├── BasisVAE ├── __init__.py ├── encoder.py ├── helpers.py ├── VAE.py └── decoder.py ├── fig ├── anim1.gif ├── anim2.gif ├── decoder.png ├── toy_data.png ├── BasisVAE_schema.png └── decoder_with_whitespace.png ├── setup.py ├── LICENSE ├── toy_data ├── generate_toy_data.R └── toy.csv └── README.md /BasisVAE/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /fig/anim1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/anim1.gif -------------------------------------------------------------------------------- /fig/anim2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/anim2.gif -------------------------------------------------------------------------------- /fig/decoder.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/decoder.png -------------------------------------------------------------------------------- /fig/toy_data.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/toy_data.png -------------------------------------------------------------------------------- /fig/BasisVAE_schema.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/BasisVAE_schema.png -------------------------------------------------------------------------------- /fig/decoder_with_whitespace.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kasparmartens/BasisVAE/HEAD/fig/decoder_with_whitespace.png -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup(name='BasisVAE', 4 | version='0.1', 5 | description='Implementation of BasisVAE', 6 | author='Kaspar Märtens', 7 | author_email='kaspar.martens@spc.ox.ac.uk', 8 | url='https://github.com/kasparmartens/BasisVAE', 9 | license='MIT', 10 | packages=['BasisVAE']) 11 | -------------------------------------------------------------------------------- /BasisVAE/encoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from torch.nn.functional import softplus 5 | 6 | class Encoder(nn.Module): 7 | 8 | def __init__(self, data_dim, hidden_dim, z_dim, nonlinearity=torch.nn.ReLU): 9 | """ 10 | Encoder for the VAE (neural network that maps P-dimensional data to [mu_z, sigma_z]) 11 | :param data_dim: 12 | :param hidden_dim: 13 | :param z_dim: 14 | :param nonlinearity: 15 | """ 16 | super().__init__() 17 | 18 | self.z_dim = z_dim 19 | 20 | # NN mapping from (Y, x) to z 21 | self.mapping = torch.nn.Sequential( 22 | torch.nn.Linear(data_dim, hidden_dim), 23 | nonlinearity(), 24 | torch.nn.Linear(hidden_dim, 2*z_dim) 25 | ) 26 | 27 | def forward(self, Y): 28 | 29 | out = self.mapping(Y) 30 | 31 | mu = out[:, 0:self.z_dim] 32 | sigma = 1e-6 + softplus(out[:, self.z_dim:(2 * self.z_dim)]) 33 | return mu, sigma 34 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Kaspar Märtens 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /toy_data/generate_toy_data.R: -------------------------------------------------------------------------------- 1 | library(tidyverse) 2 | 3 | generate_toy_data <- function(z, p, scaling, noise_sd = 0.05){ 4 | n_cols <- 4L*p 5 | N <- length(z) 6 | Y <- matrix(0, N, n_cols) 7 | 8 | transient <- function(x) (exp(-0.5*(x**2))) 9 | softplus <- function(x) 0.4*log(exp(x) + 1) 10 | 11 | for(j in 1:p){ 12 | Y[, j+0*p] <- scaling[j] * transient(z+1.5) 13 | } 14 | for(j in 1:p){ 15 | Y[, j+1*p] <- scaling[j] * transient(z) 16 | } 17 | for(j in 1:p){ 18 | Y[, j+2*p] <- scaling[j] * transient(z-1.5) 19 | } 20 | for(j in 1:p){ 21 | Y[, j+3*p] <- scaling[j] * softplus(z) 22 | } 23 | 24 | Y <- Y + noise_sd * rnorm(N*n_cols) 25 | 26 | colnames(Y) <- sprintf("feature%d", 1:ncol(Y)) 27 | 28 | data.frame(Y) 29 | } 30 | 31 | # sample size 32 | N <- 500L 33 | # number of repeated feature shapes 34 | p <- 10L 35 | 36 | set.seed(0) 37 | z <- runif(N, -3, 3) 38 | lambda <- seq(0.4, 1.6, length=p) 39 | Y <- generate_toy_data(z, p, lambda, noise_sd=0.2) 40 | 41 | write_csv(round(Y, 5), "toy_data/toy.csv") 42 | 43 | 44 | 45 | ### For visualisation purposes generate z on a grid 46 | 47 | z_grid <- seq(-3, 3, length=200) 48 | 49 | Y_grid <- generate_toy_data(z_grid, p, lambda, noise_sd=0.0) 50 | 51 | Y_grid %>% 52 | mutate(z = z_grid) %>% 53 | gather(variable, value, -z) %>% 54 | mutate(variable = forcats::fct_inorder(factor(variable))) %>% 55 | mutate(variable_group = (as.numeric(variable)-1L) %/% p) %>% 56 | ggplot(aes(z, value, col=factor(variable_group), group=variable)) + 57 | geom_path() + 58 | facet_wrap(~ variable_group, nrow=1) + 59 | theme_classic() + 60 | theme(legend.position = "none") 61 | 62 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BasisVAE 2 | 3 | This is the PyTorch implementation of our AISTATS 2020 paper [**BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders**](https://arxiv.org/abs/2003.03462) 4 | 5 | ![](fig/BasisVAE_schema.png) 6 | 7 | ### Summary 8 | 9 | It would be desirable to construct a joint modelling framework for simultaneous dimensionality reduction and clustering of features. Here, we focus on embedding such capabilities within the Variational Autoencoder (VAE) framework. Specifically, we propose the BasisVAE: a combination of the VAE and a probabilistic clustering prior, which lets us learn a one-hot basis function representation as part of the decoder network. This has been illustrated below. Furthermore, for scenarios where not all features are aligned, we develop an extension to handle translation-invariant basis functions. 10 | 11 | ![](fig/decoder_with_whitespace.png) 12 | 13 | ### Illustrations on synthetic data 14 | 15 | We illustrate BasisVAE model fitting on the following toy data set that has four groups of features: 16 | 17 | ![](fig/toy_data.png) 18 | 19 | Here is the inferred scale-invariant BasisVAE: 20 | 21 | ![](fig/anim1.gif) 22 | 23 | and here we additionally allow translation invariance 24 | 25 | ![](fig/anim2.gif) 26 | 27 | ### Demo notebook 28 | 29 | See [this Colab notebook](https://colab.research.google.com/drive/1q8rp3k4aAzog3rhsF2at76wysMSqddO5) for a toy example demo. 30 | 31 | ### Implementation details 32 | 33 | The core component of BasisVAE is its specialised decoder. See [decoder.py](https://github.com/kasparmartens/BasisVAE/blob/master/BasisVAE/decoder.py) for details about its usage and implementation. Our current implementation supports the following likelihoods `Gaussian, Bernoulli, NB, ZINB`. 34 | 35 | ### Installation 36 | 37 | ``` 38 | pip install git+https://github.com/kasparmartens/BasisVAE.git 39 | ``` 40 | 41 | -------------------------------------------------------------------------------- /BasisVAE/helpers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.distributions.uniform import Uniform 3 | from torch.distributions.normal import Normal 4 | import torch.nn.functional as F 5 | 6 | 7 | def ELBO_collapsed_Categorical(logits_phi, alpha, K, N): 8 | phi = torch.softmax(logits_phi, dim=1) 9 | 10 | sum_alpha = alpha.sum() 11 | pseudocounts = phi.sum(dim=0) 12 | term1 = torch.lgamma(sum_alpha) - torch.lgamma(sum_alpha + N) 13 | term2 = (torch.lgamma(alpha + pseudocounts) - torch.lgamma(alpha)).sum() 14 | 15 | E_q_logq = (phi * torch.log(phi + 1e-16)).sum() 16 | 17 | return -term1 - term2 + E_q_logq 18 | 19 | 20 | def KL_standard_normal(mu, sigma): 21 | p = Normal(torch.zeros_like(mu), torch.ones_like(mu)) 22 | q = Normal(mu, sigma) 23 | return torch.sum(torch.distributions.kl_divergence(q, p)) 24 | 25 | 26 | def NB_log_prob(x, mu, theta, eps=1e-8): 27 | """ 28 | Adapted from https://github.com/YosefLab/scVI/blob/master/scvi/models/log_likelihood.py 29 | """ 30 | 31 | log_theta_mu_eps = torch.log(theta + mu + eps) 32 | 33 | res = ( 34 | theta * (torch.log(theta + eps) - log_theta_mu_eps) 35 | + x * (torch.log(mu + eps) - log_theta_mu_eps) 36 | + torch.lgamma(x + theta) 37 | - torch.lgamma(theta) 38 | - torch.lgamma(x + 1) 39 | ) 40 | 41 | return res 42 | 43 | def ZINB_log_prob(x, mu, theta, pi, eps=1e-8): 44 | """ 45 | Adapted from https://github.com/YosefLab/scVI/blob/master/scvi/models/log_likelihood.py 46 | """ 47 | 48 | softplus_pi = F.softplus(-pi) 49 | log_theta_eps = torch.log(theta + eps) 50 | log_theta_mu_eps = torch.log(theta + mu + eps) 51 | pi_theta_log = -pi + theta * (log_theta_eps - log_theta_mu_eps) 52 | 53 | case_zero = F.softplus(pi_theta_log) - softplus_pi 54 | mul_case_zero = torch.mul((x < eps).type(torch.float32), case_zero) 55 | 56 | case_non_zero = ( 57 | -softplus_pi 58 | + pi_theta_log 59 | + x * (torch.log(mu + eps) - log_theta_mu_eps) 60 | + torch.lgamma(x + theta) 61 | - torch.lgamma(theta) 62 | - torch.lgamma(x + 1) 63 | ) 64 | mul_case_non_zero = torch.mul((x > eps).type(torch.float32), case_non_zero) 65 | 66 | res = mul_case_zero + mul_case_non_zero 67 | 68 | return res 69 | -------------------------------------------------------------------------------- /BasisVAE/VAE.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch 4 | 5 | from .helpers import KL_standard_normal 6 | 7 | class VAE(nn.Module): 8 | """ 9 | VAE wrapper class (for combining a standard encoder and BasisVAE decoder) 10 | """ 11 | 12 | def __init__(self, encoder, decoder, lr): 13 | super().__init__() 14 | 15 | self.encoder = encoder 16 | 17 | self.decoder = decoder 18 | 19 | self.optimizer = torch.optim.Adam(self.parameters(), lr=lr) 20 | 21 | def forward(self, Y, batch_scale=1.0, beta=1.0): 22 | """ 23 | :param Y: data matrix 24 | :param batch_scale: scaling constant for log-likelihood (typically total_sample_size / batch_size) 25 | :param beta: additional constant for KL scaling (beta * KL_terms) 26 | :return: (mu_z, sigma_z, loss) 27 | """ 28 | 29 | # encode 30 | mu_z, sigma_z = self.encoder(Y) 31 | eps = torch.randn_like(mu_z) 32 | z = mu_z + sigma_z * eps 33 | 34 | # decode 35 | y_pred, dropout_prob_logit, theta = self.decoder.forward(z) 36 | decoder_loss = self.decoder.loss(Y, y_pred, dropout_prob_logit, theta, batch_scale, beta) 37 | 38 | # latent space loss 39 | VAE_KL_loss = batch_scale * KL_standard_normal(mu_z, sigma_z) 40 | 41 | total_loss = decoder_loss + beta * VAE_KL_loss 42 | 43 | return mu_z, sigma_z, total_loss 44 | 45 | # map (Y, x) to z and calculate p(y* | x, z_mu) 46 | def calculate_test_loglik(self, Y): 47 | 48 | with torch.no_grad(): 49 | mu_z, sigma_z = self.encoder(Y) 50 | Y_pred, dropout_prob_logit, theta = self.decoder.forward(mu_z) 51 | loglik = self.decoder.loglik(Y, Y_pred, dropout_prob_logit, theta) 52 | return loglik 53 | 54 | def optimize(self, data_loader, n_epochs, beta=1.0, logging_freq=10, verbose=True): 55 | 56 | # sample size 57 | N = len(data_loader.dataset) 58 | 59 | # scaling for loglikelihood terms 60 | batch_size = data_loader.batch_size 61 | batch_scale = N / batch_size 62 | 63 | if verbose: 64 | print(f"Fitting BasisVAE.