def main(args):
theano.optimizer = 'fast_compile'
theano.config.exception_verbosity = 'high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'mse_val'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
genCase = int(args['genCase'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
loadParam = args['loadAsKelly']
target_inclusion_prob = float(args['target_inclusion_prob'])
loadAsKelly = True
if (loadParam == 'N' or loadParam == 'n' or loadParam == 'no'
or loadParam == 'NO' or loadParam == 'No'):
loadAsKelly = False
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 60 #150
p_z_dim = 60 #150
p_x_dim = 30 #250
x2s_dim = 40 #250
z2s_dim = 40 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
isKelly=loadAsKelly,
seq_per_batch=num_sequences_per_batch,
target_inclusion_prob=target_inclusion_prob)
instancesPlot = {
0: [10, 20],
2: [20, 30]
} #for now use hard coded instancesPlot for kelly sampling
if (not loadAsKelly):
instancesPlot = reader.build_dict_instances_plot(
listDates, batch_size, Xval.shape[0])
############# We switch x with y
train_data = UKdale(
name='train',
prep='normalize',
cond=False,
#path=data_path,
validTime=0,
inputX=ytrain,
labels=Xtrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=False,
#path=data_path,
X_mean=X_mean,
X_std=X_std,
validTime=1,
inputX=yval,
labels=Xval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask = train_data.theano_vars()
#valTime = train_data.theano_valTime_vars()
if (genCase == 1):
inputX = x[:-1, :]
targetX = x[1:, :]
n_steps = n_steps - 1
else:
inputX = x
targetX = x
inputX.name = 'x_original'
if debug:
inputX.tag.test_value = np.zeros((15, batch_size, x_dim),
dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize()) #Creates matrices
params = init_tparams(params) #Make the parameters theano.shared
s_0_tr = rnn.get_init_state(batch_size)
s_0_val = T.zeros((batch_size, 2 * rnn_dim), dtype=theano.config.floatX)
s_0_val = T.unbroadcast(
s_0_val, *range(s_0_val.ndim)
) #[0,1] this is to raise an error if length of dimensions are not 1
#x_1_temp = x_1.fprop([x], params)
def inner_val_fn(s_tm1):
'''
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
'''
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
x_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
x_1_t = x_1.fprop([x_t], params)
s_t = rnn.fprop([[x_1_t, z_1_t], [s_tm1]], params)
return s_t, x_t, z_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t
# prior_mu_temp_val, prior_sig_temp_val
((s_temp_val, prediction_val, z_t_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val), updates_val) =\
theano.scan(fn=inner_val_fn , n_steps=n_steps, #already 1 subtracted if doing next step
outputs_info=[s_0_val, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
def inner_train_fn(x_t, s_tm1):
x_1_t = x_1.fprop([x_t], params)
phi_1_t = phi_1.fprop([x_1_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_1_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_train_fn, sequences=[inputX],#[x_1_temp],
outputs_info=[s_0_tr, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
######### TRAINING GRAPH #########
s_temp = concatenate(
[s_0_tr[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
prediction.name = 'pred_' + str(flgAgg)
mse = T.mean(
(prediction - targetX)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - targetX))
mse.name = 'mse'
mae.name = 'mae'
x_in = inputX.reshape((batch_size * n_steps, -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
target_shape = x[:, 1:].shape
theta_mu_in = theta_mu_temp.reshape((batch_size * n_steps, -1))
theta_sig_in = theta_sig_temp.reshape((batch_size * n_steps, -1))
coeff_in = coeff_temp.reshape((batch_size * n_steps, -1))
recon = GMM(
x_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((batch_size, n_steps))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
######### TESTING GRAPH #########
s_temp_val = concatenate(
[s_0_val[None, :, :], s_temp_val[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
s_temp_val.name = 'h_1_val' #gisse
#z_1_temp_val.name = 'z_1_val'#gisse
z_t_temp_val.name = 'z_val'
theta_mu_temp_val.name = 'theta_mu_temp_val'
theta_sig_temp_val.name = 'theta_sig_temp_val'
coeff_temp_val.name = 'coeff_val'
prediction_val.name = 'generated_' + str(flgAgg)
mse_val = T.mean(
(prediction_val - targetX)**2) # As axis = None is calculated for all
mae_val = T.mean(T.abs_(prediction_val - targetX))
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
x_in_val = inputX.reshape((batch_size * n_steps, -1))
# No sense in calculate distance to a distribution because we are not calculating phi
#kl_temp_val = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
theta_mu_in_val = theta_mu_temp_val.reshape((batch_size * n_steps, -1))
theta_sig_in_val = theta_sig_temp_val.reshape((batch_size * n_steps, -1))
coeff_in_val = coeff_temp_val.reshape((batch_size * n_steps, -1))
recon_val = GMM(
x_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((batch_size, n_steps))
recon_val.name = 'gmm_out_val'
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
######################################
model.inputs = [x, mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[
recon_term_val, mse_val, mae_val, prediction_val,
theta_mu_temp_val
],
indexSep=3,
indexDDoutPlot=[(0, prediction_val)],
instancesPlot=instancesPlot, #, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
z2s_dim))
header = "epoch,log,mse,mae\n"
fLog.write(header)
for i, item in enumerate(mainloop.trainlog.monitor['recon_term_val']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term_val'][i]
d = mainloop.trainlog.monitor['mse_val'][i]
e = mainloop.trainlog.monitor['mae_val'][i]
fLog.write("{},{},{},{}\n".format(f, a, d, e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
loadParam = args['loadAsKelly']
target_inclusion_prob = float(args['target_inclusion_prob'])
loadAsKelly = True
if (loadParam == 'N' or loadParam == 'n' or loadParam == 'no'
or loadParam == 'NO' or loadParam == 'No'):
loadAsKelly = False
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 100 #150
p_z_dim = 60 #150
p_x_dim = 20 #250
x2s_dim = 40 #250
z2s_dim = 40 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
isKelly=loadAsKelly,
seq_per_batch=num_sequences_per_batch,
target_inclusion_prob=target_inclusion_prob)
instancesPlot = {
0: [4, 20],
2: [5, 10]
} #for now use hard coded instancesPlot for kelly sampling
if (not loadAsKelly):
instancesPlot = reader.build_dict_instances_plot(
listDates, batch_size, Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
'''
dissag_pred = FullyConnectedLayer(name='disag_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=num_apps,
unit='relu',
init_W=init_W,
init_b=init_b)
'''
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
'''
theta_1_temp = theta_1.fprop([z_1_temp, s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
#corr_temp.name = 'corr'
#binary_temp.name = 'binary'
if (flgAgg == -1):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs(prediction - x))
mse.name = 'mse'
pred_in = x.reshape((x_shape[0] * x_shape[1], -1))
else:
prediction.name = 'pred_' + str(flgAgg)
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse = T.mean(
(prediction - y)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - y))
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0] * y.shape[1], -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
pred_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
#recon = recon * mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = theta_sig_in.max()
mean_theta_sig = theta_sig_in.mean()
min_theta_sig = theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
coeff_max = coeff_in.max()
coeff_min = coeff_in.min()
coeff_mean_max = coeff_in.mean(axis=0).max()
coeff_mean_min = coeff_in.mean(axis=0).min()
coeff_max.name = 'coeff_max'
coeff_min.name = 'coeff_min'
coeff_mean_max.name = 'coeff_mean_max'
coeff_mean_min.name = 'coeff_mean_min'
max_phi_sig = phi_sig_temp.max()
mean_phi_sig = phi_sig_temp.mean()
min_phi_sig = phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = prior_sig_temp.max()
mean_prior_sig = prior_sig_temp.mean()
min_prior_sig = prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound,
recon_term,
kl_term,
mse,
mae,
theta_mu_temp,
theta_sig_temp,
z_t_temp,
prediction, #corr_temp, binary_temp,
s_temp,
z_1_temp
],
indexSep=5,
indexDDoutPlot=[(0, theta_mu_temp), (2, z_t_temp),
(3, prediction)],
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr, 15: (lr / 10), 70: (lr / 100)}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension,
lr_iterations=lr_iterations)
#mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write("log,kl,nll_upper_bound,mse,mae\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['nll_upper_bound'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{},{},{},{},{}\n".format(a, b, c, d, e))
def main(args):
theano.optimizer = 'fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/aggVSdisag_distrib/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps
typeLoad = int(args['typeLoad'])
flgMSE = int(args['flgMSE'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
origLR = lr
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 350
p_z_dim = 400
p_x_dim = 450
x2s_dim = 400
y2s_dim = 200
z2s_dim = 350
target_dim = k # As different appliances are separeted in theta_mu1, theta_mu2, etc... each one is just created from k different Gaussians
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=-1,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
typeLoad=typeLoad,
trainPer=0.5,
valPer=0.25,
testPer=0.25)
instancesPlot = {0: [5]}
#instancesPlot = reader.build_dict_instances_plot(listDates, batch_size, Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
y_1 = FullyConnectedLayer(name='y_1',
parent=['y_t'],
parent_dim=[y_dim],
nout=y2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1', 'y_1'],
parent_dim=[x2s_dim, z2s_dim, y2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1', 'y_1'],
