在scikit中使用BaseEstimator的GradientBoostingClassifier？

Question

我尝试在scikit-learn中使用GradientBoostingClassifier，它使用它的默认参数工作得很好。但是，当我尝试用不同的分类器替换BaseEstimator时，它不起作用，并给出以下错误：

return y - np.nan_to_num(np.exp(pred[:, k] -
IndexError: too many indices

你对这个问题有什么解决办法吗？

可以使用以下代码片段重新生成此错误：

import numpy as np
from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.utils import shuffle

mnist = datasets.fetch_mldata('MNIST original')
X, y = shuffle(mnist.data, mnist.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.01)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]

### works fine when init is None
clf_init = None
print 'Train with clf_init = None'
clf = GradientBoostingClassifier( (loss='deviance', learning_rate=0.1,
                             n_estimators=5, subsample=0.3,
                             min_samples_split=2,
                             min_samples_leaf=1,
                             max_depth=3,
                             init=clf_init,
                             random_state=None,
                             max_features=None,
                             verbose=2,
                             learn_rate=None)
clf.fit(X_train, y_train)
print 'Train with clf_init = None is done :-)'

print 'Train LogisticRegression()'
clf_init = LogisticRegression();
clf_init.fit(X_train, y_train);
print 'Train LogisticRegression() is done'

print 'Train with clf_init = LogisticRegression()'
clf = GradientBoostingClassifier(loss='deviance', learning_rate=0.1,
                             n_estimators=5, subsample=0.3,
                             min_samples_split=2,
                             min_samples_leaf=1,
                             max_depth=3,
                             init=clf_init,
                             random_state=None,
                             max_features=None,
                             verbose=2,
                             learn_rate=None)
 clf.fit(X_train, y_train) # <------ ERROR!!!!
 print 'Train with clf_init = LogisticRegression() is done'

以下是错误的完整回溯：

Traceback (most recent call last):
File "/home/mohsena/Dropbox/programing/gbm/gb_with_init.py", line 56, in <module>
   clf.fit(X_train, y_train)
File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py", line 862, in fit
   return super(GradientBoostingClassifier, self).fit(X, y)
File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py", line 614, in fit random_state)
File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py", line 475, in _fit_stage
   residual = loss.negative_gradient(y, y_pred, k=k)
File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py", line 404, in negative_gradient
   return y - np.nan_to_num(np.exp(pred[:, k] -
   IndexError: too many indices

Answer 1

根据scikit-learn开发人员的建议，这个问题可以通过使用这样的适配器来解决：

def __init__(self, est):
   self.est = est
def predict(self, X):
    return self.est.predict_proba(X)[:, 1]
def fit(self, X, y):
    self.est.fit(X, y)

Answer 2

对iampat的答案进行改进，并对scikit-developers的答案稍作修改，应该可以做到这一点。

class init:
    def __init__(self, est):
        self.est = est
    def predict(self, X):
        return self.est.predict_proba(X)[:,1][:,numpy.newaxis]
    def fit(self, X, y):
        self.est.fit(X, y)

Answer 3

在我看来，这是iampat的代码片段的一个完整且更简单的版本。

    class RandomForestClassifier_compability(RandomForestClassifier):
        def predict(self, X):
            return self.predict_proba(X)[:, 1][:,numpy.newaxis]
    base_estimator = RandomForestClassifier_compability()
    classifier = GradientBoostingClassifier(init=base_estimator)