我正在尝试使用sklearn决策树作为弱学习者来实现Adaboost算法-在每一步，我想选择一个具有一个阈值的特征对所有样本进行分类。
我有1400个长特征向量，并希望将其标记为1或-1。功能是电影分级中的单词，标签表示“差”或“好”。在某些迭代中，决策树将特征确定为阈值0.5，并将所有样本归类为-1（无论其值如何），并且在下一次迭代中选择相同的特征，这一次将样本归类为
有人可以找到原因吗？
树打印：
feat: 311
==================
|--- worst <= 0.50
---> class: 1.0
| --- worst > 0.50
---> class: -1.0
==================
alphas = 0.16872595425475514
feat: 27
==================
|--- bad <= 0.50
---> class: 1.0
|--- bad > 0.50
--->class: -1.0
==================
alphas = 0.21421414954211687
feat: 371
==================
|--- boring <= 0.50
--->class: -1.0
|--- boring > 0.50
---> class: -1.0
==================
alphas = 0.1881155411693614
feat: 371
==================
|--- boring <= 0.50
---> class: 1.0
|--- boring > 0.50
---> class: -1.0
==================
alphas = 0.12644785644997397
feat: 822
==================
|--- ridiculous <= 0.50
---> class: -1.0
|--- ridiculous > 0.50
---> class: -1.0
代码：
def run_adaboost(X_train, y_train, T):
hypotheses = []
alpha_vals = []
num_of_samples = len(X_train)
D = [1/num_of_samples for _ in range(num_of_samples)]
for t in range(T):
h = weak_learner(D, X_train, y_train)
idx, threshold = h.tree_.feature[0], h.tree_.threshold[0]
tup = (get_prediction(h, X_train[0]), idx, threshold)
print_tree(h, [vocabulary[idx] for idx in range(len(X_train[0]))])
hypotheses.append(tup)
epsilon = 1-h.score(X_train, y_train, sample_weight=D)
alpha = 0.5*np.log((1-epsilon)/epsilon)
alpha_vals.append(alpha)
D = new_distribution(D, X_train, y_train, alpha, h)
return hypotheses, alpha_vals
##############################################
def weak_learner(D, X_train, y_train):
clf = tree.DecisionTreeClassifier(max_depth=1, criterion="entropy")
clf.fit(X_train, y_train, sample_weight=D)
return clf
def new_distribution(D, X_train, y_train, alpha, h):
Z = 0
Dt = [0]*len(D)
print(f"alphas = {alpha}")
pred = h.predict(X_train)
for i in range(len(X_train)):
exponent = (-1) * alpha * y_train[i] * (pred[i])
Z += D[i]*np.exp(exponent)
for i in range(len(X_train)):
exponent = (-1) * alpha * y_train[i] * (pred[i])
Dt[i] = (D[i]*np.exp(exponent))/Z
return Dt
def get_prediction(clf, vector):
feat = clf.tree_.feature[0]
print(f"feat: {feat}")
vec = vector.copy()
vec[feat] = 0
vec = vec.reshape(1, -1)
return int(clf.predict(vec)[0])
def print_tree(clf, feat_name):
r = tree.export_text(clf, feat_name)
print(r)
print("==================")
##############################################
def main():
data = parse_data()
if not data:
return
(X_train, y_train, X_test, y_test, vocab) = data
global vocabulary, X_test_g, y_test_g
X_test_g, y_test_g = X_test, y_test
vocabulary = vocab
T = 80
run_adaboost(X_train, y_train, T)
if __name__ == '__main__':
main()