LightGBM建模
作者：互联网
LightGBM
1.读取csv数据并指定参数建模
# coding: utf-8
import json
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
# 加载数据
print('Load data...')
df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\t')
df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\t')
# 设定训练集和测试集
y_train = df_train[0].values
y_test = df_test[0].values
X_train = df_train.drop(0, axis=1).values
X_test = df_test.drop(0, axis=1).values
# 构建lgb中的Dataset格式，和xgboost中的DMatrix是对应的
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
# 参数
params = {
'task': 'train',
'boosting_type': 'gbdt',
'objective': 'regression',
'metric': {'l2', 'auc'},
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
print('开始训练...')
# 训练
gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
early_stopping_rounds=5)
# 保存模型
print('保存模型...')
# 保存模型到文件中
gbm.save_model('model.txt')
print('开始预测...')
# 预测
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
# 评估
print('预估结果的rmse为:')
print(mean_squared_error(y_test, y_pred) ** 0.5)Load data...
开始训练...
[1] valid_0's auc: 0.764496 valid_0's l2: 0.24288
Training until validation scores don't improve for 5 rounds.
[2] valid_0's auc: 0.766173 valid_0's l2: 0.239307
[3] valid_0's auc: 0.785547 valid_0's l2: 0.235559
[4] valid_0's auc: 0.797786 valid_0's l2: 0.230771
[5] valid_0's auc: 0.805155 valid_0's l2: 0.226297
[6] valid_0's auc: 0.803083 valid_0's l2: 0.22359
[7] valid_0's auc: 0.809622 valid_0's l2: 0.220982
[8] valid_0's auc: 0.808114 valid_0's l2: 0.218316
[9] valid_0's auc: 0.805671 valid_0's l2: 0.215884
[10] valid_0's auc: 0.805365 valid_0's l2: 0.213232
[11] valid_0's auc: 0.804857 valid_0's l2: 0.211087
[12] valid_0's auc: 0.805453 valid_0's l2: 0.20914
Early stopping, best iteration is:
[7] valid_0's auc: 0.809622 valid_0's l2: 0.220982
保存模型...
开始预测...
预估结果的rmse为:
0.47008692860411752.添加样本权重训练
# coding: utf-8
import json
import lightgbm as lgb
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings("ignore")
# 加载数据集
print('加载数据...')
df_train = pd.read_csv('./data/binary.train', header=None, sep='\t')
df_test = pd.read_csv('./data/binary.test', header=None, sep='\t')
W_train = pd.read_csv('./data/binary.train.weight', header=None)[0]
W_test = pd.read_csv('./data/binary.test.weight', header=None)[0]
y_train = df_train[0].values
y_test = df_test[0].values
X_train = df_train.drop(0, axis=1).values
X_test = df_test.drop(0, axis=1).values
num_train, num_feature = X_train.shape
# 加载数据的同时加载权重
lgb_train = lgb.Dataset(X_train, y_train,
weight=W_train, free_raw_data=False)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,
weight=W_test, free_raw_data=False)
# 设定参数
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'binary_logloss',
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
# 产出特征名称
feature_name = ['feature_' + str(col) for col in range(num_feature)]
print('开始训练...')
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
valid_sets=lgb_train, # 评估训练集
feature_name=feature_name,
categorical_feature=[21])加载数据...
开始训练...
