决策树实践
程序员文章站
2024-02-16 13:25:10
...
数据集：
8.iris.data
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
10.1.Iris_DecisionTree.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
def iris_type(s):
it = {b'Iris-setosa': 0,
b'Iris-versicolor': 1,
b'Iris-virginica': 2}
return it[s]
# 花萼长度、花萼宽度，花瓣长度，花瓣宽度
# iris_feature = 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
if __name__ == "__main__":
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
path = '8.iris.data' # 数据文件路径
data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type})
x, y = np.split(data, (4,), axis=1)
# 为了可视化，仅使用前两列特征
x = x[:, :2]
print(x)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
#ss = StandardScaler()
#ss = ss.fit(x_train)
# 决策树参数估计
# min_samples_split = 10：如果该结点包含的样本数目大于10，则(有可能)对其分支
# min_samples_leaf = 10：若将某结点分支后，得到的每个子结点样本数目都大于10，则完成分支；否则，不进行分支
model = Pipeline([
('ss', StandardScaler()),
('DTC', DecisionTreeClassifier(criterion='entropy', max_depth=3))])
# clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)
model = model.fit(x_train, y_train)
y_test_hat = model.predict(x_test) # 测试数据
# 保存
# dot -Tpng -o 1.png 1.dot
f = open('.\\iris_tree.dot', 'w')
tree.export_graphviz(model.get_params('DTC')['DTC'], out_file=f)
# 画图
N, M = 100, 100 # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成 v 网格采样点
x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点
# # 无意义，只是为了凑另外两个维度
# # 打开该注释前，确保注释掉x = x[:, :2]
# x3 = np.ones(x1.size) * np.average(x[:, 2])
# x4 = np.ones(x1.size) * np.average(x[:, 3])
# x_test = np.stack((x1.flat, x2.flat, x3, x4), axis=1) # 测试点
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_show_hat = model.predict(x_show) # 预测值
y_show_hat = y_show_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.figure(facecolor='w')
plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light) # 预测值的显示
plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test.ravel(), edgecolors='k', s=100, cmap=cm_dark, marker='o') # 测试数据
plt.scatter(x[:, 0], x[:, 1], c=y.ravel(), edgecolors='k', s=40, cmap=cm_dark) # 全部数据
plt.xlabel(iris_feature[0], fontsize=15)
plt.ylabel(iris_feature[1], fontsize=15)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid(True)
plt.title(u'鸢尾花数据的决策树分类', fontsize=17)
plt.show()
# 训练集上的预测结果
y_test = y_test.reshape(-1)
print(y_test_hat)
print(y_test)
result = (y_test_hat == y_test) # True则预测正确，False则预测错误
acc = np.mean(result)
