def CVAE(
model="mnist_VAE_CNN", # currently not used
steps=20000,
batch_size=100,
z_dim=6,
z_dim_true=4,
x_dim=10,
y_dim=1,
alpha_dim=4,
ntrain=5000,
No=15,
Ni=15,
lam_ML=0.000001,
gamma=0.001,
lr=0.0001,
b1=0.5,
b2=0.999,
use_ce=True,
objective="IND_UNCOND",
decoder_net="VAE_CNN", # options are ['linGauss','nonLinGauss','VAE','VAE_CNN','VAE_Imagenet','VAE_fMNIST']
classifier_net="cnn", # options are ['oneHyperplane','twoHyperplane','cnn','cnn_imagenet','cnn_fmnist']
data_type="mnist", # options are ["2dpts","mnist","imagenet","fmnist"]
break_up_ce=True, # Whether or not to break up the forward passes of the network based on alphas
randseed=None,
save_output=False,
debug_level=2,
debug_plot=False,
save_plot=False,
c_dim=1,
img_size=28):
# initialization
params = {
"steps": steps,
"batch_size": batch_size,
"z_dim": z_dim,
"z_dim_true": z_dim_true,
"x_dim": x_dim,
"y_dim": y_dim,
"alpha_dim": alpha_dim,
"ntrain": ntrain,
"No": No,
"Ni": Ni,
"lam_ML": lam_ML,
"gamma": gamma,
"lr": lr,
"b1": b1,
"b2": b2,
"use_ce": use_ce,
"objective": objective,
"decoder_net": decoder_net,
"classifier_net": classifier_net,
"data_type": data_type,
"break_up_ce": break_up_ce,
"randseed": randseed,
"save_output": save_output,
"debug_level": debug_level,
'c_dim': c_dim,
'img_size': img_size
}
params["data_std"] = 2.
if debug_level > 0:
print("Parameters:")
print(params)
# Initialize arrays for storing performance data
debug = {}
debug["loss"] = np.zeros((steps))
debug["loss_ce"] = np.zeros((steps))
debug["loss_nll"] = np.zeros((steps))
debug["loss_nll_logdet"] = np.zeros((steps))
debug["loss_nll_quadform"] = np.zeros((steps))
debug["loss_nll_mse"] = np.zeros((steps))
debug["loss_nll_kld"] = np.zeros((steps))
if decoder_net == 'linGauss':
for i in range(params["z_dim"]):
for j in range(params["z_dim_true"]):
debug["cossim_w%dwhat%d" % (j + 1, i + 1)] = np.zeros((steps))
for j in range(i + 1, params["z_dim"]):
debug["cossim_what%dwhat%d" % (i + 1, j + 1)] = np.zeros(
(steps))
if save_plot: frames = []
if data_type == 'mnist' or data_type == 'fmnist':
class_use = np.array([0, 3, 4])
class_use_str = np.array2string(class_use)
y_dim = class_use.shape[0]
newClass = range(0, y_dim)
save_dir = '/home/mnorko/Documents/Tensorflow/causal_vae/results/fmnist_class034/' + data_type + '_' + objective + '_zdim' + str(
z_dim) + '_alpha' + str(alpha_dim) + '_No' + str(No) + '_Ni' + str(
Ni) + '_lam' + str(lam_ML) + '_class' + class_use_str[1:(
len(class_use_str) - 1):2] + '/'
else:
save_dir = '/home/mnorko/Documents/Tensorflow/causal_vae/results/imagenet/' + data_type + '_' + objective + '_zdim' + str(
z_dim) + '_alpha' + str(alpha_dim) + '_No' + str(No) + '_Ni' + str(
Ni) + '_lam' + str(lam_ML) + '_cont_kl0.1/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if data_type == '2dpts':
break_up_ce = False
params['break_up_ce'] = False
# seed random number generator
if randseed is not None:
if debug_level > 0:
print('Setting random seed to ' + str(randseed) + '.')
