本文仅做摘抄记录,展示一些lgbm用作分类与回归的代码,以供学习记忆与备用。
lgbm的github:
https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
参数解释:
https://blog.youkuaiyun.com/ssswill/article/details/85235074
示例代码:https://github.com/Microsoft/LightGBM/tree/master/examples/python-guide
官方文档:
https://lightgbm.readthedocs.io/en/latest/Python-Intro.html
1.回归
1.0回归0
代码:https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py
# coding: utf-8
# pylint: disable = invalid-name, C0111
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
print('Loading data...')
# load or create your dataset
df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)
# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
# specify your configurations as a dict
params = {
'boosting_type': 'gbdt',
'objective': 'regression',
'metric': {
'l2', 'l1'},
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
print('Starting training...')
# train
gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
early_stopping_rounds=5)
print('Saving model...')
# save model to file
gbm.save_model('model.txt')
print('Starting predicting...')
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
# eval
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
1.1回归1
代码来源:https://www.kaggle.com/chauhuynh/my-first-kernel-3-699
df_train_columns = [c for c in df_train.columns if c not in ['card_id', 'first_active_month','target','outliers']]
target = df_train[