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()
output.close()
for i in test_building_list:
test_elec = test.buildings[i].elec
test_mains = test_elec.mains()
print("========== DISAGGREGATE ============")
disag_filename = "StackTest-" + str(i) + ".h5"
output = HDFDataStore(disag_filename, 'w')
disaggregator.disaggregate(test_mains,
output,
test_elec[meter_key],
sample_period=sample_period)
output.close()
print("========== RESULTS ============")
result = DataSet(disag_filename)
res_elec = result.buildings[i].elec
rpaf = metrics.recall_precision_accuracy_f1(res_elec[meter_key],
test_elec[meter_key])
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(
metrics.relative_error_total_energy(res_elec[meter_key],
test_elec[meter_key])))
print("============ Mean absolute error(in Watts): {}".format(
metrics.mean_absolute_error(res_elec[meter_key],
test_elec[meter_key])))
predicted = res_elec[APPLIANCE]
ground_truth = test_elec[APPLIANCE]
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(ground_truth.power_series_all_data(), label='ground truth')
ax.plot(predicted.power_series_all_data(), label='predicted')
#plt.xlim('2013-06-10 00:00:00', '2013-06-10 23:59:59')
#plt.ylim(0, 300)
plt.xlabel('Time')
plt.ylabel('Power [W]')
plt.title(APPLIANCE + ' Disaggregation')
myFmt = mdates.DateFormatter('%m/%d/%y')
ax.xaxis.set_major_formatter(myFmt)
ax.legend()
labels = ax.get_xticklabels()
plt.setp(labels, rotation=30, fontsize=10)
plt.savefig(APPLIANCE + "_dae.png")
import metrics
print("============ Relative error in total energy: {}".format(
metrics.relative_error_total_energy(predicted, ground_truth)))
print("============ Mean absolute error(in Watts): {}".format(
metrics.mean_absolute_error(predicted, ground_truth)))
print("============ List of percentages for every days\n")
date_series = pd.date_range(start=START_TEST, end=END_TEST, freq='D')
metrics.daily_relative_consume(predicted, ground_truth, test_mains,
date_series).to_csv('Percentages_' + APPLIANCE +
'.csv')
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)
train_mains = train_elec.mains().all_meters()[0]
test_mains = test_elec.mains().all_meters()[0]
dae = DAEDisaggregator(256)
start = time.time()
print("========== TRAIN ============")
dae.train(train_mains, train_meter, epochs=5, sample_period=1)
dae.export_model("model-redd100.h5")
end = time.time()
print("Train =", end-start, "seconds.")
print("========== DISAGGREGATE ============")
disag_filename = 'disag-out.h5'
output = HDFDataStore(disag_filename, 'w')
dae.disaggregate(test_mains, output, train_meter, sample_period=1)
output.close()
print("========== RESULTS ============")
result = DataSet(disag_filename)
res_elec = result.buildings[test_building].elec
rpaf = metrics.recall_precision_accuracy_f1(res_elec[meter_key], test_elec[meter_key])
print("============ Recall: {}".format(rpaf[0]))
print("============ Precision: {}".format(rpaf[1]))
print("============ Accuracy: {}".format(rpaf[2]))
print("============ F1 Score: {}".format(rpaf[2]))
print("============ Relative error in total energy: {}".format(metrics.relative_error_total_energy(res_elec[meter_key], test_elec[meter_key])))
print("============ Mean absolute error(in Watts): {}".format(metrics.mean_absolute_error(res_elec[meter_key], test_elec[meter_key])))
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)))
print("============ relative_error_total_energy {}".format(
metrics.relative_error_total_energy(pred, Y_batch)))
print("============ mean_absolute_error {}".format(
metrics.mean_absolute_error(pred, Y_batch)))
def runExperiment(experiment: experimentInfo, metricsResFileName,
clearMetricsFile):
dsPathsList_Test = experiment.dsList
outFileName = experiment.outName
test_building = experiment.building
meter_key = experiment.meter_key
pathOrigDS = experiment.pathOrigDS
meterTH = experiment.meterTH
print('House ', test_building)
# Load a "complete" dataset to have the test's timerange
test = DataSet(dsPathsList_Test[0])
test_elec = test.buildings[test_building].elec
testRef_meter = test_elec.submeters(
)[meter_key] # will be used as reference to align all meters based on this
# Align every test meter with testRef_meter as master
test_series_list = []
for path in dsPathsList_Test:
test = DataSet(path)
test_elec = test.buildings[test_building].elec
test_meter = test_elec.submeters()[meter_key]
# print('Stack test: ', test_meter.get_timeframe().start.date(), " - ", test_meter.get_timeframe().end.date())
aligned_meters = align_two_meters(testRef_meter, test_meter)
test_series_list.append(aligned_meters)
# Init vars for the output
MIN_CHUNK_LENGTH = 300 # Depends on the basemodels of the ensemble
timeframes = []
building_path = '/building{}'.format(test_meter.building())
mains_data_location = building_path + '/elec/meter1'
data_is_available = False
disag_filename = outFileName
output_datastore = HDFDataStore(disag_filename, 'w')
