mdbs/model/anchor_finder.py

from collections import defaultdict
import numpy as np
import copy
import json
import numpy as np
import os


def find_max_indices_numpy(L_dict):    
    keys_arr = np.array(list(L_dict.keys()))
    values_arr = np.array(list(L_dict.values()))
    max_val_from_dict = max(L_dict.values()) # 或者 np.max(values_arr)

    indices = np.where(values_arr == max_val_from_dict)[0]
    max_keys_np_way = keys_arr[indices]
    return max_keys_np_way, max_val_from_dict

def R(event_dict):
    mean = 0
    for _,v in event_dict.items():
        mean += -v
    mean /= len(event_dict)
    return mean
    

def select_social_sensors(R, L_dict, user_event_dict, event_user_dict, b, anchor_screen):
    """
    Parameters:
    - R: reward function, R(A) returns a numeric value
    - L_dict:  dict of node -> integer (event num)
    - user_event_dict: user -> event -> active_time
    - event_user_dict: event -> user_list
    - b: budget (numeric)

    Returns:
    - A: selected set of social sensors
    """
    print("Select social sensors begin!")
    anchor_screen = - anchor_screen
    user_event_dict_ = copy.deepcopy(user_event_dict)
    L_dict_ = copy.deepcopy(L_dict)
    
    V = list(L_dict_.keys())
    A = []
    f = lambda A: len(A)
    all_cas = 0
    while any(s not in A for s in V) and f(A) < b:
        
        indices_np, max_val_from_dict = find_max_indices_numpy(L_dict_)
        if max_val_from_dict == 0:
            print("no extra node in cas!!!!!")
            break
        TAR_set = set(indices_np)

        delta = {}
        cur = {}
        for s in TAR_set:
            delta[s] = float('inf')
            cur[s] = False

        c_star = []
        while True:
            s_star = max(delta, key=delta.get)
            c_star = user_event_dict_[s_star]
            if cur[s_star] == True:
                if delta[s_star] >= anchor_screen:
                    A.append(s_star)
                break
            else: 
                delta[s_star] = R(c_star)
                cur[s_star] = True

        if delta[s_star] < anchor_screen:
            L_dict_[s_star] = 0
            continue

        all_cas += len(c_star)
        for cas_id in list(c_star.keys()):
            uc = event_user_dict[cas_id]
            for v in uc:
                if v in L_dict_.keys():
                    L_dict_[v] -= 1              
                _ = user_event_dict_[v].pop(cas_id)
        print(f"Add a social sensor, sensors num is {len(A)}")
        print(f"Anchor id: {s_star}")
        print(f"all_cas is {all_cas}")
        print(f"TAR_set size: {len(TAR_set)}")
        if all_cas >= len(event_user_dict):
            user_event_dict_ = copy.deepcopy(user_event_dict)
            L_dict_ = copy.deepcopy(L_dict)
            for s in A:
                L_dict_[s] = 0
            all_cas = 0
            print(f"All cas has been perception, add extra node")
        
    
    print(f"Select social sensors finish! Get social sensors, num: {len(A)}")
    return A, all_cas

def handle_event(event_dict, obs_time):

    user_event_dict = defaultdict(dict)
    event_user_dict = defaultdict(list)
    cascades_total = 0
    for cascade_id, event_list in event_dict.items():

        cascades_total += 1

        activation_times = {}

        t_max = 0
        t_min = float('inf')
        for u_t_dict in event_list:

            time_now = int(u_t_dict['act_time'])
            if time_now > t_max:
                t_max = time_now
            if time_now < t_min:
                t_min = time_now

            node_id = u_t_dict['user_id']
            
            if time_now > obs_time and obs_time != -1:
                continue

            if node_id in activation_times.keys():
                activation_times[node_id] = min(time_now, activation_times[node_id])
            else:
                activation_times[node_id] = time_now
        
        print(f"cascade_id:{cascade_id}, t_min:{t_min}")
        

        for k,v in activation_times.items():
            event_user_dict[cascade_id].append(k)  
            if t_max > t_min:
                user_event_dict[k][cascade_id] = (v-t_min)/(t_max-t_min)
                            
    L_dict = {}

    for k,v in user_event_dict.items():
        L_dict[k] = len(v.keys())
          
    return L_dict, user_event_dict, event_user_dict

def generate_anchors(event_dict, anchor_budget, anchor_screen, obs_time, result_path, anchor_num=-1):
    
    L_dict, user_event_dict, event_user_dict = handle_event(event_dict, obs_time)

    print(f"Handle cascade file finish! Users num is {len(L_dict)}")
    os.makedirs(result_path, exist_ok=True)
    json_file = os.path.join(result_path, f'user_event.json')
    try:
        with open(json_file, 'w', encoding='utf-8') as f:
            json.dump(user_event_dict, f, indent=4, ensure_ascii=False)
        print(f"user_event_dict已成功保存到 JSON 文件: {json_file}")
    except TypeError as e:
        print(f"错误: 字典中可能包含无法序列化为 JSON 的数据类型。错误: {e}")
    except IOError:
        print(f"错误: 无法打开或写入文件 '{json_file}'。")
    except Exception as e:
        print(f"保存 JSON 文件时发生未知错误: {e}")   

    if anchor_num == -1:
        anchor_num = len(L_dict)*anchor_budget
    
    A, all_cas = select_social_sensors(R, L_dict, user_event_dict, event_user_dict, anchor_num, anchor_screen)
    os.makedirs(result_path, exist_ok=True)
    output_file = os.path.join(result_path, f'anchors_{anchor_num}.txt')
    with open(output_file, 'w') as file:
        for item in A:
            file.write(f"{item}\n")
    
    return A, all_cas, user_event_dict