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比较实用的一个自动调参代码，可以根据自己的算法进行设置
import pandas as pd
import lightgbm as lgb
from sklearn import metrics
from sklearn.datasets import load_breast_cancer
from sklearn.cross_validation import train_test_split
canceData=load_breast_cancer()
X=canceData.data
y=canceData.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0,test_size=0.2)
### 数据转换
print('数据转换')
lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,free_raw_data=False)
### 设置初始参数--不含交叉验证参数
print('设置参数')
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'nthread':4,
'learning_rate':0.1
}
### 交叉验证(调参)
print('交叉验证')
max_auc = float('0')
best_params = {}
# 准确率
print("调参1：提高准确率")
for num_leaves in range(5,100,5):
for max_depth in range(3,8,1):
params['num_leaves'] = num_leaves
params['max_depth'] = max_depth
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc = mean_auc
best_params['num_leaves'] = num_leaves
best_params['max_depth'] = max_depth
if 'num_leaves' and 'max_depth' in best_params.keys():
params['num_leaves'] = best_params['num_leaves']
params['max_depth'] = best_params['max_depth']
# 过拟合
print("调参2：降低过拟合")
for max_bin in range(5,256,10):
for min_data_in_leaf in range(1,102,10):
params['max_bin'] = max_bin
params['min_data_in_leaf'] = min_data_in_leaf
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc = mean_auc
best_params['max_bin']= max_bin
best_params['min_data_in_leaf'] = min_data_in_leaf
if 'max_bin' and 'min_data_in_leaf' in best_params.keys():
params['min_data_in_leaf'] = best_params['min_data_in_leaf']
params['max_bin'] = best_params['max_bin']
print("调参3：降低过拟合")
for feature_fraction in [0.6,0.7,0.8,0.9,1.0]:
for bagging_fraction in [0.6,0.7,0.8,0.9,1.0]:
for bagging_freq in range(0,50,5):
params['feature_fraction'] = feature_fraction
params['bagging_fraction'] = bagging_fraction
params['bagging_freq'] = bagging_freq
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['feature_fraction'] = feature_fraction
best_params['bagging_fraction'] = bagging_fraction
best_params['bagging_freq'] = bagging_freq
if 'feature_fraction' and 'bagging_fraction' and 'bagging_freq' in best_params.keys():
params['feature_fraction'] = best_params['feature_fraction']
params['bagging_fraction'] = best_params['bagging_fraction']
params['bagging_freq'] = best_params['bagging_freq']
print("调参4：降低过拟合")
for lambda_l1 in [1e-5,1e-3,1e-1,0.0,0.1,0.3,0.5,0.7,0.9,1.0]:
for lambda_l2 in [1e-5,1e-3,1e-1,0.0,0.1,0.4,0.6,0.7,0.9,1.0]:
params['lambda_l1'] = lambda_l1
params['lambda_l2'] = lambda_l2
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['lambda_l1'] = lambda_l1
best_params['lambda_l2'] = lambda_l2
if 'lambda_l1' and 'lambda_l2' in best_params.keys():
params['lambda_l1'] = best_params['lambda_l1']
params['lambda_l2'] = best_params['lambda_l2']
print("调参5：降低过拟合2")
for min_split_gain in [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]:
params['min_split_gain'] = min_split_gain
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['min_split_gain'] = min_split_gain
if 'min_split_gain' in best_params.keys():
params['min_split_gain'] = best_params['min_split_gain']
print(best_params)