{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e48c78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # 导入pandas库，用于数据处理和分析\n",
    "import numpy as np  # 导入numpy库，用于数值计算\n",
    "import xgboost as xgb  # 导入XGBoost库，用于梯度提升决策树模型\n",
    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier  # 导入决策树回归和分类模型\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier  # 导入随机森林、AdaBoost和梯度提升模型\n",
    "from sklearn import preprocessing #数据预处理：归一化\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, r2_score  # 导入评估指标，包括平均绝对误差和R^2分数\n",
    "\n",
    "import matplotlib.pyplot as plt  # 导入matplotlib库，用于绘图\n",
    "plt.rcParams['font.sans-serif']=[u'SimHei']#解决中文乱码问题\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "import warnings  # 导入警告处理库\n",
    "warnings.filterwarnings('ignore')  # 忽略警告信息\n",
    "from math import sqrt  # 导入数学库中的平方根函数\n",
    "\n",
    "def mape(actual, pred): \n",
    "    actual, pred = np.array(actual), np.array(pred)  # 将输入的实际值和预测值转换为NumPy数组\n",
    "    tt1 = (actual - pred) / actual  # 计算相对误差\n",
    "    tt2 = np.isfinite(tt1)  # 检查相对误差中是否有无穷大值\n",
    "    tt3 = tt1[tt2]  # 去除无穷大值\n",
    "    return np.mean(np.abs(tt3)) * 100  # 计算平均绝对百分比误差（MAPE）并返回\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b06e093f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   出生率(‰)  死亡率(‰)  人口老龄化率(%)  高校招生人数（万人）    性别比  流动人口数（万人）\n",
      "0   14.03    6.45        7.0      233.45  48.37      12107\n",
      "1   13.38    6.43        7.1      284.82  48.54      12604\n",
      "2   12.86    6.41        7.3      340.76  48.53      12988\n",
      "3   12.41    6.40        7.5      410.09  48.50      13544\n",
      "4   12.29    6.42        7.6      479.93  48.48      14003\n",
      "5   12.40    6.51        7.7      540.98  48.47      14735\n",
      "0    126743\n",
      "1    127627\n",
      "2    128453\n",
      "3    129227\n",
      "4    129988\n",
      "5    130756\n",
      "Name: 总人口(万人), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_excel('data1.xlsx')\n",
    "X =df[df.columns[2:8]]\n",
    "Y =df[df.columns[1]]\n",
    "print(X)\n",
    "print(Y)\n",
    "\n",
    "#归一化\n",
    "X = (X - X.min()) / (X.max() - X.min())\n",
    "Y = (Y - Y.min()) / (Y.max() - Y.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3526112d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'年份': {'Statistic': 0.9818894863128662,\n",
       "  'p-value': 0.9605551362037659,\n",
       "  'Normal Distribution': True},\n",
       " '总人口(万人)': {'Statistic': 0.9836377501487732,\n",
       "  'p-value': 0.9681457877159119,\n",
       "  'Normal Distribution': True},\n",
       " '出生率(‰)': {'Statistic': 0.8668074011802673,\n",
       "  'p-value': 0.2137729525566101,\n",
       "  'Normal Distribution': True},\n",
       " '死亡率(‰)': {'Statistic': 0.8649718761444092,\n",
       "  'p-value': 0.20688724517822266,\n",
       "  'Normal Distribution': True},\n",
       " '人口老龄化率(%)': {'Statistic': 0.940091609954834,\n",
       "  'p-value': 0.6599370241165161,\n",
       "  'Normal Distribution': True},\n",
       " '高校招生人数（万人）': {'Statistic': 0.9712461829185486,\n",
       "  'p-value': 0.9006987810134888,\n",
       "  'Normal Distribution': True},\n",
       " '性别比': {'Statistic': 0.8757383823394775,\n",
       "  'p-value': 0.2500523328781128,\n",
       "  'Normal Distribution': True},\n",
       " '流动人口数（万人）': {'Statistic': 0.9850040078163147,\n",
       "  'p-value': 0.9735586643218994,\n",
       "  'Normal Distribution': True}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "# 正态分布检测\n",
    "def test_normality(dataframe):\n",
    "    results = {}\n",
    "    for column in dataframe.columns:\n",
    "        stat, p_value = shapiro(dataframe[column])\n",
    "        results[column] = {'Statistic': stat, 'p-value': p_value, 'Normal Distribution': p_value > 0.05}\n",
    "    return results\n",
    "\n",
    "# 对df的每列数据进行正态分布检测\n",
    "normality_test_results = test_normality(df)\n",
    "normality_test_results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "06d6b2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns  # 导入seaborn库，用于绘制热力图\n",
    "\n",
    "# 计算每个数据集的斯皮尔曼相关系数\n",
    "corr_df = df[df.columns[1:8]].corr()  # 计算数据的相关系数\n",
    "\n",
    "# 定义用于绘制热力图的函数\n",
    "def plot_heatmap(corr, title):\n",
    "    plt.figure(figsize=(8, 6))  # 创建一个图表，设置图表的大小\n",
    "    sns.heatmap(corr, annot=True, cmap='coolwarm')  # 绘制热力图，设置显示相关系数数值和颜色映射\n",
    "    plt.title(title)  # 设置图表标题\n",
    "    plt.show()  # 显示图表\n",
    "\n",
    "# 绘制每个数据集的热力图\n",
    "plot_heatmap(corr_df, \"Correlation Heatmap \")  # 绘制数据的相关性热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bc657a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.XGBoost模型结果：\n",
      "  平均绝对百分比误差: 0.11863851528063135\n",
      "  r2_score 0.9999698605262693\n",
      "2.决策树模型结果：\n",
      "  平均绝对百分比误差: 0.0\n",
      "  r2_score 1.0\n",
      "3.随机森林模型结果：\n",
      "  平均绝对百分比误差: 7.652498896974044\n",
      "  r2_score 0.940395521651645\n",
      "4.Adaboost模型结果：\n",
      "  平均绝对百分比误差: 0.0\n",
      "  r2_score 1.0\n",
      "5.GBDT模型结果：\n",
      "  平均绝对百分比误差: 0.001357026503494273\n",
      "  r2_score 0.9999999992944921\n"
     ]
    }
   ],
   "source": [
    "# 构建xgboost回归模型\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=100, max_depth=3, learning_rate=0.1, random_state=42)\n",
    "xgb_model.fit(X, Y)\n",
    "xgb_y_pred = xgb_model.predict(X)\n",
    "# 输出xgboost模型评价结果\n",
    "print('1.XGBoost模型结果：')\n",
    "print(\"  平均绝对百分比误差:\",mape(Y, xgb_y_pred))\n",
    "print(\"  r2_score\",r2_score(Y,xgb_y_pred))\n",
    "\n",
    "\n",
    "# 构建决策树回归模型\n",
    "tree_model = DecisionTreeRegressor(random_state=42)\n",
    "tree_model.fit(X, Y)\n",
    "tree_y_pred = tree_model.predict(X)\n",
    "# 输出决策树模型评价结果\n",
    "print('2.决策树模型结果：')\n",
    "print(\"  平均绝对百分比误差:\",mape(Y, tree_y_pred))\n",
    "print(\"  r2_score\",r2_score(Y,tree_y_pred))\n",
    "\n",
    "\n",
    "# 构建随机森林回归模型\n",
    "rf_model = RandomForestRegressor(n_estimators=100, max_depth=3, random_state=42)\n",
    "rf_model.fit(X, Y)\n",
    "rf_y_pred = rf_model.predict(X)\n",
    "# 输出随机森林模型评价结果\n",
    "print('3.随机森林模型结果：')\n",
    "print(\"  平均绝对百分比误差:\",mape(Y, rf_y_pred))\n",
    "print(\"  r2_score\",r2_score(Y,rf_y_pred))\n",
    "\n",
    "\n",
    "# 构建Adaboost回归模型\n",
    "ada_model = AdaBoostRegressor(n_estimators=100)\n",
    "ada_model.fit(X, Y)\n",
    "ada_y_pred = ada_model.predict(X)\n",
    "# 输出Adaboost模型评价结果\n",
    "print('4.Adaboost模型结果：')\n",
    "print(\"  平均绝对百分比误差:\",mape(Y, ada_y_pred))\n",
    "print(\"  r2_score\",r2_score(Y,ada_y_pred))\n",
    "\n",
    "\n",
    "# 构建GBDT回归模型\n",
    "gbdt_model = GradientBoostingRegressor(n_estimators=100, max_depth=3, learning_rate=0.1, random_state=42)\n",
    "gbdt_model.fit(X, Y)\n",
    "gbdt_y_pred = gbdt_model.predict(X)\n",
    "# 输出GBDT模型评价结果\n",
    "print('5.GBDT模型结果：')\n",
    "print(\"  平均绝对百分比误差:\",mape(Y, gbdt_y_pred))\n",
    "print(\"  r2_score\",r2_score(Y,gbdt_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5b935fad",
   "metadata": {},
   "outputs": [
    {
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "explainer = shap.Explainer(gbdt_model) # 初始化解释器\n",
    "shap.initjs() #初始化JS\n",
    "shap_values = explainer.shap_values(X) #计算每个样本的每个特征的SHAP值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "71c049b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzO8oiIioYOIXTPRpYHsMIiIiIiKSjAkEERERERFJxgSCiIiIiIgkYwJBRERERESSMYEgIiIiIiLJciWBiI2NxYABA7BixQrJ2xw/fhxz5swxuCw+Ph4DBgzAX3/9le1+goKCMGnSJISFhUk+tlTnzp3D+fPn33s/K1aswIEDBySvHx0dDQBITEzEggUL8PDhQwBAeHi4uM6+ffsQGhr63rFlpNVq8+Q6ZsfY6xMREYGUlJQ8jIiIiIiIMmPUMK4PHjyAWq2GiYmJ3rKoqCicOXMGbdq00VumUqkAAC4uLmJZWFgYLl++bPA41tbWsLKywvLly9G4ceMsZ/+Ni4vD+fPnMWLECADA77//jvnz52d5Hl988QV27typU3bp0iWUKVMGFSpUEMs2b94MpVKJpk2bZrm/7Bw8eBDOzs7o2rVrtutGRUWhe/fumDVrFmrXro39+/ejadOmUCqV6NOnD/bt24cyZcrA19cX33zzDUaPHv1esaVTq9WYPn06wsPDsX37dnEG0ujoaMTHx0OpVEqehVkQBKjVaigUCpQrVy7b9Y25PgCwePFixMfHY8WKFShSpIikbYiIiIgodxiVQCxZsgQPHz40mECkS3+QTycIArRaLezs7PD777+L5XK5HArF/022cuHCBZ2ymjVrQi6X4+rVqwDSkpDU1FTUqlULtra24nbpsaRv16RJE2zbtg0mJiY4ePAgduzYgc2bN8PKygoAMG3aNBQrVkwv7tWrVyM0NBQBAQGws7MDAJiamkKpfP+pMpRKJYoXLy5p3RIlSmD8+PFYtGiRmOSYmppi8+bN8PT0xOeff46HDx9CrVajRYsW7x1butmzZ+Pp06dYvXq1mDwAwIEDB7B+/XqdBCI1NRVarRbm5uYG96XVaqHRaFCvXj2sWrUq22Mbc30AYN68eZg0aRImTJiAn3/+OVfqiIiIiIikMerJa+PGje99wLdv38LExAQymQwymQxqtRoajQYrV65EUlKSTlIBAIcPHwaQ9lCampqKRYsWwdbWFomJieJ+gLREJSkpCTY2NuLDqEqlQpEiRXTefAiCABsbG51jxMTEICgoCL169RKTh9ykVColP+Rev34dCoUC/fv3F9/QXL9+HZ999hkUCgWuXr2Khw8folSpUnBychK302q1UKvVMDU1NTq+Xbt24datW/D399d7kB8yZAiGDRumUzZjxgwEBwfrvcV5l1arlXR8Y64PAFhZWWHFihUYOnQoVq5ciYkTJ0reloiIiIjeT46+utVqtXBzc4MgZD3jYunSpXHkyBHxs1qtRpMmTXTWadCgAdzd3bFnzx4Aae3+ZTIZLC0txXVUKpXeW4/evXvjxYsXOp8BwM/PD25ubgCAwMBAODo66myXnmRkFBAQAI1Ggzp16uj0K0hOToZSqdQpS09kSpcurfMmJCsqlQpv376VtO61a9fw559/QqFQ4P79+wCAs2fPokiRIlCr1VCpVHj06BEiIyNRt25dnW3d3d2xfPlyScdJFxsbi59//hnLli0z+BbAULOl5ORkvWtoiNQmT8Zcn3Tm5ub48ccf0a9fP3Tq1AlVqlQxansiIiIiypkcJRByuRympqaYOHEi2rZta3Cd/fv3Y9++fboHUyqxfft2mJmZYfTo0RAEQa8JypIlS3D9+nVs2LAB9vb2CAgIgL+/P5YvX67Tnn7Dhg1QKBQIDQ3FyJEj8euvv8Le3h5FixYFkNbh+O7duxg5cqRODMnJybC2thY/q1Qq/PbbbzA1NcWsWbN01k1KSgIADBo0SCxLf4j39vZGhw4dsr1WKpUKsbGxiI2NzXZdIK0J2IgRI7BmzRq8ePECMTExGDJkCLRaLdq2bYv4+Hi0atUKM2bMEBOl8ePHo2HDhmISZYwDBw7AxcVFLxnJytu3byUlEFIYe30ycnBwQOvWrbFr1y7MnDkzV+IhIiIioqzlaePxjG3p0zk5OeHly5d4+fIlPvvsMzg4OIjLAgICcOTIEQwePBj29vYAgNq1a2Pjxo0YNGgQfH19UbNmTQBpbzeA/xuxqGjRomKZIAhYvnw5zM3N0alTJ3H/CQkJiImJEZMMANizZw+eP3+ONWvWoF69ejqxfvvtt1Aqlfj5559zfA2Cg4MhCAIePXoEtVqdbVMdrVaLn3/+GTt37sTSpUsxZswYBAcHw9/fH9bW1nj9+jVUKhVKliyJMmXKAEjrSF6+fHl89tlnRsd38eJFgx3fs/L8+XM8e/Ys06Rj8uTJkpMZY6/Pu5o3b55tp3kiIqIPKSkpKdtWGu+7/4z/pYKnsNZRxhZAWclRAiEIAlQqFRYuXIiFCxdmul76A/27zp07ByDtYXn37t1o0aIFbt68iR9//BFt27bVGVmoXLlyWL9+PYYNG4YpU6bgwIEDYofod71+/Rqpqanw9fXF+fPnMWfOHJQqVQoqlQo//PADAgMDodVqUa1aNQDAkydPsGbNGtSuXVsvecgtt27dApB2A128eFGvCVdGycnJGD9+PO7du4elS5eKfTeqV6+OoUOHYseOHUhISACQ9uDt7u4OrVaLmJiYTK91dp49e4ZKlSpJXl+tViMiIgIjRoxAy5YtdZZFRUVh5MiROm94smPM9TGkcuXKePXqFVJSUmBmZmbUtkRERHkhJCTkgzw45vZw7pT7Clsd1alTR9J6RiUQGo1GHH8/PQnITnrbdhMTE7Efw5EjR2BlZQW1Wo3Nmzfj4cOH6NOnD9q1a4dx48bpPAw+fvwYSqUSP/30E5KTk/WSh/QhYufOnYvg4GBs2LABz58/x+zZs9GuXTvx2M7OzihatCjc3d3Fb84rVKiAzZs3w8LCQuzMbWpqavDNCZCWOKWmpkKhUEj+pvz8+fNwdXVFuXLl4O/vn+UDsrm5OQYPHgw7OztUqVIF69atQ58+fVCtWjU0atQIVapUweTJk1G7dm3cu3cPQFrSpNFoULZsWUnxvEuj0WR6vobcuXMHGo0GHh4eOm+P0vcFwKjmTcZcH0PSY5faYZuIiCivVaxYMc/fQISGhsLBwQEWFhZ5dhzKuY+9joxKIG7evKnXp0Cqb7/9Ft9++y2uX7+O+/fvo3379rhx4wZ69eqFFStWYMCAAZg2bRq6du2K7777Du3btwcAbNu2DSdOnEBAQID4TXlcXBzOnDmD8+fPiyMVVahQAd9//z2qVKmCBQsWANDN+jI+mL5+/VrsMJze+Xbfvn1YtGiRwdjfbarj6+sLDw+PbM85LCwMV69ehZeXF5ydnTFgwABcuHAB7u7umW5jZ2cHjUaDoKAgbN26FZ07d8abN2/w5s0bAMDAgQPh5OQEX19fCIKAhw8fwtzcHJ9//nm28RhSrFgxPHv2TGwalp1Lly7BysoKlStX1luW/nZEagKRk+vzrmfPnsHc3DzTIWWJiIg+tA/1wGhhYSG5yQnlj4+1joxKINK/dQ8ICBDb30vRrl07mJqaQhAE+Pn5oUqVKqhYsSJu3LghjqCT/m22q6srdu/ejfbt2yMhIQGnT59G165ddYZXTW+m1LBhQ3z33XdYsWIFBg4cKO5j+PDhiImJyTSeb775BuPGjdMpa9CgAXx9fWFubi5+q718+XLI5XL873//A/B/IzA5OztLOu9169bBxsYGrVq1grW1NWrWrIlly5ahbt26mT7went7IywsDBqNBhqNBkeOHMGRI0eQkpICGxsbHDt2DPHx8Xjz5g1u376N27dvo1q1ajmeC8HZ2Rm3bt0SE7aspKamIiAgAC1btjQ4wlJ6ApGxj0lWcnJ93nXz5k04Ozsb9RaFiIiIiHLOqKfOdx8at2/fjp9//lmv+cyrV69Qu3ZtLF26VCxTKBS4desW7ty5Ay8vL51vqxs0aCCu17dvXwwaNAjnz5/H3bt3odVqdUZBAtImWzt69ChsbGzw8OFDvTgtLCzQvXt3vUntAMDNzc3gXAnlypXTmzXZ2toaSqVSHO3IGH/++Sf++OMPzJgxQ+wT4OXlhf79+2PZsmWYPn26we3S51YYNGgQatWqJSY6AwYMQPXq1SGXy1G0aFE0btwYu3fvxsOHD9GxY0ej40vXokULeHl5YcyYMdk++O/duxfR0dGZdpCOj48HIC2ByOn1yUitVmP//v345ptvsl2XiIiIiHKHtIH6/793v+UtWrQoKlWqhD179uj8a9q0qcEOrbVq1cLw4cOzHP7UxcUFnTp1wvz58+Hv748RI0agRIkSeutl1Uwmu2/jpc5PkFNhYWGYN28eatWqhS5duojlVapUwZAhQ7B//36sW7cu0+3PnTsnJk83b97EiRMnEBQUpPOg3LdvX5w8eRLPnz9/rwSicePGqFixYpad4dPPae3atWjWrJnB5ktA2pwSMpks2yZM73t90vn5+UEQBHTu3DnbdYmIiIgodxj1BuLdDkFxcXEIDAzU+4Zeq9Xiq6++MrgPQ28F3uXu7o6AgACYm5tnOs9EZjEBhoeP/VCCgoIwbtw42NrawsfHRy+WIUOGICQkBOvXr0dERAS+//57vbaSrq6umD9/Pq5evYoZM2YgMjISpUuXxr1791CqVCnI5XKUKFECSqUSxYoVe6+ESCaT4YcffsDgwYOxYcMGvVmngbSmSZMnT4aZmRm8vLwy3Vd4eDhsbW31ZhPPKDeuDwAcPXoUO3bswPLlyz/KzklEREREBdV7zQPRt29f9O3bN8t1tFotkpOT9co1Go3eyDkpKSnYsGEDtmzZgrp16+LJkycYNGgQxo8fD09PT50HZbVardOJOiNBELB+/XqsX78+23MIDw9HYmIiTExM9B7EDc1Enb5/tVoNa2trcb4KjUaDPXv2YPXq1ShZsiT8/PxQrFgxveMplUpx3oJDhw7hxo0bGD58ONq2bSs+eNva2qJNmzYoUaIELl68iJYtW6J06dJYsGABPv/8c0RGRmLWrFlo1qwZ7t27h+HDh8PPz0+MxVhffPEFFi9ejClTpiA6OhpTp04Vl7169QoTJ07EkydP4Ovrq9MXBUjrqH7ixAm8fv0aR44cybQDdG5en23btmHVqlWYNm0aGjZsmKNzJiIiIqKcMSqBMHaozH379uHXX39FXFycXpOm9BmdAeDu3bs4ffo0jhw5gjdv3mD48OEYOnQoIiIi4O3tDS8vL5QuXRpfffUVevfuDQcHByiVSixcuBCxsbFQKBQoVaqUuG+VSoUePXqgV69eejH16tVLHG4UANasWYNz587BzMws0zcXQ4cO1fms0WiQmpqKrl27YsqUKQDSOj+fOHECzZo1g7e3d5bNeBQKBRYsWIA6depg9erVOHz4MFq3bg2FQoGoqChcvHgRhw8fxn///YeRI0eiV69ekMlkaN++PTZu3IjTp09j0KBBGDVqlDgTd9++ffHdd9+he/fu2dSKYY0bN8batWvx4MEDnfILFy7g6dOnWLx4scHkoEyZMti5cyeUSiU8PT0xYcIEg/vPresDAPb29vDx8TF6yFciIiIien9GJRDpD/xS1a9fH0uWLEHVqlX1Hj7VajVSU1MBAMePH8e+ffvQqlUrDB06VByStEyZMtiwYQNOnTqF7du34/HjxzodnevUqYNXr16hZ8+eOkNkvX37FsWKFdObpyBd+lwWAPDjjz8adU6Z8fLyQqNGjbLs35GRTCZD9+7d4eHhAZlMJs6RceHCBWzatAmdOnWCr6+vzoP2+fPn8eLFC/z666+oXr06gLSxprdt24a5c+fqdQI3Vo0aNVCjRg2dsq5du6JFixaZTg5namqKY8eOZTtqUm5dHyCt4zcRERER5Q+ZkJcznSDtYT678W/VajViY2MNdpbOKDU11eAISh8bQRA4LGkBVdtfjZuR+R0FEREVRLVKATcGvFfrcEnevn2LwMBAODs7f5RzDHwMPvY6ytvhiABJF02pVGabPAD4JJIHIH87gRMRERERZSXPEwgiIiIiIvp4MIEgIiIiIiLJ8r6hHtFHxNlOBiBPuw0REVEhlfY3gujjxwSCSCKVRovt7TOfJI+IiEijFaCQM5GgjxubMBFJFHjvLuLj4/M7DMpEUlIS7t+/j6SkpPwOhTLBOiocWE/vh8kDfQqYQBBJpFKpjJ5MkT4cQRCQlJSEPB6Zmt4D66hwYD0RUXaYQBARERERkWRMIIiIiIiISDImEEREREREJBkTCCIiIiIikowJBBERERERScYEgoiIiIiIJGMCQUREREREkjGBICIiIiIiyZhAEBERERGRZEwgiCQyMTGBXM4fmYJKJpPBwsICMpksv0OhTLCOCgfWExFlR5nfARAVFs7VXGCiYAJRUFlYWKBq1ar5HQZlgXVUOBTWetJoBSjkTHqIPgQmEEQSmSjk6HdEg8BoIb9DISKiDJztZNjeXpHfYRB9MphAEBkhMFrAzcj8joKIiHTxix2iD4ntMYiIiIiISDImEEREREREJBkTCCIiIiIikowJBBERERERSVagE4jg4OBc2U98fDyio6NztO2KFStw4MCBXInDGOnxJiYmYsGCBXj48CEAIDw8XFxn3759CA0NzdXjarVahIWF5eo+pTD2OkdERCAlJSUPIyIiIiIiQ/I1gThy5AhevXplcNmhQ4cwaNAgREREvPdx/v77b7Ru3Vp8CDfGwYMHcerUqfeOwRhRUVHo1q0bzp49C7Vajf379yMqKgqPHz/G119/jadPn0KtVsPX1xdHjhzJteOq1Wp4eXlh6tSpEIT/G9EiOjoaoaGhCA8Px/PnzyX9e/bsGZ48eaKT8GTF2Ou8ePFijB49GomJiUafJxERERHlXL4N4xoXF4eVK1ciJSUF33//PZo2bYqnT5/CxMQESqUSzs7OsLKywuLFizFu3DhxO7VaDTMzM5QvXx4JCQl4+vQpzMzMoFCkjf+s0WigUqlgb2+PYsWKAQDMzc0BAKVKlTI6TqVSieLFi0teX6VS4fXr10Ydo3jx4jAxMRE/lyhRAuPHj8eiRYuwc+dOAICpqSk2b94MT09PfP7553j48CHUajVatGhh1LGyMnv2bDx9+hSrV6/WmYH0wIEDWL9+PZRKpTgTc2pqKrRarXht36XVaqHRaFCvXj2sWrUq22Mbe53nzZuHSZMmYcKECfj555+hVHJEYiIiIqIPId+eumxsbLB3717Mnz8fS5YsgVwux8yZM2FqagoLCwskJyfD3Nwcd+7cwdChQwEAgiBApVKhQYMG8PHxwcOHDzFmzBidBEKr1SI1NRVz585F8+bNAUBcZmFhYXScSqXSqIfTwMBADBkyxKhjbNq0CdWrVxc/X79+HQqFAv3798fly5fFss8++wwKhQJXr17Fw4cPUapUKTg5OYnbabVaqNVqmJqaGnV8ANi1axdu3boFf39/vQf5IUOGYNiwYTplM2bMQHBwsJjgZEar1Uo6vrHX2crKCitWrMDQoUOxcuVKTJw4UfK2RERERJRz+fq1rY2NDRYvXozw8HCUKVMGLVq0gFKpxK1btzBy5EjMmTMH7u7uEAQBWq1WTATS1alTB5cuXTK479jYWAQGBsLc3BwvX74EADx9+lTn4drBwSHbGFUqFd6+fSv5nNLfJKxYsQK1atXKct2bN29i3LhxOm8fAODatWv4888/oVAocP/+fQDA2bNnUaRIEajVaqhUKjx69AiRkZGoW7euzrbu7u5Yvny55HiBtGv1888/Y9myZQbfAqS/dcgoOTkZNjY22e7b0LaGGHudgbQ3Sz/++CP69euHTp06oUqVKkZtT0RERETGy7cE4vLlyyhVqhQqVaqE8uXLi+XR0dHw9vZG9+7d4e7uDgBYsGABEhISMG/ePMnfUl+7dg1eXl46ZX379hX/38LCAn/99VeW+1CpVIiNjUVsbKzEs/q/BMLMzAyWlpZZrmtmZgYAeuc0YsQIjBgxAmvWrMGLFy8QExODIUOGQKvVom3btoiPj0erVq0wY8YMuLm5AQDGjx+Phg0bonfv3pJjTXfgwAG4uLjoJSNZefv2raQEQoqcXOd0Dg4OaN26NXbt2oWZM2fmSjxERERElLl8SSC0Wi1WrlyJx48fo0ePHhgxYgSsra0RFRWFsWPHokGDBpg0aRK0Wi2SkpLQpUsXTJw4EdOnT8eCBQugVCrx9u1bhIWFwdTUVKe9viAI0Gg0KFu2LP7++2+YmZnh1KlTmDZtGq5cuQKZTIadO3di27Zt2cYZHBwMQRDw6NEjqNVqSclLTtriv7uNVqvFzz//jJ07d2Lp0qUYM2YMgoOD4e/vD2tra7x+/RoqlQolS5ZEmTJlAKT1KSlfvjw+++wzo49/8eJFtGnTxqht0jtKZ5Z0TJ48WXIyk5PrnFHz5s0xf/58o7YhIiIiopzJlwRCLpdj48aN8PPzw86dO6HRaDBu3Dj06tULb968wfPnz3H48GGoVCrI5XJYW1ujePHiuHLlCubOnYs5c+bgv//+w+jRo2FqagpBEJCQkIAiRYpAqVRCpVKhePHi+P333wGkfVtuaWkpJhqJiYkoUqRItnHeunULAJCUlISLFy+iSZMmeXVJRMnJyRg/fjzu3buHpUuXwsXFBQBQvXp1DB06FDt27EBCQgKAtAdvd3d3aLVaxMTEoHTp0jk65rNnz1CpUiXJ66vVakRERGDEiBFo2bKlzrKoqCiMHDkS1tbWkvf3vte5cuXKePXqFVJSUsS3OkRE9OlJSkrSGUXwY5WUlKTzXyp4CmsdZdd6Jl2+NWEyNzfHpEmT4OjoiCZNmsDc3Bxff/01rK2t4ejoKI6iZGNjI7aj37NnD1asWIFRo0ahZs2a+PvvvwGkDQG6aNEiHDx4UBx5KaOYmBjY2tqKn9MTiuycP38erq6uKFeuHPz9/T9IAmFubo7BgwfDzs4OVapUwbp169CnTx9Uq1YNjRo1QpUqVTB58mTUrl0b9+7dAwC8fv1afOuSExqNRuctTnbu3LkDjUYDDw8PvX4kGo0GAIxq3vS+1zk9dqkdtomI6OMUEhJS6B7Y3kduzwVFua+w1VGdOnUkrZfvY1927NhR/P/Ro0cjPj4eFhYWEAQBr169goWFBWQyGaKiotChQwfY2dnpNdM5ffo0PDw8UKxYMbx+/RpRUVFwdHQUl0dEROgM4fr27dtsR2QKCwvD1atX4eXlBWdnZwwYMAAXLlwQ+2XkJTs7O2g0GgQFBWHr1q3o3Lkz3rx5gzdv3gAABg4cCCcnJ/j6+kIQBDx8+BDm5ub4/PPPc3S8YsWK4dmzZ6hZs6ak9S9dugQrKytUrlxZb1n62xGpCURuXOdnz57B3Nw80yFliYjo01CxYsVP5g1EaGgoHBwccjTCJOW9j72O8j2BiIiIwJYtWzB+/HiYmZlh2rRp0Gg0mDJlCnr27ImNGzdCEAQMHToUAQEB4tCsGbe/fPky1q5dCwDw9fVFSEgItm/fLr65CAwMxBdffCFuk5iYmG1lrlu3DjY2NmjVqhWsra1Rs2ZNLFu2DHXr1s3zB1Vvb2+EhYVBo9FAo9HgyJEjOHLkCFJSUmBjY4Njx44hPj4eb968we3bt3H79m1Uq1Ytx3MhODs749atW2jfvn2266ampiIgIAAtW7Y0OMJSegJRtGhRScfOjet88+ZNODs7G/UWhYiIPj4f44NaViwsLCQ3OaH88bHWUb7ORA2kjQB06NAhxMXFiWX29vbiaEZKpVJs127ogXL79u2oUaMGateuDSBtBKPg4GAcPXoUAMQhTzN+u55dE6Y///wTf/zxB8aMGSO25ffy8sLz58+xbNmy9zthCXbu3IkLFy7AyckJ/fr1w7lz53Du3Dl88cUXaNGiBeRyOYoWLYrGjRtj9+7dOHXqFBo0aJDj47Vo0QLHjh0T33BkZe/evYiOjs60g3R8fDwAaQlEblzn9Jm6300siYiIiChv5GsCkZCQgL1796J3794oWbIkgLQH0HdnjE7/Zvndb7zv3LmDvXv3wtPTE5cuXcK+ffuwf/9+WFpaYt26dVCr1Th//jy0Wi0aNWokbpdVE6awsDDMmzcPtWrVQpcuXcTyKlWqYMiQIdi/fz/WrVuXG6efpXPnzuHu3bvQarW4efMmTpw4gaCgIHzzzTfiOn379sXJkyfx/PlznaZgxmrcuDEqVqyIhQsXZrleWFgY1q5di2bNmhlsvgSkzSkhk8mybcKUW9fZz88PgiCgc+fO2a5LRERERO8vX5swbd26FSqVCv379weQ9m1yaGgo+vTpI2n7lStXQq1WY9myZbCysoKDgwMqVKiAzp07Y9u2bTh16hR+/fVXNG/eXKcTdWZNmIKCgjBu3DjY2trCx8dHr0nMkCFDEBISgvXr1yMiIgLff/99polIREREth1nIiIiMl3m6uqK+fPn4+rVq5gxYwYiIyNRunRp3Lt3D6VKlYJcLkeJEiWgVCpRrFgxyRO2GSKTyfDDDz9g8ODB2LBhg96s00Basjd58mSYmZnpza+RUXh4OGxtbfUm/csot67z0aNHsWPHDixfvvyTe21NRERElF/yLYF49uwZtm3bhp49e4ojJ504cQLJyclo0KCB2BTGED8/P/Ts2RPjx4/H8+fPUaNGDdjb2+us4+bmhvPnz+Ply5c6MzMLgoDo6GidYVw1Gg327NmD1atXo2TJkvDz8zM4mpNSqRTnGzh06BBu3LiB4cOHo23btuIDc3rnrdmzZ0u+FoZGD7K1tUWbNm1QokQJXLx4ES1btkTp0qWxYMECfP7554iMjMSsWbPQrFkz3Lt3D8OHD4efn5/edZDqiy++wOLFizFlyhRER0dj6tSp4rJXr15h4sSJePLkCXx9fWFnZ6ezbWhoKE6cOIHXr1/jyJEjmXaAzs3rvG3bNqxatQrTpk1Dw4YNc3TORERERGS8fJtIbs6cOZDJZOLbh//++w8+Pj5o3749ihUrJrbH12q1Yn+I69evQ6FQYNOmTahXrx7q168vzpOQkSAIuHbtGvbt24cff/wRZcuWxd27d3Hw4EE8fPgQL1++RPXq1cX1vb29ceLECTRr1gze3t5ZNr9RKBRYsGAB6tSpg9WrV+Pw4cNo3bq1+GCbPozp2rVrs53Z+dq1axg5ciRSU1N1yqOionDx4kUcPnwY//33H0aOHIlevXpBJpOhffv22LhxI06fPo1BgwZh1KhRCA0NxciRI9G3b19899136N69e3ZVYFDjxo2xdu1aPHjwQKf8woULePr0KRYvXmwwOShTpgx27twJpVIJT09PTJgwweD+c/M629vbw8fH54MMrUtERERE/yffJpLr1asXoqKiYGdnh8uXL2PChAmoUaMGpkyZAgBISUkR/1ulShVUrlwZU6dOhYmJCdzc3LIcp/bVq1e4fPkyRo4cidatWwNIa1t/69YtODg4oHv37jp9Iry8vNCoUSN06NBBUvwymQzdu3eHh4cHZDKZmOAAaRPBGUulUul8vnDhAjZt2oROnTrB19dX50H7/PnzePHiBX799VcxCapYsSK2bduGuXPnoly5ckYfP6MaNWqgRo0aOmVdu3ZFixYtMp0cztTUFMeOHct21KTcvM4tWrSQtA8iIiIiyl0yoQAMmCwIAo4fP44WLVrkeCjSd6WmpsLU1DRX9pUfBEHgsKQFUG1/NW5G5ncURESUUa1SwI0B+T4y/Qfz9u1bBAYGwtnZ+aMcIvRj8LHXUYH4aZPJZGjTpk2u7rMwJw8AmDwQERERUYGU7/NAEBERERFR4cEEgoiIiIiIJGMCQUREREREkjGBICIiIiIiyQpEJ2qiwsLZTgYg3wcuIyKiDNJ+NxPRh8IEgkgilUaL7e0V+R0GEREZoNEKUMiZSBB9CGzCRCRR4L27iI+Pz+8wKBNJSUm4f/8+kpKS8jsUygTrqHAorPXE5IHow2ECQSSRSqWCVqvN7zAoE4IgICkpCQVgbkzKBOuocGA9EVF2mEAQEREREZFkTCCIiIiIiEgyJhBERERERCQZEwgiIiIiIpKMCQQREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIIgkMjExgVzOH5mCSiaTwcLCAjKZLL9DoUywjoiIPg7K/A6AqLBwruYCEwUTiILKwsICVatWze8wKAsfqo40WgEKOZMUIqK8wgSCSCIThRz9jmgQGC3kdyhElAlnOxm2t1fkdxhERB81JhBERgiMFnAzMr+jIKLMMcEnIsprbI9BRERERESSMYEgIiIiIiLJmEAQEREREZFkTCCIiIiIiEgyJhBUKKjV6jxdn4iIiIik+aQTiBUrVuDAgQP5HQYAYMmSJfj9999ztG1KSgqCgoIMPjQHBwcjNDRU8r6eP3+u8zkuLg5hYWHQarU5ik2r1WLVqlUICAjI0fYAkJCQgAEDBuDJkyeS1j927Bi8vb1zfDwiIiIiytwnnUAcPHgQp06dyu8wAAA3btww+KAvCAJ2796NlJSUTLcNCwtD7969kZSUpLds79696NevH2JjY7ONISgoCF26dMG2bdvEsoMHD6Jr167ZJhBardZgjHK5HHfv3sXmzZsNbpeamprt24KZM2fC3t4e5cuXz3Sd+Ph48fwbNGiA+/fv65wHEREREeWOT3oeCKVSieLFi0teX6VS4fXr10Ydo3jx4jAxMdErT05OhkKhEJcpFAooFApotVpotVqoVCqYmpri5MmT8PHxgUwmQ8+ePXX2MW7cOIwdO1bch0qlQlRUFKysrGBubg4AuH79OlQqFZ4+fYpixYplGevevXthYWGBdu3aiWUWFhYA0q5VRlqtFnL5/+WfT58+Rffu3cVzeXddQRDg5uamU67RaAAAs2bNQseOHQ3GdPLkSYSGhmLHjh2Qy+WIjY3FxIkTERkZiUWLFsHFxQUA4O/vj86dO6NcuXKwtbWFr68vhg0bhpYtW6J06dJZnjcRERERSffJJxDvPhhnJTAwEEOGDDHqGJs2bUL16tX1ygcMGIDg4GCdsocPH2LLli3i5wULFuCnn35Cq1at9JIHIC05SElJgZmZGQDg66+/RkpKCubPn4/mzZvjxo0bePLkCcaNG4f58+djy5YtYkLwrujoaBw+fBg9e/bMNqkKDg7G7NmzMXfuXDg4OAAAypYti0OHDsHS0hJFihQRr6ufnx8iIyMxadIk2NjYiPvQarWIiYlBcnIyihYtavA4arUaq1evxsSJE2FpaQkACAgIQHJyMqpXr44NGzZg+fLlSE1NRVRUFMqVKydu+8UXX6BTp05Yu3YtZs2aleX5EBEREZF0n3QCoVKp8PbtW8nrp3/Tv2LFCtSqVSvLdW/evIlx48YZfPsApDXLkcvlsLKygkKhwOTJk1GjRg3069cPWq0WqampWLZsGYoWLZppe/70txYymQwAcO7cOZ3l/v7+8PT0RL9+/XD9+nXMnj0bCxYs0HtDAAA///wzUlJS0KZNmyzPKykpCTNnzsSzZ890rp1SqcS2bdsgCAImTZoEAHjx4gV27NiBTp06wcrKCrt27YKrqyucnZ2h1WqxcOFCaLVaLFmyJNNraGFhgaZNm4plYWFhaNSoEWrXro2lS5cCSHtL0aJFC73tBw8ejK5du2Lq1KniGxkiIiIiej+fbB8IlUqF2NhYSX0D0qUnA2ZmZrC0tMzyX/pbgczecNSoUQMuLi5wcHBA+fLlYWpqChsbGzg4OKBSpUo4e/YsHj16BB8fH723BsnJyWJ/iaSkJLHtf1xcHF6/fo3k5GScP38e//zzD0aMGAEgrZnQo0ePMGXKFCQkJOjs79atW2In5/S4DUlJSYGXlxeePn2K5cuXo2rVquIymUwGFxcXHDp0CF5eXoiKisK0adPQvn17TJ06FXfu3MHy5cuxb98+JCQkYPLkybh69SpatWqV6TX6888/0axZM50yrVar09wLAC5duoRGjRrpbV+8eHE4OTnhypUrmZ4TERERERnnk30DERwcDEEQ8OjRI6jVaklNmYxp7pTZNi9evIBMJtMr12g0SEpKQlRUFP7++29s3LgRs2fPRpEiRRAVFQWVSgWNRoNy5cohKCgI3377LZRKJcaPHw+ZTIYiRYqgQ4cOUKvVGDNmDPz9/TFkyBDExMRg+fLlWLp0KdatW4cxY8agR48e+OWXX1C+fHnExcVh1qxZKFWqFF6+fJnpebx9+xaTJk3Cv//+i59++sngG5i2bduiTJkyUKlUWLduHe7evYu4uDh069YNUVFRGDVqFAYOHIiEhAQoFAqsX78ejo6OmR4zJCQEffv21Smzs7PD48ePYWlpCTs7OwQGBuLLL78U38K8y8XFBcHBwWjSpEmmxyGij09SUhIEQcjvMAql9C+lDA3MQQUD66jgK6x1lN5kPDufbAJx69YtAGkVe/HixQ/2gPn9998jMDDQ4LL//vsPe/bsET+/23a/XLly+P333+Hi4oI//vgDb9++hb29PS5evIjIyEh06dIFALB9+3Z8+eWXGDp0KCIjI3Hz5k1s2bIFQ4YMwcaNG/HPP/+gfPnySE5OxsSJE5GQkIBZs2Zh4sSJmcb93Xff4cmTJ1i5ciVq166d6Xqurq54+vQp+vTpg8aNG0Or1WLBggUYN24c3Nzc8M8//8DNzQ3dunXT6RNhyOvXr2FnZ6dT1qpVKwwdOhTnz5/HDz/8gICAAIwcOTLTJNDOzg4RERFZHoeIPj4hISGF7g93QWPMEOCUP1hHBV9hq6M6depIWu+TTSDOnz8PV1dXlCtXDv7+/h8sgVi/fj2USqVO3wUgLZGZNWsWLl++jHnz5um0+wfSmlxlHO50//79OHPmDPz9/fHnn38iMTERXbp0QbNmzTB16lQsW7YMcrkc9vb2WLx4MapUqQIAKFKkCDw9PQGkNVeqXr06RowYkenDfHpzp7i4OGzatEnsNG1IZGQkVqxYgStXruDw4cMICgrCihUrMHv2bDRu3Bi//PIL/P39sXHjRuzfvx9XrlzByJEj0adPH4NvEDJ2EE9XpUoV7N+/H/Hx8bC1tcX9+/dx4sQJ+Pr6omXLlpg/f77O+mZmZkhNTc00ZiL6OFWsWJFvIHIoKSkJoaGhcHBwyHTgDcpfrKOC72Ovo08ygQgLC8PVq1fh5eUFZ2dnDBgwABcuXIC7u3ueH/vdzryxsbHYvHkz/vnnH7x9+xYbN24UH/avX7+ON2/ewNPTEyYmJjodsk1MTMTXTDdu3BCTAlNTU1hZWWHq1Kn4559/dIZaBdL6BWzbtg2WlpaQyWQYN24cgLQRoDJSqVRYv369OH/Dli1bYG1tnel5nTx5ErNnz4ajoyM2bdqE5cuXY+/evbCxscHcuXOhVqthb28PKysrTJ8+HVu3bsWBAwewcuVKODg4ZNqHITo6Wi9psbOzg52dHbZu3YqOHTtizJgxGD16NDZs2ID//vtPp1lUdHS0UUP1EtHH4WP8g/2hWVhYSG7OQPmDdVTwfax19EkmEOvWrYONjQ1atWoFa2tr1KxZE8uWLUPdunU/6Gg9qamp+PXXX7Fz5040btwYc+fORdGiRfH69WusXbsWv//+OypUqAAPDw+9REChUMDU1BRPnjxBZGQk9u7diy5duohzS3h7e+u85YiOjkbv3r3Rv3//bG/kU6dOicOv1q9fH5cvX84yeQCA+vXr4+uvv8bYsWNhamqKrl27ol69eqhatSrmzJkDa2trLFmyBA8fPsS9e/dgamqKfv36oWXLlihVqpTBfZYuXRphYWEGX6dptVr8999/6NChA+Li4tCzZ0+cOnUKT58+1UkgwsLCULdu3SxjJyIiIiLpPrlRmP7880/88ccfGDNmjPhQ7OXlhefPn2PZsmUfJAZBEHDixAn07NkTBw4cAACMGDECMTEx8PHxQadOnXD69GkMHToUGzZs0EsegLRRj+RyOfbt24dmzZrB09MTK1euhEwmg0KhgI2NDeLj48VRobZv344KFSqI/SQyExcXh5UrV6Jo0aLYsmULPDw8JJ2TiYmJzqR2jo6O8PT0hL29vU7zJCcnJ3z99ddif4USJUogJSVFnFQuowYNGuD8+fMGj3fx4kU0bNhQnP3azMwMZmZmOrNhq9VqXLp0CQ0aNJB0DkRERESUvU/qDURYWBjmzZuHWrVq6TxIV6lSBUOGDMEvv/wCOzs7cejT3JacnIxjx45hx44dCAkJgYeHB2bPno1hw4Zh2rRpeP78OWrXro3p06fD09Mzy7chgiDg5cuXuHz5Mn755RdYW1vj3LlzuHPnDgDgzZs36NWrF3r27AkPDw8cOHAAa9euNZiMZGRjY4ONGzeiePHikMvlYmfzd8XExMDKygomJiaIj4/XG27VkKzeBBiacM/d3R0+Pj54/vw5ypQpo7Ps1KlTmD59ujgXRXJyMpKTk3WaLZw6dQrly5eHvb19trERERERkTSfTAIRFBSEcePGwdbWFj4+PnqddocMGYKQkBCsX78eERER+P777zNtQxsREZFtr/rMRv7ZtWsXFAoFVq1ahYYNGyIsLAwA0KhRI/Tv3x9ly5bV2yY2NhbFihXTKdNqtShdujRmz54NJycnAECFChWwb98+ABAnoJs+fTp2796NFi1aZDv5XboSJUpku87p06exadMm/PTTT6hSpQoCAgJgamoKExMTvSRl0qRJsLKywpw5c/T2o9FokJKSAltbW71lxYoVw9dff42ffvoJPj4+Ynl4eDhKlSoFU1NTmJqawtbWFr/88guCg4PF/hLJyclYtWoVZs6cKemciYiIiEiajz6B0Gg02LNnD1avXo2SJUvCz89P72EcSJuvIX0En0OHDuHGjRsYPnw42rZtK87cnD6ix+zZsyUfP32yMyCtA/Xq1athZ2en95DdoUMHg8lDcnIyvv76a4wePRrdunXTOa/g4GD4+/vj5cuXePnyJSIjI6HRaMQ4W7RogejoaPj4+GTbkTh9m4zxAv83sdzFixfFvgVqtRonT55ETEwMypQpA7lcrveGICOlUgmlUpltPwpDhg4dih49emDfvn3o3r07AODChQvo2rWruM7EiRPh6+uLLl26oFKlShAEAXPmzIGjoyMaNmxo9DGJiIiIKHMffQLh7e2NEydOoFmzZvD29s5y7gGFQoEFCxagTp06WL16NQ4fPozWrVuLCUR6O/21a9dm2zH32rVrGDlypN4QoiVLltT5XKRIEQDAsWPHUKRIEVhZWcHU1BRv375FYmIiAgICEB8fj8qVK+tsZ21tDUdHR3z++efw8PCAg4MDypQpg969e+v0J+jVqxeCgoKwc+dO1KxZE82bNzcYb/o27yYQ9evXR4kSJfC///1P71qNGDECVlZWWV6H9H2/u1+prKys4Ovri1GjRqFu3bpwcHBA7969ddZp27Yt2rZtK34+ePAgHjx4gC1btuTomERERESUuY8+gfDy8kKjRo3QoUMHSevLZDJ0794dHh4ekMlkOkOnJicnG318lUqV5fLixYujY8eO+O2337Br1y6dZUqlEuXLl8eMGTNQs2ZNnWVdunTJtEN0xo7EQNrkdUFBQYiKiso0jvRt3k14SpcujWPHjiExMVFnTHULCwvJM3Onpqbm6Nqlq1atWrZzUGTUpk0b1K1bN9uJ6oiIiIjIeDKBM+0UCIIgICUlRZw51cLCAmZmZgYnWMuJ5OTkDzpE7ceqtr8aNyPzOwoiykytUsCNAR/9d2N56u3btwgMDISzs/NHOX79x4B1VPB97HXE37IFhEwmg7m5eZ495DN5ICIiIqLc8MnNA0FERERERDnHBIKIiIiIiCRjEyYiIzjbyQCw2xBRQZX2M0pERHmJCQSRRCqNFtvbK/I7DCLKhkYrQCFnIkFElFfYhIlIosB7dxEfH5/fYVAmkpKScP/+fXEkMyp4PlQdMXkgIspbTCCIJFKpVDmeEI/yniAISEpKAkemLrhYR0REHwcmEEREREREJBkTCCIiIiIikowJBBERERERScYEgoiIiIiIJGMCQUREREREkjGBICIiIiIiyZhAEBERERGRZEwgiIiIiIhIMiYQREREREQkGRMIIolMTEwgl/NHhoiIiD5tyvwOgKiwcK7mAhMFE4iCRKMVoJDL8jsMIiKiTwoTCCKJTBRy9DuiQWC0kN+hEABnOxm2t1fkdxhERESfHCYQREYIjBZwMzK/o6A0TOSIiIjyA9tjEBERERGRZEwgiIiIiIhIMiYQREREREQkGRMIIiIiIiKSjAkEERERERFJxgSCjPLgwQPcuHEj0+VRUVEIDw/PteMlJCRg9+7dePbsmU55aGgoXr9+bXCbW7duISgoKNdiICIiIqL/w2FcySjbt29HSEgItm3bZnD52rVrcfv2bezdu9fg8gkTJuDOnTsGl61fvx6VKlXSKUtISICPjw8qVKiAsmXLiuX9+/dH9+7dMX78eL39zJkzB87OzliwYIHEsyIiIiIiqZhAkFGUSiVMTU11ypYtW4ZKlSqhS5cuMDU11VueUWpqKipXroxu3bqJZWFhYVi7di1MTEwApL3lCA0NRZs2baBUKsXjZmRmZgYzMzODxzAxMRH3RURERES5i02YyCgmJiZ6D/PHjx/H8+fPAQCmpqZZPrzL5XKUKVMGrVu3Fv+5ubkBABSKtFmFL1y4gOXLl2cZh0KhgFxu+PY1FGNERATmzJmD1NTULPdLRERERFljAkGShIeHIyIiQvw8adIk/Pvvv4iNjUV0dDSKFy+O0NBQvHnzBqmpqQgNDUVISAgePnyImJgYcTtByH72YKVSme0bBJlMlumydxOLc+fOoX///jh8+DBOnjyZ7fGJiIiIKHNswkSSbNy4EbGxsShRogSAtAf4PXv2oHXr1gDS+i+sX78eycnJUKvVGDp0KAAgJSUFXl5e6NChAwBAq9Xi1q1bmD17trjv9ARDo9HkKDZBEKBWq6FSqWBhYSGWBwUFwc/PD3/99Rdq166NCRMmwNnZOUfHICIiIqI0TCBIkvDwcFSrVg2JiYkAgPbt22P27NmwsLCAo6MjduzYAQBYvnw5bt26hc2bNxvcT+3atWFnZ4fIyEjIZDIUL14cNjY2aNu2rdH9FtKTlowCAgIApL11CAgIwBdffIHly5fD3d3dyDOmwiIpKQmCICApKUn8TAUT66hwYD0VfKyjgq+w1pGlpaWk9ZhAkCTBwcHo2LEj/v33XwCAu7s7/Pz8MH78ePTu3VvyfoYNGwYA2Lx5M1avXo3jx4/Dzs4uRzF5enqiefPmOm8gihYtCgAoWrQo5syZw8ThExASEqLzCzo0NDT/giFJWEeFA+up4GMdFXyFrY7q1KkjaT0mEJSt8PBwxMbGwtHRUUwg0t8WaLVasXlSdjQajdhR2szMDBYWFjlOHgCgcuXKYhOqd9WqVYvJwyeiYsWK4huI0NBQODg46DRlo4KDdVQ4sJ4KPtZRwfex1xETCMpWiRIlsHz5clSuXFmn3MXFBUePHsXTp0+RmJgIhUKh04larVYjJSUFlStXhrm5Ob755huEhIRAqVRCrVZDrVbjq6++0tnn77///gHPjD4G7/5itrCwkPwKlvIH66hwYD0VfKyjgu9jrSMmEJQtc3PzTL/NNzc3x+TJkxEbGwu5XI7U1FRoNBoMGjQIWq0WKSkp2L17NxwcHDBixAikpqbCxMQEu3fvxsuXL/G///0PAPD48WOsWbMm07kdiIiIiKhgYAJB7y294zKQdSfqpk2biv/v7++P6tWrw8PDAwDExMHExASpqak5mq/h8uXLcHFxMXo7IiIiIpKO80DQBxceHo779++jZs2aYllycjLkcjnMzMygUqmMSiDu3r2LUaNGYcyYMXj48GGm6/37779ITk5+n9CJiIiIPnlMIOiD0mg0WLp0KczMzHQ6QKekpIhvIUaPHo2zZ89mug+tVovk5GQ8evQIo0ePxqBBgxAbG4v58+frJCXvWrhwIbp3755r50JERET0KWITJjKKIAjibNIqlQqxsbEwMTERR1dKTU2FVqtFfHw8AECtViM1NRWlS5dGcnIy5s+fj7/++gszZ87U6VQUGhqKIkWK6B0vfZK59P0DwJ07d5CYmIhz586hRo0aevM8WFhYIDAwEEFBQVAq027xoKAg/Pfff+jRo0cuXxEiIiKiTwsTCDJKxuZFDx48wODBgw2u16xZM53PR44cwahRo/D06VN899136NKlC4C0yeB27tyJuLg4sSx930uXLkVISAhkMhk+++wzcZmrqyvatWsHDw8PeHp66h27V69eWLRokc78FDKZDDVq1BDnoSAiIiKinGECQUZJn7ANAJydnXHq1CmYmJiI3/S/S6VSQaVSwdbWFu7u7nB0dNSZN6J58+aIiIiAm5ubTjJQpUoVyOVyNG3aFM2bN4e9vb24TCaTYe7cuZnG2Lx5czRv3vx9T5WIiIiIDGACQUaZP3+++P9KpRLFihXLcv2Mw7JOnDhRb3mlSpXg7e2tV65UKrFu3bqcB0pEREREeYKdqImIiIiISDImEEREREREJBkTCCIiIiIikowJBBERERERScZO1ERGcLaTARDyOwxCel0QERHRh8YEgkgilUaL7e0V2a9IH4xGK0AhZyJBRET0IbEJE5FEgffuijNsU8HA5IGIiOjDYwJBJJFKpYJWq83vMIiIiIjyFRMIIiIiIiKSjAkEERERERFJxgSCiIiIiIgkYwJBRERERESSMYEgIiIiIiLJmEAQEREREZFkTCCIiIiIiEgyJhBERERERCQZEwgiIiIiIpKMCQSRRCYmJpDL+SNDREREnzZlfgdAVFg4V3OBiYIJRG7QaAUo5LL8DoOIiIhygAkEkUQmCjn6HdEgMFrI71AKNWc7Gba3V+R3GERERJRDTCCIjBAYLeBmZH5HUdgxASMiIirM2B6DiIiIiIgkYwJBRERERESSMYEgIiIiIiLJmEAQEREREZFkTCAo36nVaqO3uXLlCk6cOJGr+yQiIiKi7DGB+AT9/fff6NGjBx4/fvxe+9FqtVi1ahUCAgJyvI+EhAQMGDAAT548EctiY2Nx4cIFxMXFGdwmLi4O8+bNQ5kyZQwuP3bsGLy9vXMcExERERFljglEARUeHo7AwEA8fvwYoaGh2f4LCQnBw4cPcffu3Wz3XapUKYSGhuKXX36RFItWq0VKSopeuVwux927d7F582aD26Wmpmb7JmDmzJmwt7dH+fLlAQAvX77E9u3bUb16dfj4+ODt27d62yxcuBAdOnSAi4uLwX02aNAA9+/fx7Zt27I5MyIiIiIyFueBKKC2bNmCY8eOwcTEBGq1GklJSShatKi4XKPRICEhATY2NpDJ0mb0TU1NRWpqKv75558s9/3FF1+gUaNGOHv2LJ48eYIKFSpkuf7Tp0/RvXt3AIBCoTsBmFarhSAIcHNz0ynXaDQAgFmzZqFjx44G93vy5EmEhoZix44dkMvTctl9+/ahadOmKFq0KJycnPD333+jZcuW4jZ//PEHnj17hnnz5mUar62tLXx9fTFs2DC0bNkSpUuXzvL8iIiIiEg6JhAF1IwZMzBjxgwAgJ+fHwICAnD8+HFx+b///ovBgwdj27ZtBpvyCIKAxMREmJmZwcTERG95jx49YGtrKz7oZ6RSqZCSkgILCwsoFAqULVsWhw4dgqWlJYoUKQKlUinGFRkZiUmTJsHGxkbcXqvVIiYmBsnJyTpJT0ZqtRqrV6/GxIkTYWlpKZYHBQWhU6dOAAArKys8e/ZMXPby5UusWLECa9asEWPIzBdffIFOnTph7dq1mDVrVpbrEhEREZF0TCAKgYsXL6JOnTpGbRMdHY02bdpku97hw4czXebv74+qVatCqVRi27ZtEAQBkyZNAgC8ePECO3bsQKdOnWBlZYVdu3bB1dUVzs7O0Gq1WLhwIbRaLZYsWWJw3zdv3oSFhQWaNm2qU67RaBATE4Py5csjLi5OfLsiCALmzJmDwYMHw8HBQdI1GDx4MLp27YqpU6fC3Nxc0jZERERElDUmEAVceHg4/vvvP7Rr1w5hYWFiuVar1Vv377//RsOGDSGXy2Fra4sDBw6ITaC6dOmC0aNHZ9qcKCOtVguVSoWSJUsCAGQyGVxcXLBgwQK8evUKU6dOxbRp09C+fXtMnToVt27dwvLly9G+fXtMmDABM2fOxM2bNzFt2rRM3xT8+eefaNasmV75559/jt9//x0VKlTA+fPn0b9/fwDArl27IJfL0aNHD0nXDQCKFy8OJycnXLlyBU2aNJG8HRERERFljglEAbd161YIgoCffvoJP/30EwDA0tISq1ev1lnv7Nmz8PLywoABAzB69GgoFAqxY3J6R2RLS0uUKFEiR3G0bdsWZcqUgUqlwrp163D37l3ExcWhW7duiIqKwqhRozBw4EAkJCRAoVBg/fr1cHR0zHR/ISEh6Nu3r155x44dMWDAAAQEBMDW1hZubm4ICQmBv78//P39xTcSUrm4uCA4OJgJRAGUlJQEQRBydX8Z/0sFD+uocGA9FXyso4KvsNZRxmblWWECUYA9fvwYhw4dQvv27TFnzhwAgK+vLwIDA3UepPfv34/Fixejffv2GD58eJ7F4+rqiqdPn6JPnz5o3LgxtFotFixYgHHjxsHNzQ3//PMP3Nzc0K1bN50+EYa8fv0adnZ2euVOTk749ddfcfXqVTRr1gympqbw9vbGhAkTxDcixrCzs0NERITR21HeCwkJyZNfrKGhobm+T8pdrKPCgfVU8LGOCr7CVkdSm8wzgSigkpKS4O3tDVtbW0ycOFEsP3/+PNq2bSt+njdvHu7cuQMvLy907do1z+KJjIzEihUrcOXKFRw+fBhBQUFYsWIFZs+ejcaNG+OXX36Bv78/Nm7ciP379+PKlSsYOXIk+vTpY/CtQUpKCszMzAwey8XFRRyi1c/PDw4ODmjVqhVu376NuLg4NG7cWBy1CQAuX74MuVyO+vXr6+3LzMwMqampuXQVKDdVrFgx199AhIaGwsHBARYWFrm2X8o9rKPCgfVU8LGOCr6PvY6YQBRQ06dPx9OnT/HDDz/Ax8cHY8aMQVBQEF68eIFmzZqJ8ytER0djy5YtqFKlSp7FcvLkScyePRuOjo7YtGkTli9fjr1798LGxgZz586FWq2Gvb09rKysMH36dGzduhUHDhzAypUr4eDggEaNGunts3jx4oiOjs6yQ/SdO3fwxx9/YMeOHWInbmtra/j4+GDq1KkAgGXLlsHR0REREREIDAzEwIEDdfYRHR2N4sWL5+r1oNyRV79QLSwsJL+CpfzBOiocWE8FH+uo4PtY64gJRAE1ffp0hIWFwdTUFCEhIejZsydsbW3x5ZdfwtnZGf/++y+AtAfocuXK5Wks9evXx9dff42xY8fC1NQUXbt2Rb169VC1alXMmTMH1tbWWLJkCR4+fIh79+7B1NQU/fr1Q8uWLVGqVCmD+yxdujTCwsIyfVWWlJSE2bNnY+bMmbCyssKpU6fECev69esHjUaDpKQkBAUFYeLEidBoNPjmm2/0EoiwsDDUrVs3V68HERER0aeMM1EXUCVLlkTt2rXh4uICf39/1KlTB8+ePdPr45CxKU9eMTExwdixY8X5JBwdHeHp6Ql7e3ud5klOTk74+uuvxZGXSpQogZSUFINzTTRo0ADnz5/P9JjLli1D48aN4ebmhtjYWJ0J7MzNzRETE4MnT56I/SgUCoXerNVqtRqXLl1CgwYNcn7yRERERKSDbyAKgRcvXuDGjRto1qyZ3rwJ7zp8+DCqV6+e7ezSUsXHxxscbvVdWX3Lv2nTJlSvXl2nzN3dHT4+Pnj+/LneRHh//fUXbt26hW3btgFImwPizZs34vLExEQoFAq98nc75J46dQrly5eHvb19tvETERERkTRMIAq4qKgojB8/HqVLl8YPP/yQ7fr+/v4oUaIEfv75Z7FMpVJJPp5arUZKSgqKFCkCAChSpAgCAgJgamoKExMTvTcekyZNgpWVlThKVEYajQYpKSmwtbXVW1asWDF8/fXX+Omnn+Dj4yOWx8TEYNGiRfD19RU7Wdva2iI2NhZXr16FjY0NXr9+jWLFikGhUOD27dti35CMozolJydj1apVmDlzpuRzJyIiIqLsMYEowB48eIApU6ZAqVRi9erVsLa2Fpeld0A9ePAgKlasCJlMhtDQUAQHB6Ndu3Y6+0kfhcjHx0fnYT0zVlZWOHfuHIC0JlLvviHISKlUQqlU6sQm1dChQ9GjRw/s27cP3bt3F483bdo0ODs7i+vJZDJ07doVo0aNAgBxZCcbGxt89dVX6N27NwCIs2Snz1rt6OiIhg0bGh0XEREREWWOCUQBtXHjRqxbtw5OTk5YtmyZ3gRwDg4OqFq1KrZt24aUlBQAaW8Lqlatii5duuisa21tjR9//FHScbVarcFZrjOj0WiMWj8jKysr+Pr6YtSoUahbty4cHBxQtGhRuLu766373XffwcnJCW/fvtVJkGbNmgU3NzeYm5ujZcuWANKSqgcPHmDLli05iouIiIiIMscEooDq0aMHTE1N0bNnT5iamuotVyqV8Pf3l7Qvc3NztG7dOrdDBJD2diM5OTnH21erVg2bNm3KcjhXIO0tRIsWLfTKTUxM0LFjR52yNm3aoG7dutlOZkdERERExmMCUUBZW1ujf//++R1GtjZt2vTe+6hcuXIuRPJ/zM3N83xoWyIiIqJPFYdxJSIiIiIiyZhAEBERERGRZEwgiIiIiIhIMvaBIDKCs50MgJDfYRRqadeQiIiICismEEQSqTRabG+vyO8wPgoarQCFnIkEERFRYcQmTEQSBd67i/j4+PwO46PA5IGIiKjwYgJBJJFKpcrxpHlEREREHwsmEEREREREJBkTCCIiIiIikowJBBERERERScYEgoiIiIiIJGMCQUREREREkjGBICIiIiIiyZhAEBERERGRZEwgiIiIiIhIMiYQREREREQkGRMIIiIiIiKSjAkEERERERFJxgSCiIiIiIgkYwJBRERERESSMYEgIiIiIiLJmEAQEREREZFkTCCIiIiIiEgyJhBERERERCQZEwgiIiIiIpKMCQQREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIIiIiIiISDKZIAhCfgdBVNA9fPgQCQkJUCqVkMuZdxdEgiBApVLBxMQEMpksv8MhA1hHhQPrqeBjHRV8hbWOTE1N4eTklO16fBIikuDevXuIiYlh8lCARUZGIiYmplD9ov7UsI4KB9ZTwcc6Kvg+9jriGwgiCTp37gwAOHjwYD5HQplhHRV8rKPCgfVU8LGOCr6PvY74dSoREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIIiIiIiISDIO40pERERERJLxDQQREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIOiTEx0djUmTJqFJkyb45ptv8OjRI0nbaTQaLF++HM2bN0eHDh1w8uRJneVPnz7FiBEj8NVXX2HkyJGIiIjIi/A/GXlVT5cvX0avXr3g5uaGNm3aYPv27XkR/ichr+ooow0bNuDbb7/NrZA/OXldR//88w+aNGmC58+f52bYn5S8qqOAgAB06tQJjRo1Qrdu3XDixIm8CP+TkNM6Srdr1y6Dv8cK9XODQPQJ0Wq1wuDBg4UhQ4YIISEhwqFDh4QOHToIiYmJ2W67atUqoVWrVsK1a9eE27dvC61btxYCAwMFQRCE5ORkoVOnTsLkyZOFsLAwYePGjUL//v0FjUaT16f0UcqregoPDxeaNGkibNu2TYiJiRFOnDgh1KtXT7h69Wpen9JHJ6/qKKPAwEDBzc1NGD58eF6cwkcvr+soPj5eaNeunbBz5868OoWPXl7V0e3bt4VWrVoJd+7cEV69eiXs379fqFevnhAcHJzXp/TReZ86EgRB2L17t1CvXj2932OF/bmBCQR9Um7evCnUqVNHCA0NFcvGjBkjHDlyJMvtUlNTBXd3d2H//v1i2aZNm4S5c+cKgiAIR48eFdzd3YU3b96Iy7t16ybcvHkzd0/gE5FX9fTnn38Kmzdv1tmme/fuwsaNG3Mx+k9DXtVRupSUFKFHjx5Cz549mUDkUF7X0Q8//CAMHjy40DzwFER5VUdbt24Vvv/+e51tPD09hTNnzuRi9J+GnNaRIAjCoUOHhIEDBwrz58/X+z1W2J8b2ISJPikPHjxA2bJlUaFCBbGsRo0auHv3bpbbPXnyBElJSWjYsKHB7R48eAAXFxfY2NiIy6tXr57tfsmwvKqnr776CgMHDhSXJSYm4sWLFzrHIWnyqo7S+fn5wcbGBn369MndwD8heVlH58+fx5EjR/DVV1/hyJEjhavpRQGSV3Xk5OSEK1eu4O7du0hOTsa+ffug0WhQs2bNPDmPj1lO6wgAateujQ0bNqBEiRIG91uYnxuYQNAnJSEhAeXLl9cps7a2xsuXL7PdzsTEBPb29mKZjY2NuF1CQgLKlSuns03G5WScvKqnd23cuBGlSpVCkyZN3j/oT0xe1tGNGzcQEBCAuXPnQqFQ5G7gn5C8qqOUlBQsWbIEJUuWREJCAu7cuYNevXrh+PHjuX8SH7m8qqN69erBw8MDgwYNgru7O3x8fDB37lzY2trm/kl85HJaRwBQpkwZKJXKTPdbmJ8bDJ8V0UdKqVTC1NRUp8zc3BxJSUlZbqdQKPS2MzMzE7fLbPnr169zIepPT17VU0bXrl3D9u3bsXr16kx/wVPm8qqOEhMTMXv2bEyePBllypTB9evXczfwT0he1dGZM2fw8uVL7NixA46OjgAABwcHLF68GM2bN+fPkxHyqo4uXLiAv/76C2vXrkXVqlVx5coVzJ8/H0uWLOFbCCPltI6yU9ifG/gGgj4pxYoVQ3R0tE5ZQkKC3g+xoe0SExORnJwsliUmJorbGdpvYmIiTExMcinyT0te1VO6Fy9eYPr06Rg1ahTq1q2be4F/QvKqjpYuXYpq1aqhffv2uR/0Jyav6ujFixcoU6aMmDwAgIuLC+Li4hAbG5t7J/AJyKs6OnjwINq1a4e6devC0tISHh4eaN68OQ4ePJj7J/GRy2kd5WS/hem5gQkEfVJcXV3x6NEjxMfHi2X37t3TeQ1sSNmyZWFnZ4cbN26IZXfv3hW3c3V1xa1bt6DRaIzaLxmWV/UEALGxsRg3bhwaN26s0x+CjJNXdRQQEIALFy7Aw8MDHh4eWLRoEW7fvg0PDw+2szdSXtWRvb09UlJSIAiCuPz58+cwMzND0aJFc/ksPm55VUcajUbv4TQmJkbnbxRJk9M6krLfwvzcwASCPikVK1ZExYoV4efnB61Wi3v37uHcuXNo0qQJtFot4uPjDf6ClcvlaN26NdauXYvExETExMRgx44dYtt5Nzc3aDQabN26FQBw7tw53L9/n23rcyiv6ik1NRX/+9//UKxYMUyYMAFv377F27dvkZqa+qFPsdDLqzoKCAjA7t27sWPHDuzYsQMjR46Es7MzduzYYbAjImUur+roq6++glqtxvLlyxEREYGbN29i7dq1aNu2baH59rSgyKs6qlu3Ls6ePYtNmzbhxIkTWLp0KU6fPg1PT88PfYqFXk7rKDuF/blBJmT8CoHoE/Dff/9hwoQJSE5ORkJCAjp16oQZM2bg+fPn6NSpE7Zv3w4nJye97RISEjB+/Hj8999/0Gg0qFSpEtauXYsiRYoASJtQafr06ZDJZIiPj8fw4cMxbNiwD316H428qKfz589j0qRJett06NABs2fP/gBn9XHJq5+ljA4dOoRDhw7hl19++RCn9NHJqzp69OgRVqxYgX///RdyuRxNmzaFl5cXzM3NP/QpFnp5UUdqtRobNmzAsWPHEBkZieLFi+Obb75B79698+EMC7+c1lG6devW4fr163q/xwrzcwMTCPokJScn49atWyhWrBi+/PJLydtptVr8+++/UKlUqFmzpl5nwYSEBNy6dQvlypWDg4NDLkf96cmreqLcwzoq+FhHBR/rqODLaR1lp7A+NzCBICIiIiIiydgHgoiIiIiIJGMCQUREREREkjGBICIiIiIiyZhAEBERERGRZEwgiIiIiIhIMiYQRKTnn3/+gZOTE8LDw/M7lE8e64KIiAoaJhBEnzi1Wp3fIVAO5XXd8d4gIir48uN3NRMIok9YfHw8unfvjpCQkPwOhYwUEBCAKVOm5Nn+nz17hs6dOyM2NjbPjkFERO8nJCQE3bt3R3x8/Ac9LhMIok/Y5MmT8dlnn6FChQr5HQoZyd3dHXfu3MHGjRtzfd+pqakYO3YsGjRogGLFiuX6/omIKHdUqFAB9vb2efqFkiFMIIg+UUePHkVwcDB8fX0hlxv3q2Dfvn1o164dqlevjrZt2+LYsWPisvQ2+zt37tTZZvHixXByctLb165du+Dk5ITz58/rlIeHh8PJyUn85+zsDE9PT6xZswZardaoeHNbQeiXULx4cfj5+cHPzw8RERG5uu8tW7bAzMwM06dPz9X9fgoKwr1BObNq1Sp4enrmaNv031f//PNPLkdF+SW/6nT//v0G/1ZmRi6XY+nSpXj8+DGOHz+eh5G9c9wPdiQiKjDUajWWLl0KLy8vFClSxKhtd+7ciZkzZ6JBgwaYNm0arKysMHHiRDx48CBHsVy8eBEA8Pfffxtc3rRpUyxZsgQ//PADatWqhRUrVmDdunU5OtbH5ssvv0S3bt2wYsWKXNvnmzdvsH79enh7e0OhUOTafj8VlStXxpIlS1C8ePH8DkWyVatWITAwML/DoFz0Iev0Y71/ihcvjiVLlqBy5cqZrrN58+YCkTQWKVIEU6dOxdKlS6HRaD7IMZlAEH2Crl69CktLSzRv3tzobTds2IB27drhhx9+QN++fbFx40ZYWlpix44dRu9Lq9Xi8uXLAP4vkXhX5cqV0blzZ/Tp0wdLly5Fz5498csvvyA1NdXo432MRowYgePHjyM5OTlX9nfy5EnUrl0bVatWzZX9fWpKlCiBzp07w9LSMr9DkWz16tUf5QPgp+xD1unHev9YWlqic+fOKFGiRKbr+Pv748qVKx8wqsy1aNECZmZmuHHjxgc5HhMIok/Q2bNn0apVK6O302g0eP78Oezs7MQya2trjB07NkcPnHfv3sWbN2/QpUsXPHr0CC9fvsx2Gzc3N7x9+xZhYWFGH+9jZGdnh6pVq2aagBkrp/cGERHlr5YtW+L06dMf5FhMIIjywDfffIOJEydi0qRJcHV1xYwZM3D06FE0bNgQLVq0QFBQkLjusWPH0LFjR9SoUQPt2rXD3r17dfYlCALWr1+PFi1awNXVFR06dMAff/yhs056n4NDhw6hZcuWqFWrFr799lu8fv3aYHyPHz+Gq6ur0eelUChQuXJlBAQE4N9//xXLBw0ahN69exu9vwsXLkChUGDQoEEAMn8LkZGJiQkAICUlRdIx7t69CycnJ1y6dEmn3MfHB25ubuLwd1KusxROTk7Yv3+/TpmhttXZ1bsxXF1dde6p9xEUFGTw3khNTYWPjw88PDxQq1YtdO/eXefV/fHjx+Hk5IQnT57obDd