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Python-DA-and-DM/烟火图像的识别与分类/code.ipynb
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2020-08-06 19:33:32 +08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 背景介绍\n",
"- 开发一款基于户外监控摄像头的山火/非法焚烧秸秆的预防系统。希望能够\n",
"在最短的时间内基于监控画面确定是否有烟火发生,然后人工快速介入,\n",
"确定是否是山火/非法焚烧秸秆的事件。最终交由当地的联防/公安/森林\n",
"等部门进行快速响应。\n",
"我们因此采集到了海量的户外图像,其中大致分为两类:没有任何烟火的\n",
"图像,有明显的烟/火出现的图像。图像的获得是基于经过培训的人工判\n",
"断然后直接从监控画面上截图。基于提供的图像,获得一个识别烟火的图像分类器。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 导入相关库"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import time\n",
"import cv2\n",
"from PIL import Image\n",
"from PIL import ImageEnhance\n",
"import itertools\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.neural_network import MLPClassifier #神经网络\n",
"from sklearn.svm import SVC#支持向量机\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import accuracy_score, mean_squared_error, r2_score, confusion_matrix\n",
"from sklearn.metrics import roc_curve, auc,recall_score #\n",
"from sklearn.preprocessing import StandardScaler\n",
"# 绘制混淆矩阵函数\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report,confusion_matrix\n",
"#忽略警告\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据探索"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 读取图片数据"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#读取图片\n",
"nofog_path=\"fogs/0/\"\n",
"fog_path=\"fogs/1/\"\n",
"fog=os.listdir(fog_path)\n",
"nofog=os.listdir(nofog_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"* 查看烟火图片"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"有明显烟/火出现的图片:\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1800x1800 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25,25))\n",
"print(\"有明显烟/火出现的图片:\")\n",
"for i in range(1,8):\n",
" ax=plt.subplot(1,7,i)\n",
" path=fog_path+fog[i]\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" ax.imshow(Image.open(path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"* 查看无烟火图片"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"无明显烟/火出现的图片:\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x1800 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25,25))\n",
"print(\"无明显烟/火出现的图片:\")\n",
"for i in range(1,8):\n",
" ax=plt.subplot(1,7,i)\n",
" path=nofog_path+nofog[i]\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" ax.imshow(Image.open(path))\n",
" #暗通道去雾"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BarContainer object of 2 artists>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"数据集正例/负例数目\")\n",
"plt.bar([\"烟火图片数量\",\"无烟火图片数量\"],[len(fog),len(nofog)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# 数据预处理\n",
"## 暗通道去雾算法\n",
" * 这个去雾算法只针对彩色图像,而且对于低对比度的天空或者水面背景的去雾效果会产生块效应,去雾效果不好。 \n",
" ** 因此在调用去雾算法前,先提高了图片的对比度 **\n",
" * 再通过计算图片数据的像素均值作为特征进行训练"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"* 暗通道去雾算法实现"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
" \n",
"def zmMinFilterGray(src, r=7): \n",
" '''''最小值滤波,r是滤波器半径''' \n",
" return cv2.erode(src,np.ones((2*r-1,2*r-1)))\n",
"# =============================================================================\n",
"# if r <= 0: \n",
"# return src \n",
"# h, w = src.shape[:2] \n",
"# I = src \n",
"# res = np.minimum(I , I[[0]+range(h-1) , :]) \n",
"# res = np.minimum(res, I[range(1,h)+[h-1], :]) \n",
"# I = res \n",
"# res = np.minimum(I , I[:, [0]+range(w-1)]) \n",
"# res = np.minimum(res, I[:, range(1,w)+[w-1]]) \n",
"# =============================================================================\n",
" # return zmMinFilterGray(res, r-1) \n",
"def guidedfilter(I, p, r, eps): \n",
" '''''引导滤波,直接参考网上的matlab代码''' \n",
" height, width = I.shape \n",
" m_I = cv2.boxFilter(I, -1, (r,r)) \n",
" m_p = cv2.boxFilter(p, -1, (r,r)) \n",
" m_Ip = cv2.boxFilter(I*p, -1, (r,r)) \n",
" cov_Ip = m_Ip-m_I*m_p \n",
" \n",
" m_II = cv2.boxFilter(I*I, -1, (r,r)) \n",
" var_I = m_II-m_I*m_I \n",
" \n",
" a = cov_Ip/(var_I+eps) \n",
" b = m_p-a*m_I \n",
" \n",
" m_a = cv2.boxFilter(a, -1, (r,r)) \n",
" m_b = cv2.boxFilter(b, -1, (r,r)) \n",
" return m_a*I+m_b \n",
" \n",
"def getV1(m, r, eps, w, maxV1): #输入rgb图像,值范围[0,1] \n",
" '''''计算大气遮罩图像V1和光照值A, V1 = 1-t/A''' \n",
" V1 = np.min(m,2) #得到暗通道图像 \n",
" V1 = guidedfilter(V1, zmMinFilterGray(V1,7), r, eps) #使用引导滤波优化 \n",
" bins = 2000 \n",
" ht = np.histogram(V1, bins) #计算大气光照A \n",
" d = np.cumsum(ht[0])/float(V1.size) \n",
" for lmax in range(bins-1, 0, -1): \n",
" if d[lmax]<=0.999: \n",
" break \n",
" A = np.mean(m,2)[V1>=ht[1][lmax]].max() \n",
" \n",
" V1 = np.minimum(V1*w, maxV1) #对值范围进行限制 \n",
" \n",
" return V1,A \n",
" \n",
"def deHaze(m, r=81, eps=0.001, w=0.95, maxV1=0.80, bGamma=False): \n",
" Y = np.zeros(m.shape) \n",
" V1,A = getV1(m, r, eps, w, maxV1) #得到遮罩图像和大气光照 \n",
" for k in range(3): \n",
" Y[:,:,k] = (m[:,:,k]-V1)/(1-V1/A) #颜色校正 \n",
" Y = np.clip(Y, 0, 1) \n",
" if bGamma: \n",
" Y = Y**(np.log(0.5)/np.log(Y.mean())) #gamma校正,默认不进行该操作 \n",
" return Y "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 对数据集的所有图片进行处理"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def read_data(file_path):\n",
" '''\n",
" file_path:正例/负例图像的存放路径\n",
" return: features平均像素,newimg:增加对比度之后的图片\n",
" '''\n",
" pictures=os.listdir(file_path)#读取所有图片名称\n",
" features=[]\n",
" newimg=[]\n",
" for i in range(len(pictures)):\n",
" path=file_path+pictures[i]\n",
" \n",
" img=Image.open(path)\n",
" #对比度增强 \n",
" enh_con = ImageEnhance.Contrast(img) \n",
" contrast = 1.5\n",
" img_contrasted = enh_con.enhance(contrast) \n",
" img_contrasted.save(\"temp.jpg\")\n",
" \n",
" #暗通道去雾\n",
" m=deHaze(cv2.imread(\"temp.jpg\")/255.0)*255\n",
"# b=np.array(b)\n",
" #平均像素作为特征\n",
" try:\n",
" feature_matrix = np.zeros((40,40))\n",
" for i in range(0,m.shape[0]):\n",
" for j in range(0,m.shape[1]):\n",
" feature_matrix[i][j] = ((int(m[i,j,0]) + int(m[i,j,1]) + int(m[i,j,2]))/3)\n",
"\n",
" feature = np.reshape(feature_matrix, (40*40))\n",
