使用sklearn对减少的交叉验证数据集执行网格搜索的有效方法

Question

我正在使用网格搜索来找到两个模型的最佳参数。我必须用整个数据集构建一个模型，用缩减的数据集构建另一个模型(需要保持两个模型的折叠相同(。因此，在第二个模型的情况下，将从第一个模型(具有整个数据集的模型(使用的相同折叠中省略/删除数据点列表。以下是我的代码：

rkf = RepeatedKFold(n_splits=2, n_repeats=5, random_state=24)
rkf_new_indices = []
for train_idx, test_idx in rkf.split(x):
Model1x_train, Model1x_test = x[train_idx], x[test_idx]
Model1y_train, Model1y_test = y[train_idx], y[test_idx]
temp_list1 = train_idx.copy()
temp_list2 = test_idx.copy()
Model2trn_idx = remove_datapoints(temp_list1, out_list)
Model2tst_idx = remove_datapoints(temp_list2, out_list)
Model2train_idx = list(Model2trn_idx)
Model2test_idx = list(Model2tst_idx)
rkf_new_indices = np.append(Model2train_idx, Model2test_idx)
param_grid = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},]
svr_model = SVR()
# define search for model with entire dataset
BASE_SVR = GridSearchCV(svr_model, param_grid, scoring='neg_mean_absolute_error', n_jobs=-1, cv=rkf, return_train_score=True)
BASE_SVR_grid_results = BASE_SVR.fit(x, y)
# define search for model with reduced dataset
New_SVR = GridSearchCV(svr_model, param_grid, scoring='neg_mean_absolute_error', n_jobs=-1, cv=rkf_new_indices, return_train_score=True)
#         ^^^^^^^^^^^^ raises TypeError
New_SVR_grid_results = New_SVR.fit(x, y)

对于第二个GridSearch(第19行(，我得到错误：

for train, test in self.cv:
> TypeError: cannot unpack non-iterable numpy.int32 object

cv=rkf_new_indices有什么问题？我该如何解决？

Answer 1

当您在段下运行时，分割的输出是

rkf_new_indices = []
for train_idx, test_idx in rkf.split([8,8,8,8,8,8,8,8,8]):
print(train_idx, test_idx)
rkf_new_indices = np.append(train_idx, test_idx)
[0 1 2 3] [4 5 6 7 8]
[4 5 6 7 8] [0 1 2 3]
[2 3 4 7] [0 1 5 6 8]
[0 1 5 6 8] [2 3 4 7]
[1 3 7 8] [0 2 4 5 6]
[0 2 4 5 6] [1 3 7 8]
[1 4 7 8] [0 2 3 5 6]
[0 2 3 5 6] [1 4 7 8]
[1 2 6 7] [0 3 4 5 8]
[0 3 4 5 8] [1 2 6 7]

然而，rkf_new_indices = np.append(train_idx, test_idx)只得到最后一个实例：

array([0, 3, 4, 5, 8, 1, 2, 6, 7])

您可以尝试rkf_new_indices.append((train_idx, test_idx))将它们成对获取：

[(array([0, 1, 2, 3]), array([4, 5, 6, 7, 8])),
(array([4, 5, 6, 7, 8]), array([0, 1, 2, 3])),
(array([2, 3, 4, 7]), array([0, 1, 5, 6, 8])),
(array([0, 1, 5, 6, 8]), array([2, 3, 4, 7])),
(array([1, 3, 7, 8]), array([0, 2, 4, 5, 6])),
(array([0, 2, 4, 5, 6]), array([1, 3, 7, 8])),
(array([1, 4, 7, 8]), array([0, 2, 3, 5, 6])),
(array([0, 2, 3, 5, 6]), array([1, 4, 7, 8])),
(array([1, 2, 6, 7]), array([0, 3, 4, 5, 8])),
(array([0, 3, 4, 5, 8]), array([1, 2, 6, 7]))]