随机森林代码
1.描述
鸢尾花判断
2.代码
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 = {'Iris-setosa': 0, 'Iris-versicolor': 1, '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.Regression\\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] #取前两列
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) # 测试集选30%
#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([ #管道：使过程更加完成明显
('ss', StandardScaler()), #先进行标准化，保证均值0，方差1，提高效果
('DTC', DecisionTreeClassifier(criterion='entropy', max_depth=3))]) #最大深度
# clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)
model = model.fit(x_train, y_train) # 进行训练
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) #输出到图形可视化
# 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) #第0列取了100个值
t2 = np.linspace(x2_min, x2_max, M) # 第1列取了100个值
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点，t1t2拼到一起
x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点，把里面每个数据都写成一列，然后合起来
# # 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']) # 制作浅色的图例
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) # 制作深色的图例
y_show_hat = model.predict(x_show) # 预测值，现在仍然是一个向量
y_show_hat = y_show_hat.reshape(x1.shape) # 使之与输入的形状相同reshape
plt.figure(facecolor='w') # 设置底，w白色，k黑色，默认灰色
plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light) # 预测值的显示，背景块
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，设置圆圈的颜色，大小，形状
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) # X 轴的标注
plt.ylabel(iris_feature[1], fontsize=15)
plt.xlim(x1_min, x1_max) # X轴画图的范围
plt.ylim(x2_min, x2_max)
plt.grid(True) # 是否加网格
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) # 层数的范围，进行遍历
err_list = []
for d in depth:
clf = DecisionTreeClassifier(criterion='entropy', max_depth=d) # 决策树
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) # result均值就是准确率，用1减是错误率
err_list.append(err)
print d, ' 错误率: %.2f%%' % (100 * err)
plt.figure(facecolor='w')
plt.plot(depth, err_list, 'ro-', lw=2) # 做出层数与错误率的图
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()
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 = {'Iris-setosa': 0, 'Iris-versicolor': 1, '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)] # 做枚举，取不同的位置
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) #设置叶节点的样本数不能小于3个
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()
决策树做回归
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)，本来是[0,1)
x.sort()
y = np.sin(x) + np.random.randn(N) * 0.05 #加一点噪声
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) #决策树做回归，用均方误差作为准则而不是丄增益率
dt = reg.fit(x, y) #训练
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') # 实际值
plt.plot(x_test, y_hat, 'g-', linewidth=2, label='Predict') # 预测值
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] # 做遍历
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()
多输出案例，当结果之间存在相关性的时候可以考虑做多输出
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() #进行从小到大排序
y1 = np.sin(x) + 3 + np.random.randn(N) * 0.1 # 得到正弦和余弦，带有扰动
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)) #做垂直方向的堆叠
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) # 用均方误差做回归，三层
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()
随机森林时间
5.Iris_RandomForest_Enuum.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 #提取随机森林
def iris_type(s):
it = {'Iris-setosa': 0, 'Iris-versicolor': 1, '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') # 做一个窗口，背景全白
for i, pair in enumerate(feature_pairs): # 对特征对进行遍历，i表示第几个
# 准备数据
x = x_prime[:, pair]
# 随机森林
clf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=4) # 就用熵作为标准
rf_clf = clf.fit(x, y.ravel()) # 训练
# 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) # 对测试集进行预测
y = y.reshape(-1)
c = np.count_nonzero(y_hat == y) # 统计预测正确的个数，也可以直接数非0项
print '特征： ', iris_feature[pair[0]], ' + ', iris_feature[pair[1]], # 打印特征
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) # 设置图与边界之间的距离
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) # 设置总标题
plt.show()