"# feature=np.reshape((b+g+r)/3,40*40)\n",
" features.append(feature)\n",
" newimg.append(m)\n",
" except:\n",
" pass\n",
" \n",
" return features,newimg"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据集大小: (3609, 1601)\n"
]
},
{
"data": {
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],
"text/plain": [
" 0 1 2 3 4 5 \\\n",
"0 149.666667 148.666667 145.666667 144.666667 144.666667 145.666667 \n",
"1 106.333333 110.333333 114.333333 113.333333 108.333333 105.000000 \n",
"2 144.000000 140.333333 133.333333 126.000000 123.000000 119.333333 \n",
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"4 61.000000 86.000000 37.000000 39.333333 48.333333 40.333333 \n",
"\n",
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"0 146.666667 148.333333 140.333333 140.333333 ... 30.666667 \n",
"1 108.000000 114.000000 110.333333 119.000000 ... 70.333333 \n",
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"\n",
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"\n",
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"\n",
"[5 rows x 1601 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#处理所有的有明火的图片\n",
"features,fog_contrasted=read_data(fog_path)\n",
"fogs=pd.DataFrame(features)\n",
"fogs['label']=[1 for i in range(len(fogs))]\n",
"# 处理所有无明火的图片\n",
"features,nofog_contrasted=read_data(nofog_path)\n",
"no_fogs=pd.DataFrame(features)\n",
"no_fogs['label']=[0 for i in range(len(no_fogs))]\n",
"#整合成新的DataFrame数据集\n",
"df=pd.concat([fogs,no_fogs],axis=0)\n",
"\n",
"#处理后所得到的图片\n",
"imgs=[]\n",
"imgs.extend(nofog_contrasted)\n",
"imgs.extend(fog_contrasted)\n",
"#将图片像素不为40*40的转换为40*40\n",
"for i in range(len(imgs)):\n",
" if(imgs[i].shape!=(40,40,3)):\n",
" imgs[i]=np.resize(imgs[i],(40,40,3)) \n",
"\n",
"print(\"数据集大小:\",df.shape)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 预处理后的图片效果"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"有明显烟/火出现的图片:\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1800x1800 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25,25))\n",
"print(\"有明显烟/火出现的图片:\")\n",
"for i in range(1,8):\n",
" ax=plt.subplot(1,7,i)\n",
"\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" ax.imshow(fog_contrasted[i]/255)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"无明显烟/火出现的图片:\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x1800 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25,25))\n",
"print(\"无明显烟/火出现的图片:\")\n",
"for i in range(1,8):\n",
" ax=plt.subplot(1,7,i)\n",
"\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" ax.imshow(nofog_contrasted[i]/255)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据建模\n",
"### 预先定义模型评估方法\n",
" * 绘制混淆矩阵\n",
" * 模型性能评估(准确率、召回率、漏报率、误报率)\n",
" * 绘制ROC曲线"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# 绘制混淆矩阵函数\n",
"def plot_confusion_matrix(cm, classes,\n",
" normalize=False,\n",
" title='Confusion matrix',\n",
" cmap=plt.cm.Blues):\n",
" plt.figure()\n",
" plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(classes))\n",
" plt.xticks(tick_marks, classes, rotation=45)\n",
" plt.yticks(tick_marks, classes)\n",
"\n",
" fmt = '.2f' if normalize else 'd'\n",
" thresh = cm.max() / 2.\n",
" for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
" plt.text(j, i, format(cm[i, j], fmt),\n",
" horizontalalignment=\"center\",\n",
" color=\"white\" if cm[i, j] > thresh else \"black\")\n",
"\n",
" plt.tight_layout()\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
" plt.show()\n",
" \n",
"# 模型性能评估\n",
"def model_performance_evaluation(model_name, test, pred,spend_time):\n",
" acc= accuracy_score(test, pred)\n",
" print(model_name, '| 准确率: %.4f' %acc)\n",
" pred=pred.astype('float64')\n",
" false_positive_rate,true_positive_rate,thresholds=roc_curve(test, pred)\n",
" roc_auc=auc(false_positive_rate, true_positive_rate)\n",
" print(model_name, '| AUC: %.4f' %roc_auc)\n",
" cm=confusion_matrix(test,pred)\n",
" miss_report=cm[0][1] / (1.0 * cm[0][1] + cm[1][1])\n",
" false_report=cm[1][0] / (1.0 * cm[0][0] + cm[1][0])\n",
" print(model_name,\"| 漏报率为:%.4f\"%miss_report)\n",
" print(model_name,\"| 误报率为:%.4f\"%false_report)\n",
" print(model_name,\"| 训练时长(秒):%.4f\"%spend_time)\n",
" return acc,roc_auc,miss_report,false_report,spend_time\n",
" \n",
"#绘制ROC曲线\n",
"def plot_ROC_curve(y_test,y_predict):\n",
" false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test, y_predict)\n",
" roc_auc=auc(false_positive_rate, true_positive_rate)\n",
" plt.title('ROC')\n",
" plt.plot(false_positive_rate, true_positive_rate,'b',label='AUC = %0.2f'% roc_auc)\n",
" plt.legend(loc='lower right')\n",
" plt.plot([0,1],[0,1],'r--')\n",
" plt.ylabel('TPR')\n",
" plt.xlabel('FPR')\n",
" plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 使用传统机器学习模型:支持向量机、随机森林、神经网络、集成学习Adaboost进行训练"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def train_model(label,data,model_name):\n",
" '''\n",
" data:训练数据\n",
" model_name:模型名称\n",
" model:sklearn模型\n",
" '''\n",
" y=label\n",
" X=data\n",
" #划分数据集\n",
" X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=0)\n",
"\n",
" #搜索最优参数\n",
" if model_name=='决策树':\n",
" grid=DecisionTreeClassifier(min_samples_leaf=10,random_state = 0)\n",
" if model_name=='随机森林':\n",
" grid=RandomForestClassifier(n_estimators=100,min_samples_leaf=10,random_state = 0)\n",
" \n",
" if model_name=='支持向量机':\n",
" grid=SVC(kernel='rbf',C=10,random_state = 0)\n",
" \n",
" if model_name=='神经网络':\n",
" grid=MLPClassifier(random_state =0)\n",
" \n",
" if model_name=='adaboost':\n",
" grid=AdaBoostClassifier(n_estimators=100,random_state = 0)\n",
" #模型训练\n",
" clf=grid\n",
" start = time.time()\n",
" model=clf.fit(X_train,y_train)\n",
" end=time.time()\n",
" spend_time=end-start\n",
" \n",
" #模型评估\n",
" y_pred=model.predict(X_test)\n",
" # 绘制混淆矩阵\n",
" print(model_name+\"分类评估报告\")\n",
" cnf_matrix = confusion_matrix(y_test, y_pred)\n",
" np.set_printoptions(precision=2) # 设置打印数量的阈值\n",
