LinkPrediction.py 文件源码

def Prediction_Experiment(G, Predictor, Probe_Set, Top_L, Deleted_Ratio):
    print "Prediction_Experiment!"
    #Get Evaluation Link Set--------
    #Top_L = (G.number_of_edges() - 0) / Top_k #The top proportion 1/Top_k of edges are considered
    #Probe_Set = Probe_Set_Correspond_Training(G, Top_L, fpname)  #****Get the probe set for evaluation*****
    #Get Ranking List with different deleted links ratio----------
    Edge_Num = float(G.number_of_edges())

    '''AUC = Performance_Evaluation_AUC(Predictor, G, Probe_Set)'''
    Unobserved_links = nx.non_edges(G)
    Non_existing_links = list(set(Unobserved_links).difference(set(Probe_Set)))
    AUC = Performance_Evaluation_AUC(Predictor, G, Probe_Set, Non_existing_links)

    Rank_List_Set = Prediction_LinkScores_Ratio(G, Predictor, Deleted_Ratio, 50, 30) #Prediction_LinkScores_Ratio(G, Predictor, Proportion, Toleration, Predict_Gap)
    #----Performance Evaluation with Precision under different Training Data Ratio----
    Precision_Set = []
    X_Set = []
    Coefficient_Set = []
    Avg_PathLen_Set = []
    for key in sorted(Rank_List_Set.keys()):
        Rank_List_Sorted = sorted(Rank_List_Set[key][0], key=lambda edge: edge[2], reverse=True)
        Top_L_Rank_List = Rank_List_Sorted[0:Top_L]
        Coefficient_Set.append(Rank_List_Set[key][1])
        Avg_PathLen_Set.append(Rank_List_Set[key][2])
        #AUC_Set.append(Rank_List_Set[key][3])
        #print key, Performance_Evaluation_Precision(Top_L_Rank_List, Probe_Set)
        X_Set.append(float(key)/Edge_Num)
        Precision_Set.append(Performance_Evaluation_Precision(Top_L_Rank_List, Probe_Set))
        '''
        #Draw Curve Graph
        if key%100 == 0:
            data = []
            for edge in Rank_List_Sorted:
                data.append(edge[2])
            matploit(data)
        '''
    #end for
    print "*Different deleted links ratio:", X_Set
    print "*Precision_Set with different deleted links ratio:", Precision_Set
    print "*Coefficient_Set:", Coefficient_Set
    print "*Avg_PathLen_Set:", Avg_PathLen_Set
    print "*AUC Value:", AUC


    return 1




#def Native_Prediction_Experiment(G, Predictor, Probe_Set, Top_L, Deleted_Ratio):