def experiment(key_name, start_e, end_e):
if (key_name not in allowed_key_names):
conf_filename = "appconf/{}.json".format(key_name)
with open(conf_filename) as data_file:
conf = json.load(data_file)
input_window = conf['lookback']
threshold = conf['on_threshold']
mamax = 5000
memax = conf['memax']
mean = conf['mean']
std = conf['std']
train_buildings = conf['train_buildings']
test_building = conf['test_building']
on_threshold = conf['on_threshold']
meter_key = conf['nilmtk_key']
save_path = conf['save_path']
X_train = np.load("dataset/trainsets/X-{}.npy".format(key_name))
X_train = normalize(X_train, mamax, mean, std)
y_train = np.load("dataset/trainsets/Y-{}.npy".format(key_name))
y_train = normalize(y_train, memax, mean, std)
model = create_model(input_window)
if start_e>0:
model = load_model(save_path+"CHECKPOINT-{}-{}epochs.hdf5".format(key_name, start_e))
if end_e > start_e:
filepath = save_path+"CHECKPOINT-"+key_name+"-{epoch:01d}epochs.hdf5"
checkpoint = ModelCheckpoint(filepath, verbose=1, save_best_only=False)
history = model.fit(X_train, y_train, batch_size=128, epochs=end_e, shuffle=True, initial_epoch=start_e, callbacks=[checkpoint])
losses = history.history['loss']
model.save("{}CHECKPOINT-{}-{}epochs.hdf5".format(save_path, key_name, end_e),model)
try:
a = np.loadtxt("{}losses.csv".format(save_path))
losses = np.append(a,losses)
except:
pass
np.savetxt("{}losses.csv".format(save_path), losses, delimiter=",")
mains, meter = opends(test_building, key_name)
X_test = normalize(mains, mamax, mean, std)
y_test = meter
X_batch, Y_batch = gen_batch(X_test, y_test, len(X_test)-input_window, 0, input_window)
pred = model.predict(X_batch)
pred = denormalize(pred, memax, mean, std)
pred[pred<0] = 0
pred = np.transpose(pred)[0]
np.save("{}pred-{}-epochs{}".format(save_path, key_name, end_e), pred)
rpaf = metrics.recall_precision_accuracy_f1(pred, Y_batch, threshold)
rete = metrics.relative_error_total_energy(pred, Y_batch)
mae = metrics.mean_absolute_error(pred, Y_batch)
res_out = open("{}results-pred-{}-{}epochs".format(save_path, key_name, end_e), 'w')
for r in rpaf:
res_out.write(str(r))
res_out.write(',')
res_out.write(str(rete))
res_out.write(',')
res_out.write(str(mae))
res_out.close()
if __name__ == "__main__":
if len(sys.argv) == 1 or sys.argv[1] == "":
exit()
key_name = sys.argv[1]
experiment(key_name, 0, 7)
def experiment(key_name, start_e, end_e):
'''Trains a network and disaggregates the testset
Displays the metrics for the disaggregated part
Parameters
----------
key_name : The string key of the appliance
start_e : The starting number of epochs for Training
end_e: The ending number of epochs for Training
'''
# ======= Open configuration file
if (key_name not in allowed_key_names):
print(" Device {} not available".format(key_name))
print(" Available device names: {}", allowed_key_names)
conf_filename = "appconf/{}.json".format(key_name)
with open(conf_filename) as data_file:
conf = json.load(data_file)
input_window = conf['lookback']
threshold = conf['on_threshold']
mamax = 5000
memax = conf['memax']
mean = conf['mean']
std = conf['std']
train_buildings = conf['train_buildings']
test_building = conf['test_building']
on_threshold = conf['on_threshold']
meter_key = conf['nilmtk_key']
save_path = conf['save_path']
# ======= Training phase
print("Training for device: {}".format(key_name))
print(" train_buildings: {}".format(train_buildings))
# Open train sets
X_train = np.load("dataset/trainsets/X-{}.npy".format(key_name))
X_train = normalize(X_train, mamax, mean, std)
y_train = np.load("dataset/trainsets/Y-{}.npy".format(key_name))
y_train = normalize(y_train, memax, mean, std)
model = create_model(input_window)
# Train model and save checkpoints
if start_e > 0:
model = load_model(
save_path +
"CHECKPOINT-{}-{}epochs.hdf5".format(key_name, start_e))
if end_e > start_e:
filepath = save_path + "CHECKPOINT-" + key_name + "-{epoch:01d}epochs.hdf5"
checkpoint = ModelCheckpoint(filepath, verbose=1, save_best_only=False)
history = model.fit(X_train,
y_train,
batch_size=128,
epochs=(end_e - start_e),
shuffle=True,
initial_epoch=start_e,
callbacks=[checkpoint])
losses = history.history['loss']
model.save(
"{}CHECKPOINT-{}-{}epochs.hdf5".format(save_path, key_name, end_e),
model)
# Save training loss per epoch
try:
a = np.loadtxt("{}losses.csv".format(save_path))
losses = np.append(a, losses)
except:
pass
np.savetxt("{}losses.csv".format(save_path), losses, delimiter=",")
# ======= Disaggregation phase
mains, meter = opends(test_building, key_name)
X_test = normalize(mains, mamax, mean, std)
y_test = meter
# Predict data
X_batch, Y_batch = gen_batch(X_test, y_test,
len(X_test) - input_window, 0, input_window)
pred = model.predict(X_batch)
pred = denormalize(pred, memax, mean, std)
pred[pred < 0] = 0
pred = np.transpose(pred)[0]
# Save results
np.save("{}pred-{}-epochs{}".format(save_path, key_name, end_e), pred)
rpaf = metrics.recall_precision_accuracy_f1(pred, Y_batch, threshold)
rete = metrics.relative_error_total_energy(pred, Y_batch)
mae = metrics.mean_absolute_error(pred, Y_batch)
print("============ Recall: {}".format(rpaf[0]))
print("============ Precision: {}".format(rpaf[1]))
print("============ Accuracy: {}".format(rpaf[2]))
print("============ F1 Score: {}".format(rpaf[3]))
print("============ Relative error in total energy: {}".format(rete))
print("============ Mean absolute error(in Watts): {}".format(mae))
res_out = open(
"{}results-pred-{}-{}epochs".format(save_path, key_name, end_e), 'w')
for r in rpaf:
res_out.write(str(r))
res_out.write(',')
res_out.write(str(rete))
res_out.write(',')
res_out.write(str(mae))
res_out.close()
model = create_model(input_window)
# Train model and save checkpoints
epochs_per_checkpoint = 1
for epochs in range(0, 1, epochs_per_checkpoint):
model.fit(X_train,
y_train,
batch_size=128,
epochs=epochs_per_checkpoint,
shuffle=True)
model.save(
"SYNTH-LOOKBACK-{}-ALL-{}epochs-1WIN.h5".format(
key_name, epochs + epochs_per_checkpoint), model)
# ======= Disaggregation phase
mains, meter = opends(test_building, key_name)
X_test = mains
y_test = meter * mmax
# Predict data
X_batch, Y_batch = gen_batch(X_test, y_test,
len(X_test) - input_window, 0, input_window)
pred = model.predict(X_batch) * mmax
pred[pred < 0] = 0
pred = np.transpose(pred)[0]
# Save results
np.save('pred.results', pred)
# Calculate and show metrics
print("============ Recall Precision Accurracy F1 {}".format(
metrics.recall_precision_accuracy_f1(pred, Y_batch, threshold)))