def mean():
# METRICS
ALGO = 'Mean'
scores[ALGO] = {}
aggregate_predictions = None
for appliance in APPLIANCES:
y_true_fname = join(
BASE_DIRECTORY, EXPERIMENT_DIRECTORIES[appliance],
'targets.npy')
y_true = np.load(y_true_fname)
n = len(y_true)
y_pred = np.zeros(n) + y_true.mean()
print(appliance, y_true.mean())
scores[ALGO][appliance] = run_metrics(
y_true, y_pred, mains.values)
if aggregate_predictions is None:
aggregate_predictions = y_pred
else:
n_agg = min(len(aggregate_predictions), len(y_pred))
aggregate_predictions = aggregate_predictions[:n_agg]
aggregate_predictions += y_pred[:n_agg]
scores[ALGO] = across_all_appliances(
scores[ALGO], mains, aggregate_predictions)
def calculate_metrics():
scores = {}
for architecture in ['ae', 'rectangles']:
scores[architecture] = {}
for appliance, buildings in APPLIANCES:
scores[architecture][appliance] = {}
aggregate_predictions = None
for building_i in buildings:
y_true, y_pred, mains = load(
architecture, building_i, appliance)
# Truncate
n = min(len(y_true), len(y_pred))
y_true = y_true[:n]
y_pred = y_pred[:n]
if aggregate_predictions is None:
aggregate_predictions = y_pred
else:
n_agg = min(len(aggregate_predictions), len(y_pred))
aggregate_predictions = aggregate_predictions[:n_agg]
aggregate_predictions += y_pred[:n_agg]
scores[architecture][appliance][building_i] = run_metrics(
y_true, y_pred, mains)
return scores
def fhmm_metrics():
# METRICS
scores['FHMM'] = {}
aggregate_predictions = None
for appliance_type in APPLIANCES:
y_pred = np.load(
join(BASE_DIRECTORY, 'FHMM_' + appliance_type + '.npy'))
y_true_fname = join(
BASE_DIRECTORY, EXPERIMENT_DIRECTORIES[appliance_type],
'targets.npy')
y_true = np.load(y_true_fname)
n = min(len(y_true), len(y_pred))
y_true = y_true[:n]
y_pred = y_pred[:n]
if appliance_type in ['fridge freezer']:
m = fridge_mains
elif appliance_type in ['kettle']:
m = kettle_mains
else:
m = mains
if PLOT:
fig, axes = plt.subplots(nrows=2, sharex=True)
axes[0].plot(y_true[:20000], label='y_true')
axes[0].plot(y_pred[:20000], label='y_pred')
axes[0].set_title(appliance_type)
axes[0].legend()
axes[1].plot(m.values[:20000], label='mains')
axes[1].set_title('Mains')
plt.show(block=True)
scores['FHMM'][appliance_type] = run_metrics(
y_true, y_pred, m.values)
if aggregate_predictions is None:
aggregate_predictions = y_pred
else:
n_agg = min(len(aggregate_predictions), len(y_pred))
aggregate_predictions = aggregate_predictions[:n_agg]
aggregate_predictions += y_pred[:n_agg]
scores['FHMM'] = across_all_appliances(
scores['FHMM'], mains, aggregate_predictions)