, value in FIXED_FLOWS.items():
@@ -144,33 +176,40 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
     #Multiplizier den node Wert mit infow + outflow
     model.ModelSense = GRB.MAXIMIZE
 
-    #Multiply node value with infow or outflow
+    #Multiply edgelikelihood with the edge use boolean
     model.setObjectiveN(
-        quicksum(enmax[e_id] * x[e_id] for e_id, (_, _) in hyperedges.items()),
+        quicksum(enmax[e_id] * b[e_id] for e_id, (_, _) in hyperedges.items()),
         index = 0,
         priority = 2,
         name = "maximize_nmr_similarity",
     )  
-
+    #Minimize the overall flow
     model.setObjectiveN(
-        quicksum(x[e_id] for e_id in hyperedges),
+        quicksum(-1 * x[e_id] for e_id in hyperedges),
         index=1,
-        priority= - 1,
+        priority= 1,
         name="minimize_used_hyperedges",
     )
-
+    
     #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) 
     #Restrict number of used edges to prevent using all available
-    #model.addConstr(quicksum(x[e_id] for e_id in hyperedges) <= 10)
+    #model.addConstr(quicksum(x[e_id] for e_id in hyperedges) <= 16)
 
-    #2 Butadien create first different molecule:
+    #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)
 
     #Every item created has to be consumed:
-    
+    for mol in vertices:
+        for edge, (heads, tails) in hyperedges.items():
+            count[mol] += x[edge] * (heads.count(mol) - tails.count(mol))
+        #model.addConstr(count[mol] == 0)
+
 
     #No cyclic reaction pairs:
+
     for e_id1, (heads1, tails1) in hyperedges.items():
         for e_id2, (heads2, tails2) in hyperedges.items():
             if (heads1, tails1) == (tails2, heads2):