np.random.seed(randseed)
torch.manual_seed(randseed)
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Generate data
if data_type == '2dpts':
# --- construct projection matrices ---
# 'true' orthogonal columns used to generate data from latent factors
#Wsquare = sp.linalg.orth(np.random.rand(x_dim,x_dim))
Wsquare = np.identity(x_dim)
W = Wsquare[:, :z_dim_true]
# 1st column of W
w1 = np.expand_dims(W[:, 0], axis=1)
# 2nd column of W
w2 = np.expand_dims(W[:, 1], axis=1)
# form projection matrices
Pw1 = util.formProjMat(w1)
Pw2 = util.formProjMat(w2)
# convert to torch matrices
Pw1_torch = torch.from_numpy(Pw1).float()
Pw2_torch = torch.from_numpy(Pw2).float()
# --- construct data ---
# ntrain instances of alpha and x
Alpha = params["data_std"] * np.random.randn(ntrain, z_dim_true)
X = np.matmul(Alpha, W.T)
elif data_type == 'mnist':
test_size = 64
X, Y, tridx = load_mnist_classSelect('train', class_use, newClass)
vaX, vaY, vaidx = load_mnist_classSelect('val', class_use, newClass)
sample_inputs = vaX[0:test_size]
sample_inputs_torch = torch.from_numpy(sample_inputs)
sample_inputs_torch = sample_inputs_torch.permute(0, 3, 1,
2).float().to(device)
ntrain = X.shape[0]
elif data_type == 'fmnist':
test_size = 64
X, Y, tridx = load_fashion_mnist_classSelect('train', class_use,
newClass)
vaX, vaY, vaidx = load_fashion_mnist_classSelect(
'val', class_use, newClass)
sample_inputs = vaX[0:test_size]
sample_inputs_torch = torch.from_numpy(sample_inputs)
sample_inputs_torch = sample_inputs_torch.permute(0, 3, 1,
2).float().to(device)
ntrain = X.shape[0]
elif data_type == 'svhn':
X, Y, tridx = load_svhn_classSelect('train', class_use, newClass)
vaX, vaY, vaidx = load_svhn_classSelect('val', class_use, newClass)
sample_inputs = vaX[0:test_size]
sample_inputs_torch = torch.from_numpy(sample_inputs)
sample_inputs_torch = sample_inputs_torch.float().to(device)
ntrain = X.shape[0]
elif data_type == 'imagenet':
transform_train = transforms.Compose([
transforms.RandomCrop(128, padding=4, pad_if_needed=True),
transforms.RandomHorizontalFlip(),
transforms.Resize([32, 32]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
transform_test = transforms.Compose([
transforms.CenterCrop(128),
transforms.Resize([32, 32]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
file_open = open('imagenet_zebra_gorilla.pkl', 'rb')
train_fileType_2 = pickle.load(file_open)
train_imgName_2 = pickle.load(file_open)
train_imgLabel_2 = pickle.load(file_open)
val_fileType_2 = pickle.load(file_open)
val_imgName_2 = pickle.load(file_open)
val_imgLabel_2 = pickle.load(file_open)
file_open.close()
train_set = Imagenet_Gor_Zeb(train_imgName_2,
train_imgLabel_2,
train_fileType_2,
transforms=transform_train)
trainloader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=True)
test_size = 16
val_set = Imagenet_Gor_Zeb(val_imgName_2,
val_imgLabel_2,
val_fileType_2,
transforms=transform_test)
valloader = torch.utils.data.DataLoader(val_set,
batch_size=test_size,
shuffle=True)
dataiter = iter(trainloader)
dataiter_val = iter(valloader)
sample_inputs_torch, _ = dataiter_val.next()
#sample_inputs_torch.to(device)
sample_inputs_torch = sample_inputs_torch.to(device)
# --- initialize decoder ---
if decoder_net == 'linGauss':
from linGaussModel import Decoder
decoder = Decoder(x_dim, z_dim).to(device)
elif decoder_net == 'nonLinGauss':
from VAEModel import Decoder_samp
z_num_samp = No
decoder = Decoder_samp(x_dim, z_dim).to(device)
elif decoder_net == 'VAE':
from VAEModel import Decoder, Encoder
encoder = Encoder(x_dim, z_dim).to(device)
encoder.apply(weights_init_normal)
decoder = Decoder(x_dim, z_dim).to(device)
decoder.apply(weights_init_normal)
elif decoder_net == 'VAE_CNN' or 'VAE_fMNIST':
from VAEModel_CNN import Decoder, Encoder
encoder = Encoder(z_dim, c_dim, img_size).to(device)
encoder.apply(weights_init_normal)
decoder = Decoder(z_dim, c_dim, img_size).to(device)
decoder.apply(weights_init_normal)
elif decoder_net == 'VAE_Imagenet':
checkpoint = torch.load(
'/home/mnorko/Documents/Tensorflow/causal_vae/results/imagenet/imagenet_JOINT_UNCOND_zdim40_alpha0_No20_Ni1_lam0.001/'
+ 'network_batch' + str(batch_size) + '.pt')
from VAEModel_CNN_imagenet import Decoder, Encoder
encoder = Encoder(z_dim, c_dim, img_size).to(device)
decoder = Decoder(z_dim, c_dim, img_size).to(device)
encoder.load_state_dict(checkpoint['model_state_dict_encoder'])
decoder.load_state_dict(checkpoint['model_state_dict_decoder'])
else:
print("Error: decoder_net should be one of: linGauss nonLinGauss VAE")
# --- initialize classifier ---
if classifier_net == 'oneHyperplane':
from hyperplaneClassifierModel import OneHyperplaneClassifier
classifier = OneHyperplaneClassifier(x_dim, y_dim, Pw1_torch,
ksig=5.).to(device)
classifier.apply(weights_init_normal)
elif classifier_net == 'twoHyperplane':
from hyperplaneClassifierModel import TwoHyperplaneClassifier
classifier = TwoHyperplaneClassifier(x_dim,
y_dim,
Pw1_torch,
Pw2_torch,
ksig=5.).to(device)
classifier.apply(weights_init_normal)
elif classifier_net == 'cnn':
from cnnClassifierModel import CNN
classifier = CNN(y_dim).to(device)
batch_orig = 64
