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Decision_tree.py
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Decision_tree.py
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#该程序用于实现决策树对随机生成数据的分类
# -*- coding: utf-8 -*-
"""
Created on 05 May, 2019
@author jswanglp
requirements:
numpy==1.15.4
tensorflow==1.12.0
scipy==1.1.0
hmmlearn==0.2.3
matplotlib==2.0.2
graphviz==0.14
scikit_learn==0.23.1
"""
from sklearn import tree
import numpy as np
import graphviz
import matplotlib.pyplot as plt
from sklearn.externals import joblib
from mpl_toolkits.mplot3d import Axes3D
class Bunch(dict):
def __init__(self,*args,**kwds):
super(Bunch,self).__init__(*args,**kwds)
self.__dict__ = self
# 生成带标签的随机数据
def generate_random(sigma, N, mu1=[15., 25., 10], mu2=[30., 40., 30], mu3=[25., 10., 20], mu4=[40., 30., 40]):
c = sigma.shape[-1] #生成N行c维的随机测试数据，比较kmeans与decision tree
X = np.zeros((N, c)) # 初始化X，2行N列。2维数据，N个样本
target = np.zeros((N,1))
for i in range(N):
if np.random.random(1) < 0.25: # 生成0-1之间随机数
X[i, :] = np.random.multivariate_normal(mu1, sigma[0, :, :], 1) #用第一个高斯模型生成2维数据
target[i] = 1
elif 0.25 <= np.random.random(1) < 0.5:
X[i, :] = np.random.multivariate_normal(mu2, sigma[1, :, :], 1) #用第二个高斯模型生成2维数据
target[i] = 2
elif 0.5 <= np.random.random(1) < 0.75:
X[i, :] = np.random.multivariate_normal(mu3, sigma[2, :, :], 1) #用第三个高斯模型生成2维数据
target[i] = 3
else:
X[i, :] = np.random.multivariate_normal(mu4, sigma[3, :, :], 1) #用第四个高斯模型生成2维数据
target[i] = 4
return X, target
if __name__ == '__main__':
k, N = 4, 400
# 初始化方差，生成样本与标签
sigma = np.zeros((k, 3, 3))
for i in range(k):
sigma[i, :, :] = np.diag(np.random.randint(10, 25, size=(3, )))
sample, target = generate_random(sigma, N)
feature_names = ['x_label', 'y_label', 'z_label'] # 特征数
target_names = ['gaussian1', 'gaussian2', 'gaussian3', 'gaussian4'] # 类别
data = Bunch(sample=sample, feature_names=feature_names, target=target, target_names=target_names)
# 训练及保存模型
# sklearn.tree.DecisionTreeClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=None) # 决策树分类器
clf = clf.fit(data.sample, data.target)
# joblib.dump(clf, 'GoogleDrive/My Drive/Models/model_dt.pkl') # 存储生成的模型
# model = joblib.load('GoogleDrive/My Drive/Models/model_dt.pkl')
dot_data = tree.export_graphviz(clf,out_file=None, # 定义dot_data的属性
feature_names=data.feature_names,
class_names=data.target_names,
filled=True, rounded=True,
special_characters=True)
graph = graphviz.Source(dot_data)
graph.render("data") # 存储dot数据与pdf文件
f = open('data') # 读取已存储的dot数据并用graph显示
txt = f.read()
graph = graphviz.Source(txt)
graph
#graph#生成决策树，将数据的每个特征划分为2^r-2种（假设每个特征数的样本为r=N），计算他们各自的gini值
#选取gini值最小的分类方式（决策树中各x_label的值由分成的两类中低类的最大值与高类的最小值的均值决定）
#然后计算每个特征的分类方式，通过计算不纯度的降低来判定用哪一个特征作为节点
# 计算每类的中心
data_t, _ = generate_random(sigma, N)
tar_pre = clf.predict(data_t) - 1
mu_data_t = np.zeros((4, 3))
for j in range(4):
num = 0
for m, i in enumerate(tar_pre):
if i == j:
mu_data_t[j, :] += data_t[m, :]
num += 1.
mu_data_t[j, :] = mu_data_t[j, :] / num
# 显示训练，测试数据的分布
titles = ['Random training data', 'Random testing data']
DATA = [data.sample, data_t]
fig = plt.figure(1, figsize=(16, 8))
fig.subplots_adjust(wspace=.01, hspace=.02)
for i, title, data_n in zip([1, 2], titles, DATA):
ax = fig.add_subplot(1, 2, i, projection='3d')
ax.scatter(data_n[:,0], data_n[:,1], data_n[:,2], c='b', s=35, alpha=0.4, marker='o')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(elev=20., azim=-25)
ax.set_title(title, fontsize=14)
# 显示决策树对训练，测试数据的分类情况
titles = ['Classified training data by DT', 'Classified testing data by DT']
TAR = [np.array(clf.predict(data.sample) - 1, dtype=np.uint8),
np.array(clf.predict(data_t) - 1, dtype=np.uint8)]
fig = plt.figure(2, figsize=(16, 8))
fig.subplots_adjust(wspace=.01, hspace=.02)
for i, title, data_n, tar in zip([1, 2], titles, DATA, TAR):
ax = fig.add_subplot(1, 2, i, projection='3d')
color=['b','r','g','y']
for j in range(N):
ax.scatter(data_n[j, 0], data_n[j, 1], data_n[j, 2], c=color[tar[j]], s=35, alpha=0.4, marker='P')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(elev=20., azim=-25)
ax.set_title(title, fontsize=14)
plt.show()