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# use bayes_opt
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score,KFold
from bayes_opt import BayesianOptimization
import numpy as np
# 产生随机分类数据集，10个特征， 2个类别
x, y = make_classification(n_samples=1000,n_features=10,n_classes=2)# 尝试一下用未调参的随机森林模型进行交叉验证
rf = RandomForestClassifier()
# 这里会输出5个值，取得均值
cv_score = cross_val_score(rf, x, y, scoring="f1", cv=5).mean()
cv_score# 定义一个函数，输入一些超参数，这些超参数就是需要进行调整的参数
def rf_cv(n_estimators, min_samples_split, max_features, max_depth):cv_score = cross_val_score(RandomForestClassifier(n_estimators=int(n_estimators),min_samples_split=int(min_samples_split),max_features=float(max_features), max_depth=int(max_depth),random_state=2),x, y, scoring="f1", cv=5).mean()# 必须返回一个值，如果像误差的话（回归算法）这里是需要加上一个负号的return cv_score
rf_bo = BayesianOptimization(rf_cv,{'n_estimators': (10, 250),'min_samples_split': (2, 25),'max_features': (0.1, 0.999),'max_depth': (5, 15)})
# 输出不同迭代参数组合下的得分
rf_bo.maximize()
# 输出最高得分下的参数组合
rf_bo.max
# 带入最佳参数进行计算
rf = RandomForestClassifier(random_state=2,max_depth=12,max_features=0.2694,min_samples_split=6,n_estimators=103)
np.mean(cross_val_score(rf, x, y, cv=4, scoring='f1'))
使用lightgbm尝试一波
# 设置几个参数
def lgb_cv(colsample_bytree, min_child_samples, num_leaves, subsample, max_depth):model = lgb.LGBMClassifier(boosting_type='gbdt',objective='binary',colsample_bytree=float(colsample_bytree), learning_rate=0.01,min_child_samples=int(min_child_samples), min_child_weight=0.001, n_estimators=800, n_jobs=-1, num_leaves=int(num_leaves),random_state=None, reg_alpha=0.0, reg_lambda=0.0,max_depth=int(max_depth),subsample=float(subsample))cv_score = cross_val_score(model, x, y, scoring="f1", cv=5).mean()return cv_score
# 使用贝叶斯优化
lgb_bo = BayesianOptimization(lgb_cv,{'colsample_bytree': (0.7,1),'min_child_samples': (2, 25),'num_leaves': (5, 250),'subsample': (0.7, 1),'max_depth':(2,10)})
lgb_bo.maximize()
lgb_bo.max
# 将优化好的参数带入进行使用
model = lgb.LGBMClassifier(boosting_type='gbdt',objective='binary',colsample_bytree=0.7111, learning_rate=0.01,min_child_samples=9, min_child_weight=0.001, n_estimators=800, n_jobs=-1, num_leaves=188,random_state=None, reg_alpha=0.0, reg_lambda=0.0,max_depth=2,subsample=0.91)
cv_score = cross_val_score(model, x, y, scoring="f1", cv=5).mean()
cv_score
# 结果就不放出来了，也就是一个值，大家可以模仿一下，跑跑代码。
接下里是采用lightgbm的自带的train方法配合交叉验证
def LGB_CV(max_depth,num_leaves,min_data_in_leaf,feature_fraction,bagging_fraction,lambda_l1):# 这里就不采用交叉验证的cv_score = cross_val_score(model, x, y, scoring="f1", cv=5)kf = KFold(n_splits=5,shuffle=True)# f是准备存储预测值的，交叉验证下，用五份数据作为验证集，最后将这五份数据放回f里f = np.zeros(x.shape[0])for index, (train_index, val_index) in enumerate(kf.split(x)):print("fold--{}".format(index))train_data = lgb.Dataset(x[train_index], label=y[train_index])val_data = lgb.Dataset(x[val_index], label=y[val_index])params = {'num_leaves': int(num_leaves),'min_data_in_leaf': int(min_data_in_leaf), 'objective':'binary','max_depth': int(max_depth),'learning_rate': 0.01,"boosting": "gbdt","feature_fraction": feature_fraction,"bagging_fraction": bagging_fraction ,"metric": 'auc',"lambda_l1": lambda_l1,}# 因为是交叉验证的算法，这里直接使用train，valid_sets就是要评估的数据集model = lgb.train(params, train_data, valid_sets=[train_data, val_data],num_boost_round=700, verbose_eval=500,early_stopping_rounds = 20)# 返回迭代中最好的数据,这里的predict里面的数据（不需要经过dataset）不需要再进行转化，如果是xgboost就需要，需要把x_test进行转化DMatrix(x_test),这里x_test不包含类别特征f[val_index] = model.predict(x[val_index], num_iteration=model.best_iteration)# predict里面的验证集不需要进行dataset，但是xgboost算法时需要dmatrix，并且只需要DMatrix(x_test),这里x_test不包含类别特征,很多地方这里都会出错，直接带着类别就去预测del model, train_index, val_index# 由于输出的是概率值，转化为0，1的整型值 f = np.array([1 if i>0.5 else 0 for i in oof])return metrics.f1_score(f, y)# 最后进行调参
LGB_CV(max_depth=5,num_leaves=32,min_data_in_leaf=15,feature_fraction=0.8,bagging_fraction=0.8,lambda_l1=None)# 采用贝叶斯优化算法
lgb_ba = BayesianOptimization(LGB_CV, {"max_depth":(2,12),"num_leaves":(5,130),"min_data_in_leaf":(5,30),"feature_fraction":(0.7,1),"bagging_fraction":(0.7,1),"lambda_l1":(0,6)})
lgb_ba.maximize()
lgb_ba.max["params"]
# 把设置好的参数带入
kf = KFold(n_splits=5,shuffle=True)
f = np.zeros(x.shape[0])
# 设置测试集数据
x_test = x[:200]
y_test = y[:200]
prediction = np.zeros(x_test.shape[0])
for index, (train_index, val_index) in enumerate(kf.split(x)):print("fold--{}".format(index))train_data = lgb.Dataset(x[train_index], label=y[train_index])val_data = lgb.Dataset(x[val_index], label=y[val_index])params = {'num_leaves': 44,'min_data_in_leaf': 19, 'objective':'binary','max_depth': 11,'learning_rate': 0.01,"boosting": "gbdt","feature_fraction": 0.81,"bagging_fraction": 0.84 ,"metric": 'auc',"lambda_l1": 1.8,}# 因为是交叉验证的算法，这里直接使用train，valid_sets就是要评估的数据集model = lgb.train(params, train_data, valid_sets=[train_data, val_data],num_boost_round=700, verbose_eval=500,early_stopping_rounds=20)f[val_index] = model.predict(x[val_index], num_iteration=model.best_iteration)# predict里面的数据不需要进行datasetprediction +=model.predict(x_test)num_iteration=model.best_iteration)/kf.n_splits
metrics.f1_score(np.array([1 if i>0.5 else 0 for i in prediction]), y_test)
题外话
这是kaggle上面的一种做法，也是非常高效的
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