金融风控-->申请评分卡模型-->logisticRegression建模

上一篇博文中，我们对数据进行了特征工程处理，包括特征分箱，WOE编码，计算IV值，进行单变量，多变量的分析等一系列的处理。总结特征分箱的一些处理办法如下图的流程图：

那么本篇博文在上篇博文的基础上，建立logisticRegression模型，其主要思想的流程图如下，其中特征分箱部分就是对应上面流程图：

logsiticRegression介绍

有关logsiticRegression详细介绍请看我的另外一篇博文logistic回归

特征选择

在实际场景中，金融数据的维度可能会非常大，那么有必要做一些特征选择，选择一些比较重要的特征来训练模型。有关特征选择详细的说明可以参考我的另外一篇博文sklean特征选择

逻辑回归中的权重问题

两类错误

• 第一类错误:将逾期人群预测成非逾期
• 第二类错误:将非逾期人群预测成逾期

以上两种误判的代价不一样!

增加逾期类样本的权重
　在目标函数或者是损失函数中增加逾期权重。然后梯度下降法对参数进行估计。
　
评分卡模型中

• 逾期样本的权重总是高于非逾期样本的权重
• 可以用交叉验证法选择合适的权重
• 也可以跟业务相结合:权重通常跟利率有关。利率高,逾期样本的权重相对低

模型训练

对特征进行了单变量，多变量分析以后，剔除了一些冗余，多重共线性的变量以后。

sm.Logit:

import statsmodels.api as smvar_WOE_list = [i+'_WOE' for i in var_IV_sortet_2]y = trainData['target']X = trainData[var_WOE_list]X['intercept'] = [1]*X.shape[0]LR = sm.Logit(y, X).fit()summary = LR.summary()pvals = LR.pvaluespvals = pvals.to_dict()## 获取每个变量的显著性p值，p值越大则越不显著。### Some features are not significant, so we need to delete feature one by one.varLargeP = {k: v for k,v in pvals.items() if v >= 0.1}varLargeP = sorted(varLargeP.iteritems(), key=lambda d:d[1], reverse = True)while(len(varLargeP) > 0 and len(var_WOE_list) > 0):    # In each iteration, we remove the most insignificant feature and build the regression again, until    # (1) all the features are significant or    # (2) no feature to be selected    varMaxP = varLargeP[0][0]    if varMaxP == 'intercept':        print 'the intercept is not significant!'        break    var_WOE_list.remove(varMaxP)    y = trainData['target']    X = trainData[var_WOE]    X['intercept'] = [1] * X.shape[0]    LR = sm.Logit(y, X).fit()    summary = LR.summary()    pvals = LR.pvalues    pvals = pvals.to_dict()    varLargeP = {k: v for k, v in pvals.items() if v >= 0.1}    varLargeP = sorted(varLargeP.iteritems(), key=lambda d: d[1], reverse=True)

注意这里调用的是statsmodels.api里的逻辑回归。这个回归模型可以获取每个变量的显著性p值，p值越大越不显著，当我们发现多于一个变量不显著时，不能一次性剔除所有的不显著变量，因为里面可能存在我们还未发现的多变量的多重共线性，我们需要迭代的每次剔除最不显著的那个变量。
上面迭代的终止条件：
①剔除了所有的不显著变量
②剔除了某一个或某几个变量后，剩余的不显著变量变得显著了。（说明之前存在多重共线性）

RandomForest:

X = trainData[var_WOE_list]X = np.matrix(X)y = trainData['target']y = np.array(y)RFC = RandomForestClassifier()RFC_Model = RFC.fit(X,y)features_rfc = trainData[var_WOE_list].columnsfeatureImportance = {features_rfc[i]:RFC_Model.feature_importances_[i] for i in range(len(features_rfc))}featureImportanceSorted = sorted(featureImportance.iteritems(),key=lambda x: x[1], reverse=True)# we selecte the top 10 featuresfeatures_selection = [k[0] for k in featureImportanceSorted[:10]]y = trainData['target']X = trainData[features_selection]X['intercept'] = [1]*X.shape[0]LR = sm.Logit(y, X).fit()summary = LR.summary()

sklearn中的logsiticRegressionCV：

X = trainData[var_WOE_list]   #by default  LogisticRegressionCV() fill fit the interceptX = np.matrix(X)y = trainData['target']y = np.array(y)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)X_train.shape, y_train.shapemodel_parameter = {}for C_penalty in np.arange(0.005, 0.2,0.005):## 选择适合的参数Cs，Cs越大选择的特征数越少。    for bad_weight in range(2, 101, 2):## 选择适合的正负样本的权重比例，对正负样本给予不同重视程度。        LR_model_2 = LogisticRegressionCV(Cs=[C_penalty], penalty='l1', solver='liblinear', class_weight={1:bad_weight, 0:1})## 运用L1正则化        LR_model_2_fit = LR_model_2.fit(X_train,y_train)        y_pred = LR_model_2_fit.predict_proba(X_test)[:,1]        scorecard_result = pd.DataFrame({'prob':y_pred, 'target':y_test})        performance = KS_AR(scorecard_result,'prob','target')        KS = performance['KS']        model_parameter[(C_penalty, bad_weight)] = KS

选择在ks指标上效果最好的参数进行模型训练。

### Calculate the KS and AR for the socrecard modeldef KS_AR(df, score, target):    '''    :param df: the dataset containing probability and bad indicator    :param score:    :param target:    :return:    '''    total = df.groupby([score])[target].count()    bad = df.groupby([score])[target].sum()    all = pd.DataFrame({'total':total, 'bad':bad})    all['good'] = all['total'] - all['bad']    all[score] = all.index    all = all.sort_values(by=score,ascending=False)    all.index = range(len(all))    all['badCumRate'] = all['bad'].cumsum() / all['bad'].sum()    all['goodCumRate'] = all['good'].cumsum() / all['good'].sum()    all['totalPcnt'] = all['total'] / all['total'].sum()    arList = [0.5 * all.loc[0, 'badCumRate'] * all.loc[0, 'totalPcnt']]    for j in range(1, len(all)):        ar0 = 0.5 * sum(all.loc[j - 1:j, 'badCumRate']) * all.loc[j, 'totalPcnt']        arList.append(ar0)    arIndex = (2 * sum(arList) - 1) / (all['good'].sum() * 1.0 / all['total'].sum())    KS = all.apply(lambda x: x.badCumRate - x.goodCumRate, axis=1)    return {'AR':arIndex, 'KS': max(KS)}