print('准确度: %.2f%%' % (100 * acc))
# 过拟合：错误率
depth = np.arange(1, 15)
err_list = []
for d in depth:
clf = DecisionTreeClassifier(criterion='entropy', max_depth=d)
clf = clf.fit(x_train, y_train)
y_test_hat = clf.predict(x_test) # 测试数据
result = (y_test_hat == y_test) # True则预测正确，False则预测错误
err = 1 - np.mean(result)
err_list.append(err)
print(d, ' 准确度: %.2f%%' % (100 * err))
plt.figure(facecolor='w')
plt.plot(depth, err_list, 'ro-', lw=2)
plt.xlabel(u'决策树深度', fontsize=15)
plt.ylabel(u'错误率', fontsize=15)
plt.title(u'决策树深度与过拟合', fontsize=17)
plt.grid(True)
plt.show()
结果：
E:\pythonwork\venv\Scripts\python.exe E:/pythonspace/10.RandomForest/10.1.Iris_DecisionTree.py
[[5.1 3.5]
[4.9 3. ]
[4.7 3.2]
[4.6 3.1]
[5. 3.6]
[5.4 3.9]
[4.6 3.4]
[5. 3.4]
[4.4 2.9]
[4.9 3.1]
[5.4 3.7]
[4.8 3.4]
[4.8 3. ]
[4.3 3. ]
[5.8 4. ]
[5.7 4.4]
[5.4 3.9]
[5.1 3.5]
[5.7 3.8]
[5.1 3.8]
[5.4 3.4]
[5.1 3.7]
[4.6 3.6]
[5.1 3.3]
[4.8 3.4]
[5. 3. ]
[5. 3.4]
[5.2 3.5]
[5.2 3.4]
[4.7 3.2]
[4.8 3.1]
[5.4 3.4]
[5.2 4.1]
[5.5 4.2]
[4.9 3.1]
[5. 3.2]
[5.5 3.5]
[4.9 3.1]
[4.4 3. ]
[5.1 3.4]
[5. 3.5]
[4.5 2.3]
[4.4 3.2]
[5. 3.5]
[5.1 3.8]
[4.8 3. ]
[5.1 3.8]
[4.6 3.2]
[5.3 3.7]
[5. 3.3]
[7. 3.2]
[6.4 3.2]
[6.9 3.1]
[5.5 2.3]
[6.5 2.8]
[5.7 2.8]
[6.3 3.3]
[4.9 2.4]
[6.6 2.9]
[5.2 2.7]
[5. 2. ]
[5.9 3. ]
[6. 2.2]
[6.1 2.9]
[5.6 2.9]
[6.7 3.1]
[5.6 3. ]
[5.8 2.7]
[6.2 2.2]
[5.6 2.5]
[5.9 3.2]
[6.1 2.8]
[6.3 2.5]
[6.1 2.8]
[6.4 2.9]
[6.6 3. ]
[6.8 2.8]
[6.7 3. ]
[6. 2.9]
[5.7 2.6]
[5.5 2.4]
[5.5 2.4]
[5.8 2.7]
[6. 2.7]
[5.4 3. ]
[6. 3.4]
[6.7 3.1]
[6.3 2.3]
[5.6 3. ]
[5.5 2.5]
[5.5 2.6]
[6.1 3. ]
[5.8 2.6]
[5. 2.3]
[5.6 2.7]
[5.7 3. ]
[5.7 2.9]
[6.2 2.9]
[5.1 2.5]
[5.7 2.8]
[6.3 3.3]
[5.8 2.7]
[7.1 3. ]
[6.3 2.9]
[6.5 3. ]
[7.6 3. ]
[4.9 2.5]
[7.3 2.9]
[6.7 2.5]
[7.2 3.6]
[6.5 3.2]
[6.4 2.7]
[6.8 3. ]
[5.7 2.5]
[5.8 2.8]
[6.4 3.2]
[6.5 3. ]
[7.7 3.8]
[7.7 2.6]
[6. 2.2]
[6.9 3.2]
[5.6 2.8]
[7.7 2.8]
[6.3 2.7]
[6.7 3.3]
[7.2 3.2]
[6.2 2.8]
[6.1 3. ]
[6.4 2.8]
[7.2 3. ]
[7.4 2.8]
[7.9 3.8]
[6.4 2.8]
[6.3 2.8]
[6.1 2.6]
[7.7 3. ]
[6.3 3.4]
[6.4 3.1]
[6. 3. ]
[6.9 3.1]
[6.7 3.1]
[6.9 3.1]
[5.8 2.7]
[6.8 3.2]
[6.7 3.3]
[6.7 3. ]
[6.3 2.5]
[6.5 3. ]
[6.2 3.4]
[5.9 3. ]]
[0. 1. 2. 0. 2. 2. 2. 0. 0. 2. 1. 0. 2. 2. 1. 0. 1. 1. 0. 0. 1. 0. 2. 0.
2. 1. 0. 0. 1. 2. 1. 2. 1. 2. 1. 0. 1. 0. 2. 2. 2. 0. 1. 2. 2.]
[0. 1. 1. 0. 2. 1. 2. 0. 0. 2. 1. 0. 2. 1. 1. 0. 1. 1. 0. 0. 1. 1. 1. 0.
2. 1. 0. 0. 1. 2. 1. 2. 1. 2. 2. 0. 1. 0. 1. 2. 2. 0. 2. 2. 1.]