" class_names = [0,1]\n",
" test_report=classification_report(y_test,y_pred)\n",
" print(test_report)\n",
" plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix')\n",
" plot_ROC_curve(y_test,y_pred)\n",
" \n",
" print(model_name+\"在训练集上的性能 -- \")\n",
" model_performance_evaluation(model_name, y_train, clf.predict(X_train),spend_time)\n",
" print(\"=========================================\")\n",
" print(model_name+\"在测试集上的性能 -- \")\n",
" return list(model_performance_evaluation(model_name, y_test, y_pred,spend_time))\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"data=df[df.columns[:-1]]\n",
"label=df['label']\n",
"#数据标准化\n",
"scaler = StandardScaler()\n",
"data= scaler.fit_transform(data)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"决策树分类评估报告\n",
" precision recall f1-score support\n",
"\n",
" 0 0.75 0.75 0.75 197\n",
" 1 0.70 0.70 0.70 164\n",
"\n",
"avg / total 0.72 0.72 0.72 361\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"决策树在训练集上的性能 -- \n",
"决策树 | 准确率: 0.9070\n",
"决策树 | AUC: 0.9047\n",
"决策树 | 漏报率为:0.0919\n",
"决策树 | 误报率为:0.0938\n",
"决策树 | 训练时长(秒):4.5754\n",
"=========================================\n",
"决策树在测试集上的性能 -- \n",
"决策树 | 准确率: 0.7230\n",
"决策树 | AUC: 0.7207\n",
"决策树 | 漏报率为:0.3049\n",
"决策树 | 误报率为:0.2538\n",
"决策树 | 训练时长(秒):4.5754\n"
]
}
],
"source": [
"DT_result=train_model(label,data,\"决策树\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"随机森林分类评估报告\n",
" precision recall f1-score support\n",
"\n",
" 0 0.83 0.87 0.85 197\n",
" 1 0.84 0.79 0.81 164\n",
"\n",
"avg / total 0.83 0.83 0.83 361\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"随机森林在训练集上的性能 -- \n",
"随机森林 | 准确率: 0.9406\n",
"随机森林 | AUC: 0.9386\n",
"随机森林 | 漏报率为:0.0531\n",
"随机森林 | 误报率为:0.0643\n",
"随机森林 | 训练时长(秒):8.0432\n",
"=========================================\n",
"随机森林在测试集上的性能 -- \n",
"随机森林 | 准确率: 0.8338\n",
"随机森林 | AUC: 0.8298\n",
"随机森林 | 漏报率为:0.1623\n",
"随机森林 | 误报率为:0.1691\n",
"随机森林 | 训练时长(秒):8.0432\n"
]
}
],
"source": [
"RF_result=train_model(label,data,\"随机森林\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"支持向量机分类评估报告\n",
" precision recall f1-score support\n",
"\n",
" 0 0.90 0.82 0.86 197\n",
" 1 0.81 0.89 0.85 164\n",
"\n",
"avg / total 0.86 0.85 0.85 361\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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LNd09iykRhOzVV9VkTtLE9u1+lbDdu6FNG1i0CF54wV8eKllNiSBksRgceCAcf3zYkUikzZ3ry0CjRpU2iTvooHBjkpRRIgjRtm1+bQ41mZPQbN7s+wL17g07dviuh2oSFzmaRxCimTPVZE5CNngwvPUW/OQnMGYMNGkSdkQSAiWCEMViajInIVi3zl/9s88+cN99/nS0e/ewo5IQqTQUkpImcwMHwt57hx2NRMaLL8LRR5c2ievRQ0lAlAjC8v77vm+XykKSEt98A0OGwLBhfqWwCy8MOyJJI0oEIYnFoF49NZmTFHj1Vd8W4vXX/QLy8+b5K4RE4jRGEJKSJnMtWoQdiWS9jh3hhBN8v6Ajjgg7GklDOiMIwX//6ydrqiwkgSguht/8Bq64wj8++mh/WaiSgFRCiSAEajIngVm82M8JuPFGPwilJnGSACWCEMRicOyx0KFD2JFI1tixw88DOO44+PxzmDTJLxyjJnGSACWCFFu71s/mV1lIkmrDBnj8cTjnHH9WcOGFmq4uCQskEZjZRDN718zurGa78WY2KIgY0lVJkzmVhaTOtm3zA8AlTeL+9S/461/9fZEaSHoiMLMhQD3nXHego5kdXsl2vYG2zrkpyY4hncVicMAB0K1b2JFIRps9218Cev31vkUE+D8skVoI4owgD3g+fn860Kv8Bma2F/AHYJmZVVgkMbMRZjbfzOYXFBQEEGbqbd9e2mTueyrKSW1s2gTXXgunnAK7dsEbb0DfvmFHJRkuiMNRY2BF/P46IKeCbS4BFgMPAyea2fXlN3DOTXDO5Trnclu3bh1AmKk3cyZs2aKykNTB4MHw+9/DT3/qS0FKApIEQUwoKwQaxe83oeJkcxwwwTm30swmAfcDYwOIJa3EYr6546mnhh2JZJQ1a3yDuH32gfvv94PAJ58cdlSSRYI4I1hAaTmoC7Csgm3+C3SM388FvgggjrSiJnNSY875wd+jj4Zf/tI/1727koAkXRCJYDJwsZk9BgwHPjazMeW2mQj8wMxmA9cCjwYQR1qZP9/3/VJZSBKyYoUvA/3oR37CySWXhB2RZLGkl4acc5vMLA/oDzzsnFsJfFRum83AsGS/dzoraTJ35plhRyJp75VX/DyAnTvh0Uf9LOF69cKOSrJYIE3nnHPrKb1ySPBtJXr3VpM5ScBhh/l1AsaO9fdFAqaLGFNgyRL4979VFpJKFBf7WcGXXeYfH3WUbxmtJCApokSQArGY/6q2ErKHjz+Gnj3hZz/zVwepSZyEQIkgBfLz4ZhjfFt4EcA3ibv3Xt8k7n//g+ee85eVqUmchECJIGDr1sGcOSoLSTkbNsCTT/qlIxcv9lcHqUmchESJIGCvvupLwCoLCVu3+gVjiotLm8Q9+yxkycx5yVxKBAHLz4f994fc3LAjkVC99ZZfhOLGG2HWLP/c/vuHGpJICSWCABUVwdSpMGiQmsxF1saNcPXVvq+ImU8I6g8kaUaL1wdo5kwoLFRZKNIGD/Yto3/xCxg92vcLEkkzVSYCM6sH9AN2OOfeij9nwFDn3IspiC+j5edD48ZqMhc5BQX+F7/PPvCrX/lZwSecEHZUIpWqrmDxHHAecI2ZPWlmPwEWUcEaA/Jdu3f7RHDaaboiMDKc85eBlm0Sd/LJSgKS9qorDR3snOsRPwtYCowHejvnNgQfWmZbsAC+/lplochYvhyuucb3CTrppNJZwiIZoLpE0NDMuuPPHNYBc4FOZoZz7p3Ao8tg+fl+gFhN5iIgPx8uuqi0VcT116tJnGSU6hLBR8BVFdx3gBJBFWIx6NULWrYMOxIJ3BFH+F/2uHGaPi4ZqbpEcDtwPbAV+E28fbRUY+lSP1fo178OOxIJxK5d8MQTsGgR/OUvvknca6+FHZVIrVU3WPwX4GNgA358QBKQn++/anwgCy1a5FcJ+8Uv/ELyahInWaC6RNDAOfesc24ccHAqAsoGsRh06gSHHhp2JJI0RUX+SqBu3eDLL+H55+Hll3VJmGSF6kpDrc3sAsCANvH7ADjnngs0sgy1bp2fP3TzzWFHIkm1aROMH++bwz3+uAZ/JKtUlwh2AIfH7/+tzH2pxOuvq8lc1tiyBSZMgBtu8I3h/v1vyMkJOyqRpKsuEaxzzt2TkkiyRCwGbdtqDlHGe/NNuOoqP/LfpYufHq4kIFmqujGCk83s83K3/5jZ5ymJLsMUFfkzAjWZy2AbNsCVV0K/flC/PvzjH+oRIlmvujOC95xzP0hJJFlg1iw1mct455zjVxK65RY/ONyoUdgRiQSuukSgxnI1EIv5PmP6AJlhVq2CJk18o7gHH/RnAt26hR2VSMpUWcBwzv02VYFkOudKm8zpQ2SGcA7+3//z1/qWNIk76SQlAYkcVbKTZOFCWLFCZaGM8eWXvhHUJZfAkUfCFVeEHZFIaLQwTZLEYmoylzFiMd8kzjm/gPy116pJnESaEkGSxGLQsye0ahV2JFIp5/xykUcdBXl5MHYstG8fdlQioVNpKAmWLfMtaFQWSlO7dsFDD8HFF/vHRx4JU6YoCYjEKREkQUmTubPOCjcOqcBHH/kB4Ftvha1b1SROpAJKBEkQi/nVCQ9XA470sX073Hkn5Ob6UfwXX4SXXlKTOJEKKBHU0fr1fvKpykJpZvNmeOopuPBCWLwYhg4NOyKRtKVEUEdqMpdGCgvh0Uf9L6R1a58AnnkGWrQIOzKRtBZIIjCziWb2rpndWc12OWb2QRAxpEos5nuRnXhi2JFE3PTpcMwxvv/37Nn+udatw41JJEMkPRGY2RCgnnOuO9DRzKqqnD8KZOw83B071GQudOvWweWX+yndDRv6PkE/UHsskZoI4vCVBzwfvz8d6FXRRmZ2KrAFWFnJ90eY2Xwzm19QUBBAmHU3a5YvRassFKJzzvFtIm6/HT780E/mEJEaCWJCWWNgRfz+OuD48huYWQPgLuAcYHJFL+KcmwBMAMjNzXUBxFlnJU3m+vYNO5KIWbkSmjb1TeIeeQQaNICuXcOOSiRjBXFGUEhpuadJJe9xKzDeObchgPdPiZImcwMGqMlcyjjnB387dYK77/bPnXiikoBIHQWRCBZQWg7qAiyrYJt+wCgzmwV0NbM/BhBHoD74AJYvV1koZZYtg4ED/XhA584wYkTYEYlkjSBKQ5OBOWZ2AHA6cL6ZjXHOfXsFkXOuT8l9M5vlnLsygDgCpSZzKfTyy749hBmMGwfXXKPReZEkSnoicM5tMrM8oD/wsHNuJfBRFdvnJTuGVMjPhx49dIVioEqaxHXu7JeO/M1voF27sKMSyTqBfKxyzq13zj0fTwJZ54sv/AUqKgsFZOdOeOABPysY4IgjYPJkJQGRgOj8uhbUZC5ACxf6AeA77vAzhIuKwo5IJOspEdRCfr5vaX/EEWFHkkW2bYPbbvNJYOVKPy7wt7/B3nuHHZlI1lMiqKENG/xEMpWFkmzLFpg4ES691PcIGjw47IhEIkOJoIZef92vc6KyUBJs3gwPP+xLQK1a+QQwcSI0bx52ZCKRokRQQ/n50KaNX+tE6mDqVN8k7tZbfX8g0DqfIiFRIqiBHTvgtdd8kzmtdV5La9f68s/pp/sWEW+/7dcPFpHQaPH6GvjHP2DTJpWF6mTIEHjnHbjrLn9lkAaDRUKnRFAD+fm+r1C/fmFHkmG++cY3iWvSxC8c06ABdOkSdlQiEqfSUIKc820lBgzwHUclAc7B00/7BZ1LmsSdcIKSgEiaUSJI0IcfwldfqSyUsCVLfNa84gp/4B85MuyIRKQSKg0lKD/ft7354Q/DjiQDvPSSbxJXrx787ne+U6iaxImkLSWCBMVivslcmzZhR5LGSprEHXusbxn9xBNw8MFhRyUi1dDHtAR8+aVff0BloUrs2AFjxsAFF/hkcPjh8Pe/KwmIZAglggRMmeK/qq1EBebP9wPAd93lH+/YEW48IlJjSgQJiMXgyCP9TeK2bYObb/ZTrNes8T+k//s/zQsQyUBKBNXYuNE3mVNZqJwtW/z6wVdcAR9/rB+QSAZTIqjG1Kl+nRSVhfDTqh98sLRJ3CefwIQJ0KxZ2JGJSB0oEVQjFvPLUZ58ctiRhOzVV/2SkXfcUdokrmXLcGMSkaRQIqjCzp2+ydwPfxjhJnMFBX7JyB/+EPbbz/cJUpM4kayieQRVmD3bjxFEuiw0dCjMmwejR/sVxBo0CDsiEUkyJYIqxGLQsCH07x92JCm2YoX/9N+kCTz+uL8S6Jhjwo5KRAKi0lAlSprM9e8foSZzzsEf/gCdOpU2ievWTUlAJMspEVRi0SI/ozgyZaH//Q/69vV9gbp1g1Gjwo5IRFJEiaASsViEmsy9+KLvD7Rggb8c9M034dBDw45KRFJEYwSViMX8JaM5OWFHEqCSJnFdusCZZ/rxgIMOCjsqEUkxnRFUYPlyWLgwi8tCO3bAPffA+eeXNol74QUlAZGIUiKoQH6+/5qVieCf//RjAKNHQ/36ahInIkoEFYnF/IfkrGoyt3Ur/Pzn0L07rF/vW6o++6yaxImIEkF5mzbBW2/5swGzsKNJom3bYNIkf1XQ4sURGQUXkURosLicrGoyt3EjjBsHt9zi+wJ98gk0bx52VCKSZgI5IzCziWb2rpndWcn39zOz181supm9bGZp07cgFvONNbt3DzuSOpoypXRi2Ny5/jklARGpQNITgZkNAeo557oDHc3s8Ao2uxB4zDk3AFgJDEx2HLWRFU3mCgrgRz/y6wO0bAnvvacmcSJSpSBKQ3nA8/H704FewH/KbuCcG1/mYWtgdfkXMbMRwAiAQw45JIAw9zRnDmzYkOFloZImcffe60tCahInItUIIhE0BlbE768Djq9sQzPrDjR3zs0r/z3n3ARgAkBubq4LIM49ZGyTueXL/eIwTZrAE0/4K4E6dw47KhHJEEGMERQCjeL3m1T2HmbWAhgL/DiAGGrMOT9/oF8/aNw47GgStHs3PPWUHwsoWTz++OOVBESkRoJIBAvw5SCALsCy8hvEB4dfAG5zzn0RQAw19q9/wbJlGVQW+s9/4NRTYeRIOPFEuP76sCMSkQwVRCKYDFxsZo8Bw4GPzWxMuW2uwJeM7jCzWWZ2XgBx1EhGNZl74QX4/vfhww9h4kSYMQM6dgw7KhHJUEkfI3DObTKzPKA/8LBzbiXwUbltfgf8LtnvXRf5+XDSSdC2bdiRVKGkSdxxx/lTl8cegwMOCDsqEclwgcwjcM6td849H08CaW/FCpg/P43LQkVFfj7A8OE+GRx2GPz1r0oCIpIUajFBaZO5s84KN44KzZvnB4Dvuw8aNVKTOBFJOiUCfCI47DA4+uiwIyljyxb46U+hRw/YvNnPdPvLX9QkTkSSLvKJYPNmmDkzDZvMbd/uyz/XXgsffwynnx52RCKSpSLfdG7qVF9tSYuy0IYNMHYs3HZbaZO4Zs3CjkpEslzkzwjy8/0xt0ePkAOZPNlPDLvnHnjnHf+ckoCIpECkE8HOnfDqq37uQP2wzo1WrfJXA51zDrRp45vE9ekTUjAiEkWRLg3NnesX6wq1LHTuuX75yDFj4OabYa+9QgxGRKIo0okgP99fhDNgQIrf+Msv/doATZvCk0/6IDp1SnEQIiJeZEtDzvm2Ev36+aadKbF7N/z2t74p3N13++eOO05JQERCFdlE8O9/w9KlKSwLffYZnHIKXHedX/7sJz9J0RuLiFQtsomgZDbxoEEpeLPnn4cuXXz2+dOfYNo0aN8+BW8sIlK9yCaCWMw3mdt//wDfxMXX0+nWDYYM8fMCLrsszWauiUjURTIRfP01vP9+gGWh7dvhjjv8FUHOwaGHwnPPpXlrUxGJqkgmgilT/NdAuo2+844fAH7gAX9VkJrEiUiai2QiiMX8h/SkXqxTWAg33AC9esHWrb53xTPPqEmciKS9yCWCzZvhzTd9WSippfodO+DFF2HUKD8ofNppSXxxEZHgRG5C2fTp/pidlLLQunV+Qtidd0KLFn4weL/9kvDCIiKpE7kzgljMH7N79qzjC/397762NGZMaZM4JQERyUCRSgS7dvkmc2eeWYcmc998A0OH+iuCDjjAr3GpJnEiksEiVRp6+21fzalTWWj4cH/t6YMPwk3spmx8AAAKMElEQVQ3hdi2VEQkOSJ1FIvFoEGDWozjfvGFryc1beoXjmnUCI48MpAYRURSLTKloZImc3371qDJ3O7d/sDfuTPcdZd/rmtXJQERySqROSNYvBiWLPEt/xPy6adw5ZW+njRwoF9IXkTqbOfOnSxfvpzt27eHHUrWaNiwIQcddBB71XI9k8gkgljMf02oydxf/wqXXupPHf7yF7joIvUHEkmS5cuX07RpU9q3b4/p/1WdOedYu3Yty5cvp0OHDrV6jciUhmIxOOEEf6FPpXbv9l9POAGGDfOnERdfrCQgkkTbt2+nZcuWSgJJYma0bNmyTmdYkUgE33zjV4Os9Gqhbdvg1lv9ZaElTeImTYKcnJTGKRIVSgLJVdefZyQSQZVN5ubM8QPADz0ELVv6Fe1FRCIkEokgFoMOHfzFP9/avNn3BerTxx/8Z8yAP/7RX18qIllt3bp1NG3a9NtyymWXXcbcuXMBGD16NJMmTaK4uJgRI0bQu3dvLr30UnaXlI5raefOnQwaNIiePXvy9NNPV7rdkiVL6Nu3L127duW+++77zvcGDRrEhx9+WKc4KpL1g8WFhb7J3DXXlCv179wJkyfDjTf6NhGNG4cWo0hU3XgjJPu41rUrPPFE1dvMmDGD7du3M3v2bAYMGFDhNn/7298oKipizpw53HLLLUyePJkhQ4bssd3ZZ5/Nxo0bv318wQUXMGLEiD22Gzt2LN26dWP06NGcccYZDBs2jKZNm+6x3bhx47j33nvp2bMnvXr1YuTIkbRu3Zpnn32WQw89lK5du1bzE6i5rD8jmD4dioriZaG1a/2i8bt2+Qlin34Kjz+uJCASMVOnTmXUqFFMnTq10m2mTZvGmWeeCcB5551H69atK9wuFosxa9asb28VJQGAWbNmMXz4cAD69OnD/PnzK9yuZcuWLFq0iFWrVlFUVESzZs1Yt24dN910E82bN+ett96qya4mJOvPCGIxaN7M0Xvli3Dedb7HRP/+0Lu3nyksIqGp7pN7UN59913mzp1L3759K91m1apVtGjRAoDjjz++zu+5ZcsWDjzwQABatGjBqlWrKtxu4MCBPPnkkyxfvpxTTz2V+vXr8/jjjzNs2DCuvvpqbrvtNjZv3sxZSVxiMZAzAjObaGbvmtmdddmmrnbtggVTvmZ60yHU+9FwOPhg3ySud++g3lJE0tyiRYtYs2YN5557LsuWLeOrr77a46obM2PfffelsLAQgMmTJzNp0qQKX+/ss88mLy/v29uECRMq3K5JkyZs27YNgMLCwkrHHB588EGeeeYZ7r//frZt28aMGTP44IMPGDVqFG3btmX48OHMmjWrlntfsaQnAjMbAtRzznUHOprZ4bXZJhneeQd+v344x62cCg8/DPPmQZcuQbyViGSIadOmcfvttzNr1ixuuOEGpk2bRk5ODkuWLAH8YG3btm3p2bMnM2bMAPyYQrNmzSp8vURLQ926dft2QPqjjz6iffv2FW63dOlSvvrqK7Zv387ChQsxMw477LBv45s/fz7t2rWry49gT865pN6AJ4Ez4vfPBy6v5TYjgPnA/EMOOcTVxty5zl3T40O3ecFntfr3IpJ8ixcvDvX9+/bt6z744APnnHMzZ850Q4cOdUuXLnU9evRwvXr1csOGDXPFxcVu69at7vzzz3c9e/Z0F110kSsuLq7T+y5btsx16tTJ3XDDDS43N9ft2rXLvfnmm27s2LHf2e6VV15xHTp0cE2aNHHnn3++27Vrl1uxYoU7/fTTXY8ePVy/fv3cpk2b9nj9in6uwHyXwHHb/LbJY2YTgSedcx+Z2QDgeOfcgzXdpqzc3FxX2cCKiGSWTz75hKOPPjrsMELx9ddfM3fuXE477TT2S/JCVhX9XM1sgXMut7p/G8RgcSHQKH6/CRWXnxLZRkQkqxxwwAHfXjmUToI4AC8AesXvdwGW1XIbEclSya5ERF1df55BnBFMBuaY2QHA6cD5ZjbGOXdnFducHEAcIpKGGjZsyNq1a9V4LklcvPtow4YNa/0aSU8EzrlNZpYH9Aceds6tBD6qZpuNe7yQiGSlgw46iOXLl1NQUBB2KFmjZD2C2gpkQplzbj3wfF23EZHss9dee9W6b74EQ4O0IiIRp0QgIhJxSgQiIhGX9AllQTCzAuCLWv7zVsCaJIaTCbTP0aB9joa67HM751zFbVPLyIhEUBdmNj+RmXXZRPscDdrnaEjFPqs0JCIScUoEIiIRF4VEUHFz8OymfY4G7XM0BL7PWT9GICIiVYvCGYGIiFRBiUBEJOKyJhGkyzrJqVTd/pjZfmb2uplNN7OXzaxBqmNMtkR/h2aWY2YfpCquINVgn8eb2aBUxRWkBP62m5vZa2Y238yeSnV8QYj/zc6p4vt7mdkUM3vbzH6czPfOikSQTuskp0qC+3Mh8JhzbgCwEhiYyhiTrYa/w0cpXfwoYyW6z2bWG2jrnJuS0gADkOA+Xww8G7++vqmZZfTcAjNrDvwZaFzFZtcDC5xzPYFzzaxpst4/KxIBkEdpJ9PplC56U9NtMkke1eyPc268c25G/GFrYHVqQgtMHgn8Ds3sVGALPvllujyq2Wcz2wv4A7DMzM5OXWiByaP63/Na4BgzawYcDHyVmtACUwycB2yqYps8Sn8us4GkJb9sSQSNgRXx++uAnFpuk0kS3h8z6w40d87NS0VgAap2n+Plr7uAW1MYV5AS+T1fAiwGHgZONLPrUxRbUBLZ57lAO+AG4JP4dhnLObcpgXVZAjuGZUsiiOI6yQntj5m1AMYCSa0phiSRfb4VGO+c25CyqIKVyD4fB0yILwI1CfhBimILSiL7/EtgpHPuXuBT4PIUxRamwI5hmX4wLBHFdZKr3Z/4p+MXgNucc7Vt2pdOEvkd9gNGmdksoKuZ/TE1oQUmkX3+L9Axfj+X2jdoTBeJ7HNz4FgzqwecBERhQlRwxzDnXMbfgH3xy2E+hj9N7AKMqWab/cKOOwX7fA2wHpgVv50XdtxB73O57WeFHXOKfs9N8Ql/NvAucGDYcadgn08EPsZ/Sp4BNAk77iTt+6z411OB68p9r118n38DvI8fUE/K+2bNzOL4qHt/YLbzp8i12iaTZNv+JEL7rH2OMjM7AH9WMM0lca33rEkEIiJSO9kyRiAiIrWkRCAiEnFKBCIiEVc/7ABEMoGZjcbP/FwVf+oYoCB+24lvefAA/gqX7cBy4ALn3M6UBytSQzojEEnc/c65POdcHjAu/rgP8Cd8HxiA653vkVOIn9MgkvaUCETqrjmwreSBmRl+5ueO0CISqQElApHE3WFms8xsfJnHs4GT8ZN8wLfzWIYvIc1MfYgiNacxApHE3e+cmwTfjhl8+zj+HPgSUS+gyGmSjmQInRGIJN9TwBXxPjgiaU+JQCTJnHPr8WWhoWHHIpIItZgQEYk4nRGIiEScEoGISMQpEYiIRJwSgYhIxCkRiIhEnBKBiEjE/X/MvAlFwbcAyAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"支持向量机在训练集上的性能 -- \n",
