510 lines
20 KiB
Python
510 lines
20 KiB
Python
from collections import defaultdict
|
||
import time
|
||
import numpy as np
|
||
import copy
|
||
import argparse
|
||
import json
|
||
from scipy.optimize import curve_fit
|
||
import matplotlib.pyplot as plt
|
||
import numpy as np
|
||
import time
|
||
from matplotlib import font_manager
|
||
import seaborn as sns
|
||
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
|
||
|
||
def R(event_dict):
|
||
sum = 0
|
||
for _,v in event_dict.items():
|
||
sum += -v
|
||
return sum
|
||
|
||
|
||
def select_social_sensors(R, L_dict, user_event_dict, event_user_dict, b):
|
||
"""
|
||
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!")
|
||
user_event_dict = copy.deepcopy(user_event_dict)
|
||
|
||
V = list(L_dict.keys())
|
||
A = set()
|
||
f = lambda A: len(A)
|
||
all_cas = 0
|
||
while any(s not in A for s in V) and f(A) < b:
|
||
|
||
indices_np = find_max_indices_numpy(L_dict)
|
||
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:
|
||
A.add(s_star)
|
||
break
|
||
else:
|
||
delta[s_star] = R(c_star)
|
||
cur[s_star] = True
|
||
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)}")
|
||
|
||
|
||
print(f"Select social sensors finish! Get social sensors, num: {len(A)}")
|
||
return A, all_cas
|
||
|
||
def handle_cas(args):
|
||
filename, obs_time = args.input_file, args.obs_time
|
||
print("Handle cascade begin!")
|
||
user_event_dict = defaultdict(dict)
|
||
event_user_dict = defaultdict(list)
|
||
cascades_total = 0
|
||
with open(filename) as file:
|
||
for line in file:
|
||
|
||
cascades_total += 1
|
||
if cascades_total > args.max_cas_num and args.max_cas_num != -1:
|
||
break
|
||
parts = line.split(',')
|
||
cascade_id = parts[0]
|
||
activation_times = {}
|
||
|
||
paths = parts[1:]
|
||
|
||
t_max = 0
|
||
t_min = float('inf')
|
||
for p in paths:
|
||
# observed adoption/participant
|
||
nodes = p.split(':')[0].split('/')
|
||
time_now = int(p.split(':')[1])
|
||
if time_now > t_max:
|
||
t_max = time_now
|
||
if time_now < t_min:
|
||
t_min = time_now
|
||
node = nodes[-1]
|
||
node_id = int(node)
|
||
|
||
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
|
||
|
||
|
||
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())
|
||
|
||
print(f"Handle cascade file finish! Users num is {len(L_dict)}")
|
||
|
||
return L_dict, user_event_dict, event_user_dict
|
||
|
||
def generate_anchors(args):
|
||
result_path, anchor_budget = args.result_path, args.anchor_budget
|
||
|
||
L_dict, user_event_dict, event_user_dict = handle_cas(args)
|
||
|
||
num_nodes = len(L_dict.keys())
|
||
|
||
anchor_num = 0
|
||
|
||
if anchor_budget > 0.02:
|
||
max_anchor_num = int(num_nodes*0.02)
|
||
print(f"Max anchor num is {max_anchor_num}, anchor_budget is set to {max_anchor_num}")
|
||
anchor_num = max_anchor_num
|
||
else:
|
||
anchor_num = int(num_nodes*anchor_budget)
|
||
|
||
A, all_cas = select_social_sensors(R, L_dict, user_event_dict, event_user_dict, anchor_num)
|
||
os.makedirs(result_path, exist_ok=True)
|
||
output_file = os.path.join(result_path, f'anchors_{args.anchor_budget}.txt')
|
||
with open(output_file, 'w') as file:
|
||
for item in A:
|
||
file.write(f"{item}\n")
