银行分控模型的建立

1. cm_polt函数
   

   # -*- coding: utf-8 -*-
   
   def cm_plot(y, yp):
       from sklearn.metrics import confusion_matrix  # 导入混淆矩阵函数
   
       cm = confusion_matrix(y, yp)  # 混淆矩阵
   
       import matplotlib.pyplot as plt  # 导入作图库
       plt.matshow(cm, cmap=plt.cm.Greens)  # 画混淆矩阵图，配色风格使用cm.Greens，更多风格请参考官网。
       plt.colorbar()  # 颜色标签
   
       for x in range(len(cm)):  # 数据标签
           for y in range(len(cm)):
               plt.annotate(cm[x, y], xy=(x, y), horizontalalignment='center', verticalalignment='center')
   
       plt.ylabel('True label')  # 坐标轴标签
       plt.xlabel('Predicted label')  # 坐标轴标签
       return plt

    

2. ANNS算法实现
   

   # -*- coding: utf-8 -*-
   
   import pandas as pd
   import numpy as np
   #导入划分数据集函数
   from sklearn.linear_model import LogisticRegression as LR
   from sklearn.model_selection import train_test_split
   #读取数据
   datafile = '../data/bankloan.xls'#文件路径
   data = pd.read_excel(datafile)
   x = data.iloc[:,:8]
   y = data.iloc[:,8]
   #划分数据集
   x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)
   #导入模型和函数
   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import Dense,Dropout
   #导入指标
   from tensorflow.keras.metrics import BinaryAccuracy
   #导入时间库计时
   import time
   start_time = time.time()
   #-------------------------------------------------------#
   model = Sequential()
   model.add(Dense(input_dim=8,units=800,activation='relu'))#激活函数relu
   model.add(Dropout(0.5))#防止过拟合的掉落函数
   model.add(Dense(input_dim=800,units=400,activation='relu'))
   model.add(Dropout(0.5))
   model.add(Dense(input_dim=400,units=1,activation='sigmoid'))
   
   model.compile(loss='binary_crossentropy', optimizer='adam',metrics=[BinaryAccuracy()])
   model.fit(x_train,y_train,epochs=100,batch_size=128)    #调参 epochs:训练次数，此处为100次
   loss,binary_accuracy = model.evaluate(x,y,batch_size=128)
   #--------------------------------------------------------#
   end_time = time.time()
   run_time = end_time-start_time#运行时间
   
   print('模型运行时间：{}'.format(run_time))
   print('模型损失值：{}'.format(loss))
   print('模型精度：{}'.format(binary_accuracy))
   
   yp = model.predict(x).reshape(len(y))
   yp = np.around(yp,0).astype(int) #转换为整型
   from cm_plot import *  # 导入自行编写的混淆矩阵可视化函数
   
   cm_plot(y,yp).show()  # 显示混淆矩阵可视化结果

   运行结果
   

    

    

    

3. SVM算法实现
   

   # -*- coding: utf-8 -*-
   
   from sklearn import svm
   from sklearn.metrics import accuracy_score
   from sklearn.metrics import confusion_matrix
   from matplotlib import pyplot as plt
   import seaborn as sns
   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   data_load = "../data/bankloan.xls"
   data = pd.read_excel(data_load)
   data.describe()
   data.columns
   data.index
   ## 转为np 数据切割
   X = np.array(data.iloc[:,0:-1])
   y = np.array(data.iloc[:,-1])
   X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, train_size=0.8, test_size=0.2, shuffle=True)
   svm = svm.SVC()
   svm.fit(X_test,y_test)
   y_pred = svm.predict(X_test)
   accuracy_score(y_test, y_pred)
   print(accuracy_score(y_test, y_pred))
   cm = confusion_matrix(y_test, y_pred)
   heatmap = sns.heatmap(cm, annot=True, fmt='d')
   heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
   heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
   plt.ylabel("true label")
   plt.xlabel("predict label")
   plt.show()

   运行结果