def modelValid(x_data,y_data):
error = 0
err_list =[]
x_data = np.reshape(x_data, (-1, 1, 6))
y_list = np.unique(y_data)
y_count =[]
for j in range(len(y_list)):
err_list.append(0)
y_count.append(0)
model_dir = "Model/Regression/"+load_date+"/"
name = model_dir + gas_type + "_reg"
cnn = load_model(name+'.pkl')
for i in range(len(x_data)):
result = cnn.test(torch.unsqueeze(torch.Tensor(x_data[i]).float(), dim=0)).tolist()
result_int = int(result[0][0])
err = round(abs(y_data[i] - result_int) / y_data[i] * 100, 1)
error = error + err
for j in range(len(y_list)):
if y_data[i] == y_list[j]:
err_list[j] = err_list[j]+err
y_count[j] = y_count[j] + 1
print(result_int,"-",y_data[i],":",err) # μΈνΈ
for j in range(len(y_list)):
print(y_list[j], ":", round(err_list[j] / y_count[j], 1))
print(gas_type, "Error :", round(sum(err_list) / sum(y_count), 1))
print("")
def modelValid(x_data,y_data,name):
dnn = load_model(name+'.pkl')
dnn.train(False)
for i in range(len(x_data)):
x = torch.Tensor(x_data[i]).float()
x = x.unsqueeze(dim=0)
result = dnn.forward(x.cuda())
print(np.round(result.tolist(), 2), y_data[i])
def modelValid(x_data,y_data):
error = 0
err_list =[]
y_list = np.unique(y_data)
y_count =[]
for j in range(len(y_list)):
err_list.append(0)
y_count.append(0)
model_dir = "Model/Regression/"+load_date+"/"
name = model_dir + gas_type + "_reg"
dnn = load_model(name+'.pkl')
dnn.train(False)
for i in range(len(x_data)):
x = torch.Tensor(x_data[i]).float()
x = x.unsqueeze(dim=0)
result = dnn.forward(x.cuda())
result_int = int(result.tolist()[0][0])
err = round(abs(y_data[i] - result_int)/y_data[i] * 100,1)
error = error + err
for j in range(len(y_list)):
if y_data[i] == y_list[j]:
err_list[j] = err_list[j]+err
y_count[j] = y_count[j] + 1
print(result_int, "-", y_data[i], ":", err) # μΈνΈ
for j in range(len(y_list)):
print(y_list[j], ":", round(err_list[j] / y_count[j], 1))
print(gas_type, "Error :", round(sum(err_list) / sum(y_count), 1))
print("")
from datetime import datetime
from datetime import datetime
print("- Port :", Port)
print("- DB :", Database)
print("- GPU Use :", gpu)
print("")
class_name = ['Normal','H2S','NH3','CH3SH','CO','CH4','H2S+NH3','H2S+CH3SH','H2S+CO','H2S+CH4','NH3+CH3SH','NH3+CO','NH3+CH4','CH3SH+CO','CH3SH+CH4','CO+CH4']
clf_name = ['model/Classify/Clf_all.pkl', 'model/Classify/Clf_single.pkl', 'model/Classify/Clf_mix.pkl']
reg_name = ['model/Regression/H2S_Reg.pkl', 'model/Regression/NH3_Reg.pkl', 'model/Regression/CH3SH_Reg.pkl',
'model/Regression/CO_Reg.pkl', 'model/Regression/CH4_Reg.pkl']
scaler_model = [joblib.load('model/Classify/all_scaler.pkl'),joblib.load('model/Classify/single_scaler.pkl'),joblib.load('model/Classify/mix_scaler.pkl')]
print("- Model Loaded")
clf_model = []
reg_model = []
if gpu:
for i in range(len(clf_name)):
tmp_clf = load_model(clf_name[i])
tmp_clf.train(False)
clf_model.append(tmp_clf)
print("- DNN CLF Model Loaded")
for i in range(len(reg_name)):
tmp_reg = load_model(reg_name[i])
tmp_reg.train(False)
reg_model.append(tmp_reg)
print("- DNN REG Model Loaded")
else:
for i in range(len(clf_name)):
tmp_clf = load_model(clf_name[i], map_location='cpu')
tmp_clf.train(False)
clf_model.append(tmp_clf)
print("- DNN CLF Model Loaded")
for i in range(len(reg_name)):
from datetime import datetime
