自动调参库hyperopt+lightgbm 调参demo

在此之前，调参要么网格调参，要么随机调参，要么肉眼调参。虽然调参到一定程度，进步有限，但仍然很耗精力。 自动调参库hyperopt可用tpe算法自动调参，实测强于随机调参。 hyperopt 需要自己写个输入参数，返回模型分数的函数（只能求最小化，如果分数是求最大化的，加个负号），设置参数空间。 本来最优参数fmin函数会自己输出的，但是出了意外，参数会强制转化整数，没办法只好自己动手了。 demo如下： import lightgbm as lgb from sklearn.metrics import roc_auc_score as auc def get_set(n1,data='trained.csv',n_splits=10,y=False,random_state=0): from sklearn.model_selection import KFold data=pd.read_csv(data) kf = KFold(n_splits=n_splits,shuffle=True,random_state=random_state) if y: train,test=pd.DataFrame(),pd.DataFrame() clas=list(data[y].unique()) for cla in clas: i=0 dd=data[data[y]==cla] for train_index,test_index in kf.split(dd): i=i+1 if n1==i: train=train.append(data.loc[list(train_index)]) test=test.append(data.loc[list(test_index)]) else: i=0 for train_index,test_index in kf.split(data): i=i+1 if n1==i: train=data.iloc[list(train_index),:] test=data.iloc[list(test_index),:] return train,test def scorer(yp,data): yt= data.get_label() score=auc(yt,yp) return 'auc',score,True def peropt(param): conf=['num_leaves','max_depth','min_child_samples','max_bin'] for i in conf: param[i]=int(param[i]) evals_result={} lgb.train(param, dtrain, 2000, feval=scorer, valid_sets=[dval], verbose_eval=None, evals_result=evals_result, early_stopping_rounds=10) best_score=evals_result['valid_0']['auc'][-11] #print(param,best_score,len(evals_result['valid_0']['auc'])-10) result.append((param,best_score,len(evals_result['valid_0']['auc'])-10)) return -best_score if 0:#数据集 i=1 x_train,x_test=get_set(i,n_splits=5) x_train.pop('CaseId') x_test.pop('CaseId') y_train=x_train.pop('Evaluation') y_test=x_test.pop('Evaluation') dtrain=lgb.Dataset(x_train,y_train) dval=lgb.Dataset(x_test,y_test) if 1:#调参 from hyperopt import fmin,tpe,hp#,rand#,pyll#,partial space={ 'num_leaves': hp.quniform('num_leaves',50,70,1) ,'max_depth':hp.quniform('max_depth',7,15,1) ,'min_child_samples':hp.quniform('min_child_samples',5,20,1) ,'max_bin':hp.quniform('max_bin',100,150,5) ,'learning_rate':hp.choice('learning_rate',[0.01]) ,'subsample':hp.uniform('subsample',0.9,1) ,'colsample_bytree':hp.uniform('colsample_bytree',0.95,1) ,'min_split_gain':hp.loguniform('min_split_gain',-5,2) ,'reg_alpha':hp.loguniform('reg_alpha',-5,2) ,'reg_lambda':hp.loguniform('reg_lambda',-5,2) } result=[] #print(pyll.stochastic.sample(space))#抽样 #algo=partial(tpe.suggest,n_startup_jobs=10)#作用未知 fmin(peropt, space=space, algo=tpe.suggest, max_evals=100 ) sort=sorted(result,key=lambda x:x[1],reverse=True)