class TestInference(TestCase):
def setUp(self):
self.home = 'xusuqian'
self.datastore = Data_store(redd_hdf5_path='/home/uftp/hubei/30%s.h5' %
self.home)
self.datarange = [
pd.Timestamp('2017-12-2 00:00:00'),
pd.Timestamp('2018-1-1 00:00:00')
]
print('')
def setUp_dynamic(self):
# select total power data in a period
# datastore = Data_store(redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xiayurong.h5')
# datastore = Data_store(redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xusuqian.h5')
# datastore = Data_store(redd_hdf5_path='/home/uftp/hubei/30xusuqian.h5')
# datastore = Data_store(redd_hdf5_path='/home/uftp/hubei/30fake.h5')
pss = []
# datarange = [pd.Timestamp('2017-12-15 10:00:00'), pd.Timestamp('2017-12-15 12:00:00')]
appliance_truth = {}
appliance_consumtion = {}
for app in self.datastore.appliance_names:
if (app in ['meter', 'TVbox', 'TV']) and self.home == 'xusuqian':
if app == 'meter':
totalpower = self.datastore.get_instance_ps(
appliance_name=app,
instance='1').loc[self.datarange[0]:self.datarange[-1]]
continue
theps = self.datastore.get_instance_ps(
appliance_name=app,
instance='1').loc[self.datarange[0]:self.datarange[-1]]
appliance_truth[app] = theps
pss.append(theps)
appliance_consumtion[app] = Tools.ps_consumption(theps=theps)
# if(app=='lamp'):
# print()
knownps = aggregate_with_resample(pss)
appliance_truth['unknown'] = totalpower - knownps
appliance_consumtion['unknown'] = Tools.ps_consumption(
theps=appliance_truth['unknown'])
ps = totalpower
cluster = Clustering()
print('miaomiaomiao?')
del pss
# ps.plot()
# ps=median_filter(ps=ps)
# ps.plot()
# plt.show()
# 获得states_list
from readData.getdistributions import getDistribitions
self.appliance_truth = appliance_truth
# centers_list, states_list = getDistribitions(ps=ps, redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xusuqian.h5',
# center_path='D:\SJTU\湖北项目\数据\ori\\xusuqian')
# centers_list, states_list = getDistribitions(ps=ps)
centers_list, states_list = getDistribitions(
ps=ps,
redd_hdf5_path='/home/uftp/hubei/30%s.h5' % self.home,
center_path='/home/uftp/hubei/ori/%s' % self.home,
load=False)
self.appliance_consumtion = appliance_consumtion
self.inference = Inference(total_ps=ps, states_list=states_list)
def test_infer_operating_list_to_chunks(self):
self.setUp_dynamic()
infer_result = self.inference.infer_operating_list_to_chunks_dynamic()
infer_result.get_estimated_ps_dict()
print(self.appliance_consumtion)
performance = Performance(infer_result=infer_result,
appliance_truth=self.appliance_truth)
error = performance.dissagga_metric()
print(1 - error)
print(Parameters())
print()
def test_infer_operating_list_to_chunks_static(self):
import staticGO
appliance_truth, ps, states_list, centers, state_r3_list, appliance_consumtion = staticGO.staticGo(
self.datastore, self.datarange)
self.inference = Inference(total_ps=ps,
states_list=states_list,
centers_list=centers)
infer_result = self.inference.infer_operating_list_to_chunks_static(
state_r3_list=state_r3_list)
infer_result.get_estimated_ps_dict()
print(appliance_consumtion)
performance = Performance(infer_result=infer_result,
appliance_truth=appliance_truth)
df1, df2 = performance.dissagga_metric2()
error = performance.dissagga_metric()
print(1 - error)
print(self.inference.para)
print()
# t=TestInference()
# t.setUp()
# t.test_infer_operating_list_to_chunks()
class TestInference(TestCase):
def setUp(self):
self.unknown = True
self.home = 'xiayurong'
self.datastore = Data_store(
redd_hdf5_path='/home/uftp/hubei/4test/30%s.h5' % self.home)
self.datarange = [
pd.Timestamp('2017-12-10 00:00:00'),
pd.Timestamp('2017-12-17 00:00:00')
]
print('')
def setUp_dynamic(self):
