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寻找 2 个模型的最佳参数。我必须构建一个包含整个数据集的模型和另一个包含减少的数据集的模型（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，数据点列表将从用于第一个模型（具有整个数据集的模型）的相同折叠中省略/删除。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），我收到错误：

for train, test in self.cv:

> TypeError: cannot unpack non-iterable numpy.int32 object

我在cv=rkf_new_indices这里做错了什么，我该如何解决这个问题？

Answer 1

当你运行低于分段时，拆分的输出是

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]

但是，rkf_new_indices = np.append(train_idx, test_idx) 仅获取最后一个实例：

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

您可以尝试 rkf_new_indices.append((train_idx, test_idx)) 将它们全部配对：

[(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]))]

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

Efficient way to perform grid search for reduced set of cross-validated data using sklearn

python

machine-learning

cross-validation

hyperparameters

data-science