def __init__(self,
EVALUATION_DATA_PATH,
TARGET_APPLIANCE,
ON_POWER_THRESHOLD,
MAX_TARGET_POWER,
disagg_func,
disagg_kwargs,
remove_vampire_power=False,
pad_mains=True,
pad_appliance=False,
downsample_factor=1):
self.EVALUATION_DATA_PATH = EVALUATION_DATA_PATH
self.BUILDINGS = []
self.TARGET_APPLIANCE = TARGET_APPLIANCE
if TARGET_APPLIANCE == "dish washer":
self.ON_POWER_THRESHOLD = 15
else:
self.ON_POWER_THRESHOLD = ON_POWER_THRESHOLD
self.MAX_TARGET_POWER = MAX_TARGET_POWER
self.PAD_WIDTH = 1536
self.pad_mains = pad_mains
self.pad_appliance = pad_appliance
self.disagg_func = disagg_func
self.disagg_kwargs = disagg_kwargs
self.downsample_factor = downsample_factor
self.metrics = MetricsAccumulator(self.ON_POWER_THRESHOLD, 4200)
self._init_data(remove_vampire_power)
def run_experiment(dataset, INPUT_MEAN, INPUT_STD, SOURCE_TYPES,
VALIDATION_SOURCE_TYPES, DOWNSAMPLE_FACTOR, SEQ_LENGTH,
TARGET_SEQ_LENGTH, MAX_TARGET_POWER, TARGET_APPLIANCE,
TRAINING_SEED, VERBOSE_TRAINING, LEARNING_RATE,
NUM_SEQ_PER_BATCH, EPOCHS, STEPS_PER_EPOCH, USE_CUDA,
CHECKPOINT_BEST_MSE, CHECKPOINTING_EVERY_N_EPOCHS,
TEST_DISAGGREGATE_EVERY_N_EPOCHS, _run):
torch.manual_seed(TRAINING_SEED)
OUTPUT_FOLDER = os.path.join(ex.get_experiment_info()['name'], "output")
for observer in _run.observers:
if type(observer) is FileStorageObserver:
OUTPUT_FOLDER = os.path.join(observer.basedir, str(_run._id))
VERBOSE_TRAINING = 0
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
writer = SummaryWriter(log_dir=OUTPUT_FOLDER)
# From dataset Ingredient
TRAIN_BUILDINGS = dataset["TRAIN_BUILDINGS"]
ON_POWER_THRESHOLD = dataset["ON_POWER_THRESHOLD"]
##############################################################################################
#PREPARE DATASET (DATALOADERs)
##############################################################################################
running_data_processes = [] # stop these at the end
sources, validation_sources = get_sources(
training_source_names=SOURCE_TYPES,
validation_source_names=VALIDATION_SOURCE_TYPES,
seq_length=SEQ_LENGTH,
sources_seed=TRAINING_SEED,
validation_stride=128)
input_processing_steps = [
Add(-INPUT_MEAN),
DivideBy(INPUT_STD),
Transpose((0, 2, 1))
]
target_processing_steps = [
Add(-INPUT_MEAN),
DivideBy(INPUT_STD),
Transpose((0, 2, 1))
]
if DOWNSAMPLE_FACTOR > 1:
downsample_rng = np.random.RandomState(TRAINING_SEED)
input_processing_steps_training = [
DownSample(DOWNSAMPLE_FACTOR, downsample_rng)
] + input_processing_steps
else:
input_processing_steps_training = input_processing_steps
validation_pipeline = DataPipeline(
sources=validation_sources,
num_seq_per_batch=NUM_SEQ_PER_BATCH,
input_processing=input_processing_steps_training,
target_processing=target_processing_steps)
validation_batches = get_validation_batches(validation_pipeline)
print("appliance {} has {} validation batches".format(
TARGET_APPLIANCE,
sum([len(v) for k, v in validation_batches.items()])))
data_pipeline = DataPipeline(
sources=sources,
num_seq_per_batch=NUM_SEQ_PER_BATCH,
input_processing=input_processing_steps_training,
target_processing=target_processing_steps)
data_thread = DataProcess(data_pipeline)
data_thread.start()
running_data_processes.append(data_thread)
