kaggle平台学习复习笔记 | XGBoost、LightGBM and Catboost

news2024/9/28 19:20:46

这里写目录

1.XGBoost
- 官方文档
- 介绍与使用
2.LightGBM
- 官方文档
- 介绍与使用
3.CatBoost
- 官方文档
- 介绍与使用
对比

数据预处理如下，下文不再重复

import lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

def rmse(y_true, y_pred):
    return round(np.sqrt(mean_squared_error(y_true, y_pred)), 5)

def load_preprocessed_dfs(drop_full_visitor_id=True):
    """
    Loads files `TRAIN`, `TEST` and `Y` generated by preprocess() into variables
    """
    X_train = pd.read_csv(TRAIN, converters={'fullVisitorId': str})
    X_test = pd.read_csv(TEST, converters={'fullVisitorId': str})
    y_train = pd.read_csv(Y, names=['LogRevenue']).T.squeeze()
    
    # This is the only `object` column, we drop it for train and evaluation
    if drop_full_visitor_id: 
        X_train = X_train.drop(['fullVisitorId'], axis=1)
        X_test = X_test.drop(['fullVisitorId'], axis=1)
    return X_train, y_train, X_test

X, y, X_test = load_preprocessed_dfs()
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.15, random_state=1)

print(f"Train shape: {X_train.shape}")
print(f"Validation shape: {X_val.shape}")
print(f"Test (submit) shape: {X_test.shape}")

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1.XGBoost

官方文档

https://xgboost.readthedocs.io/en/stable/parameter.html

介绍与使用

介绍
高阶树模型，它的出现略晚于随机森林

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使用

pip install xgboost

原生接口

def run_xgb(X_train, y_train, X_val, y_val, X_test):
    # 参数参考官方文档
    params = {'objective': 'reg:linear',
              'eval_metric': 'rmse',
              'eta': 0.001,
              'max_depth': 10,
              'subsample': 0.6,
              'colsample_bytree': 0.6,
              'alpha':0.001,
              'random_state': 42,
              'silent': True}

    xgb_train_data = xgb.DMatrix(X_train, y_train)
    xgb_val_data = xgb.DMatrix(X_val, y_val)
    xgb_submit_data = xgb.DMatrix(X_test)

    model = xgb.train(params, xgb_train_data, 
                      num_boost_round=2000, # 训练轮数
                      evals= [(xgb_train_data, 'train'), (xgb_val_data, 'valid')],
                      early_stopping_rounds=100, # 超过100epchos可以提前停止
                      verbose_eval=500  #每500次打印一次精度
                     )

    y_pred_train = model.predict(xgb_train_data, ntree_limit=model.best_ntree_limit)
    y_pred_val = model.predict(xgb_val_data, ntree_limit=model.best_ntree_limit)
    y_pred_submit = model.predict(xgb_submit_data, ntree_limit=model.best_ntree_limit)

    print(f"XGB : RMSE val: {rmse(y_val, y_pred_val)}  - RMSE train: {rmse(y_train, y_pred_train)}")
    return y_pred_submit, model


%%time
xgb_preds, xgb_model = run_xgb(X_train, y_train, X_val, y_val, X_test)

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2.LightGBM

官方文档

https://lightgbm.readthedocs.io/en/latest/

介绍与使用

LightGBM是对XGBoost节点分裂方面的一些改进

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使用

pip install lightgbm

原生接口

def run_lgb(X_train, y_train, X_val, y_val, X_test):
    # 参数作用参考官方文档
    params = {
        "objective" : "regression",
        "metric" : "rmse",
        "num_leaves" : 40,
        "learning_rate" : 0.005,
        "bagging_fraction" : 0.6,
        "feature_fraction" : 0.6,
        "bagging_frequency" : 6,
        "bagging_seed" : 42,
        "verbosity" : -1,
        "seed": 42
    }
    # 数据集封装
    lgb_train_data = lgb.Dataset(X_train, label=y_train)
    lgb_val_data = lgb.Dataset(X_val, label=y_val)
	
	# train的参数如上
    model = lgb.train(params, lgb_train_data, 
                      num_boost_round=5000,
                      valid_sets=[lgb_train_data, lgb_val_data],
                      early_stopping_rounds=100,
                      verbose_eval=500)

    y_pred_train = model.predict(X_train, num_iteration=model.best_iteration)
    y_pred_val = model.predict(X_val, num_iteration=model.best_iteration)
    y_pred_submit = model.predict(X_test, num_iteration=model.best_iteration)

    print(f"LGBM: RMSE val: {rmse(y_val, y_pred_val)}  - RMSE train: {rmse(y_train, y_pred_train)}")
    return y_pred_submit, model

结果：
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最优解相关数据

print("LightGBM features importance...")
gain = lgb_model.feature_importance('gain')
featureimp = pd.DataFrame({'feature': lgb_model.feature_name(), 
                   'split': lgb_model.feature_importance('split'), 
                   'gain': 100 * gain / gain.sum()}).sort_values('gain', ascending=False)
print(featureimp[:10])