main-tep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import math\n",
    "import json\n",
    "import heapq  \n",
    "from utils.contribution import contribution_chart\n",
    "import sys\n",
    "import os\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# 保证绝对路径可以被检测到\n",
    "sys.path.append(\"\\\\\".join(sys.path[0].split(\"\\\\\")[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_test = 0\n",
    "id_temporal_len = 100\n",
    "graph_type='raw'#'raw' or 'simple'\n",
    "# graph_type='simple'\n",
    "test_set = 'd01_te'\n",
    "test_path = 'E:/Lab/TEP-KG/data_tep/TEP-dataset/test/'+test_set+'.dat'\n",
    "dataset_type = 'tep'\n",
    "folder_path = './fig/'+dataset_type+'/'+test_set+'/'\n",
    "if not os.path.isdir(folder_path):\n",
    "    os.makedirs(folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def preprocess_cont_chart(id_test,id_temporal_len,graph_type,test_path):\n",
    "    t2_cont_list,spe_cont_list,rbc_cont_list,num_pc = contribution_chart(test_path,num_pc=0,PCA_components_rate=0.5,temporal=True,id_test=id_test,id_temporal_len=id_temporal_len,graph_type=graph_type)\n",
    "    print(num_pc)\n",
    "    cont_list = rbc_cont_list\n",
    "    cont_list = cont_list[id_test:id_test+id_temporal_len]\n",
    "    cont_list=cont_list/np.sum(cont_list,axis=1).reshape(-1,1)\n",
    "    plt.figure(figsize=(16,5))\n",
    "    ax = sns.heatmap(cont_list.transpose(),cbar=True) # vmin和vmax是自定义显示颜色的范围\n",
    "    plt.title(\"temporal contribution chart\") # 设置标题\n",
    "    plt.xlabel(\"time\", fontsize=14) # 设置x标题\n",
    "    plt.ylabel(\"contribution\", fontsize=14) # 设置y轴标题\n",
    "    # plt.savefig(fig_name,bbox_inches='tight',dpi=300)\n",
    "    plt.show()\n",
    "    return cont_list\n",
    "cont_list = preprocess_cont_chart(id_test,id_temporal_len,graph_type,test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x4': 0.5598314514916005,\n",
       " 'x45': 0.5570058746257773,\n",
       " 'Stream 4': 0.5558175416906115,\n",
       " 'C': 0.5444769857332318,\n",
       " 'A': 0.5410065731911387,\n",
       " 'B': 0.5381552799722619,\n",
       " 'x1': 0.5236871765897404,\n",
       " 'x44': 0.523407832375857,\n",
       " 'Stream 1': 0.5200597526913736,\n",
       " 'x20': 0.5163701789192771,\n",
       " 'x50': 0.5116024034535642,\n",
       " 'Compressor': 0.5097907969198525,\n",
       " 'x46': 0.5093389527362073,\n",
       " 'x19': 0.5063752076987383,\n",
       " 'x16': 0.5044849485851937,\n",
       " 'x15': 0.5011597145978837,\n",
       " 'x18': 0.5011337239055743,\n",
       " 'Stripper': 0.500518793867538,\n",
       " 'x11': 0.49279947222026943,\n",
       " 'x13': 0.49227637089849974,\n",
       " 'Stream 14': 0.491902919816143,\n",
       " 'Separator': 0.491902919816143,\n",
       " 'x22': 0.4917901146922884,\n",
       " 'Stream 10': 0.4912355084916663,\n",
       " 'x12': 0.4911553147233687,\n",
       " 'x23': 0.48995326469261796,\n",
       " 'x48': 0.4898061157901592,\n",
       " 'x14': 0.48881171984246913,\n",
       " 'x25': 0.4872205521606958,\n",
       " 'x24': 0.4833919953529125,\n",
       " 'Stream 6': 0.48299426485673863,\n",
       " 'x6': 0.4827731083857185,\n",
       " 'Stream 8': 0.47883247208024,\n",
       " 'Stream 5': 0.4782462564000078,\n",
       " 'x27': 0.47746406813642855,\n",
       " 'Stream 7': 0.4769533841251784,\n",
       " 'Condenser': 0.4769533841251784,\n",
       " 'x28': 0.4769008954530315,\n",
       " 'x5': 0.47645857116479645,\n",
       " 'Stream 13': 0.4760233523686485,\n",
       " 'x26': 0.47395142853025995,\n",
       " 'x52': 0.47241217060957713,\n",
       " 'x31': 0.47115696272115304,\n",
       " 'F': 0.4693057760240886,\n",
       " 'x29': 0.46864410276245244,\n",
       " 'x30': 0.4641936582298768,\n",
       " 'Stream 3': 0.46163693219806934,\n",
