BP神经网络算法 ID3决策树算法

# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 17:49:56 2022

@author: Administrator
"""

import pandas as pd
from keras.models import Sequential
from keras.layers.core import Dense, Activation
# 参数初始化
inputfile = 'bankloan.xls'
data = pd.read_excel(inputfile)  # 导入数据

x = data.iloc[:,:8].values
y = data.iloc[:,8].values

# 建立模型
model = Sequential()  # 建立模型
model.add(Dense(input_dim = 8, units = 8))
model.add(Activation('relu'))  # 用relu函数作为激活函数，能够大幅提供准确度
model.add(Dense(input_dim = 8, units = 1))
model.add(Activation('sigmoid'))  # 由于是0-1输出，用sigmoid函数作为激活函数

model.compile(loss = 'mean_squared_error', optimizer = 'adam')
# 编译模型。由于我们做的是二元分类，所以我们指定损失函数为binary_crossentropy，以及模式为binary
# 另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。
# 求解方法我们指定用adam，还有sgd、rmsprop等可选

model.fit(x, y, epochs = 1000, batch_size = 10)  # 训练模型，学习一千次
#yp = model.predict_classes(x).reshape(len(y))  # 分类预测
import numpy as np
predict_x=model.predict(x) 
classes_x=np.argmax(predict_x,axis=1)
yp = classes_x.reshape(len(y))

score = model.evaluate(x, y, batch_size=128)  #分类预测精确度
print(score)

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

# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 17:49:56 2022

@author: Administrator
"""

def cm_plot(y, yp):
  
  from sklearn.metrics import confusion_matrix 

  cm = confusion_matrix(y, yp) 
  
  import matplotlib.pyplot as plt 
  plt.matshow(cm, cmap=plt.cm.Greens) 
  plt.colorbar() 
  
  for x in range(len(cm)): 
    for y in range(len(cm)):
      plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center')
  
  plt.ylabel('True label') 
  plt.xlabel('Predicted label') 
  return plt

二、ID3决策树算法

# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 17:49:56 2022

@author: Administrator
"""

import pandas as pd
# 参数初始化
filename = 'bankloan.xls'
data_tr = pd.read_excel(filename)
#print(data_tr) # 导入数据
#训练数据
#读取数据
x_tr = data_tr.iloc[:,:8].astype(int)
y_tr = data_tr.iloc[:,8].astype(int)
from sklearn.tree import DecisionTreeClassifier as DTC
dtc = DTC(criterion='entropy')  # 建立决策树模型，基于信息熵
dtc.fit(x_tr, y_tr)  # 训练模型
# 导入相关函数，可视化决策树。
# 导出的结果是一个dot文件，需要安装Graphviz才能将它转换为pdf或png等格式。
from sklearn.tree import export_graphviz
x = pd.DataFrame(x_tr)

string1 = '''
edge [fontname="NSimSun"];
node [ fontname="NSimSun" size="15,15"];
{
'''
string2 = '}'

with open("dt1.dot", 'w') as f:
    export_graphviz(dtc, feature_names = x.columns, out_file = f)
    f.close()

from IPython.display import Image  
from sklearn import tree
import pydotplus 

dot_data = tree.export_graphviz(dtc, out_file=None,  #regr_1 是对应分类器
                         feature_names=data_tr.columns[:8],   #对应特征的名字
                         class_names=data_tr.columns[8],    #对应类别的名字
                         filled=True, rounded=True,  
                         special_characters=True)  
#手动修改树结构的字体类型，以免出来的图片出现中文乱码
dot_data = dot_data.replace('helvetica', 'MicrosoftYaHei')
graph = pydotplus.graph_from_dot_data(dot_data)  
graph.write_png('p.png')    #保存图像
Image(graph.create_png())

#显示决策树图片
import matplotlib.pyplot as plt
img = plt.imread('p.png')
fig = plt.figure('show picture')
plt.imshow(img)