def LR_under_sample_test(data):
# 下采样后的新数据
X_under_sample, y_under_sample = UnderSample.under_sample_own(data)
X_under_sample_train, X_under_sample_test, y_under_sample_train, y_under_sample_test = DataTools.data_split(
X_under_sample, y_under_sample)
# 训练LR，得到C参数
c_param_scores = ClassifierLR.c_param_scores(X_under_sample_train,
y_under_sample_train)
# 训练模型
y_predict = ClassifierLR.fit_model_LR(X_under_sample_train,
y_under_sample_train,
X_under_sample_test,
c_param_scores)
# 计算混淆矩阵
cnf_matrix = DataTools.compute_confusion_matrix(
y_under_sample_test, y_predict)
# 绘制混淆矩阵图
PlotTools.plot_confusion_matrix(cnf_matrix, title='Confusion matrix')
# 绘制ROC曲线
ResultLR.LR_plot_ROC(X_under_sample_train, y_under_sample_train,
X_under_sample_test, y_under_sample_test,
c_param_scores)
# 绘制阈值图
# ResultLR.LR_plot_threshold(X_under_sample_train, y_under_sample_train, X_under_sample_test,
# y_under_sample_test, c_param_scores)
# 绘制精度-召回率曲线
ResultLR.LR_plot_precision_recall(X_under_sample_train,
y_under_sample_train,
X_under_sample_test,
y_under_sample_test, c_param_scores)
def LR_origin_data(data):
# 采样前的数据
X, y = DataPreprocessing.read_X_y(data)
X_train, X_test, y_train, y_test = DataTools.data_split(X, y)
# 训练LR，得到C参数
c_param_scores = ClassifierLR.c_param_scores(X_train, y_train)
# 训练模型
y_predict = ClassifierLR.fit_model_LR(X_train, y_train, X_test,
c_param_scores)
# 计算混淆矩阵
cnf_matrix = DataTools.compute_confusion_matrix(y_test, y_predict)
# 绘制混淆矩阵图
PlotTools.plot_confusion_matrix(cnf_matrix, title='Confusion matrix')
# 绘制ROC曲线
ResultLR.LR_plot_ROC(X_train, y_train, X_test, y_test, c_param_scores)
def LR_plot_precision_recall(X_train, y_train, X_test, y_test,
c_param_scores):
y_pred_proba = ClassifierLR.lr_predict_proba(X_train, y_train, X_test,
c_param_scores)
PlotTools.plot_precision_recall(y_test, y_pred_proba)
def LR_plot_ROC(X_train, y_train, X_test, y_test, c_param_scores):
y_predict_score = ClassifierLR.lr_roc_curve_score(
X_train, y_train, X_test, c_param_scores)
PlotTools.plot_roc_curve(y_test, y_predict_score)
def LR_plot_threshold(X_train, y_train, X_test, y_test, c_param_scores):
y_pred_proba = ClassifierLR.lr_predict_proba(X_train, y_train, X_test,
c_param_scores)
PlotTools.plot_thresholds(y_test, y_pred_proba)
def LR_EE_smote(data):
# 子集数目
num_subsets = 10
X, y = DataPreprocessing.read_X_y(data)
print('原始数据：')
DataTools.print_data_ratio(y)
X_train, X_test, y_train, y_test = DataTools.data_split(X, y)
print('分割后的测试集：')
DataTools.print_data_ratio(y_test)
print('分割后的训练集：')
DataTools.print_data_ratio(y_train)
result = {}
result_recall = []
result_acc = []
result_precision = []
result_f1 = []
result_auc = []
result_gmean = []
result_fpr_temps = []
result_tpr_temps = []
start_time = time.clock()
for i in (range(num_subsets)):
print(
'******************************************************************************'
)
print('第 ', i + 1, ' 个分类器开始：')
# EE&smote后的数据
X_ee_smote, y_ee_smote = SmoteEE.smoteEE_own(X_train, y_train)
DataTools.print_data_ratio(y_ee_smote)
# 训练参数
# print('训练集子集%d：' % (i + 1))
# ClassifierLR.lr_grid_search_cv(X_ee_smote, y_ee_smote)
pd.concat([X_ee_smote, y_ee_smote],
axis=1).to_csv('data/subsets/lr_subset%d.csv' % (i + 1))
print('第%d个 子集导出成功！' % (i + 1))
print('训练集子集%d：' % (i + 1))
DataTools.print_data_ratio(y_ee_smote)
y_predict = ClassifierLR.fit_model_LR(X_ee_smote, y_ee_smote,
X_test, 0.1)
y_predict_prob = ClassifierLR.lr_predict_proba(
X_ee_smote, y_ee_smote, X_test, 0.1)
recall_, accuracy_, precision_, f1_, auc_, g_mean_, fpr_, tpr_ = \
DataTools.compute_score(y_test, y_predict, y_predict_prob)
result_recall.append(recall_)
result_acc.append(accuracy_)
result_precision.append(precision_)
result_f1.append(f1_)
result_auc.append(auc_)
result_gmean.append(g_mean_)
result_fpr_temps.append(fpr_)
result_tpr_temps.append(tpr_)
end_time = time.clock()
result['time'] = end_time - start_time
result['recall'] = np.mean(result_recall)
result['acc'] = np.mean(result_acc)
result['precision'] = np.mean(result_precision)
result['f1'] = np.mean(result_f1)
result['auc'] = np.mean(result_auc)
result['gmean'] = np.mean(result_gmean)
result['fpr'] = pd.DataFrame(result_fpr_temps).mean()
result['tpr'] = pd.DataFrame(result_tpr_temps).mean()
pd.DataFrame(result).to_csv('data/score/lr_ee_tuned.csv')
print('结果已保存至score文件夹下 ^_^')