parent_dim=[x2s_dim, rnn_dim, y2s_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu1 = FullyConnectedLayer(name='theta_mu1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu2 = FullyConnectedLayer(name='theta_mu2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu3 = FullyConnectedLayer(name='theta_mu3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu4 = FullyConnectedLayer(name='theta_mu4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu5 = FullyConnectedLayer(name='theta_mu5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig1 = FullyConnectedLayer(name='theta_sig1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig2 = FullyConnectedLayer(name='theta_sig2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig3 = FullyConnectedLayer(name='theta_sig3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig4 = FullyConnectedLayer(name='theta_sig4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig5 = FullyConnectedLayer(name='theta_sig5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff1 = FullyConnectedLayer(name='coeff1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff2 = FullyConnectedLayer(name='coeff2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff3 = FullyConnectedLayer(name='coeff3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff4 = FullyConnectedLayer(name='coeff4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff5 = FullyConnectedLayer(name='coeff5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu1,
theta_sig1,
coeff1,
theta_mu2,
theta_sig2,
coeff2,
theta_mu3,
theta_sig3,
coeff3,
theta_mu4,
theta_sig4,
coeff4,
theta_mu5,
theta_sig5,
coeff5
]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_test(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
prior_mu_t, prior_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu1_t = theta_mu1.fprop([theta_1_t], params)
theta_sig1_t = theta_sig1.fprop([theta_1_t], params)
coeff1_t = coeff1.fprop([theta_1_t], params)
y_pred1 = GMM_sampleY(
theta_mu1_t, theta_sig1_t,
coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
theta_mu2_t = theta_mu2.fprop([theta_1_t], params)
theta_sig2_t = theta_sig2.fprop([theta_1_t], params)
coeff2_t = coeff2.fprop([theta_1_t], params)
y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t)
y_pred1 = T.concatenate([y_pred1, y_pred2], axis=1)
theta_mu3_t = theta_mu3.fprop([theta_1_t], params)
theta_sig3_t = theta_sig3.fprop([theta_1_t], params)
coeff3_t = coeff3.fprop([theta_1_t], params)
y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t)
y_pred1 = T.concatenate([y_pred1, y_pred3], axis=1)
theta_mu4_t = theta_mu4.fprop([theta_1_t], params)
theta_sig4_t = theta_sig4.fprop([theta_1_t], params)
coeff4_t = coeff4.fprop([theta_1_t], params)
y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t)
y_pred1 = T.concatenate([y_pred1, y_pred4], axis=1)
theta_mu5_t = theta_mu5.fprop([theta_1_t], params)
theta_sig5_t = theta_sig5.fprop([theta_1_t], params)
coeff5_t = coeff5.fprop([theta_1_t], params)
y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t)
y_pred1 = T.concatenate([y_pred1, y_pred5], axis=1)
pred_1_t = y_1.fprop([y_pred1], params)
#y_pred = [GMM_sampleY(theta_mu_t[i], theta_sig_t[i], coeff_t[i]) for i in range(y_dim)]#T.stack([y_pred1,y_pred2],axis = 0 )
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1, theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2, theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3, theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4, theta_mu5_t, theta_sig5_t, coeff5_t, y_pred5
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, theta_mu1_temp_val,
theta_sig1_temp_val, coeff1_temp_val, y_pred1_temp_val,
theta_mu2_temp_val, theta_sig2_temp_val, coeff2_temp_val,
y_pred2_temp_val, theta_mu3_temp_val, theta_sig3_temp_val,
coeff3_temp_val, y_pred3_temp_val, theta_mu4_temp_val,
theta_sig4_temp_val, coeff4_temp_val, y_pred4_temp_val,
theta_mu5_temp_val, theta_sig5_temp_val, coeff5_temp_val,
y_pred5_temp_val),
updates_val) = theano.scan(fn=inner_fn_test,
sequences=[x_1_temp],
outputs_info=[
s_0, None, None, None, None, None, None,
None, None, None, None, None, None, None,
None, None, None, None, None, None, None,
None, None
])
for k, v in updates_val.iteritems():
k.default_update = v
def inner_fn(x_t, y_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
phi_mu_t, phi_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu1_t = theta_mu1.fprop([theta_1_t], params)
theta_sig1_t = theta_sig1.fprop([theta_1_t], params)
coeff1_t = coeff1.fprop([theta_1_t], params)
y_pred1 = GMM_sampleY(
theta_mu1_t, theta_sig1_t,
coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
theta_mu2_t = theta_mu2.fprop([theta_1_t], params)
theta_sig2_t = theta_sig2.fprop([theta_1_t], params)
coeff2_t = coeff2.fprop([theta_1_t], params)
y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t)
theta_mu3_t = theta_mu3.fprop([theta_1_t], params)
theta_sig3_t = theta_sig3.fprop([theta_1_t], params)
coeff3_t = coeff3.fprop([theta_1_t], params)
y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t)
theta_mu4_t = theta_mu4.fprop([theta_1_t], params)
theta_sig4_t = theta_sig4.fprop([theta_1_t], params)
coeff4_t = coeff4.fprop([theta_1_t], params)
y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t)
theta_mu5_t = theta_mu5.fprop([theta_1_t], params)
theta_sig5_t = theta_sig5.fprop([theta_1_t], params)
coeff5_t = coeff5.fprop([theta_1_t], params)
y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t)
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1, theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2, theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3, theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4, theta_mu5_t, theta_sig5_t, coeff5_t, y_pred5
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,
theta_mu1_temp, theta_sig1_temp, coeff1_temp, y_pred1_temp,
theta_mu2_temp, theta_sig2_temp, coeff2_temp, y_pred2_temp,
theta_mu3_temp, theta_sig3_temp, coeff3_temp, y_pred3_temp,
theta_mu4_temp, theta_sig4_temp, coeff4_temp, y_pred4_temp,
theta_mu5_temp, theta_sig5_temp, coeff5_temp, y_pred5_temp),
updates) = theano.scan(fn=inner_fn,
sequences=[x_1_temp, y_1_temp],
outputs_info=[
s_0, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None,
None
])
for k, v in updates.iteritems():
k.default_update = v
theta_mu1_temp.name = 'theta_mu1'
theta_sig1_temp.name = 'theta_sig1'
coeff1_temp.name = 'coeff1'
y_pred1_temp.name = 'disaggregation1'
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse1 = T.mean((y_pred1_temp - y[:, :, 0].reshape(
(y.shape[0], y.shape[1], 1)))**2)
mae1 = T.mean(
T.abs_(y_pred1_temp - y[:, :, 0].reshape((y.shape[0], y.shape[1], 1))))
mse1.name = 'mse1'
mae1.name = 'mae1'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
y_shape = y.shape
theta_mu2_temp.name = 'theta_mu2'
theta_sig2_temp.name = 'theta_sig2'
coeff2_temp.name = 'coeff2'
y_pred2_temp.name = 'disaggregation2'
mse2 = T.mean((y_pred2_temp - y[:, :, 1].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae2 = T.mean(
T.abs_(y_pred2_temp - y[:, :, 1].reshape((y.shape[0], y.shape[1], 1))))
mse2.name = 'mse2'
mae2.name = 'mae2'
theta_mu3_temp.name = 'theta_mu3'
theta_sig3_temp.name = 'theta_sig3'
coeff3_temp.name = 'coeff3'
y_pred3_temp.name = 'disaggregation3'
mse3 = T.mean((y_pred3_temp - y[:, :, 2].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae3 = T.mean(
T.abs_(y_pred3_temp - y[:, :, 2].reshape((y.shape[0], y.shape[1], 1))))
mse3.name = 'mse3'
mae3.name = 'mae3'
theta_mu4_temp.name = 'theta_mu4'
theta_sig4_temp.name = 'theta_sig4'
coeff4_temp.name = 'coeff4'
y_pred4_temp.name = 'disaggregation4'
mse4 = T.mean((y_pred4_temp - y[:, :, 3].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae4 = T.mean(
T.abs_(y_pred4_temp - y[:, :, 3].reshape((y.shape[0], y.shape[1], 1))))
mse4.name = 'mse4'
mae4.name = 'mae4'
theta_mu5_temp.name = 'theta_mu5'
theta_sig5_temp.name = 'theta_sig5'
coeff5_temp.name = 'coeff5'
y_pred5_temp.name = 'disaggregation5'
mse5 = T.mean((y_pred5_temp - y[:, :, 4].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae5 = T.mean(
T.abs_(y_pred5_temp - y[:, :, 4].reshape((y.shape[0], y.shape[1], 1))))
mse5.name = 'mse5'
mae5.name = 'mae5'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
theta_mu1_in = theta_mu1_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig1_in = theta_sig1_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff1_in = coeff1_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu2_in = theta_mu2_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig2_in = theta_sig2_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff2_in = coeff2_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu3_in = theta_mu3_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig3_in = theta_sig3_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff3_in = coeff3_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu4_in = theta_mu4_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig4_in = theta_sig4_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff4_in = coeff4_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu5_in = theta_mu5_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig5_in = theta_sig5_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff5_in = coeff5_temp.reshape((x_shape[0] * x_shape[1], -1))
x_shape = x.shape
y_shape = y.shape
#x_in = x.reshape((x_shape[0]*x_shape[1], -1))
y_in = y.reshape((y_shape[0] * y_shape[1], -1))
recon = GMMdisagMulti(y_dim, y_in, theta_mu1_in, theta_sig1_in, coeff1_in,
theta_mu2_in, theta_sig2_in, coeff2_in, theta_mu3_in,
theta_sig3_in, coeff3_in, theta_mu4_in,
theta_sig4_in, coeff4_in, theta_mu5_in,
theta_sig5_in, coeff5_in)
#recon = GMMdisagMulti(y_dim, y_in, theta_mu1_in, theta_sig1_in, coeff1_in, theta_mu2_in, theta_sig2_in, coeff2_in,theta_mu3_in, theta_sig3_in, coeff3_in,theta_mu4_in, theta_sig4_in, coeff4_in,theta_mu5_in, theta_sig5_in, coeff5_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
'''
recon5 = GMM(y_in[:,4, None], theta_mu5_in, theta_sig5_in, coeff5_in)
recon5 = recon.reshape((x_shape[0], x_shape[1]))
'''
recon_term = recon.sum(axis=0).mean()
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
######################## TEST (GENERATION) TIME
#s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
theta_mu1_temp_val.name = 'theta_mu1_val'
theta_sig1_temp_val.name = 'theta_sig1_val'
coeff1_temp_val.name = 'coeff1_val'
y_pred1_temp_val.name = 'disaggregation1_val'
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse1_val = T.mean((y_pred1_temp_val - y[:, :, 0].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae1_val = T.mean(
T.abs_(y_pred1_temp_val -
y[:, :, 0].reshape((y.shape[0], y.shape[1], 1))))
#NEURALNILM #(sum_output - sum_target) / max(sum_output, sum_target))
totPred = T.sum(y_pred1_temp_val)
totReal = T.sum(y[:, :, 0])
relErr1_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned1_val = 1 - T.sum(