checkpoint = torch.load('./mnist_batch64_lr0.1_class38/network_batch' +
str(batch_orig) + '.pt')
#checkpoint = torch.load('/home/mnorko/Documents/Tensorflow/causal_vae/results/mnist_batch64_lr0.1_class149/network_batch' + str(batch_orig) + '.pt')
classifier.load_state_dict(checkpoint['model_state_dict_classifier'])
elif classifier_net == 'cnn_fmnist':
from cnnClassifierModel import CNN
classifier = CNN(y_dim).to(device)
batch_orig = 64
checkpoint = torch.load(
'./fmnist_batch64_lr0.1_class034/network_batch' + str(batch_orig) +
'.pt')
#checkpoint = torch.load('/home/mnorko/Documents/Tensorflow/causal_vae/results/mnist_batch64_lr0.1_class149/network_batch' + str(batch_orig) + '.pt')
classifier.load_state_dict(checkpoint['model_state_dict_classifier'])
elif classifier_net == 'cnn_imagenet':
from cnnImageNetClassifierModel import CNN
classImagenetIdx = [366, 340]
classifier_model = models.vgg16_bn(pretrained=True)
classifier = CNN(classifier_model, classImagenetIdx).to(device)
else:
print(
"Error: classifier should be one of: oneHyperplane twoHyperplane")
##
if decoder_net == 'linGauss':
What = Variable(torch.mul(torch.randn(x_dim, z_dim, dtype=torch.float),
0.5),
requires_grad=True)
else:
What = None
# --- specify optimizer ---
# NOTE: we only include the decoder parameters in the optimizer
# because we don't want to update the classifier parameters
if decoder_net == 'VAE' or decoder_net == 'VAE_CNN' or decoder_net == 'VAE_Imagenet' or decoder_net == 'VAE_fMNIST':
params_use = list(decoder.parameters()) + list(encoder.parameters())
else:
params_use = list(decoder.parameters())
optimizer_NN = torch.optim.Adam(params_use, lr=lr, betas=(b1, b2))
# --- train ---
start_time = time.time()
for k in range(0, steps):
# --- reset gradients to zero ---
# (you always need to do this in pytorch or the gradients are
# accumulated from one batch to the next)
optimizer_NN.zero_grad()
# --- compute negative log likelihood ---
# randomly subsample batch_size samples of x
randIdx = np.random.randint(0, ntrain, batch_size)
if data_type == '2dpts':
Xbatch = torch.from_numpy(X[randIdx, :]).float()
elif data_type == 'mnist' or data_type == 'fmnist':
Xbatch = torch.from_numpy(X[randIdx]).float()
Xbatch = Xbatch.permute(0, 3, 1, 2)
elif data_type == 'imagenet':
try:
Xbatch, _ = dataiter.next()
except:
dataiter = iter(trainloader)
Xbatch, _ = dataiter.next()
Xbatch = Xbatch.to(device)
if decoder_net == 'linGauss':
nll = loss_functions.linGauss_NLL_loss(Xbatch, What, gamma)
elif decoder_net == 'nonLinGauss':
randBatch = torch.from_numpy(np.random.randn(z_num_samp,
z_dim)).float()
Xest, Xmu, Xlogvar = decoder(randBatch)
Xcov = torch.exp(Xlogvar)
nll = loss_functions.nonLinGauss_NLL_loss(Xbatch, Xmu, Xcov)
elif decoder_net == 'VAE' or decoder_net == 'VAE_CNN' or decoder_net == 'VAE_Imagenet' or decoder_net == 'VAE_fMNIST':
latent_out, mu, logvar = encoder(Xbatch)
Xest = decoder(latent_out)
nll, nll_mse, nll_kld = loss_functions.VAE_LL_loss(
Xbatch, Xest, logvar, mu)
# --- compute mutual information causal effect term ---
if objective == "IND_UNCOND":
causalEffect, ceDebug = causaleffect.ind_uncond(params,
decoder,
classifier,
device,
What=What)
elif objective == "IND_COND":
causalEffect, ceDebug = causaleffect.ind_cond(params,
decoder,
classifier,
device,
What=What)
elif objective == "JOINT_UNCOND":
causalEffect, ceDebug = causaleffect.joint_uncond(params,
decoder,
classifier,
device,
What=What)
elif objective == "JOINT_COND":
causalEffect, ceDebug = causaleffect.joint_cond(params,
decoder,
classifier,
device,
What=What)
# --- compute gradients ---
# total loss
loss = use_ce * causalEffect + lam_ML * nll
# backward step to compute the gradients
loss.backward()
if decoder_net == 'linGauss':
# update What with the computed gradient
What.data.sub_(lr * What.grad.data)
# reset the What gradients to 0
What.grad.data.zero_()
else:
optimizer_NN.step()
# --- save debug info for this step ---
debug["loss"][k] = loss.item()
debug["loss_ce"][k] = causalEffect.item()
debug["loss_nll"][k] = (lam_ML * nll).item()
if decoder_net == 'VAE' or decoder_net == 'VAE_CNN' or decoder_net == 'VAE_Imagenet' or decoder_net == 'VAE_fMNIST':
debug["loss_nll_mse"][k] = (lam_ML * nll_mse).item()
debug["loss_nll_kld"][k] = (lam_ML * nll_kld).item()
if debug_level > 1:
if decoder_net == 'linGauss':
Wnorm = W / np.linalg.norm(W, axis=0, keepdims=True)
Whatnorm = What.detach().numpy() / np.linalg.norm(
What.detach().numpy(), axis=0, keepdims=True)
# cosine similarities between columns of W and What
for i in range(params["z_dim"]):
for j in range(params["z_dim_true"]):
debug["cossim_w%dwhat%d" %
(j + 1, i + 1)][k] = np.matmul(
Wnorm[:, j], Whatnorm[:, i])
for j in range(i + 1, params["z_dim"]):
debug["cossim_what%dwhat%d" %
(i + 1, j + 1)][k] = np.matmul(
Whatnorm[:, i], Whatnorm[:, j])
# --- print step information ---
if debug_level > 0:
print("[Step %d/%d] time: %4.2f [CE: %g] [ML: %g] [loss: %g]" % \
(k, steps, time.time() - start_time, debug["loss_ce"][k],
debug["loss_nll"][k], debug["loss"][k]))
# --- debug plot ---
if debug_plot and k % 1000 == 0:
print('Generating plot frame...')
