def getBatches(period, filename, target_inclusion_prob, windows, appliances,
num_batches, num_seq_per_batch, seq_length, numApp):
activations = load_nilmtk_activations(appliances=appliances,
filename=filename,
sample_period=period,
windows=windows)
list_of_Xbatches = []
list_of_Ybatches = []
if (numApp == -1):
for target_appliance in appliances:
print("Getting batches for", target_appliance)
pipeline = get_pipeline(period, filename, target_inclusion_prob,
windows, appliances, target_appliance,
activations, seq_length, num_seq_per_batch)
for i in range(0, num_batches):
batch = pipeline.get_batch(
) #define sequence length in get_pipeline()
list_of_Xbatches.append(batch.input)
list_of_Ybatches.append(batch.target)
else:
pipeline = get_pipeline(period, filename, target_inclusion_prob,
windows, appliances, appliances[numApp],
activations, seq_length, num_seq_per_batch)
for i in range(0, num_batches):
batch = pipeline.get_batch(
) #define sequence length in get_pipeline()
list_of_Xbatches.append(batch.input)
list_of_Ybatches.append(batch.target)
return np.array(list_of_Xbatches), np.array(list_of_Ybatches)
def run(root_experiment_name):
activations = load_nilmtk_activations(appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
for get_net in [ae]:
for target_appliance in ['kettle']:
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(
net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__, target_appliance
],
metrics=Metrics(
state_boundaries=[2]), # was 3 up until 230000ish
learning_rates={0: 1E-2},
repeat_callbacks=[(5000, Trainer.validate),
(5000, Trainer.save_params),
(5000, Trainer.plot_estimates)])
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(None)
def main():
set_log_level()
parse_args()
load_config()
# load the activations
print('Loading activations ...')
activations = load_nilmtk_activations(
appliances=[TARGET_APPLIANCE],
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS
)
# generate pipeline
pipeline, input_std, target_std = get_pipeline(activations)
# determine the input shape
print('Determining input shape ... ', end='')
batch = pipeline.get_batch()
input_shape = batch.input.shape[1:]
print(input_shape)
# look for an existing model only when OVERRIDE is not on; if none, then
# build a new one
print('Looking for an existing model ... ', end='')
model_filename = os.path.join(dirs.MODELS_DIR, DATASET + '_' + TARGET_APPLIANCE + '_' + strftime('%Y-%m-%d_%H_%M') +'.h5')
if not OVERRIDE and os.path.exists(model_filename):
print('Found; loading it ...')
from keras.models import load_model
model = load_model(model_filename)
else:
if OVERRIDE:
print('Overridden; building a new one with the specified topology ...')
else:
print('Not found; building a new one with the specified topology ...')
# define accuracy
#import keras.backend as K
#ON_POWER_THRESHOLD = DivideBy(target_std)(10)
#def acc(y_true, y_pred):
# return K.mean(K.equal(K.greater_equal(y_true, ON_POWER_THRESHOLD),
# K.greater_equal(y_pred, ON_POWER_THRESHOLD)))
# build model
topology_module = importlib.import_module(dirs.TOPOLOGIES_DIR + '.' + TOPOLOGY_NAME, __name__)
model = topology_module.build_model(input_shape)
print (model.summary())
# train
print('Preparing the training process ...')
train(pipeline, model)
# save the model
print('Saving the model to ' + model_filename + ' ...')