net = _Net(SEQ_LENGTH)
print(net)
metrics_accu = MetricsAccumulator(on_power_threshold=ON_POWER_THRESHOLD,
max_power=MAX_TARGET_POWER)
# note: MSE - Mean Squared Error
criterion = torch.nn.MSELoss()
stop_training = False
best_mse = None
# PREPARE TEST DISAGGREGATOR
if TEST_DISAGGREGATE_EVERY_N_EPOCHS is not None:
test_disaggregator = Disaggregator(
EVALUATION_DATA_PATH=dataset['EVALUATION_DATA_PATH'],
TARGET_APPLIANCE=TARGET_APPLIANCE,
ON_POWER_THRESHOLD=ON_POWER_THRESHOLD,
MAX_TARGET_POWER=MAX_TARGET_POWER,
pad_mains=True,
pad_appliance=False,
disagg_func=disag_seq2seq,
downsample_factor=DOWNSAMPLE_FACTOR,
disagg_kwargs=dict(model=net,
input_processing=input_processing_steps,
target_processing=target_processing_steps,
n_seq_per_batch=NUM_SEQ_PER_BATCH,
seq_length=SEQ_LENGTH,
target_seq_length=TARGET_SEQ_LENGTH,
USE_CUDA=USE_CUDA,
stride=1))
# PREPARE TENSORS, WHICH WILL BE FED USED DURING TRAINING AND VALIDATION
input = torch.FloatTensor(NUM_SEQ_PER_BATCH, 1, SEQ_LENGTH)
target = torch.FloatTensor(NUM_SEQ_PER_BATCH, 1, TARGET_SEQ_LENGTH)
if USE_CUDA:
# note: push to GPU
net.cuda()
criterion.cuda()
input, target = input.cuda(), target.cuda()
# setup optimizer. TODO: Should we use 'Adam' for disaggregator?
optimizer = optim.Adam(net.parameters(),
lr=LEARNING_RATE,
betas=(0.9, 0.999))
#optimizer = optim.SGD(net.parameters(), momentum=0.9, nesterov=True, lr=LEARNING_RATE)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[50, 75],
gamma=0.1)
history = {}
csvpath = os.path.join(OUTPUT_FOLDER, "history.csv")
if os.path.exists(csvpath):
print("Already exists: {}".format(csvpath))
return -1
progbar_epoch = tqdm(desc="Epoch",
total=EPOCHS,
unit="epoch",
disable=(not VERBOSE_TRAINING))
for epoch in range(EPOCHS):
# TRAINING
metrics_log = {'training': {}}
training_loss = 0.0
progbar = tqdm(desc="Train",
total=STEPS_PER_EPOCH,
leave=False,
disable=(not VERBOSE_TRAINING))
for i in range(STEPS_PER_EPOCH):
net.zero_grad()
batch = data_thread.get_batch()
while batch is None:
batch = data_thread.get_batch()
qsize = data_thread._queue.qsize()
aggregated_signal = torch.from_numpy(batch.after_processing.input)
target_signal = torch.from_numpy(batch.after_processing.target)
if USE_CUDA:
aggregated_signal = aggregated_signal.cuda()
target_signal = target_signal.cuda()
input.resize_as_(aggregated_signal).copy_(aggregated_signal)
target.resize_as_(target_signal).copy_(target_signal)
inputv = Variable(input, requires_grad=False)
targetv = Variable(target, requires_grad=False)
output = net(inputv)
loss = criterion(output, targetv)
loss.backward()
optimizer.step()
training_loss += loss.item()
progbar.set_postfix(dict(loss="{:.4f}".format(loss.item()),
qsize=qsize),
refresh=False)
progbar.update()
metrics_log['training']['loss'] = float(training_loss /
STEPS_PER_EPOCH)
metrics_log['training']['lr'] = optimizer.param_groups[0]['lr']
# VALIDATION
#pr_num_thresholds = 127
for fold in validation_batches:
metrics_accu.reset_accumulator()
#accumulated_pr = {}
#for cl in ["tp", "tn", "fp", "fn"]:
# accumulated_pr[cl] = torch.LongTensor(pr_num_thresholds).zero_()
for batch in validation_batches[fold]:
aggregated_signal = torch.from_numpy(
batch.after_processing.input)
