金融风控-->客户流失预警模型-->神经网络建模

上一篇博文中，我们对金融数据建立了GBDT模型。这篇博文将利用深度学习框架pytorch对金融数据建立一个人工神经网络模型。

有关人工神经网络的详细介绍请看我的另外一篇博文人工神经网络。

人工神经网络数据预处理：
不能有缺失值
移除常量型特征（即这个特征的最大值和最小值相等）
不能接受非数值形式的输入,字符型变量需要编码:

• One hot编码
• Dummy编码
• 浓度编码

变量归一化/标准化

人工神经网络中的参数设置：

• 输入层节点个数
• 隐藏层层数
• 隐藏层节点个数
• 隐藏层联接状态
• 激活函数
• 损失函数
• 学习速率
• 迭代次数

模型的效果

AUC score,超过0.7为佳,越大说明效果越好
调参时,可以选择AUC最高的一组参数

下面是代码部分，这里我们只用一个隐藏层，只调了隐藏层神经元个数，dropOut这两个参数。其他参数调优类似。

#coding:utf-8import pandas as pdimport numpyimport torchfrom torch.autograd import Variableimport torch.nn.functional as Fimport torch.nn as nnfrom sklearn import metricsimport torch.utils.data as Datafrom sklearn.model_selection import train_test_splitimport operatormodelData = pd.read_csv('modelData.csv', header = 0)allFeatures = list(modelData.columns)#remove the class label and cust id from featuresallFeatures.remove('CUST_ID')allFeatures.remove('CHURN_CUST_IND')modelData2 = modelData.copy()#normalize the features using max-min to convert the values into [0,1] intervaldef MaxMinNorm(df,col):    ma, mi = max(df[col]), min(df[col])    rangeVal = ma - mi    if rangeVal == 0:        print col    df[col] = df[col].map(lambda x:(x-mi)*1.0/rangeVal)for numFeatures in allFeatures:    MaxMinNorm(modelData2, numFeatures)#we need to remove columns of 'TELEBANK_ALL_TX_NUM', 'RATIO_21' since they are constantsallFeatures.remove('TELEBANK_ALL_TX_NUM')allFeatures.remove('RATIO_21')x_train, x_test, y_train, y_test = train_test_split(modelData2[allFeatures],modelData2['CHURN_CUST_IND'], test_size=0.4,random_state=9)train_x=x_train.valuestrain_y=y_train.valuestensor_x=torch.from_numpy(train_x)tensor_y=torch.from_numpy(train_y)torch_dataset=torch.utils.data.TensorDataset(data_tensor=tensor_x,target_tensor=tensor_y)Batch_size=1500train_loader=torch.utils.data.DataLoader(    dataset=torch_dataset,    batch_size=Batch_size,    shuffle=True,    num_workers=2,)x=Variable(torch.from_numpy(train_x)).type(torch.FloatTensor)y=Variable(torch.from_numpy(train_y)).type(torch.LongTensor)test_x=x_test.valuesV_test_x=Variable(torch.from_numpy(test_x)).type(torch.FloatTensor)test_y=y_test.valuesclass Net(torch.nn.Module):    def __init__(self,n_feature,n_hidden,n_output,drop_out):        super(Net,self).__init__()        self.hidden=torch.nn.Linear(n_feature,n_hidden)        torch.nn.Dropout(drop_out)        self.predict=torch.nn.Linear(n_hidden,n_output)    def forward(self, x):        x=F.relu(self.hidden(x))        x=self.predict(x)        return x## 只有一个隐藏层，这里挑选一个在auc上效果最好的隐藏层的神经元个数hidden_units_selection={}for hidden_units in range(10,101,10):    net=Net(n_feature=174,n_hidden=800,n_output=2,drop_out=0.5)    optimizer=torch.optim.Adam(net.parameters(),lr=0.01)    ##loss_func=nn.CrossEntropyLoss(weight=torch.FloatTensor([1,35]))    loss_func=nn.CrossEntropyLoss()    for epoch in range(200):        for step,(b_x,b_y) in enumerate(train_loader):            batch_x=Variable(b_x).type(torch.FloatTensor)            batch_y=Variable(b_y).type(torch.LongTensor)            out=net(batch_x)            loss=loss_func(out,batch_y)            optimizer.zero_grad()            loss.backward()            optimizer.step()            if epoch%5==0:                ##F.softmax(out)得出二维的概率值，表示每个样本在０类，１类上概率值，是个Variabel类型                # 1表示在列这个维度上得出每行的最大值，返回最大值和在这个行上的位置index local                pred = net(V_test_x)                ## 将Variabel 类型转成numpy类型                clf_pred_proba=F.softmax(pred).data.numpy()                ## 取每个样本为类别１的概率                pred_proba=[i[1] for i in clf_pred_proba]                auc_score=metrics.roc_auc_score(test_y,pred_proba)                print('Epoch: ', epoch, '| roc_auc_score: %.4f' % auc_score)    pred = net(V_test_x)    clf_pred_proba=F.softmax(pred).data.numpy()    pred_proba=[i[1] for i in clf_pred_proba]    auc_score=metrics.roc_auc_score(test_y,pred_proba)    hidden_units_selection[hidden_units]=auc_scorebest_hidden_units=max(hidden_units_selection.iteritems(),key=operator.itemgetter(1))[0]## 只有一个隐藏层，这里挑选一个在auc上效果最好的dropOutdrop_out_selection={}for drop_out in numpy.linspace(0,0.99,100):    net=Net(n_feature=174,n_hidden=best_hidden_units,n_output=2,drop_out=drop_out)    optimizer=torch.optim.Adam(net.parameters(),lr=0.01)    ##loss_func=nn.CrossEntropyLoss(weight=torch.FloatTensor([1,35]))    loss_func=nn.CrossEntropyLoss()    for epoch in range(200):        for step,(b_x,b_y) in enumerate(train_loader):            batch_x=Variable(b_x).type(torch.FloatTensor)            batch_y=Variable(b_y).type(torch.LongTensor)            out=net(batch_x)            loss=loss_func(out,batch_y)            optimizer.zero_grad()            loss.backward()            optimizer.step()            if epoch%5==0:                pred = net(V_test_x)                clf_pred_proba=F.softmax(pred).data.numpy()                pred_proba=[i[1] for i in clf_pred_proba]                auc_score=metrics.roc_auc_score(test_y,pred_proba)                print('Epoch: ', epoch, '| roc_auc_score: %.4f' % auc_score)    pred = net(V_test_x)    clf_pred_proba=F.softmax(pred).data.numpy()    pred_proba=[i[1] for i in clf_pred_proba]    auc_score=metrics.roc_auc_score(test_y,pred_proba)    drop_out_selection[drop_out]=auc_scorebest_drop_out=max(drop_out_selection.iteritems(),key=operator.itemgetter(1))[0]