if data_type == '2dpts':
# generate samples of p(x | alpha_i = alphahat_i)
decoded_points = {}
decoded_points["ai_vals"] = lfplot_aihat_vals
decoded_points["samples"] = np.zeros(
(2, lfplot_nsamp, len(lfplot_aihat_vals), params["z_dim"]))
for l in range(params["z_dim"]): # loop over latent dimensions
for i, aihat in enumerate(
lfplot_aihat_vals): # loop over fixed aihat values
for m in range(
lfplot_nsamp): # loop over samples to generate
z = np.random.randn(params["z_dim"])
z[l] = aihat
x = decoder(
torch.from_numpy(z).float(), What, gamma)
decoded_points["samples"][:, m, i,
l] = x.detach().numpy()
frame = plotting.debugPlot_frame(X, ceDebug["Xhat"], W, What,
k, steps, debug, params,
classifier, decoded_points)
if save_plot:
frames.append(frame)
elif data_type == 'mnist' or data_type == 'imagenet' or data_type == 'fmnist':
torch.save(
{
'step': k,
'model_state_dict_classifier': classifier.state_dict(),
'model_state_dict_encoder': encoder.state_dict(),
'model_state_dict_decoder': decoder.state_dict(),
'optimizer_state_dict': optimizer_NN.state_dict(),
'loss': loss,
}, save_dir + 'network_batch' + str(batch_size) + '.pt')
sample_latent, mu, var = encoder(sample_inputs_torch)
sample_inputs_torch_new = sample_inputs_torch.permute(
0, 2, 3, 1)
sample_inputs_np = sample_inputs_torch_new.detach().cpu(
).numpy()
sample_img = decoder(sample_latent)
sample_latent_small = sample_latent[0:10, :]
imgOut_real, probOut_real, latentOut_real = sweepLatentFactors(
sample_latent_small, decoder, classifier, device, img_size,
c_dim, y_dim, False)
rand_latent = torch.from_numpy(np.random.randn(
10, z_dim)).float().to(device)
imgOut_rand, probOut_rand, latentOut_rand = sweepLatentFactors(
rand_latent, decoder, classifier, device, img_size, c_dim,
y_dim, False)
samples = sample_img
samples = samples.permute(0, 2, 3, 1)
samples = samples.detach().cpu().numpy()
save_images(samples, [8, 8],
'{}train_{:04d}.png'.format(save_dir, k))
#sio.savemat(save_dir + 'sweepLatentFactors.mat',{'imgOut_real':imgOut_real,'probOut_real':probOut_real,'latentOut_real':latentOut_real,'loss_total':debug["loss"][:k],'loss_ce':debug["loss_ce"][:k],'loss_nll':debug['loss_nll'][:k],'samples_out':samples,'sample_inputs':sample_inputs_np})
sio.savemat(
save_dir + 'sweepLatentFactors.mat', {
'imgOut_real': imgOut_real,
'probOut_real': probOut_real,
'latentOut_real': latentOut_real,
'imgOut_rand': imgOut_rand,
'probOut_rand': probOut_rand,
'latentOut_rand': latentOut_rand,
'loss_total': debug["loss"][:k],
'loss_ce': debug["loss_ce"][:k],
'loss_nll': debug['loss_nll'][:k],
'samples_out': samples,
'sample_inputs': sample_inputs_np
})
# --- save all debug data ---
debug["X"] = Xbatch.detach().cpu().numpy()
if not decoder_net == 'linGauss':
debug["Xest"] = Xest.detach().cpu().numpy()
if save_output:
datestamp = ''.join(
re.findall(r'\d+',
str(datetime.datetime.now())[:10]))
timestamp = ''.join(
re.findall(r'\d+',
str(datetime.datetime.now())[11:19]))
# results_folder = './results/tests_kSig5_lr0001_' + objective + '_lam' \
# + str(lam_ML) + '_No' + str(No) + '_Ni' + str(Ni) + '_' \
# + datestamp + '_' + timestamp + '/'
# if not os.path.exists(results_folder):
# os.makedirs(results_folder)
results_folder = save_dir
matfilename = 'results_' + datestamp + '_' + timestamp + '.mat'
sio.savemat(results_folder + matfilename, {
'params': params,
'data': debug
})
if debug_level > 0:
print('Finished saving data to ' + matfilename)
if save_plot:
print('Saving plot...')
gif.save(frames, "results.gif", duration=100)
print('Done!')
return debug
def CVAE_test_twohyperplaneVAE(
model="linGauss_multiHP", # currently not used
steps=6000,
batch_size=100,
z_dim=4,
z_dim_true=4,
x_dim=10,
y_dim=1,
alpha_dim=2,
ntrain=5000,
No=15,
Ni=15,
lam_ML=0.000001,
gamma=0.001,
lr=0.0001,
b1=0.5,
b2=0.999,
use_ce=True,
objective="IND_UNCOND",
decoder_net="linGauss",
classifier_net="hyperplane",
randseed=None,
save_output=False,
debug_level=2,
debug_plot=False,
save_plot=False):
# initialization
params = {
"steps": steps,
"batch_size": batch_size,
"z_dim": z_dim,
"z_dim_true": z_dim_true,
"x_dim": x_dim,
"y_dim": y_dim,
"alpha_dim": alpha_dim,
"ntrain": ntrain,
"No": No,
"Ni": Ni,
"lam_ML": lam_ML,
"gamma": gamma,
"lr": lr,
"b1": b1,
"b2": b2,
"use_ce": use_ce,
"objective": objective,
"decoder_net": decoder_net,
"classifier_net": classifier_net,
"randseed": randseed,
"save_output": save_output,
"debug_level": debug_level
}
params["plot_batchsize"] = 500
params["data_std"] = 2.
