demo3-1.py

import numpy as np
import matplotlib

matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import zero_one_loss
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier

# 设置 AdaBoost 迭代次数
n_estimators = 200
# 使用
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
# 从 12000 个数据中取前 2000 行作为测试集，其余作为训练集
test_x, test_y = X[2000:], y[2000:]
train_x, train_y = X[:2000], y[:2000]
# 弱分类器
dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
dt_stump.fit(train_x, train_y)
dt_stump_err = 1.0 - dt_stump.score(test_x, test_y)
# 决策树分类器
dt = DecisionTreeClassifier()
dt.fit(train_x, train_y)
dt_err = 1.0 - dt.score(test_x, test_y)
# AdaBoost 分类器
ada = AdaBoostClassifier(base_estimator=dt_stump, n_estimators=n_estimators)
ada.fit(train_x, train_y)
# 三个分类器的错误率可视化
fig = plt.figure()
# 设置 plt 正确显示中文
plt.rcParams['font.sans-serif'] = ['SimHei']
ax = fig.add_subplot(111)
ax.plot([1, n_estimators], [dt_stump_err] * 2, 'k-', label=u'决策树弱分类器 错误率')
ax.plot([1, n_estimators], [dt_err] * 2, 'k--', label=u'决策树模型 错误率')
ada_err = np.zeros((n_estimators,))
# 遍历每次迭代的结果 i 为迭代次数, pred_y 为预测结果
for i, pred_y in enumerate(ada.staged_predict(test_x)):
  # 统计错误率
  ada_err[i] = zero_one_loss(pred_y, test_y)
# 绘制每次迭代的 AdaBoost 错误率
ax.plot(np.arange(n_estimators) + 1, ada_err, label='AdaBoost Test 错误率', color='orange')
ax.set_xlabel('迭代次数')
ax.set_ylabel('错误率')
leg = ax.legend(loc='upper right', fancybox=True)
plt.show()