def compare(cfg):
data_loader = make_dataloader(cfg, True)
train_data, test_data, train_label, test_label = data_loader.load_data()
cfg_cp = cfg.clone()
for m in cfg.MODEL.ARCHITECTURES:
cfg_cp.MODEL.ARCHITECTURE = m
model = build_classification_model(cfg_cp)
model.load_model()
pre_train_data, pre_train_label, pre_test_data, pre_test_label = model.data_preprocess(
train_data, train_label, test_data, test_label)
acc = model.get_accuracy(pre_test_data, pre_test_label)
prediction = model.predict_prob(pre_test_data)
fpr, tpr, thresholds = roc_curve(pre_test_label, prediction)
roc_auc = auc(fpr, tpr)
plt.plot(fpr,
tpr,
lw=1,
label=model.model_name + '(area = {0:.2f})'.format(roc_auc))
print(model.model_name + "测试集正确率为：" + "{:.2f}".format(acc * 100) + "%")
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck') # 画对角线
plt.xlim([-0.05, 1.05]) # 设置x、y轴的上下限，设置宽一点，以免和边缘重合，可以更好的观察图像的整体
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate') # 可以使用中文，但需要导入一些库即字体
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
def train(cfg):
model = build_classification_model(cfg)
data_loader = make_dataloader(cfg, True)
train_data, val_data, train_label, val_label = data_loader.load_data()
train_data, train_label, val_data, val_label = model.data_preprocess(train_data, train_label, val_data, val_label)
model.train(train_data, train_label, val_data, val_label)
acc = model.get_accuracy(val_data,val_label)
prediction = model.predict_prob(val_data)
fpr, tpr, thresholds = roc_curve(val_label,prediction)
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, lw=1, label='ROC fold {0:.2f} (area = {1:.2f})'.format(1, roc_auc))
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck') # 画对角线
plt.xlim([-0.05, 1.05]) # 设置x、y轴的上下限，设置宽一点，以免和边缘重合，可以更好的观察图像的整体
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate') # 可以使用中文，但需要导入一些库即字体
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
print("测试集正确率为：" + "{:.2f}".format(acc * 100) + "%")
plt.show()
model.save_model()
def cross_val(cfg):
KF = KFold(n_splits=5, shuffle=True)
tprs = []
aucs = []
accs = []
mean_fpr = np.linspace(0, 1, 100)
model = build_classification_model(cfg)
data_loader = make_dataloader(cfg, True)
data, label_value, label_classify = data_loader.split_data_lable()
for i, (train_idx, val_idx) in enumerate(KF.split(data)):
train_data, val_data = data[train_idx], data[val_idx]
train_label, val_label = label_classify[train_idx], label_classify[
val_idx]
train_data, train_label, val_data, val_label = model.data_preprocess(
train_data, train_label, val_data, val_label)
model.train(train_data, train_label, val_data, val_label)
acc = model.get_accuracy(val_data, val_label)
accs.append(acc)
prediction = model.predict_prob(val_data)
fpr, tpr, thresholds = roc_curve(val_label, prediction)
roc_auc = auc(fpr, tpr)
tprs.append(interp(mean_fpr, fpr, tpr))
tprs[-1][0] = 0.0
aucs.append(roc_auc)
print(model.model_name + "测试集正确率为：" + "{0:.3f}".format(acc * 100) +
"%")
plt.plot(mean_fpr,
tprs[-1],
lw=1,
alpha=0.3,
label='ROC fold %d(area=%0.2f)' % (i, roc_auc))
mean_acc = np.mean(accs)
print(model.model_name + "测试集平均正确率为：" + "{0:.3f}".format(mean_acc * 100) +
"%")
# 画对角线
plt.plot([0, 1], [0, 1],
linestyle='--',
lw=2,
color='r',
label='Luck',
alpha=.8)
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr) # 计算平均AUC值
plt.plot(mean_fpr,
mean_tpr,
color='b',
label=r'Mean ROC (area=%0.3f)' % mean_auc,
lw=2,
alpha=.8)
std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
plt.fill_between(mean_tpr, tprs_lower, tprs_upper, color='gray', alpha=.2)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title(model.model_name + ':ROC')
plt.legend(loc='lower right')
plt.show()
np.savez(cfg.MODEL.CROSS_VAL_RESULTS_PATH + model.model_name + '-' +
cfg.DATA_LOADER.DATA_PATH.split('/')[-1].split('.')[0] + '.npz',
mean_fpr=mean_fpr,
tprs=tprs,
aucs=aucs,
accs=accs)
def optimize(cfg):
model =build_classification_model(cfg)
dataloader = make_dataloader(cfg)
optimizer = build_optimizer(cfg,model)
data, label_value, label_classify = dataloader.split_data_lable()
optimizer.select_param(data=data, label=label_classify)