内容简介：版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。版权声
版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。
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
data = pd.read_csv("creditcard.csv")
data.head()
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from sklearn.preprocessing import StandardScaler data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1)) data = data.drop(['Time','Amount'],axis=1) data.head() 复制代码
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
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版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。
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
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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
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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 ********************************************************************************* 复制代码
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')
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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()
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7 混合矩阵作用于全数据集X_test.values的展示
版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。 lr = LogisticRegression(C = best_c, penalty = 'l1') lr.fit(X_train_undersample,y_train_undersample.values.ravel()) y_pred = lr.predict(X_test.values)
# 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()
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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 ********************************************************************************* 复制代码
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()
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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
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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
credit_cards=pd.read_csv('creditcard.csv')
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()
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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。
版权声明：本套技术专栏是作者（秦凯新）平时工作的总结和升华，通过从真实商业环境抽取案例进行总结和分享，并给出商业应用的调优建议和集群环境容量规划等内容，请持续关注本套博客。QQ邮箱地址：1120746959@qq.com，如有任何学术交流，可随时联系。
秦凯新 于深圳 201812081811
以上所述就是小编给大家介绍的《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》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
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