项目实例---金融---用机器学习构建模型，进行信用卡反欺诈预测-优快云博客

本文介绍了一个信用卡欺诈检测项目，使用机器学习方法对极不平衡的数据集进行处理，并通过重采样技术提高欺诈交易的检测率。

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来源：
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
这里写图片描述

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2013年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())

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	…	V21	V22	V23	V24	V25	V26	V27	V28	Amount
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	…	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	…	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69
2	1.0	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	…	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	378.66
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	…	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	…	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99

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    284315
1       492
Name: Class, dtype: int64

这里写图片描述

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 case
Changing the performance metric:
- Use the confusio nmatrix to calculate Precision, Recall
- F1score (weighted average of precision recall)
- Use Kappa - which is a classification accuracy normalized by the imbalance of the * classes in the data
- ROC 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 StandardScaler

data['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 class
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)

# Picking the indices of the normal classes
normal_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 indices
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])

# Under sample dataset
under_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 ratio
print("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:  0.5
Percentage of fraud transactions:  0.5
Total 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 dataset
X_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 dataset
X_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:  199364
Number transactions test dataset:  85443
Total number of transactions:  284807

Number transactions train dataset:  688
Number transactions test dataset:  296
Total number of transactions:  984

逻辑回归分类（在重采样数据集上）

Accuracy = (TP+TN)/total
Precision = TP/(TP+FP)
Recall = TP/(TP+FN)

from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation  import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report


def printing_Kfold_scores(x_train_data, y_train_data):
    fold = KFold(len(y_train_data), 5, shuffle=False)

    # Different C parameters
    c_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 = 0
    for 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 parameter
            lr = 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 data
            y_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_parameter
            recall_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 += 1
        print('')
        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_c


best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)

结果如下：

-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.931506849315
Iteration  2 : recall score =  0.931506849315
Iteration  3 : recall score =  1.0
Iteration  4 : recall score =  0.972972972973
Iteration  5 : recall score =  0.969696969697

Mean recall score  0.96113672826

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.949152542373
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.909090909091

Mean recall score  0.900600930204

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.890410958904
Iteration  3 : recall score =  0.983050847458
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.924242424242

Mean recall score  0.915890346306

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.86301369863
Iteration  2 : recall score =  0.876712328767
Iteration  3 : recall score =  0.983050847458
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.909090909091

Mean recall score  0.912860043276

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.876712328767
Iteration  3 : recall score =  0.983050847458
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.909090909091

Mean 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 itertools

def 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')