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() 复制代码
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 复制代码
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 复制代码
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 复制代码
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') 复制代码
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() 复制代码
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() 复制代码
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() 复制代码
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 复制代码
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() 复制代码
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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