model.fit(train_data, y=label_data, eval_set=eval_dataset)
eval_dataset = Pool(val_data, val_labels)
model = CatBoostClassifier(depth=8 or 10, iterations=10, task_type="GPU", devices='0-2', eval_metric='Accuracy', boosting_type="Ordered", bagging_temperature=0, use_best_model=True)
当我运行上述代码时（在2个单独的运行中/深度设置为8或10），我会得到以下结果：
深度为10：0.6864865 深度为8：0.6756757
我想以一种方式设置和运行GridSearch，使其运行完全相同的组合并产生与我手动运行代码时完全相同的结果。
GridSearch 代码：
model = CatBoostClassifier(iterations=10, task_type="GPU", devices='0-2', eval_metric='Accuracy', boosting_type="Ordered", depth=10, bagging_temperature=0, use_best_model=True)
grid = {'depth': [8,10]}
grid_search_result = GridSearchCV(model, grid, cv=2)
results = grid_search_result.fit(train_data, y=label_data, eval_set=eval_dataset)
问题：
我希望GridSearch使用我的"eval_set"来比较/验证所有不同的运行结果（就像手动运行一样）-但它使用了其他我不理解的东西，似乎根本没有看"eval_set"？
它不仅生成2个结果-而是根据"cv"参数（交叉验证拆分策略）运行3、5、7、9或11次？我不想要那样。
我尝试通过调试器遍历整个“results”对象-但我根本找不到最佳运行或所有其他运行的验证“准确性”分数。我可以找到很多其他值-但没有一个与我要找的匹配。这些数字与“eval_set”数据集产生的数字不相符？
我通过实现自己简单的GridSearch解决了我的问题（如果有助于/激励其他人：-））：如果您对代码有任何评论，请告诉我：-)
import pandas as pd
from catboost import CatBoostClassifier, Pool
from sklearn.model_selection import GridSearchCV
import csv
from datetime import datetime
# Initialize data
train_data = pd.read_csv('./train_x.csv')
label_data = pd.read_csv('./labels_train_x.csv')
val_data = pd.read_csv('./val_x.csv')
val_labels = pd.read_csv('./labels_val_x.csv')
eval_dataset = Pool(val_data, val_labels)
ite = [1000,2000]
depth = [6,7,8,9,10]
max_bin = [None,32,46,100,254]
l2_leaf_reg = [None,2,10,20,30]
bagging_temperature = [None,0,0.5,1]
random_strength = [None,1,5,10]
total_runs = len(ite) * len(depth) * len(max_bin) * len(l2_leaf_reg) * len(bagging_temperature) * len(random_strength)
print('Total runs: ' + str(total_runs))
counter = 0
file_name = './Results/Catboost_' + str(datetime.now().strftime("%d_%m_%Y_%H_%M_%S")) + '.csv'
row = ['Validation Accuray','Logloss','Iterations', 'Depth', 'Max_bin', 'L2_leaf_reg', 'Bagging_temperature', 'Random_strength']
with open(file_name, 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(row)
csvFile.close()
for a in ite:
for b in depth:
for c in max_bin:
for d in l2_leaf_reg:
for e in bagging_temperature:
for f in random_strength:
model = CatBoostClassifier(task_type="GPU", devices='0-2', eval_metric='Accuracy', boosting_type="Ordered", use_best_model=True,
iterations=a, depth=b, max_bin=c, l2_leaf_reg=d, bagging_temperature=e, random_strength=f)
counter += 1
print('Run # ' + str(counter) + '/' + str(total_runs))
result = model.fit(train_data, y=label_data, eval_set=eval_dataset, verbose=1)
accuracy = float(result.best_score_['validation']['Accuracy'])
logLoss = result.best_score_['validation']['Logloss']
row = [ accuracy, logLoss,
('Auto' if a == None else a),
('Auto' if b == None else b),
('Auto' if c == None else c),
('Auto' if d == None else d),
('Auto' if e == None else e),
('Auto' if f == None else f)]
with open(file_name, 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(row)
csvFile.close()