174 lines
5.5 KiB
Python
174 lines
5.5 KiB
Python
from collections import defaultdict
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import numpy as np
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import copy
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import argparse
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import json
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import numpy as np
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import os
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def find_max_indices_numpy(L_dict):
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keys_arr = np.array(list(L_dict.keys()))
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values_arr = np.array(list(L_dict.values()))
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max_val_from_dict = max(L_dict.values()) # 或者 np.max(values_arr)
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indices = np.where(values_arr == max_val_from_dict)[0]
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max_keys_np_way = keys_arr[indices]
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return max_keys_np_way, max_val_from_dict
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def R(event_dict):
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mean = 0
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for _,v in event_dict.items():
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mean += -v
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mean /= len(event_dict)
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return mean
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def select_social_sensors(R, L_dict, user_event_dict, event_user_dict, b, anchor_screen):
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"""
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Parameters:
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- R: reward function, R(A) returns a numeric value
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- L_dict: dict of node -> integer (event num)
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- user_event_dict: user -> event -> active_time
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- event_user_dict: event -> user_list
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- b: budget (numeric)
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Returns:
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- A: selected set of social sensors
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"""
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print("Select social sensors begin!")
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user_event_dict_ = copy.deepcopy(user_event_dict)
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L_dict_ = copy.deepcopy(L_dict)
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V = list(L_dict_.keys())
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A = []
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f = lambda A: len(A)
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all_cas = 0
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while any(s not in A for s in V) and f(A) < b:
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indices_np, max_val_from_dict = find_max_indices_numpy(L_dict_)
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if max_val_from_dict == 0:
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print("no extra node in cas!!!!!")
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break
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TAR_set = set(indices_np)
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delta = {}
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cur = {}
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for s in TAR_set:
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delta[s] = float('inf')
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cur[s] = False
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c_star = []
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while True:
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s_star = max(delta, key=delta.get)
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c_star = user_event_dict_[s_star]
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if cur[s_star] == True:
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if delta[s_star] >= anchor_screen:
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A.append(s_star)
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break
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else:
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delta[s_star] = R(c_star)
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cur[s_star] = True
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if delta[s_star] < anchor_screen:
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L_dict_[s_star] = 0
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continue
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all_cas += len(c_star)
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for cas_id in list(c_star.keys()):
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uc = event_user_dict[cas_id]
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for v in uc:
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if v in L_dict_.keys():
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L_dict_[v] -= 1
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_ = user_event_dict_[v].pop(cas_id)
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print(f"Add a social sensor, sensors num is {len(A)}")
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print(f"Anchor id: {s_star}")
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print(f"all_cas is {all_cas}")
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print(f"TAR_set size: {len(TAR_set)}")
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if all_cas >= len(event_user_dict):
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user_event_dict_ = copy.deepcopy(user_event_dict)
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L_dict_ = copy.deepcopy(L_dict)
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for s in A:
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L_dict_[s] = 0
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all_cas = 0
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print(f"All cas has been perception, add extra node")
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print(f"Select social sensors finish! Get social sensors, num: {len(A)}")
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return A, all_cas
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def handle_event(event_dict, obs_time):
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user_event_dict = defaultdict(dict)
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event_user_dict = defaultdict(list)
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cascades_total = 0
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for cascade_id, event_list in event_dict.items():
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cascades_total += 1
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activation_times = {}
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t_max = 0
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t_min = float('inf')
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for u_t_dict in event_list:
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time_now = int(u_t_dict['act_time'])
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if time_now > t_max:
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t_max = time_now
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if time_now < t_min:
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t_min = time_now
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node_id = u_t_dict['user_id']
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if time_now > obs_time and obs_time != -1:
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continue
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if node_id in activation_times.keys():
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activation_times[node_id] = min(time_now, activation_times[node_id])
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else:
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activation_times[node_id] = time_now
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print(f"cascade_id:{cascade_id}, t_min:{t_min}")
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for k,v in activation_times.items():
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event_user_dict[cascade_id].append(k)
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if t_max > t_min:
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user_event_dict[k][cascade_id] = (v-t_min)/(t_max-t_min)
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L_dict = {}
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for k,v in user_event_dict.items():
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L_dict[k] = len(v.keys())
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return L_dict, user_event_dict, event_user_dict
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def generate_anchors(event_dict, anchor_budget, anchor_screen, obs_time, result_path, anchor_num=-1):
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L_dict, user_event_dict, event_user_dict = handle_event(event_dict, obs_time)
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print(f"Handle cascade file finish! Users num is {len(L_dict)}")
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os.makedirs(result_path, exist_ok=True)
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json_file = os.path.join(result_path, f'user_event.json')
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try:
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with open(json_file, 'w', encoding='utf-8') as f:
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json.dump(user_event_dict, f, indent=4, ensure_ascii=False)
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print(f"user_event_dict已成功保存到 JSON 文件: {json_file}")
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except TypeError as e:
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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}")
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except IOError:
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print(f"错误: 无法打开或写入文件 '{json_file}'。")
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except Exception as e:
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print(f"保存 JSON 文件时发生未知错误: {e}")
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if anchor_num == -1:
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anchor_num = len(L_dict)*anchor_budget
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A, all_cas = select_social_sensors(R, L_dict, user_event_dict, event_user_dict, anchor_num, anchor_screen)
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os.makedirs(result_path, exist_ok=True)
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output_file = os.path.join(result_path, f'anchors_{anchor_num}.txt')
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with open(output_file, 'w') as file:
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for item in A:
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file.write(f"{item}\n")
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return A, all_cas, user_event_dict |