if debug_level > 0:
print("Parameters:")
print(params)
# Initialize arrays for storing performance data
debug = {}
vis_samples = 35
debug["yhat_min"] = np.zeros((steps))
debug["loss"] = np.zeros((steps))
debug["loss_ce"] = np.zeros((steps))
debug["loss_nll"] = np.zeros((steps))
debug["loss_nll_logdet"] = np.zeros((steps))
debug["loss_nll_quadform"] = np.zeros((steps))
debug["loss_nll_mse"] = np.zeros((steps))
debug["loss_nll_kld"] = np.zeros((steps))
debug["What"] = np.zeros((z_dim, x_dim, steps))
debug["xhat_a1"] = np.zeros((x_dim, vis_samples, steps))
debug["xhat_a2"] = np.zeros((x_dim, vis_samples, steps))
debug["Yhat_a1"] = np.zeros((x_dim, vis_samples, steps))
debug["Yhat_a2"] = np.zeros((x_dim, vis_samples, steps))
# seed random number generator
if randseed is not None:
if debug_level > 0:
print('Setting random seed to ' + str(randseed) + '.')
np.random.seed(randseed)
torch.manual_seed(randseed)
# --- construct projection matrices ---
# 'true' orthogonal columns used to generate data from latent factors
#Wsquare = sp.linalg.orth(np.random.rand(x_dim,x_dim))
Wsquare = np.identity(x_dim)
W = Wsquare
w1 = np.expand_dims(W[:, 0], axis=1)
w2 = np.expand_dims(W[:, 1], axis=1)
# form projection matrices
Pw1 = util.formProjMat(w1)
Pw2 = util.formProjMat(w2)
# convert to torch matrices
Pw1_torch = torch.from_numpy(Pw1).float()
Pw2_torch = torch.from_numpy(Pw2).float()
# --- construct data ---
# ntrain instances of alpha and x
Alpha = params["data_std"] * np.random.randn(ntrain, z_dim_true)
X = np.matmul(Alpha, W.T)
# --- initialize decoder ---
if decoder_net == 'linGauss':
from linGaussModel import Decoder
decoder = Decoder(x_dim, z_dim)
elif decoder_net == 'nonLinGauss':
from VAEModel import Decoder_samp
z_num_samp = No
decoder = Decoder_samp(x_dim, z_dim)
elif decoder_net == 'VAE':
from VAEModel import Decoder, Encoder
encoder = Encoder(x_dim, z_dim)
encoder.apply(weights_init_normal)
decoder = Decoder(x_dim, z_dim)
else:
print("Error: decoder_net should be one of: linGauss nonLinGauss VAE")
decoder.apply(weights_init_normal)
# --- initialize classifier ---
if classifier_net == 'oneHyperplane':
from hyperplaneClassifierModel import OneHyperplaneClassifier
classifier = OneHyperplaneClassifier(x_dim,
y_dim,
Pw1_torch,
ksig=100.,
a1=w1.reshape((1, 2)))
elif classifier_net == 'twoHyperplane':
from hyperplaneClassifierModel import TwoHyperplaneClassifier
classifier = TwoHyperplaneClassifier(x_dim,
y_dim,
Pw1_torch,
Pw2_torch,
ksig=100.,
a1=w1.reshape((1, 2)),
a2=w2.reshape((1, 2)))
else:
print(
"Error: classifier should be one of: oneHyperplane twoHyperplane")
classifier.apply(weights_init_normal)
if decoder_net == 'linGauss':
What = Variable(torch.mul(torch.randn(x_dim, z_dim, dtype=torch.float),
0.5),
requires_grad=True)
else:
What = None
# --- specify optimizer ---
# NOTE: we only include the decoder parameters in the optimizer
# because we don't want to update the classifier parameters
if decoder_net == 'VAE':
params_use = list(decoder.parameters()) + list(encoder.parameters())
else:
params_use = list(decoder.parameters())
optimizer_NN = torch.optim.Adam(params_use, lr=lr, betas=(b1, b2))
# --- train ---
start_time = time.time()
for k in range(0, steps):
# --- reset gradients to zero ---
# (you always need to do this in pytorch or the gradients are
# accumulated from one batch to the next)
optimizer_NN.zero_grad()
# --- compute negative log likelihood ---
# randomly subsample batch_size samples of x
randIdx = np.random.randint(0, ntrain, batch_size)
Xbatch = torch.from_numpy(X[randIdx, :]).float()
if decoder_net == 'linGauss':
nll = loss_functions.linGauss_NLL_loss(Xbatch, What, gamma)
elif decoder_net == 'nonLinGauss':
randBatch = torch.from_numpy(np.random.randn(z_num_samp,
z_dim)).float()
Xest, Xmu, Xlogvar = decoder(randBatch)
Xcov = torch.exp(Xlogvar)
nll = loss_functions.nonLinGauss_NLL_loss(Xbatch, Xmu, Xcov)
elif decoder_net == 'VAE':
latent_out, mu, logvar = encoder(Xbatch)
Xest = decoder(latent_out)
nll, nll_mse, nll_kld = loss_functions.VAE_LL_loss(
Xbatch, Xest, logvar, mu)
# --- compute mutual information causal effect term ---
if objective == "IND_UNCOND":
