def main():
print('--- Adaboost ---')
data = datasets.load_digits()
X, y = data.data, data.target
digit1 = 1
digit2 = 8
idx = np.append(np.where(y == digit1)[0], np.where(y == digit2)[0])
y = data.target[idx]
y[y == digit1] = 1
y[y == digit2] = -1
X = data.data[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
clf = Adaboost(n_estimators=5)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_pred, y_test)
clf_tree = ClassificationTree()
clf_tree.fit(X_train, y_train)
y_pred_tree = clf_tree.predict(X_test)
acc_tree = accuracy_score(y_pred, y_test)
print("Adaboost_Accuracy:", acc)
print("Tree_Accuracy:", acc_tree)
def fit(self, X, y):
n_samples, n_features = X.shape[0], X.shape[1]
# 初始化各样本的权重
w = np.full(n_samples, (1 / n_samples))
# 储存每个分类器
self.clfs = []
for i in tqdm(range(self.n_clfs)):
# 实例化一个分类器
clf = ClassificationTree()
# 训练
clf.fit(X, y)
# 得到训练结果
y_pred = clf.predict(X)
# 计算训练误差
print(accuracy_score(y, y_pred))
error = sum(w[y != y_pred])
# 对于错误率大于0.5的分类器，因为是二分类问题(Adaboost只能解决二分类问题)，我们可以翻转
# 分类器的预测结果来使得其错误率为1-error>0.5
print(error)
if error > 0.5:
self.polarity[i] = -1
y_pred *= -1
error = 1 - error
self.alphas[i] = 0.5 * np.log((1.0 - error) / (error + 1e-10))
predictions = np.array(self.polarity[i] * y_pred)
w *= np.exp(-self.alphas[i] * y * predictions)
w /= sum(w)
self.clfs.append(clf)