# 信用卡欺诈行为逻辑回归数据分析-大数据ML样本集案例实战

## 1 信用卡欺诈行为案例集预处理

```import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

```from sklearn.preprocessing import StandardScaler
data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)

## 2 K折交叉验证

```def printing_Kfold_scores(x_train_data, y_train_data):

fold = KFold(len(y_train_data),5,shuffle=False)
# Different C parameters
# 0.01 倒数其实是100
# 0.1其实是10
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):
lr = LogisticRegression(C = c_param, penalty = 'l1')]
lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())
y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)
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)
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

## 3 不均衡问题处理策略（OverSample与UnderSample）

```# 找出非class列
X = data.ix[:, data.columns != 'Class']
# 找出class列
y = data.ix[:, data.columns == 'Class']

# 找出欺诈的个数和索引492
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)

# Picking the indices of the normal classes（找出正常的索引）
normal_indices = data[data.Class == 0].index

# Out of the indices we picked, randomly select "x" number (number_records_fraud)（从正常的行为中选择接近欺诈的样本索引）492
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(索引组合) 892
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])

# iloc通过行号获取行数据
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))

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

## 4 训练集与测试集划分

```from sklearn.cross_validation import train_test_split

X特征输入,y表示label,test_size划分的测试集比例，没有设置random_state，每次取得的
结果就不一样，它的随机数种子与当前系统时间有关。其实就是该组随机数的编号，在需要重
复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到
随机数组是一样的。但填0或不填，每次都不一样。随机数的产生取决于种子，随机数和种子之
间的关系遵从以下两个规则：种子不同，产生不同的随机数；种子相同，即使实例不同也产生
相同的随机数。

全部样本拆分
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))

Number transactions train dataset:  199364
Number transactions test dataset:  85443
Total number of transactions:  284807

# 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))

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

## 5基于低采样数据集X_test_undersample模型训练与测试（均衡数据）

```#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,recall_score,classification_report

函数调用
best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)

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

Iteration  1 : recall score =  0.958904109589
Iteration  2 : recall score =  0.917808219178
Iteration  3 : recall score =  1.0
Iteration  4 : recall score =  0.972972972973
Iteration  5 : recall score =  0.954545454545

Mean recall score  0.960846151257

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

Iteration  1 : recall score =  0.835616438356
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.915254237288
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.878787878788

Mean recall score  0.885020937099

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

Iteration  1 : recall score =  0.835616438356
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.945945945946
Iteration  5 : recall score =  0.893939393939

Mean recall score  0.900923434357

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

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.959459459459
Iteration  5 : recall score =  0.893939393939

Mean recall score  0.906365863087

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

Iteration  1 : recall score =  0.86301369863
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.959459459459
Iteration  5 : recall score =  0.893939393939

Mean recall score  0.909105589115

*********************************************************************************
Best model to choose from cross validation is with C parameter =  0.01
*********************************************************************************

## 5 混合矩阵

```def plot_confusion_matrix(cm, classes,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
"""
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)

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')

## 6 混合矩阵作用于低采样数据集X_test_undersample的展示

```import itertools
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample = lr.predict(X_test_undersample.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
, classes=class_names
, title='Confusion matrix')
plt.show()

## 7 混合矩阵作用于全数据集X_test.values的展示

```# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
, classes=class_names
, title='Confusion matrix')
plt.show()

## 8 基于全数据集进行k折交叉验证（不均衡数据）

### 8.1 全数据集进行k折交叉验证

```best_c = printing_Kfold_scores(X_train,y_train)
-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.492537313433
Iteration  2 : recall score =  0.602739726027
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.569230769231
Iteration  5 : recall score =  0.45

Mean recall score  0.559568228405

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

Iteration  1 : recall score =  0.567164179104
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.584615384615
Iteration  5 : recall score =  0.525

Mean recall score  0.595310250644

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

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.716666666667
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.5625

Mean recall score  0.612645688837

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

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

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

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

*********************************************************************************
Best model to choose from cross validation is with C parameter =  10.0
*********************************************************************************

### 8.2 全数据集混合矩阵

```# 不均衡样本偏向于多的样本，误伤率低
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train,y_train.values.ravel())
y_pred_undersample = lr.predict(X_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred_undersample)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
, classes=class_names
, title='Confusion matrix')
plt.show()

## 9 逻辑回归基于阈值进行判断（概率）

```lr = LogisticRegression(C = 0.01, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)

thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

plt.figure(figsize=(10,10))

j = 1
for i in thresholds:
y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i

plt.subplot(3,3,j)
j += 1

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plot_confusion_matrix(cnf_matrix
, classes=class_names
, title='Threshold >= %s'%i)

Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  0.986394557823
Recall metric in the testing dataset:  0.931972789116
Recall metric in the testing dataset:  0.884353741497
Recall metric in the testing dataset:  0.836734693878
Recall metric in the testing dataset:  0.748299319728
Recall metric in the testing dataset:  0.571428571429

## 10 基于SMOTE 进行数据预处理

```import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split

columns=credit_cards.columns
# The labels are in the last column ('Class'). Simply remove it to obtain features columns
features_columns=columns.delete(len(columns)-1)

features=credit_cards[features_columns]
labels=credit_cards['Class']
features_train, features_test, labels_train, labels_test = train_test_split(features,
labels,
test_size=0.2,
random_state=0)
oversampler=SMOTE(random_state=0)
os_features,os_labels=oversampler.fit_sample(features_train,labels_train)

len(os_labels[os_labels==1])
227454

os_features = pd.DataFrame(os_features)
os_labels = pd.DataFrame(os_labels)
best_c = printing_Kfold_scores(os_features,os_labels)

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

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.968861347792
Iteration  4 : recall score =  0.957595541926
Iteration  5 : recall score =  0.958430881173

Mean recall score  0.933989438728

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

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970410534469
Iteration  4 : recall score =  0.959980655302
Iteration  5 : recall score =  0.960178498807

Mean recall score  0.935125822266

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

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970454796946
Iteration  4 : recall score =  0.96014552489
Iteration  5 : recall score =  0.960596168431

Mean recall score  0.935251182603

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

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.97065397809
Iteration  4 : recall score =  0.960343368396
Iteration  5 : recall score =  0.960530220596

Mean recall score  0.935317397966

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

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970543321899
Iteration  4 : recall score =  0.960211472725
Iteration  5 : recall score =  0.960903924995

Mean recall score  0.935343628474

*********************************************************************************
Best model to choose from cross validation is with C parameter =  100.0
*********************************************************************************

lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(os_features,os_labels.values.ravel())
y_pred = lr.predict(features_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix( ,y_pred)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
, classes=class_names
, title='Confusion matrix')
plt.show()

## 11 总结

OverSample与UnderSample对比发现，基于SMOTE，数据的准确率和召回率得到了很大程度的提高。