T.abs_(y_pred1_temp_val - y[:, :, 0].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
#y_unNormalize = (y[:,:,0] * reader.stdTraining[0]) + reader.meanTraining[0]
#y_pred1_temp_val = (y_pred1_temp_val * reader.stdTraining[0]) + reader.meanTraining[0]
#mse1_valUnNorm = T.mean((y_pred1_temp_val - y_unNormalize.reshape((y.shape[0],y.shape[1],1)))**2) # As axis = None is calculated for all
#mae1_valUnNorm = T.mean( T.abs_(y_pred1_temp_val - y_unNormalize.reshape((y.shape[0],y.shape[1],1))))
mse1_val.name = 'mse1_val'
mae1_val.name = 'mae1_val'
theta_mu1_in_val = theta_mu1_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig1_in_val = theta_sig1_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff1_in_val = coeff1_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_mu2_temp_val.name = 'theta_mu2_val'
theta_sig2_temp_val.name = 'theta_sig2_val'
coeff2_temp_val.name = 'coeff2_val'
y_pred2_temp_val.name = 'disaggregation2_val'
mse2_val = T.mean((y_pred2_temp_val - y[:, :, 1].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae2_val = T.mean(
T.abs_(y_pred2_temp_val -
y[:, :, 1].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred2_temp_val)
totReal = T.sum(y[:, :, 1])
relErr2_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned2_val = 1 - T.sum(
T.abs_(y_pred2_temp_val - y[:, :, 1].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse2_val.name = 'mse2_val'
mae2_val.name = 'mae2_val'
theta_mu2_in_val = theta_mu2_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig2_in_val = theta_sig2_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff2_in_val = coeff2_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_mu3_temp_val.name = 'theta_mu3_val'
theta_sig3_temp_val.name = 'theta_sig3_val'
coeff3_temp_val.name = 'coeff3_val'
y_pred3_temp_val.name = 'disaggregation3_val'
mse3_val = T.mean((y_pred3_temp_val - y[:, :, 2].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae3_val = T.mean(
T.abs_(y_pred3_temp_val -
y[:, :, 2].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred3_temp_val)
totReal = T.sum(y[:, :, 2])
relErr3_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned3_val = 1 - T.sum(
T.abs_(y_pred3_temp_val - y[:, :, 2].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse3_val.name = 'mse3_val'
mae3_val.name = 'mae3_val'
theta_mu3_in_val = theta_mu3_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig3_in_val = theta_sig3_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff3_in_val = coeff3_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_mu4_temp_val.name = 'theta_mu4_val'
theta_sig4_temp_val.name = 'theta_sig4_val'
coeff4_temp_val.name = 'coeff4_val'
y_pred4_temp_val.name = 'disaggregation4_val'
mse4_val = T.mean((y_pred4_temp_val - y[:, :, 3].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae4_val = T.mean(
T.abs_(y_pred4_temp_val -
y[:, :, 3].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred4_temp_val)
totReal = T.sum(y[:, :, 3])
relErr4_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned4_val = 1 - T.sum(
T.abs_(y_pred4_temp_val - y[:, :, 3].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse4_val.name = 'mse4_val'
mae4_val.name = 'mae4_val'
theta_mu4_in_val = theta_mu4_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig4_in_val = theta_sig4_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff4_in_val = coeff4_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_mu5_temp_val.name = 'theta_mu5_val'
theta_sig5_temp_val.name = 'theta_sig5_val'
coeff5_temp_val.name = 'coeff5_val'
y_pred5_temp_val.name = 'disaggregation5_val'
mse5_val = T.mean((y_pred5_temp_val - y[:, :, 4].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae5_val = T.mean(
T.abs_(y_pred5_temp_val -
y[:, :, 4].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred5_temp_val)
totReal = T.sum(y[:, :, 4])
relErr5_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned5_val = 1 - T.sum(
T.abs_(y_pred5_temp_val - y[:, :, 4].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse5_val.name = 'mse5_val'
mae5_val.name = 'mae5_val'
theta_mu5_in_val = theta_mu5_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig5_in_val = theta_sig5_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff5_in_val = coeff5_temp_val.reshape((x_shape[0] * x_shape[1], -1))
prediction_val = T.concatenate([
y_pred1_temp_val, y_pred2_temp_val, y_pred3_temp_val, y_pred4_temp_val,
y_pred5_temp_val
],
axis=2)
recon_val = GMMdisagMulti(
y_dim, y_in, theta_mu1_in_val, theta_sig1_in_val, coeff1_in_val,
theta_mu2_in_val, theta_sig2_in_val, coeff2_in_val, theta_mu3_in_val,
theta_sig3_in_val, coeff3_in_val, theta_mu4_in_val, theta_sig4_in_val,
coeff4_in_val, theta_mu5_in_val, theta_sig5_in_val, coeff5_in_val)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out'
totaMSE_val = (mse1_val + mse2_val + mse3_val + mse4_val +
mse5_val) / y_dim
totaMAE_val = (mae1_val + mae2_val + mae3_val + mae4_val +
mae5_val) / y_dim
'''
recon5 = GMM(y_in[:,4, None], theta_mu5_in, theta_sig5_in, coeff5_in)
recon5 = recon.reshape((x_shape[0], x_shape[1]))
'''
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term'
######################
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound, recon_term, kl_term, mse1, mae1, mse2, mae2,
mse3, mae3, mse4, mae4, mse5, mae5, y_pred1_temp, y_pred2_temp,
y_pred3_temp, y_pred4_temp, y_pred5_temp
],
indexSep=13,
indexDDoutPlot=[13], # adding indexes of ddout for the plotting
#, (6,y_pred_temp)
instancesPlot=instancesPlot, #0-150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=30)
mainloop.run()
data = Iterator(test_data, batch_size)
test_fn = theano.function(
inputs=[x, y], #[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[
prediction_val, recon_term_val, totaMSE_val, totaMAE_val, mse1_val,
mse2_val, mse3_val, mse4_val, mse5_val, mae1_val, mae2_val,
mae3_val, mae4_val, mae5_val, relErr1_val, relErr2_val,
relErr3_val, relErr4_val, relErr5_val, propAssigned1_val,
propAssigned2_val, propAssigned3_val, propAssigned4_val,
propAssigned5_val
] #prediction_val, mse_val, mae_val
,
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
testMetrics2 = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2])
testOutput.append(outputGeneration[1:14])
testMetrics2.append(outputGeneration[14:])
#{0:[4,20], 2:[5,10]}
#if (numBatchTest==0):
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],
[1, 0, 2])[4]) #ORIGINAL 1,0,2
plt.savefig(save_path +
"/vrnn_dis_generated{}_Pred_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(batch[2], [1, 0, 2])[4])
plt.savefig(save_path +
"/vrnn_dis_generated{}_RealDisag_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(batch[0], [1, 0, 2])[4]) #ORIGINAL 1,0,2
plt.savefig(save_path +
"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest += 1
testOutput = np.asarray(testOutput)
testMetrics2 = np.asarray(testMetrics2)
print(testOutput.shape)
print(testMetrics2.shape)
recon_test = testOutput[:, 0].mean()
mse_test = testOutput[:, 1].mean()
mae_test = testOutput[:, 2].mean()
mse1_test = testOutput[:, 3].mean()
mae1_test = testOutput[:, 8].mean()
mse2_test = testOutput[:, 4].mean()
mae2_test = testOutput[:, 9].mean()
mse3_test = testOutput[:, 5].mean()
mae3_test = testOutput[:, 10].mean()
mse4_test = testOutput[:, 6].mean()
mae4_test = testOutput[:, 11].mean()
mse5_test = testOutput[:, 7].mean()
mae5_test = testOutput[:, 12].mean()
relErr1_test = testMetrics2[:, 0].mean()
relErr2_test = testMetrics2[:, 1].mean()
relErr3_test = testMetrics2[:, 2].mean()
relErr4_test = testMetrics2[:, 3].mean()
relErr5_test = testMetrics2[:, 4].mean()
propAssigned1_test = testMetrics2[:, 5].mean()
propAssigned2_test = testMetrics2[:, 6].mean()
propAssigned3_test = testMetrics2[:, 7].mean()
propAssigned4_test = testMetrics2[:, 8].mean()
propAssigned5_test = testMetrics2[:, 9].mean()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(appliances) + "\n")
fLog.write(str(windows) + "\n")
fLog.write(
"logTest,mse1_test,mse2_test,mse3_test,mse4_test,mse5_test,mae1_test,mae2_test,mae3_test,mae4_test,mae5_test,mseTest,maeTest\n"
)
fLog.write("{},{},{},{},{},{},{},{},{},{},{},{},{}\n\n".format(
recon_test, mse1_test, mse2_test, mse3_test, mse4_test, mse5_test,
mae1_test, mae2_test, mae3_test, mae4_test, mae5_test, mse_test,
mae_test))
fLog.write(
"relErr1,relErr2,relErr3,relErr4,relErr5,propAssigned1,propAssigned2,propAssigned3,propAssigned4,propAssigned5\n"
)
fLog.write("{},{},{},{},{},{},{},{},{},{}\n".format(
relErr1_test, relErr2_test, relErr3_test, relErr4_test, relErr5_test,
propAssigned1_test, propAssigned2_test, propAssigned3_test,
propAssigned4_test, propAssigned5_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
fLog.write(
"epoch,log,kl,mse1,mse2,mse3,mse4,mse5,mae1,mae2,mae3,mae4,mae5\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
d, e, f, g, j, k, l, m = 0, 0, 0, 0, 0, 0, 0, 0
ep = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['mse1'][i]
h = mainloop.trainlog.monitor['mae1'][i]
d = mainloop.trainlog.monitor['mse2'][i]
j = mainloop.trainlog.monitor['mae2'][i]
e = mainloop.trainlog.monitor['mse3'][i]
k = mainloop.trainlog.monitor['mae3'][i]
f = mainloop.trainlog.monitor['mse4'][i]
l = mainloop.trainlog.monitor['mae4'][i]
g = mainloop.trainlog.monitor['mse5'][i]
m = mainloop.trainlog.monitor['mae5'][i]
fLog.write(
"{:d},{:.2f},{:.2f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f}\n"
.format(ep, a, b, c, d, e, f, g, h, j, k, l, m))
f = open(save_path + '/outputRealGeneration.pkl', 'wb')
pickle.dump(outputGeneration, f, -1)
f.close()
def main(args):
theano.optimizer = 'fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/aggVSdisag_distrib/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps
flgMSE = int(args['flgMSE'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
origLR = lr
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 20 #150
p_z_dim = 20 #150
p_x_dim = 20 #250
x2s_dim = 20 #250
z2s_dim = 20 #150
target_dim = k # As different appliances are separeted in theta_mu1, theta_mu2, etc... each one is just created from k different Gaussians
model = Model()
Xtrain, ytrain, Xval, yval, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=-1,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1)
instancesPlot = reader.build_dict_instances_plot(listDates, batch_size,
Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
x.name = 'x_original'
x_input = x #[:,:-1,:]
y_input = y #[:,1:,:]