model.save(model_filename)
def create_data_pipeline(conf,
sample_period,
num_seq_per_batch,
source_probabilities=(.5, .5),
windows_key='windows'):
appliances = conf['distracting_appliances']
appliances.append(conf['target_appliance'])
data_file_path = conf['data_file'] if os.path.isabs(
conf['data_file']) else os.path.join(
os.path.dirname(__file__) + '/../', conf['data_file'])
windows = {}
for window_name, window in conf[windows_key].items():
windows[window_name] = {}
for house, window_selection in window.items():
windows[window_name][int(house)] = window_selection
appliance_activations = load_nilmtk_activations(
appliances=appliances,
filename=data_file_path,
sample_period=sample_period,
windows=windows)
synthetic_agg_source = SyntheticAggregateSource(
activations=appliance_activations,
target_appliance=conf['target_appliance'],
seq_length=conf['seq_length'],
sample_period=sample_period)
real_agg_source = RealAggregateSource(
activations=appliance_activations,
target_appliance=conf['target_appliance'],
seq_length=conf['seq_length'],
filename=data_file_path,
windows=windows,
sample_period=sample_period)
sample = next(real_agg_source.get_batch(num_seq_per_batch=1024))
sample = sample.before_processing
real_input_std = sample.input.flatten().std()
real_target_std = sample.target.flatten().std()
real_avg_power = sample.target.flatten().sum() / 1024 / conf['seq_length']
pipeline = RectangleDataPipeline(
[synthetic_agg_source, real_agg_source],
num_seq_per_batch=num_seq_per_batch,
source_probabilities=source_probabilities,
input_processing=[DivideBy(conf['input_std']),
IndependentlyCenter()],
target_processing=[
DivideBy(conf['target_std']), start_and_end_and_mean
])
return pipeline, real_input_std, real_target_std, real_avg_power
def run(root_experiment_name):
activations = load_nilmtk_activations(
appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS
)
for get_net in [ae]:
for target_appliance in APPLIANCES[2:]:
print("Starting training for net {}, appliance {}."
.format(get_net.__name__, target_appliance))
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(
net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__, target_appliance],
metrics=Metrics(state_boundaries=[2.5]),
learning_rates={
0: 1e-2,
200000: 1e-3
},
repeat_callbacks=[
(25000, Trainer.validate),
(25000, Trainer.save_params),
(25000, Trainer.plot_estimates)
]
)
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(300000)
def run(root_experiment_name):
activations = load_nilmtk_activations(appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
for get_net in [ae]:
for target_appliance in APPLIANCES[2:]:
print("Starting training for net {}, appliance {}.".format(
get_net.__name__, target_appliance))
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__,
target_appliance
],
metrics=Metrics(state_boundaries=[2.5]),
learning_rates={
0: 1e-2,
200000: 1e-3
},
repeat_callbacks=[(25000, Trainer.validate),
(25000, Trainer.save_params),
(25000, Trainer.plot_estimates)
])
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(300000)
def run(root_experiment_name):
activations = load_nilmtk_activations(
appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS
)
for get_net in [ae]:
for target_appliance in ['kettle']:
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(
net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__, target_appliance],
metrics=Metrics(state_boundaries=[2]), # was 3 up until 230000ish
learning_rates={0: 1E-2},
repeat_callbacks=[
(5000, Trainer.validate),
(5000, Trainer.save_params),
(5000, Trainer.plot_estimates)
]
)
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(None)
6: ("2011-05-22", "2011-06-14"),
},
'unseen_activations_of_seen_appliances': {
1: ("2011-04-19", None),
2: ("2011-04-19", None),
3: ("2011-04-19", None),
6: ("2011-05-22", None),
},
'unseen_appliances': {
5: ("2011-04-19", None)
}
}
# get the dictionary of activations for each appliance
activations = load_nilmtk_activations(appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
# get pipeline for the fridge example
num_seq_per_batch = 16
target_appliance = 'fridge'
seq_length = 512
train_buildings = [1, 2, 3, 6]
unseen_buildings = [5]
DATA_FOLD_NAMES = ('train', 'unseen_appliances',
'unseen_activations_of_seen_appliances')