target_signal = torch.from_numpy(batch.after_processing.target)
if USE_CUDA:
aggregated_signal = aggregated_signal.cuda()
target_signal = target_signal.cuda()
input.resize_as_(aggregated_signal).copy_(aggregated_signal)
target.resize_as_(target_signal).copy_(target_signal)
with torch.no_grad():
inputv = Variable(input)
targetv = Variable(target)
output = net(inputv)
val_loss = criterion(output, targetv)
loss_value = val_loss.item()
# other metrics
pred_y = data_pipeline.apply_inverse_processing(
output.cpu().data.numpy(), 'target')
true_y = batch.before_processing.target
metrics_accu.accumulate_metrics(true_y,
pred_y,
val_loss=loss_value)
#calculate_pr_curve_torch(accumulated_pr, MAX_TARGET_POWER, true_y, pred_y, num_thresholds=pr_num_thresholds)
for key, value in metrics_accu.finalize_metrics().items():
metrics_log.setdefault(fold[0],
{}).setdefault(key, {})[fold[1]] = value
#precision = accumulated_pr["tp"] / (accumulated_pr["tp"] + accumulated_pr["fp"])
#recall = accumulated_pr["tp"] / (accumulated_pr["tp"] + accumulated_pr["fn"])
#writer.add_pr_curve_raw("pr_{}/{}".format(fold[0], fold[1]),
# true_positive_counts=accumulated_pr["tp"],
# false_positive_counts=accumulated_pr["fp"],
# true_negative_counts=accumulated_pr["tn"],
# false_negative_counts=accumulated_pr["fn"],
# precision=precision, recall=recall,
# global_step=(epoch+1)*STEPS_PER_EPOCH, num_thresholds=pr_num_thresholds)
# LR Scheduler
val_loss = metrics_log['unseen_activations']['val_loss']['rss']
#val_loss = metrics_log['mean_squared_error']['unseen_activations']['rss']
#scheduler.step(val_loss)
scheduler.step()
# PRINT STATS
if not VERBOSE_TRAINING:
print('[{:d}/{:d}] {}'.format(epoch + 1, EPOCHS,
metrics_log['training']))
else:
progbar_epoch.set_postfix(
dict(loss=metrics_log['training']['loss']), refresh=False)
progbar_epoch.update()
progbar.close()
# store in history / tensorboard
for fold, metrics_for_fold in metrics_log.items():
for metric_name, value in metrics_for_fold.items():
if type(value) == dict:
SW_add_scalars2(writer, "{}/{}".format(fold, metric_name),
value, (epoch + 1) * STEPS_PER_EPOCH)
for k, v in value.items():
name = "{}/{}/{}".format(fold, metric_name, k)
history.setdefault(name, []).append(v)
else:
name = "{}/{}".format(fold, metric_name)
writer.add_scalar(name, value,
(epoch + 1) * STEPS_PER_EPOCH)
history.setdefault(name, []).append(value)
# CHECKPOINTING
if CHECKPOINT_BEST_MSE:
mse = val_loss
if best_mse is None:
best_mse = mse
if best_mse > mse:
msg = "[{:d}/{:d}] MSE improved from {:.4f} to {:.4f} (d={:f}), saving model...".format(
epoch + 1, EPOCHS, best_mse, mse, best_mse - mse)
if not VERBOSE_TRAINING:
print(msg)
else:
progbar_epoch.write(msg)
torch.save(
{
'epoch': epoch + 1,
'step': (epoch + 1) * STEPS_PER_EPOCH,
'mse': mse,
'model': net.state_dict()
}, '{}/net_best_mse.pth.tar'.format(OUTPUT_FOLDER))
best_mse = mse
if CHECKPOINTING_EVERY_N_EPOCHS is not None:
if (epoch + 1) % CHECKPOINTING_EVERY_N_EPOCHS == 0:
torch.save(
net.state_dict(), '{}/net_step_{:06d}.pth'.format(
OUTPUT_FOLDER, (epoch + 1) * STEPS_PER_EPOCH))
if TEST_DISAGGREGATE_EVERY_N_EPOCHS is not None:
if (epoch + 1) % TEST_DISAGGREGATE_EVERY_N_EPOCHS == 0:
