如何在scikit learn中使用带有GridSearchCV对象的TimeSeriesSplit来优化模型？

Question

我已经搜索了sklearn docs for ^{}和docs for cross-validation但是还没有找到一个有效的例子。

我使用的是sklearn版本0.19。

这是我的设置

import xgboost as xgb
from sklearn.model_selection import TimeSeriesSplit
from sklearn.grid_search import GridSearchCV
import numpy as np
X = np.array([[4, 5, 6, 1, 0, 2], [3.1, 3.5, 1.0, 2.1, 8.3, 1.1]]).T
y = np.array([1, 6, 7, 1, 2, 3])
tscv = TimeSeriesSplit(n_splits=2)
for train, test in tscv.split(X):
    print(train, test)

给出：

[0 1] [2 3]
[0 1 2 3] [4 5]

如果我尝试：

model = xgb.XGBRegressor()
param_search = {'max_depth' : [3, 5]}

my_cv = TimeSeriesSplit(n_splits=2).split(X)
gsearch = GridSearchCV(estimator=model, cv=my_cv,
                        param_grid=param_search)
gsearch.fit(X, y)

它给出：TypeError: object of type 'generator' has no len()

我得到了一个问题：GridSearchCV试图调用len(cv)，但是my_cv是一个没有长度的迭代器。但是，我可以使用docs for ^{}状态

int, cross-validation generator or an iterable, optional

我试着在没有.split(X)的情况下使用TimeSeriesSplit，但仍然没有成功。

我肯定我忽略了一些简单的事情，谢谢！！

Answer 1

结果发现问题是我在使用sklearn.grid_search中的GridSearchCV，这是不推荐的。从sklearn.model_selection导入GridSearchCV解决了问题：

import xgboost as xgb
from sklearn.model_selection import TimeSeriesSplit, GridSearchCV
import numpy as np
X = np.array([[4, 5, 6, 1, 0, 2], [3.1, 3.5, 1.0, 2.1, 8.3, 1.1]]).T
y = np.array([1, 6, 7, 1, 2, 3])

model = xgb.XGBRegressor()
param_search = {'max_depth' : [3, 5]}

tscv = TimeSeriesSplit(n_splits=2)
gsearch = GridSearchCV(estimator=model, cv=tscv,
                        param_grid=param_search)
gsearch.fit(X, y)

给出：

GridSearchCV(cv=<generator object TimeSeriesSplit.split at 0x11ab4abf8>,
       error_score='raise',
       estimator=XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,
       learning_rate=0.1, max_delta_step=0, max_depth=3,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='reg:linear', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1),
       fit_params=None, iid=True, n_jobs=1,
       param_grid={'max_depth': [3, 5]}, pre_dispatch='2*n_jobs',
       refit=True, return_train_score=True, scoring=None, verbose=0)