       " 'Stream 9': 0.45875031465036775,\n",
       " 'x34': 0.4585484840802808,\n",
       " 'x33': 0.4583472733976751,\n",
       " 'x35': 0.4581819848381978,\n",
       " 'x36': 0.4577133444780679,\n",
       " 'x10': 0.45714271234239484,\n",
       " 'x47': 0.4568610183405205,\n",
       " 'E': 0.4551891062811311,\n",
       " 'x32': 0.4547056989690846,\n",
       " 'G': 0.4524953584557899,\n",
       " 'x43': 0.4522420681480317,\n",
       " 'H': 0.45166085856390836,\n",
       " 'x3': 0.45136111947657753,\n",
       " 'x7': 0.4486661765770729,\n",
       " 'Reactor': 0.44792214748456316,\n",
       " 'x8': 0.4460552085715204,\n",
       " 'x9': 0.4460117089541426,\n",
       " 'Stream 2': 0.44422642105820387,\n",
       " 'Stream 12': 0.44363503680677563,\n",
       " 'x21': 0.4429535491032903,\n",
       " 'x51': 0.44162829889672744,\n",
       " 'D': 0.4397063099771318,\n",
       " 'x2': 0.43213490738685256,\n",
       " 'x42': 0.43201389614120733,\n",
       " 'x38': 0.40449661531982195,\n",
       " 'x39': 0.40070474512243553,\n",
       " 'Stream 11': 0.39743039734282337,\n",
       " 'x49': 0.39719454999887316,\n",
       " 'x37': 0.39670194800426395,\n",
       " 'x17': 0.39608671893421477,\n",
       " 'x40': 0.3952657258796064,\n",
       " 'x41': 0.3947942296518809}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best_x =[17.,  34.,  49., 200.,   5.,  10.,  10.,  13.]\n",
    "best_x =[10,  30, 50., 200.,   1.,  5.,   8.,  20]\n",
    "param_State_loss = best_x[0]/10\n",
    "param_Output_loss = best_x[1]/10\n",
    "param_Contain_loss = best_x[2]/10\n",
    "param_Generate_loss = best_x[3]/10\n",
    "param_State_disp = 1\n",
    "param_Output_disp = 1\n",
    "param_Contain_disp = 1\n",
    "param_Generate_disp = 1\n",
    "param_State_time = best_x[4]\n",
    "param_Output_time = best_x[5]\n",
    "param_Contain_time = best_x[6]\n",
    "param_Generate_time = best_x[7]\n",
    "relation_loss_dict = {'State_of':param_State_loss,'State':param_State_loss,'Output':param_Output_loss,'Contain':param_Contain_loss,'Contained_by':param_Contain_loss,'Generate':param_Generate_loss}\n",
    "distance_punishment = {'State_of':param_State_disp,'State':param_State_disp,'Output':param_Output_disp,'Contain':param_Contain_disp,'Contained_by':param_Contain_disp,'Generate':param_Generate_disp}\n",
    "relation_time_delay = {'State_of':param_State_time,'State':param_State_time,'Output':param_Output_time,'Contain':param_Contain_time,'Contained_by':param_Contain_time,'Generate':param_Generate_time}\n",
    "\n",
    "if graph_type=='raw':\n",
    "    variable_names = ['x'+str(i) for i in range(1,53)]\n",
    "    with open('data_tep/graph.json', 'r') as f:\n",
    "        kg_graph = json.load(f)\n",
    "\n",
    "if graph_type=='simple':\n",
    "    variable_names = ['x'+str(i) for i in range(1,23)]+['x'+str(i) for i in range(42,53)]\n",
    "    with open('data_tep/simple_graph.json', 'r') as f:\n",
    "        kg_graph = json.load(f)\n",
    "\n",
    "stream_names = ['Stream '+str(i) for i in range(1,15)]\n",
    "device_names = ['Reactor','Separator','Stripper','Compressor','Condenser']\n",
    "substance_names = ['A','B','C','D','E','F','G','H']\n",
    "key_order = variable_names+stream_names+device_names+substance_names\n",
    "sorted_items = sorted(kg_graph.items(), key=lambda item: key_order.index(item[0]))\n",
    "kg_graph = dict(sorted_items)\n",
    "#节点最大传播次数\n",
    "max_propagate_times = 10\n",
    "mode = \"simulate\"\n",
    "sequence_cont = [np.sum(cont) for cont in cont_list.transpose()]/np.sum(cont_list)\n",
    "\n",
    "\n",
    "def func_sigmoid(x,p):\n",
    "    sigmoid_x = 1 / (1 + np.exp(-p*x))\n",
    "    y = (1-sigmoid_x)*2\n",
    "    return y\n",
    "\n",
    "def Depre_path_loss(path_loss):\n",
    "    #根据路径长度计算折旧\n",
    "    param_path_depre = 0.1\n",
    "    # return math.exp(-param_path_depre*path_loss)\n",
    "    return func_sigmoid(path_loss,param_path_depre)\n",
    "\n",
    "def Depre_child_node(count_child):\n",
    "    #子节点数目过多带来的损耗\n",
    "    min_child_node = 1\n",
    "    if count_child<min_child_node:\n",
    "        return 1\n",
    "    param_child_node = 0.01\n",
    "    # return math.exp(-param_child_node*count_child)\n",
    "    return func_sigmoid(count_child-min_child_node,param_child_node)\n",
    "\n",
    "def Depre_propagated(propagate_times,dis_punish):\n",
    "    #被传播带来的折旧\n",
    "    return 1/(propagate_times*dis_punish)\n",
    "    \n",
    "class PriorityQueue(object):\n",
    "    def __init__(self):\n",
    "        self._queue = []\n",
    "        self._index = 0\n",
    "\n",
    "    def push(self, item):\n",
    "        \"\"\"\n",
    "        队列由 (priority, index, item) 形式组成\n",
    "        priority 增加 \"-\" 号是因为 heappush 默认是最小堆\n",
    "        index 是为了当两个对象的优先级一致时，按照插入顺序排列\n",
    "        \"\"\"\n",
    "        priority = item.time\n",
    "        heapq.heappush(self._queue, (priority, self._index, item))\n",
    "        self._index += 1\n",
    "\n",
    "    def pop(self):\n",
    "        \"\"\"\n",
    "        弹出优先级最高的对象\n",
    "        \"\"\"\n",
    "        return heapq.heappop(self._queue)[-1]\n",
    "\n",
    "    def qsize(self):\n",
    "        return len(self._queue)\n",
    "\n",
    "    def empty(self):\n",
    "        return True if not self._queue else False\n",
    "\n",
    "class TimeNode(object):\n",
    "    def __init__(self, node, time):\n",
    "        self.node = node\n",
    "        self.time = time\n",
    "\n",
    "    def __repr__(self):\n",
    "        return \"{0} - {1}\".format(self.node, self.time)\n",
    "\n",
    "\n",
    "def bfs_simulate(startnode,sequence_cont,relation_loss_dict,distance_punishment,relation_time_delay):\n",
    "    pq = PriorityQueue()\n",
    "\n",
    "    map_node_visit = {}#节点被访问次数，相当于节点被别的节点传播损耗的次数\n",
    "    map_node_propagate = {}#节点作为visit节点的次数，相当于节点向别的节点传播损耗的次数\n",
    "    map_node_simu_score = {}#节点的模拟得分\n",
    "    map_node_propagated_score = {} #节点被其他节点传播的量，可以据此回溯到其主要影响节点\n",
    "    for node in kg_graph.keys():\n",
    "        map_node_visit[node] = 0\n",
    "        map_node_propagate[node] = 0\n",
    "        map_node_simu_score[node] = 0\n",
    "        map_node_propagated_score[node] = {}\n",
    "    timer = 0#计时器\n",
    "    pq.push(TimeNode(startnode,timer))\n",
    "    map_node_visit[startnode] = 1\n",
    "    map_node_simu_score[startnode] = 1\n",
    "    while pq.qsize():#保障还有元素\n",
    "        for _ in range(pq.qsize()):#同一层级的node遍历\n",
    "            #子节点得分计算\n",
    "            visit_timenode = pq.pop()\n",
    "            visit_node = visit_timenode.node\n",
    "            map_node_propagate[visit_node]+=1\n",
    "            for node in kg_graph[visit_node].keys():\n",
    "                count_child = len(kg_graph[visit_node])\n",
    "                #原始的广度优先搜索，每个节点只能被访问一次\n",
    "                if mode == \"bfs\": \n",
    "                    if not map_node_visit[node]:\n",
    "                        map_node_visit[node]+=1\n",
    "                        relation = kg_graph[visit_node][node]\n",
    "                        map_node_simu_score[node] += map_node_simu_score[visit_node]*Depre_path_loss(relation_loss_dict[relation])*1/count_child\n",
    "                        pq.push(TimeNode(node,1))#不考虑时间问题\n",
    "                #基于BFS的流程模拟方法，每个节点向别的节点传播损耗的次数有限制，当只能传播一次时，相当于访问了所有的边\n",
    "                elif mode == \"simulate\":\n",
    "                    if map_node_propagate[visit_node]<=max_propagate_times:\n",
    "                        #visit_node->node 误差传播过程\n",
