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PL_order/PL_order.py

+import numpy as np
+from cylp.cy import CyClpSimplex
+from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
+from cylp.py.mip import SimpleNodeCompare
+from cylp.cy.CyCgl import CyCglGomory, CyCglClique, CyCglKnapsackCover
+ 
+from cylp.py.utils.sparseUtil import csr_matrixPlus
+import networkx as nx
+import numpy as np
+import time
+import sys
+
+
+
+from scipy.sparse import csc_matrix
+
+
+def load_graph(file_path, isdirected): 
+    f = open(file_path,"r")
+    if isdirected :
+        G = nx.DiGraph()	# the graph is directed
+    else:
+        G = nx.Graph()		# the graph is undirected
+    lines = f.readlines()
+    for line in lines :
+        nodes = line.split('\t')
+        if int(nodes[0])!=int(nodes[1]):
+            #G.add_node(int(nodes[0]))
+            #G.add_node(int(nodes[1]))
+            G.add_edge(int(nodes[0]),int(nodes[1]))
+    return G
+
+
+def PL_variables(G,t,pr): 
+    
+    all_paths = []
+    edges_dict ={}  # key : edge ,  value : edge index
+    dict_paths  ={} # key : edge ,  value : path index
+    # init 
+    for n in G.nodes() : 
+        for n2 in list(G.neighbors(n)):
+            dict_paths[(n,n2)] = []
+            #dict_paths[(n2,n)] = []
+    # Extract all simple path with length <= t 
+    edges= list(G.edges())
+    i = 0
+    nodes = list(G.nodes())
+    for i1 in range(len(nodes)-1):
+        n = nodes[i1]
+        for i2 in range(i1+1,len(nodes)) : 
+           l = nodes[i2]
+           paths =  nx.all_simple_paths(G,source =n ,target = l, cutoff= t) 
+           l_path  = [ list(p) for p in map(nx.utils.pairwise, paths)]
+           for p in l_path  :
+                all_paths.append(p)
+                if  l in list(G.neighbors(n)) :
+                    dict_paths[(n,l)].append(len(edges)+i)
+                    dict_paths[(l,n)].append(len(edges)+i)
+                i+=1
+
+    print("The number of paths : " + str(len(all_paths)))
+    # creating edges dict 
+
+
+    edges_dict ={}
+    i = 0 
+    for e in edges :
+        u = e[0]
+        v = e[1]
+        edges_dict[(u,v)] = i 
+        edges_dict[(v,u)] = i 
+        i+=1
+    # creating pl variables 
+    objs = [1 for i in range(len(edges))] + [0 for j in range(len(all_paths))]
+    lhs_ineqs_r = []
+    lhs_ineqs_c = []
+    lhs_ineqs_d = []
+
+    lhs_ineqs2_r = []
+    lhs_ineqs2_c = []
+    lhs_ineqs2_d = []
+
+    
+    rhs_ineqs = []
+    rhs_ineqs2 = []
+    # inequality (3) 
+    r_i = 0
+    path_index = len(edges)
+    for path in all_paths : 
+        for e in path :
+            lhs_ineqs_r.append(r_i)
+            lhs_ineqs_c.append(edges_dict[e])
+            lhs_ineqs_d.append(-1)
+            lhs_ineqs_r.append(r_i)
+            lhs_ineqs_c.append(path_index)
+            lhs_ineqs_d.append(1)
+            rhs_ineqs.append(0)
+            r_i+=1
+        path_index+=1
+        
+    # inequality (4)
+    for e in edges :
+        u,v = e
+        if v < u:
+            e =(v,u) 
+        find =False 
+        for  i in dict_paths[e] :
+            find = True
+            lhs_ineqs_r.append(r_i)
+            lhs_ineqs_c.append(i)
+            lhs_ineqs_d.append(1)
+        if find : 
+            r_i+=1
+            rhs_ineqs.append(1)
+
+    # inequality (5)
+    r_i2 = 0 
+    for i in range(1,t+1):
+        for  u in G.nodes(): 
+            find = False
+            for v in  list(G.neighbors(u)):
+                pis =  dict_paths[(u,v)]