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu1 = FullyConnectedLayer(name='theta_mu1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu2 = FullyConnectedLayer(name='theta_mu2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu3 = FullyConnectedLayer(name='theta_mu3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu4 = FullyConnectedLayer(name='theta_mu4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_mu5 = FullyConnectedLayer(name='theta_mu5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig1 = FullyConnectedLayer(name='theta_sig1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig2 = FullyConnectedLayer(name='theta_sig2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig3 = FullyConnectedLayer(name='theta_sig3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig4 = FullyConnectedLayer(name='theta_sig4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_sig5 = FullyConnectedLayer(name='theta_sig5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff1 = FullyConnectedLayer(name='coeff1',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff2 = FullyConnectedLayer(name='coeff2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff3 = FullyConnectedLayer(name='coeff3',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff4 = FullyConnectedLayer(name='coeff4',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
coeff5 = FullyConnectedLayer(name='coeff5',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu1,
theta_mu2,
theta_mu3,
theta_mu4,
theta_mu5,
theta_sig1,
theta_sig2,
theta_sig3,
theta_sig4,
theta_sig5,
coeff1,
coeff2,
coeff3,
coeff4,
coeff5
]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x_input], params)
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
phi_mu_t, phi_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu1_t = theta_mu1.fprop([theta_1_t], params)
theta_sig1_t = theta_sig1.fprop([theta_1_t], params)
coeff1_t = coeff1.fprop([theta_1_t], params)
theta_mu2_t = theta_mu2.fprop([theta_1_t], params)
theta_sig2_t = theta_sig2.fprop([theta_1_t], params)
coeff2_t = coeff2.fprop([theta_1_t], params)
theta_mu3_t = theta_mu3.fprop([theta_1_t], params)
theta_sig3_t = theta_sig3.fprop([theta_1_t], params)
coeff3_t = coeff3.fprop([theta_1_t], params)
theta_mu4_t = theta_mu4.fprop([theta_1_t], params)
theta_sig4_t = theta_sig4.fprop([theta_1_t], params)
coeff4_t = coeff4.fprop([theta_1_t], params)
theta_mu5_t = theta_mu5.fprop([theta_1_t], params)
theta_sig5_t = theta_sig5.fprop([theta_1_t], params)
coeff5_t = coeff5.fprop([theta_1_t], params)
y_pred1 = GMM_sampleY(
theta_mu1_t, theta_sig1_t,
coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t)
y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t)
y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t)
y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t)
#y_pred = [GMM_sampleY(theta_mu_t[i], theta_sig_t[i], coeff_t[i]) for i in range(y_dim)]#T.stack([y_pred1,y_pred2],axis = 0 )
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return (s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t,
theta_1_t, theta_mu1_t, theta_sig1_t, coeff1_t, theta_mu2_t,
theta_sig2_t, coeff2_t, theta_mu3_t, theta_sig3_t, coeff3_t,
theta_mu4_t, theta_sig4_t, coeff4_t, theta_mu5_t, theta_sig5_t,
coeff5_t, y_pred1, y_pred2, y_pred3, y_pred4, y_pred5)
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp,
theta_mu1_temp, theta_sig1_temp, coeff1_temp, theta_mu2_temp, theta_sig2_temp, coeff2_temp,
theta_mu3_temp, theta_sig3_temp, coeff3_temp, theta_mu4_temp, theta_sig4_temp, coeff4_temp,
theta_mu5_temp, theta_sig5_temp, coeff5_temp,
y_pred1_temp, y_pred2_temp, y_pred3_temp, y_pred4_temp, y_pred5_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None,None, None, None,
None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu1_temp.name = 'theta_mu1'
theta_sig1_temp.name = 'theta_sig1'
coeff1_temp.name = 'coeff1'
theta_mu2_temp.name = 'theta_mu2'
theta_sig2_temp.name = 'theta_sig2'
coeff2_temp.name = 'coeff2'
theta_mu3_temp.name = 'theta_mu3'
theta_sig3_temp.name = 'theta_sig3'
coeff3_temp.name = 'coeff3'
theta_mu4_temp.name = 'theta_mu4'
theta_sig4_temp.name = 'theta_sig4'
coeff4_temp.name = 'coeff4'
theta_mu5_temp.name = 'theta_mu5'
theta_sig5_temp.name = 'theta_sig5'
coeff5_temp.name = 'coeff5'
#corr_temp.name = 'corr'
#binary_temp.name = 'binary'
#x_pred_temp.name = 'x_reconstructed'
y_pred1_temp.name = 'disaggregation1'
y_pred2_temp.name = 'disaggregation2'
y_pred3_temp.name = 'disaggregation3'
y_pred4_temp.name = 'disaggregation4'
y_pred5_temp.name = 'disaggregation5'
'''
y_pred_temp = T.stack([y_pred1_temp, y_pred2_temp, y_pred3_temp, y_pred4_temp], axis=2)
y_pred_temp = y_pred_temp.flatten(3)# because of the stack, i guess, there's a 4th dimension created
mse = T.mean((y_pred_temp - y.reshape((y.shape[0], y.shape[1],-1)))**2) # cause mse can be 26000
'''
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse1 = T.mean((y_pred1_temp - y_input[:, :, 0].reshape(
(y_input.shape[0], y_input.shape[1],
1)))**2) # As axis = None is calculated for all
mae1 = T.mean(
T.abs_(y_pred1_temp - y_input[:, :, 0].reshape((y_input.shape[0],
y_input.shape[1], 1))))
mse1.name = 'mse1'
mae1.name = 'mae1'
mse2 = T.mean((y_pred2_temp - y_input[:, :, 1].reshape(
(y_input.shape[0], y_input.shape[1],
1)))**2) # As axis = None is calculated for all
mae2 = T.mean(
T.abs_(y_pred2_temp - y_input[:, :, 1].reshape((y_input.shape[0],
y_input.shape[1], 1))))
mse2.name = 'mse2'
mae2.name = 'mae2'
mse3 = T.mean((y_pred3_temp - y_input[:, :, 2].reshape(
(y_input.shape[0], y_input.shape[1],
1)))**2) # As axis = None is calculated for all
mae3 = T.mean(
T.abs_(y_pred3_temp - y_input[:, :, 2].reshape((y_input.shape[0],
y_input.shape[1], 1))))
mse3.name = 'mse3'
mae3.name = 'mae3'
mse4 = T.mean((y_pred4_temp - y_input[:, :, 3].reshape(
(y_input.shape[0], y_input.shape[1],
1)))**2) # As axis = None is calculated for all
mae4 = T.mean(
T.abs_(y_pred4_temp - y_input[:, :, 3].reshape((y_input.shape[0],
y_input.shape[1], 1))))
mse4.name = 'mse4'
mae4.name = 'mae4'
mse5 = T.mean((y_pred5_temp - y_input[:, :, 4].reshape(
(y_input.shape[0], y_input.shape[1],
1)))**2) # As axis = None is calculated for all
mae5 = T.mean(
T.abs_(y_pred5_temp - y_input[:, :, 4].reshape((y_input.shape[0],
y_input.shape[1], 1))))
mse5.name = 'mse5'
mae5.name = 'mae5'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x_input.shape
y_shape = y_input.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
y_in = y.reshape((y_shape[0] * y_shape[1], -1))
theta_mu1_in = theta_mu1_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig1_in = theta_sig1_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff1_in = coeff1_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu2_in = theta_mu2_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig2_in = theta_sig2_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff2_in = coeff2_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu3_in = theta_mu3_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig3_in = theta_sig3_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff3_in = coeff3_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu4_in = theta_mu4_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig4_in = theta_sig4_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff4_in = coeff4_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_mu5_in = theta_mu5_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig5_in = theta_sig5_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff5_in = coeff5_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMMdisag5(
y_in, theta_mu1_in, theta_sig1_in, coeff1_in, theta_mu2_in,
theta_sig2_in, coeff2_in, theta_mu3_in, theta_sig3_in, coeff3_in,
theta_mu4_in, theta_sig4_in, coeff4_in, theta_mu5_in, theta_sig5_in,
coeff5_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
'''
recon5 = GMM(y_in[:,4, None], theta_mu5_in, theta_sig5_in, coeff5_in)
recon5 = recon.reshape((x_shape[0], x_shape[1]))
'''
recon_term = recon.sum(axis=0).mean()
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
#nll_upper_bound_0 = recon_term + kl_term
#nll_upper_bound_0.name = 'nll_upper_bound_0'
if (flgMSE == 1):
nll_upper_bound = recon_term + kl_term + mse1 + mse2 + mse3 + mse4 + mse5
else:
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
'''
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = theta_sig_in.max()
mean_theta_sig = theta_sig_in.mean()
min_theta_sig = theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
coeff_max = coeff_in.max()
coeff_min = coeff_in.min()
coeff_mean_max = coeff_in.mean(axis=0).max()
coeff_mean_min = coeff_in.mean(axis=0).min()
coeff_max.name = 'coeff_max'
coeff_min.name = 'coeff_min'
coeff_mean_max.name = 'coeff_mean_max'
coeff_mean_min.name = 'coeff_mean_min'
max_phi_sig = phi_sig_temp.max()
mean_phi_sig = phi_sig_temp.mean()
min_phi_sig = phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = prior_sig_temp.max()
mean_prior_sig = prior_sig_temp.mean()
min_prior_sig = prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
'''
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound, recon_term, kl_term, mse1, mse2, mse3, mse4,
mse5, mae1, mae2, mae3, mae4, mae5, y_pred1_temp, y_pred2_temp,
y_pred3_temp, y_pred4_temp, y_pred5_temp
],
indexSep=13,
indexDDoutPlot=[13], # adding indexes of ddout for the plotting
#, (6,y_pred_temp)
instancesPlot=instancesPlot, #0-150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr, 150: (lr / 10)}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
lr_iterations = {0: origLR, 100: (origLR / 10)}
fLog.write(str(lr_iterations) + "\n")
fLog.write(
"log,kl,nll_upper_bound,mse1,mae1,mse2,mae2,mse3,mae3,mse4,mae4,mse5,mae5\n"
)
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['nll_upper_bound'][i]
d = mainloop.trainlog.monitor['mse1'][i]
e = mainloop.trainlog.monitor['mae1'][i]
f = mainloop.trainlog.monitor['mse2'][i]
g = mainloop.trainlog.monitor['mae2'][i]
h = mainloop.trainlog.monitor['mse3'][i]
j = mainloop.trainlog.monitor['mae3'][i]
k = mainloop.trainlog.monitor['mse4'][i]
l = mainloop.trainlog.monitor['mae4'][i]
m = mainloop.trainlog.monitor['mse5'][i]
n = mainloop.trainlog.monitor['mae5'][i]
fLog.write("{},{},{},{},{},{},{},{},{},{},{},{},{}\n".format(
a, b, c, d, e, f, g, h, j, k, l, m, n))
def main(args):
'''
Arguments:
args : A dictionary containing all parameters required for the training ( read from config_AE.txt ).
