Sklearn for gbm支持init参数，该参数提供了训练初始模型并将其通过init参数传递给另一个模型的选项。
我正在尝试使用相同的概念进行回归。下面是我的代码。
gbm_base=GradientBoostingRegressor(random_state=1,verbose=True)
gbm_base.fit(X_train, y_train)
gbm_withEstimator=
GradientBoostingRegressor(init=gbm_base,random_state=1,verbose=True)
gbm_withEstimator.fit(X_train, y_train)
但这给了我以下错误。
~/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/gradient_boosting.py in
update_terminal_regions(self, tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate, k)
499 """
500 # update predictions
--> 501 y_pred[:, k] += learning_rate * tree.predict(X).ravel()
502
503 def _update_terminal_region(self, tree, terminal_regions, leaf, X, y,
IndexError: too many indices for array
我认为这是错误的，因为在回归中ypred始终是一维数组，但是在这里的代码中它假定它是二维
答案 0 :(得分：3)
这是一个已知的错误。看看GradientBoosting fails when using init estimator parameter.和[MRG] FIX gradient boosting with sklearn estimator as init #12436
了解更多背景信息。
同时，您可以将GradientBoostingRegressor子类化，以避免出现以下问题：
from sklearn.utils import check_array
class GBR_Init(GradientBoostingRegressor):
def predict(self,X):
X = check_array(X, dtype=np.float32, order='C', accept_sparse='csr')
return self._decision_function(X)
然后，您可以使用GBR_Init类代替GradientBoostingRegressor。
一个例子：
import numpy as np
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor as GBR
from sklearn.utils import check array
class GBR_Init(GradientBoostingRegressor):
def predict(self,X):
X = check_array(X, dtype=np.float32, order='C', accept_sparse='csr')
return self._decision_function(X)
boston = load_boston()
X = boston.data
y = boston.target
base = GBR_Init(random_state=1, verbose=True)
base.fit(X, y)
Iter Train Loss Remaining Time
1 71.3024 0.00s
2 60.6243 0.00s
3 51.6694 0.00s
4 44.3657 0.00s
5 38.2831 0.00s
6 33.2863 0.00s
7 28.9190 0.00s
8 25.2967 0.18s
9 22.2587 0.16s
10 19.6923 0.14s
20 8.3119 0.13s
30 5.4763 0.07s
40 4.1906 0.07s
50 3.4663 0.05s
60 3.0437 0.04s
70 2.6753 0.03s
80 2.4451 0.02s
90 2.2376 0.01s
100 2.0142 0.00s
GBR_Init(alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.1,
loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_iter_no_change=None, presort='auto',
random_state=1, subsample=1.0, tol=0.0001, validation_fraction=0.1,
verbose=True, warm_start=False)
est = GBR_Init(init=base, random_state=1, verbose=True)
est.fit(X, y)
est.fit(X, y)
Iter Train Loss Remaining Time
1 71.3024 0.00s
2 60.6243 0.00s
3 51.6694 0.00s
4 44.3657 0.00s
5 38.2831 0.00s
6 33.2863 0.00s
7 28.9190 0.00s
8 25.2967 0.18s
9 22.2587 0.16s
10 19.6923 0.14s
20 8.3119 0.06s
30 5.4763 0.07s
40 4.1906 0.05s
50 3.4663 0.05s
60 3.0437 0.03s
70 2.6753 0.03s
80 2.4451 0.02s
90 2.2376 0.01s
100 2.0142 0.00s
Iter Train Loss Remaining Time
1 2.0069 0.00s
2 1.9844 0.00s
3 1.9729 0.00s
4 1.9670 0.00s
5 1.9409 0.00s
6 1.9026 0.00s
7 1.8850 0.00s
8 1.8690 0.00s
9 1.8450 0.00s
10 1.8391 0.14s
20 1.6879 0.06s
30 1.5695 0.04s
40 1.4469 0.05s
50 1.3431 0.03s
60 1.2329 0.03s
70 1.1370 0.02s
80 1.0616 0.02s
90 0.9904 0.01s
100 0.9228 0.00s
GBR_Init(alpha=0.9, criterion='friedman_mse',
init=GBR_Init(alpha=0.9, criterion='friedman_mse', init=None, learning_rate
=0.1,
loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_iter_no_change=None, presort='auto',
random_state=1, subsample=1.0, tol=0.0001, validation_fraction=0.1,
verbose=True, warm_start=False),
learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100, n_iter_no_change=None,
presort='auto', random_state=1, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=True, warm_start=False)