+                if v < u :
+                    pis =  dict_paths[(v,u)]
+                for pi in pis:
+                    if (len(all_paths[pi-len(edges)])<= i):
+                        find = True
+                        lhs_ineqs2_r.append(r_i2)
+                        lhs_ineqs2_c.append(pi)
+                        lhs_ineqs2_d.append(1)
+            if find :
+                r_i2+=1
+                prsn = pr[i]*len(list(G.neighbors(u))) 
+                rhs_ineqs2.append(prsn)
+ 
+    row = np.array(lhs_ineqs_r)
+    col = np.array(lhs_ineqs_c)
+    data = np.array(lhs_ineqs_d)
+    lhs_ineqs = csc_matrix((data, (row, col)), shape=(r_i, len(objs)))   
+    row = np.array(lhs_ineqs2_r)
+    col = np.array(lhs_ineqs2_c)
+    data = np.array(lhs_ineqs2_d)
+    lhs_ineqs2 = csc_matrix((data, (row, col)), shape=(r_i2, len(objs)))   
+    return objs,lhs_ineqs,rhs_ineqs,lhs_ineqs2,rhs_ineqs2
+
+
+
+
+
+def save_graph(G,filename):
+    file = open(file_name,"w") 
+    for e in G.edges():
+        file.write(str(e[0])+'\t'+str(e[1])+ "\n")
+    file.close()
+
+
+
+
+
+
+
+
+
+if __name__ == "__main__":
+    
+    np.set_printoptions(threshold=sys.maxsize)
+    for i in range(30):
+        file_name = sys.argv[1]  + str(i)  + ".txt" 
+        file_name_o = sys.argv[2] + str(i) + "_PL.txt"
+        algo =  sys.argv[3]
+        G =  load_graph( file_name , False)
+        t = 2
+        p = [0,0.0,0.5]
+        print ("Computing variables . . .  ")
+        start = time.time()
+        objs,lhs_ineqs,rhs_ineqs,lhs_ineqs2,rhs_ineqs2 = PL_variables(G,t,p)
+        end = time.time()
+        print("Runtime : " + str( end-start ))
+    
+        print("Solving the linear problem . . . ")
+        s =  CyClpSimplex()
+        x =  s.addVariable('x',len(objs),isInt=True)
+        #s.setInteger(x[0:len(objs)])
+        # Create coefficients and bounds
+        A = lhs_ineqs
+        a = CyLPArray(rhs_ineqs)
+        B = lhs_ineqs2
+        b = CyLPArray(rhs_ineqs2)
+    
+        # Add constraints  
+        s += A * x <= a
+        s += 0 <= x <= 1 
+        s += B * x >= b
+
+
+        # Set the objective function
+        c = CyLPArray(objs)
+        s.objective = c * x 
+        if ( algo == '1' ) : 
+            print("Relaxed problem")
+            s.primal()
+            sol = s.primalVariableSolution['x']
+        else :
+            print("Integer linear programming")
+            s.copyInIntegerInformation(np.array ( s.nCols * [ True ], np.uint8 ))
+            cbcModel =  s.getCbcModel ()
+            cbcModel.solve()
+            sol = cbcModel.primalVariableSolution['x']
+
+        end = time.time()
+        print("Runtime : " + str( end-start ))    
+        data = np.asarray(sol)
+
+        score_list = list(data)
+        scores   = score_list[0:len(list(G.edges()))]
+        edges = list(G.edges())
+
+        for i in range(len(scores)) :
+            if scores[i] < 0 :
+                scores[i] = 0
+        # sorting the edges 
+        dict_edges_scores = {}
+        for i in range(len(scores)) :
+            dict_edges_scores[i] = scores[i]
+        
+        sorted_edges =  list({k: v for k, v in sorted(dict_edges_scores.items(), key=lambda item: item[1],reverse=True)}.keys())
+        
+
+        file = open(file_name_o,"w") 
+        for s in sorted_edges:
+            file.write(str(edges[s][0])+'\t'+str(edges[s][1])+'\t'+ str(dict_edges_scores[s])+ "\n")
+        file.close()
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