本文共 17548 字，大约阅读时间需要 58 分钟。

import pandas as pdimport matplotlib.pyplot as pltimport numpy as np%matplotlib inline

data = pd.read_csv("creditcard.csv")data.head()

#查看样本是否平衡

count_classes = pd.value_counts(data['Class'], sort = True).sort_index()count_classes.plot(kind = 'bar')plt.title("Fraud class histogram")plt.xlabel("Class")plt.ylabel("Frequency")

显然样本不平衡，现在有两种策略来平衡样本，一种是下采样策略，让0和1样本一样少；另外一种是过采样策略，让1样本生成到与0同样多

另外Time列数据用不到，Amount列的数据起伏较大，在机器学习过程中可能误以为数值大的权重较大，故需要进行标准化或者归一化：

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

#下采样策略

X = data.ix[:, data.columns != 'Class']y = data.ix[:, data.columns == 'Class']# Number of data points in the minority classnumber_records_fraud = len(data[data.Class == 1])fraud_indices = np.array(data[data.Class == 1].index)# Picking the indices of the normal classesnormal_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 indicesunder_sample_indices = np.concatenate([fraud_indices,random_normal_indices])# Under sample datasetunder_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 ratioprint("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:  0.5Percentage of fraud transactions:  0.5Total number of transactions in resampled data:  984

from sklearn.cross_validation import train_test_split# Whole datasetX_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 datasetX_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:  199364Number transactions test dataset:  85443Total number of transactions:  284807Number transactions train dataset:  688Number transactions test dataset:  296Total number of transactions:  984

#模型评估方法

#Recall = TP/(TP+FN)from sklearn.linear_model import LogisticRegressionfrom sklearn.cross_validation import KFold, cross_val_scorefrom sklearn.metrics import confusion_matrix,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    #正则化惩罚项    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)

-------------------------------------------C parameter:  0.01-------------------------------------------Iteration  1 : recall score =  0.958904109589Iteration  2 : recall score =  0.917808219178Iteration  3 : recall score =  1.0Iteration  4 : recall score =  0.972972972973Iteration  5 : recall score =  0.954545454545Mean recall score  0.960846151257-------------------------------------------C parameter:  0.1-------------------------------------------Iteration  1 : recall score =  0.835616438356Iteration  2 : recall score =  0.86301369863Iteration  3 : recall score =  0.915254237288Iteration  4 : recall score =  0.932432432432Iteration  5 : recall score =  0.878787878788Mean recall score  0.885020937099-------------------------------------------C parameter:  1-------------------------------------------Iteration  1 : recall score =  0.835616438356Iteration  2 : recall score =  0.86301369863Iteration  3 : recall score =  0.966101694915Iteration  4 : recall score =  0.945945945946Iteration  5 : recall score =  0.893939393939Mean recall score  0.900923434357-------------------------------------------C parameter:  10-------------------------------------------Iteration  1 : recall score =  0.849315068493Iteration  2 : recall score =  0.86301369863Iteration  3 : recall score =  0.966101694915Iteration  4 : recall score =  0.959459459459Iteration  5 : recall score =  0.893939393939Mean recall score  0.906365863087-------------------------------------------C parameter:  100-------------------------------------------Iteration  1 : recall score =  0.86301369863Iteration  2 : recall score =  0.86301369863Iteration  3 : recall score =  0.966101694915Iteration  4 : recall score =  0.959459459459Iteration  5 : recall score =  0.893939393939Mean recall score  0.909105589115*********************************************************************************Best model to choose from cross validation is with C parameter =  0.01*********************************************************************************

#混淆矩阵

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')

#下采样策略混淆矩阵import itertoolslr = 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 matrixcnf_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 matrixclass_names = [0,1]plt.figure()plot_confusion_matrix(cnf_matrix                      , classes=class_names                      , title='Confusion matrix')plt.show()

#完整数据集混淆矩阵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 matrixcnf_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 matrixclass_names = [0,1]plt.figure()plot_confusion_matrix(cnf_matrix                      , classes=class_names                      , title='Confusion matrix')plt.show()

best_c = printing_Kfold_scores(X_train,y_train)

