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feat: implement chunkwise training for AFHMM+SAC.
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Fixes #42
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@levaphenyl
levaphenyl committed May 16, 2021
1 parent 98ac737 commit a2c03a6
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Showing 2 changed files with 71 additions and 120 deletions.
91 changes: 35 additions & 56 deletions nilmtk_contrib/disaggregate/afhmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,9 @@ class AFHMM(Disaggregator):
def __init__(self, params):
self.MODEL_NAME = 'AFHMM'
self.models = []
self.num_appliances = 0
self.appliances = []
self.means_vector = OrderedDict()
self.pi_s_vector = OrderedDict()
self.transmat_vector = OrderedDict()
self.signal_aggregates = OrderedDict()
self.time_period = params.get("time_period", 720)
self.default_num_states = params.get("default_num_states", 2)
Expand All @@ -29,61 +30,39 @@ def __init__(self, params):
self.load_model(self.load_model_path)

def partial_fit(self, train_main, train_appliances, **load_kwargs):
self.models = []
self.num_appliances = 0
self.appliances = []
train_main = pd.concat(train_main, axis=0)
train_app_tmp = []
for app_name, df_list in train_appliances:
df_list = pd.concat(df_list, axis=0)
train_app_tmp.append((app_name,df_list))

# All the initializations required by the model
train_appliances = train_app_tmp
learnt_model = OrderedDict()
means_vector = []
one_hot_states_vector = []
pi_s_vector = []
transmat_vector = []
states_vector = []
train_main = train_main.values.flatten().reshape((-1,1))

for appliance_name, power in train_appliances:
# Learning the pi's and transistion probablities for each appliance using a simple HMM
self.appliances.append(appliance_name)
X = power.values.reshape((-1,1))
learnt_model[appliance_name] = hmm.GaussianHMM(self.default_num_states, "full")
# Fit
learnt_model[appliance_name].fit(X)
means = learnt_model[appliance_name].means_.flatten().reshape((-1,1))
states = learnt_model[appliance_name].predict(X)
transmat = learnt_model[appliance_name].transmat_
"""
train_main: pd.DataFrame It will contain the mains reading.
train_appliances: list of tuples [('appliance1', [ df1 ]),('appliance2', [ df2 ]),...]
"""
for appli_name, df_list in train_appliances:
# Compute model parameters for this chunk.
app_df = pd.concat(df_list, axis=0)
X = app_df.values.reshape(( -1, 1 ))
learnt_model = hmm.GaussianHMM(self.default_num_states, "full")
learnt_model.fit(X)
means = learnt_model.means_.flatten().reshape(( -1, 1 ))
states = learnt_model.predict(X)
transmat = learnt_model.transmat_.T
counter = Counter(states.flatten())
total = 0
keys = list(counter.keys())
keys.sort()

for i in keys:
total+=counter[i]
pi = []

for i in keys:
pi.append(counter[i]/total)
pi = np.array(pi)
nb_classes = self.default_num_states
targets = states.reshape(-1)
means_vector.append(means)
pi_s_vector.append(pi)
transmat_vector.append(transmat.T)
states_vector.append(states)
self.num_appliances+=1
self.signal_aggregates[appliance_name] = (np.mean(X)*self.time_period).reshape((-1,))

self.means_vector = means_vector
self.pi_s_vector = pi_s_vector
self.means_vector = means_vector
self.transmat_vector = transmat_vector
print ("Finished Training")
total = sum(counter.values())
pi = np.array([ v/total for v in counter.values() ])
sigagg = (np.mean(X) * self.time_period).reshape(( -1, ))
# Merge with previous values.
# Hypothesis 1: chunk size is constant. (mean of means)
# Hypothesis 2: if the appliance is already registered in
# self.means_vector, then it is also known in all other dicts.
if appli_name in self.means_vector:
self.means_vector[appli_name] = (self.means_vector[appli_name] + means) / 2
self.pi_s_vector[appli_name] = (self.pi_s_vector[appli_name] + pi) / 2
self.transmat_vector[appli_name] = (self.transmat_vector[appli_name] + transmat) / 2
self.signal_aggregates[appli_name] = (self.signal_aggregates[appli_name] + sigagg) / 2
else:
self.means_vector[appli_name] = means
self.pi_s_vector[appli_name] = pi
self.transmat_vector[appli_name] = transmat
self.signal_aggregates[appli_name] = sigagg

print ("{}: Finished training".format(self.MODEL_NAME))