filtered_windows = select_windows(train_buildings, unseen_buildings)
filtered_activations = filter_activations(filtered_windows, activations)
synthetic_agg_source = SyntheticAggregateSource(
def getNILMbatches(period, filename, target_inclusion_prob, windows,
appliances, pTrain, pVal, pTest, num_seq_per_batch,
seq_length, numApp):
activations = load_nilmtk_activations(appliances=appliances,
filename=filename,
sample_period=period,
windows=windows)
filtered_activations = filter_activations(windows, appliances, activations)
list_of_Xbatches = []
list_of_Ybatches = []
trainSize = int(num_seq_per_batch * pTrain)
valSize = int(num_seq_per_batch * pVal)
testSize = int(num_seq_per_batch * pTest)
if (numApp == -1):
print("not implemented")
#return None, None
##############3getbatch(enable_all_appliances=True)
lenApps = len(appliances)
trainSize = trainSize / lenApps
valSize = valSizee / lenApps
testSize = testSize / lenApps
totalX = {
'train': np.empty([0, self.time_steps]),
'val': np.empty([0, self.time_steps]),
'test': np.empty([0, self.time_steps])
}
totalY = {
'train': np.empty([0, self.time_steps, lenApps]),
'val': np.empty([0, self.time_steps, lenApps]),
'test': np.empty([0, self.time_steps, lenApps])
}
for target_appliance in appliances:
real_agg_source = RealAggregateSource(
activations=filtered_activations,
target_appliance=target_appliance,
seq_length=seq_length,
filename=filename,
windows=windows,
sample_period=period,
target_inclusion_prob=target_inclusion_prob)
#print('train')
sampleTrain = real_agg_source.get_batch(
num_seq_per_batch=trainSize, fold='train',
validation=False).next()
Xtrain = sampleTrain.before_processing
input_std = Xtrain.input.flatten().std()
target_std = Xtrain.target.flatten().std()
input_processing = [DivideBy(input_std), IndependentlyCenter()]
target_processing = [DivideBy(target_std)]
Xtrain, Ytrain = Xtrain.input, Xtrain.target
for step in input_processing:
Xtrain = step(Xtrain)
for step in target_processing:
Ytrain = step(Xtrain)
#print('validate')
sampleVal = real_agg_source.get_batch(num_seq_per_batch=valSize,
fold='val',
validation=True).next()
Xval = sampleVal.before_processing
Xval, Yval = Xval.input, Xval.target
for step in input_processing:
Xval = step(Xval)
for step in target_processing:
Yval = step(Yval)
#print('test')
sampleTest = real_agg_source.get_batch(num_seq_per_batch=testSize,
fold='test',
validation=True).next()
Xtest = sampleTest.before_processing
Xtest, Ytest = Xtest.input, Xtest.target
for step in input_processing:
Xtest = step(Xtest)
for step in target_processing:
Ytest = step(Ytest)
'''
pipeline = get_pipeline(period, filename, target_inclusion_prob, windows, appliances, appliances[numApp], activations, seq_length, num_seq_per_batch)
batchTrain = pipeline.get_batch(fold='train',validation=False) #define sequence length in get_pipeline()
batchVal = pipeline.get_batch(fold='val',validation=True)
batchTest = pipeline.get_batch(fold='test',validation=True)
'''
'''
print(Xtrain[0])
print(Xtrain[499])
print(Xval[0])
print(Xval[249])
print(Xtest[0])
print(Xtest[249])
'''
totalX = {
'train': np.squeeze(np.array(Xtrain)),
'val': np.squeeze(np.array(Xval)),
'test': np.squeeze(np.array(Xtest))
}
totalY = {
'train': np.squeeze(np.array(Ytrain)),
'val': np.squeeze(np.array(Yval)),
'test': np.squeeze(np.array(Ytest))
}
else:
target_appliance = appliances[numApp]
real_agg_source = RealAggregateSource(
activations=filtered_activations,
target_appliance=target_appliance,
seq_length=seq_length,
filename=filename,
windows=windows,
sample_period=period,
target_inclusion_prob=target_inclusion_prob)
#print('train')
sampleTrain = real_agg_source.get_batch(num_seq_per_batch=trainSize,
fold='train',
validation=False).next()
Xtrain = sampleTrain.before_processing
input_std = Xtrain.input.flatten().std()
target_std = Xtrain.target.flatten().std()
input_processing = [DivideBy(input_std), IndependentlyCenter()]
target_processing = [DivideBy(target_std)]
Xtrain, Ytrain = Xtrain.input, Xtrain.target
for step in input_processing:
Xtrain = step(Xtrain)
for step in target_processing:
Ytrain = step(Xtrain)
#print('validate')
sampleVal = real_agg_source.get_batch(num_seq_per_batch=valSize,
fold='val',
validation=True).next()
Xval = sampleVal.before_processing
Xval, Yval = Xval.input, Xval.target