准确度: 80.00%
1 准确度: 44.44%
2 准确度: 40.00%
3 准确度: 20.00%
4 准确度: 24.44%
5 准确度: 24.44%
6 准确度: 26.67%
7 准确度: 35.56%
8 准确度: 40.00%
9 准确度: 35.56%
10 准确度: 40.00%
11 准确度: 37.78%
12 准确度: 37.78%
13 准确度: 40.00%
14 准确度: 37.78%
Process finished with exit code 0
10.2.Iris_DecisionTree_Enum.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.tree import DecisionTreeClassifier
def iris_type(s):
it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
return it[s]
# 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
if __name__ == "__main__":
mpl.rcParams['font.sans-serif'] = [u'SimHei'] # 黑体 FangSong/KaiTi
mpl.rcParams['axes.unicode_minus'] = False
path = '8.iris.data' # 数据文件路径
data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type})
x_prime, y = np.split(data, (4,), axis=1)
feature_pairs = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]
plt.figure(figsize=(10, 9), facecolor='#FFFFFF')
for i, pair in enumerate(feature_pairs):
# 准备数据
x = x_prime[:, pair]
# 决策树学习
clf = DecisionTreeClassifier(criterion='entropy', min_samples_leaf=3)
dt_clf = clf.fit(x, y)
# 画图
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点
# 训练集上的预测结果
y_hat = dt_clf.predict(x)
y = y.reshape(-1)
c = np.count_nonzero(y_hat == y) # 统计预测正确的个数
print('特征： ', iris_feature[pair[0]], ' + ', iris_feature[pair[1]],)
print('\t预测正确数目：', c,)
print('\t准确率: %.2f%%' % (100 * float(c) / float(len(y))))
# 显示
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = dt_clf.predict(x_test) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.subplot(2, 3, i+1)
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值
plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', cmap=cm_dark) # 样本
plt.xlabel(iris_feature[pair[0]], fontsize=14)
plt.ylabel(iris_feature[pair[1]], fontsize=14)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.suptitle(u'决策树对鸢尾花数据的两特征组合的分类结果', fontsize=18)
plt.tight_layout(2)
plt.subplots_adjust(top=0.92)
plt.show()
结果：
E:\pythonwork\venv\Scripts\python.exe E:/pythonspace/10.RandomForest/10.2.Iris_DecisionTree_Enum.py
特征： 花萼长度 + 花萼宽度
预测正确数目： 123
准确率: 82.00%
特征： 花萼长度 + 花瓣长度
预测正确数目： 145
准确率: 96.67%
特征： 花萼长度 + 花瓣宽度
预测正确数目： 144
准确率: 96.00%
特征： 花萼宽度 + 花瓣长度
预测正确数目： 143
准确率: 95.33%
特征： 花萼宽度 + 花瓣宽度
预测正确数目： 145
准确率: 96.67%
特征： 花瓣长度 + 花瓣宽度
预测正确数目： 147
准确率: 98.00%
Process finished with exit code 0
10.3.DecisionTreeRegressor.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor
if __name__ == "__main__":
N = 100
x = np.random.rand(N) * 6 - 3 # [-3,3)
x.sort()
y = np.sin(x) + np.random.randn(N) * 0.05
print(y)
x = x.reshape(-1, 1) # 转置后，得到N个样本，每个样本都是1维的
print(x)
reg = DecisionTreeRegressor(criterion='mse', max_depth=9)
dt = reg.fit(x, y)
x_test = np.linspace(-3, 3, 50).reshape(-1, 1)
y_hat = dt.predict(x_test)
plt.plot(x, y, 'r*', linewidth=2, label='Actual')