causalEffect, ceDebug = causaleffect.ind_uncond(params,
decoder,
classifier,
What=What)
elif objective == "IND_COND":
causalEffect, ceDebug = causaleffect.ind_cond(params,
decoder,
classifier,
What=What)
elif objective == "JOINT_UNCOND":
causalEffect, ceDebug = causaleffect.joint_uncond(params,
decoder,
classifier,
What=What)
elif objective == "JOINT_COND":
causalEffect, ceDebug = causaleffect.joint_cond(params,
decoder,
classifier,
What=What)
yhat_np = ceDebug["yhat"].detach().numpy()
# --- compute gradients ---
# total loss
if use_ce:
loss = causalEffect + lam_ML * nll
else:
loss = lam_ML * nll
# backward step to compute the gradients
loss.backward()
if decoder_net == 'linGauss':
# update What with the computed gradient
What.data.sub_(lr * What.grad.data)
# reset the What gradients to 0
What.grad.data.zero_()
else:
optimizer_NN.step()
# --- save debug info for this step ---
debug["yhat_min"][k] = yhat_np.min()
# components of objective
debug["loss"][k] = loss.detach().numpy()
debug["loss_ce"][k] = causalEffect.detach().numpy()
debug["loss_nll"][k] = (lam_ML * nll).detach().numpy()
if debug_level > 1: # and k == steps-1:
if decoder_net == 'linGauss':
debug["What"][:, :, k] = What.detach().numpy()
elif decoder_net == 'VAE':
debug["loss_nll_mse"][k] = (lam_ML * nll_mse).detach().numpy()
debug["loss_nll_kld"][k] = (lam_ML * nll_kld).detach().numpy()
v_sweep = np.linspace(-5., 5., vis_samples)
# samples Yhat | x[1], x[2], x[3]
for ix1, x1 in enumerate(v_sweep):
for ix2, x2 in enumerate(v_sweep):
xs = np.zeros((250, 3))
xs[:, 0] = x1
xs[:, 1] = x2
xs[:, 2] = params["data_std"] * np.random.randn(250)
yhats = classifier(torch.from_numpy(xs).float())[0]
debug["yhat_x1x2"][ix1, ix2, k] = np.mean(
yhats.detach().numpy()[:, 0])
for ix1, x1 in enumerate(v_sweep):
for ix3, x3 in enumerate(v_sweep):
xs = np.zeros((250, 3))
xs[:, 0] = x1
xs[:, 1] = params["data_std"] * np.random.randn(250)
xs[:, 2] = x3
yhats = classifier(torch.from_numpy(xs).float())[0]
debug["yhat_x1x3"][ix1, ix3, k] = np.mean(
yhats.detach().numpy()[:, 0])
for ix2, x2 in enumerate(v_sweep):
for ix3, x3 in enumerate(v_sweep):
xs = np.zeros((250, 3))
xs[:, 0] = params["data_std"] * np.random.randn(250)
xs[:, 1] = x2
xs[:, 2] = x3
yhats = classifier(torch.from_numpy(xs).float())[0]
debug["yhat_x2x3"][ix2, ix3, k] = np.mean(
yhats.detach().numpy()[:, 0])
# samples x | alpha[1], alpha[2], beta
for ia1, a1 in enumerate(v_sweep):
for ia2, a2 in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = a1
zs[:, 1] = a2
zs[:, 2] = np.random.randn(250)
xs = decoder(
torch.from_numpy(zs).float()).detach().numpy()
debug["x1_a1a2"][ia1, ia2, k] = np.mean(xs[:, 0])
debug["x2_a1a2"][ia1, ia2, k] = np.mean(xs[:, 1])
debug["x3_a1a2"][ia1, ia2, k] = np.mean(xs[:, 2])
for ia1, a1 in enumerate(v_sweep):
for ib, b in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = a1
zs[:, 1] = np.random.randn(250)
zs[:, 2] = b
xs = decoder(
torch.from_numpy(zs).float()).detach().numpy()
debug["x1_a1b"][ia1, ib, k] = np.mean(xs[:, 0])
debug["x2_a1b"][ia1, ib, k] = np.mean(xs[:, 1])
debug["x3_a1b"][ia1, ib, k] = np.mean(xs[:, 2])
for ia2, a2 in enumerate(v_sweep):
for ib, b in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = np.random.randn(250)
zs[:, 1] = a2
zs[:, 2] = b
xs = decoder(
torch.from_numpy(zs).float()).detach().numpy()
debug["x1_a2b"][ia2, ib, k] = np.mean(xs[:, 0])
debug["x2_a2b"][ia2, ib, k] = np.mean(xs[:, 1])
debug["x3_a2b"][ia2, ib, k] = np.mean(xs[:, 2])
# samples yhat | alpha[1], alpha[2], beta
for ia1, a1 in enumerate(v_sweep):
for ia2, a2 in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = a1
zs[:, 1] = a2
zs[:, 2] = params["data_std"] * np.random.randn(250)
xs = decoder(torch.from_numpy(zs).float())
yhats = classifier(xs)[0]
debug["yhat_a1a2"][ia1, ia2, k] = np.mean(
yhats.detach().numpy()[:, 0])
for ia1, a1 in enumerate(v_sweep):
for ib, b in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = a1
zs[:, 1] = np.random.randn(250)
zs[:, 2] = b
xs = decoder(torch.from_numpy(zs).float())
yhats = classifier(xs)[0]
debug["yhat_a1b"][ia1, ib, k] = np.mean(
