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deci_learn.py
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# -*- coding: utf-8 -*-
"""
Created on Sun May 1 09:18:30 2022
@author: QAJ
"""
### 决策树
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
plt.rcParams["font.sans-serif"]=["SimHei"] #设置字体
plt.rcParams["axes.unicode_minus"]=False #该语句解决图像中的“-”负号的乱码问题
clf = DecisionTreeClassifier() # 建立决策树分类器
iris = load_iris() # 获取iris数据
cross_val_score(clf, iris.data, iris.target, cv=10) #交叉验证，第一个参数是分类器
data = iris.data
a = pd.DataFrame(range(150))
select = a.sample(frac = 0.8)
exerData = data[select]
exer = exerData.reshape(120,4)
exerName = iris.target[select]
name = exerName[:,0]
clf.fit(exer, name) # 训练决策树模型
c = list(select[0])
b = a.drop(a.index[c]) # b是a的补集
test = data[b[0]] #得到测试集
t = clf.predict(test) # 利用决策树模型，进行测试集的预测
### 下面把测试集预测值和真实值用图示的方法表示出来，借用上面的PCA代码。
y = iris.target[b[0]] #使用y表示原来的标签
X = data #使用 X表示数据集中的属性数据
pca = PCA(n_components=2)
#加载 PCA 算法，设置降维后主成分数目为 2
reduced_X = pca.fit_transform(X)
# 对原始数据进行降维，保存在 reduced_X 中
red_x, red_y = [], [] # 第1类数据点
blue_x, blue_y = [], [] # 第2类数据点
green_x, green_y = [], [] # 第3类数据点
points = pd.DataFrame(reduced_X)
testPoints = points.iloc[b[0]]
selectPoints = testPoints.values
### 第一个子图
plt.subplot(1, 2, 1)
plt.title("原图")
for i in range(len(b)):
if y[i] == 0:
red_x.append(selectPoints[i][0])
red_y.append(selectPoints[i][1])
elif y[i] == 1:
blue_x.append(selectPoints[i][0])
blue_y.append(selectPoints[i][1])
else:
green_x.append(selectPoints[i][0])
green_y.append(selectPoints[i][1])
plt.scatter(red_x, red_y, c='r', marker='x')
plt.scatter(blue_x, blue_y, c='b', marker='D')
plt.scatter(green_x, green_y, c='g', marker='.')
# 参考线
plt.axvline(x=0, c = 'k',ls = '--', lw = 1)
plt.axhline(y=0, c = 'k',ls = '--', lw = 1)
### 第二个子图
plt.subplot(1, 2, 2)
plt.title("预测图")
for i in range(len(b)):
if t[i] == 0:
red_x.append(selectPoints[i][0])
red_y.append(selectPoints[i][1])
elif t[i] == 1:
blue_x.append(selectPoints[i][0])
blue_y.append(selectPoints[i][1])
else:
green_x.append(selectPoints[i][0])
green_y.append(selectPoints[i][1])
### 因为要标注错误的预测点，所以需要逐个画点。
for i in range(len(b)):
if t[i] == 0:
if t[i] == y[i]:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'r', marker = 'x')
else:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'm', marker = '+')
elif t[i] == 1:
if t[i] == y[i]:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'b', marker = 'D')
else:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'y', marker = '1')
else:
if t[i] == y[i]:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'g', marker = '.')
else:
plt.scatter(selectPoints[i][0], selectPoints[i][1], c = 'k', marker = '*')
# 参考线
plt.axvline(x=0, c = 'k',ls = '--', lw = 1)
plt.axhline(y=0, c = 'k',ls = '--', lw = 1)
plt.show()