plt.plot(x_test, y_hat, 'g-', linewidth=2, label='Predict')
plt.legend(loc='upper left')
plt.grid()
plt.show()
# 比较决策树的深度影响
depth = [2, 4, 6, 8, 10]
clr = 'rgbmy'
reg = [DecisionTreeRegressor(criterion='mse', max_depth=depth[0]),
DecisionTreeRegressor(criterion='mse', max_depth=depth[1]),
DecisionTreeRegressor(criterion='mse', max_depth=depth[2]),
DecisionTreeRegressor(criterion='mse', max_depth=depth[3]),
DecisionTreeRegressor(criterion='mse', max_depth=depth[4])]
plt.plot(x, y, 'k^', linewidth=2, label='Actual')
x_test = np.linspace(-3, 3, 50).reshape(-1, 1)
for i, r in enumerate(reg):
dt = r.fit(x, y)
y_hat = dt.predict(x_test)
plt.plot(x_test, y_hat, '-', color=clr[i], linewidth=2, label='Depth=%d' % depth[i])
plt.legend(loc='upper left')
plt.grid()
plt.show()
结果：
E:\pythonwork\venv\Scripts\python.exe E:/pythonspace/10.RandomForest/10.3.DecisionTreeRegressor.py
[-0.08762283 -0.16771537 -0.23860516 -0.38278713 -0.40841552 -0.48093607
-0.56326155 -0.54950973 -0.66609012 -0.71334252 -0.64798177 -0.65805192
-0.74970801 -0.70198992 -0.78982299 -0.84627162 -0.90004589 -0.75272558
-0.83448298 -0.83091698 -0.93088584 -0.88510208 -0.90669696 -0.92614916
-0.9927603 -0.92036213 -0.97683139 -1.02773104 -0.93678639 -1.01801533
-1.01569934 -0.98660534 -0.98409135 -1.09264675 -1.00724295 -1.05969848
-1.03651372 -0.94403054 -0.87392559 -0.90135942 -0.95865273 -0.8578595
-0.90945654 -0.92196075 -0.81153918 -0.93168369 -0.77832897 -0.76346297
-0.68488173 -0.61783284 -0.64327791 -0.58193051 -0.35848254 -0.32665048
-0.32879428 -0.26418869 -0.18945475 -0.12244273 -0.03867582 0.11229909
0.24854313 0.59109719 0.75895249 0.708417 0.83726958 0.78888503
0.90070104 0.98627956 0.86199038 0.94321235 0.95233268 0.96197201
0.9828884 0.89584501 0.92839125 0.94633715 0.95625243 1.07204747
0.96827918 0.8980396 0.89179345 0.78041181 0.7454409 0.76607445
0.59841426 0.70471736 0.67932723 0.63846437 0.76913785 0.658829
0.60404674 0.62633004 0.53566953 0.64420922 0.56715467 0.4657827
0.39855462 0.36194898 0.40706424 0.15147884]
[[-2.97990265]
[-2.97337443]
[-2.95321606]
[-2.77951944]
[-2.75069817]
[-2.64802897]
[-2.51622648]
[-2.47450801]
[-2.40686662]
[-2.38829177]
[-2.35118118]
[-2.32818686]
[-2.32724123]
[-2.27010567]
[-2.26636734]
[-2.23939071]
[-2.2191937 ]
[-2.19141504]
[-2.18275017]
[-2.08399601]
[-2.07001052]
[-2.05862414]
[-2.00281178]
[-1.99638772]
[-1.97154131]
[-1.89581255]
[-1.89194748]
[-1.7566021 ]
[-1.735978 ]
[-1.71384172]
[-1.59511853]
[-1.5843659 ]
[-1.56736464]
[-1.53189255]
[-1.50034428]
[-1.47751661]
[-1.46088067]
[-1.46071084]
[-1.41078229]
[-1.3144557 ]
[-1.28915245]
[-1.13610951]
[-1.11582631]
[-1.02311451]
[-0.99076348]
[-0.98395691]
[-0.92780808]
[-0.83087654]
[-0.77773532]
[-0.67825078]