yhats.detach().numpy()[:, 0])
for ia2, a2 in enumerate(v_sweep):
for ib, b in enumerate(v_sweep):
zs = np.zeros((250, 3))
zs[:, 0] = np.random.randn(250)
zs[:, 1] = a2
zs[:, 2] = b
xs = decoder(torch.from_numpy(zs).float())
yhats = classifier(xs)[0]
debug["yhat_a2b"][ia2, ib, k] = np.mean(
yhats.detach().numpy()[:, 0])
# samples x | alpha[i]
for ia1, a1 in enumerate(range(-3, 4)):
zs = np.zeros((250, 3))
zs[:, 0] = a1
zs[:, 1] = np.random.randn(250)
zs[:, 2] = np.random.randn(250)
xs = decoder(torch.from_numpy(zs).float())
debug["xhat_a1"][ia1, :, :,
k] = xs.detach().numpy().transpose()
for ia2, a2 in enumerate(range(-3, 4)):
zs = np.zeros((250, 3))
zs[:, 0] = np.random.randn(250)
zs[:, 1] = a2
zs[:, 2] = np.random.randn(250)
xs = decoder(torch.from_numpy(zs).float())
debug["xhat_a2"][ia2, :, :,
k] = xs.detach().numpy().transpose()
# --- print step information ---
if debug_level > 0:
print("[Step %d/%d] time: %4.2f [CE: %g] [ML: %g] [loss: %g]" % \
(k, steps, time.time() - start_time, debug["loss_ce"][k],
debug["loss_nll"][k], debug["loss"][k]))
# --- debug plot ---
if debug_plot and k % 500 == 0:
print('Generating plot frame...')
# generate samples of p(x | alpha_i = alphahat_i)
decoded_points = {}
decoded_points["ai_vals"] = lfplot_aihat_vals
decoded_points["samples"] = np.zeros(
(2, lfplot_nsamp, len(lfplot_aihat_vals), params["z_dim"]))
for l in range(params["z_dim"]): # loop over latent dimensions
for i, aihat in enumerate(
lfplot_aihat_vals): # loop over fixed aihat values
for m in range(
lfplot_nsamp): # loop over samples to generate
z = np.random.randn(params["z_dim"])
z[l] = aihat
x = decoder(torch.from_numpy(z).float(), What, gamma)
decoded_points["samples"][:, m, i,
l] = x.detach().numpy()
frame = plotting.debugPlot_frame(X, ceDebug["Xhat"], W, What, k,
steps, debug, params, classifier,
decoded_points)
if save_plot:
frames.append(frame)
# --- save all debug data ---
debug["X"] = X
if save_output:
datestamp = ''.join(
re.findall(r'\d+',
str(datetime.datetime.now())[:10]))
timestamp = ''.join(
re.findall(r'\d+',
str(datetime.datetime.now())[11:19]))
results_folder = './results/tests_kSig5_lr0001_' + objective + '_lam' \
+ str(lam_ML) + '_No' + str(No) + '_Ni' + str(Ni) + '_' \
+ datestamp + '_' + timestamp + '/'
if not os.path.exists(results_folder):
os.makedirs(results_folder)
matfilename = 'results_' + datestamp + '_' + timestamp + '.mat'
sio.savemat(results_folder + matfilename, {
'params': params,
'data': debug
})
if debug_level > 0:
print('Finished saving data to ' + matfilename)
if save_plot:
print('Saving plot...')
gif.save(frames, "results.gif", duration=100)
print('Done!')
return debug, params
classifier,
device,
What=What)
nce_ic, _ = causaleffect.ind_cond(params,
decoder,
classifier,
device,
What=What)
nce_ju, _ = causaleffect.joint_uncond(params,
decoder,
classifier,
device,
What=What)
nce_jc, _ = causaleffect.joint_cond(params,
decoder,
classifier,
device,
What=What)
# compute likelihood
data["loglik"][ia1, ia2] = -loss_functions.linGauss_NLL_loss(
torch.from_numpy(X).float(), What, params["gamma"])
# store results
data["ce_iu"][ia1, ia2] = -nce_iu.detach().numpy()
data["ce_ic"][ia1, ia2] = -nce_ic.detach().numpy()
data["ce_ju"][ia1, ia2] = -nce_ju.detach().numpy()
data["ce_jc"][ia1, ia2] = -nce_jc.detach().numpy()
data["nentropy"][ia1, ia2] = info_iu["negHYhat"].detach().numpy()
data["expcondentropy"][
ia1, ia2] = info_iu["EalphaHYhatgivenalpha"].detach().numpy()
data["adj_iu_ic"][ia1,
ia2] = data["ce_iu"][ia1, ia2] - data["ce_ic"][ia1,
def train(self, X, K, L,
steps = 50000,
Nalpha = 50,
Nbeta = 50,
lam = 0.0001,
causal_obj = 'JOINT_UNCOND',
batch_size = 100,
lr = 0.0001,
b1 = 0.5,
b2 = 0.999,
use_ce = True):
# initialize
self.K = K
self.L = L
ntrain = X.shape[0]
sample_input = torch.from_numpy(X[0]).unsqueeze(0).float().permute(0,3,1,2)
M = self.classifier(sample_input.to(self.device))[0].shape[1]
self.train_params = {
'K' : K,
'L' : L,
'steps' : steps,
'Nalpha' : Nalpha,
'Nbeta' : Nbeta,
'lambda' : lam,
'causal_obj' : causal_obj,
'batch_size' : batch_size,
'lr' : lr,
'b1' : b1,
'b2' : b2,
'use_ce' : use_ce}
self.ceparams = {