Returns: None
'''
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'mse'
data_path = args['data_path']
save_path = args['save_path']#+'/aggVSdisag_distrib/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps
typeLoad = int(args['typeLoad'])
flgMSE = int(args['flgMSE'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
origLR = lr
debug = int(args['debug'])
print "-------------------------------------------------------------------\n"
print "trial no. %d" % trial
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
print "Epochs: %d" % epoch
print "-------------------------------------------------------------------\n"
n_appl = 5 # number of appliance or the size of 3rd dimension
#Data Preprocessing
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(data_path, windows, appliances,numApps=-1, period=period,n_steps= n_steps,
stride_train = stride_train, stride_test = stride_test,
flgAggSumScaled = 1, flgFilterZeros = 1, typeLoad = typeLoad)
print(np.shape(Xtrain))
#file name to save test data
save_file = save_path+'/testData_'+str(epoch)+'_'+str(lr)+".npy"
if not os.path.exists(save_file):
np.save(save_file,np.array([np.reshape(Xtest,[-1,]),np.reshape(ytest,[-1,])]))
#creating a argument parser for the model
parser = argparse.ArgumentParser()
parser.add_argument('--rnn_size', type=int, default=rnn_dim,
help='size of RNN hidden state')
parser.add_argument('--latent_size', type=int, default=z_dim,
help='size of latent space')
parser.add_argument('--batch_size', type=int, default=batch_size,
help='minibatch size')
parser.add_argument('--seq_length', type=int, default=n_steps,
help='RNN sequence length')
parser.add_argument('--num_epochs', type=int, default=epoch,
help='number of epochs')
parser.add_argument('--save_every', type=int, default=monitoring_freq,
help='save frequency')
parser.add_argument('--grad_clip', type=float, default=10.,
help='clip gradients at this value')
parser.add_argument('--learning_rate', type=float, default=lr,
help='learning rate')
parser.add_argument('--decay_rate', type=float, default=1.,
help='decay of learning rate')
parser.add_argument('--chunk_samples', type=int, default=n_appl,
help='number of samples per mdct chunk')
parser.add_argument('--target_dim', type=int, default=k,
help='mixture of K Gaussian functions')
parser.add_argument('--test_file', type=str, default=save_file,
help='test data')
args = parser.parse_args()
model = VRNN(args) #create the VRNN model
states = train(args, model,(Xtrain,ytrain), (Xval, yval))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'nll_upper_bound'
data_path = args['data_path']
save_path = args['save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
genCase = int(args['genCase'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps#int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
typeLoad = int(args['typeLoad'])
target_inclusion_prob = float(args['target_inclusion_prob'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 200
x2s_dim = 100
y2s_dim = 100
z2s_dim = 100
target_dim = k#x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(data_path, windows, appliances,numApps=flgAgg, period=period,
n_steps= n_steps, stride_train = stride_train, stride_test = stride_test,
typeLoad= typeLoad, flgAggSumScaled = 1, flgFilterZeros = 1,
seq_per_batch=num_sequences_per_batch, target_inclusion_prob=target_inclusion_prob)
instancesPlot = {0:[4,20], 2:[5,10]} #for now use hard coded instancesPlot for kelly sampling
if(typeLoad==0):
instancesPlot = reader.build_dict_instances_plot(listDates, batch_size, Xval.shape[0])
train_data = UKdale(name='train',
prep='normalize',
cond=True,# False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels = yval)
test_data = UKdale(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels = ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y , y_mask = train_data.theano_vars()
if (genCase ==1):
inputX = x[:-1,:]
targetX = x[1:,:]
n_steps = n_steps-1
else:
inputX = x
targetX = x
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
y_1 = FullyConnectedLayer(name='y_1',
parent=['y_t'],
parent_dim=[y_dim],
nout=y2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1','y_1'],
parent_dim=[x2s_dim, z2s_dim, y_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1','y_1'],
parent_dim=[x2s_dim, rnn_dim,y2s_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [rnn,
x_1, y_1, z_1, #dissag_pred,
phi_1, phi_mu, phi_sig,
prior_1, prior_mu, prior_sig,
theta_1, theta_mu, theta_sig, coeff]#, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t, coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred_t#, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, z_t_temp_val, z_1_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
def inner_fn_train(x_t, y_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1,y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t, coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred#, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn_train,
sequences=[x_1_temp, y_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
s_temp.name = 'h_1'#gisse
z_1_temp.name = 'z_1'#gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
if (flgAgg == -1 ):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean( T.abs(prediction - x) )
mse.name = 'mse'
pred_in = x.reshape((x_shape[0]*x_shape[1], -1))
else:
prediction.name = 'pred_'+str(flgAgg)
mse = T.mean((prediction - y)**2) # As axis = None is calculated for all
mae = T.mean( T.abs_(prediction - y) )
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0]*y.shape[1],-1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0]*x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0]*x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(pred_in, theta_mu_in, theta_sig_in, coeff_in)# BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
######################## TEST (GENERATION) TIME
prediction_val.name = 'generated__'+str(flgAgg)
mse_val = T.mean((prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean( T.abs_(prediction_val - y) )
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0]*y.shape[1],-1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape((x_shape[0]*x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0]*x_shape[1], -1))
recon_val = GMM(pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val)# BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val= recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(
lr=lr
)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(freq=monitoring_freq,
ddout=[nll_upper_bound, recon_term, kl_term, mse, mae,
theta_mu_temp,prediction],
indexSep=5,
instancesPlot = instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder = save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq, path=save_path, channel=channel_name),
WeightNorm()
]
lr_iterations = {0:lr, 75:(lr/10), 150:(lr/100)}
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
extension=extension,
lr_iterations=lr_iterations
)
mainloop.run()
z_t_temp_val.name='z_temp_val'
s_temp_val.name='s_temp_val'
theta_mu_temp_val.name='mu_temp'
data=Iterator(test_data, batch_size)
test_fn = theano.function(inputs=[x, y],#[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[z_t_temp_val, s_temp_val, theta_mu_temp_val, prediction_val, recon_term_val, mse_val, mae_val]#prediction_val, mse_val, mae_val
,updates=updates_val#, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2])
testOutput.append(outputGeneration[4:])
#{0:[4,20], 2:[5,10]}
#if (numBatchTest==0):
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_z_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_s_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_theta_0-4".format(numBatchTest))
plt.clf()
'''
plt.figure(4)
plt.plot(np.transpose(outputGeneration[3],[1,0,2])[4])
plt.plot(np.transpose(batch[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_RealAndPred_0-4".format(numBatchTest))
plt.clf()
plt.figure(4)
plt.plot(np.transpose(batch[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest+=1
testOutput = np.asarray(testOutput)
print(testOutput.shape)
recon_test = this_mean = testOutput[:, 0].mean()
mse_test = this_mean = testOutput[:, 1].mean()
mae_test = this_mean = testOutput[:, 2].mean()
fLog = open(save_path+'/output.csv', 'w')
fLog.write(str(lr_iterations)+"\n")
fLog.write(str(windows)+"\n")
fLog.write("logTest,mseTest,maeTest\n")
fLog.write("{},{},{}\n".format(recon_test,mse_test,mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i , item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f,a,b,d,e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'mse'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/aggVSdisag_distrib/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps
typeLoad = int(args['typeLoad'])
flgMSE = int(args['flgMSE'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
origLR = lr
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 350
p_z_dim = 400
p_x_dim = 450
x2s_dim = 400
y2s_dim = 200
z2s_dim = 350
target_dim = k # As different appliances are separeted in theta_mu1, theta_mu2, etc... each one is just created from k different Gaussians
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=-1,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
typeLoad=typeLoad)
instancesPlot = {0: [10]}
#instancesPlot = reader.build_dict_instances_plot(listDates, batch_size, Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
schedMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
fmodel = open('vrnn_gmm_disall_best.pkl', 'rb')
mainloop = cPickle.load(fmodel)
fmodel.close()
model = mainloop.model
#print(mainloop.model.__dict__)
rnn = mainloop.model.nodes[0]
x_1 = mainloop.model.nodes[1]
y_1 = mainloop.model.nodes[2]
z_1 = mainloop.model.nodes[3]
phi_1 = mainloop.model.nodes[4]
phi_mu = mainloop.model.nodes[5]
phi_sig = mainloop.model.nodes[6]