[1] training's binary_logloss: 0.680298
[2] training's binary_logloss: 0.672021
[3] training's binary_logloss: 0.664444
[4] training's binary_logloss: 0.655536
[5] training's binary_logloss: 0.647375
[6] training's binary_logloss: 0.64095
[7] training's binary_logloss: 0.63514
[8] training's binary_logloss: 0.628769
[9] training's binary_logloss: 0.622774
[10] training's binary_logloss: 0.6168953.模型的载入与预测
# 查看特征名称
print('完成10轮训练...')
print('第7个特征为:')
print(repr(lgb_train.feature_name[6]))
# 存储模型
gbm.save_model('./model/lgb_model.txt')
# 特征名称
print('特征名称:')
print(gbm.feature_name())
# 特征重要度
print('特征重要度:')
print(list(gbm.feature_importance()))
# lgb.Booster加载模型
print('加载模型用于预测')
bst = lgb.Booster(model_file='./model/lgb_model.txt')
# 预测
y_pred = bst.predict(X_test)
# 在测试集评估效果
print('在测试集上的rmse为:')
print(mean_squared_error(y_test, y_pred) ** 0.5)完成10轮训练...
第7个特征为:
'feature_6'
特征名称:
['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_13', 'feature_14', 'feature_15', 'feature_16', 'feature_17', 'feature_18', 'feature_19', 'feature_20', 'feature_21', 'feature_22', 'feature_23', 'feature_24', 'feature_25', 'feature_26', 'feature_27']
特征重要度:
[9, 6, 1, 15, 5, 40, 3, 0, 0, 8, 2, 1, 0, 9, 2, 0, 0, 6, 2, 6, 0, 0, 37, 2, 30, 50, 37, 29]
加载模型用于预测
在测试集上的rmse为:
0.46241117632267294.接着之前的模型继续训练
# 继续训练
# 从./model/model.txt中加载模型初始化
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model='./model/lgb_model.txt',
valid_sets=lgb_eval)
print('以旧模型为初始化，完成第 10-20 轮训练...')
# 在训练的过程中调整超参数
# 比如这里调整的是学习率
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
learning_rates=lambda iter: 0.05 * (0.99 ** iter),
valid_sets=lgb_eval)
print('逐步调整学习率完成第 20-30 轮训练...')
# 调整其他超参数
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
valid_sets=lgb_eval,
callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)])
print('逐步调整bagging比率完成第 30-40 轮训练...')[11] valid_0's binary_logloss: 0.614214
[12] valid_0's binary_logloss: 0.609777
[13] valid_0's binary_logloss: 0.605236
[14] valid_0's binary_logloss: 0.601523
[15] valid_0's binary_logloss: 0.598256
[16] valid_0's binary_logloss: 0.595957
[17] valid_0's binary_logloss: 0.591773
[18] valid_0's binary_logloss: 0.588163
[19] valid_0's binary_logloss: 0.585106
[20] valid_0's binary_logloss: 0.582878
以旧模型为初始化，完成第 10-20 轮训练...
[21] valid_0's binary_logloss: 0.614214
[22] valid_0's binary_logloss: 0.60982
[23] valid_0's binary_logloss: 0.605366
[24] valid_0's binary_logloss: 0.601754
[25] valid_0's binary_logloss: 0.598598
[26] valid_0's binary_logloss: 0.596394
[27] valid_0's binary_logloss: 0.59243
[28] valid_0's binary_logloss: 0.58903
[29] valid_0's binary_logloss: 0.586164
[30] valid_0's binary_logloss: 0.583693
逐步调整学习率完成第 20-30 轮训练...