run = True
chunkDataForOutput = None
# -- Used to hold necessary data for saving the results using NILMTK (e.g. timeframes).
# -- (in case where chunks have different size (not in current implementation), must use the chunk whose windowsSize is the least (to have all the data))
while run:
try:
testX = []
columnInd = 0
# Get Next chunk of each series
for testXGen in test_series_list:
chunkALL = next(testXGen)
chunk = chunkALL[
'slave'] # slave is the meter needed (master is only for aligning)
chunk.fillna(0, inplace=True)
if (columnInd == 0):
chunkDataForOutput = chunk # Use 1st found chunk for it's metadata
if (testX == []):
testX = np.zeros(
[len(chunk), len(test_series_list)]
) # Initialize the array that will hold all of the series as columns
testX[:, columnInd] = chunk[:]
columnInd += 1
testX = scaler.transform(testX)
except:
run = False
break
if len(chunkDataForOutput) < MIN_CHUNK_LENGTH:
continue
# print("New sensible chunk: {}".format(len(chunk)))
startTime = chunkDataForOutput.index[0]
endTime = chunkDataForOutput.index[
-1] # chunkDataForOutput.shape[0] - 1
# print('Start:',startTime,'End:',endTime)
timeframes.append(TimeFrame(
startTime, endTime)) #info needed for output for use with NILMTK
measurement = ('power', 'active')
pred = clf.predict(testX)
column = pd.Series(pred, index=chunkDataForOutput.index, name=0)
appliance_powers_dict = {}
appliance_powers_dict[0] = column
appliance_power = pd.DataFrame(appliance_powers_dict)
appliance_power[appliance_power < 0] = 0
# Append prediction to output
data_is_available = True
cols = pd.MultiIndex.from_tuples([measurement])
meter_instance = test_meter.instance()
df = pd.DataFrame(appliance_power.values,
index=appliance_power.index,
columns=cols,
dtype="float32")
key = '{}/elec/meter{}'.format(building_path, meter_instance)
output_datastore.append(key, df)
# Append aggregate data to output
mains_df = pd.DataFrame(chunkDataForOutput,
columns=cols,
dtype="float32")
# Note (For later): not 100% right. Should be mains. But it won't be used anywhere, so it doesn't matter in this case
output_datastore.append(key=mains_data_location, value=mains_df)
# Save metadata to output
if data_is_available:
disagr = Disaggregator()
disagr.MODEL_NAME = 'Stacked model'
disagr._save_metadata_for_disaggregation(
output_datastore=output_datastore,
sample_period=sample_period,
measurement=measurement,
timeframes=timeframes,
building=test_meter.building(),
meters=[test_meter])
#======================== Calculate Metrics =====================================
testYDS = DataSet(pathOrigDS)
testYDS.set_window(start=test_meter.get_timeframe().start.date(),
end=test_meter.get_timeframe().end.date())
testY_elec = testYDS.buildings[test_building].elec
testY_meter = testY_elec.submeters()[meter_key]
test_mains = testY_elec.mains()
result = DataSet(disag_filename)
res_elec = result.buildings[test_building].elec
rpaf = metrics.recall_precision_accuracy_f1(res_elec[meter_key],
testY_meter, meterTH, meterTH)
relError = metrics.relative_error_total_energy(res_elec[meter_key],
testY_meter)
MAE = metrics.mean_absolute_error(res_elec[meter_key], testY_meter)
RMSE = metrics.RMSE(res_elec[meter_key], testY_meter)
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(relError))
print("============ Mean absolute error(in Watts): {}".format(MAE))
print("=== For docs: {:.4}\t{:.4}\t{:.4}\t{:.4}\t{:.4}\t{:.4}".format(
rpaf[0], rpaf[1], rpaf[2], rpaf[3], relError, MAE))
# print("============ RMSE: {}".format(RMSE))
# print("============ TECA: {}".format(metrics.TECA([res_elec[meter_key]],[testY_meter],test_mains)))
resDict = {
'model': 'TEST',
'building': test_building,
'Appliance': meter_key,
'Appliance_Type': 2,
'Recall': rpaf[0],
'Precision': rpaf[1],
'Accuracy': rpaf[2],
'F1': rpaf[3],
'relError': relError,
'MAE': MAE,
'RMSE': RMSE
}
metrics.writeResultsToCSV(resDict, metricsResFileName, clearMetricsFile)