prior_1 = mainloop.model.nodes[7]
prior_mu = mainloop.model.nodes[8]
prior_sig = mainloop.model.nodes[9]
theta_1 = mainloop.model.nodes[10]
theta_mu1 = mainloop.model.nodes[11]
theta_sig1 = mainloop.model.nodes[12]
coeff1 = mainloop.model.nodes[13]
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu1,
theta_sig1,
coeff1
]
params = mainloop.model.params
dynamicOutput = [None, None, None, None, None, None, None, None]
#dynamicOutput_val = [None, None, None, None, None, None,None, None, None]
if (y_dim > 1):
theta_mu2 = mainloop.model.nodes[14]
theta_sig2 = mainloop.model.nodes[15]
coeff2 = mainloop.model.nodes[16]
nodes = nodes + [theta_mu2, theta_sig2, coeff2]
dynamicOutput = dynamicOutput + [None, None, None, None
] #mu, sig, coef and pred
if (y_dim > 2):
theta_mu3 = mainloop.model.nodes[17]
theta_sig3 = mainloop.model.nodes[18]
coeff3 = mainloop.model.nodes[19]
nodes = nodes + [theta_mu3, theta_sig3, coeff3]
dynamicOutput = dynamicOutput + [None, None, None, None]
if (y_dim > 3):
theta_mu4 = mainloop.model.nodes[20]
theta_sig4 = mainloop.model.nodes[21]
coeff4 = mainloop.model.nodes[22]
nodes = nodes + [theta_mu4, theta_sig4, coeff4]
dynamicOutput = dynamicOutput + [None, None, None, None]
if (y_dim > 4):
theta_mu5 = mainloop.model.nodes[23]
theta_sig5 = mainloop.model.nodes[24]
coeff5 = mainloop.model.nodes[25]
nodes = nodes + [theta_mu5, theta_sig5, coeff5]
dynamicOutput = dynamicOutput + [None, None, None, None]
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
output_fn = [s_0] + dynamicOutput
output_fn_val = [s_0] + dynamicOutput[2:]
print(len(output_fn), len(output_fn_val))
def inner_fn_test(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
prior_mu_t, prior_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu1_t = theta_mu1.fprop([theta_1_t], params)
theta_sig1_t = theta_sig1.fprop([theta_1_t], params)
coeff1_t = coeff1.fprop([theta_1_t], params)
y_pred1 = GMM_sampleY(
theta_mu1_t, theta_sig1_t,
coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
tupleMulti = prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1
if (y_dim > 1):
theta_mu2_t = theta_mu2.fprop([theta_1_t], params)
theta_sig2_t = theta_sig2.fprop([theta_1_t], params)
coeff2_t = coeff2.fprop([theta_1_t], params)
y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t)
y_pred1 = T.concatenate([y_pred1, y_pred2], axis=1)
tupleMulti = tupleMulti + (theta_mu2_t, theta_sig2_t, coeff2_t,
y_pred2)
if (y_dim > 2):
theta_mu3_t = theta_mu3.fprop([theta_1_t], params)
theta_sig3_t = theta_sig3.fprop([theta_1_t], params)
coeff3_t = coeff3.fprop([theta_1_t], params)
y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t)
y_pred1 = T.concatenate([y_pred1, y_pred3], axis=1)
tupleMulti = tupleMulti + (theta_mu3_t, theta_sig3_t, coeff3_t,
y_pred3)
if (y_dim > 3):
theta_mu4_t = theta_mu4.fprop([theta_1_t], params)
theta_sig4_t = theta_sig4.fprop([theta_1_t], params)
coeff4_t = coeff4.fprop([theta_1_t], params)
y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t)
y_pred1 = T.concatenate([y_pred1, y_pred4], axis=1)
tupleMulti = tupleMulti + (theta_mu4_t, theta_sig4_t, coeff4_t,
y_pred4)
if (y_dim > 4):
theta_mu5_t = theta_mu5.fprop([theta_1_t], params)
theta_sig5_t = theta_sig5.fprop([theta_1_t], params)
coeff5_t = coeff5.fprop([theta_1_t], params)
y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t)
y_pred1 = T.concatenate([y_pred1, y_pred5], axis=1)
tupleMulti = tupleMulti + (theta_mu5_t, theta_sig5_t, coeff5_t,
y_pred5)
pred_1_t = y_1.fprop([y_pred1], params)
#y_pred = [GMM_sampleY(theta_mu_t[i], theta_sig_t[i], coeff_t[i]) for i in range(y_dim)]#T.stack([y_pred1,y_pred2],axis = 0 )
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return (s_t, ) + tupleMulti
#corr_temp, binary_temp
(restResults_val, updates_val) = theano.scan(fn=inner_fn_test,
sequences=[x_1_temp],
outputs_info=output_fn_val)
for k, v in updates_val.iteritems():
k.default_update = v
def inner_fn(x_t, y_t, schedMask, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
phi_mu_t, phi_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu1_t = theta_mu1.fprop([theta_1_t], params)
theta_sig1_t = theta_sig1.fprop([theta_1_t], params)
coeff1_t = coeff1.fprop([theta_1_t], params)
y_pred1 = GMM_sampleY(
theta_mu1_t, theta_sig1_t,
coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
y_pred = y_pred1
tupleMulti = phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1
if (y_dim > 1):
theta_mu2_t = theta_mu2.fprop([theta_1_t], params)
theta_sig2_t = theta_sig2.fprop([theta_1_t], params)
coeff2_t = coeff2.fprop([theta_1_t], params)
y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t)
y_pred = T.concatenate([y_pred, y_pred2], axis=1)
tupleMulti = tupleMulti + (theta_mu2_t, theta_sig2_t, coeff2_t,
y_pred2)
if (y_dim > 2):
theta_mu3_t = theta_mu3.fprop([theta_1_t], params)
theta_sig3_t = theta_sig3.fprop([theta_1_t], params)
coeff3_t = coeff3.fprop([theta_1_t], params)
y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t)
y_pred = T.concatenate([y_pred, y_pred3], axis=1)
tupleMulti = tupleMulti + (theta_mu3_t, theta_sig3_t, coeff3_t,
y_pred3)
if (y_dim > 3):
theta_mu4_t = theta_mu4.fprop([theta_1_t], params)
theta_sig4_t = theta_sig4.fprop([theta_1_t], params)
coeff4_t = coeff4.fprop([theta_1_t], params)
y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t)
y_pred = T.concatenate([y_pred, y_pred4], axis=1)
tupleMulti = tupleMulti + (theta_mu4_t, theta_sig4_t, coeff4_t,
y_pred4)
if (y_dim > 4):
theta_mu5_t = theta_mu5.fprop([theta_1_t], params)
theta_sig5_t = theta_sig5.fprop([theta_1_t], params)
coeff5_t = coeff5.fprop([theta_1_t], params)
y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t)
y_pred = T.concatenate([y_pred, y_pred5], axis=1)
tupleMulti = tupleMulti + (theta_mu5_t, theta_sig5_t, coeff5_t,
y_pred5)
if (schedMask == 1):
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
else:
y_t_aux = y_1.fprop([y_pred], params)
s_t = rnn.fprop([[x_t, z_1_t, y_t_aux], [s_tm1]], params)
return (s_t, ) + tupleMulti
#corr_temp, binary_temp
(restResults,
updates) = theano.scan(fn=inner_fn,
sequences=[x_1_temp, y_1_temp, schedMask],
outputs_info=output_fn)
'''
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp,
theta_mu1_temp, theta_sig1_temp, coeff1_temp, theta_mu2_temp, theta_sig2_temp, coeff2_temp,
theta_mu3_temp, theta_sig3_temp, coeff3_temp, theta_mu4_temp, theta_sig4_temp, coeff4_temp,
theta_mu5_temp, theta_sig5_temp, coeff5_temp,
y_pred1_temp, y_pred2_temp, y_pred3_temp, y_pred4_temp, y_pred5_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp, y_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None,None, None, None,
None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None])
'''
s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,\
theta_mu1_temp, theta_sig1_temp, coeff1_temp, y_pred1_temp = restResults[:9]
restResults = restResults[9:]
for k, v in updates.iteritems():
k.default_update = v
#s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
theta_mu1_temp.name = 'theta_mu1'
theta_sig1_temp.name = 'theta_sig1'
coeff1_temp.name = 'coeff1'
y_pred1_temp.name = 'disaggregation1'
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse1 = T.mean((y_pred1_temp - y[:, :, 0].reshape(
(y.shape[0], y.shape[1], 1)))**2)
mae1 = T.mean(
T.abs_(y_pred1_temp - y[:, :, 0].reshape((y.shape[0], y.shape[1], 1))))
mse1.name = 'mse1'
mae1.name = 'mae1'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
y_shape = y.shape
#x_in = x.reshape((x_shape[0]*x_shape[1], -1))
y_in = y.reshape((y_shape[0] * y_shape[1], -1))
theta_mu1_in = theta_mu1_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig1_in = theta_sig1_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff1_in = coeff1_temp.reshape((x_shape[0] * x_shape[1], -1))
ddoutMSEA = []
ddoutYpreds = [y_pred1_temp]
indexSepDynamic = 6 # plus one for totaMSE
#totaMSE = T.copy(mse1)
mse2 = T.zeros((1, ))
mae2 = T.zeros((1, ))
mse3 = T.zeros((1, ))
mae3 = T.zeros((1, ))
mse4 = T.zeros((1, ))
mae4 = T.zeros((1, ))
mse5 = T.zeros((1, ))
mae5 = T.zeros((1, ))
if (y_dim > 1):
theta_mu2_temp, theta_sig2_temp, coeff2_temp, y_pred2_temp = restResults[:
4]
restResults = restResults[4:]
theta_mu2_temp.name = 'theta_mu2'
theta_sig2_temp.name = 'theta_sig2'
coeff2_temp.name = 'coeff2'
y_pred2_temp.name = 'disaggregation2'
mse2 = T.mean((y_pred2_temp - y[:, :, 1].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae2 = T.mean(
T.abs_(y_pred2_temp -
y[:, :, 1].reshape((y.shape[0], y.shape[1], 1))))
mse2.name = 'mse2'
mae2.name = 'mae2'
theta_mu2_in = theta_mu2_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig2_in = theta_sig2_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff2_in = coeff2_temp.reshape((x_shape[0] * x_shape[1], -1))
argsGMM = theta_mu2_in, theta_sig2_in, coeff2_in
ddoutMSEA = ddoutMSEA + [mse2, mae2]
ddoutYpreds = ddoutYpreds + [y_pred2_temp]
#totaMSE+=mse2
indexSepDynamic += 2
if (y_dim > 2):
theta_mu3_temp, theta_sig3_temp, coeff3_temp, y_pred3_temp = restResults[:
4]
restResults = restResults[4:]
theta_mu3_temp.name = 'theta_mu3'
theta_sig3_temp.name = 'theta_sig3'
coeff3_temp.name = 'coeff3'
y_pred3_temp.name = 'disaggregation3'
mse3 = T.mean((y_pred3_temp - y[:, :, 2].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae3 = T.mean(
T.abs_(y_pred3_temp -
y[:, :, 2].reshape((y.shape[0], y.shape[1], 1))))
mse3.name = 'mse3'
mae3.name = 'mae3'
theta_mu3_in = theta_mu3_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig3_in = theta_sig3_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff3_in = coeff3_temp.reshape((x_shape[0] * x_shape[1], -1))
argsGMM = argsGMM + (theta_mu3_in, theta_sig3_in, coeff3_in)
ddoutMSEA = ddoutMSEA + [mse3, mae3]
ddoutYpreds = ddoutYpreds + [y_pred3_temp]
#totaMSE+=mse3
indexSepDynamic += 2
if (y_dim > 3):
theta_mu4_temp, theta_sig4_temp, coeff4_temp, y_pred4_temp = restResults[:
4]
