mdbs/anchor.py

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
import pandas as pd

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)
    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 = 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.append(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)}")
        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")
            # break
        
    
    print(f"Select social sensors finish! Get social sensors, num: {len(A)}")
    return A, all_cas

def handle_cas(args):
    filename, result_path, obs_time = args.input_file, args.result_path, args.obs_time
    print("Handle cascade begin!")
    user_event_dict = defaultdict(dict)
    event_user_dict = defaultdict(list)
    cascades_total = 0
    
    # t_min_cas = -1
    # t_min_all = 0

    with open(filename) as file:
        for line in file:

            cascades_total += 1
            if cascades_total > args.obs_cas_num and args.obs_cas_num >0:
                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
                n_t = p.split(':')
                if len(n_t) < 2:
                    continue
                nodes = n_t[0].split('/')
                time_now = int(n_t[1])
                if time_now > t_max:
                    t_max = time_now
                if time_now < t_min:
                    t_min = time_now
                node = nodes[-1]
                node_id = 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
            
            print(f"cascade_id:{cascade_id}, t_min:{t_min}")
            
            # if t_min > t_min_all:
            #     t_min_all = t_min
            #     t_min_cas = cascade_id 
            
            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)
                    
            

    
    # print(f"t_min_cas: {t_min_cas}")           
    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)}")
    os.makedirs(result_path, exist_ok=True)
    json_file = os.path.join(result_path, f'user_event_{args.obs_cas_num}.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}")
        
    return L_dict, user_event_dict, event_user_dict

def generate_anchors(args):
    input_file, result_path, anchor_budget, obs_time = args.input_file, args.result_path, args.anchor_budget, args.obs_time
    
    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)
    # anchor_num = 10
    
    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.obs_cas_num and args.obs_cas_num >0:
                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 = 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, (t_max - t_min)/3600]
            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, set_name):
    result_path = args.result_path
    t0_dict = prepare['t0']
    t0_mean = prepare['t0_mean']
    anchors_activation_times_before_t0_ = defaultdict(list)
    anchors_activation_times_before_t0 = defaultdict(list)
    anchors_activation_times_ = 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][0])
                anchors_activation_times_before_t0[k].append((ac_time - t0_dict[cas_id][0])*t0_dict[cas_id][1])
                anchors_activation_times_[k].append(ac_time)
                anchors_activation_times[k].append(ac_time*t0_dict[cas_id][1])
    print(f"感知到的级联数量：{len(cas_set)}")
    
    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_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)
    
    
    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_)
    result_path = os.path.join(result_path, f'{set_name}')
    os.makedirs(result_path, exist_ok=True)
    
    anchor_event_dict = {}
    for anchor in node_list:
        anchor_event_dict[anchor] = user_event_dict[anchor]
    
    anchor_event_file = os.path.join(result_path, f'anchor_event_{args.anchor_budget}.json')
    try:
        with open(anchor_event_file, 'w', encoding='utf-8') as f:
            json.dump(anchor_event_dict, f, indent=4, ensure_ascii=False)
        print(f"anchor_event_dictt已成功保存到 JSON 文件: {anchor_event_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 '{anchor_event_file}'。")
    except Exception as e:
        print(f"保存 JSON 文件时发生未知错误: {e}")
    
    
    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_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
    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

    # --- 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=0.0002, type=float, help='Anchors num')
    parser.add_argument('--obs_cas_num', default=-1, type=int, help='Anchors observe cas 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_file', default='/home/zjy/project/CasDO/dataset/nh/key_node_10', 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)
    set_name = 'anchor'
    analyse_result(A, user_event_dict, prepare, args, set_name)
    
    
    
    
    
    # # # #===========读取其他算法挖掘重要节点=================
    # with open(args.key_node_file, 'r') as file:
    #     # 使用列表推导式，strip() 去掉每行的换行符，然后将每行转换为整数
    #     # 过滤掉可能存在的空行或无效数据
    #     key_node_list = [line.strip() for line in file if line.strip().isdigit()]

    # key_node_set = set(key_node_list[:len(A)])
    
    # json_file = os.path.join(args.result_path, 'user_event.json')
    # user_event_dict = {}
    # if os.path.exists(json_file):
    #     print(f"文件 {json_file} 存在。")
    #     with open(json_file, 'r', encoding='utf-8') as f:
    #         user_event_dict = json.load(f)
            
    # # prepare = get_t0(args)
    
    # set_name_d = 'degree'
    # analyse_result(key_node_set, user_event_dict, prepare, args, set_name_d)