scores = test_disaggregator.calculate_metrics()
scores_by_metric = {}
for building_i, building in scores.items():
for metric, value in building.items():
scores_by_metric.setdefault(metric,
{})[building_i] = value
for metric, building_d in scores_by_metric.items():
SW_add_scalars2(writer, "test_score/{}".format(metric),
building_d, (epoch + 1) * STEPS_PER_EPOCH)
if stop_training:
break
# CHECKPOINTING at end
torch.save(
{
'epoch': epoch + 1,
'step': (epoch + 1) * STEPS_PER_EPOCH,
'model': net.state_dict(),
'optimizer': optimizer.state_dict(),
#'scheduler': scheduler.state_dict()
# TODO: scheduler is not saved this way, scheduler.state_dict() does not exist
},
'{}/net_step_{:06d}.pth.tar'.format(OUTPUT_FOLDER,
(epoch + 1) * STEPS_PER_EPOCH))
df = pd.DataFrame(history)
df.to_csv(csvpath)
for p in running_data_processes:
p.stop()
writer.close()
#return 42
return metrics_log['training']['loss']
class Disaggregator():
def __init__(self,
EVALUATION_DATA_PATH,
TARGET_APPLIANCE,
ON_POWER_THRESHOLD,
MAX_TARGET_POWER,
disagg_func,
disagg_kwargs,
remove_vampire_power=False,
pad_mains=True,
pad_appliance=False,
downsample_factor=1):
self.EVALUATION_DATA_PATH = EVALUATION_DATA_PATH
self.BUILDINGS = []
self.TARGET_APPLIANCE = TARGET_APPLIANCE
if TARGET_APPLIANCE == "dish washer":
self.ON_POWER_THRESHOLD = 15
else:
self.ON_POWER_THRESHOLD = ON_POWER_THRESHOLD
self.MAX_TARGET_POWER = MAX_TARGET_POWER
self.PAD_WIDTH = 1536
self.pad_mains = pad_mains
self.pad_appliance = pad_appliance
self.disagg_func = disagg_func
self.disagg_kwargs = disagg_kwargs
self.downsample_factor = downsample_factor
self.metrics = MetricsAccumulator(self.ON_POWER_THRESHOLD, 4200)
self._init_data(remove_vampire_power)
def _init_data(self, remove_vampire_power):
re_building_filename = re.compile("^((.*)_(.*))\\.pkl$")
self.mains = {}
self.appliance_y_true = {}
for filename in os.listdir(self.EVALUATION_DATA_PATH):
re_match = re_building_filename.match(filename)
if re_match:
building_i = re_match.group(1)
mains, y_true = (
self._load_data(filename,
remove_vampire_power,
pad_mains=self.pad_mains,
pad_appliance=self.pad_appliance))
if mains is None:
continue
self.BUILDINGS.append(building_i)
self.mains[building_i] = mains
self.appliance_y_true[building_i] = y_true
def _load_data(self, filename, remove_vampire_power, pad_mains=True, pad_appliance=False):
# Load mains
filename = os.path.join(self.EVALUATION_DATA_PATH, filename)
df = pd.read_pickle(filename)
if not self.TARGET_APPLIANCE in df:
return None, None
mains = df['mains'].values
if remove_vampire_power:
vampire_power = df['mains'].quantile(0.0002)
mains = np.clip(mains-vampire_power, 0, None)
if self.downsample_factor > 1:
mains = self._resample_mains(mains)
# Pad
if pad_mains:
mains = np.pad(
mains, pad_width=(self.PAD_WIDTH, self.PAD_WIDTH), mode='constant')
y_true = df[self.TARGET_APPLIANCE].values
if pad_appliance:
y_true = np.pad(
y_true, pad_width=(self.PAD_WIDTH, self.PAD_WIDTH), mode='constant')
return mains, y_true
def _resample_mains(self, mains):
mains_length_odd = len(mains)
downsample_factor = self.downsample_factor
n_samples_new = int(np.ceil(mains_length_odd/downsample_factor))
mains_length_even = n_samples_new*downsample_factor
