new file: main.py
new file: model/anchor_finder.py
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main.py
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main.py
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import argparse
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from collections import defaultdict
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import json
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import os
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from model import anchor_finder
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def handle_cas(args):
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filename, result_path = args.input_file, args.result_path
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print("Handle cascade begin!")
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event_dict = defaultdict(dict)
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with open(filename) as file:
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for line in file:
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cascades_total += 1
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parts = line.split(',')
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cascade_id = parts[0]
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paths = parts[1:]
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for p in paths:
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# observed adoption/participant
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n_t = p.split(':')
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if len(n_t) < 2:
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continue
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nodes = n_t[0].split('/')
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time_now = n_t[1]
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node = nodes[-1]
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node_id = node
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u_t_dict = {
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'user_id': node_id,
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'act_time': time_now
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}
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event_dict[cascade_id].append(u_t_dict)
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print(f"cascade_id:{cascade_id}")
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print(f"Handle cascade file finish!")
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os.makedirs(result_path, exist_ok=True)
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json_file = os.path.join(result_path, f'event_dict.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(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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return event_dict
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def parse_args():
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parser = argparse.ArgumentParser(description='Parameters')
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parser.add_argument('--input_file', default='./dataset_for_anchor.txt', type=str, help='Cascade file')
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parser.add_argument('--result_path', default='./result/', type=str, help='result save path')
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parser.add_argument('--anchor_budget', default=0.005, type=float, help='Anchors proportion')
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parser.add_argument('--anchor_num', default=1000, type=int, help='Anchors num')
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parser.add_argument('--obs_cas_num', default=-1, type=int, help='Anchors observe cas num')
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parser.add_argument('--obs_time', default=-1, type=int, help='Anchors observe time, default seeting is -1, meaning can observe all')
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parser.add_argument('--key_node_file', default='/home/zjy/project/CasDO/dataset/nh/key_node_10', type=str, help='key_node_json save file')
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parser.add_argument('--anchor_screen', default=-0.8, type=float, help='anchor_screen')
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parser.add_argument('--cas_min_length', default=20, type=int, help='Cas min length')
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parser.add_argument('--t_max', default=1751299200, type=int, help='Cas min length')
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parser.add_argument('--t_min', default=1751299200, type=int, help='Cas min length')
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_args()
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input_file, result_path, anchor_budget, obs_time, anchor_num = args.input_file, args.result_path, args.anchor_budget, args.obs_time, args.anchor_num
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event_dict = handle_cas(args)
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print(f"anchor_num is set to {anchor_num}")
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#===========执行m点挖掘算法, 返回m点以及感知的级联数量=================
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A, all_cas, user_event_dict = anchor_finder.generate_anchors(event_dict, anchor_budget, obs_time, result_path, anchor_num)
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model/anchor_finder.py
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model/anchor_finder.py
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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):
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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] >= args.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] < args.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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# break
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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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# t_min_cas = -1
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t_min_all = args.t_min
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for cascade_id, event_list in event_dict:
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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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if len(activation_times) < args.cas_min_length:
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print(f"Too short Cas! Cas_id:{cascade_id}")
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continue
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if t_min > args.t_max or t_min < args.t_min:
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print(f"Cas is not in proper time! Cas_id:{cascade_id}")
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continue
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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, 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)
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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
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