機器學習算法--python實作通過自适應增強來利用弱學習者

在增強過程中，內建是由很簡單的常被稱為弱學習者的基本分類器所組成，性能僅比随機猜測略優，弱學習者的典型例子是單層決策樹。增強背後的關鍵概念是專注于難以分類的訓練樣本，即讓弱學習者随後從訓練樣本的分類錯誤中學習以提高內建的性能。

AdaBoost用完整的訓練集來訓練弱學習者，在每次疊代中重新定義訓練樣本的權重，來建構可以從內建中弱學習者的錯誤中不斷地學習的強大的分類器。

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier

df_wine = pd.read_csv("xxx\\wine.data",
                      header=None)

df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
                   'Alcalinity of ash', 'Magnesium', 'Total phenols',
                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
                   'Proline']

df_wine = df_wine[df_wine['Class label'] != 1]

y = df_wine['Class label'].values
X = df_wine[['Alcohol', 'OD280/OD315 of diluted wines']].values

le = LabelEncoder()
y = le.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y,
                             test_size=0.2,
                             random_state=1,
                             stratify=y)


tree = DecisionTreeClassifier(criterion='entropy',
                              max_depth=1,
                              random_state=1)

ada = AdaBoostClassifier(base_estimator=tree,
                         n_estimators=500,
                         learning_rate=0.1,
                         random_state=1)

tree = tree.fit(X_train, y_train)
y_train_pred = tree.predict(X_train)
y_test_pred = tree.predict(X_test)

tree_train = accuracy_score(y_train, y_train_pred)
tree_test = accuracy_score(y_test, y_test_pred)
# 單層決策樹似乎對訓練資料欠拟合
print('Decision tree train/test accuracies %.3f/%.3f'
      % (tree_train, tree_test))

ada = ada.fit(X_train, y_train)
y_train_pred = ada.predict(X_train)
y_test_pred = ada.predict(X_test)

ada_train = accuracy_score(y_train, y_train_pred)
ada_test = accuracy_score(y_test, y_test_pred)
# AdaBoost模型正确地預測了訓練集的所有分類标簽，而且與單層決策樹相比，
# 其測試性能也略有改善。然而，也看到因為試圖減少模型偏差，
# 在訓練集和測試集之間的性能上存在着較大的差距，是以也引入了額外的方差。
print('AdaBoost train/test accuracies %.3f/%.3f'
      % (ada_train, ada_test))

x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))

f, axarr = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(8, 3))


for idx, clf, tt in zip([0, 1],
                        [tree, ada],
                        ['Decision tree', 'AdaBoost']):
    clf.fit(X_train, y_train)

    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    axarr[idx].contourf(xx, yy, Z, alpha=0.3)
    axarr[idx].scatter(X_train[y_train == 0, 0],
                       X_train[y_train == 0, 1],
                       c='blue', marker='^')
    axarr[idx].scatter(X_train[y_train == 1, 0],
                       X_train[y_train == 1, 1],
                       c='green', marker='o')
    axarr[idx].set_title(tt)

axarr[0].set_ylabel('Alcohol', fontsize=12)
plt.text(10.2, -0.5,
         s='OD280/OD315 of diluted wines',
         ha='center', va='center', fontsize=12)

plt.tight_layout()
#plt.savefig('images/07_11.png', dpi=300, bbox_inches='tight')
plt.show()

           

運作結果：

Decision tree train/test accuracies 0.916/0.875

AdaBoost train/test accuracies 1.000/0.917

運作結果圖：