[31] valid_0's binary_logloss: 0.613881
[32] valid_0's binary_logloss: 0.608822
[33] valid_0's binary_logloss: 0.604746
[34] valid_0's binary_logloss: 0.600465
[35] valid_0's binary_logloss: 0.596407
[36] valid_0's binary_logloss: 0.593572
[37] valid_0's binary_logloss: 0.589196
[38] valid_0's binary_logloss: 0.586633
[39] valid_0's binary_logloss: 0.583136
[40] valid_0's binary_logloss: 0.579651
逐步调整bagging比率完成第 30-40 轮训练...5.自定义损失函数
# 类似在xgboost中的形式
# 自定义损失函数需要
def loglikelood(preds, train_data):
labels = train_data.get_label()
preds = 1. / (1. + np.exp(-preds))
grad = preds - labels
hess = preds * (1. - preds)
return grad, hess
# 自定义评估函数
def binary_error(preds, train_data):
labels = train_data.get_label()
return 'error', np.mean(labels != (preds > 0.5)), False
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
fobj=loglikelood,
feval=binary_error,
valid_sets=lgb_eval)
print('用自定义的损失函数与评估标准完成第40-50轮...')[41] valid_0's binary_logloss: 4.61573 valid_0's error: 0.394
[42] valid_0's binary_logloss: 4.66615 valid_0's error: 0.386
[43] valid_0's binary_logloss: 4.58473 valid_0's error: 0.388
[44] valid_0's binary_logloss: 4.63403 valid_0's error: 0.388
[45] valid_0's binary_logloss: 4.81468 valid_0's error: 0.38
[46] valid_0's binary_logloss: 4.86387 valid_0's error: 0.366
[47] valid_0's binary_logloss: 4.71095 valid_0's error: 0.37
[48] valid_0's binary_logloss: 4.81772 valid_0's error: 0.358
[49] valid_0's binary_logloss: 4.87924 valid_0's error: 0.358
[50] valid_0's binary_logloss: 4.86966 valid_0's error: 0.352
用自定义的损失函数与评估标准完成第40-50轮...sklearn与LightGBM配合使用
1.LightGBM建模，sklearn评估
# coding: utf-8
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
# 加载数据
print('加载数据...')
df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\t')
df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\t')
# 取出特征和标签
y_train = df_train[0].values
y_test = df_test[0].values
X_train = df_train.drop(0, axis=1).values
X_test = df_test.drop(0, axis=1).values
print('开始训练...')
# 直接初始化LGBMRegressor
# 这个LightGBM的Regressor和sklearn中其他Regressor基本是一致的
gbm = lgb.LGBMRegressor(objective='regression',
num_leaves=31,
learning_rate=0.05,
n_estimators=20)
# 使用fit函数拟合
gbm.fit(X_train, y_train,
eval_set=[(X_test, y_test)],
eval_metric='l1',
early_stopping_rounds=5)
# 预测
print('开始预测...')
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# 评估预测结果
print('预测结果的rmse是:')
print(mean_squared_error(y_test, y_pred) ** 0.5)加载数据...
开始训练...
[1] valid_0's l1: 0.491735 valid_0's l2: 0.242763
Training until validation scores don't improve for 5 rounds.
[2] valid_0's l1: 0.486563 valid_0's l2: 0.237895
[3] valid_0's l1: 0.481489 valid_0's l2: 0.233277
[4] valid_0's l1: 0.476848 valid_0's l2: 0.22925
[5] valid_0's l1: 0.47305 valid_0's l2: 0.226155
[6] valid_0's l1: 0.469049 valid_0's l2: 0.222963
[7] valid_0's l1: 0.465556 valid_0's l2: 0.220364
[8] valid_0's l1: 0.462208 valid_0's l2: 0.217872
[9] valid_0's l1: 0.458676 valid_0's l2: 0.215328
[10] valid_0's l1: 0.454998 valid_0's l2: 0.212743
[11] valid_0's l1: 0.452047 valid_0's l2: 0.210805
[12] valid_0's l1: 0.449158 valid_0's l2: 0.208945
[13] valid_0's l1: 0.44608 valid_0's l2: 0.206986
[14] valid_0's l1: 0.443554 valid_0's l2: 0.205513
[15] valid_0's l1: 0.440643 valid_0's l2: 0.203728
[16] valid_0's l1: 0.437687 valid_0's l2: 0.201865
[17] valid_0's l1: 0.435454 valid_0's l2: 0.200639
[18] valid_0's l1: 0.433288 valid_0's l2: 0.199522
[19] valid_0's l1: 0.431297 valid_0's l2: 0.198552
[20] valid_0's l1: 0.428946 valid_0's l2: 0.197238
Did not meet early stopping. Best iteration is:
[20] valid_0's l1: 0.428946 valid_0's l2: 0.197238
开始预测...