\n" 65 | f"\tScale-invariance={self.decoder.scale_invariance}" 66 | f"\tTranslation-invariance={self.decoder.translation_invariance}\n") 67 | print(f"\tData set size {N}, batch size {batch_size}, number of basis funs {self.decoder.n_basis}.\n") 68 | 69 | for epoch in range(n_epochs): 70 | 71 | train_loss = 0.0 72 | 73 | for batch_idx, (Y_subset, ) in enumerate(data_loader): 74 | 75 | mu_z, sigma_z, loss = self.forward(Y_subset, batch_scale=batch_scale, beta=beta) 76 | 77 | self.optimizer.zero_grad() 78 | loss.backward() 79 | self.optimizer.step() 80 | 81 | train_loss += loss.item() 82 | 83 | if epoch % logging_freq == 0: 84 | print(f"\tEpoch: {epoch:2}. Total loss: {train_loss:11.2f}") 85 | -------------------------------------------------------------------------------- /BasisVAE/decoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from torch.nn.functional import softplus 5 | from torch.nn import functional as F 6 | 7 | from torch.distributions.uniform import Uniform 8 | from torch.distributions.gamma import Gamma 9 | from torch.distributions.dirichlet import Dirichlet 10 | 11 | from .helpers import NB_log_prob, ZINB_log_prob, ELBO_collapsed_Categorical 12 | 13 | class BasisDecoder(nn.Module): 14 | 15 | """ 16 | Core component of BasisVAE: decoder where the last layer contains probabilistic Categorical random variables 17 | """ 18 | 19 | def __init__(self, data_dim, hidden_dim, z_dim, 20 | n_basis, 21 | scale_invariance, 22 | translation_invariance, 23 | likelihood="Gaussian", 24 | nonlinearity=nn.Softplus, 25 | inference = "collapsed", 26 | alpha=1.0, 27 | qalpha_init=None, 28 | max_delta=1.5, 29 | min_lambda=0.25, 30 | max_lambda=1.75, 31 | device="cpu"): 32 | """ 33 | :param data_dim: Data dimensionality 34 | :param hidden_dim: The number of neurons in the hidden layer (we assume one-hidden-layer NN) 35 | :param z_dim: Dimensionality of latent space 36 | :param n_basis: Number of basis functions (K) 37 | :param scale_invariance: Is BasisVAE scale-invariant? 38 | :param translation_invariance: Is BasisVAE translation-invariant? 39 | :param likelihood: Likelihood (options include "Gaussian", "Bernoulli", and for single-cell applications "NB" and "ZINB") 40 | :param nonlinearity: Type of non-linearity in NNs 41 | :param inference: Inference approach ("collapsed" is recommended) 42 | :param alpha: The Dirichlet alpha parameter (scalar) 43 | :param qalpha_init: Only relevant for non-collapsed inference 44 | :param max_delta: The range of delta values can be restricted for identifiability 45 | :param min_lambda: Lower bound on lambda values 46 | :param max_lambda: Upper bound on lambda values 47 | :param device: CPU or GPU 48 | """ 49 | super().