    "                        relation = kg_graph[visit_node][node]\n",
    "                        #visit节点的损耗需要根据其被传播的损耗进行折旧，然后再乘上路径损耗\n",
    "                        propagate_error = map_node_simu_score[visit_node]*Depre_propagated(map_node_visit[visit_node],distance_punishment[relation])*Depre_path_loss(relation_loss_dict[relation])*Depre_child_node(count_child)\n",
    "                        pq.push(TimeNode(node,timer+relation_time_delay[relation]))\n",
    "                        map_node_simu_score[node] +=propagate_error\n",
    "                        if visit_node not in map_node_propagated_score[node].keys():\n",
    "                            map_node_propagated_score[node][visit_node]=0\n",
    "                        map_node_propagated_score[node][visit_node]+=propagate_error\n",
    "                        map_node_visit[node]+=1\n",
    "                timer+=1\n",
    "    #计算相似度\n",
    "    start_cont = 1\n",
    "    for i in range(0,len(variable_names)):\n",
    "        if variable_names[i]==startnode:\n",
    "            start_cont = sequence_cont[i]\n",
    "    sequence_simu = [v*start_cont/map_node_simu_score[startnode] for v in map_node_simu_score.values()]\n",
    "    sequence_cont = np.array(sequence_cont)\n",
    "    sequence_simu = np.array(sequence_simu)[0:len(variable_names)]\n",
    "    sim_score = np.dot(sequence_cont, sequence_simu) / (np.linalg.norm(sequence_cont) * np.linalg.norm(sequence_simu))\n",
    "    return sim_score,sequence_simu,map_node_propagated_score\n",
    "\n",
    "dict_simu_score = {}\n",
    "dict_sequence_simu = {}\n",
    "for startnode in key_order:\n",
    "    dict_simu_score[startnode],dict_sequence_simu[startnode],map_node_propagated_score = bfs_simulate(startnode,sequence_cont,relation_loss_dict,distance_punishment,relation_time_delay)\n",
    "sorted_items = sorted(dict_simu_score.items(), key=lambda item: item[1],reverse=True)\n",
    "dict_simu_score = dict(sorted_items)\n",
    "dict_simu_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for startnode in dict_simu_score.keys():\n",
    "    if startnode in variable_names:\n",
    "        break\n",
    "dict_simu_score[startnode],dict_sequence_simu[startnode],map_node_propagated_score = bfs_simulate(startnode,sequence_cont,relation_loss_dict,distance_punishment,relation_time_delay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x1', 'x4', 'x16', 'x19', 'x20', 'x31', 'x38', 'x50']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_cont\n",
    "indices_sorted = np.argsort(sequence_cont, axis=0)[-8:]\n",
    "sorted_var = [variable_names[i] for i in sorted(indices_sorted)]\n",
    "sorted_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('x1', 'Stream 1'), ('Stream 1', 'A'), ('A', 'x4')]\n",
      "[('x4', 'Stream 4'), ('Stream 4', 'B'), ('B', 'x4')]\n",
      "[('x16', 'Stripper'), ('Stripper', 'Stream 4'), ('Stream 4', 'x4')]\n",
      "[('x19', 'Stripper'), ('Stripper', 'Stream 4'), ('Stream 4', 'x4')]\n",
      "[('x20', 'Compressor'), ('Compressor', 'Separator'), ('Separator', 'Stream 14'), ('Stream 14', 'Condenser'), ('Condenser', 'Stream 7'), ('Stream 7', 'Reactor'), ('Reactor', 'Stream 6'), ('Stream 6', 'Stream 8'), ('Stream 8', 'Stream 5'), ('Stream 5', 'Stripper'), ('Stripper', 'Stream 4'), ('Stream 4', 'x4')]\n",
      "[('x31', 'C'), ('C', 'x4')]\n",
      "[('x38', 'Stripper'), ('Stripper', 'Stream 4'), ('Stream 4', 'x4')]\n",
      "[('x50', 'Stripper'), ('Stripper', 'Stream 4'), ('Stream 4', 'x4')]\n"
     ]
    }
   ],
   "source": [
    "def backtrack(node,startnode):\n",
    "    back_list = []\n",
    "    while True:\n",
    "        current_node = node\n",
    "        current_path = map_node_propagated_score[current_node]\n",
    "        max_node = ''\n",
    "        max_value = 0\n",
    "        for node in current_path.keys():\n",