|
||
return A, all_cas, user_event_dict
|
||
|
||
# Logistic 函数定义
|
||
def logistic(t, K, r, t0):
|
||
return K / (1 + np.exp(-r * (t - t0)))
|
||
|
||
def find_inflection_logistic(t_list, c_list):
|
||
t_array = np.array(t_list)
|
||
c_array = np.array(c_list)
|
||
|
||
# 时间归一化到 [0, 1]
|
||
t_min = t_array.min()
|
||
t_max = t_array.max()
|
||
|
||
if len(set(c_array)) < 3 or max(c_array) <= 1 or t_max - t_min == 0:
|
||
return None
|
||
t_array_normalized = (t_array - t_min) / (t_max - t_min)
|
||
|
||
try:
|
||
p0 = [max(c_array) if max(c_array) > 0 else 1.0, 1.0, 0.5] # 确保 K 初始值不为0，如果c_array全是0
|
||
|
||
# 检查 c_array 是否有效
|
||
if max(c_array) <= 0: # 如果c_array都是0或负数，也无法拟合S曲线
|
||
print("警告: 累计值 (c_list) 没有正增长，无法拟合S曲线。")
|
||
return None
|
||
|
||
# 调整 bounds，如果 K 的下限是0，max(c_array)也可能是0，可能导致问题
|
||
# bounds = ([0, 0, 0], [np.inf, 10, 1]) # K r t0
|
||
# K > 0 (所以下限设为很小的正数，或基于数据)
|
||
# r > 0 (增长率)
|
||
# t0 在 [0, 1] 范围内
|
||
bounds = ([1e-6, 1e-6, 0], [np.inf, 10, 1]) # K, r 应该为正
|
||
|
||
popt, _ = curve_fit(logistic, t_array_normalized, c_array, p0=p0, bounds=bounds, maxfev=10000)
|
||
K, r, t0 = popt
|
||
return t0
|
||
except (RuntimeError, ValueError):
|
||
print(f"警告: 曲线拟合失败或参数无效")
|
||
return None
|
||
|
||
# 获取所有事件的爆发时间
|
||
def get_t0(args):
|
||
input_file, result_path = args.input_file, args.result_path
|
||
|
||
print("To get All Cas' T0")
|
||
os.makedirs(result_path, exist_ok=True)
|
||
json_file = os.path.join(result_path, 'prepare.json')
|
||
prepare = {}
|
||
if os.path.exists(json_file):
|
||
print(f"文件 {json_file} 存在。")
|
||
with open(json_file, 'r', encoding='utf-8') as f:
|
||
prepare = json.load(f)
|
||
return prepare
|
||
t0_dict = {}
|
||
num_time_points = 50 # 你希望在平均趋势图上采样的点数，可以调整
|
||
common_normalized_timeline = np.linspace(0, 1, num_time_points)
|
||
nodes_in_bins = {i: 0 for i in range(len(common_normalized_timeline) - 1)}
|
||
|
||
interpolated_counts_all_cascades = []
|
||
cascades_total = 0
|
||
t0_mean = 0
|
||
with open(input_file) as file:
|
||
for line in file:
|
||
cascades_total += 1
|
||
if cascades_total > args.max_cas_num and args.max_cas_num != -1:
|
||
break
|
||
parts = line.split(',')
|
||
cascade_id = parts[0]
|
||
|
||
paths = parts[1:]
|
||
t_max = 0
|
||
t_min = float('inf')
|
||
cas_set = set()
|
||
times = []
|
||
counts = []
|
||
for p in paths:
|
||
nodes = p.split(':')[0].split('/')
|
||
time_now = int(p.split(':')[1])
|
||
if time_now > t_max:
|
||
t_max = time_now
|
||
if time_now < t_min:
|
||
t_min = time_now
|
||
node = nodes[-1]
|
||
node_id = int(node)
|
||
cas_set.add(node_id)
|
||
times.append(time_now)
|
||
counts.append(len(cas_set))
|
||
|
||
sorted_times = sorted(times)
|
||
if t_max - t_min <= 0:
|
||
continue
|
||
nodes_in_bin = {i: 0 for i in range(len(common_normalized_timeline) - 1)}
|
||
for i in range(len(times)):
|
||
participation_time = (sorted_times[i] - t_min)/(t_max - t_min)
|
||
sorted_times[i] = participation_time
|
||
bin_index = np.digitize(participation_time, common_normalized_timeline, right=False) - 1
|
||
if bin_index == -1 and participation_time == common_normalized_timeline[0]:
|
||
bin_index = 0
|
||
elif bin_index == len(common_normalized_timeline) - 1:
