信用评分系统运行原理下篇（1）

corr = train.corr() # 计算各变量的相关性系数
xticks = ['x0','x1','x2','x3','x4','x5','x6','x7','x8','x9','x10'] # x轴标签
yticks = list(corr.index) # y轴标签
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1)
sns.heatmap(corr, annot=True, cmap='rainbow', ax=ax,
            annot_kws={'size': 12, 'weight': 'bold', 'color': 'blue'}) # 绘制相关性系数热力图
ax.set_xticklabels(xticks, rotation=0, fontsize=12)
ax.set_yticklabels(yticks, rotation=0, fontsize=12)
plt.show()

上图可以看出变量之间的相关性都较小，但是 NumberOfOpenCreditLinesAndLoans 和 NumberRealEstateLoansOrLines 相对来说较大为0.43

ivlist = [ivx1, ivx2, ivx3, ivx4, ivx5, ivx6, ivx7, ivx8, ivx9, ivx10]  # 各变量IV
index = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10']  # x轴的标签
fig1 = plt.figure(figsize=(8, 8))
ax1 = fig1.add_subplot(1, 1, 1)
x = np.arange(len(index)) + 1
ax1.bar(x, ivlist, width=0.4)  # 生成柱状图
ax1.set_xticks(x)
ax1.set_xticklabels(index, rotation=0, fontsize=12)
ax1.set_ylabel('IV(Information Value)', fontsize=12)
# 在柱状图上添加数字标签
for a, b in zip(x, ivlist):
    plt.text(a, b + 0.01, '%.4f' % b, ha='center', va='bottom', fontsize=10)
plt.show()

通过IV值判断变量预测能力的标准是：
< 0.02: unpredictive
0.02 to 0.1: weak
0.1 to 0.3: medium
0.3 to 0.5: strong
> 0.5: suspicious
DebtRatio、MonthlyIncome、NumberRealEstateLoansOrLines 和 NumberOfDependents 变量的IV值明显较低

# 替换成woe函数
def replace_woe(series, cut, woe):
    list = []
    i = 0
    while i < len(series):
        value = series[i]
        j = len(cut) - 2
        m = len(cut) - 2
        while j >= 0:
            if value >= cut[j]:
                j = -1
            else:
                j -= 1
                m -= 1
        list.append(woe[m])
        i += 1
    return list
train['RevolvingUtilizationOfUnsecuredLines'] = Series(
    replace_woe(train['RevolvingUtilizationOfUnsecuredLines'], cutx1, woex1))

这段代码的意思是 获取每一行的值
依次和 0.75分位、0.5分位、0.25分位的值比较
要是当前值大于某一分位的值则记录该分位的值

将训练数据集划分为K份，K一般为10
依次取其中一份为验证集，其余为训练集训练分类器，测试分类器在验证集上的精度 
取K次实验的平均精度为该分类器的平均精度

# 导入逻辑回归
from sklearn.linear_model import LogisticRegression
# 导入分类器
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier
# k最近邻分类
from sklearn.neighbors import KNeighborsClassifier

knMod = KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2,
                             metric='minkowski', metric_params=None)
lrMod = LogisticRegression(penalty='l1', dual=False, tol=0.0001, C=1.0, fit_intercept=True,
                           intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100,
                           multi_class='ovr', verbose=2)
adaMod = AdaBoostClassifier(base_estimator=None, n_estimators=200, learning_rate=1.0)
gbMod = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, subsample=1.0,
                                   min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3,
                                   init=None, random_state=None, max_features=None, verbose=0)
rfMod = RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2,
                               min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto',
                               max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None,
                               verbose=0)

# 入参是训练数据信息
def cvDictGen(functions, scr, X_train=X_train, Y_train=Y_train, cv=10, verbose=1):
    cvDict = {}
    for func in functions:
        # cross_val_score将交叉验证的整个过程连接起来，不用再进行手动的分割数据
        # cv参数用于规定将原始数据分成多少份
        cvScore = cross_val_score(func, X_train, Y_train, cv=cv, verbose=verbose, scoring=scr)
        cvDict[str(func).split('(')[0]] = [cvScore.mean(), cvScore.std()]
    return cvDict
cvD = cvDictGen(functions=[knMod, lrMod, adaMod, gbMod, rfMod], scr='roc_auc')    
def cvDictNormalize(cvDict):
    cvDictNormalized = {}
    for key in cvDict.keys():
        for i in cvDict[key]:
            cvDictNormalized[key] = ['{:0.2f}'.format((cvDict[key][0] / cvDict[list(cvDict.keys())[0]][0])),
                                     '{:0.2f}'.format((cvDict[key][1] / cvDict[list(cvDict.keys())[0]][1]))]
    return cvDictNormalized
cvDictNormalize(cvD)

cvD:
'KNeighborsClassifier': [0.5887365163416062, 0.011300179653818953], 'LogisticRegression': [0.8500902765971645, 0.0036164412715674102], 'AdaBoostClassifier': [0.8583319753215507, 0.004050825383307547], 'GradientBoostingClassifier': [0.8639129158346284, 0.003503053433053003], 'RandomForestClassifier': [0.7803945135123486, 0.010025212199131]}
平均值、方差
标准化处理结果：