'Nalpha' : Nalpha,
'Nbeta' : Nbeta,
'K' : K,
'L' : L,
'z_dim' : K+L,
'M' : M}
debug = {'loss' : np.zeros((steps)),
'loss_ce' : np.zeros((steps)),
'loss_nll' : np.zeros((steps)),
'loss_nll_logdet' : np.zeros((steps)),
'loss_nll_quadform' : np.zeros((steps)),
'loss_nll_mse' : np.zeros((steps)),
'loss_nll_kld' : np.zeros((steps))}
# initialize for training
opt_params = list(self.decoder.parameters()) + list(self.encoder.parameters())
self.opt = torch.optim.Adam(opt_params, lr=lr, betas=(b1, b2))
start_time = time.time()
# training loop
for k in range(0, steps):
# reset gradient
self.opt.zero_grad()
# compute negative log-likelihood
randIdx = np.random.randint(0, ntrain, batch_size)
Xbatch = torch.from_numpy(X[randIdx]).float().permute(0,3,1,2).to(self.device)
z, mu, logvar = self.encoder(Xbatch)
Xhat = self.decoder(z)
nll, nll_mse, nll_kld = loss_functions.VAE_LL_loss(Xbatch, Xhat, logvar, mu)
# compute causal effect
if causal_obj == 'IND_UNCOND':
causalEffect, ceDebug = causaleffect.ind_uncond(
self.ceparams, self.decoder, self.classifier, self.device)
elif causal_obj == 'IND_COND':
causalEffect, ceDebug = causaleffect.ind_cond(
self.ceparams, self.decoder, self.classifier, self.device)
elif causal_obj == 'JOINT_UNCOND':
causalEffect, ceDebug = causaleffect.joint_uncond(
self.ceparams, self.decoder, self.classifier, self.device)
elif causal_obj == 'JOINT_COND':
causalEffect, ceDebug = causaleffect.joint_cond(
self.ceparams, self.decoder, self.classifier, self.device)
else:
print('Invalid causal objective!')
# compute gradient
loss = use_ce*causalEffect + lam*nll
loss.backward()
self.opt.step()
# save debug info for this step
debug['loss'][k] = loss.item()
debug['loss_ce'][k] = causalEffect.item()
debug['loss_nll'][k] = (lam*nll).item()
debug['loss_nll_mse'][k] = (lam*nll_mse).item()
debug['loss_nll_kld'][k] = (lam*nll_kld).item()
if self.params['debug_print']:
print("[Step %d/%d] time: %4.2f [CE: %g] [ML: %g] [loss: %g]" % \
(k+1, steps, time.time() - start_time, debug['loss_ce'][k],
debug['loss_nll'][k], debug['loss'][k]))
if self.params['save_output'] and k % 1000 == 0:
torch.save({
'step': k,
'model_state_dict_classifier' : self.classifier.state_dict(),
'model_state_dict_encoder' : self.encoder.state_dict(),
'model_state_dict_decoder' : self.decoder.state_dict(),
'optimizer_state_dict' : self.opt.state_dict(),
'loss' : loss,
}, '%s_batch_%d.pt' % \
(self.params['save_dir'], self.params['batch_size']))
# save/return debug data from entire training run
debug['Xbatch'] = Xbatch.detach().cpu().numpy()
debug['Xhat'] = Xhat.detach().cpu().numpy()
if self.params['save_output']:
datestamp = ''.join(re.findall(r'\d+', str(datetime.datetime.now())[:10]))
timestamp = ''.join(re.findall(r'\d+', str(datetime.datetime.now())[11:19]))
matfilename = 'results_' + datestamp + '_' + timestamp + '.mat'
sio.savemat(save_dir + matfilename, {'params' : params, 'data' : debug})
if self.params['debug_print']:
print('Finished saving data to ' + matfilename)
return debug
print(
'Computing causal effect for alpha=%.2f (%d/%d), beta=%.2f (%d/%d)...'
% (theta_alpha, ia, len(thetas_alpha), theta_beta, ib,
len(thetas_beta)))
# form generative map for this (theta1, theta2)
what1 = np.array([[np.cos(theta_alpha)], [np.sin(theta_alpha)]])
what2 = np.array([[np.cos(theta_beta)], [np.sin(theta_beta)]])
What = torch.from_numpy(np.hstack((what1, what2))).float()
# sample-based estimate of causal effect
CEs[ia, ib, 0] = -causaleffect.ind_uncond(
params, decoder, classifier, device, What=What)[0]
CEs[ia, ib, 1] = -causaleffect.ind_cond(
params, decoder, classifier, device, What=What)[0]
CEs[ia, ib, 2] = -causaleffect.joint_uncond(
params, decoder, classifier, device, What=What)[0]
CEs[ia, ib, 3] = -causaleffect.joint_cond(
params, decoder, classifier, device, What=What)[0]
# --- save results ---
print('Done! Saving results...')
sio.savemat(
'results/visualize_causalobj_linear.mat', {
'CEs': CEs,
'thetas_alpha': thetas_alpha,
'thetas_beta': thetas_beta,
'params': params
})
print('Done!')
#%% make debug plot (see visualize_causalobj_plot.m for plots in paper)
import numpy as np