def train():
""" Training
"""
dataset = 'cus_mnist'
dataroot = 'E:/ProjectSet/Pycharm/WAIBAO/Code01/GAN/data/cus_mnist'
opt = Options().parse(dataset)
opt.load_weights = False
dataloader = load_data(opt)
print(opt)
# LOAD MODEL
model = Ganomaly(opt, dataloader)
model.train()
def train():
""" Training
"""
##
# ARGUMENTS
opt = Options().parse()
##
# LOAD DATA
dataloader = load_data(opt)
##
# LOAD MODEL
model = Ganomaly(opt, dataloader)
##
# TRAIN MODEL
model.train()
def run(self): #训练模型
# 1. 模型训练
self.option.signalInfo.emit(-1,"开始导入数据...")
dataloader = load_data(self.option)
model = Ganomaly(self.option, dataloader)
self.option.signalInfo.emit(10, "导入数据完毕！")
self.modelINFO = model.train()
# 2. 对训练结果进行处理，生成一个字典
self.option.signalInfo.emit(100,"")
self.modelINFO['opt'] = vars(self.option)
self.modelINFO['opt'].pop('signalInfo')
signal = self.modelINFO['opt'].pop('signal')
self.modelINFO['modelName'] = self.modelINFO['opt'].pop('dataset')
self.modelINFO['raw_path'] = self.modelINFO['opt'].pop('dataroot')
self.modelINFO['desc'] = self.modelINFO['opt'].pop('desc')
# 3. 将训练结果字典保存到json文件中
## 默认保存路径./output/modelsData/models.json
# filename = './output/modelsData/models.json'
# data = {}
# with open(filename,'r',encoding='utf-8') as f:
# try:
# data = json.load(f)
# except json.decoder.JSONDecodeError: # 此处源文件没有数据,即尚未有模型被训练
# data = {}
# with open(filename, 'w', encoding="utf-8") as f:
# data[self.modelINFO['modelName']] = self.modelINFO
# json.dump(data,f,sort_keys=True,indent=2)
# 3. 将训练结果字典通过信号传递给主函数
signal.emit(copy.deepcopy(self.modelINFO))
def train(self):
""" Training
"""
self.textEdit.append('开始训练...')
dataset = 'cus_mnist_2'
# dataroot = './data/cus_mnist'
dataroot = 'E:\ProjectSet\Pycharm\WAIBAO\cus_mnist2'
opt = Options().parse(dataset,dataroot)
opt.signal = self.Signal_TrainFinished
opt.load_weights = False
dataloader = load_data(opt)
print(opt)
# LOAD MODEL
opt.showProcess = self.progressBar
opt.showText = self.textEdit
model = Ganomaly(opt, dataloader)
model.train()
def train():
""" Training
"""
##
# ARGUMENTS
opt = Options().parse()
##
# LOAD DATA
dataloader = load_data(opt)
##
# LOAD MODEL
model = Ganomaly(opt, dataloader)
##
if opt.phase == 'train':
# TRAIN MODEL
model.train()
elif opt.phase =='test':
performance=model.test()
print(performance)
from lib.data_preprocess_KDD import load_data_kdd from lib.data_preprocess_arr import load_data_arr from lib.data_preprocess_ucr import load_data_ucr ## # def main(): """ Training """ ## # ARGUMENTS opt = Options().parse() print(opt.anomaly_class) ## # LOAD DATA # dataloader = load_data(opt) # dataloader = load_data_kdd(opt) # dataloader = load_data_arr(opt) dataloader = load_data_ucr(opt) ## # LOAD MODEL model = Ganomaly(opt, dataloader) ## # TRAIN MODEL model.train() # if __name__ == '__main__': # main()
def train():
""" Training
"""
##
# ARGUMENTS
opt = Options().parse()
##
# LOAD DATA
dataloader = load_data(opt)
##
# LOAD MODEL
model = Ganomaly(opt, dataloader)
##
# TRAIN MODEL
model.train()
train_1 = model.train_final()
train_1 = train_1.cpu().numpy()
test_1, y_true, y_true_original, auroc_value, auprc_value = model.test_final(
)
test_1 = test_1.cpu().numpy()
y_true = y_true.cpu().numpy()
y_true_original = y_true_original.cpu().numpy()
test_path = os.path.join(opt.outf, opt.dataset, 'test', 'OCSVM',
'abnormal' + str(opt.abnormal_class),
'seed' + str(opt.manualseed))
if not os.path.isdir(test_path):
os.makedirs(test_path)
print("GANomaly AUROC: {}".format(auroc_value))
np.save(test_path + '/ganomaly_aucroc.npy', auroc_value)
for i in range(len(y_true)):
if y_true[i] == 1:
y_true[i] = 0
else:
y_true[i] = 1
################################
cf = svm.OneClassSVM(gamma='scale', nu=0.1)
train_ind = np.random.choice(train_1.shape[0], 10000, replace=False)
cf.fit(train_1[train_ind, :])
y_scores = cf.score_samples(test_1)
y_scores = (y_scores - min(y_scores)) / (max(y_scores) - min(y_scores))
auroc = metrics.roc_auc_score(y_true, y_scores)
print("HybridGAN AUROC: {}".format(auroc))
np.save(test_path + '/svm_aucroc1.npy', auroc)
np.save(test_path + '/svm_aucroc1_transduct_' + str(0) + '.npy', auroc)
bandwidth = get_bandwidth(y_scores, test_1)
for trans_iter in np.arange(0, 30, 1):
optimal_threshold = find_optimal_threshold(y_scores=y_scores,
train_1=train_1,
test_1=test_1,
y_true=y_true,
train_ind=train_ind,
test_path=test_path,
bandwidth=bandwidth)
abn_idx = np.where(
y_scores < np.percentile(y_scores, optimal_threshold))
abn_tst_latent = test_1[abn_idx]
kmeans = KMeans(n_clusters=1, random_state=0).fit(abn_tst_latent)
train_1 = np.concatenate((train_1, kmeans.transform(train_1)), axis=1)
test_1 = np.concatenate((test_1, kmeans.transform(test_1)), axis=1)
cf = svm.OneClassSVM(gamma='scale', nu=0.1)
cf.fit(train_1[train_ind, :])
y_scores = cf.score_samples(test_1)
y_scores = (y_scores - min(y_scores)) / (max(y_scores) - min(y_scores))
auroc = metrics.roc_auc_score(y_true, y_scores)
print("TransdeepOCSVM AUROC after {} iterations: {}".format(
trans_iter + 1, auroc))
print("Optimal_threshold after {} iterations: {}".format(
trans_iter + 1, optimal_threshold[0]))
np.save(
test_path + '/svm_aucroc1_transduct_' + str(trans_iter + 1) +
'.npy', auroc)
np.save(
test_path + '/optimal_threshold_' + str(trans_iter + 1) + '.npy',
optimal_threshold)