mains_resampled = np.pad(mains, pad_width=(0, mains_length_even-mains_length_odd), mode='constant')
mains_resampled = mains_resampled.reshape((n_samples_new, downsample_factor))
mains_resampled[:, :] = mains_resampled.mean(axis=1)[:, np.newaxis]
return mains_resampled.reshape((-1))[:mains_length_odd]
def get_mains(self, building_i):
if pad_mains:
return self.mains[building_i][self.PAD_WIDTH:-self.PAD_WIDTH]
else:
return self.mains[building_i]
def disaggregate(self, building_i, return_sequences=False):
kwargs = dict(
mains=self.mains[building_i],
target=self.appliance_y_true[building_i],
max_target_power=self.MAX_TARGET_POWER,
building_i=building_i,
return_sequences=return_sequences
)
kwargs.update(self.disagg_kwargs)
if return_sequences:
estimates, sequences = self.disagg_func(**kwargs)
else:
estimates = self.disagg_func(**kwargs)
if self.pad_mains and not self.pad_appliance:
estimates = estimates[self.PAD_WIDTH:-self.PAD_WIDTH] # remove padding
estimates = np.round(estimates).astype(int)
if return_sequences:
return estimates, sequences
else:
return estimates
def calculate_metrics(self, return_sequences=False):
scores = {}
estimates = {}
sequences = {}
for building_i in self.BUILDINGS:
mains = self.mains[building_i]
y_true = self.appliance_y_true[building_i]
if return_sequences:
y_pred, y_sequences = self.disaggregate(building_i, return_sequences=True)
else:
y_pred = self.disaggregate(building_i)
if self.pad_appliance:
y_true = y_true[self.PAD_WIDTH:-self.PAD_WIDTH] # remove padding
y_pred = y_pred[self.PAD_WIDTH:-self.PAD_WIDTH]
# Truncate
n = min(len(y_true), len(y_pred))
y_true = y_true[:n]
y_pred = y_pred[:n]
#np.savez("building_{}".format(building_i), y_true=y_true, y_pred=y_pred, mains=mains)
scores[building_i] = self.metrics.run_metrics(y_true, y_pred, mains)
if return_sequences:
sequences[building_i] = y_sequences
estimates[building_i] = y_pred
if return_sequences:
return scores, estimates, sequences
else:
return scores
def save_disaggregation_data(self,
model_name,
estimates,
sequences,
SAVETO_DIR='./disaggregation_data/'):
model_name = model_name.replace(" ", "_")
SAVETO_DIR = os.path.join(SAVETO_DIR, model_name)
for building_i in sequences:
SAVETO_PartialPATH = os.path.join(SAVETO_DIR, str(building_i))
os.makedirs(SAVETO_PartialPATH, exist_ok=True)
SAVETO_PATHs = [os.path.join(SAVETO_PartialPATH, "estimate_windows.npz"),
os.path.join(SAVETO_PartialPATH, "estimate_average.npy"),
os.path.join(SAVETO_PartialPATH, "mains.npy"),
os.path.join(SAVETO_PartialPATH, "appliance.npy")]
for SAVETO_PATH in SAVETO_PATHs:
assert not os.path.exists(SAVETO_PATH), "ERROR: File {} already exists !!!!!!".format(SAVETO_PATH)
np.savez(SAVETO_PATHs[0], **sequences[building_i])
np.save(SAVETO_PATHs[1], estimates[building_i])
np.save(SAVETO_PATHs[2], self.mains[building_i])
np.save(SAVETO_PATHs[3], self.appliance_y_true[building_i])
print("INFO: saved estimates windows to {}".format(SAVETO_PATHs[0]))
print("INFO: saved estimates averages to {}".format(SAVETO_PATHs[1]))
print("INFO: saved mains to {}".format(SAVETO_PATHs[2]))
print("INFO: saved appliance_y_true to {}".format(SAVETO_PATHs[3]))
def load_disaggregation_data(PATH):
return np.load(PATH)