[-0.66767348]
[-0.60995641]
[-0.43246033]
[-0.24100792]
[-0.23800074]
[-0.2252397 ]
[-0.2160786 ]
[-0.11953865]
[-0.08764956]
[ 0.07279476]
[ 0.25360206]
[ 0.62929412]
[ 0.8148173 ]
[ 0.87717764]
[ 0.94656319]
[ 0.95059124]
[ 1.14393197]
[ 1.24160707]
[ 1.26547816]
[ 1.36455416]
[ 1.54873863]
[ 1.5678806 ]
[ 1.57543481]
[ 1.57789541]
[ 1.65970316]
[ 1.74724827]
[ 1.78461289]
[ 1.86563055]
[ 1.87887338]
[ 1.95357623]
[ 1.97593249]
[ 2.16488433]
[ 2.30660908]
[ 2.31776991]
[ 2.3268286 ]
[ 2.34399191]
[ 2.37361178]
[ 2.40385108]
[ 2.41127997]
[ 2.42744508]
[ 2.45560025]
[ 2.46993779]
[ 2.50433988]
[ 2.52071851]
[ 2.60606967]
[ 2.66188751]
[ 2.66838645]
[ 2.76367578]
[ 2.77732631]
[ 2.95712184]]
Process finished with exit code 0
10.4.MultiOutput_DTR.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor
if __name__ == "__main__":
N = 300
x = np.random.rand(N) * 8 - 4 # [-4,4)
x.sort()
y1 = np.sin(x) + 3 + np.random.randn(N) * 0.1
y2 = np.cos(0.3*x) + np.random.randn(N) * 0.01
# y1 = np.sin(x) + np.random.randn(N) * 0.05
# y2 = np.cos(x) + np.random.randn(N) * 0.1
y = np.vstack((y1, y2))
y = np.vstack((y1, y2)).T
x = x.reshape(-1, 1) # 转置后，得到N个样本，每个样本都是1维的
deep = 3
reg = DecisionTreeRegressor(criterion='mse', max_depth=deep)
dt = reg.fit(x, y)
x_test = np.linspace(-4, 4, num=1000).reshape(-1, 1)
print(x_test)
y_hat = dt.predict(x_test)
print(y_hat)
plt.scatter(y[:, 0], y[:, 1], c='r', s=40, label='Actual')
plt.scatter(y_hat[:, 0], y_hat[:, 1], c='g', marker='s', s=100, label='Depth=%d' % deep, alpha=1)
plt.legend(loc='upper left')
plt.xlabel('y1')
plt.ylabel('y2')
plt.grid()
plt.show()
结果：
E:\pythonwork\venv\Scripts\python.exe E:/pythonspace/10.RandomForest/10.4.MultiOutput_DTR.py
[[-4. ]
[-3.99199199]
[-3.98398398]
[-3.97597598]
[-3.96796797]
[-3.95995996]
[-3.95195195]
[-3.94394394]
[-3.93593594]
[-3.92792793]
[-3.91991992]
[-3.91191191]
[-3.9039039 ]
[-3.8958959 ]
[-3.88788789]
[-3.87987988]
[-3.87187187]
[-3.86386386]
[-3.85585586]
[-3.84784785]
[-3.83983984]
[-3.83183183]
[-3.82382382]
[-3.81581582]
[-3.80780781]
[-3.7997998 ]
[-3.79179179]
[-3.78378378]
[-3.77577578]
[-3.76776777]
[-3.75975976]
[-3.75175175]
[-3.74374374]
[-3.73573574]
[-3.72772773]
[-3.71971972]
[-3.71171171]
[-3.7037037 ]
[-3.6956957 ]
[-3.68768769]
[-3.67967968]
[-3.67167167]
[-3.66366366]
[-3.65565566]
[-3.64764765]
[-3.63963964]
[-3.63163163]
[-3.62362362]
[-3.61561562]
[-3.60760761]
[-3.5995996 ]
[-3.59159159]
[-3.58358358]
[-3.57557558]
[-3.56756757]
[-3.55955956]
[-3.55155155]
[-3.54354354]
[-3.53553554]
[-3.52752753]
[-3.51951952]
[-3.51151151]
[-3.5035035 ]
[-3.4954955 ]
[-3.48748749]
[-3.47947948]
[-3.47147147]
[-3.46346346]
[-3.45545546]
[-3.44744745]
[-3.43943944]
[-3.43143143]
[-3.42342342]
[-3.41541542]
[-3.40740741]