"支持向量机 | 准确率: 0.9717\n",
"支持向量机 | AUC: 0.9731\n",
"支持向量机 | 漏报率为:0.0483\n",
"支持向量机 | 误报率为:0.0109\n",
"支持向量机 | 训练时长(秒):12.7980\n",
"=========================================\n",
"支持向量机在测试集上的性能 -- \n",
"支持向量机 | 准确率: 0.8532\n",
"支持向量机 | AUC: 0.8563\n",
"支持向量机 | 漏报率为:0.1934\n",
"支持向量机 | 误报率为:0.1000\n",
"支持向量机 | 训练时长(秒):12.7980\n"
]
}
],
"source": [
"SVM_result=train_model(label,data,\"支持向量机\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"神经网络分类评估报告\n",
" precision recall f1-score support\n",
"\n",
" 0 0.81 0.65 0.72 197\n",
" 1 0.66 0.82 0.73 164\n",
"\n",
"avg / total 0.74 0.73 0.73 361\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"神经网络在训练集上的性能 -- \n",
"神经网络 | 准确率: 0.9704\n",
"神经网络 | AUC: 0.9710\n",
"神经网络 | 漏报率为:0.0418\n",
"神经网络 | 误报率为:0.0193\n",
"神经网络 | 训练时长(秒):9.8903\n",
"=========================================\n",
"神经网络在测试集上的性能 -- \n",
"神经网络 | 准确率: 0.7285\n",
"神经网络 | AUC: 0.7359\n",
"神经网络 | 漏报率为:0.3366\n",
"神经网络 | 误报率为:0.1887\n",
"神经网络 | 训练时长(秒):9.8903\n"
]
}
],
"source": [
"MLP_result=train_model(label,data,\"神经网络\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adaboost分类评估报告\n",
" precision recall f1-score support\n",
"\n",
" 0 0.77 0.64 0.70 197\n",
" 1 0.64 0.77 0.70 164\n",
"\n",
"avg / total 0.71 0.70 0.70 361\n",
"\n"
]
},
{
"data": {
"image/png": 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/lGV1lXQNmT133po7Zl7Q6NtYH2vZ+9OpQ7A6e+nxeQ0pt1+PczQza4T2pVmrysnRzJLpl68Pmpk1WpWTo9eDMbMksjVk6jPOUVKLpD9ImiXpB/m5SyXdmc8qVpqTo5klU8fJbo8CfpKvaz1M0ueBpojYB9hO0g5lY3Oz2sySqePzmOeAXSUNB7YCFrNq/ZgbySbLeaRMgTUlR0m7kk1V9gTwZEQsK3MTM7POsvkcS2XHEZJmdTieFhHT8v3bgYOBTwF/B9YFnso/WwCMKRtfLVOWnQ+MIpu27L+AbwH/UfZGZmadlezXa8ubzUW+DHw8X5L1s8DXgEvyz4aWv1VtX9gtIg4DFkXE74GNyt7EzKyIVPvWgxZgN0lNwN7AN1k17+xoYE7Z2GppVs+XdBrQIunDwNyyNzEz60z1nafxG8APgW2AO4FzgRmSRpGtnjqubIG11Bw/RNa5eSdZrfHosjcxMytSr5pjRNwdEf8WEUMjYv+IWAK0AncBEyNicdnYaqk5vg9YSLb6YOTHV5a9kZlZRwLWaeAo8IhYyKon1qXVUnNUvjUDhwL7dn+5mVlt6tjnWHe1THZ7RYfDiyVd1MB4zGygqG1wdzK1DOXpWFPcFNilceGY2UCiCq8/WEufY8d5HV8Gjm9QLGY2gGSDwFNH0bVamtWn90UgZjbwVDk59vhARtIf+yIQMxt4qrz6YC1Pq++TdEjDIzGzAaW9WV2nWXnqrpY+x7cAJ0q6D1iOF9gys3rorwtsteuLhbbMbGCq8tKsXTar3ZQ2s0aqerO6uz5Hr69pZg0kmlT71te6a1aPk/Rwp3Mi63PcsYExmdkAIPpvn+NM9zeaWcP049cHr+6zKMxsQKryA5kuk2NEXNiXgZjZwNKfm9VmZg3VL2uOZmaNVuHc6ORoZmmIXiwJ2IecHM0sDVG3CSUkfQKYkh8OJ1vWZR2y+Wd/HxFnli2zyonbzNZyKrF1JyK+HxGtEdEKzAD+ATRFxD7AdpJ2KBuba45mlkT2+mCpmuMISbM6HE+LiGmvK1PaAhhJthhg++JaN5KtYf1ImZs5OZpZMiUHgbdFxNgerjke+D7w/4Cn8nMLgDGlYyv7BTOz+qh9otta+iYlDSJb1mU6sIxsxVSAofQi1zk5mlkS7U+ra91qMIHstecA7iVrSgOMBuaUjc/NajNLps7LHxwI3JbvXwPMkDQKmAyMK1uYk6OZJVPP1BgRX+qwv0RSK7A/8O2IWFy2PCdHM0ujjuMci0TEQlY9sS7NydHMkvAbMmZmXUix5GqtnBzNLJnqpkYnRzNLqMIVRydHM0sj63OsbnZ0cjSzZFxzNDNbjTwTuJlZZ25Wm5kVkZvVZmaFnBzNzArIzWozs9fLZgJPHUXXqvxqY7+2YMECbrn5Jtra2lKHYlZZKvFPX3NybICFCxdy6CHvYtY9d3PQpInMnz+f97z7nbxt77Gc8InjUodnvbDZxsO4+b8/BcBWm7dwww9O4I8XH88Fp0x53XVXn/sxdt9xixQh9ktS7Vtfc3JsgPvv+xvfOuscTv7iKUw64EB+/tOrmPKBI7lj5iyWLlvKvbNm9VyIVcbwYc1ccvqRbNC8LgDHHvpWPvWNXzL54xey5cjh7Lr9KADeP3kvHvtXG397+KnuirMOXHMcYCbs+3b2HjeO22fcxqx77mb48OE8+MD9LFq0iH89+SRbbrVV6hCthBUrV3LUFy9n6bIXAfjKRb9n9px5AGyy0RCeW7SMlg034BufeQ8Ll7zAvmO3Txluv9He51jr1tcakhwljZQ0oxFl9xcRwdW/+DktLS1MeHsrTzzxOBdd8D122mlnNt5449ThWQlLl7/EkjwxdnT4/nvy4D/n8kzbEk48spXf3PwXLv31HRx58L9z8L67Joi0vylTb1wLao6SWoArgCH1Lrs/kcR3z7+QXXfbnQMntXL+hRfzpVNPY8c378SVl/8wdXi2hrbdYhM+c9R+nPSdXwMw+s1bcvEvZzDvuaX86qY/s+9erj32qEStsdaao6SLJL07379U0p2STu1NeI2oOa4ApgBLGlB2v3D2Wd/iJz+6EoBFixaxeNEi7r/vPlasWME998ys9ASf1rPhw5q58usf4rgzrnqtRvnPJ+fzxi1GADBml614Yu7ClCH2C1mzWjVvPZYnTQA2j4jrJB0KNEXEPsB2knYoG1/dk2NELOluMRtJUyXNkjRrftv8et++Eo796FSu+smPmDRxX1auWMG1v7ueEz45lZGbbMTCBQs44v0fSB2irYHPHT2JLTdv4ZyTDuOGH5zA+DFv4pwrbuHjR0zg1ks/zfg9t+eKa+9KHWa/oBIbMKI9d+Tb1NfKkQYDlwBzJB0CtLJq/ZgbWbVMa+2xZUu81p+k6RHR2t01e+01Nu6Y6Se3a5uWvT+dOgSrs5ce+hkrl8+ra5Nn5932jB9e8z81X7/P9i33RsTYos8kHQscDHwSOBH4AjAmIv4q6YB8/5tl4vMbMmaWTB0ftOwJTIuIuZJ+DLwVaM4/G0ovWskeymNmydRxEPijwHb5/lhgW1Y1pUcDc8rG1rCaY09NajOzOrbTLwUuk/R+YDBZn+NvJY0CJgPjyhboZrWZpVOn7BgRS4H3va5oqRXYH/h2dw+Ju+LkaGZJZE+hGzesLSIWsuqJdWlOjmaWhmcCNzMrVuHc6ORoZqmo0m+LOTmaWTIVzo1OjmaWRofXAivJydHM0qlwdnRyNLNkvPqgmVkB9zmamRWocG50cjSzRCr+RMbJ0cyScZ+jmVknwn2OZmaFKpwbnRzNLKEKZ0cnRzNLppZVBVNxcjSzZKqbGp0czSylCmdHJ0czS6LRM4GvKSdHM0ujjjOBS1oH+Ge+QbZ29eHAO4G7I+L4smV6aVYzS0Ylth7sDvw0IlrzlU/XJVua9d+BZyVNKhuba45mlk65muMISbM6HE+LiGn5/jjgXZImAvcBs4FfRURIuoFsedaby9zMydHMElHZPse2iBjbxWf3AJMi4hlJVwLNZAkSYAEwsmx0To5mlkwdhzn+LSJeyvdnAYPJEiTAUHrRheg+RzNLokx/Yw059EeSRktqAt4DDCHrcwQYDcwpG59rjmaWTv1qjmcAV+Ul/hY4E5gh6TzgoHwrxcnRzJKp1+uDEXE/2RPr1+RPqA8GzouIx8qW6eRoZsk0cgh4RLwAXN3b7zs5mlkadRwE3ghOjmaWUHWzo5OjmSXhmcDNzLpQ4dzo5Ghm6bjmaGZWwFOWmZkVqW5udHI0s3QqnBudHM0sDXmco5lZMVU4Ozo5mlky1U2NTo5mllCFK45OjmaWSumZwPuUk6OZJVH11wc9E7iZWQHXHM0smSrXHJ0czSyZKvc5ulltZmlo1UDwWraaipRGSvpzvn+ppDslndqb8JwczSyJOq8+2O5soFnSoUBTROwDbCdph7LxOTmaWTrlsuMISbM6bFNfV5S0H7AcmAu0Ar/IP7qRVcu01sx9jmaWTMk+x7aIGFtYjrQu8F/Ae4FryNatfir/eAEwpmxsTo5mlsyg+j2P+QJwUUQsyt/XXgY0558NpRetZCdHM0unfslxErCfpOOBPYCtgSeBu4DRwOyyBTo5mlky9RrKExH7vlamNB34D2CGpFHAZGBc2TL9QMbMkmh/fbCeQ3kAIqI1IpaQPZS5C5gYEYtLxxcRZb9TN5LmA48nC6BvjQDaUgdhdTdQ/ly3iYhN61mgpOvJ/v3Vqi0iDqpnDN1JmhwHEkmzunrSZv2X/1zXXm5Wm5kVcHI0Myvg5Nh3pqUOwBrCf65rKfc5mpkVcM3RzKyAk6OZWQEnxwaTNCR1DGZWnpNjg0gaJGkacI6kQyXtljomqw9Jn0sdgzWek2PjHAc0kU2jtDEwWdIBaUOyOvmwpLNSB2GN5eTYOH8DVgKLgF8C9wFvlVT6BXirBmVagL8Am0q6OHVM1jhOjo3zEPACsHf+0vv/Ac8C/5Y0Kuu1yCwEPhERRwMvOkGuvZwcGyQiniMbIPxBSa3AUrLkeJyk5u6+a5X3IkBEfAZYJunHieOxBvAg8AaTtBPwPmA3IIBbIsJvVfRzkhT5/zySvgacHxFzE4dldeTk2AckrQ/sSDaF3SMR8XzikKwOJA2KiJWp47DGcHI0MyvgPkczswJOjmZmBZwczcwKODmamRVwcuzHJH1F0t8l3SbplnwZyt6W09rh+Ls1fGcPSXv09h5dXDN9TcsoU55Zd5wc+7+v5Wv2/hA4sR4F5oObe7JHvpmtldZJHYDVTQvZ64rtNaZ7gN0j4kBJGwBXApsB90XE8fk7wr8kmxxDwPT2giRNj4jWfH994HJgS7L3xI8gm0zjvfnnR0XEO8reoyeShgJXA0OARyPiI/lHn5V0OvAMcCTZ++vTyMaRzgemRMSKWu9j1hXXHPu/UyTdBowDzsvPjQPujIgD8+OpwP15DfMNknbPz/0uIiYCr3RT/lTgrxExHvgVsGtEfBH4JvDNiHhHHe5R5A3A+cAkYFtJI/PzsyLi7cBi4N3AIcDg/NwTwMEl72NWyDXH/u9rEdH53d77I+LXHY7fTDYjUCswHNgCeCPw8/zzWd2UvxNZUoSsBtmVNblHkVeAjwIfIZvyrf199Jn5zz8BbyL7b3ifvLY8FPh7yfuYFXLNce20rNPxbOC7eVP5VLIa1hOsmiGou77Dh4C35PtfIktYkDXhN4DsPeM1vEeRY8ma1R8Alnc4v1f+c3dgTn7fn+X3/QzwYMn7mBVychwYLiGbbPc24OPAk2T9dIflNa4Ne/jumPy6McCP8vM3AYdKugOYsIb3KHIT8EXg1vx4i/znBEn/C4wErgV+C4zKz50JPF7yPmaF/G61mVkB1xzNzAo4OZqZFXByNDMr4ORoZlbAydHMrICTo5lZgf8PmOwrXetgVOcAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"adaboost在训练集上的性能 -- \n",