restResults = restResults[4:]
theta_mu4_temp.name = 'theta_mu4'
theta_sig4_temp.name = 'theta_sig4'
coeff4_temp.name = 'coeff4'
y_pred4_temp.name = 'disaggregation4'
mse4 = T.mean((y_pred4_temp - y[:, :, 3].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae4 = T.mean(
T.abs_(y_pred4_temp -
y[:, :, 3].reshape((y.shape[0], y.shape[1], 1))))
mse4.name = 'mse4'
mae4.name = 'mae4'
theta_mu4_in = theta_mu4_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig4_in = theta_sig4_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff4_in = coeff4_temp.reshape((x_shape[0] * x_shape[1], -1))
argsGMM = argsGMM + (theta_mu4_in, theta_sig4_in, coeff4_in)
ddoutMSEA = ddoutMSEA + [mse4, mae4]
ddoutYpreds = ddoutYpreds + [y_pred4_temp]
#totaMSE+=mse4
indexSepDynamic += 2
if (y_dim > 4):
theta_mu5_temp, theta_sig5_temp, coeff5_temp, y_pred5_temp = restResults[:
4]
restResults = restResults[4:]
theta_mu5_temp.name = 'theta_mu5'
theta_sig5_temp.name = 'theta_sig5'
coeff5_temp.name = 'coeff5'
y_pred5_temp.name = 'disaggregation5'
mse5 = T.mean((y_pred5_temp - y[:, :, 4].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae5 = T.mean(
T.abs_(y_pred5_temp -
y[:, :, 4].reshape((y.shape[0], y.shape[1], 1))))
mse5.name = 'mse5'
mae5.name = 'mae5'
theta_mu5_in = theta_mu5_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig5_in = theta_sig5_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff5_in = coeff5_temp.reshape((x_shape[0] * x_shape[1], -1))
argsGMM = argsGMM + (theta_mu5_in, theta_sig5_in, coeff5_in)
ddoutMSEA = ddoutMSEA + [mse5, mae5]
ddoutYpreds = ddoutYpreds + [y_pred5_temp]
#totaMSE+=mse5
indexSepDynamic += 2
totaMSE = (mse1 + mse2 + mse3 + mse4 + mse5) / y_dim
totaMSE.name = 'mse'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
y_shape = y.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
y_in = y.reshape((y_shape[0] * y_shape[1], -1))
recon = GMMdisagMulti(
y_dim, y_in, theta_mu1_in, theta_sig1_in, coeff1_in, *argsGMM
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
'''
recon5 = GMM(y_in[:,4, None], theta_mu5_in, theta_sig5_in, coeff5_in)
recon5 = recon.reshape((x_shape[0], x_shape[1]))
'''
recon_term = recon.sum(axis=0).mean()
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
#nll_upper_bound_0 = recon_term + kl_term
#nll_upper_bound_0.name = 'nll_upper_bound_0'
if (flgMSE == 1):
nll_upper_bound = recon_term + kl_term + totaMSE
else:
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
######################## TEST (GENERATION) TIME
s_temp_val, prior_mu_temp_val, prior_sig_temp_val, \
theta_mu1_temp_val, theta_sig1_temp_val, coeff1_temp_val, y_pred1_temp_val = restResults_val[:7]
restResults_val = restResults_val[7:]
#s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
theta_mu1_temp_val.name = 'theta_mu1_val'
theta_sig1_temp_val.name = 'theta_sig1_val'
coeff1_temp_val.name = 'coeff1_val'
y_pred1_temp_val.name = 'disaggregation1_val'
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse1_val = T.mean((y_pred1_temp_val - y[:, :, 0].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae1_val = T.mean(
T.abs_(y_pred1_temp_val -
y[:, :, 0].reshape((y.shape[0], y.shape[1], 1))))
#NEURALNILM #(sum_output - sum_target) / max(sum_output, sum_target))
totPred = T.sum(y_pred1_temp_val)
totReal = T.sum(y[:, :, 0])
relErr1_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned1_val = 1 - T.sum(
T.abs_(y_pred1_temp_val - y[:, :, 0].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
#y_unNormalize = (y[:,:,0] * reader.stdTraining[0]) + reader.meanTraining[0]
#y_pred1_temp_val = (y_pred1_temp_val * reader.stdTraining[0]) + reader.meanTraining[0]
#mse1_valUnNorm = T.mean((y_pred1_temp_val - y_unNormalize.reshape((y.shape[0],y.shape[1],1)))**2) # As axis = None is calculated for all
#mae1_valUnNorm = T.mean( T.abs_(y_pred1_temp_val - y_unNormalize.reshape((y.shape[0],y.shape[1],1))))
mse1_val.name = 'mse1_val'
mae1_val.name = 'mae1_val'
theta_mu1_in_val = theta_mu1_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig1_in_val = theta_sig1_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff1_in_val = coeff1_temp_val.reshape((x_shape[0] * x_shape[1], -1))
ddoutMSEA_val = []
ddoutYpreds_val = [y_pred1_temp_val]
totaMSE_val = mse1_val
totaMAE_val = mae1_val
indexSepDynamic_val = 5
prediction_val = y_pred1_temp_val
#Initializing values of mse and mae
mse2_val = T.zeros((1, ))
mae2_val = T.zeros((1, ))
mse3_val = T.zeros((1, ))
mae3_val = T.zeros((1, ))
mse4_val = T.zeros((1, ))
mae4_val = T.zeros((1, ))
mse5_val = T.zeros((1, ))
mae5_val = T.zeros((1, ))
relErr2_val = T.zeros((1, ))
relErr3_val = T.zeros((1, ))
relErr4_val = T.zeros((1, ))
relErr5_val = T.zeros((1, ))
propAssigned2_val = T.zeros((1, ))
propAssigned3_val = T.zeros((1, ))
propAssigned4_val = T.zeros((1, ))
propAssigned5_val = T.zeros((1, ))
if (y_dim > 1):
theta_mu2_temp_val, theta_sig2_temp_val, coeff2_temp_val, y_pred2_temp_val = restResults_val[:
4]
restResults_val = restResults_val[4:]
theta_mu2_temp_val.name = 'theta_mu2_val'
theta_sig2_temp_val.name = 'theta_sig2_val'
coeff2_temp_val.name = 'coeff2_val'
y_pred2_temp_val.name = 'disaggregation2_val'
mse2_val = T.mean((y_pred2_temp_val - y[:, :, 1].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae2_val = T.mean(
T.abs_(y_pred2_temp_val -
y[:, :, 1].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred2_temp_val)
totReal = T.sum(y[:, :, 1])
relErr2_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned2_val = 1 - T.sum(
T.abs_(y_pred2_temp_val - y[:, :, 1].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse2_val.name = 'mse2_val'
mae2_val.name = 'mae2_val'
theta_mu2_in_val = theta_mu2_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig2_in_val = theta_sig2_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff2_in_val = coeff2_temp_val.reshape((x_shape[0] * x_shape[1], -1))
argsGMM_val = theta_mu2_in_val, theta_sig2_in_val, coeff2_in_val
ddoutMSEA_val = ddoutMSEA_val + [mse2_val, mae2_val]
ddoutYpreds_val = ddoutYpreds_val + [y_pred2_temp_val]
totaMSE_val += mse2_val
totaMAE_val += mae2_val
indexSepDynamic_val += 2
prediction_val = T.concatenate([prediction_val, y_pred2_temp_val],
axis=2)
if (y_dim > 2):
theta_mu3_temp_val, theta_sig3_temp_val, coeff3_temp_val, y_pred3_temp_val = restResults_val[:
4]
restResults_val = restResults_val[4:]
theta_mu3_temp_val.name = 'theta_mu3_val'
theta_sig3_temp_val.name = 'theta_sig3_val'
coeff3_temp_val.name = 'coeff3_val'
y_pred3_temp_val.name = 'disaggregation3_val'
mse3_val = T.mean((y_pred3_temp_val - y[:, :, 2].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae3_val = T.mean(
T.abs_(y_pred3_temp_val -
y[:, :, 2].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred3_temp_val)
totReal = T.sum(y[:, :, 2])
relErr3_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned3_val = 1 - T.sum(
T.abs_(y_pred3_temp_val - y[:, :, 2].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse3_val.name = 'mse3_val'
mae3_val.name = 'mae3_val'
theta_mu3_in_val = theta_mu3_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig3_in_val = theta_sig3_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff3_in_val = coeff3_temp_val.reshape((x_shape[0] * x_shape[1], -1))
argsGMM_val = argsGMM_val + (theta_mu3_in_val, theta_sig3_in_val,
coeff3_in_val)
ddoutMSEA_val = ddoutMSEA_val + [mse3_val, mae3_val]
ddoutYpreds_val = ddoutYpreds_val + [y_pred3_temp_val]
totaMSE_val += mse3_val
totaMAE_val += mae3_val
indexSepDynamic_val += 2
prediction_val = T.concatenate([prediction_val, y_pred3_temp_val],
axis=2)
if (y_dim > 3):
theta_mu4_temp_val, theta_sig4_temp_val, coeff4_temp_val, y_pred4_temp_val = restResults_val[:
4]
restResults_val = restResults_val[4:]
theta_mu4_temp_val.name = 'theta_mu4_val'
theta_sig4_temp_val.name = 'theta_sig4_val'
coeff4_temp_val.name = 'coeff4_val'
y_pred4_temp_val.name = 'disaggregation4_val'
mse4_val = T.mean((y_pred4_temp_val - y[:, :, 3].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae4_val = T.mean(
T.abs_(y_pred4_temp_val -
y[:, :, 3].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred4_temp_val)
totReal = T.sum(y[:, :, 3])
relErr4_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned4_val = 1 - T.sum(
T.abs_(y_pred4_temp_val - y[:, :, 3].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse4_val.name = 'mse4_val'
mae4_val.name = 'mae4_val'
theta_mu4_in_val = theta_mu4_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig4_in_val = theta_sig4_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff4_in_val = coeff4_temp_val.reshape((x_shape[0] * x_shape[1], -1))
argsGMM_val = argsGMM_val + (theta_mu4_in_val, theta_sig4_in_val,
coeff4_in_val)
ddoutMSEA_val = ddoutMSEA_val + [mse4_val, mae4_val]
ddoutYpreds_val = ddoutYpreds_val + [y_pred4_temp_val]
totaMSE_val += mse4_val
totaMAE_val += mae4_val
indexSepDynamic_val += 2
prediction_val = T.concatenate([prediction_val, y_pred4_temp_val],
axis=2)
if (y_dim > 4):
theta_mu5_temp_val, theta_sig5_temp_val, coeff5_temp_val, y_pred5_temp_val = restResults_val[:
4]
restResults_val = restResults_val[4:]
theta_mu5_temp_val.name = 'theta_mu5_val'
theta_sig5_temp_val.name = 'theta_sig5_val'
coeff5_temp_val.name = 'coeff5_val'
y_pred5_temp_val.name = 'disaggregation5_val'
mse5_val = T.mean((y_pred5_temp_val - y[:, :, 4].reshape(
(y.shape[0], y.shape[1],
1)))**2) # As axis = None is calculated for all
mae5_val = T.mean(
T.abs_(y_pred5_temp_val -
y[:, :, 4].reshape((y.shape[0], y.shape[1], 1))))
totPred = T.sum(y_pred5_temp_val)
totReal = T.sum(y[:, :, 4])
relErr5_val = (totPred - totReal) / T.maximum(totPred, totReal)
propAssigned5_val = 1 - T.sum(
T.abs_(y_pred5_temp_val - y[:, :, 4].reshape(
(y.shape[0], y.shape[1], 1)))) / (2 * T.sum(x))
mse5_val.name = 'mse5_val'
mae5_val.name = 'mae5_val'
theta_mu5_in_val = theta_mu5_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