[-3.3993994 ]
[-3.39139139]
[-3.38338338]
[-3.37537538]
[-3.36736737]
[-3.35935936]
[-3.35135135]
[-3.34334334]
[-3.33533534]
[-3.32732733]
[-3.31931932]
[-3.31131131]
[-3.3033033 ]
[-3.2952953 ]
[-3.28728729]
[-3.27927928]
[-3.27127127]
[-3.26326326]
[-3.25525526]
[-3.24724725]
[-3.23923924]
[-3.23123123]
[-3.22322322]
[-3.21521522]
[-3.20720721]
[-3.1991992 ]
[-3.19119119]
[-3.18318318]
[-3.17517518]
[-3.16716717]
[-3.15915916]
[-3.15115115]
[-3.14314314]
[-3.13513514]
[-3.12712713]
[-3.11911912]
[-3.11111111]
[-3.1031031 ]
[-3.0950951 ]
[-3.08708709]
[-3.07907908]
[-3.07107107]
[-3.06306306]
[-3.05505506]
[-3.04704705]
[-3.03903904]
[-3.03103103]
[-3.02302302]
[-3.01501502]
[-3.00700701]
[-2.998999 ]
[-2.99099099]
[-2.98298298]
[-2.97497497]
[-2.96696697]
[-2.95895896]
[-2.95095095]
[-2.94294294]
[-2.93493493]
[-2.92692693]
[-2.91891892]
[-2.91091091]
[-2.9029029 ]
[-2.89489489]
[-2.88688689]
[-2.87887888]
[-2.87087087]
[-2.86286286]
[-2.85485485]
[-2.84684685]
[-2.83883884]
[-2.83083083]
[-2.82282282]
[-2.81481481]
[-2.80680681]
[-2.7987988 ]
[-2.79079079]
[-2.78278278]
[-2.77477477]
[-2.76676677]
[-2.75875876]
[-2.75075075]
[-2.74274274]
[-2.73473473]
[-2.72672673]
[-2.71871872]
[-2.71071071]
[-2.7027027 ]
[-2.69469469]
[-2.68668669]
[-2.67867868]
[-2.67067067]
[-2.66266266]
[-2.65465465]
[-2.64664665]
[-2.63863864]
[-2.63063063]
[-2.62262262]
[-2.61461461]
[-2.60660661]
[-2.5985986 ]
[-2.59059059]
[-2.58258258]
[-2.57457457]
[-2.56656657]
[-2.55855856]
[-2.55055055]
[-2.54254254]
[-2.53453453]
[-2.52652653]
[-2.51851852]
[-2.51051051]
[-2.5025025 ]
[-2.49449449]
[-2.48648649]
[-2.47847848]
[-2.47047047]
[-2.46246246]
[-2.45445445]
[-2.44644645]
[-2.43843844]
[-2.43043043]
[-2.42242242]
[-2.41441441]
[-2.40640641]
[-2.3983984 ]
[-2.39039039]
[-2.38238238]
[-2.37437437]
[-2.36636637]
[-2.35835836]
[-2.35035035]
[-2.34234234]
[-2.33433433]
[-2.32632633]
[-2.31831832]
[-2.31031031]
[-2.3023023 ]
[-2.29429429]
[-2.28628629]
[-2.27827828]
[-2.27027027]
[-2.26226226]
[-2.25425425]
[-2.24624625]
[-2.23823824]
[-2.23023023]
[-2.22222222]
[-2.21421421]
[-2.20620621]
[-2.1981982 ]
[-2.19019019]
[-2.18218218]
[-2.17417417]
[-2.16616617]
[-2.15815816]
[-2.15015015]
[-2.14214214]
[-2.13413413]
[-2.12612613]
[-2.11811812]
[-2.11011011]
[-2.1021021 ]
[-2.09409409]
[-2.08608609]
[-2.07807808]
[-2.07007007]
[-2.06206206]
[-2.05405405]
[-2.04604605]
[-2.03803804]
[-2.03003003]
[-2.02202202]
[-2.01401401]
[-2.00600601]
[-1.997998 ]
[-1.98998999]
[-1.98198198]
[-1.97397397]
[-1.96596597]
[-1.95795796]
[-1.94994995]
[-1.94194194]
[-1.93393393]
[-1.92592593]
[-1.91791792]
[-1.90990991]
[-1.9019019 ]
[-1.89389389]
[-1.88588589]
[-1.87787788]
[-1.86986987]
[-1.86186186]
[-1.85385385]
[-1.84584585]
[-1.83783784]
[-1.82982983]