"adaboost | 准确率: 0.8150\n",
"adaboost | AUC: 0.8167\n",
"adaboost | 漏报率为:0.2282\n",
"adaboost | 误报率为:0.1445\n",
"adaboost | 训练时长(秒):41.4722\n",
"=========================================\n",
"adaboost在测试集上的性能 -- \n",
"adaboost | 准确率: 0.7008\n",
"adaboost | AUC: 0.7065\n",
"adaboost | 漏报率为:0.3571\n",
"adaboost | 误报率为:0.2303\n",
"adaboost | 训练时长(秒):41.4722\n"
]
}
],
"source": [
"ADA_result=train_model(label,data,\"adaboost\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用CNN进行建模训练"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras\n",
"from keras.preprocessing.image import img_to_array#图片转为array\n",
"from keras.utils import to_categorical#相当于one-hot\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"import random\n",
"from keras.optimizers import Adam\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,BatchNormalization,Dropout\n",
"from keras.optimizers import SGD\n",
"from keras.models import Sequential\n",
"from keras.optimizers import RMSprop\n",
"import keras.backend as K"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"101/101 [==============================] - 15s 146ms/step - loss: 0.4985 - accuracy: 0.7525 - val_loss: 0.3981 - val_accuracy: 0.8310\n",
"Epoch 2/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.4047 - accuracy: 0.8268 - val_loss: 0.4070 - val_accuracy: 0.8255\n",
"Epoch 3/30\n",
"101/101 [==============================] - 14s 140ms/step - loss: 0.3889 - accuracy: 0.8290 - val_loss: 0.3179 - val_accuracy: 0.8532\n",
"Epoch 4/30\n",
"101/101 [==============================] - 14s 139ms/step - loss: 0.3427 - accuracy: 0.8582 - val_loss: 0.2948 - val_accuracy: 0.8698\n",
"Epoch 5/30\n",
"101/101 [==============================] - 14s 141ms/step - loss: 0.3286 - accuracy: 0.8638 - val_loss: 0.2685 - val_accuracy: 0.8920\n",
"Epoch 6/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.3107 - accuracy: 0.8772 - val_loss: 0.2443 - val_accuracy: 0.9169\n",
"Epoch 7/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.2799 - accuracy: 0.8862 - val_loss: 0.2267 - val_accuracy: 0.9114\n",
"Epoch 8/30\n",
"101/101 [==============================] - 15s 147ms/step - loss: 0.2592 - accuracy: 0.8940 - val_loss: 0.2720 - val_accuracy: 0.9086\n",
"Epoch 9/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.2544 - accuracy: 0.9021 - val_loss: 0.2290 - val_accuracy: 0.9058\n",
"Epoch 10/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.2306 - accuracy: 0.9086 - val_loss: 0.2372 - val_accuracy: 0.9003\n",
"Epoch 11/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.2390 - accuracy: 0.9061 - val_loss: 0.2358 - val_accuracy: 0.9086\n",
"Epoch 12/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.2285 - accuracy: 0.9132 - val_loss: 0.2062 - val_accuracy: 0.9224\n",
"Epoch 13/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.2279 - accuracy: 0.9092 - val_loss: 0.2306 - val_accuracy: 0.9058\n",
"Epoch 14/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.2017 - accuracy: 0.9179 - val_loss: 0.2795 - val_accuracy: 0.9086\n",
"Epoch 15/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.1983 - accuracy: 0.9220 - val_loss: 0.2309 - val_accuracy: 0.9224\n",
"Epoch 16/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.2143 - accuracy: 0.9185 - val_loss: 0.1826 - val_accuracy: 0.9280\n",
"Epoch 17/30\n",
"101/101 [==============================] - 14s 139ms/step - loss: 0.2041 - accuracy: 0.9211 - val_loss: 0.1954 - val_accuracy: 0.9307\n",
"Epoch 18/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.2010 - accuracy: 0.9247 - val_loss: 0.2079 - val_accuracy: 0.9391\n",
"Epoch 19/30\n",
"101/101 [==============================] - 14s 140ms/step - loss: 0.2013 - accuracy: 0.9257 - val_loss: 0.1842 - val_accuracy: 0.9363\n",
"Epoch 20/30\n",
"101/101 [==============================] - 14s 136ms/step - loss: 0.2129 - accuracy: 0.9153 - val_loss: 0.1857 - val_accuracy: 0.9391\n",
"Epoch 21/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.1897 - accuracy: 0.9233 - val_loss: 0.1850 - val_accuracy: 0.9391\n",
"Epoch 22/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.1879 - accuracy: 0.9235 - val_loss: 0.2047 - val_accuracy: 0.9307\n",
"Epoch 23/30\n",
"101/101 [==============================] - 14s 136ms/step - loss: 0.1862 - accuracy: 0.9266 - val_loss: 0.1795 - val_accuracy: 0.9335\n",
"Epoch 24/30\n",
"101/101 [==============================] - 14s 140ms/step - loss: 0.1964 - accuracy: 0.9230 - val_loss: 0.1855 - val_accuracy: 0.9391\n",
"Epoch 25/30\n",
"101/101 [==============================] - 14s 137ms/step - loss: 0.1861 - accuracy: 0.9310 - val_loss: 0.1963 - val_accuracy: 0.9252\n",
"Epoch 26/30\n",
"101/101 [==============================] - 14s 136ms/step - loss: 0.1612 - accuracy: 0.9450 - val_loss: 0.2083 - val_accuracy: 0.9058\n",
"Epoch 27/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.1702 - accuracy: 0.9341 - val_loss: 0.1957 - val_accuracy: 0.9252\n",
"Epoch 28/30\n",
"101/101 [==============================] - 14s 136ms/step - loss: 0.1739 - accuracy: 0.9303 - val_loss: 0.1600 - val_accuracy: 0.9474\n",
"Epoch 29/30\n",
"101/101 [==============================] - 14s 136ms/step - loss: 0.1584 - accuracy: 0.9391 - val_loss: 0.2235 - val_accuracy: 0.9197\n",
