记一次失败的kaggle比赛（1）：赛题简单介绍与初次尝试

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

持续更新兴许经验。欢迎关注^_^