[-1.82182182]
[-1.81381381]
[-1.80580581]
[-1.7977978 ]
[-1.78978979]
[-1.78178178]
[-1.77377377]
[-1.76576577]
[-1.75775776]
[-1.74974975]
[-1.74174174]
[-1.73373373]
[-1.72572573]
[-1.71771772]
[-1.70970971]
[-1.7017017 ]
[-1.69369369]
[-1.68568569]
[-1.67767768]
[-1.66966967]
[-1.66166166]
[-1.65365365]
[-1.64564565]
[-1.63763764]
[-1.62962963]
[-1.62162162]
[-1.61361361]
[-1.60560561]
[-1.5975976 ]
[-1.58958959]
[-1.58158158]
[-1.57357357]
[-1.56556557]
[-1.55755756]
[-1.54954955]
[-1.54154154]
[-1.53353353]
[-1.52552553]
[-1.51751752]
[-1.50950951]
[-1.5015015 ]
[-1.49349349]
[-1.48548549]
[-1.47747748]
[-1.46946947]
[-1.46146146]
[-1.45345345]
[-1.44544545]
[-1.43743744]
[-1.42942943]
[-1.42142142]
[-1.41341341]
[-1.40540541]
[-1.3973974 ]
[-1.38938939]
[-1.38138138]
[-1.37337337]
[-1.36536537]
[-1.35735736]
[-1.34934935]
[-1.34134134]
[-1.33333333]
[-1.32532533]
[-1.31731732]
[-1.30930931]
[-1.3013013 ]
[-1.29329329]
[-1.28528529]
[-1.27727728]
[-1.26926927]
[-1.26126126]
[-1.25325325]
[-1.24524525]
[-1.23723724]
[-1.22922923]
[-1.22122122]
[-1.21321321]
[-1.20520521]
[-1.1971972 ]
[-1.18918919]
[-1.18118118]
[-1.17317317]
[-1.16516517]
[-1.15715716]
[-1.14914915]
[-1.14114114]
[-1.13313313]
[-1.12512513]
[-1.11711712]
[-1.10910911]
[-1.1011011 ]
[-1.09309309]
[-1.08508509]
[-1.07707708]
[-1.06906907]
[-1.06106106]
[-1.05305305]
[-1.04504505]
[-1.03703704]
[-1.02902903]
[-1.02102102]
[-1.01301301]
[-1.00500501]
[-0.996997 ]
[-0.98898899]
[-0.98098098]
[-0.97297297]
[-0.96496496]
[-0.95695696]
[-0.94894895]
[-0.94094094]
[-0.93293293]
[-0.92492492]
[-0.91691692]
[-0.90890891]
[-0.9009009 ]
[-0.89289289]
[-0.88488488]
[-0.87687688]
[-0.86886887]
[-0.86086086]
[-0.85285285]
[-0.84484484]
[-0.83683684]
[-0.82882883]
[-0.82082082]
[-0.81281281]
[-0.8048048 ]
[-0.7967968 ]
[-0.78878879]
[-0.78078078]
[-0.77277277]
[-0.76476476]
[-0.75675676]
[-0.74874875]
[-0.74074074]
[-0.73273273]
[-0.72472472]
[-0.71671672]
[-0.70870871]
[-0.7007007 ]
[-0.69269269]
[-0.68468468]
[-0.67667668]
[-0.66866867]
[-0.66066066]
[-0.65265265]
[-0.64464464]
[-0.63663664]
[-0.62862863]
[-0.62062062]
[-0.61261261]
[-0.6046046 ]
[-0.5965966 ]
[-0.58858859]
[-0.58058058]
[-0.57257257]
[-0.56456456]
[-0.55655656]
[-0.54854855]
[-0.54054054]
[-0.53253253]
[-0.52452452]
[-0.51651652]
[-0.50850851]
[-0.5005005 ]
[-0.49249249]
[-0.48448448]
[-0.47647648]
[-0.46846847]
[-0.46046046]
[-0.45245245]
[-0.44444444]
[-0.43643644]
[-0.42842843]
[-0.42042042]
[-0.41241241]
[-0.4044044 ]
[-0.3963964 ]
[-0.38838839]
[-0.38038038]
[-0.37237237]
[-0.36436436]
[-0.35635636]
[-0.34834835]
[-0.34034034]
[-0.33233233]
[-0.32432432]
[-0.31631632]
[-0.30830831]
[-0.3003003 ]
[-0.29229229]
[-0.28428428]
[-0.27627628]
[-0.26826827]
[-0.26026026]