+/Hh06tRJp2z79u1o3bo1XF1d0aVLF5w5c0ZnecZ+BFeuXMGgQYNQt25dnXWk1tfGjRvh4eGBevXqYfr06fDz84Obmxs2b94srnPp0iX06tULNWrUQMuWLfHLL7/kuBlAZn0gvLy80L9/f/z444+oVasWhg0bhkuXLqFZs2Zwd3cXv8V0cnLCmjVrMHToUFSvXh2enp4G3+799ttvaNeuHVxdXdGqVSts375db51Tp06hc+fOqFGjBlq3bo1NmzaJ/YfS21mnt7WeNm2a+Pnd+1eK8+fPo0uXLqhevTratGmD33//XWe5p6cnfH198ffff6Njx46oWbMm+vbti6dPn0o+htRrKCUeQRCwZs0auLu7o1atWpgxY4bB3ym5eW9kR8o1XLVqlU5ZxvbyUutUyj2WW8fKjjF1KuWeP3jwIFq3bo3atWtjzJgx2LJlC7766iv8+OOPANL+Nk+YMAH37t1Dz5494erqis6dO+v8PUuXWR+I9J9xJycnPHv2DKtXrxY/Z7xm33zzDby8vAxum/H3w549e+Dp6QlXV1eMGTMGb9680YslMDAQgwcPhqurKzw8PLB48WKD92tu/i3IDhMIojxy/PhxmJubw8PDA/v27cPSpUsxatQoREdHY8+ePQCA33//HRMmTECtWrXg7e0NV1dXzJw5U+dhctOmTfD19cVXX32FmTNnwsnJCZMmTcLjx491jnfu3DnMnz8fPXv2RL9+/XDhwgX89NNPBmOLiopCyZIlc3Res2bNgkqlQo8ePTB69OhMX10nJSXh9evX4j9DTWwuXryIL7/8Es7OzrCzs8u0H0RGjx49gkwmw+effy4pXhcXF5QrVw4nT57UKT99+jSaN28OpVIJQPp1zg1S6t0YJUuWRFRUVK7EFh0dbfDeWLx4MTZv3oxOnTph2rRpKFasGEaPHi0mqU2bNoWlpSVOnTolbpOamoo///wTrVu3Fst+/vlnLFy4EJ6enpg5cybs7e0xatQog8nj+fPnMWTIEMhkMr23IlLq6+jRo1iyZAk6dOiA8ePH4/Llyzh58iS8vb3h5uYGIO0P+rBhw2Bvbw9vb280a9YMy5Yt03t4yg03b94Uh8f966+/MGHCBAwcOBByuRxbtmzRuUZarRZeXl6oWLEi5syZg3379onLjx8/junTp+PLL7/EzJkz4e7ujrlz5+r8/Bw9ehRjxoxB0aJFMW3aNLi5uWHRokVYv349AKBevXpYsmQJlixZAgDo1auX+LlevXpGndfp06cxcuRIsR22g4MDpk6dqjeQQmBgIMaNG4cWLVpg5MiRuHfvHmbPnp3r11BKPGvWrMHy5ctRp04dTJo0CYGBgXoPpB/y3pB6DbNiTJ1md4/l5rGyI6VOpdzzt2/fxtSpU1G/fn1MnToVT58+xcaNG+Hl5aXzOygiIgLDhg2Dq6srJkyYgIiICEyePFlyvOn9nJYsWQJbW1u0bNlS/NyyZUujzv3gwYPw9vZGhQoVMGXKFMTHx2PlypU66zx+/Bh9+/aFIAiYPn06unbtiu3bt2PmzJl6+8vNvwXZEogo1/Xv31/o0KGDIAiCcOHCBcHR0VE4duyYIAiC0KtXL2Hq1KmCRqMR3N3dhSlTpgjR0dHiv6+//lro1q2buK9FixYJixcvFj9rNBqhXr16wtatW8UyR0dHwcnJSbh165ZYNnbsWKFdu3YG42vdurUQFBSUafyXL18WHB0dhbCwMIPLQ0NDhYkTJwrOzs7Cl19+KSxatEjQaDQ622b2L11CQoJQrVo1Yd68eYIgCMJ3330nNGrUSNBqtYIgCEJYWJjg6OgozJ49W4iOjhbCw8OFAwcOCHXr1hWmTJmSaeyGLFq0SGjSpIm476CgIMHR0VE4d+6czjrZXWcp18fR0VH47bffdMpWrlwpNGvWTNyvlHqXcqx0O3bsEH744YdsroI0Li4uQnJysl75999/L2zYsEH8HBsbKzg6OgpnzpwRy8aOHSv07t1b/Hzu3DnB0dFRvNfevHkj1KhRQ1i2bJl43lFRUULjxo2F//3vf+J26edcu3Zt4c8//zQYp5T6mjt3rtC1a1fx89atW4UaNWro7Kdnz55C//79depi9OjRQoMGDbK9VoZkVl9Tp04V6tevL6SkpAghISGCo6OjeD0nTpwo9O/fXxCEtPunbdu2gkqlEs+rQ4cOQqtWrcR9rV+/XvDy8hLvZ0EQhE6dOgmLFi0SBEEQtFqt0LRpU6F79+7iz6UgCMKQIUOE5s2b68Vs6J6VSqvVCp6enkKXLl0EtVotlg8fPlyoXbu2kJCQIAiCIDRr1kxwdHQUjh49Kq6zaNEiwdXVVfKxpFxDKfGkpqYKdevWFQYNGiQuj4uLE+rWrSv+nAqCcfdG+u+ry5cvSz6fdMZcw5UrV+ps+9tvv+n8Xk2XVZ1Kucdy61jZkfpzkd09LwiC8Ouvvwr169cXP585c0ZwdHQUoqKixLL+/fsLjo6Owvr168Wybdu2CY6OjkJ0dLRObFLq1NB1ynisqVOn6pS9+/uhffv2Qps2bcS6SE1NFVq1aqVznSdMmCC0atVKePXqlXgfzp8/X3B2dhbi4+N19h8UFCS0adMm03hzk/LDpClEn570V7vp33A7OzsD+L8mOCEhIYiMjMTBgwdx8OBBnW0zdsCcOnUqAOC///7D9evX8ffff+PNmzeIiYnR2Sb9FWi6ypUr4+7duwZjs7OzQ3R0dJajS2SlQoUKWLp0Kb777jvMnj0bGzduhKmpKSZMmCCuM2DAADRr1kz8vHv3bp0mJlevXoVKpUKVKlXw+vVrODk54dSpU3j48CG+/PJLcb0dO3bovF6vV6+e3mvh7LRp0wYbN27Ev//+ixo1auDkyZOwsbFBo0aNxHWkXuf3JbXejREVFZVlRz9j2NnZISoqCmXLltUpX7x4MbRaLf7991/cuHFDbHaU8fq0adMGkyZNEuM5deoUqlSpIt5nN2/eRHJyMtauXYu1a9fq7D84OFgvlh49euCrr74yGKeU+nJxccH+/ftx9epV2Nvb4+zZsyhXrpy4/O3bt7hz5w60Wi0aNmyod4yYmBjY2tpmeb2MUalSJZiammb6OyFdmzZtxHXkcjmaNWuGdevWISEhAVZWVhg2bBgA4MmTJ7h27Rr++ecfPHr0SNxfcHAwXrx4gWHDhukM0bx+/XoIgpBr5wMAoaGhCA8Px8yZM3VG7erZsyfOnz+PW7duoXHjxgDSRg1r27atuE7lypWRlJRk1PGyu4ZS4ilZsiTi4uJ03mpZW1vD09MTV69eBfBh7w1jrmFuye4e+5Ck/Fxkd88DaT/vcXFxOHnyJKpVq4bjx4+jSJEienPZ2NnZYcCAATrHB9Lq/EOOnpaQkIBHjx5h+PDh4rmbmJigffv28PPzE9e7fPkyoqOjDd4DoaGhcHFxET/n5t+C7DCBIMoj6b8Q0r07JGb6g87YsWNRu3ZtnWUymUz8/wsXLsDb2xvPnz9H2bJlUb9+fYOTe1WvXl3nc1ZzO9jb2+PJkyeoX7++pHNJFx8fj8jISJQvXx6mpqaoXLkyNm3ahP79+2PTpk0YO3asuG6lSpV0HtD/+usvnX1duHABQFqTqFmzZonlf//9t04C0aZNG/Tq1QsymQz29vaoWLGiUTEDae1Cy5Qpg9OnT6NGjRo4deoUPD09df5ASb3OOZGQkCD+v9R6N0ZoaCgaNGiQ8wAzSL833k0gDh48iAULFuDNmzeoUKECGjZsqDf6iIeHB0xNTXHmzBn06NEDZ8+eRc+ePcXl6bNaz5kzR68Jmrm5uV4sPXr0yDROKfWV3pSif//+ANJGSMo45G1cXBy0Wi369OljsOO4sUMcZye73wnpMg5SAEB8UE1MTISVlRXu3r0LLy8vPHr0CCVLlkTdunV1EqP06/xuUzRj53uRIr0J27vHKl26tM5ywLjfUZnJ7hpKicfMzAyA/nUuVaqU+P8f8t4w5hq+K+PvFmNkd4/l5rGyI+XnIrt7HkhLUEuWLIkxY8YAAKysrLBw4UK9/Tk5OcHU1FT8nBc/F5nJeA3T/z+r+xBI+3lu1aoV+vTpo7e/d3+PPnnyBPb29rkVbpaYQBDlk/Rf2CVKlNB50I6IiEBERAS0Wi3evn2LcePGoW7duti9e7f4i8VQO0tjHnbd3d1x/PjxLB/QDElvn7p3717UqFEDQNovf09PT9y8edOob+svXrwIBwcHneRh/PjxuHjxIoYOHSqWlSlTRuf65FTLli1x6tQp9O3bF3fv3sV3330nLktISJB8nXMiY6c2KfVuzB80lUqFCxcuGNWGNyvu7u44ffq0TmxPnjwR295OmTIFRYsWBQCxL086S0tLuLu7i28eXr16pdP2OP0eLVu2rM7+nz59iri4OL1Y0h/03iW1vmbNmoUJEyagSZMmiIuLg6Ojo06iYmNjA5lMBmtra514Xr9+jadPn+bbhIWvXr3S+RwdHQ0A4oPd//73PxQvXhwnT54UHyDSByIA/u86v9sW+tKlSzh48CAmT56ca99Spt/P78YcGRmpszxjXHlJSjzp1zE90UqXfp2BD3tvGHMN35XTDrPZ3WO5eazckN09DwC+vr5o27YtBg4ciMjISFSpUsXg+XyI+zAzGftopSehWd2HQFq8JiYmOvdhQkKCwfo4ffo02rVrl4sRZ46dqInyScWKFVGqVCkcO3ZMp1nBjBkzMGHCBMjlcoSEhCAhIQGdO3cWH5KuXr2KZ8+evdexPTw8cOXKFaNny/3iiy8A6E/6duvWLRQtWlTvm5TMvHz5Eo8fP0aTJk3QqFEj8Z+bmxuuXbuWJ3M8tG7dGkFBQVi/fr34oJsuN6+zhYWFzoNbUFCQTgdhKfVujOPHj6NChQr47LPPjI7VkJYtW+Lo0aM635QFBgZCrVajR48eYvIQEBBgcDSa1q1b49KlS/j999/h4OCgM6NqzZo1YWZmhqNHj4plWq0Wo0aNwqJFiyTHKLW+goODYWZmBgcHB9SoUUPvLYelpaXYpC3jPbd8+XJ88803kuPJbX/88Yc4OphGo8GZM2fg6OiIIkWK4PXr13j27BlatWolPkgFBwfj9u3b4vaVKlWCvb09Dh48qPOgGxAQgKNHj+o9VBUrVizH3y5XrFgRZcuWxYEDB3Tuh71796JIkSKoWbNmjvabU1LiqVSpEiwtLXWGu0xMTMTZs2fFzx/y3pB6DS0sLHQe/F+/fo1Dhw4Z3Gd2dZrVPZbbx3pfUu759DIzMzOUKVMGNWvW/CBNsbI693evYWpqqk6neGtra1SoUAFnz54Vf041Go3eaHJubm5iE810O3bsQK9evXTKwsPDcf36dXh4eOTGqWWLbyCI8olcLsekSZMwdepUDB06FC1atMC9e/dw4cIF8Vv58uXLw8zMDOvXr0dCQgL+++8/7NmzBzKZTPIwpobY2tqid+/eWLx4sVEjinzxxRdo0aIF/Pz8EB0djSpVquDKlSs4ffq0UQ+/6QnIu28WGjZsiBMnTuD69esoX7689BOSoHbt2ihdujS2bt2KDh066LzCzs3rXLNmTezatQtNmjRBamoqvv/+e1SqVEls6y2l3qVKSkqCr68v5s2bZ9R2WXFyckL9+vWxevVqsa9J5cqVIZPJ8NNPP6Fdu3a4efMmDh06ZPD6pA9Xu3v3bowcOVJnWdGiRTFy5EisWLECSUlJaNCgAS5cuIDHjx8bNbu41PqqXr06Vq5ciejoaJQpUwZFihRBuXLldNpNT5w4EUOHDkXfvn3RtWtXhIeHY9++fRg8eLDBZlUfwsuXLzFs2DC0bNkSZ86cwePHj7F06VIAaT+7JUqUwO7du2FtbY3nz59j586dUKlU4rnLZDJMnToVEydOxKBBg9CmTRs8ePAABw4cwLfffqt3Xo0aNcKWLVtgamoKtVqNe/fuYeHChZJilclkmDZtGsaOHSse68KFCzh79ix++OGHD96eXmo833zzDdatW4dJkyahdu3a+P3335GYmAgLCwtxXx/q3pAac82aNXHs2DF06tQJxYoVw8yZM1GmTBmD30RnV6dZ3WO5faz3JeWeB9J+3rdt2waZTIYKFSqgSJEi+Oyzz1CtWrVMmwu+r0aNGmHv3r0oW7YsTExMcPHiRfFvas2aNeHn54dTp07ByckJCxcu1OvnNnToUPzwww/49ttv0axZM5w5c0ZvKOwxY8bg3Llz6NmzJ/r06YP4+Hj8+uuvaNu2rc7fycWLF6Nv377ilzx5jW8giPJRly5d8NNPP+HVq1dYuHAhrl+/jvnz56Nv374A0r7dWLFiBVJTUzFv3jxcvnwZCxYsgKur63tPHDZq1CjcunXL6BmkFy9ejG7duuHo0aNYuHAhHj9+jB9//FHvYTErf//9N5RKpd5wf+mdFdP7R+SmjEOBtmnTRmdZbl7n2bNno1SpUujVqxcmT56Mb7/9Vu942dW7FML/H9Lvyy+/zLSjcU5NmTIFv/32m/iN7BdffIH58+cjLCwM8+bNQ1BQENatW4fSpUvrXR8rKyuxs1/G5kvpvvvuO3h7e+P+/ftYsGABwsPDsXr1ajRt2lRyfFLrq3379oiPj8e2bdswffp0jBkzBl26dMGgQYPEtz8NGjTAhg0bIJfLsWjRIpw4cQLjx4/HxIkTjb5uuWX06NGwsLDA4sWLERISgvnz56NDhw4A0u7j1atXw9bWFgsXLsQff/yBsWPHom3btrh+/br4QNWuXTusXr0asbGxWLRoEa5duwZvb2+dgQ7SeXt7o1q1avDx8YGPjw8SExONirdly5ZYs2YN4uLisHDhQgQHB2PRokXo16/f+1+MHJASz/jx4zF69GhcunQJS5YsQbly5fTi/ZD3hpSYJ0yYgJo1a2L48OEYOnQomjdvjiFDhhjcX3Z1mtU9ltvHel9S7/k2bdpArVbjt99+ww8//ID//e9/6NGjB7p27ZrrMaUbPXo0WrVqBT8/P/z444+IiIgQl6Ung1OnTkWvXr3g4OCA6dOn62zfq1cveHt7IygoSHwLm96HI13lypWxfft2fPbZZ/jpp5+wd+9e9OnTRydJ27FjB27fvo1vv/02T87TEJmQ20MyEFGhcefOHQwcOBC//fabOBIFkDb++YABA3D69Gm9jmr0YWVWF3v37sX69euxd+/ePPnG6dSpU/Dy8sLp06c/2DdauSk0NBRt2rTBwIED4ejoCIVCgeTkZJw6dQqXLl3C5cuXYW1tnd9h6nFycsL8+fON7p9EJNXHeI+9ffsWbm5uaN++PerUqQMTExOkpqbin3/+weHDh3H48GGxCe7HJjg4GN26dcOWLVvEvokfApswEX3CatSogT179ugkD1Q4dOzYEQ0aNMizh/sWLVpg586dhTJ5ANKaOY0cORInT57Enj17kJqaCmtra1SrVg1+fn4FMnkgopyxtLTEpEmTcPDgQZw8eRJJSUmwsrKCo6MjFi1a9NEmD0Ban6c9e/Z88HPkGwgiIiIiIpKMfSCIiIiIiEgyJhBERERERCQZEwgiIiIiIpKMCQQREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIIiIiIiISLL/B5ouJO1sSbPKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#指标重要性\n",
    "shap.summary_plot(shap_values, X,  plot_type=\"bar\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b5f7507e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iSC0XD3U1IVGLCDLVIETP'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.512334911537503, \"outValue\": 0.6189854552009156, \"link\": \"identity\", \"featureNames\": [\"\\u51fa\\u751f\\u7387(\\u2030)\", \"\\u6b7b\\u4ea1\\u7387(\\u2030)\", \"\\u4eba\\u53e3\\u8001\\u9f84\\u5316\\u7387(%)\", \"\\u9ad8\\u6821\\u62db\\u751f\\u4eba\\u6570\\uff08\\u4e07\\u4eba\\uff09\", \"\\u6027\\u522b\\u6bd4\", \"\\u6d41\\u52a8\\u4eba\\u53e3\\u6570\\uff08\\u4e07\\u4eba\\uff09\"], \"features\": {\"0\": {\"effect\": 0.0782212718908012, \"value\": 0.3275862068965518}, \"1\": {\"effect\": -0.030447895099085118, \"value\": 0.09090909090908944}, \"2\": {\"effect\": 0.018434425952837456, \"value\": 0.4285714285714282}, \"3\": {\"effect\": 0.04600925677418634, \"value\": 0.3489415666764218}, \"4\": {\"effect\": -0.03767750019356099, \"value\": 0.9411764705882476}, \"5\": {\"effect\": 0.03211098433823378, \"value\": 0.3352359208523592}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iSC0XD3U1IVGLCDLVIETP')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x17bf756f1d0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value, \n",
    "                shap_values[3,:], \n",
    "                X.iloc[2,:], figsize=(4,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3273a291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>出生率(‰)</th>\n",
       "      <th>死亡率(‰)</th>\n",
       "      <th>人口老龄化率(%)</th>\n",
       "      <th>高校招生人数（万人）</th>\n",
       "      <th>性别比</th>\n",
       "      <th>流动人口数（万人）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.184429</td>\n",
       "      <td>-0.019554</td>\n",
       "      <td>-0.071408</td>\n",
       "      <td>-0.129012</td>\n",
       "      <td>0.007697</td>\n",
       "      <td>-0.115615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.081801</td>\n",
       "      <td>-0.003912</td>\n",
       "      <td>-0.050965</td>\n",
       "      <td>-0.061122</td>\n",
       "      <td>-0.009777</td>\n",
       "      <td>-0.084467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.041934</td>\n",
       "      <td>0.003928</td>\n",
       "      <td>-0.013090</td>\n",
       "      <td>-0.029017</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>-0.009487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.078221</td>\n",
       "      <td>-0.030448</td>\n",
       "      <td>0.018434</td>\n",
       "      <td>0.046009</td>\n",
       "      <td>-0.037678</td>\n",
       "      <td>0.032111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.111858</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>0.044638</td>\n",
       "      <td>0.073768</td>\n",
       "      <td>-0.004979</td>\n",
       "      <td>0.072443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.122405</td>\n",
       "      <td>0.034844</td>\n",
       "      <td>0.069339</td>\n",
       "      <td>0.111432</td>\n",
       "      <td>0.051971</td>\n",
       "      <td>0.097660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     出生率(‰)    死亡率(‰)  人口老龄化率(%)  高校招生人数（万人）       性别比  流动人口数（万人）\n",
       "0 -0.184429 -0.019554  -0.071408   -0.129012  0.007697  -0.115615\n",
       "1 -0.081801 -0.003912  -0.050965   -0.061122 -0.009777  -0.084467\n",
       "2 -0.041934  0.003928  -0.013090   -0.029017  0.003382  -0.009487\n",
       "3  0.078221 -0.030448   0.018434    0.046009 -0.037678   0.032111\n",
       "4  0.111858 -0.001450   0.044638    0.073768 -0.004979   0.072443\n",
       "5  0.122405  0.034844   0.069339    0.111432  0.051971   0.097660"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# 初始化SHAP模型\n",
    "explainer = shap.Explainer(gbdt_model)\n",
    "\n",
    "# 计算SHAP值\n",
    "shap_values = explainer(X)\n",
    "\n",
    "shap_df = pd.DataFrame(shap_values.values, columns=X.columns)\n",
    "display(shap_df)\n",
    "# 输出每个特征的SHAP值（绝对值）\n",
    "features=[]\n",
    "abs_mean_shap_values=[]\n",
    "for i, feature in enumerate(X.columns):\n",
    "    abs_mean_shap_value = np.abs(shap_df.values[:, i]).mean()\n",
    "    features.append(feature)\n",
    "    abs_mean_shap_values.append(abs_mean_shap_value)\n",
    "    \n",
    "ddf=pd.DataFrame(features,columns=['features'])\n",
    "ddf['shap_values']=abs_mean_shap_values\n",
    "\n",
    "sns.barplot(x='features', y='shap_values', data=ddf)\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b19b0a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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