题目描写叙述:https://www.kaggle.com/c/santander-customer-satisfaction
简单总结:一堆匿名属性;label是0/1。目标是最大化AUC。
第一次尝试:
特征:
因为时间比較充裕,直接用了暴力搜索提取较好的特征:
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! better use a RFC or GBC as the clf #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! because the final predict model are those two #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! we should select better feature for RFC or GBC, not for LR clf = LogisticRegression(class_weight='balanced', penalty='l2', n_jobs=-1) selectedFeaInds=GreedyFeatureAdd(clf, trainX, trainY, scoreType="auc", goodFeatures=[], maxFeaNum=150) joblib.dump(selectedFeaInds, 'modelPersistence/selectedFeaInds.pkl') #selectedFeaInds=joblib.load('modelPersistence/selectedFeaInds.pkl') trainX=trainX[:,selectedFeaInds] testX=testX[:,selectedFeaInds] print trainX.shape
模型:
直接使用sklearn中最经常使用的三个模型:
trainN=len(trainY) print "Creating train and test sets for blending..." #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! always use a seed for randomized procedures models=[ RandomForestClassifier(n_estimators=1999, criterion='gini', n_jobs=-1, random_state=SEED), RandomForestClassifier(n_estimators=1999, criterion='entropy', n_jobs=-1, random_state=SEED), ExtraTreesClassifier(n_estimators=1999, criterion='gini', n_jobs=-1, random_state=SEED), ExtraTreesClassifier(n_estimators=1999, criterion='entropy', n_jobs=-1, random_state=SEED), GradientBoostingClassifier(learning_rate=0.1, n_estimators=101, subsample=0.6, max_depth=8, random_state=SEED) ] #StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set. #kfcv=KFold(n=trainN, n_folds=nFold, shuffle=True, random_state=SEED) kfcv=StratifiedKFold(y=trainY, n_folds=nFold, shuffle=True, random_state=SEED) dataset_trainBlend=np.zeros( ( trainN, len(models) ) ) dataset_testBlend=np.zeros( ( len(testX), len(models) ) ) meanAUC=0.0 for i, model in enumerate(models): print "model ", i, "=="*20 dataset_testBlend_j=np.zeros( ( len(testX), nFold ) ) for j, (trainI, testI) in enumerate(kfcv): print "Fold ", j, "^"*20
终于结果:
通过blending全部模型的结果:
print "Blending models..." #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! if we want to predict some real values, use RidgeCV model=LogisticRegression(n_jobs=-1) C=np.linspace(0.001,1.0,1000) trainAucList=[] for c in C: model.C=c model.fit(dataset_trainBlend,trainY) trainProba=model.predict_proba(dataset_trainBlend)[:,1] trainAuc=metrics.roc_auc_score(trainY, trainProba) trainAucList.append((trainAuc, c)) sortedtrainAucList=sorted(trainAucList) for trainAuc, c in sortedtrainAucList: print "c=%f => trainAuc=%f" % (c, trainAuc)
model.C=sortedtrainAucList[-1][1] #0.05 model.fit(dataset_trainBlend,trainY) trainProba=model.predict_proba(dataset_trainBlend)[:,1] print "train auc: %f" % metrics.roc_auc_score(trainY, trainProba) #0.821439 print "model.coef_: ", model.coef_ print "Predict and saving results..." submitProba=model.predict_proba(dataset_testBlend)[:,1] df=pd.DataFrame(submitProba) print df.describe() SaveFile(submitID, submitProba, fileName="1submit.csv")
归一化:
print "MinMaxScaler predictions to [0,1]..." mms=preprocessing.MinMaxScaler(feature_range=(0, 1)) submitProba=mms.fit_transform(submitProba) df=pd.DataFrame(submitProba) print df.describe() SaveFile(submitID, submitProba, fileName="1submitScale.csv")
从測试结果中总结经验:
第一:暴力搜索特征的方式在特征数较多的情况下不可取。较少的情况下能够考虑(<200)
第二:sklearn中的这几个模型,ExtraTreesClassifier效果最差,RandomForestClassifier效果较好且速度比較快,GradientBoostingClassifier结果最好但速度很慢(由于不能并行)
第三:当某一个模型(GradientBoostingClassifier)比其它模型效果好非常多时,不要使用blending的方法(尤其是特征空间一样,分类器类似的情况。比方这里的五个分类器都在同一组特征上建模。并且都是基于树的分类器),由于blending往往会使总体效果低于单独使用最好的一个模型
第四:对于AUC,实际上关心的是样本间的排名。而不是详细数值的大小,所以结果不是必需做归一化处理;关于这个结论。自行搜索资料理解
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