theta_sig5_in_val = theta_sig5_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff5_in_val = coeff5_temp_val.reshape((x_shape[0] * x_shape[1], -1))
argsGMM_val = argsGMM_val + (theta_mu5_in_val, theta_sig5_in_val,
coeff5_in_val)
ddoutMSEA_val = ddoutMSEA_val + [mse5_val, mae5_val]
ddoutYpreds_val = ddoutYpreds_val + [y_pred5_temp_val]
totaMSE_val += mse5_val
totaMAE_val += mae5_val
indexSepDynamic_val += 2
prediction_val = T.concatenate([prediction_val, y_pred5_temp_val],
axis=2)
recon_val = GMMdisagMulti(
y_dim, y_in, theta_mu1_in_val, theta_sig1_in_val, coeff1_in_val,
*argsGMM_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out'
totaMSE_val = totaMSE_val / y_dim
totaMAE_val = totaMAE_val / y_dim
'''
recon5 = GMM(y_in[:,4, None], theta_mu5_in, theta_sig5_in, coeff5_in)
recon5 = recon.reshape((x_shape[0], x_shape[1]))
'''
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term'
######################
model.inputs = [x, mask, y, y_mask, schedMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[nll_upper_bound, recon_term, kl_term, totaMSE, mse1, mae1] +
ddoutMSEA + ddoutYpreds,
indexSep=indexSepDynamic,
indexDDoutPlot=[13], # adding indexes of ddout for the plotting
#, (6,y_pred_temp)
instancesPlot=instancesPlot, #0-150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
"""mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
n_steps = n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp
)"""
mainloop.restore(data=Iterator(train_data, batch_size),
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp)
mainloop.run()
data = Iterator(test_data, batch_size)
test_fn = theano.function(
inputs=[x, y], #[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[
prediction_val, recon_term_val, totaMSE_val, totaMAE_val, mse1_val,
mse2_val, mse3_val, mse4_val, mse5_val, mae1_val, mae2_val,
mae3_val, mae4_val, mae5_val, relErr1_val, relErr2_val,
relErr3_val, relErr4_val, relErr5_val, propAssigned1_val,
propAssigned2_val, propAssigned3_val, propAssigned4_val,
propAssigned5_val
] #prediction_val, mse_val, mae_val
,
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
testMetrics2 = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2])
testOutput.append(outputGeneration[1:14])
testMetrics2.append(outputGeneration[14:])
#{0:[4,20], 2:[5,10]}
#if (numBatchTest==0):
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],
[1, 0, 2])[4]) #ORIGINAL 1,0,2
plt.savefig(save_path +
"/vrnn_dis_generated{}_Pred_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(batch[2], [1, 0, 2])[4])
plt.savefig(save_path +
"/vrnn_dis_generated{}_RealDisag_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(batch[0], [1, 0, 2])[4]) #ORIGINAL 1,0,2
plt.savefig(save_path +
"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest += 1
testOutput = np.asarray(testOutput)
testMetrics2 = np.asarray(testMetrics2)
print(testOutput.shape)
print(testMetrics2.shape)
recon_test = testOutput[:, 0].mean()
mse_test = testOutput[:, 1].mean()
mae_test = testOutput[:, 2].mean()
mse1_test = testOutput[:, 3].mean()
mae1_test = testOutput[:, 8].mean()
mse2_test = testOutput[:, 4].mean()
mae2_test = testOutput[:, 9].mean()
mse3_test = testOutput[:, 5].mean()
mae3_test = testOutput[:, 10].mean()
mse4_test = testOutput[:, 6].mean()
mae4_test = testOutput[:, 11].mean()
mse5_test = testOutput[:, 7].mean()
mae5_test = testOutput[:, 12].mean()
relErr1_test = testMetrics2[:, 0].mean()
relErr2_test = testMetrics2[:, 1].mean()
relErr3_test = testMetrics2[:, 2].mean()
relErr4_test = testMetrics2[:, 3].mean()
relErr5_test = testMetrics2[:, 4].mean()
propAssigned1_test = testMetrics2[:, 5].mean()
propAssigned2_test = testMetrics2[:, 6].mean()
propAssigned3_test = testMetrics2[:, 7].mean()
propAssigned4_test = testMetrics2[:, 8].mean()
propAssigned5_test = testMetrics2[:, 9].mean()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(appliances) + "\n")
fLog.write(str(windows) + "\n")
fLog.write(
"logTest,mse1_test,mse2_test,mse3_test,mse4_test,mse5_test,mae1_test,mae2_test,mae3_test,mae4_test,mae5_test,mseTest,maeTest\n"
)
fLog.write("{},{},{},{},{},{},{},{},{},{},{},{},{}\n\n".format(
recon_test, mse1_test, mse2_test, mse3_test, mse4_test, mse5_test,
mae1_test, mae2_test, mae3_test, mae4_test, mae5_test, mse_test,
mae_test))
fLog.write(
"relErr1,relErr2,relErr3,relErr4,relErr5,propAssigned1,propAssigned2,propAssigned3,propAssigned4,propAssigned5\n"
)
fLog.write("{},{},{},{},{},{},{},{},{},{}\n".format(
relErr1_test, relErr2_test, relErr3_test, relErr4_test, relErr5_test,
propAssigned1_test, propAssigned2_test, propAssigned3_test,
propAssigned4_test, propAssigned5_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
fLog.write(
"epoch,log,kl,mse1,mse2,mse3,mse4,mse5,mae1,mae2,mae3,mae4,mae5\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
d, e, f, g, j, k, l, m = 0, 0, 0, 0, 0, 0, 0, 0
ep = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['mse1'][i]
h = mainloop.trainlog.monitor['mae1'][i]
if (y_dim > 1):
d = mainloop.trainlog.monitor['mse2'][i]
j = mainloop.trainlog.monitor['mae2'][i]
if (y_dim > 2):
e = mainloop.trainlog.monitor['mse3'][i]
k = mainloop.trainlog.monitor['mae3'][i]
if (y_dim > 3):
f = mainloop.trainlog.monitor['mse4'][i]
l = mainloop.trainlog.monitor['mae4'][i]
if (y_dim > 4):
g = mainloop.trainlog.monitor['mse5'][i]
m = mainloop.trainlog.monitor['mae5'][i]
fLog.write(
"{:d},{:.2f},{:.2f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f},{:.3f}\n"
.format(ep, a, b, c, d, e, f, g, h, j, k, l, m))
f = open(save_path + '/outputRealGeneration.pkl', 'wb')
pickle.dump(outputGeneration, f, -1)
f.close()
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
typeLoad = int(args['typeLoad'])
target_inclusion_prob = float(args['target_inclusion_prob'])
n_steps_val = n_steps
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150 #150
p_z_dim = 150 #150
p_x_dim = 250 #250
x2s_dim = 250 #250
z2s_dim = 150 #150
target_dim = k #x_dim #(x_dim-1)*k
'''
f = open(sample_path+'vrnn_gmm_1_best.pkl', 'rb')
mainloop = cPickle.load(f)
f.close()
'''
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
typeLoad=typeLoad,
seq_per_batch=num_sequences_per_batch,
target_inclusion_prob=target_inclusion_prob)
instancesPlot = {
0: [4]
} #for now use hard coded instancesPlot for kelly sampling
if (typeLoad == 0): #original split according time
instancesPlot = reader.build_dict_instances_plot(
listDates, batch_size, Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=ytrain,
labels=Xtrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=yval,
labels=Xval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
'''
dissag_pred = FullyConnectedLayer(name='disag_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=num_apps,
unit='relu',
init_W=init_W,
init_b=init_b)
'''
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_val_fn(s_tm1):
'''
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
'''
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = x_1.fprop([pred_t], params)
s_t = rnn.fprop([[pred_1_t, z_1_t], [s_tm1]], params)
return s_t, pred_t, z_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t
# prior_mu_temp_val, prior_sig_temp_val
((s_temp_val, prediction_val, z_t_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val), updates_val) =\
theano.scan(fn=inner_val_fn , n_steps=n_steps_val, #already 1 subtracted if doing next step
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
'''
theta_1_temp = theta_1.fprop([z_1_temp, s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
prediction.name = 'pred_' + str(flgAgg)
mse = T.mean((prediction - x)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - x))
mse.name = 'mse'
mae.name = 'mae'
x_in = x.reshape((batch_size * n_steps, -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
x_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
#recon = recon * mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
############## TEST ###############
theta_mu_in_val = theta_mu_temp_val.reshape((batch_size * n_steps, -1))
theta_sig_in_val = theta_sig_temp_val.reshape((batch_size * n_steps, -1))
coeff_in_val = coeff_temp_val.reshape((batch_size * n_steps, -1))
pred_in = prediction_val.reshape((batch_size * n_steps, -1))
recon_val = GMM(
pred_in, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((batch_size, n_steps))
recon_val.name = 'gmm_out_val'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound, recon_term, kl_term, mse, mae, theta_mu_temp,
prediction
],
indexSep=5,
indexDDoutPlot=[(0, theta_mu_temp), (2, z_t_temp),
(3, prediction)],
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
test_fn = theano.function(
inputs=[],
outputs=[prediction_val, recon_val],
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
outputGeneration = test_fn()
#{0:[4,20], 2:[5,10]}
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated_z_0-4")
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated_s_0-4")
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated_theta_0-4")
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[4])
plt.savefig(save_path + "/vrnn_dis_generated_pred_4.ps")
plt.figure(2)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[10])
plt.savefig(save_path + "/vrnn_dis_generated_pred_10.ps")
plt.figure(3)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[20])
plt.savefig(save_path + "/vrnn_dis_generated_pred_20.ps")
testLogLike = np.asarray(outputGeneration[1]).mean()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("Test-log-likelihood\n")
fLog.write("{}\n".format(testLogLike))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
z2s_dim))
#fLog.write("{}\n".format(outputGeneration[0]))#logLIkelihood in the test set
fLog.write("epoch,log,kl,mse,mae\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f, a, b, d, e))
f = open(save_path + '/outputRealGeneration.pkl', 'wb')
pickle.dump(outputGeneration, f, -1)
f.close()
