Question

我尝试将AdaBoostClassifier与决策树桩一起用作基本分类器。我注意到AdaBoostClassifier完成的重量调整给了我SAMME.R和SAMME选项的错误。

这里简要介绍了我正在做的事情

def train_adaboost(features, labels):
    uniqLabels = np.unique(labels)
    allLearners = []
    for targetLab in uniqLabels:
        runs=[]
        for rrr in xrange(10):
            feats,labs = get_binary_sets(features, labels, targetLab)
            baseClf = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
            baseClf.fit(feats, labs)

            ada_real = AdaBoostClassifier( base_estimator=baseClf, 
                                           learning_rate=1, 
                                           n_estimators=20, 
                                           algorithm="SAMME")
            runs.append(ada_real.fit(feats, labs))
        allLearners.append(runs)

    return allLearners

我查看了每个决策树分类器的拟合情况，他们能够预测一些标签。然而，当我使用这个基础分类器查看AdaBoostClassifier时，我得到了关于权重提升算法的错误。

def compute_confidence(allLearners, dada, labbo):
    for ii,thisLab in enumerate(allLearners):
        for jj, thisLearner in enumerate(thisLab):
            #accessing thisLearner's methods here

这些方法会产生如下错误：

ipdb> thisLearner.predict_proba(myData)

PATHTOPACKAGE/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.py:727: RuntimeWarning: invalid value encountered in double_scalars proba /= self.estimator_weights_.sum() *** ValueError: 'axis' entry is out of bounds

ipdb> thisLearner.predict(myData)

PATHTOPACKAGE/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.py:639: RuntimeWarning: invalid value encountered in double_scalars pred /= self.estimator_weights_.sum() *** IndexError: 0-d arrays can only use a single () or a list of newaxes (and a single ...) as an index

我为adaboost尝试了SAMME.R算法，但由于此错误，我甚至无法适应adaboost [...]

File "PATH/sklearn/ensemble/weight_boosting.py", line 388, in fit return super(AdaBoostClassifier, self).fit(X, y, sample_weight)

File "PATH/sklearn/ensemble/weight_boosting.py", line 124, in fit X_argsorted=X_argsorted)

File "PATH/sklearn/ensemble/weight_boosting.py", line 435, in _boost X_argsorted=X_argsorted)

File "PATH/sklearn/ensemble/weight_boosting.py", line 498, in _boost_real (estimator_weight < 0)))

ValueError: non-broadcastable output operand with shape (1000) doesn't match the broadcast shape (1000,1000)

数据的维度实际上与分类器期望的格式兼容，无论是在使用adaboost之前还是在我尝试测试训练的分类器时。这些错误表明了什么？

Answer 1

这有点违反直觉，来自Matlab编码。

显然，实验室的尺寸是问题，即（1000,1）。它需要是（1000，）

添加此行解决了它：

labs = labs [：，0]

scikit-learn's adaboost中的体重问题

1 个答案: