Python gridsearch后如何绘制热图并为决策树找到最佳超参数

我需要在网格搜索donorschoose数据集（可从kaggle获得）后，绘制一个热图，以找到决策树的最佳超参数

这里我有两个超参数：

max_depth=[1, 5, 10, 50, 100, 500]
min_samples_split=[5, 10, 100, 500]

X_tr_bow = hstack((X_train_price_norm,X_train_categories_ohe,X_train_state_ohe,X_train_teacher_ohe,X_train_grade_ohe,X_train_essay__bow,X_train_clean_title__bow)).tocsr()

X_tr_bow

是我在gridsearch中拟合的数据

X_tru_撑弓的尺寸

-（535317980）（53531，）

我在这里面临的错误

Best cross-validation score: 0.59
Best parameters:  {'max_depth': 50, 'min_samples_split': 500}
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py in create_block_manager_from_blocks(blocks, axes)
   1650                 blocks = [make_block(values=blocks[0],
-> 1651                                      placement=slice(0, len(axes[0])))]
   1652 

6 frames
ValueError: Wrong number of items passed 1, placement implies 24

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
   1689         raise ValueError("Empty data passed with indices specified.")
   1690     raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 1691         passed, implied))
   1692 
   1693 

ValueError: Shape of passed values is (24, 1), indices imply (24, 24)

---

万一有人还在找asnwer，下面的代码对我很有用

results = pd.DataFrame.from_dict(rand_search_cv.cv_results_)

max_scores = results.groupby(['param_min_samples_split', 'param_max_depth']).max()
max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]
sn.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g');

results = pd.DataFrame.from_dict(rand_search_cv.cv_results_)

max_scores = results.groupby(['param_min_samples_split', 'param_max_depth']).max()
max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]
sn.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g');