def estimate_model(model_path, model_type, inputs, y_label, key=''):
batch, _, _ = inputs.size()
files = get_file_list(model_path)
model_names = []
if 'cnn' in model_type:
model_names += [f for f in files if (f.endswith('.cnn') and key in f)]
if 'lstm' in model_type:
model_names += [f for f in files if (f.endswith('.lstm') and key in f)]
for name in model_names:
roc_name = 'ROC' + name
d = torch.load(model_path + name)
d.eval()
bg = time.clock() # time start
outputs, _, _ = d(inputs)
ed = time.clock() # time end
logging.info('{}, predict time={} for a {} batch'.format(name, ed-bg, batch))
print('{}, predict time={} for a {} batch'.format(name, ed-bg, batch))
y_score = outputs.detach().numpy()
# statistics
roc = plotROC()
roc.analyse(y_label, y_score)
auc = roc.auc(roc_type)
logging.info('{}, {} auc = {}'.format(name, roc_type, auc))
print('{}, {}, auc = {}'.format(name, roc_type, auc))
roc.plot(roc_type, view=False, file=model_path+roc_name)
def load_normal_data(self, file_path, fault_time=None, snr=None):
"""
load normal data where all data files are in file_path
If fault_time=None, all data are normal,
Else data before fault_time is normal.
fault_time belongs to (0, 1)
"""
list_files = get_file_list(file_path)
for file in list_files:
sig = read_normal_data(file_path + file, fault_time, snr)
if self._data is None:
self._data = sig
else:
self._data = np.concatenate((self._data, sig))
self._data = discrete_data(self._data, self._dn)
type=str,
choices=['cnn', 'lstm'],
help="choose the network")
parser.add_argument("-b", "--batch", type=int, help="set batch size")
args = parser.parse_args()
system = ['bpsk', 'mt'] if args.system is None else [args.system]
network = ['cnn', 'lstm'] if args.network is None else [args.network]
test_batch = 20000 if args.batch is None else args.batch
var_list = ['m', 'p', 'c', 's0', 's1']
mode_list = ['N', 'TMA', 'PCR', 'CAR', 'MPL', 'AMP', 'TMB']
# BPSK
if 'bpsk' in system:
data_path = parentdir + '\\bpsk_navigate\\data\\train\\'
mana = BpskDataTank()
list_files = get_file_list(data_path)
for file in list_files:
mana.read_data(data_path + file, step_len=128, snr=20)
inputs, _, _, _ = mana.random_batch(test_batch,
normal=1 / 7,
single_fault=10,
two_fault=0)
# CNN
if 'cnn' in network:
ann = 'bpsk_cnn_distill_(8, 16, 32, 64).cnn'
ann = parentdir + '\\ann_diagnoser\\bpsk\\train\\20db\\{}\\'.format(
args.index) + ann
important_vars = heat_map_feature_input(
ann,
inputs,
figname=
#data amount
small_data = True
#settings
PATH = parentdir
DATA_PATH = PATH + "\\bpsk_navigate\\data\\" + ("big_data\\" if not small_data
else "small_data\\")
ANN_PATH = PATH + "\\ddd\\ann_model\\" + ("big_data\\" if not small_data else
"small_data\\")
step_len = 100
criterion = CrossEntropy
hdia_name = "HFE.pkl"
#prepare data
mana = BpskDataTank()
list_files = get_file_list(DATA_PATH)
for file in list_files:
mana.read_data(DATA_PATH + file, step_len=step_len, snr=20, norm=True)
diagnoser = HBlockScanFE()
print(diagnoser)
optimizer = optim.Adam(diagnoser.parameters(), lr=0.001, weight_decay=8e-3)
#train
epoch = 2000
batch = 2000 if not small_data else 1000
train_loss = []
running_loss = 0.0
for i in range(epoch):
inputs, labels, _, res = mana.random_batch(batch,
normal=0.4,
predict_vector = []
for p in predict:
vector = [0]*(fault_num+1)
vector[p] = 1
predict_vector.append(vector)
predict_vector = np.array(predict_vector)
roc = plotROC()
roc.analyse(labels, predict_vector)
auc = roc.auc('micro')
return auc
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--system", type=str, choices=['bpsk', 'mt'], help="choose the system")
parser.add_argument("-l", "--length", type=int, help="step length")
parser.add_argument("-t", "--strategy", type=str, help="strategy")
parser.add_argument("-i", "--index", type=int, help="index")
args = parser.parse_args()
path = parentdir + '\\utilities\\{}\\{}\\'.format(args.system, args.index)
files = get_file_list(path)
files = [f for f in files if f.endswith('.txt')]
for f in files:
if f.startswith('CNN') or f.startswith('LSTM'):
auc = weka_estimator(path + f)
else:
auc = weka_slide_window_estimator(path+f, args.length, args.strategy)
print('AUC of {} is: {}.'.format(f, auc))
type=str,
choices=['bpsk', 'mt'],
help="choose the system")
parser.add_argument("-b", "--batch", type=int, help="set batch size")
args = parser.parse_args()
snr = 20
batch = 8000 if args.batch is None else args.batch
if args.system == 'bpsk':
mode_list = ['N', 'TMA', 'PCR', 'CAR', 'MPL', 'AMP', 'TMB']
step_len = 128
pca_selection = PCA_feature_selection(0.95)
# train
train_path = parentdir + '\\bpsk_navigate\\data\\train\\'
mana_train = BpskDataTank()
list_files = get_file_list(train_path)
for file in list_files:
mana_train.read_data(train_path + file, step_len=step_len, snr=snr)
inputs, labels, _, _ = mana_train.random_batch(batch,
normal=0.4,
single_fault=10,
two_fault=0)
inputs = inputs.detach().numpy()
labels = torch.sum(labels * torch.Tensor([1, 2, 3, 4, 5, 6]), 1).long()
labels = labels.detach().numpy()
batch, variable, step = inputs.shape
inputs = inputs.transpose((0, 2, 1))
inputs = inputs.reshape((batch * step, variable))
inputs = pca_selection.learn_from(inputs)
labels = np.repeat(labels, step)
_, fe_num = inputs.shape
def load_bpsk_data(data_path, snr):
mana = BpskDataTank()
list_files = get_file_list(data_path)
for file in list_files:
mana.read_data(data_path+file, step_len=128, snr=snr)
return mana
model_name = "dcnn.pkl"
elif dia == "dscnn":
model_name = "dscnn.pkl"
elif dia == "rdscnn":
model_name = "rdscnn.pkl"
norm = True
elif dia == "rdsecnn":
model_name = "rdsecnn.pkl"
norm = True
else:
print("unkown object!")
exit(0)
dia_name.append(model_name)
mana = BpskDataTank()
list_files = get_file_list(TEST_DATA_PATH)
for file in list_files:
mana.read_data(TEST_DATA_PATH + file,
step_len=step_len,
snr=snr,
norm=norm)
#load diagnoser
diagnoser = []
for name in dia_name:
d = torch.load(ANN_PATH + name)
d.eval()
diagnoser.append(d)
batch = 10000
inputs, labels, _, res = mana.random_batch(batch,
normal=0.4,
