項目實例---金融---用機器學習構建模型，進行信用卡反欺詐預測

來源：
用機器學習構建模型，進行信用卡反欺詐預測
反欺詐中所用到的機器學習模型有哪些？
Credit card fraud detection
構建信用卡反欺詐預測模型——機器學習

信用卡交易數據相關知識收集
交易渠道、交易日期，商戶名稱/網點名稱、卡號、交易幣種、交易金額幾個字段
刷卡供應商的信任度、插卡讓購買行爲（時空維度）、IP地址等等

信用貸款的不同維度

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

項目背景

數據集包含由歐洲持卡人於2013年9月使用信用卡進行交的數據。此數據集顯示兩天內發生的交易，其中284,807筆交易中有492筆被盜刷。數據集非常不平衡，積極的類（被盜刷）佔所有交易的0.172％。

它只包含作爲PCA轉換結果的數字輸入變量。不幸的是，由於保密問題，我們無法提供有關數據的原始功能和更多背景信息。特徵V1，V2，… V28是使用PCA獲得的主要組件，沒有用PCA轉換的唯一特徵是“時間”和“量”。特徵’時間’包含數據集中每個事務和第一個事務之間經過的秒數。特徵“金額”是交易金額，此特徵可用於實例依賴的成本認知學習。特徵’類’是響應變量，如果發生被盜刷，則取值1，否則爲0。

加載數據

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	Class
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	0
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	0
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	0
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	0
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	0

查看數據標籤分佈

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()

結果如下：

0    284315
1       492
Name: Class, dtype: int64

從上可以看到正負樣本極不平衡,如果全樣本訓練，標籤爲1的樣本就被淹沒了；結果是，標籤爲0的樣本容易套上模型，而標籤爲1的樣本不容易套上模型；導致在交叉驗證預測分類時，整個結果的精度看起來依然很高，但容易把真實標籤爲1的樣本預測錯。

對於不平衡樣本的解決方案：

• 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)

預採用的整體方案

• 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.

特徵值處理和重採樣

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))

結果如下：

Percentage of normal transactions:  0.5
Percentage of fraud transactions:  0.5
Total number of transactions in resampled data:  984

拆分數據爲訓練集和測試集

爲了交叉驗證的需要

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))

結果如下：

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

邏輯迴歸分類（在重採樣數據集上）

• 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)

結果如下：

-------------------------------------------
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
*********************************************************************************

混淆函數的可視化函數

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')

在測試集上進行類別預測並計算混淆函數