预测结果的rmse是:
0.44411533442542082.网格搜索查找最优超参数
# 配合scikit-learn的网格搜索交叉验证选择最优超参数
estimator = lgb.LGBMRegressor(num_leaves=31)
param_grid = {
'learning_rate': [0.01, 0.1, 1],
'n_estimators': [20, 40]
}
gbm = GridSearchCV(estimator, param_grid)
gbm.fit(X_train, y_train)
print('用网格搜索找到的最优超参数为:')
print(gbm.best_params_)用网格搜索找到的最优超参数为:
{'learning_rate': 0.1, 'n_estimators': 40}3.绘图解释
# coding: utf-8
import lightgbm as lgb
import pandas as pd
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError('You need to install matplotlib for plotting.')
# 加载数据集
print('加载数据...')
df_train = pd.read_csv('./data/regression.train.txt', header=None, sep='\t')
df_test = pd.read_csv('./data/regression.test.txt', header=None, sep='\t')
# 取出特征和标签
y_train = df_train[0].values
y_test = df_test[0].values
X_train = df_train.drop(0, axis=1).values
X_test = df_test.drop(0, axis=1).values
# 构建lgb中的Dataset数据格式
lgb_train = lgb.Dataset(X_train, y_train)
lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)
# 设定参数
params = {
'num_leaves': 5,
'metric': ('l1', 'l2'),
'verbose': 0
}
evals_result = {} # to record eval results for plotting
print('开始训练...')
# 训练
gbm = lgb.train(params,
lgb_train,
num_boost_round=100,
valid_sets=[lgb_train, lgb_test],
feature_name=['f' + str(i + 1) for i in range(28)],
categorical_feature=[21],
evals_result=evals_result,
verbose_eval=10)
print('在训练过程中绘图...')
ax = lgb.plot_metric(evals_result, metric='l1')
plt.show()
print('画出特征重要度...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()
print('画出第84颗树...')
ax = lgb.plot_tree(gbm, tree_index=83, figsize=(20, 8), show_info=['split_gain'])
plt.show()
#print('用graphviz画出第84颗树...')
#graph = lgb.create_tree_digraph(gbm, tree_index=83, name='Tree84')
#graph.render(view=True)加载数据...
开始训练...
[10] training's l1: 0.457448 training's l2: 0.217995 valid_1's l1: 0.456464 valid_1's l2: 0.21641
[20] training's l1: 0.436869 training's l2: 0.205099 valid_1's l1: 0.434057 valid_1's l2: 0.201616
[30] training's l1: 0.421302 training's l2: 0.197421 valid_1's l1: 0.417019 valid_1's l2: 0.192514
[40] training's l1: 0.411107 training's l2: 0.192856 valid_1's l1: 0.406303 valid_1's l2: 0.187258
[50] training's l1: 0.403695 training's l2: 0.189593 valid_1's l1: 0.398997 valid_1's l2: 0.183688
[60] training's l1: 0.398704 training's l2: 0.187043 valid_1's l1: 0.393977 valid_1's l2: 0.181009
[70] training's l1: 0.394876 training's l2: 0.184982 valid_1's l1: 0.389805 valid_1's l2: 0.178803
[80] training's l1: 0.391147 training's l2: 0.1828 valid_1's l1: 0.386476 valid_1's l2: 0.176799
[90] training's l1: 0.388101 training's l2: 0.180817 valid_1's l1: 0.384404 valid_1's l2: 0.175775
[100] training's l1: 0.385174 training's l2: 0.179171 valid_1's l1: 0.382929 valid_1's l2: 0.175321
在训练过程中绘图...
画出特征重要度...
画出第84颗树...
标签：binary,LightGBM,lgb,建模,logloss,train,l2,valid 来源： https://www.cnblogs.com/chenxiangzhen/p/10962983.html