stacking融合

import pandas as pd
import numpy as np
from sklearn.linear_model import Lasso
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.model_selection import KFold, cross_val_score, train_test_split
import xgboost as xgb
import lightgbm as lgb

## 采用stacking的方法训练预测，最终的提交文件为stacking_submit.csv

all_train = pd.read_csv('all_train.csv',sep='\t')
test_set = pd.read_csv('test_set.csv',sep='\t')

result_name = test_set[['USRID']]
train = all_train.drop(['USRID', 'FLAG'], axis=1)
y_train = all_train['FLAG'].values
test = test_set.drop(['USRID'], axis=1)

#线下的交叉验证函数
n_folds = 5

def auc_cv(model):
    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)
    auc = cross_val_score(model, train.values, y_train, scoring="roc_auc", cv = kf)
    return(auc)

lasso = make_pipeline(RobustScaler(), Lasso(max_iter=1000,alpha=0.0005,fit_intercept=True,random_state=1))

GBoost = GradientBoostingRegressor(n_estimators=500, learning_rate=0.01,
                                   max_depth=18, max_features='sqrt',
                                   min_samples_leaf=16, min_samples_split=10,
                                   random_state =5)

model_xgb = xgb.XGBRegressor(colsample_bytree=0.9, objective = 'binary:logistic',
                             learning_rate=0.02, max_depth=6, eval_metric = 'auc',
                             min_child_weight=10, n_estimators=842,
                             subsample=0.7, silent=1,
                             random_state =0, nthread = -1)

model_lgb = lgb.LGBMRegressor(objective='binary',metric ='auc',num_leaves=35,
                              learning_rate=0.01, n_estimators=842,
                              max_bin = 55, bagging_fraction = 0.8,
                              bagging_freq = 3, feature_fraction = 0.9,
                              feature_fraction_seed=9, bagging_seed=9,
                              min_data_in_leaf =370, min_sum_hessian_in_leaf = 11)

# 单模型的线下得分
score_lasso = auc_cv(lasso)
print("\nLasso score: {:.4f} ({:.4f})\n".format(score_lasso.mean(), score_lasso.std()))

score_GBoost = auc_cv(GBoost)
print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score_GBoost.mean(), score_GBoost.std()))

score_lgb = auc_cv(model_lgb)
print("LightGBM score: {:.4f} ({:.4f})\n".format(score_lgb.mean(), score_lgb.std()))

score_xgb = auc_cv(model_xgb)
print("XGBoost score: {:.4f} ({:.4f})\n".format(score_xgb.mean(), score_xgb.std()))

## 定义stacking的类

class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):
    def __init__(self, base_models, meta_model, n_folds=5):
        self.base_models = base_models
        self.meta_model = meta_model
        self.n_folds = n_folds

    def fit(self, X, y):
        self.base_models_ = [list() for x in self.base_models]
        self.meta_model_ = clone(self.meta_model)
        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)

        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))  ##初始化矩阵
        for i, model in enumerate(self.base_models):
            for train_index, holdout_index in kfold.split(X, y):
                instance = clone(model)
                self.base_models_[i].append(instance)  ##五折交叉验证，一个基模型有5个instance
                instance.fit(X[train_index], y[train_index])
                y_pred = instance.predict(X[holdout_index])
                out_of_fold_predictions[holdout_index, i] = y_pred

        self.meta_model_.fit(out_of_fold_predictions, y)
        return self

    def predict(self, X):
        meta_features = np.column_stack([
            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)
            for base_models in self.base_models_])
        return self.meta_model_.predict(meta_features)

stacked_averaged_models = StackingAveragedModels(base_models = (GBoost,model_xgb,model_lgb),
                                                 meta_model = lasso)
# stacking模型的线下得分
score = auc_cv(stacked_averaged_models)
print("Stacking Averaged models score: {:.4f} ({:.4f})".format(score.mean(), score.std()))

stacked_averaged_models.fit(train.values, y_train)
stacked_pred = stacked_averaged_models.predict(test.values)

result_name['RST'] = stacked_pred
result_name.to_csv('stacking_submit.csv',index=None,sep='\t')

 

 

普通加权融合，主要是根据线上的分数进行决定权重。（但是有时候存在对等分对线上更有帮助。

加权平均结合sigmoid反函数
    主要步骤：首先将各个模型的结果代入到sigmoid反函数中，然后得到其均值，对其结果使用sigmoid函数。相较于普通的加权平均，这种方法更适合于结果具有较小差异性的。
def f(x):
    res=1/(1+np.e**(-x))
    return res

def f_ver(x):
    res=np.log(x/(1-x))
    return res

 https://www.sohu.com/a/196885191_116235