目录
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AdaBoost算法代码(鸢尾花分类)
一、导入模块
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.font_manager import FontProperties
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
%matplotlib inline
font = FontProperties(fname='/Library/Fonts/Heiti.ttc')
二、导入数据
X = iris_data.data[:, [2, 3]]
y = iris_data.target
label_list = ['山鸢尾', '杂色鸢尾', '维吉尼亚鸢尾']
三、构造决策边界
def plot_decision_regions(X, y, classifier=None):
marker_list = ['o', 'x', 's']
color_list = ['r', 'b', 'g']
cmap = ListedColormap(color_list[:len(np.unique(y))])
x1_min, x1_max = X[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
x2_min, x2_max = X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
t1 = np.linspace(x1_min, x1_max, <span class="hljs-number">666</span>)
t2 = np.linspace(x2_min, x2_max, <span class="hljs-number">666</span>)
x1, x2 = np.meshgrid(t1, t2)
y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T)
y_hat = y_hat.reshape(x1.shape)
plt.contourf(x1, x2, y_hat, alpha=<span class="hljs-number">0.2</span>, cmap=cmap)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
<span class="hljs-keyword">for</span> ind, clas <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(np.unique(y)):
plt.scatter(X[y == clas, <span class="hljs-number">0</span>], X[y == clas, <span class="hljs-number">1</span>], alpha=<span class="hljs-number">0.8</span>, s=<span class="hljs-number">50</span>,
c=color_list[ind], marker=marker_list[ind], label=label_list[clas])
四、训练模型
4.1 训练模型(n_e=10, l_r=0.8)
adbt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm="SAMME", n_estimators=10, learning_rate=0.8)
adbt.fit(X, y)
AdaBoostClassifier(algorithm='SAMME',
base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=5, min_samples_split=20,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best'),
learning_rate=0.8, n_estimators=10, random_state=None)
4.2 可视化
plot_decision_regions(X, y, classifier=adbt)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('AdaBoost算法代码(鸢尾花分类, n_e=10, l_r=0.8)',
fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()
print("Score:{}".format(adbt.score(X, y)))
Score:0.9866666666666667
4.3 训练模型(n_estimators=300, learning_rate=0.8)
adbt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm="SAMME", n_estimators=300, learning_rate=0.8)
adbt.fit(X, y)
print("Score:{}".format(adbt.score(X, y)))
Score:0.9933333333333333
由于样本太少，可能效果不明显，但是对比上一个模型可以发现，相同步长的情况下，如果弱学习个数越多，拟合效果越好，但如果过多则可能过拟合。
4.4 训练模型(n_estimators=300, learning_rate=0.5)
adbt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm="SAMME", n_estimators=300, learning_rate=0.001)
adbt.fit(X, y)
print("Score:{}".format(adbt.score(X, y)))
Score:0.9533333333333334
相同迭代次数的情况下，对比上一个模型可以发现，如果步长越大，则模型效果越好。
4.5 训练模型(n_estimators=600, learning_rate=0.7)
adbt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm="SAMME", n_estimators=600, learning_rate=0.8)
adbt.fit(X, y)
print("Score:{}".format(adbt.score(X, y)))
Score:0.9933333333333333
对比第二个模型，可以发现即使增加迭代次数，算法准确率也没有提高，所以n_estimators=300的时候其实算法就已经收敛了。