|
||
if participation_time == common_normalized_timeline[-1]:
|
||
bin_index = len(common_normalized_timeline) - 2
|
||
else:
|
||
continue
|
||
if 0 <= bin_index < len(nodes_in_bin):
|
||
nodes_in_bins[bin_index] +=1
|
||
nodes_in_bin[bin_index] += 1
|
||
|
||
interpolated_counts = np.interp(common_normalized_timeline,
|
||
sorted_times,
|
||
counts)
|
||
estimated_increments_between_common_times = np.diff(interpolated_counts)
|
||
interpolated_counts_all_cascades.append(estimated_increments_between_common_times)
|
||
|
||
t0 = find_inflection_logistic(sorted_times, counts)
|
||
if t0 is None:
|
||
continue
|
||
t0_dict[cascade_id] = t0
|
||
t0_mean += t0
|
||
|
||
|
||
|
||
t0_mean /= len(t0_dict)
|
||
stacked_interpolated_counts = np.vstack(interpolated_counts_all_cascades).tolist()
|
||
counts_0 = np.array([nodes_in_bins[i] for i in sorted(nodes_in_bins.keys())])
|
||
all_counts = (counts_0 / counts_0.max()).tolist()
|
||
|
||
|
||
prepare['t0'] = t0_dict
|
||
prepare['t0_mean'] = t0_mean
|
||
prepare['interpolated_counts_sum'] = stacked_interpolated_counts
|
||
prepare['interpolated_counts_step_avg'] = all_counts
|
||
|
||
try:
|
||
with open(json_file, 'w', encoding='utf-8') as f:
|
||
json.dump(prepare, f, indent=4, ensure_ascii=False)
|
||
print(f"t0_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}")
|
||
|
||
return prepare
|
||
|
||
|
||
def analyse_result(node_list, user_event_dict, prepare, args):
|
||
result_path = args.result_path
|
||
t0_dict = prepare['t0']
|
||
t0_mean = prepare['t0_mean']
|
||
anchors_activation_times_before_t0 = defaultdict(list)
|
||
anchors_activation_times = defaultdict(list)
|
||
cas_set = set()
|
||
for k,v in user_event_dict.items():
|
||
if k in node_list:
|
||
for cas_id, ac_time in v.items():
|
||
cas_set.add(cas_id)
|
||
if cas_id not in t0_dict:
|
||
continue
|
||
anchors_activation_times_before_t0[k].append(ac_time - t0_dict[cas_id])
|
||
anchors_activation_times[k].append(ac_time)
|
||
|
||
anchors_avg_activation_time_before_t0 = dict()
|
||
anchors_avg_activation_time = dict()
|
||
|
||
for node_id, times in anchors_activation_times_before_t0.items():
|
||
if times:
|
||
anchors_avg_activation_time_before_t0[node_id] = (sum(times) / len(times))
|
||
else:
|
||
anchors_avg_activation_time_before_t0[node_id] = None
|
||
|
||
for node_id, times in anchors_activation_times.items():
|
||
if times:
|
||
anchors_avg_activation_time[node_id] = (sum(times) / len(times))
|
||
else:
|
||
anchors_avg_activation_time[node_id] = None
|
||
|
||
anchors_total_avg_time_before_t0 = sum(t for t in anchors_avg_activation_time_before_t0.values() if t is not None) / len(anchors_avg_activation_time_before_t0)
|
||
anchors_total_avg_time = sum(t for t in anchors_avg_activation_time.values() if t is not None) / len(anchors_avg_activation_time)
|
||
|
||
os.makedirs(result_path, exist_ok=True)
|
||
|
||
anylyze_result = {}
|
||
anylyze_result['anchors_act_cas_num'] = len(cas_set)
|
||
anylyze_result['act_cas'] = list(cas_set)
|
||
anylyze_result['anchors_total_avg_time_before_t0'] = anchors_total_avg_time_before_t0
|
||
anylyze_result['anchors_avg_activation_time_before_t0'] = anchors_avg_activation_time_before_t0
