信用卡欺诈检测
数据集在kaggle在搜索即可。此数据正样本和负样本的的比例不平衡，因此可以采用下采样或者过采样的方法使样本均衡。
import matplotlib.pylab as plt import numpy as np import pandas as pd data = pd.read_csv("D:/creditcard.csv") # print(data.head()) #0 - 正常的样本，1 - 有问题的样本 count_classes = pd.value_counts(data['Class'], sort=True).sort_index() count_classes.plot(kind = 'bar', alpha=0.5) plt.title('Fraud class histogram') plt.xlabel('Class') plt.ylabel('Frequency') # plt.show() from sklearn.preprocessing import StandardScaler #fit_transform()：对数据进行一个变化，变化号的列作为一个新属性添加到data data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1)) data = data.drop(['Time', 'Amount'], axis=1) # print(data.head()) #下采样实现 X = data.ix[:, data.columns != 'Class'] y = data.ix[:, data.columns == 'Class'] # print(X) # print(y) #样本的个数 number_records_fraud = len(data[data.Class == 1]) #样本的索引，所有值为1的索引 fraud_indices = np.array(data[data.Class == 1].index) #值为0的数据的索引 normal_indices = data[data.Class == 0].index # print(number_records_fraud) # print(fraud_indices) # print(normal_indices) #使两个样本一样少 #随机选取样本中的数据，随机选择：np.random.choice(Class为0的数据索引=>样本，选择多少数据量，是否选择代替=False) random_normal_indeics = np.random.choice(normal_indices, number_records_fraud, replace=False) random_normal_indeics = np.array(random_normal_indeics) #连接Class=1的数据索引和随机选取的Class=0的数据索引 under_sample_indeice = np.concatenate([fraud_indices, random_normal_indeics]) # print(under_sample_indeice) #下采样（选取该索引下的数据） under_sample_data = data.iloc[under_sample_indeice, :] 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 oif normal transactions:', len(under_sample_data[under_sample_data.Class == 0]) / len(under_sample_data)) # print('Percentage oif 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)) #交叉验证， train_test_split: 切分数据 from sklearn.cross_validation import train_test_split #将数据集分成训练集0.7和测试集0.3 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 transaction:', len(X_train) + len(X_test)) #对下采样数据集进行相同的操作 #只采用下采样数据集进行训练，最终用原始数据集测试 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('Number transactions train dataset:', len(X_train_undersample)) # print('Number transactions test dataset:', len(X_test_undersample)) # print('Total number of transaction:', len(X_train_undersample) + len(X_test_undersample)) #在类不平衡问题中，用精度来衡量指标是骗人的！没有意义(1000个人，全部预测为正常， 0个癌症) 精度 = TP / Total # 这里使用Recall(召回率, 查全率) = TP / TP+FN , 1000个人(990个正常,10个癌症),如果检测出0个病人 0/10=0,检测出2个病人2/10=0.2 # 检测任务上，通常用Recall作为模型的评估标准 from sklearn.linear_model import LogisticRegression #KFold->做几倍的交叉验证 from sklearn.cross_validation import KFold # confusion_matrix:混淆矩阵 from sklearn.metrics import confusion_matrix, recall_score def printing_Kfold_score(x_train_data, y_train_data): #切分成5部分，将原始数据集进行切分 fold = KFold(len(y_train_data), 5, shuffle=False) #正则化惩罚项 #希望当前模型的泛化能力更好，不仅满足训练数据，还要在测试数据上尽可能满足 #浮动的差异小 ====> 过拟合的风险小 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 j = 0 #查看哪一个c值比较好 for c_param in c_param_range: print('------------------------------------') print('C paramter:', c_param) print('------------------------------------') print('') recall_accs = [] for iteration, indices in enumerate(fold, start=1): #L1惩罚或者L2惩罚 lr = LogisticRegression(C = c_param, penalty='l1') #进行训练，交叉验证数据 ---> 建立模型 lr.fit(x_train_data.iloc[indices[0], :], y_train_data.iloc[indices[0], :].values.ravel()) #进行预测，用交叉验证中的验证集 再进行一个验证测操作 y_pred_undersameple = lr.predict(x_train_data.iloc[indices[1], :].values) #计算当前模型的recall(indices[1]:测试集) recall_acc = recall_score(y_train_data.iloc[indices[1], :].values, y_pred_undersameple) recall_accs.append(recall_acc) print("Iterator", 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('') # print(results_table) # 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 printing_Kfold_score(X_train_undersample, y_train_undersample) #混淆矩阵 def plot_confusion_matrix(cm, classes, title = 'Confusion matrix', cmap = plt.cm.Blues): 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') import itertools lr = LogisticRegression(C = 0.1, penalty='l1') lr.fit(X_train_undersample, y_train_undersample.values.ravel()) y_pred_undersample = lr.predict(X_test_undersample) #计算混淆矩阵 cnf_matrix = confusion_matrix(y_test_undersample, y_pred_undersample) np.set_printoptions(precision=2) print('Recal metric in the testing dataset: ', cnf_matrix[1, 1] / (cnf_matrix[1, 0] + cnf_matrix[1, 1])) class_names = [0,1] plt.figure() plot_confusion_matrix(cnf_matrix, classes=class_names, title = 'Confusion matrix') # plt.show() lr = LogisticRegression(C = 0.1, penalty='l1') lr.fit(X_train_undersample, y_train_undersample.values.ravel()) y_pred = lr.predict(X_test.values) #用原数据来生成混淆矩阵 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])) class_names = [0, 1] plt.figure() plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix') # plt.show() #测试阈值对结果的影响 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) #设置不同的阈值 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_undersample_high_recall = y_pred_undersample_proba[:, 1] > i plt.subplot(3, 3, j) j += 1 #计算混淆矩阵 cnf_matrix = confusion_matrix(y_test_undersample, y_test_undersample_high_recall) np.set_printoptions(precision=2) print('Recal metrix in the testing dataset:', cnf_matrix[1, 1] / (cnf_matrix[1, 0] + cnf_matrix[1, 1])) class_names = [0, i] plot_confusion_matrix(cnf_matrix, classes=class_names, title='Threshold >= %s' %i) # plt.show() # #采取过采样的策略 ---- SMOTE样本生成策略 # from imblearn.over_sampling import SMOTE # from sklearn.ensemble import RandomForestClassifier # # # 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 cloumns # 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])
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