-------------------------------------------C parameter:  0.01-------------------------------------------Iteration  1 : recall score =  0.492537313433Iteration  2 : recall score =  0.602739726027Iteration  3 : recall score =  0.683333333333Iteration  4 : recall score =  0.569230769231Iteration  5 : recall score =  0.45Mean recall score  0.559568228405-------------------------------------------C parameter:  0.1-------------------------------------------Iteration  1 : recall score =  0.567164179104Iteration  2 : recall score =  0.616438356164Iteration  3 : recall score =  0.683333333333Iteration  4 : recall score =  0.584615384615Iteration  5 : recall score =  0.525Mean recall score  0.595310250644-------------------------------------------C parameter:  1-------------------------------------------Iteration  1 : recall score =  0.55223880597Iteration  2 : recall score =  0.616438356164Iteration  3 : recall score =  0.716666666667Iteration  4 : recall score =  0.615384615385Iteration  5 : recall score =  0.5625Mean recall score  0.612645688837-------------------------------------------C parameter:  10-------------------------------------------Iteration  1 : recall score =  0.55223880597Iteration  2 : recall score =  0.616438356164Iteration  3 : recall score =  0.733333333333Iteration  4 : recall score =  0.615384615385Iteration  5 : recall score =  0.575Mean recall score  0.61847902217-------------------------------------------C parameter:  100-------------------------------------------Iteration  1 : recall score =  0.55223880597Iteration  2 : recall score =  0.616438356164Iteration  3 : recall score =  0.733333333333Iteration  4 : recall score =  0.615384615385Iteration  5 : recall score =  0.575Mean recall score  0.61847902217*********************************************************************************Best model to choose from cross validation is with C parameter =  10.0*********************************************************************************

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 matrixcnf_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 matrixclass_names = [0,1]plt.figure()plot_confusion_matrix(cnf_matrix                      , classes=class_names                      , title='Confusion matrix')plt.show()

#逻辑回归阈值对结果的影响

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)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 = 1for 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:  1.0Recall metric in the testing dataset:  1.0Recall metric in the testing dataset:  1.0Recall metric in the testing dataset:  0.986394557823Recall metric in the testing dataset:  0.931972789116Recall metric in the testing dataset:  0.884353741497Recall metric in the testing dataset:  0.836734693878Recall metric in the testing dataset:  0.748299319728Recall metric in the testing dataset:  0.571428571429

#过采样策略、SMOTE生成策略

import pandas as pdfrom imblearn.over_sampling import SMOTEfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import confusion_matrixfrom 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 columnsfeatures_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 =  0.890322580645Iteration  2 : recall score =  0.894736842105Iteration  3 : recall score =  0.968861347792Iteration  4 : recall score =  0.957595541926Iteration  5 : recall score =  0.958430881173Mean recall score  0.933989438728-------------------------------------------C parameter:  0.1-------------------------------------------Iteration  1 : recall score =  0.890322580645Iteration  2 : recall score =  0.894736842105Iteration  3 : recall score =  0.970410534469Iteration  4 : recall score =  0.959980655302Iteration  5 : recall score =  0.960178498807Mean recall score  0.935125822266-------------------------------------------C parameter:  1-------------------------------------------Iteration  1 : recall score =  0.890322580645Iteration  2 : recall score =  0.894736842105Iteration  3 : recall score =  0.970454796946Iteration  4 : recall score =  0.96014552489Iteration  5 : recall score =  0.960596168431Mean recall score  0.935251182603-------------------------------------------C parameter:  10-------------------------------------------Iteration  1 : recall score =  0.890322580645Iteration  2 : recall score =  0.894736842105Iteration  3 : recall score =  0.97065397809Iteration  4 : recall score =  0.960343368396Iteration  5 : recall score =  0.960530220596Mean recall score  0.935317397966-------------------------------------------C parameter:  100-------------------------------------------Iteration  1 : recall score =  0.890322580645Iteration  2 : recall score =  0.894736842105Iteration  3 : recall score =  0.970543321899Iteration  4 : recall score =  0.960211472725Iteration  5 : recall score =  0.960903924995Mean 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 matrixcnf_matrix = confusion_matrix(labels_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 matrixclass_names = [0,1]plt.figure()plot_confusion_matrix(cnf_matrix                      , classes=class_names                      , title='Confusion matrix')plt.show()