def fhmm_disag():
# TRAIN
disag = fhmm_exact.FHMM()
disag.train(meters)
# TEST
appliance_powers = disag.disaggregate_chunk(mains)
for meter, df in appliance_powers.iteritems():
appliance = meter.dominant_appliance()
appliance_type = appliance.identifier.type
if appliance_type in MAINS_APPLIANCES:
y_pred = df.values
np.save(join(BASE_DIRECTORY, 'FHMM_' + appliance_type), y_pred)
appliance_powers = disag.disaggregate_chunk(fridge_mains)
for meter, df in appliance_powers.iteritems():
appliance = meter.dominant_appliance()
appliance_type = appliance.identifier.type
if appliance_type in ['fridge freezer']:
y_pred = df.values
np.save(join(BASE_DIRECTORY, 'FHMM_' + appliance_type), y_pred)
appliance_powers = disag.disaggregate_chunk(kettle_mains)
for meter, df in appliance_powers.iteritems():
appliance = meter.dominant_appliance()
appliance_type = appliance.identifier.type
if appliance_type in ['kettle']:
y_pred = df.values
np.save(join(BASE_DIRECTORY, 'FHMM_' + appliance_type), y_pred)
def import_nilm_model(self, filepath, m_type):
if m_type is "FHMM":
self.model = fhmm_exact.FHMM()
self.model.import_model(filepath)
elif m_type is "CombOpt":
self.model = combinatorial_optimisation.CombinatorialOptimisation()
self.model.import_model(filepath)
def fhmm(start_train, end_train, start_test, end_test, train_elec):
#Start training
data.set_window(start_train, end_train)
elec = data.buildings[1].elec
fhmm = fhmm_exact.FHMM()
fhmm.train(train_elec, sample_period=1)
#Start disaggregating
data.set_window(start_test, end_test)
disag_filename = './build/disagg_sum_fhmm_{}_k.h5'.format(
len(train_elec.meters))
output = HDFDataStore(disag_filename, 'w')
fhmm.disaggregate(elec.mains(), output)
output.close()
dates_dict = {
"start_train": start_train,
"end_train": end_train,
"start_test": start_test,
"end_test": end_test
}
# write test and train timeframe into json file
with open(disag_filename + ".json", 'w') as dates_file:
json.dump(dates_dict, dates_file)
disag = DataSet(disag_filename)
disag_elec = disag.buildings[1].elec
disag_elec.plot()
plt.title("FHMM")
plt.show()
#Calculate F1-Score
f1 = f1_score(disag_elec, train_elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance')
plt.xlabel('f-score')
plt.title("FHMM")
plt.show()
import matplotlib.pyplot as plt
"""
This file replicates issue #376 (which should now be fixed)
https://github.com/nilmtk/nilmtk/issues/376
"""
data_dir = '/data'
building_number = 3
disag_filename = join(data_dir, 'disag-fhmm' + str(building_number) + '.h5')
data = DataSet(join(data_dir, 'redd.h5'))
print("Loading building " + str(building_number))
elec = data.buildings[building_number].elec
top_train_elec = elec.submeters().select_top_k(k=5)
fhmm = fhmm_exact.FHMM()
fhmm.train(top_train_elec)
output = HDFDataStore(disag_filename, 'w')
fhmm.disaggregate(elec.mains(), output)
output.close()
### f1score fhmm
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec
f1 = f1_score(disag_elec, elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance')
plt.xlabel('f-score')
df_all_noNan = df_all.dropna() a = df_all_noNan.keys() middleTime = a[int(math.floor(a.size/2))] middleTimeStr = "%d-%02d-%02d %02d:%02d:%02d" % (middleTime.year, middleTime.month, middleTime.day, middleTime.hour, middleTime.minute, middleTime.second) print(middleTimeStr) train.set_window(end=middleTimeStr) test.set_window(start=middleTimeStr) train_elec = train.buildings[building_number].elec test_elec = test.buildings[building_number].elec top_train_elec = train_elec.submeters().select_top_k(k=5) fhmm = fhmm_exact.FHMM() #mk change this later to default fhmm.train(top_train_elec, sample_period=60, resample=True) outputAddress = "/nilmtk/data/iawe_449_3.h5" output = HDFDataStore(outputAddress, 'w') fhmm.disaggregate(test_elec.mains(), output, sample_period=60, resample=True) output.close() disag = DataSet(outputAddress) #load FHMM prediction disag_elec = disag.buildings[building_number].elec #disag_elec.plot() # plot all disaggregated data f1 = f1_score(disag_elec, test_elec) f1.index = disag_elec.get_labels(f1.index) f1.plot(kind='barh') disag.store.window = TimeFrame(start='2013-07-10 18:00:00-05:00', end='2013-07-17 04:00:00-05:00')
test = DataSet(testPath)
train_elec = train.buildings[trainBuilding].elec
test_elec = test.buildings[testBuilding].elec
# select the larger sampling period between the train and the test set
samplePeriod = next(iter(
train.metadata['meter_devices'].values()))['sample_period']
testSamples = next(iter(
test.metadata['meter_devices'].values()))['sample_period']
if samplePeriod < testSamples:
samplePeriod = testSamples
# train the appropriate algorithm
clf = ''
if algorithm == 'fhmm':
clf = fhmm_exact.FHMM()
elif algorithm == 'combOpt':
print('here')
clf = CombinatorialOptimisation()
start = time.time()
clf.train(train_elec, sample_period=samplePeriod)
end = time.time()
print('Training runtime =', end - start, 'seconds.')
# make predicitons
pred = {}
testChunks = test_elec.mains().load(sample_period=samplePeriod)
for i, chunk in enumerate(testChunks):
chunk_drop_na = chunk.dropna()
pred[i] = clf.disaggregate_chunk(chunk_drop_na)
print('---------------------------------')
def create_nilm_model(self, m_type):
if m_type is "FHMM":
self.model = fhmm_exact.FHMM()
elif m_type is "CombOpt":
self.model = combinatorial_optimisation.CombinatorialOptimisation()
