#training_set, test_set = data.generate_clean_data(data_dir="../data/REDD/", appliance='Dishwasher', window_size=params.DISHWASHER_WINDOW_SIZE, proportion=[0, 0], threshold=10, test=True, test_on='All', stride=1) #stride=1
#training_set = dataset.REDDDataset(data=training_set)
#test_set = dataset.REDDDataset(data=test_set)
#mean, std = training_set.get_mean_and_std()
mean = 395.890660893
std = 615.928606564
# mean = 200
# std = 450
#training_set.init_transformation(torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))
#del training_set, test_set
training_set, test_set = data.generate_clean_data(
data_dir="../data/REDD/",
appliance='Dishwasher',
window_size=params.DISHWASHER_WINDOW_SIZE,
proportion=[3, 0],
threshold=10,
test=True,
test_on='h1',
stride=100) #stride=1
mean = 395.890660893
std = 615.928606564
# test_set = data.read_from_home('../data/REDD/', 'h2', appliance='Dishwasher', window_size=params.DISHWASHER_WINDOW_SIZE)
test_set = dataset.REDDDataset(data=test_set)
test_set.init_transformation(
torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))
print('Size of test set: ', len(test_set))
# del training_set
net = ConvDishNILM()
try:
net = torch.load('models/dishwasher_lucky_shot_house1.pt')
import redd_parameters as params
import redd_data as data
import dataset
from matplotlib import pyplot as plt
from transformations import Normalize
import torch.nn as nn
import torch.optim as optim
from models import *
from torch.autograd import Variable
import torchvision
import scores
#Creating custom dataset
train_set, test_set = data.generate_clean_data(
data_dir="../data/REDD/",
appliance='Refrigerator',
window_size=params.REFRIGERATOR_WINDOW_SIZE,
proportion=[0, 0],
threshold=80,
test=True,
test_on='All') #stride=5
# initialization of custom pytorch datasets
refrigerator_train_set = dataset.REDDDataset(data=train_set)
refrigerator_test_set = dataset.REDDDataset(data=test_set)
#Getting mean and standard deviation so Normalization can be performed
mean, std = refrigerator_train_set.get_mean_and_std()
refrigerator_train_set.init_transformation(
torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))
refrigerator_test_set.init_transformation(
torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))
print('Training set size: ', len(refrigerator_train_set))
print('Test set size: ', len(refrigerator_test_set))
import redd_parameters as params
import redd_data as data
import dataset
from matplotlib import pyplot as plt
from transformations import Normalize
import torch.nn as nn
import torch.optim as optim
from models import *
from torch.autograd import Variable
import torchvision
import scores
#Initialization of dataset
train_set, test_set = data.generate_clean_data(
data_dir="../data/REDD/",
appliance='Microwave',
window_size=params.MICROWAVE_WINDOW_SIZE,
proportion=[15, 0],
threshold=200,
test=True,
test_on='All') #stride=1
# initialization of custom pytorch datasets
microwave_train_set = dataset.REDDDataset(data=train_set)
microwave_test_set = dataset.REDDDataset(data=test_set)
#Getting mean and standard deviation so Normalization can be performed
#mean, std = microwave_train_set.get_mean_and_std()
mean = 444.516250434
std = 828.08954202
microwave_train_set.init_transformation(
torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))
microwave_test_set.init_transformation(
torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))