    "            # if node not in variable_names and (node,current_node) not in back_list:\n",
    "            if node not in variable_names or node==startnode:\n",
    "                if (current_node,node) not in back_list and (node,current_node) not in back_list:\n",
    "                    if current_path[node]>max_value:\n",
    "                        max_value = current_path[node]\n",
    "                        max_node = node\n",
    "        if max_value>0:\n",
    "            node = max_node\n",
    "            back_list.append((current_node,node))\n",
    "            if node==startnode:\n",
    "                break\n",
    "        else:\n",
    "            break  \n",
    "    return back_list\n",
    "for var in sorted_var:\n",
    "    path = backtrack(var,startnode)\n",
    "    print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_node = [startnode for startnode in dict_simu_score.keys()]\n",
    "for startnode in rank_node:\n",
    "    if startnode in variable_names:\n",
    "        best_start_node = startnode\n",
    "        break\n",
    "\n",
    "xlablefront = 12\n",
    "ylablefront = 12\n",
    "titlefront = 14\n",
    "figxsize = 10\n",
    "figysize = 4\n",
    "\n",
    "def markdown_x(v):\n",
    "    return '$'+v[:1]+'_{'+v[1:]+'}$'\n",
    "\n",
    "def markdown_Stream(v):\n",
    "    return v[:6]+' '+v[7:]\n",
    "\n",
    "# plt.figure(figsize=(20,12))\n",
    "plt.figure(figsize=(figxsize,figysize))\n",
    "# plt.subplot(3,2,1)\n",
    "# plt.bar([v[1:] for v in variable_names], sequence_cont, color='blue')\n",
    "plt.bar([str(x) for x in range(1,53)], sequence_cont, color='blue')\n",
    "plt.xticks([str(x) for x in range(5,53,5)])\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable', fontsize=xlablefront)\n",
    "plt.ylabel('Contribution rate / %', fontsize=ylablefront)\n",
    "# plt.title('RBC Contribution plot', fontsize=titlefront)\n",
    "plt.ylim(0, max(sequence_cont) * 1.25)\n",
    "plt.tight_layout()\n",
    "plt.savefig(folder_path+'contribution.png')\n",
    "\n",
    "# plt.subplot(3,2,2)\n",
    "plt.figure(figsize=(figxsize,figysize))\n",
    "plt.bar([markdown_x(v) for v in variable_names], dict_sequence_simu[best_start_node], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable', fontsize=xlablefront)\n",
    "plt.ylabel('Proportion of fault quantity /%', fontsize=ylablefront)\n",
    "plt.title('Fault Simulation Plot', fontsize=titlefront)\n",
    "plt.ylim(0, max(sequence_cont) * 1.25)\n",
    "plt.tight_layout()\n",
    "plt.savefig(folder_path+'Simulation.png')\n",
    "\n",
    "rank_var = [v for v in rank_node if v in variable_names][0:20]\n",
    "# plt.subplot(3,2,3)\n",
    "plt.figure(figsize=(figxsize,figysize))\n",
    "plt.bar([markdown_x(v) for v in rank_var], [dict_simu_score[v] for v in rank_var], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable entity', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('Root cause score of Variable', fontsize=titlefront)\n",
    "plt.tight_layout()\n",
    "plt.savefig(folder_path+'Variable_score.png')\n",
    "\n",
    "rank_sd = [v for v in rank_node if v in stream_names or v in device_names][0:10]\n",
    "# plt.subplot(3,2,4)\n",
    "plt.figure(figsize=(figxsize,figysize))\n",
    "plt.bar([v for v in rank_sd], [dict_simu_score[v] for v in rank_sd], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Stream and Device entity', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('Root cause score of Stream and Device', fontsize=titlefront)\n",
    "# plt.tick_params(labelsize=8)\n",
    "plt.tight_layout()\n",
    "plt.savefig(folder_path+'Stream_Device_score.png')\n",
    "\n",
    "# plt.subplot(3,2,5)\n",
    "plt.figure(figsize=(figxsize,figysize))\n",
