python lgb代码 使用numpy

import numpy as np
import lightgbm as lgb

# 准备训练数据和标签
X_train = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y_train = np.array([0, 1, 0])

# 准备测试数据和标签
X_test = np.array([[2, 3, 4], [5, 6, 7]])
y_test = np.array([1, 0])

# 将数据转换为lgb特定的数据格式
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# 定义模型参数
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'binary_logloss',
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9
}

# 训练模型
num_round = 100
bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])

# 预测
y_pred = bst.predict(X_test)

# 输出预测结果
print(y_pred)

相关问题

python lgb stacking代码

很抱歉，作为AI语言模型，我不具备编写代码的能力。但是，以下是一个LGBMRegressor模型的Stacking实现的代码示例，供参考：

import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from lightgbm import LGBMRegressor

# Load train and test datasets
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# Define target variable and features
target = 'target'
features = [col for col in train_df.columns if col not in [target]]

# Initialize parameters for LGBMRegressor model
lgb_params = {
    'objective': 'regression',
    'boosting_type': 'gbdt',
    'metric': 'rmse',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': -1
}

# Define number of folds for cross-validation
n_folds = 5

# Initialize empty array to store predictions
train_preds = np.zeros(train_df.shape[0])
test_preds = np.zeros(test_df.shape[0])

# Initialize KFold cross-validation
kf = KFold(n_splits=n_folds, shuffle=True, random_state=42)

# Loop through each fold
for fold, (train_idx, val_idx) in enumerate(kf.split(train_df)):
    print('Fold:', fold+1)
    
    # Split train and validation datasets
    X_train, y_train = train_df[features].iloc[train_idx], train_df[target].iloc[train_idx]
    X_val, y_val = train_df[features].iloc[val_idx], train_df[target].iloc[val_idx]
    
    # Initialize LGBMRegressor model
    model = LGBMRegressor(**lgb_params)
    
    # Fit model on train dataset
    model.fit(X_train, y_train, eval_set=[(X_val, y_val)], early_stopping_rounds=100, verbose=100)
    
    # Generate predictions on train and validation datasets
    train_preds[val_idx] = model.predict(X_val)
    test_preds += model.predict(test_df[features]) / n_folds
    
    # Calculate RMSE for validation dataset
    rmse = np.sqrt(mean_squared_error(y_val, train_preds[val_idx]))
    print('RMSE:', rmse)

# Calculate overall RMSE for train dataset
rmse = np.sqrt(mean_squared_error(train_df[target], train_preds))
print('Overall RMSE:', rmse)

# Save predictions to CSV file
submission = pd.DataFrame({'id': test_df['id'], 'target': test_preds})
submission.to_csv('submission.csv', index=False)

python lgb

你好！关于 "python lgb"，我猜测你可能在问 LightGBM（LGB）在 Python 中的使用方法。

要在 Python 中使用 LightGBM，首先需要安装 LightGBM 库。可以通过以下命令使用 pip 进行安装：

pip install lightgbm

安装完成后，可以在 Python 脚本中导入 LightGBM 并使用它进行机器学习任务。以下是一个简单的示例代码，演示了如何使用 LightGBM 进行二分类任务：

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

# 加载数据
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1)
y = data['target']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建 LightGBM 数据集
train_data = lgb.Dataset(X_train, label=y_train)

# 设置模型参数
params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'boosting_type': 'gbdt',    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9
}

# 训练模型
model = lgb.train(params, train_data, num_boost_round=100)

# 预测
y_pred = model.predict(X_test)

# 输出预测结果
print(y_pred)

# 相关问题: