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用机器学习构建模型，进行信用卡反欺诈预测
反欺诈中所用到的机器学习模型有哪些？
Credit card fraud detection
构建信用卡反欺诈预测模型——机器学习
信用卡交易数据相关知识收集
交易渠道、交易日期，商户名称/网点名称、卡号、交易币种、交易金额几个字段
刷卡供应商的信任度、插卡让购买行为（时空维度）、IP地址等等
信用贷款的不同维度
index, id, member_id, loan_amnt, funded_amnt, funded_amnt_inv, term, int_rate, installment, grade, sub_grade, emp_title, emp_length, home_ownership, annual_inc, verification_status, issue_d, loan_status, pymnt_plan, url, desc, purpose, title, zip_code, addr_state, dti, delinq_2yrs, earliest_cr_line, inq_last_6mths, mths_since_last_delinq, mths_since_last_record, open_acc, pub_rec, revol_bal, revol_util, total_acc, initial_list_status, out_prncp, out_prncp_inv, total_pymnt, total_pymnt_inv, total_rec_prncp, total_rec_int, total_rec_late_fee, recoveries, collection_recovery_fee, last_pymnt_d, last_pymnt_amnt, next_pymnt_d, last_credit_pull_d, collections_12_mths_ex_med, mths_since_last_major_derog, policy_code, application_type, annual_inc_joint, dti_joint, verification_status_joint, acc_now_delinq, tot_coll_amt, tot_cur_bal, open_acc_6m, open_il_6m, open_il_12m, open_il_24m, mths_since_rcnt_il, total_bal_il, il_util, open_rv_12m, open_rv_24m, max_bal_bc, all_util, total_rev_hi_lim, inq_fi, total_cu_tl, inq_last_12m
项目背景
数据集包含由欧洲持卡人于9月使用信用卡进行交的数据。此数据集显示两天内发生的交易，其中284,807笔交易中有492笔被盗刷。数据集非常不平衡，积极的类（被盗刷）占所有交易的0.172％。
它只包含作为PCA转换结果的数字输入变量。不幸的是，由于保密问题，我们无法提供有关数据的原始功能和更多背景信息。特征V1，V2，… V28是使用PCA获得的主要组件，没有用PCA转换的唯一特征是“时间”和“量”。特征’时间’包含数据集中每个事务和第一个事务之间经过的秒数。特征“金额”是交易金额，此特征可用于实例依赖的成本认知学习。特征’类’是响应变量，如果发生被盗刷，则取值1，否则为0。
加载数据
data = pd.read_csv("../input/creditcard.csv")print(data.head())
查看数据标签分布
count_classes = pd.value_counts(data['Class'], sort = True).sort_index()count_classes.plot(kind = 'bar')print(count_classes)plt.title("Fraud class histogram")plt.xlabel("Class")plt.ylabel("Frequency")plt.show()
结果如下：
0 2843151 492Name: Class, dtype: int64
从上可以看到正负样本极不平衡,如果全样本训练，标签为1的样本就被淹没了；结果是，标签为0的样本容易套上模型，而标签为1的样本不容易套上模型；导致在交叉验证预测分类时，整个结果的精度看起来依然很高，但容易把真实标签为1的样本预测错。
对于不平衡样本的解决方案：
Collect more data? Nice strategy but not applicable in this caseChanging the performance metric:
Use the confusio nmatrix to calculate Precision, RecallF1score (weighted average of precision recall)Use Kappa - which is a classification accuracy normalized by the imbalance of the * classes in the dataROC curves - calculates sensitivity/specificity ratio.Resampling the dataset
Essentially this is a method that will process the data to have an approximate 50-50 ratio.One way to achieve this is by OVER-sampling, which is adding copies of the under-represented class (better when you have little data)Another is UNDER-sampling, which deletes instances from the over-represented class (better when he have lot’s of data)
预采用的整体方案
We are not going to perform feature engineering in first instance. The dataset has been downgraded in order to contain 30 features (28 anonamised + time + amount).We will then compare what happens when using resampling and when not using it. We will test this approach using a simple logistic regression classifier.We will evaluate the models by using some of the performance metrics mentioned above.We will repeat the best resampling/not resampling method, by tuning the parameters in the logistic regression classifier.We will finally perform classifications model using other classification algorithms.特征值处理和重采样
from sklearn.preprocessing import StandardScalerdata['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1))data = data.drop(['Time','Amount'],axis=1)# print(data.head())X = data.ix[:, data.columns != 'Class']y = data.ix[:, data.columns == 'Class']# Number of data points in the minority classnumber_records_fraud = len(data[data.Class == 1])fraud_indices = np.array(data[data.Class == 1].index)# Picking the indices of the normal classesnormal_indices = data[data.Class == 0].index# Out of the indices we picked, randomly select "x" number (number_records_fraud)random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)random_normal_indices = np.array(random_normal_indices)# Appending the 2 indicesunder_sample_indices = np.concatenate([fraud_indices,random_normal_indices])# Under sample datasetunder_sample_data = data.iloc[under_sample_indices,:]X_undersample = under_sample_data.ix[:, under_sample_data.columns != 'Class']y_undersample = under_sample_data.ix[:, under_sample_data.columns == 'Class']# Showing ratioprint("Percentage of normal transactions: ", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))print("Percentage of fraud transactions: ", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))print("Total number of transactions in resampled data: ", len(under_sample_data))
结果如下：
Percentage of normal transactions: 0.5Percentage of fraud transactions: 0.5Total number of transactions in resampled data: 984
拆分数据为训练集和测试集
为了交叉验证的需要
from sklearn.model_selection import train_test_split# Whole datasetX_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0)print("Number transactions train dataset: ", len(X_train))print("Number transactions test dataset: ", len(X_test))print("Total number of transactions: ", len(X_train)+len(X_test))# Undersampled datasetX_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample,y_undersample,test_size = 0.3,random_state = 0)print("")print("Number transactions train dataset: ", len(X_train_undersample))print("Number transactions test dataset: ", len(X_test_undersample))print("Total number of transactions: ", len(X_train_undersample)+len(X_test_undersample))
结果如下：
Number transactions train dataset: 199364Number transactions test dataset: 85443Total number of transactions: 284807Number transactions train dataset: 688Number transactions test dataset: 296Total number of transactions: 984
逻辑回归分类（在重采样数据集上）
Accuracy = (TP+TN)/totalPrecision = TP/(TP+FP)Recall = TP/(TP+FN)from sklearn.linear_model import LogisticRegressionfrom sklearn.cross_validation import KFold, cross_val_scorefrom sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_reportdef printing_Kfold_scores(x_train_data, y_train_data):fold = KFold(len(y_train_data), 5, shuffle=False)# Different C parametersc_param_range = [0.01, 0.1, 1, 10, 100]results_table = pd.DataFrame(index=range(len(c_param_range), 2), columns=['C_parameter', 'Mean recall score'])results_table['C_parameter'] = c_param_range# the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]j = 0for c_param in c_param_range:print('-------------------------------------------')print('C parameter: ', c_param)print('-------------------------------------------')print('')recall_accs = []for iteration, indices in enumerate(fold, start=1):# Call the logistic regression model with a certain C parameterlr = LogisticRegression(C=c_param, penalty='l1')# Use the training data to fit the model. In this case, we use the portion of the fold to train the model# with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]lr.fit(x_train_data.iloc[indices[0], :], y_train_data.iloc[indices[0], :].values.ravel())# Predict values using the test indices in the training datay_pred_undersample = lr.predict(x_train_data.iloc[indices[1], :].values)# Calculate the recall score and append it to a list for recall scores representing the current c_parameterrecall_acc = recall_score(y_train_data.iloc[indices[1], :].values, y_pred_undersample)recall_accs.append(recall_acc)print('Iteration ', iteration, ': recall score = ', recall_acc)# The mean value of those recall scores is the metric we want to save and get hold of.results_table.ix[j, 'Mean recall score'] = np.mean(recall_accs)j += 1print('')print('Mean recall score ', np.mean(recall_accs))print('')best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']# Finally, we can check which C parameter is the best amongst the chosen.print('*********************************************************************************')print('Best model to choose from cross validation is with C parameter = ', best_c)print('*********************************************************************************')return best_cbest_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)
结果如下：
-------------------------------------------C parameter: 0.01-------------------------------------------Iteration 1 : recall score = 0.931506849315Iteration 2 : recall score = 0.931506849315Iteration 3 : recall score = 1.0Iteration 4 : recall score = 0.972972972973Iteration 5 : recall score = 0.969696969697Mean recall score 0.96113672826-------------------------------------------C parameter: 0.1-------------------------------------------Iteration 1 : recall score = 0.849315068493Iteration 2 : recall score = 0.86301369863Iteration 3 : recall score = 0.949152542373Iteration 4 : recall score = 0.932432432432Iteration 5 : recall score = 0.909090909091Mean recall score 0.900600930204-------------------------------------------C parameter: 1-------------------------------------------Iteration 1 : recall score = 0.849315068493Iteration 2 : recall score = 0.890410958904Iteration 3 : recall score = 0.983050847458Iteration 4 : recall score = 0.932432432432Iteration 5 : recall score = 0.924242424242Mean recall score 0.915890346306-------------------------------------------C parameter: 10-------------------------------------------Iteration 1 : recall score = 0.86301369863Iteration 2 : recall score = 0.876712328767Iteration 3 : recall score = 0.983050847458Iteration 4 : recall score = 0.932432432432Iteration 5 : recall score = 0.909090909091Mean recall score 0.912860043276-------------------------------------------C parameter: 100-------------------------------------------Iteration 1 : recall score = 0.849315068493Iteration 2 : recall score = 0.876712328767Iteration 3 : recall score = 0.983050847458Iteration 4 : recall score = 0.932432432432Iteration 5 : recall score = 0.909090909091Mean recall score 0.910120317248*********************************************************************************Best model to choose from cross validation is with C parameter = 0.01*********************************************************************************
混淆函数的可视化函数
import itertoolsdef plot_confusion_matrix(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.Blues):"""This function prints and plots the confusion matrix.Normalization can be applied by setting `normalize=True`."""plt.imshow(cm, interpolation='nearest', cmap=cmap)plt.title(title)plt.colorbar()tick_marks = np.arange(len(classes))plt.xticks(tick_marks, classes, rotation=0)plt.yticks(tick_marks, classes)if normalize:cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]#print("Normalized confusion matrix")else:1#print('Confusion matrix, without normalization')#print(cm)thresh = cm.max() / 2.for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):plt.text(j, i, cm[i, j],horizontalalignment="center",color="white" if cm[i, j] > thresh else "black")plt.tight_layout()plt.ylabel('True label')plt.xlabel('Predicted label')
在测试集上进行类别预测并计算混淆函数
