scikit学习中带有BaseEstimator的GradientBoostingClassifier

Question

我尝试在scikit learn中使用GradientBoostingClassifier，它与默认参数配合良好。然而，当我试图用不同的分类器替换BaseEstimator时，它没有起作用，并给了我以下错误，

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

你有解决这个问题的办法吗。

此错误可以使用以下代码段重新生成：

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

iampat答案的改进版本和scikit开发人员答案的轻微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 2

正如scikit-learn开发人员所建议的，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 3

在我看来，这是iampat代码片段的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)

Answer 4

Gradient Boosting通常要求基础学习器是一种执行数值预测的算法，而不是分类。我想这是你的问题。