|
||
anylyze_result['anchors_total_avg_time'] = anchors_total_avg_time
|
||
anylyze_result['anchors_avg_activation'] = anchors_avg_activation_time
|
||
|
||
json_file = os.path.join(result_path, f'anylyze_result_{args.anchor_budget}.json')
|
||
try:
|
||
with open(json_file, 'w', encoding='utf-8') as f:
|
||
json.dump(anylyze_result, f, indent=4, ensure_ascii=False)
|
||
print(f"anylyze_result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}")
|
||
|
||
print("开始绘制激活时间图 ")
|
||
# --- 绘制激活时间图 ---
|
||
important_times = [t for t in anchors_avg_activation_time_before_t0.values() if t is not None]
|
||
# 然后继续绘制图表
|
||
# 假设已经有 important_times 和 random_times
|
||
plt.figure(figsize=(10, 6))
|
||
sns.kdeplot(important_times, label='anchors', fill=True, color='red', linewidth=2)
|
||
|
||
plt.title('Anchors activate time')
|
||
plt.xlabel('Time')
|
||
plt.ylabel('P')
|
||
plt.legend()
|
||
plt.grid(True)
|
||
plt.tight_layout()
|
||
|
||
fig_file_1 = os.path.join(result_path, f'anchor_act_time_{args.anchor_budget}.png')
|
||
plt.savefig(fig_file_1, # 文件名
|
||
dpi=300, # 可选：分辨率 (dots per inch)
|
||
bbox_inches='tight',# 可选：尝试裁剪掉空白边缘
|
||
)
|
||
plt.close()
|
||
|
||
print("开始绘制趋势图 ")
|
||
|
||
# --- 绘制趋势图 ---
|
||
min_time = 0.0
|
||
max_time = 1 # 假设最大观察时间是 0.5，你可以根据你的数据调整
|
||
bin_width = 0.01
|
||
num_bins = int(np.ceil((max_time - min_time) / bin_width)) # 向上取整计算bin的数量
|
||
|
||
# 创建时间段的边界
|
||
# 例如：[0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
|
||
time_bin_edges = np.linspace(min_time, max_time, num_bins + 1)
|
||
|
||
nodes_in_bins = {i: 0 for i in range(len(time_bin_edges) - 1)}
|
||
for node, participation_time in anchors_avg_activation_time.items():
|
||
bin_index = np.digitize(participation_time, time_bin_edges, right=False) - 1
|
||
if bin_index == -1 and participation_time == time_bin_edges[0]:
|
||
bin_index = 0
|
||
elif bin_index == len(time_bin_edges) - 1:
|
||
if participation_time == time_bin_edges[-1]:
|
||
bin_index = len(time_bin_edges) - 2
|
||
else:
|
||
continue
|
||
if 0 <= bin_index < len(nodes_in_bins):
|
||
nodes_in_bins[bin_index] += 1
|
||
|
||
|
||
plt.figure(figsize=(10, 6))
|
||
|
||
bin_labels = []
|
||
for i in range(len(time_bin_edges) - 1):
|
||
start_time = time_bin_edges[i]
|
||
end_time = time_bin_edges[i+1]
|
||
# 你可以用区间的字符串表示，或者区间的中心点
|
||
bin_labels.append(f"[{start_time:.2f}-{end_time:.2f})")
|
||
# bin_labels.append(f"{start_time:.1f}-") # 更简洁的标签，只显示起始
|
||
|
||
# 获取每个 bin 的计数值
|
||
counts_0 = np.array([nodes_in_bins[i] for i in sorted(nodes_in_bins.keys())])
|
||
counts = (counts_0 / counts_0.max()).tolist()
|
||
# X 轴的位置 (用于条形图)
|
||
x_positions = time_bin_edges[:-1] + bin_width/2
|
||
bars = plt.bar(x_positions, counts,
|
||
width=bin_width, # 条形的宽度
|
||
color='skyblue', # 条形的颜色
|
||
edgecolor='black') # 条形的边框颜色
|
||
# bar_index = 0
|
||
# for bar in bars:
|
||
# yval = bar.get_height()
|
||
# if yval > 0: # 只为非零的条形添加文本
|
||
# plt.text(bar.get_x() + bar.get_width()/2.0, yval + 0.05, # 文本位置微调
|
||
# counts_0[bar_index],
|
||
# ha='center', # 水平居中
|
||
# va='bottom') # 垂直对齐方式
|
||
# bar_index +=1
|
||
|
||
# stacked_interpolated_counts = np.array(prepare['interpolated_counts'])
|
||
|
||