for step in input_processing:
Xval = step(Xval)
for step in target_processing:
Yval = step(Yval)
#print('test')
sampleTest = real_agg_source.get_batch(num_seq_per_batch=testSize,
fold='test',
validation=True).next()
Xtest = sampleTest.before_processing
Xtest, Ytest = Xtest.input, Xtest.target
for step in input_processing:
Xtest = step(Xtest)
for step in target_processing:
Ytest = step(Ytest)
'''
pipeline = get_pipeline(period, filename, target_inclusion_prob, windows, appliances, appliances[numApp], activations, seq_length, num_seq_per_batch)
batchTrain = pipeline.get_batch(fold='train',validation=False) #define sequence length in get_pipeline()
batchVal = pipeline.get_batch(fold='val',validation=True)
batchTest = pipeline.get_batch(fold='test',validation=True)
'''
'''
print(Xtrain[0])
print(Xtrain[499])
print(Xval[0])
print(Xval[249])
print(Xtest[0])
print(Xtest[249])
'''
totalX = {
'train': np.squeeze(np.array(Xtrain)),
'val': np.squeeze(np.array(Xval)),
'test': np.squeeze(np.array(Xtest))
}
totalY = {
'train': np.squeeze(np.array(Ytrain)),
'val': np.squeeze(np.array(Yval)),
'test': np.squeeze(np.array(Ytest))
}
return totalX, totalY, input_std, target_std
},
'unseen_activations_of_seen_appliances': {
1: ("2011-04-19", None),
2: ("2011-04-19", None),
3: ("2011-04-19", None),
6: ("2011-05-22", None),
},
'unseen_appliances': {
5: ("2011-04-19", None)
}
}
# get the dictionary of activations for each appliance
activations = load_nilmtk_activations(
appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS
)
# get pipeline for the fridge example
num_seq_per_batch = 16
target_appliance = 'fridge'
seq_length = 512
train_buildings = [1, 2, 3, 6]
unseen_buildings = [5]
DATA_FOLD_NAMES = (
'train', 'unseen_appliances', 'unseen_activations_of_seen_appliances')
filtered_windows = select_windows(train_buildings, unseen_buildings)
filtered_activations = filter_activations(filtered_windows, activations)
def main():
global BUILDINGS_APPLIANCES, seq_length
set_log_level()
parse_args()
load_config()
pipeline = None
pipe_path = path.join(
dirs.MODELS_DIR,
'pipe_' + DATASET + '_[' + TARGET_APPLIANCE + ']' + '.pkl')
if LOAD_PIPELINE:
print('Loading pipeline ...')
with open(pipe_path, 'rb') as fp:
pipeline = cPickle.load(fp)
seq_period = SEQ_PERIODS[APPLIANCES[0]]
seq_length = seq_period // SAMPLE_PERIOD
else:
# load the activations
print('Loading activations ...')
BUILDINGS_APPLIANCES = BUILDINGS.keys()
activations = load_nilmtk_activations(appliances=BUILDINGS_APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
# activations_processor = Preprocess(activations)
# activations_processor.activations_pruning()
# activations_processor.median_filter(k_factor=3)
# activations = activations_processor.get_activations()
# generate pipeline
pipeline, input_std, target_std = get_pipeline(activations)
with open(pipe_path, 'wb') as fp:
cPickle.dump(pipeline, fp, True)
# determine the input shape
print('Determining input shape ... ', end='')
batch = pipeline.get_batch()
input_shape = batch.input.reshape(NUM_SEQ_PER_BATCH, seq_length,
1).shape[1:]
print(input_shape)
# look for an existing model only when OVERRIDE is not on; if none, then
# build a new one
print('Looking for an existing model ... ', end='')
model_filename = path.join(
dirs.MODELS_DIR, DATASET + '_[' + TARGET_APPLIANCE + ']_' +
strftime('%Y-%m-%d_%H_%m') + '.h5')
if not OVERRIDE and path.exists(model_filename):
print('Found; loading it ...')
from keras.models import load_model
model = load_model(model_filename)
else:
if OVERRIDE:
print(
'Overridden; building a new one with the specified topology ...'
)
else:
print(
'Not found; building a new one with the specified topology ...'
)
# define accuracy
#import keras.backend as K
#ON_POWER_THRESHOLD = DivideBy(target_std)(10)
#def acc(y_true, y_pred):
# return K.mean(K.equal(K.greater_equal(y_true, ON_POWER_THRESHOLD),
# K.greater_equal(y_pred, ON_POWER_THRESHOLD)))
# build model
topology_module = importlib.import_module(
dirs.TOPOLOGIES_DIR + '.' + TOPOLOGY_NAME, __name__)
model = topology_module.build_model(input_shape, APPLIANCES)
print(model.summary())
# train
print('Preparing the training process ...')
train(pipeline, model)
# save the model
print('Saving the model to ' + model_filename + ' ...')
model.save(model_filename)
