LightGBMError (train_data->num_features()) > (0)

Question

我收到了错误

[LightGBM] [Fatal] 检查失败：(train_data->num_features()) > (0)

对于形状为 (40,7) 的数据集 X。我正在尝试为自定义损失函数运行梯度提升

将不胜感激任何解决方案或提示。

在线出现错误

gbm.fit(
    X_train,
    y_train,
    eval_set=[(X_valid, y_valid)],
    eval_metric=custom_asymmetric_valid,
    verbose=False,
)

完整代码如下：

import lightgbm
import pandas as pd 
from sklearn.model_selection import train_test_split
import numpy as np

train = pd.read_csv("Data_Train.csv")
X, y = train.iloc[:, 1:-1], train.iloc[:, -1] 

X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.20, random_state=42)
print(np.shape(X_train),np.shape(X_valid))


test = pd.read_csv("Data_Test.csv")
X_test, y_test = test.iloc[:, 1:-1], test.iloc[:, -1] 

# Defining custom loss function

def custom_asymmetric_train(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    grad = np.where(residual<0, -2*10.0*residual, -2*residual)
    hess = np.where(residual<0, 2*10.0, 2.0)
    return grad, hess

def custom_asymmetric_valid(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    loss = np.where(residual < 0, (residual**2)*10.0, residual**2) 
    return "custom_asymmetric_eval", np.mean(loss), False

# default lightgbm model with sklearn api
gbm = lightgbm.LGBMRegressor(random_state=33) 

# updating objective function to custom
# default is "regression"
# also adding metrics to check different scores
gbm.set_params(**{'objective': custom_asymmetric_train}, metrics = ["mse", 'mae'])

# fitting model 
gbm.fit(
    X_train,
    y_train,
    eval_set=[(X_valid, y_valid)],
    eval_metric=custom_asymmetric_valid,
    verbose=False,
)

y_pred = gbm.predict(X_valid)


# create dataset for lightgbm

lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
lgb_eval = lgb.Dataset(X_valid, y_valid, reference=lgb_train, free_raw_data=False)


params = {
    'objective': 'regression',
    'verbose': 0
}

gbm = lgb.train(params,
                lgb_train,
                num_boost_round=10,
                init_model=gbm,
                fobj=custom_asymmetric_train,
                feval=custom_asymmetric_valid,
                valid_sets=lgb_eval)
                
y_pred = gbm.predict(X_valid)

Answer 1

您的原始示例无法完全重现（因为 "Data_Train.csv" 的内容未共享），但我可以使用 LightGBM 3.1.1（与 @987654325 一起安装）使用以下代码可靠地重现您提到的错误消息@)。

import lightgbm as lgb
import numpy as np
import pandas as pd

np.random.seed(708)

def custom_asymmetric_train(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    grad = np.where(residual<0, -2*10.0*residual, -2*residual)
    hess = np.where(residual<0, 2*10.0, 2.0)
    return grad, hess

# create a training dataset of shape (40, 7)
X = pd.DataFrame({
    f"feat_{i}": np.random.random((40,))
    for i in range(7)
})
y = np.random.random((40,))

gbm = lgb.LGBMRegressor()
gbm.set_params(**{'objective': custom_asymmetric_train}, metrics = ["mse", 'mae'])
gbm.fit(X, y)

LightGBMError：检查失败：(train_data->num_features()) > (0)

LightGBM 有一些用于防止过拟合的参数。在这种情况下有两个相关：

• min_data_in_leaf（默认=20）
• min_sum_hessian_in_leaf（默认=0.001）

默认情况下，在Dataset对象的构造过程中，LightGBM会根据这些条件过滤掉无法分割的特征（参见feature_pre_filter。

LightGBM 的参数默认值旨在为中等大小的数据集提供良好的性能。形状为(40, 7) 的数据集非常小，这增加了所有特征不可分割的风险。

为了适应如此小的数据集，您可以覆盖默认值并将它们设置为 0 或更小的值。下面的代码训练成功，没有错误。

import lightgbm as lgb
import numpy as np
import pandas as pd

np.random.seed(708)

def custom_asymmetric_train(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    grad = np.where(residual<0, -2*10.0*residual, -2*residual)
    hess = np.where(residual<0, 2*10.0, 2.0)
    return grad, hess

# create a training dataset of shape (40, 7)
X = pd.DataFrame({
    f"feat_{i}": np.random.random((40,))
    for i in range(7)
})
y = np.random.random((40,))

gbm = lgb.LGBMRegressor(
    min_sum_in_hessian=0,
    min_data_in_leaf=0
)
gbm.set_params(**{'objective': custom_asymmetric_train}, metrics = ["mse", 'mae'])
gbm.fit(X, y)