def logistics_mod_xgb_woe_SMOTE(X_woe_train,Y_woe_train,X_woe_test,Y_woe_test,minority,combined,show_result,nthread, learning_rate, n_estimators, max_depth, gamma,subsample, colsample_bytree):
X_woe_train_SMOTE, Y_woe_train_SMOTE, Transdata_woe_train_SMOTE = processing.data_SMOTE(X_woe_train, Y_woe_train,
minority=minority,
combined=combined, WOE=True)
mod_xgb_woe_SMOTE = xgb.XGBClassifier(nthread=nthread, #含义：nthread=-1时，使用全部CPU进行并行运算（默认）, nthread=1时，使用1个CPU进行运算。
learning_rate=learning_rate, # 含义：学习率，控制每次迭代更新权重时的步长，默认0.3。调参：值越小，训练越慢。典型值为0.01-0.2。
n_estimators=n_estimators, # 含义：总共迭代的次数，即决策树的个数
max_depth=max_depth, # 含义：树的深度，默认值为6，典型值3-10。调参：值越大，越容易过拟合；值越小，越容易欠拟合
gamma=gamma, # 含义：惩罚项系数，指定节点分裂所需的最小损失函数下降值。
subsample=subsample,
# 含义：训练每棵树时，使用的数据占全部训练集的比例。默认值为1，典型值为0.5-1。调参：防止overfitting。
colsample_bytree=colsample_bytree) # 训练每棵树时，使用的特征占全部特征的比例。默认值为1，典型值为0.5-1。调参：防止overfitting。
mod_xgb_woe_SMOTE.fit(X_woe_train_SMOTE, Y_woe_train_SMOTE)
X_train_leaves = mod_xgb_woe_SMOTE.apply(X_woe_train_SMOTE)
X_test_leaves = mod_xgb_woe_SMOTE.apply(X_woe_test.values)
train_rows = X_train_leaves.shape[0]
# 合并编码后的训练数据和测试数据
X_leaves = np.concatenate((X_train_leaves, X_test_leaves), axis=0)
X_leaves = X_leaves.astype(np.int32)
XGBencoder = OneHotEncoder()
X_trans = XGBencoder.fit_transform(X_leaves)
X_train_xgb = hstack([X_trans[:train_rows, :], X_woe_train_SMOTE.astype(float)])
X_test_xgb = hstack([X_trans[train_rows:, :], X_woe_test.astype(np.float64)])
logistics_mod_xgb = linear_model.LogisticRegression(solver='liblinear', C=1.0, tol=1e-6) # ,verbose=1)#penalty='l2'
logistics_mod_xgb.fit(X_train_xgb, Y_woe_train_SMOTE)
predictions_xgblr = logistics_mod_xgb.predict_proba(X_test_xgb)
model_measurement.plotROC(predictions_xgblr, Y_woe_test, name='XGBlr+WOE+SMOTE', show=show_result)
def NN_SMOTE_mod(X_train,Y_train,X_test,Y_test,minority,combined,loss,intermediate_dim,epochs,batch_size,dropout,activation,output_activation,show_result):
X_train_SMOTE, Y_train_SMOTE, Transdata_train_SMOTE = processing.data_SMOTE(X_train, Y_train, minority=minority,
combined=combined, WOE=False)
X_train_NN, X_test_NN, Y_train_NN, Y_test_NN = NN.NN_processing(X_train_SMOTE, X_test.values, Y_train_SMOTE, Y_test.values)
model = NN.NN_main_func(X_train_NN.shape[1],intermediate_dim,loss=loss,dropout = dropout,activation=activation,output_activation=output_activation)
model.fit(X_train_NN, Y_train_NN, epochs = epochs, batch_size = batch_size, validation_data = (X_test_NN, Y_test_NN),verbose=show_result)
predictions_NN = model.predict(X_test_NN)
model_measurement.plotROC(predictions_NN,Y_test_NN,show=show_result,name='NN+SMOTE',method='NN')
def SVM_woe_SMOTE(X_woe_train,X_woe_test,Y_woe_train,Y_woe_test,kernel,C,drop_feature,minority,combined,show_result):
X_woe_train_SMOTE, Y_woe_train_SMOTE, Transdata_woe_train_SMOTE = processing.data_SMOTE(X_woe_train, Y_woe_train, minority=minority, combined=combined, WOE=True)
grid_svc_best = svm.SVC(C = C, kernel=kernel,gamma='auto')
X_woe_train_SMOTE1 = Transdata_woe_train_SMOTE.drop(['是否是诈骗电话'] + drop_feature, axis=1)
grid_svc_best.fit(X_woe_train_SMOTE1, Y_woe_train_SMOTE)
X_woe_test1 = X_woe_test.drop(drop_feature, axis=1)
pred_SVM_woe_SMOTE = grid_svc_best.predict(X_woe_test1)
model_measurement.plotROC(pred_SVM_woe_SMOTE, Y_woe_test, name='SVM+WOE+SMOTE',method='SVM+WOE+SMOTE',show=show_result)
def logistics_mod_woe_SMOTE(X_woe_train,X_woe_test,Y_woe_train,Y_woe_test,drop_feature,minority,combined,show_result):
X_woe_train_SMOTE, Y_woe_train_SMOTE, Transdata_woe_train_SMOTE = processing.data_SMOTE(X_woe_train, Y_woe_train, minority=minority, combined=combined, WOE = True)
print(Transdata_woe_train_SMOTE.groupby(['是否是诈骗电话']).count())
logistics_mod_woe_SMOTE = linear_model.LogisticRegression(solver='liblinear',C=1.0, tol=1e-6,verbose=0)#,penalty='l2')
X_woe_train_SMOTE1=Transdata_woe_train_SMOTE.drop(['是否是诈骗电话']+ drop_feature,axis=1)
logistics_mod_woe_SMOTE.fit(X_woe_train_SMOTE1, Y_woe_train_SMOTE)
X_woe_test1=X_woe_test.drop(drop_feature,axis=1)
predictions_woe_SMOTE = logistics_mod_woe_SMOTE.predict_proba(X_woe_test1)#每一类的概率
model_measurement.plotROC(predictions_woe_SMOTE,Y_woe_test,'LR+WOE+SMOTE',show=show_result)
if show_result:
model_measurement.plotKS(predictions_woe_SMOTE, Y_woe_test, 'LR+WOE+SMOTE')
model_measurement.VIF(X_woe_train_SMOTE1, name='X_woe_train_SMOTE1', combined=combined, WOE=True)
else:
pass
def mod_xgb_SMOTE(X_train,Y_train,X_test,Y_test,minority,combined,show_result,nthread, learning_rate, n_estimators, max_depth, gamma,subsample, colsample_bytree):
X_train_SMOTE, Y_train_SMOTE, Transdata_train_SMOTE = processing.data_SMOTE(X_train, Y_train, minority=minority,
combined=combined, WOE=False)
mod_xgb_SMOTE = xgb.XGBClassifier(nthread=nthread, #含义：nthread=-1时，使用全部CPU进行并行运算（默认）, nthread=1时，使用1个CPU进行运算。
learning_rate=learning_rate, # 含义：学习率，控制每次迭代更新权重时的步长，默认0.3。调参：值越小，训练越慢。典型值为0.01-0.2。
n_estimators=n_estimators, # 含义：总共迭代的次数，即决策树的个数
max_depth=max_depth, # 含义：树的深度，默认值为6，典型值3-10。调参：值越大，越容易过拟合；值越小，越容易欠拟合
gamma=gamma, # 含义：惩罚项系数，指定节点分裂所需的最小损失函数下降值。
subsample=subsample, # 含义：训练每棵树时，使用的数据占全部训练集的比例。默认值为1，典型值为0.5-1。调参：防止overfitting。
colsample_bytree=colsample_bytree) # 训练每棵树时，使用的特征占全部特征的比例。默认值为1，典型值为0.5-1。调参：防止overfitting。
mod_xgb_SMOTE.fit(X_train_SMOTE, Y_train_SMOTE)