[-0.25225225]
[-0.24424424]
[-0.23623624]
[-0.22822823]
[-0.22022022]
[-0.21221221]
[-0.2042042 ]
[-0.1961962 ]
[-0.18818819]
[-0.18018018]
[-0.17217217]
[-0.16416416]
[-0.15615616]
[-0.14814815]
[-0.14014014]
[-0.13213213]
[-0.12412412]
[-0.11611612]
[-0.10810811]
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[ 4. ]]
[[3.60980674 0.41652999]
[3.60980674 0.41652999]
[3.60980674 0.41652999]
...
[2.4296138 0.43140993]
[2.4296138 0.43140993]
[2.4296138 0.43140993]]
Process finished with exit code 0
10.5.Iris_RandomForest_Enum.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.ensemble import RandomForestClassifier
def iris_type(s):
it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
return it[s]
# 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
if __name__ == "__main__":
mpl.rcParams['font.sans-serif'] = [u'SimHei'] # 黑体 FangSong/KaiTi
mpl.rcParams['axes.unicode_minus'] = False
path = '..\\8.Regression\\8.iris.data' # 数据文件路径
data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type})
x_prime, y = np.split(data, (4,), axis=1)
feature_pairs = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]
plt.figure(figsize=(10, 9), facecolor='#FFFFFF')
for i, pair in enumerate(feature_pairs):
# 准备数据
x = x_prime[:, pair]
# 随机森林
clf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=4)
rf_clf = clf.fit(x, y.ravel())
# 画图
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点
# 训练集上的预测结果
y_hat = rf_clf.predict(x)
y = y.reshape(-1)
c = np.count_nonzero(y_hat == y) # 统计预测正确的个数
print('特征： ', iris_feature[pair[0]], ' + ', iris_feature[pair[1]],)
print('\t预测正确数目：', c,)
print('\t准确率: %.2f%%' % (100 * float(c) / float(len(y))))
# 显示
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = rf_clf.predict(x_test) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.subplot(2, 3, i+1)
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值
plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', cmap=cm_dark) # 样本
plt.xlabel(iris_feature[pair[0]], fontsize=14)
plt.ylabel(iris_feature[pair[1]], fontsize=14)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.tight_layout(2.5)
plt.subplots_adjust(top=0.92)
plt.suptitle(u'随机森林对鸢尾花数据的两特征组合的分类结果', fontsize=18)
plt.show()
结果：
E:\pythonwork\venv\Scripts\python.exe E:/pythonspace/10.RandomForest/10.5.Iris_RandomForest_Enum.py
E:\pythonwork\venv\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
from numpy.core.umath_tests import inner1d
特征： 花萼长度 + 花萼宽度
预测正确数目： 125
准确率: 83.33%
特征： 花萼长度 + 花瓣长度
预测正确数目： 145
准确率: 96.67%
特征： 花萼长度 + 花瓣宽度
预测正确数目： 146
准确率: 97.33%
特征： 花萼宽度 + 花瓣长度
预测正确数目： 144
准确率: 96.00%
特征： 花萼宽度 + 花瓣宽度
预测正确数目： 145
准确率: 96.67%
特征： 花瓣长度 + 花瓣宽度
预测正确数目： 146
准确率: 97.33%
Process finished with exit code 0
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