# select total power data in a period
# datastore = Data_store(redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xiayurong.h5')
# datastore = Data_store(redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xusuqian.h5')
# datastore = Data_store(redd_hdf5_path='/home/uftp/hubei/30xusuqian.h5')
# datastore = Data_store(redd_hdf5_path='/home/uftp/hubei/30fake.h5')
pss = []
# datarange = [pd.Timestamp('2017-12-15 10:00:00'), pd.Timestamp('2017-12-15 12:00:00')]
appliance_truth = {}
appliance_consumtion = {}
if self.unknown:
for app in self.datastore.appliance_names:
if (app in ['TVbox', 'TV']) and self.home == 'xusuqian':
continue
if (app in ['lamp', 'TV']) and self.home == 'xiayurong':
continue
if (app in ['sterilizer', 'iron', 'kitchen', 'TV'
]) and self.home == 'zhouqi':
continue
if (app == 'meter'):
for key in self.datastore.keys_dict[app]:
meterdata = self.datastore.get_instance_ps(
appliance_name=app, instance=key
).loc[self.datarange[0]:self.datarange[-1]]
try:
ps += meterdata
except:
ps = meterdata
continue
for key in self.datastore.keys_dict[app]:
theps = self.datastore.get_instance_ps(
appliance_name=app,
instance=key).loc[self.datarange[0]:self.datarange[-1]]
appliance_truth[app + '_' + key] = theps
appliance_consumtion[app + '_' +
key] = Tools.ps_consumption(
theps=theps)
else:
for app in self.datastore.appliance_names:
if (app in ['TVbox', 'TV']) and self.home == 'xusuqian':
continue
if (app in ['lamp', 'TV']) and self.home == 'xiayurong':
continue
if (app in ['sterilizer', 'iron', 'kitchen', 'TV'
]) and self.home == 'zhouqi':
continue
if (app in ['meter', 'unknown']): continue
for key in self.datastore.keys_dict[app]:
theps = self.datastore.get_instance_ps(
appliance_name=app,
instance=key).loc[self.datarange[0]:self.datarange[-1]]
appliance_truth[app + '_' + key] = theps
appliance_consumtion[app + '_' +
key] = Tools.ps_consumption(
theps=theps)
pss.append(theps)
ps = aggregate_with_resample(pss)
del pss
# ps.plot()
# ps=median_filter(ps=ps)
# ps.plot()
# plt.show()
# 获得states_list
from readData.getdistributions import getDistribitions
self.appliance_truth = appliance_truth
# centers_list, states_list = getDistribitions(ps=ps, redd_hdf5_path='D:\SJTU\湖北项目\数据\h5s/30xusuqian.h5',
# center_path='D:\SJTU\湖北项目\数据\ori\\xusuqian')
# centers_list, states_list = getDistribitions(ps=ps)
centers_list, states_list = getDistribitions(
ps=ps,
redd_hdf5_path='/home/uftp/hubei/30%s.h5' % self.home,
center_path='/home/uftp/hubei/ori/%s' % self.home,
load=False)
self.appliance_consumtion = appliance_consumtion
self.inference = Inference(total_ps=ps, states_list=states_list)
def test_infer_operating_list_to_chunks(self):
self.setUp_dynamic()
infer_result = self.inference.infer_operating_list_to_chunks_dynamic()
infer_result.get_estimated_ps_dict()
print(self.appliance_consumtion)
performance = Performance(infer_result=infer_result,
appliance_truth=self.appliance_truth)
error = performance.dissagga_metric()
print(1 - error)
print(Parameters())
print()
def test_infer_operating_list_to_chunks_static(self):
import staticGO
appliance_truth, ps, states_list, centers, state_r3_list,appliance_consumtion\
=staticGO.staticGo(self.datastore,self.datarange, home=self.home, unknown=self.unknown)
print(datetime.datetime.now())
self.inference = Inference(total_ps=ps,
states_list=states_list,
centers_list=centers)
infer_result = self.inference.infer_operating_list_to_chunks_static(
state_r3_list=state_r3_list)
infer_result.get_estimated_ps_dict()
print(appliance_consumtion)
performance = Performance(infer_result=infer_result,
appliance_truth=appliance_truth)
df1, df2 = performance.dissagga_metric2()
error = performance.dissagga_metric()
print(1 - error)
print(self.inference.para)
print()
print(datetime.datetime.now())
print()
# t=TestInference()
# t.setUp()
# t.test_infer_operating_list_to_chunks()
