1.样本下采样选择

# 下采样取样本数据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))# 下采样后的数据进行训练、验证数据集拆分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))

 

交叉验证选择最优参数：

#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    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_cbest_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)

 

 绘制混淆矩阵

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')

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()

 

查看不同阈值对应召回率

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 = 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)