    "plt.bar([v for v in rank_node if v in substance_names], [dict_simu_score[v] for v in rank_node if v in substance_names], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Substance entity', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('Root cause score of Substance', fontsize=titlefront)\n",
    "plt.tight_layout()\n",
    "plt.savefig(folder_path+'Substance_score.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_csv_path = folder_path+'data.csv'\n",
    "save_size = 10\n",
    "concatenated_data = []\n",
    "for i in range(0,save_size):\n",
    "    concatenated_data.append([rank_var[i],round(dict_simu_score[rank_var[i]],5),rank_sd[i],round(dict_simu_score[rank_sd[i]],5)])\n",
    "triples_df = pd.DataFrame(concatenated_data, columns=['variable','score','stream or device','score'])\n",
    "triples_df.to_csv(save_csv_path, index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_node = [startnode for startnode in dict_simu_score.keys()]\n",
    "for startnode in rank_node:\n",
    "    if startnode in variable_names:\n",
    "        best_start_node = startnode\n",
    "        break\n",
    "\n",
    "xlablefront = 12\n",
    "ylablefront = 12\n",
    "titlefront = 14\n",
    "\n",
    "def markdown_x(v):\n",
    "    return '$'+v[:1]+'_{'+v[1:]+'}$'\n",
    "\n",
    "def markdown_Stream(v):\n",
    "    return v[:6]+' '+v[7:]\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.subplot(3,2,1)\n",
    "plt.bar([markdown_x(v) for v in variable_names], sequence_cont, color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable number', fontsize=xlablefront)\n",
    "plt.ylabel('Proportion of SPE statistics /%', fontsize=ylablefront)\n",
    "plt.title('RBC Contribution plot', fontsize=titlefront)\n",
    "plt.ylim(0, max(sequence_cont) * 1.15)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(3,2,2)\n",
    "plt.bar([markdown_x(v) for v in variable_names], dict_sequence_simu[best_start_node], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable number', fontsize=xlablefront)\n",
    "plt.ylabel('Proportion of fault quantity /%', fontsize=ylablefront)\n",
    "plt.title('Fault Simulation Plot', fontsize=titlefront)\n",
    "plt.ylim(0, max(sequence_cont) * 1.15)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "rank_var = [v for v in rank_node if v in variable_names][0:20]\n",
    "plt.subplot(3,2,3)\n",
    "plt.bar([markdown_x(v) for v in rank_var], [dict_simu_score[v] for v in rank_var], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Variable name', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('KG simulate variable rank', fontsize=titlefront)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(3,2,4)\n",
    "plt.bar([v for v in rank_node if v in device_names], [dict_simu_score[v] for v in rank_node if v in device_names], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Device name', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('KG simulate device rank', fontsize=titlefront)\n",
    "plt.tight_layout()\n",
    "\n",
    "rank_stream = [v for v in rank_node if v in stream_names][0:10]\n",
    "plt.subplot(3,2,5)\n",
    "plt.bar([markdown_Stream(v) for v in rank_stream], [dict_simu_score[v] for v in rank_stream], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Stream name', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('KG simulate stream rank', fontsize=titlefront)\n",
    "# plt.tick_params(labelsize=8)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "plt.bar([v for v in rank_node if v in substance_names], [dict_simu_score[v] for v in rank_node if v in substance_names], color='blue')\n",
    "# plt.xticks(rotation=90)\n",
    "plt.xlabel('Substance name', fontsize=xlablefront)\n",
    "plt.ylabel('Simulate similar score', fontsize=ylablefront)\n",
    "plt.title('KG simulate substance rank', fontsize=titlefront)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}