"Epoch 30/30\n",
"101/101 [==============================] - 14s 138ms/step - loss: 0.1730 - accuracy: 0.9341 - val_loss: 0.1590 - val_accuracy: 0.9418\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"420.0632104873657"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"def cnn(channel,height,width,classes):\n",
" input_shape = (channel,height,width)\n",
" if K.image_data_format() == \"channels_last\":\n",
" input_shape = (height,width,channel)\n",
" model = Sequential()\n",
" model.add(Conv2D(32,(5,5),padding=\"same\",activation=\"relu\",input_shape=input_shape,name=\"conv1\"))\n",
" model.add(Conv2D(32,(5,5),padding=\"same\",activation=\"relu\",name=\"conv2\"))\n",
" model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),name=\"pool1\"))\n",
" \n",
" model.add(Conv2D(64,(3,3),padding=\"same\",activation=\"relu\",name=\"conv3\"))\n",
" model.add(Conv2D(64,(3,3),padding=\"same\",activation=\"relu\",name=\"conv4\"))\n",
" model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),name=\"pool2\"))\n",
"\n",
" # 全连接层,展开操作,\n",
" model.add(Flatten())\n",
" model.add(Dense(256,activation=\"relu\",name=\"fc1\"))\n",
" model.add(Dense(classes,activation=\"softmax\",name=\"fc2\"))\n",
" return model\n",
"\n",
"def train(aug, model,train_x,train_y,test_x,test_y):\n",
" start=time.time()\n",
" model.compile(loss=\"categorical_crossentropy\",optimizer=\"Adam\",metrics=[\"accuracy\"])\n",
" _history = model.fit_generator(aug.flow(train_x,train_y,batch_size=batch_size),\n",
" validation_data=(test_x,test_y),steps_per_epoch=len(train_x)//batch_size,\n",
" epochs=epochs,verbose=1)\n",
" end=time.time()\n",
" spend_time=end-start\n",
"\n",
" plt.figure()\n",
" N = epochs\n",
" #model的history有四个属性,loss,val_loss,acc,val_acc\n",
" plt.plot(np.arange(0,N),_history.history[\"loss\"],label =\"train_loss\")\n",
" plt.plot(np.arange(0,N),_history.history[\"val_loss\"],label=\"val_loss\")\n",
" plt.plot(np.arange(0,N),_history.history[\"accuracy\"],label=\"train_acc\")\n",
" plt.plot(np.arange(0,N),_history.history[\"val_accuracy\"],label=\"val_acc\")\n",
" plt.title(\"loss and accuracy\")\n",
" plt.xlabel(\"epoch\")\n",
" plt.ylabel(\"acc/loss\")\n",
" plt.legend(loc=\"best\")\n",
"# plt.savefig(\"../result/result.png\")\n",
" plt.show()\n",
" return spend_time\n",
"\n",
"\n",
"#模型参数设置\n",
"channel = 1\n",
"height = 40\n",
"width = 40\n",
"class_num = 2\n",
"norm_size = 32#参数\n",
"batch_size = 32\n",
"epochs = 30\n",
"\n",
"data=data.reshape(-1,40,40,1)\n",
"label=np.array(df['label'].astype('int'))\n",
"label = to_categorical(label)\n",
"train_x,test_x, train_y,test_y = train_test_split(data,label,test_size=0.1,random_state=0)\n",
"#构建模型\n",
"model = cnn(channel=channel, height=height,width=width, classes=class_num)\n",
"\n",
"aug = ImageDataGenerator(rotation_range=30,width_shift_range=0.1,\n",
" height_shift_range=0.1,shear_range=0.2,zoom_range=0.2,\n",
" horizontal_flip=True,fill_mode=\"nearest\")#数据增强,生成迭代器\n",
"\n",
"\n",
"spend_time=train(aug,model,train_x,train_y,test_x,test_y)#训练\n",
"spend_time"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------\n",
"cnn测试集分类报告:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.95 0.95 197\n",
" 1 0.94 0.93 0.94 164\n",
"\n",
"avg / total 0.94 0.94 0.94 361\n",
"\n",
"cnn | 准确率: 0.9418\n",
"cnn | AUC: 0.9411\n",
"cnn | 漏报率为:0.0613\n",
"cnn | 误报率为:0.0556\n",
"cnn | 训练时长(秒):420.0632\n"
]
},
{
"data": {
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0OMB2ZRdlZr1DT+05AiDpMmAnYDjwJtmV6oM6/JKZWRUaNxqru1r9l8AHgeeAQ4B1pVZkZr2ClN0EXu2yqVVztfpNslt4moBjyHqQZmbd1sBH1VX1HI8GngW+COwGfL7Uisys16j3wBOSRkuak7/uK+kWSXMlTau0rZJOwzEiVkXEcxGxKCJmRMScqqo0M+tEPQeeyKdivQIYmG86FZgfEQcCR0saXGFbIY+wY2ZJiOrPN+bnHEdKmtdmmd6uybXAVGB5vt7Mhom0ZgP7VdhWyHPImFkatQ9FtiQiKoZZRCyHd9weNBB4KX+9lGzA7qJthZLOITN+tx2Y++DFZe/GNrEdPtPprJfWw7Qsaiml3ZLvc1xJdn/2MmBQvl60rZAPq80sidapWatdumA+G+7J3ht4vsK2Qp5gy8ySKfnxwSuA2yVNAnYnGx/ipYJtxbWVWpqZWQfKGLIsIprzn4uAKcBc4LCIWFu0rVI77jmaWRLZLTrldh3zmVKv7WxbEYejmSXTI0flMTMrWyM/PlhVOErag2yosheAP0VExcvfZmbVyMZzbNx0rGbIsouBMWTDlp0N/AfwkZLrMrNeoJGvCFdT254R8QngjYi4DRhack1m1kvU89nqeqvmsPo1STOA4ZJOAF4tuSYz6wWUaJzGalXTc/x7skdtHiDrNZ5YZkFm1ns0cs+xmnA8Bmghu5P8jXzdzKxbBGzRR1Uvm1o14ah8GQAcBRzc8cfNzKrTyD3HTs85RsQVbVZ/IOmSEusxs96ixscCN7VqbuVp21McRfawtplZt6mB5x+s5mp123Ed/wycUlItZtaLZDeBp66ismoOq8/ZFIWYWe/TyOHY6QUZSb/YFIWYWe9T79kH66maq9WPS/po6ZWYWa/Selhd7/Ec66Wac47vBU6V9DiwCk+wZWb1kOgWnWpVc86x9Im2zKx36pGPD/pQ2szKVM/DaknDJd2ez2f9w3zbZZIekHRWV+rr6JzjP3WlQTOz6lQ/82AVsw8eD1yVz2s9WNJXgaaImAiMk7RzrdV1dFh9gKRnNvptsnOOu9S6IzOztkRdzzm+DuwhaRiwPdlgOa3zxNxFNh3rs7U02FE4PuTzjWZWmtqvQo+UNK/N+syImJm/vh/4EPAF4EmgH9k0rABLgQm1ltdROF5fa2NmZrWo8YLMkvywucjXgc9GxHJJXwLOAy7N3xtEFwYdr/iFiPherY2ZmVWr9bC6TqPyDAf2lNQE7A+cT3YoDbA38Hyt9Xn2QTNLpo638vw78GNgLNnA3N8G5kgaAxwBHFBrgw5HM0umXtkYEQ8D73ln22oGpgAXRMSyWtt0OJpZEqLc2QcjooUNV6xr5nA0szREkgElquVwNLNkGjcaHY5mlkj2+GDjxqPD0cySaeTBbh2OZpZImkFsq+VwNLMkyr5a3V0ORzNLxj1HM7MCjRuNDkczS8X3OZqZbcznHM3MKnDP0cysQONGo8PRzBJq4I6jw9HM0sjOOTZuOjoczSwZ9xzNzDYiDzxhZtaeD6vNzIpUN3FWMo18D6aZbebqOPtg3p4ukfTh/PVlkh6QdFZXanM4mlkyquGfTtuSJgHbRsQtko4CmiJiIjBO0s611uZwNLMkspHAq1+AkZLmtVmmr29L6gtcCjwv6aNAMxsm17qLDXNYV83nHM3qZNjAfuw9djiPv9DC0pV/Tl1Oj1BNj7CNJRGxX4X3/h5YAFwAnAqcAlyWv7cUmFBrbe45lmjx4sUcNvng9etPPfkkxxz1sYQVWVeNGtKfm0+bDMC2wwbw2Df/hhu/0syNX2lm60H9GbpVX676wkHss9MIbsi3WefqeM5xH2BmRLwKXAnMBgbk7w2iC1nnnmNJWlpaOPmkE1m1ahUAf1i4kDNO/yqrVq5MW5jVbOhWfbl42vvYqn/212XfcSP4zm1Pcvmshes/M3GXUcy45jHm/2Epwwb2Y6+xw7jvicWpSu4xauw5duQ5YFz+ej9gR7JD6QeBvYGna23QPceSNDU18dOrrmbIkCEADBo8mP++5vrEVVlXrF0XnPzDB1mxeg0A+47bmuMm7cQvZ0zhjKP2BOCBZ15j/h+WcsAuI9lnpxE8svD1lCX3CF0459iRy4DJkmYDnyc753i8pAuBY4Hbaq2vlJ6jpNHA9RExqYz2e4LWUGy1zTbbJKrEumvlW2+/Y/2ex1/hP29dwOr/Xcv1Xz6E3d89lAUvLgPgY+/dgTdW/Zm310aKUnuY6q5CVyMiVgDHvKN1qRmYAlwQEctqbbPuPUdJw4ErgIH1btusETyy8HVWvfU26yL4/QstjBs9aP17X7vqURa8uIzDx49JWGEPUUOvsStTuEZES0Rcm5+HrFkZh9VrganA8hLaNkvumi8ezDZDt2RAvyYOec+2PPnick49YleOnTgWyM5RLn/TV6s7kx1Wq+plU6v7YXVELIfKI/zm9yZNB9h+hx3qvXuz0n3r5ie48SvNrHl7HVfMWsjCxSv4ya8W8l+fnchxB4/jqZeW+WJMlRr46UEUUc65EUmzIqK5o89M2He/mPvgI6Xs39IZ+9nrUpdgddby89NZs2RhXbNstz33iR/fdF/Vn5/4l8Pnd3CfY935Vh4zS6aOt/LUncPRzJJp5FF5SgvHzg6pzcwaOBvdczSzhBo4HR2OZpaE8DlHM7ONNfhI4A5HM0umgbPR4WhmqajiwyKNwOFoZsk0cDY6HM0sDeHDajOzYg2cjg5HM0vGt/KYmRXwOUczswINnI0ORzNLpMGvyHiCLTNLRjX8U1V70mhJv8lfXybpAUlndaU2h6OZJSHqOm91q28BAyQdBTRFxERgnKSda63P4WhmyaiGBRgpaV6bZfo72pI+AKwCXiWbmvXa/K27yOawronPOZpZOrWdc1xSaZoESf2As4GPAzeRzX76Uv72UmBCraU5HM0smTrOKvg14JKIeCN/XnslMCB/bxBdOEp2OJpZMnW8WH0Y8AFJpwDjgR2APwEPAnsDT9faoMPRzNKpUzpGxMHrm5RmAR8B5kgaAxwBHFBrm74gY2ZJtI4EXs9beSCbvyoilpNdlHkQmBwRy2qtzz1HM0uj5JHAI6KFDVesa+ZwNLNkGvgBGYejmSXUwOnocDSzRGo7l7ipORzNLBkPWWZm1k6DD8rjcDSzhBo4HR2OZpZMHR8frDuHo5kl07jR6HA0s1RKvgm8uxyOZpZQ46ajw9HMkmgdCbxRORzNLJkGzkaHo5ml456jmVkBPz5oZlakcbPR4Whm6TRwNjoczSyNGuej3uQcjmaWjOqUjpKGAlcDTWRzV08Fvg/sDtwWEefW2qbnkDGzZFTD0onjgAsj4q+BV4FPAk0RMREYJ2nnWmtzz9HMkqmx4zhS0rw26zMjYiZARFzSZvso4O+A7+TrdwEHAc/WsjOHo5klUvNI4EsiYr8OW5QmAsOB54GX8s1LgQm1VufDajNLovXxwWqXTtuTRgAXA9OAlcCA/K1BdCHrHI5m1uNJ6gdcB5weEYuA+WSH0gB7k/Uka+JwNLNk6thzPIns0PlMSbPIOqbHS7oQOBa4rdbafM7RzJKp1+ODEfF9slt3NrQt3QxMAS6IiGW1tulwNLM0Sr4JPCJagGu7+n2Ho5kl4dkHzcwqaeB0dDiaWTIesszMrECfxs1Gh6OZJeRwNDPbmA+rzczaafTZBxUR6XYuvQYsSlbApjUSWJK6CKu73vLnOjYiRtWzQUl3kP37q9aSiPhgPWvoSNJw7E0kzetsRBHrefznuvnys9VmZgUcjmZmBRyOm87M1AVYKfznupnyOUczswLuOZqZFXA4mpkVcDiWTNLA1DWYWe0cjiWR1EfSTOBCSUdJ2jN1TVYfkr6cugYrn8OxPJ8BmoCzgRHAEZL+Om1JVicnSPpm6iKsXA7H8vwOWAe8QTYr2uPA+yUdkLQq6zJlhgOPAaMk/SB1TVYeh2N5ngJWA/vnk/v8Gvgf4D1Jq7Iui0wL8LmIOBF4ywG5+XI4liQiXie7QfhTkpqBFWTh+BlJAzr6rjW8twAi4p+BlZKuTFyPlcA3gZdM0q7AMcCeQAD3RISfqujhJCnyvzySzgMujohXE5dldeRw3AQkbQnsQjaE3bMR8WbikqwOJPWJiHWp67ByOBzNzAr4nKOZWQGHo5lZAYejmVkBh6OZWQGHYw8m6RuSnpQ0W9I9ksZ0o53mNuvfqeI74yWN7+o+KnxmVnfbqKU9s444HHu+8yLiYODHwKn1aDC/ubkz4/PFbLPkeas3H8PJHlds7TE9AuwVEYdL2gr4CbAN8HhEnJI/I3wd2eAYAma1NiRpVkQ056+3BC4H3k32nPixZINpfDx///iIOLTWfXRG0iDgemAg8FxEfDp/60uSzgFeAY4je359Jtl9pK8BUyNibbX7MavEPcee70xJs4EDgIvybQcAD0TE4fn6dOD3eQ/zXZL2yrfdGhGTgTUdtD8d+G1EHAT8P2CPiDgdOB84PyIOrcM+irwLuBg4DNhR0uh8+7yIOARYBnwY+CjQN9/2AvChGvdjVsg9x57vvIho/2zv7yPihjbrf0U2IlAzMAzYDtgJuCZ/f14H7e9KFoqQ9SAr6c4+iqwB/gH4NNmQb63Poz+U/3wU+Auy/4Yn5r3lQcCTNe7HrJB7jpunle3Wnwa+kx8qn0XWw3qBDSMEdXTu8CngvfnrM8gCC7JD+K0ge864m/sochLZYfXfAqvabN83/7kX8Hy+36vz/f4zsKDG/ZgVcjj2DpeSDbY7G/gs8Cey83SfyHtcQzr57oT8cxOAn+bb7waOkjQXmNTNfRS5GzgduDdf3y7/OUnSr4DRwM+Bm4Ex+bZzgUU17seskJ+tNjMr4J6jmVkBh6OZWQGHo5lZAYejmVkBh6OZWQGHo5lZgf8PtOcBuFvHQ40AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred= model.predict(test_x, batch_size=32)\n",
"\n",
"pred=pred.argmax(axis=1)\n",
"testY=test_y.argmax(axis=1)\n",
"\n",
"test_acc=accuracy_score(testY,pred)\n",
"cnf_matrix=confusion_matrix(testY,pred)\n",
"\n",
"test_report=classification_report(testY,pred)\n",
"print(\"-----------------\")\n",
"print(\"cnn测试集分类报告:\\n\",test_report)\n",
"\n",
"CNN_result=model_performance_evaluation(\"cnn\",testY,pred,spend_time)\n",
"plot_confusion_matrix(cnf_matrix, classes=[0,1], title='Confusion matrix')\n",
"plot_ROC_curve(pred,testY)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 模型性能评估,以及不同模型性能比较"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>Name</th>\n",
" <th>准确率</th>\n",
" <th>AUC</th>\n",
" <th>漏报率</th>\n",
" <th>误报率</th>\n",
" <th>训练时长</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>决策树</th>\n",
" <td>0.722992</td>\n",
" <td>0.720657</td>\n",
" <td>0.304878</td>\n",
" <td>0.253807</td>\n",
" <td>4.575365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>随机森林</th>\n",
" <td>0.833795</td>\n",
" <td>0.829841</td>\n",
" <td>0.162338</td>\n",
" <td>0.169082</td>\n",
" <td>8.043200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>支持向量机</th>\n",
" <td>0.853186</td>\n",
" <td>0.856289</td>\n",
" <td>0.193370</td>\n",
" <td>0.100000</td>\n",
" <td>12.798042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>神经网络</th>\n",
" <td>0.728532</td>\n",
" <td>0.735948</td>\n",
" <td>0.336634</td>\n",
" <td>0.188679</td>\n",
" <td>9.890273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adaboost</th>\n",
" <td>0.700831</td>\n",
" <td>0.706481</td>\n",
" <td>0.357143</td>\n",
" <td>0.230303</td>\n",
" <td>41.472241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CNN</th>\n",
" <td>0.941828</td>\n",
" <td>0.941083</td>\n",
" <td>0.061350</td>\n",
" <td>0.055556</td>\n",
" <td>420.063210</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Name 准确率 AUC 漏报率 误报率 训练时长\n",
"决策树 0.722992 0.720657 0.304878 0.253807 4.575365\n",
"随机森林 0.833795 0.829841 0.162338 0.169082 8.043200\n",
"支持向量机 0.853186 0.856289 0.193370 0.100000 12.798042\n",
"神经网络 0.728532 0.735948 0.336634 0.188679 9.890273\n",
"Adaboost 0.700831 0.706481 0.357143 0.230303 41.472241\n",
"CNN 0.941828 0.941083 0.061350 0.055556 420.063210"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result=pd.DataFrame({\n",
" \"决策树\":DT_result,\n",
" \"随机森林\":RF_result,\n",
" \"支持向量机\":SVM_result,\n",
" \"神经网络\":MLP_result,\n",
" \"Adaboost\":ADA_result,\n",
" \"CNN\":CNN_result,\n",
" \"Name\":[\"准确率\",\"AUC\",\"漏报率\",\"误报率\",\"训练时长\"]\n",
"})\n",
"result=result.set_index(\"Name\")\n",
"result= pd.DataFrame(result.values.T, index=result.columns, columns=result.index)\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result.iloc[:,:-1].plot()\n",
"plt.title(\"各个模型性能比较\")\n",
"plt.xticks(np.arange(6),result.index)\n",
"plt.ylim([0,1])\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result['训练时长'].plot(title=\"各模型训练时长\")\n",
"plt.xticks(np.arange(6),result.index)\n",
"plt.ylabel(\"秒\")\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 对于误报率与漏报率,相对而言,漏报率更重要。对于误报率偏高我们可以通过优化算法降低这个结果。但是当漏报率偏高时,会对模型最后的结果影响较大,很可能会使得实际的应用中不能有效地发挥作用,同时也会增加人工的筛查操作,降低效率。\n",
"### 由上可见,使用CNN的效果最好,准确率为0.94,AUC为0.94。相比传统机器学习模型,准确率有显著的提升\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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