lightgbm eval metric 질문입니다.

오류 마지막 결과를 보면, raise ValueError("Found input variables with inconsistent numbers of" ValueError: Found input variables with inconsistent numbers of samples: [650, 325] 650과 325가 안맞는다고 나옵니다. 325가 650의 절반인데, evaluate_macreF1_lgb의 reshape과정에서 문제가 생기는 것은 아닐까 생각되는데 어떻게 해결해야할지 잘 모르겠습니다... print(re_train_x.shape[0], re_train_y.shape[0]) print(valid_x.shape[0], valid_y.shape[0]) print(test_x.shape[0], test_y.shape[0]) 를 해보면 결과는 1947 1947 650 650 649 649 이 나옵니다. 피처와 레이블의 갯수는 잘 맞는 것 같습니다..

lightgbm eval metric 질문입니다.

Eval_set을 검증만 넣어서 clf.fit(re_train_x, re_train_y, eval_set=[(valid_x, valid_y)], eval_metric= evaluate_macroF1_lgb, verbose= 100, early_stopping_rounds= 1000) 위와 같이 fit 시켰을 때 다음과 같은 오류가 나옵니다.. Traceback (most recent call last): File "C:\Users\user\PycharmProjects\mb\MB_final_code_11_05.py", line 122, in clf.fit(re_train_x, re_train_y, eval_set=[(valid_x, valid_y)], File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 890, in fit super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets, File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 683, in fit self._Booster = train(params, train_set, File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\engine.py", line 256, in train evaluation_result_list.extend(booster.eval_valid(feval)) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\basic.py", line 2888, in eval_valid return [item for i in range(1, self.__num_dataset) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\basic.py", line 2889, in for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)] File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\basic.py", line 3402, in __inner_eval feval_ret = eval_function(self.__inner_predict(data_idx), cur_data) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 168, in __call__ return self.func(labels, preds) File "C:\Users\user\PycharmProjects\mb\MB_final_code_11_05.py", line 49, in evaluate_macroF1_lgb f1 = f1_score(truth, pred_labels, average='macro') File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score return fbeta_score(y_true, y_pred, beta=1, labels=labels, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1277, in _check_set_wise_labels y_type, y_true, y_pred = _check_targets(y_true, y_pred) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 83, in _check_targets check_consistent_length(y_true, y_pred) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 319, in check_consistent_length raise ValueError("Found input variables with inconsistent numbers of" ValueError: Found input variables with inconsistent numbers of samples: [650, 325]

lightgbm eval metric 질문입니다.

def evaluate_macroF1_lgb(truth, predictions): pred_labels = predictions.reshape(len(np.unique(truth)),-1).argmax(axis=0) f1 = f1_score(truth, pred_labels, average='macro') return ('macroF1', f1, True)clf = LGBMClassifier( n_jobs=-1, n_estimators=1000000, learning_rate=0.0001, num_leaves=48, subsample=0.8, max_depth=20, silent=-1, verbose=-1) clf.fit(re_train_x, re_train_y, eval_set=[(re_train_x, re_train_y), (valid_x, valid_y)], eval_metric= evaluate_macroF1_lgb, verbose= 100, early_stopping_rounds= 1000) 코드를 위와 같이 작성하고, eval_metric에만 저 식을 적용해주었는데, 계속 에러가 납니다. eval metric을 loss나 auc로 하면 작동은 잘 됩니다. Traceback (most recent call last): File "C:/Users/user/PycharmProjects/mb/MB_final_code_11_03.py", line 116, in clf.fit(re_train_x, re_train_y, eval_set=[(re_train_x, re_train_y), (valid_x, valid_y)], File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 890, in fit super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets, File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 683, in fit self._Booster = train(params, train_set, File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\engine.py", line 255, in train evaluation_result_list.extend(booster.eval_train(feval)) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\basic.py", line 2856, in eval_train return self.__inner_eval(self._train_data_name, 0, feval) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\basic.py", line 3402, in __inner_eval feval_ret = eval_function(self.__inner_predict(data_idx), cur_data) File "C:\Users\user\.conda\envs\mb\lib\site-packages\lightgbm\sklearn.py", line 168, in __call__ return self.func(labels, preds) File "C:/Users/user/PycharmProjects/mb/MB_final_code_11_03.py", line 38, in evaluate_macroF1_lgb f1 = f1_score(truth, pred_labels, average='macro') File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score return fbeta_score(y_true, y_pred, beta=1, labels=labels, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 1277, in _check_set_wise_labels y_type, y_true, y_pred = _check_targets(y_true, y_pred) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\metrics\_classification.py", line 83, in _check_targets check_consistent_length(y_true, y_pred) File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 316, in check_consistent_length lengths = [_num_samples(X) for X in arrays if X is not None] File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 316, in lengths = [_num_samples(X) for X in arrays if X is not None] File "C:\Users\user\.conda\envs\mb\lib\site-packages\sklearn\utils\validation.py", line 259, in _num_samples raise TypeError("Singleton array %r cannot be considered" TypeError: Singleton array 80 cannot be considered a valid collection. 어떻게 해결해야할까요? 인터넷에서 찾아본 다른 f1 score 코드를 이용해도 모두 에러가 나옵니다. 문제는 0과 1로 된 이진분류 문제입니다.. 감사합니다.