# stacked_log_counts = stacked_interpolated_counts
|
||
# # 沿着级联的维度 (axis=0) 计算平均值
|
||
# average_counts_trend = np.mean(stacked_log_counts, axis=0) # (num_time_points,)
|
||
# max_counts = average_counts_trend.max()
|
||
|
||
# std_counts_trend = np.std(stacked_log_counts, axis=0)
|
||
# sem_counts_trend = std_counts_trend / np.sqrt( average_counts_trend.shape[0])
|
||
# # 计算95%置信区间 (使用1.96作为Z分数)
|
||
# confidence_interval_upper = average_counts_trend + 1.96 * sem_counts_trend
|
||
# confidence_interval_lower = average_counts_trend - 1.96 * sem_counts_trend
|
||
|
||
# average_counts_trend_normalized = average_counts_trend / max_counts
|
||
# confidence_interval_upper_normalized = confidence_interval_upper / max_counts
|
||
# confidence_interval_lower_normalized = confidence_interval_lower / max_counts
|
||
# confidence_interval_lower_corrected = np.maximum(0, confidence_interval_lower_normalized)
|
||
|
||
# common_normalized_timeline = np.linspace(0, 1, average_counts_trend.shape[0])
|
||
# plt.plot(common_normalized_timeline, average_counts_trend_normalized, label='Average Participation Trend', color='blue', linewidth=2)
|
||
|
||
# # (可选) 绘制置信区间或标准差区域
|
||
# plt.fill_between(common_normalized_timeline,
|
||
# confidence_interval_lower_corrected,
|
||
# confidence_interval_upper_normalized,
|
||
# color='blue', alpha=0.2, label='95% Confidence Interval')
|
||
|
||
# counts_step = prepare['interpolated_counts_step_avg']
|
||
# common_normalized_timeline = np.linspace(0, 1, len(counts_step))
|
||
# plt.plot(common_normalized_timeline, counts_step, label='Average Participation Trend', color='blue', linewidth=2)
|
||
|
||
|
||
# --- 使用 plt.axvline() 绘制垂直红线 ---
|
||
plt.axvline(x=t0_mean, # 线的 x 坐标
|
||
color='red', # 线的颜色
|
||
linestyle='--', # 线型 (例如 'solid', '--', '-.', ':')
|
||
linewidth=2, # 线的宽度
|
||
label=f'Average outbreak time = {t0_mean}')
|
||
fig_file_2 = os.path.join(result_path, f'trend_{args.anchor_budget}.png')
|
||
plt.savefig(fig_file_2, # 文件名
|
||
dpi=300, # 可选：分辨率 (dots per inch)
|
||
bbox_inches='tight',# 可选：尝试裁剪掉空白边缘
|
||
)
|
||
plt.close()
|
||
return
|
||
|
||
def parse_args():
|
||
parser = argparse.ArgumentParser(description='Parameters')
|
||
parser.add_argument('--input_file', default='./dataset_for_anchor.txt', type=str, help='Cascade file')
|
||
parser.add_argument('--result_path', default='./result/', type=str, help='result save path')
|
||
parser.add_argument('--anchor_budget', default=0.005, type=float, help='Anchors num')
|
||
parser.add_argument('--max_cas_num', default=-1, type=int, help='Cascade num')
|
||
parser.add_argument('--obs_time', default=-1, type=int, help='Anchors observe time, default seeting is -1, meaning can observe all')
|
||
parser.add_argument('--key_node_json', default='./key_node.json', type=str, help='key_node_json save file')
|
||
return parser.parse_args()
|
||
|
||
if __name__ == '__main__':
|
||
args = parse_args()
|
||
|
||
#===========执行m点挖掘算法, 返回m点以及感知的级联数量=================
|
||
A, all_cas, user_event_dict = generate_anchors(args)
|
||
|
||
#===========分析结果=================
|
||
print("开始分析结果")
|
||
prepare = get_t0(args)
|
||
analyse_result(A, user_event_dict, prepare, args)
|
||
|
||
|
||
|