深度学习模型融合stacking

当你的深度学习模型变得很多时，选一个确定的模型也是一个头痛的问题。或者你可以把他们都用起来，就进行模型融合。我主要使用stacking和blend方法。先把代码贴出来，大家可以看一下。

 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve
 
 SEED = 222
 np.random.seed(SEED)
 from sklearn.model_selection import train_test_split
 
 from sklearn.metrics import roc_auc_score
 from sklearn.svm import SVC,LinearSVC
 from sklearn.naive_bayes import GaussianNB
 from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.neural_network import MLPClassifier
 
 df = pd.read_csv('input.csv')
 
 def get_train_test():   # 数据处理
 
     y = 1 * (df.cand_pty_affiliation == "REP")
     x = df.drop(['cand_pty_affiliation'],axis=1)
     x = pd.get_dummies(x,sparse=True)
     x.drop(x.columns[x.std()==0],axis=1,inplace=True)
     return train_test_split(x,y,test_size=0.95,random_state=SEED)
 
 def get_models():   # 模型定义
     nb = GaussianNB()
     svc = SVC(C=100,probability=True)
     knn = KNeighborsClassifier(n_neighbors=3)
     lr = LogisticRegression(C=100,random_state=SEED)
     nn = MLPClassifier((80, 10), early_stopping=False, random_state=SEED)
     gb = GradientBoostingClassifier(n_estimators =100, random_state = SEED)
     rf = RandomForestClassifier(n_estimators=1,max_depth=3,random_state=SEED)
 
     models = {'svm':svc,
               'knn':knn,
               'naive bayes':nb,
               'mlp-nn':nn,
               'random forest':rf,
               'gbm':gb,
               'logistic':lr,
               }
     return models
 
 def train_base_learnres(base_learners,inp,out,verbose=True):    # 训练基本模型
     if verbose:print("fitting models.")
     for i,(name,m) in enumerate(base_learners.items()):
         if verbose:print("%s..." % name,end=" ",flush=False)
         m.fit(inp,out)
         if verbose:print("done")
 
 def predict_base_learners(pred_base_learners,inp,verbose=True): # 把基本学习器的输出作为融合学习的特征，这里计算特征
     p = np.zeros((inp.shape[0],len(pred_base_learners)))
     if verbose:print("Generating base learner predictions.")
     for i,(name,m) in enumerate(pred_base_learners.items()):
         if verbose:print("%s..." % name,end=" ",flush=False)
         p_ = m.predict_proba(inp)
         p[:,i] = p_[:,1]
         if verbose:print("done")
     return p
 
 def ensemble_predict(base_learners,meta_learner,inp,verbose=True):  # 融合学习进行预测
     p_pred = predict_base_learners(base_learners,inp,verbose=verbose)    # 测试数据必须先经过基本学习器计算特征
     return p_pred,meta_learner.predict_proba(p_pred)[:,1]
 
 def ensenmble_by_blend():   # blend融合
     xtrain_base, xpred_base, ytrain_base, ypred_base = train_test_split(
         xtrain, ytrain, test_size=0.5, random_state=SEED
     )   # 把数据切分成两部分
 
     train_base_learnres(base_learners, xtrain_base, ytrain_base)  # 训练基本模型
 
     p_base = predict_base_learners(base_learners, xpred_base) # 把基本学习器的输出作为融合学习的特征，这里计算特征
     meta_learner.fit(p_base, ypred_base)    # 融合学习器的训练
     p_pred, p = ensemble_predict(base_learners, meta_learner, xtest)  # 融合学习进行预测
     print("\nEnsemble ROC-AUC score: %.3f" % roc_auc_score(ytest, p))
 
 from sklearn.base import clone
 def stacking(base_learners,meta_learner,X,y,generator): # stacking进行融合
     print("Fitting final base learners...",end="")
     train_base_learnres(base_learners,X,y,verbose=False)
     print("done")
 
     print("Generating cross-validated predictions...")
     cv_preds,cv_y = [],[]
     for i,(train_inx,test_idx) in enumerate(generator.split(X)):
         fold_xtrain,fold_ytrain = X[train_inx,:],y[train_inx]
         fold_xtest,fold_ytest = X[test_idx,:],y[test_idx]
 
         fold_base_learners = {name:clone(model)
                               for name,model in base_learners.items()}
         train_base_learnres(fold_base_learners,fold_xtrain,fold_ytrain,verbose=False)
         fold_P_base = predict_base_learners(fold_base_learners,fold_xtest,verbose=False)
 
         cv_preds.append(fold_P_base)
         cv_y.append(fold_ytest)
 
         print("Fold %i done" %(i+1))
     print("CV-predictions done")
     cv_preds = np.vstack(cv_preds)
     cv_y = np.hstack(cv_y)
 
     print("Fitting meta learner...",end="")
     meta_learner.fit(cv_preds,cv_y)
     print("done")
 
     return base_learners,meta_learner
 
 def ensemble_by_stack():
     from sklearn.model_selection import KFold
     cv_base_learners,cv_meta_learner = stacking(
         get_models(),clone(meta_learner),xtrain.values,ytrain.values,KFold(2))
     P_pred,p = ensemble_predict(cv_base_learners,cv_meta_learner,xtest,verbose=False)
     print("\nEnsemble ROC-AUC score: %.3f" %roc_auc_score(ytest,p))
 
 def plot_roc_curve(ytest,p_base_learners,p_ensemble,labels,ens_label):
     plt.figure(figsize=(10,8))
     plt.plot([0,1],[0,1],'k--')
     cm = [plt.cm.rainbow(i)
         for i in np.linspace(0,1.0, p_base_learners.shape[1] +1)]
     for i in range(p_base_learners.shape[1]):
         p = p_base_learners[:,i]
         fpr,tpr,_ = roc_curve(ytest,p)
         plt.plot(fpr,tpr,label = labels[i],c=cm[i+1])
     fpr, tpr, _ = roc_curve(ytest, p_ensemble)
     plt.plot(fpr, tpr, label=ens_label, c=cm[0])
     plt.xlabel('False positive rate')
     plt.ylabel('True positive rate')
     plt.title('ROC curve')
     plt.legend(frameon=False)
     plt.show()
 
 from mlens.ensemble import SuperLearner
 def use_pack():
     sl =SuperLearner(
         folds=10,random_state=SEED,verbose=2,
         # backend="multiprocessing"
     )
     # Add the base learners and the meta learner
     sl.add(list(base_learners.values()),proba=True)
     sl.add_meta(meta_learner,proba=True)
     # Train the ensemble
     sl.fit(xtrain,ytrain)
     # Predict the test set
     p_sl=sl.predict_proba(xtest)
 
     print("\nSuper Learner ROC-AUC score: %.3f" % roc_auc_score(ytest,p_sl[:,1]))
 
 if __name__ == "__main__":
     xtrain, xtest, ytrain, ytest = get_train_test()
     base_learners = get_models()
 
     meta_learner = GradientBoostingClassifier(
         n_estimators=1000,
         loss="exponential",
         max_depth=4,
         subsample=0.5,
         learning_rate=0.005,
         random_state=SEED
     )
 
     # ensenmble_by_blend() # blend进行融合
     # ensemble_by_stack()   # stack进行融合
     use_pack()  # 调用包进行融合