Project: s41467-024-45598-0 | Function: topol_stacking_temporal_with_edgelist (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Performs temporal link prediction using stacked topological features across multiple network layers for completely unobserved target layer

**KEY TERMS:** {
  "edges_orig": "list of edge lists from previous time layers",
  "target_layer": "target layer to predict",
  "predict_num": "number of previous layers to use",
  "df_t_tr": "dataframe of true edge features",
  "df_f_tr": "dataframe of false edge features",
  "n_depth": "random forest tree depth",
  "n_est": "number of estimators"
}

**CONTEXT CODE:**     A_ho_aux = -1*A_ho[-1] + 1
    ne_ho = sparse.find(sparse.triu(A_ho_aux,1)) # false candidates
    Nsamples = 10000 # number of samples
    edge_t = [] # list of true edges (positive samples)
    edge_f = [] # list of false edges (negative samples)
    train_final = []
    
    for ll in range(Nsamples):
        edge_t_idx_aux = np.random.randint(len(e_diff[0]))
        edge_f_idx_aux = np.random.randint(len(ne_ho[0]))
        edge_t.append((e_diff[0][edge_t_idx_aux],e_diff[1][edge_t_idx_aux]))
        edge_f.append((ne_ho[0][edge_f_idx_aux],ne_ho[1][edge_f_idx_aux]))
    # store for later use
    try:
        os.mkdir("./edge_tf_tr/")
    except:
        print("")
    #if not os.path.isdir("./edge_tf_tr/"):
        #os.mkdir("./edge_tf_tr/")
    np.savetxt("./edge_tf_tr/edge_t_final"+"_"+str(name)+".txt",edge_t,fmt='%u')
    np.savetxt("./edge_tf_tr/edge_f_final"+"_"+str(name)+".txt",edge_f,fmt='%u')
    
    
    A_diff = A - A_ho[-1]
    e_diff = sparse.find(sparse.triu(A_diff,1)) # true candidates
    A_orig_aux = -1*A + 1
    ne_orig = sparse.find(sparse.triu(A_orig_aux,1)) # false candidates
    Nsamples = 10000 # number of samples
    edge_tt = [] # list of true edges (positive samples)
    edge_ff = [] # list of false edges (negative samples)
    
    for ll in range(Nsamples):
        edge_tt_idx_aux = np.random.randint(len(e_diff[0]))
        edge_ff_idx_aux = np.random.randint(len(ne_orig[0]))
        edge_tt.append((e_diff[0][edge_tt_idx_aux],e_diff[1][edge_tt_idx_aux]))
        edge_ff.append((ne_orig[0][edge_ff_idx_aux],ne_orig[1][edge_ff_idx_aux]))
    try:
        os.mkdir("./edge_tf_true/")
    except:
        print("")
    #if not os.path.isdir("./edge_tf_true/"):
        #os.mkdir("./edge_tf_true/")
    np.savetxt("./edge_tf_true/edge_t"+"_"+str(name)+".txt",edge_tt,fmt='%u')
    np.savetxt("./edge_tf_true/edge_f"+"_"+str(name)+".txt",edge_ff,fmt='%u')        


# #################


""
def topol_stacking_temporal_with_edgelist(edges_orig, target_layer, predict_num,name): 
    
    """ 
    Assuming we have no information at all for the target layer
    """
    
    # MASKED: Performs temporal link prediction using stacked topological features across multiple network layers for completely unobserved target layer implementation
    raise NotImplementedError("This function is masked for testing purposes.")


def topol_stacking_temporal_with_adjmatrix(adj_orig, target_layer, predict_num,name): 
    """
    input: 
    1. adj_orig: a list of dense metrices before the target layer matrix
    2. target_layer: the layer we aim to predict
    3. predict_num: number of layers wanted to use
    4. name: name of the dataset
    
    """

    A_tr = adj_orig
    A = target_layer
    num_nodes = int(A.shape[0]+1)
    
    edge_t_tr = []
    edge_f_tr = []
    df_f_tr = [] 
    df_t_tr = [] 
        
    
    
    sample_true_false_edges_temporal(A, A_tr, predict_num, name)

    
    for i in range(predict_num, len(A_tr)):
        edge_t_tr.append(np.loadtxt("./edge_tf_tr/edge_t_{}".format(i)+"_"+str(name)+".txt").astype('int'))
        edge_f_tr.append(np.loadtxt("./edge_tf_tr/edge_f_{}".format(i)+"_"+str(name)+".txt").astype('int'))
        df_temp, time_temp = gen_topol_feats_temporal(A_tr[i], A_tr[i-predict_num :i], edge_f_tr[i-predict_num])
        df_f_tr.append(df_temp)
        df_temp, time_temp = gen_topol_feats_temporal(A_tr[i], A_tr[i-predict_num :i], edge_t_tr[i-predict_num])
        df_t_tr.append(df_temp)
    
    
    edge_t_ho= np.loadtxt("./edge_tf_true/edge_t"+"_"+str(name)+".txt").astype('int')
    edge_f_ho= np.loadtxt("./edge_tf_true/edge_f"+"_"+str(name)+".txt").astype('int')
    df_f_ho, time1 = gen_topol_feats_temporal(A, A_tr[ -(predict_num):], edge_f_ho)
    df_t_ho, time2 = gen_topol_feats_temporal(A, A_tr[ -(predict_num):], edge_t_ho)


#    return df_f_tr, df_t_tr, df_f_ho
    df_t_tr_columns = df_t_tr[0].columns
    df_f_tr_columns = df_f_tr[0].columns    
    
    v1 = df_t_tr[0].values
    v2 = df_f_tr[0].values

    
    # Stack Together for df_f_ho, df_f_tr, df_t_ho, df_t_tr
    # Let the randomforest make selection
    
    for i in range(1, len(A_tr)-predict_num):
        v1 = np.vstack((v1,df_t_tr[i].values))
        v2 = np.vstack((v2,df_f_tr[i].values))

#    return df_t_tr, df_f_tr, df_t_ho, df_f_ho  
    df_t_tr_ = pd.DataFrame(data=v1, columns = df_t_tr_columns)
    df_f_tr_ = pd.DataFrame(data=v2, columns = df_f_tr_columns)
    
    
    
    
    
    column_name = list(df_t_tr_.columns)
    
    for j in range(1,predict_num+1):  
        for i in range (44*j, 44*(j+1)):
            column_name[i] = column_name[i]+"_"+str(j)
    
    #print(column_name)
    
    df_t_tr_.columns = column_name
    df_f_tr_.columns = column_name
    df_t_ho.columns = column_name
    df_f_ho.columns = column_name
    
    feat_path = "./ef_gen_tr/"+str(name)+"/"
    if not os.path.isdir(feat_path):
        os.makedirs(feat_path)
    df_t_tr_.to_pickle(feat_path + 'df_t')
    df_f_tr_.to_pickle(feat_path + 'df_f')
    
    feat_path = "./ef_gen_ho/"+str(name)+"/"
    if not os.path.isdir(feat_path):
        os.makedirs(feat_path)
    df_t_ho.to_pickle(feat_path + 'df_t')
    df_f_ho.to_pickle(feat_path + 'df_f')
    
    
    #return df_t_tr_,df_f_tr_,df_t_ho,df_f_ho
    


Complete the function implementation replacing "raise NotImplementedError".