predictions_xgb_SMOTE = mod_xgb_SMOTE.predict_proba(X_test.values)#每一类的概率
model_measurement.plotROC(predictions_xgb_SMOTE,Y_test,name='XGB+SMOTE',show=show_result)
def logistics_mod_SMOTE(X_train,Y_train,X_test,Y_test,drop_feature,minority,combined,show_result):
X_train_SMOTE, Y_train_SMOTE, Transdata_train_SMOTE = processing.data_SMOTE(X_train,
Y_train, minority= minority, combined= combined, WOE = False)
print(Transdata_train_SMOTE.groupby(['是否是诈骗电话']).count())
logistics_mod_SMOTE = linear_model.LogisticRegression(solver='liblinear',C=1.0, tol=1e-6,verbose=0)#,penalty='l2')
if combined:
X_train1_SMOTE = Transdata_train_SMOTE.drop(['是否是诈骗电话','主叫_离散率组合_(1.0, 1.0)', '归属地是否未知_0.0'] + drop_feature, axis=1)
X_test1 = X_test.drop(drop_feature+['主叫_离散率组合_(1.0, 1.0)', '归属地是否未知_0.0'], axis=1)
else:
X_train1_SMOTE = Transdata_train_SMOTE.drop(['是否是诈骗电话'] + drop_feature, axis=1)
X_test1 = X_test.drop(drop_feature, axis=1)
logistics_mod_SMOTE.fit(X_train1_SMOTE, Y_train_SMOTE)
predictions_SMOTE = logistics_mod_SMOTE.predict_proba(X_test1)#每一类的概率
model_measurement.plotROC(predictions_SMOTE,Y_test,'LR+SMOTE',show=show_result)
if show_result:
model_measurement.plotKS(predictions_SMOTE, Y_test, 'LR+SMOTE')
model_measurement.VIF(X_train1_SMOTE, name='X_train_SMOTE1', combined=combined, WOE=False)
model_measurement.coef(logistics_mod_SMOTE, X_train1_SMOTE, Y_train_SMOTE)
else:
pass
def SVM_woe_SMOTE_grid(X_woe_train,X_woe_test,Y_woe_train,Y_woe_test,grid_kernel,grid_C,n_jobs,drop_feature,minority,combined,show_result):
X_woe_train_SMOTE, Y_woe_train_SMOTE, Transdata_woe_train_SMOTE = processing.data_SMOTE(X_woe_train, Y_woe_train, minority=minority, combined=combined, WOE=True)
# 使用网格搜索法，选择非线性可分SVM“类”中的最佳C值和核函数
parameters = {'kernel': grid_kernel, 'C': grid_C}
grid_svc = model_selection.GridSearchCV(estimator=svm.SVC(gamma = 'auto'),
param_grid=parameters,
scoring='roc_auc', cv=5, verbose=1,n_jobs=n_jobs)
# 模型在训练数据集上的拟合
X_woe_train_SMOTE1 = Transdata_woe_train_SMOTE.drop(['是否是诈骗电话'] + drop_feature, axis=1)
grid_svc.fit(X_woe_train_SMOTE1, Y_woe_train_SMOTE)
print('the best params:{}, the best training AUC:{:0.3f}'.format(grid_svc.best_params_, grid_svc.best_score_))
grid_svc_best = svm.SVC(C = grid_svc.best_params_['C'], kernel=grid_svc.best_params_['kernel'],gamma='auto')
X_woe_train_SMOTE1 = Transdata_woe_train_SMOTE.drop(['是否是诈骗电话'] + drop_feature, axis=1)
grid_svc_best.fit(X_woe_train_SMOTE1, Y_woe_train_SMOTE)
X_woe_test1 = X_woe_test.drop(drop_feature, axis=1)
pred_SVM_woe_SMOTE = grid_svc_best.predict(X_woe_test1)
model_measurement.plotROC(pred_SVM_woe_SMOTE, Y_woe_test, name='SVM+WOE+SMOTE(Grid)',method='SVM+WOE+SMOTE',show=show_result)
