Scikit learn 多类多标签分类的GBM训练中的维数相关问题？

我想用LightGBM算法对多类多标签分类，但我在训练过程中遇到了一个问题，因为我不是输入列表。 实际行的长度为

10000

dataset = pd.read_csv('Data.csv') 
X = dataset.iloc[:,np.r_[0:6, 7:27]].values
y = dataset.iloc[:,np.r_[6]].values

x_train, x_test, y_train, y_test = train_test_split(X, y,test_size = 0.25, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
import lightgbm as lgb
d_train = lgb.Dataset(x_train, label=y_train)
params = {}
params['learning_rate'] = 0.003
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'binary_logloss'
params['sub_feature'] = 0.5
params['num_leaves'] = 10
params['min_data'] = 50
params['max_depth'] = 10
clf = lgb.train(params, d_train, 100)

y_pred=clf.predict(x_test)

for i in range(0,99):
 if y_pred[i]>=.5:
    y_pred[i]=1
 else:
    y_pred[i]=0

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

我遇到这个问题：

    clf = lgb.train(params, d_train, 100)
  File "..\lightgbm\engine.py", line 228, in train
    ...
  File "..\lightgbm\basic.py", line 1336, in set_label
    label = list_to_1d_numpy(_label_from_pandas(label), name='label')
  File "..\lightgbm\basic.py", line 86, in list_to_1d_numpy
    "It should be list, numpy 1-D array or pandas Series".format(type(data).__name__, name))

在函数“”中将数据转换为numpy 1-D数组“”中发现此错误，而我已通过将数据更改为1D

y_train = np.reshape(y_train, [1,trainsize])
x_train = np.reshape(x_train, [1,trainsize*26])

问题没有解决！ 然后我使用

ravel

为

x\u列车、y\u列车创建1D

x_train = np.ravel(x_train)
y_train = np.ravel(y_train)

但新的错误如下所示：

---

\lib\site packages\lightgbm\basic.py”，第872行，来自np2d的 raise VALUE ERROR（'Input numpy.ndarray必须是二维的'）
ValueError:输入numpy.ndarray必须是二维的

---

怎么了？我怎么解决这个问题呢？

什么是

x\u-train

和

y\u-train

形状？你能给我们提供

x\u-train.shape

和

y\u-train.shape

的输出吗？试着把

y-train

重塑成

（7500，）

\lib\site-packages\lightgbm\basic.py），第872行，在NPU的初始位置（'Input numpy.ndarray必须是二维的'）值错误：Input numpy.ndarray必须是二维的