# Loading data from HDF5 store
dataset = DataSet()
t1 = time.time()
dataset.load_hdf5(EXPORT_PATH)
t2 = time.time()
print("Runtime to import from HDF5 = {:.2f}".format(t2 - t1))
# Experiment on first (and only) building
b = dataset.buildings[1]
# Filtering to include only top 8 appliances
b = filter_top_k_appliances(b, 3)
# Dividing the data into train and test
train, test = train_test_split(b)
# Again subdivide data into train, test for testing on even smaller data
#train, test = train_test_split(train, test_size=.5)
# Initializing FHMM Disaggregator
disaggregator = FHMM()
train_mains = train.utility.electric.mains[
train.utility.electric.mains.keys()[0]][DISAGG_FEATURE]
# Get appliances data
app = train.utility.electric.appliances
train_appliances = pd.DataFrame({appliance: app[appliance][DISAGG_FEATURE] for appliance in app if DISAGG_FEATURE in app[appliance]})
# Feature to perform disaggregation on
DISAGG_FEATURE = Measurement('power', 'active')
# Load data
dataset.load(PATH)
# Get data of Home_01
building = dataset.buildings[1]
# Let us filter out appliances contributing less than 5%
building = prepb.filter_contribution_less_than_x(building, x=5)
print('Dividing data into test and train')
train, test = train_test_split(building, train_size = 0.5)
# Train
disaggregator = CO_1d()
disaggregator_name = "CO_1d"
t1 = time.time()
disaggregator.train(train, disagg_features=[DISAGG_FEATURE])
t2 = time.time()
print("Runtime to train for {} = {:.2f} seconds".format(disaggregator_name, t2 - t1))
train_time=t2-t1
# Disaggregate
t1 = time.time()
disaggregator.disaggregate(test)
t2 = time.time()
print("Runtime to disaggregate for {}= {:.2f} seconds".format(disaggregator_name, t2 - t1))
t1 = time.time()
dataset.export(EXPORT_PATH)
t2 = time.time()
print("Runtime to export to HDF5 = {:.2f}".format(t2 - t1))
# Loading data from HDF5 store
dataset = ampds.AMPDS()
t1 = time.time()
dataset.load_hdf5(EXPORT_PATH)
t2 = time.time()
print("Runtime to import from HDF5 = {:.2f}".format(t2 - t1))
# Dividing the data into train and test
b = dataset.buildings[1]
train, test = train_test_split(b, test_size=.005)
# Initializing CO 1D Disaggregator
disaggregator = CO_1d()
train_mains = train.utility.electric.mains[
train.utility.electric.mains.keys()[0]][DISAGG_FEATURE]
# Get appliances data
app = train.utility.electric.appliances
train_appliances = pd.DataFrame({appliance: app[appliance][DISAGG_FEATURE] for appliance in app})
# Train
disaggregator.train(train_mains, train_appliances)
# Disaggregate
disaggregator.disaggregate(test.utility.electric.mains[
# Feature to perform disaggregation on
DISAGG_FEATURE = Measurement('power', 'active')
# Load data
dataset.load_electricity(PATH)
# Get data of Home_01
building = dataset.buildings[1]
# Preprocessing:
print(' :::::::::: Dividing data into test and train')
lenToUse = len(building.utility.electric.appliances.items()[0][1])
train, test = train_test_split(
building,
train_size=int(float(1) / 2.0 * lenToUse),
test_size=int(float(1) / 2.0 * lenToUse)
) # This splits the dataset in 1/2 for training and 1/2 for testing. Test-training ratio should be set by users
### ---------- Disaggregation with FHMM ---------- #
# --- training model
disaggregator = FHMM()
disaggregator_name = "FHMM"
print(' :::::::::: Training FHMM disaggregator')
t1 = time.time()
disaggregator.train(train, disagg_features=[DISAGG_FEATURE])
t2 = time.time()
print("Runtime to train for {} = {:.2f} seconds".format(
disaggregator_name, t2 - t1))
train_time = t2 - t1