def disaggregate_thread(self, test_mains,index,d):
means_vector = self.means_vector
Expand Down
100 changes: 36 additions & 64 deletions nilmtk_contrib/disaggregate/afhmm_sac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,9 @@ class AFHMM_SAC(Disaggregator):
def __init__(self, params):
self.MODEL_NAME = 'AFHMM_SAC'
self.models = []
self.num_appliances = 0
self.appliances = []
self.means_vector = OrderedDict()
self.pi_s_vector = OrderedDict()
self.transmat_vector = OrderedDict()
self.signal_aggregates = OrderedDict()
self.time_period = params.get("time_period", 720)
self.default_num_states = params.get("default_num_states", 2)
Expand All @@ -29,70 +30,41 @@ def __init__(self, params):
if self.load_model_path:
self.load_model(self.load_model_path)

@nilmtk.docinherit.doc_inherit
def partial_fit(self, train_main, train_appliances, **load_kwargs):
self.models = []
self.num_appliances = 0
self.appliances = []
'''
train_main :- pd.DataFrame It will contain the mains reading.
train_appliances :- list of tuples [('appliance1',df1),('appliance2',df2),...]
'''
train_main = pd.concat(train_main, axis=0)
train_app_tmp = []

for app_name, df_list in train_appliances:
df_list = pd.concat(df_list, axis=0)
train_app_tmp.append((app_name,df_list))

train_appliances = train_app_tmp
learnt_model = OrderedDict()
means_vector = []
one_hot_states_vector = []
pi_s_vector = []
transmat_vector = []
states_vector = []

train_main = train_main.values.flatten().reshape((-1,1))

for appliance_name, power in train_appliances:
#print (appliance_name)
self.appliances.append(appliance_name)
X = power.values.reshape((-1,1))
learnt_model[appliance_name] = hmm.GaussianHMM(self.default_num_states, "full")
# Fit
learnt_model[appliance_name].fit(X)
means = learnt_model[appliance_name].means_.flatten().reshape((-1,1))
states = learnt_model[appliance_name].predict(X)
transmat = learnt_model[appliance_name].transmat_
"""
train_main: pd.DataFrame It will contain the mains reading.
train_appliances: list of tuples [('appliance1', [ df1 ]),('appliance2', [ df2 ]),...]
"""
for appli_name, df_list in train_appliances:
# Compute model parameters for this chunk.
app_df = pd.concat(df_list, axis=0)
X = app_df.values.reshape(( -1, 1 ))
learnt_model = hmm.GaussianHMM(self.default_num_states, "full")
learnt_model.fit(X)
means = learnt_model.means_.flatten().reshape(( -1, 1 ))
states = learnt_model.predict(X)
transmat = learnt_model.transmat_.T
counter = Counter(states.flatten())
total = 0
keys = list(counter.keys())
keys.sort()

for i in keys:
total+=counter[i]

pi = []
for i in keys:
pi.append(counter[i]/total)

pi = np.array(pi)

nb_classes = self.default_num_states
targets = states.reshape(-1)

means_vector.append(means)
pi_s_vector.append(pi)
transmat_vector.append(transmat.T)
states_vector.append(states)
self.num_appliances+=1
self.signal_aggregates[appliance_name] = (np.mean(X)*self.time_period).reshape((-1,))

self.means_vector = means_vector
self.pi_s_vector = pi_s_vector
self.means_vector = means_vector
self.transmat_vector = transmat_vector
print ("Finished Training")
total = sum(counter.values())
pi = np.array([ v/total for v in counter.values() ])
sigagg = (np.mean(X) * self.time_period).reshape(( -1, ))
# Merge with previous values.
# Hypothesis 1: chunk size is constant. (mean of means)
# Hypothesis 2: if the appliance is already registered in
# self.means_vector, then it is also known in all other dicts.
if appli_name in self.means_vector:
self.means_vector[appli_name] = (self.means_vector[appli_name] + means) / 2
self.pi_s_vector[appli_name] = (self.pi_s_vector[appli_name] + pi) / 2
self.transmat_vector[appli_name] = (self.transmat_vector[appli_name] + transmat) / 2
self.signal_aggregates[appli_name] = (self.signal_aggregates[appli_name] + sigagg) / 2
else:
self.means_vector[appli_name] = means
self.pi_s_vector[appli_name] = pi
self.transmat_vector[appli_name] = transmat
self.signal_aggregates[appli_name] = sigagg

print ("{}: Finished training".format(self.MODEL_NAME))

def disaggregate_thread(self, test_mains,index,d):
means_vector = self.means_vector
Expand Down

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