__init__() 50 | 51 | self.data_dim = data_dim 52 | self.likelihood = likelihood 53 | self.inference = inference 54 | self.device = device 55 | 56 | self.scale_invariance = scale_invariance 57 | self.translation_invariance = translation_invariance 58 | self.n_basis = n_basis 59 | self.max_delta = max_delta 60 | self.min_lambda = min_lambda 61 | self.max_lambda = max_lambda 62 | 63 | # we will set up a neural network with one hidden layer 64 | if self.translation_invariance: 65 | # for translation-invariant case, we do the computations manually for computational efficiency 66 | self.linear1 = nn.Linear(z_dim, hidden_dim) 67 | self.linear2 = nn.Linear(hidden_dim, self.n_basis) 68 | self.nonlinearity = nonlinearity() 69 | else: 70 | # for non-translation-invariant case 71 | self.mapping_z = nn.Sequential( 72 | nn.Linear(z_dim, hidden_dim), 73 | nonlinearity(), 74 | nn.Linear(hidden_dim, self.n_basis) 75 | ) 76 | 77 | # feature-specific variances 78 | if self.likelihood == "Gaussian": 79 | self.noise_sd = nn.Parameter(-2.0 * torch.ones(1, data_dim)) 80 | 81 | elif self.likelihood == "NB": 82 | self.nb_theta = nn.Parameter(torch.zeros(1, data_dim)) 83 | 84 | elif self.likelihood == "ZINB": 85 | self.nb_theta = nn.Parameter(torch.zeros(1, data_dim)) 86 | 87 | self.dropout_decoder = nn.Sequential( 88 | nn.Linear(1, hidden_dim), 89 | nonlinearity(), 90 | nn.Linear(hidden_dim, data_dim) 91 | ) 92 | 93 | elif self.likelihood == "Bernoulli": 94 | self.noise_sd = None 95 | else: 96 | raise ValueError("Unknown likelihood") 97 | 98 | self.intercept = nn.Parameter(torch.zeros(1, data_dim)) 99 | 100 | # we assume vector (alpha, ..., alpha) 101 | self.alpha_z = alpha * torch.ones(n_basis, device=self.device) 102 | 103 | # q(phi) parameters 104 | self.qphi_logits = nn.Parameter(torch.zeros([self.data_dim, self.n_basis])) 105 | 106 | if self.inference == "non-collapsed": 107 | # for non-collapsed inference, q(alpha) 108 | if qalpha_init is None: 109 | raise ValueError("For non-collapsed inference need to specify q(alpha)") 110 | self.qalpha_z = nn.Parameter(torch.Tensor(qalpha_init)) 111 | 112 | if self.scale_invariance: 113 | self.scaling_z = nn.Parameter(torch.zeros([self.data_dim, self.n_basis])) 114 | 115 | if self.translation_invariance: 116 | self.shift_z = nn.Parameter(torch.zeros([self.data_dim, self.n_basis, 1])) 117 | 118 | def get_delta(self): 119 | # delta values (constrained within [-max_delta, max_delta] 120 | return self.max_delta * torch.tanh(self.shift_z) 121 | 122 | def get_lambda(self): 123 | # lambda values 124 | return self.min_lambda + (self.max_lambda - self.min_lambda) * torch.sigmoid(self.scaling_z) 125 | 126 | def get_phi(self): 127 | return torch.softmax(self.qphi_logits, dim=-1) 128 | 129 | def get_basis(self, z): 130 | 131 | if self.translation_invariance: 132 | 133 | z_tilde = self.get_delta()[None, :, :, :] + z[:, None, None, :] 134 | # first hidden layer representation, [N, output_dim, n_basis_z, n_hidden_units] 135 | hidden = self.nonlinearity(self.linear1(z_tilde)) 136 | 137 | # [N, output_dim, n_basis_z] 138 | basis0 = torch.sum(hidden * self.linear2.weight, dim=3) 139 | 140 | # [output_dim, n_basis_z] 141 | scaling = self.get_lambda() 142 | 143 | # [N, output_dim, n_basis_z] 144 | basis = basis0 * scaling 145 | 146 | else: 147 | basis0 = self.mapping_z(z) 148 | 149 | if self.scale_invariance: 150 | # shape [output_dim, n_basis_z] 151 | scaling = self.get_lambda() 152 | 153 | # shape [N, output_dim, n_basis_z] 154 | basis = basis0[:, None, :] * scaling 155 | else: 156 | # shape [N, output_dim, n_basis_z] 157 | basis = basis0[:, None, :].repeat(1, self.data_dim, 1) 158 | 159 | return basis 160 | 161 | def KL_phi(self): 162 | 163 | if self.inference == "collapsed": 164 | return ELBO_collapsed_Categorical(self.qphi_logits, self.alpha_z, K=self.n_basis, N=self.data_dim) 165 | 166 | elif self.inference == "fixed_pi": 167 | qphi = self.get_phi() 168 | pi = torch.ones_like(qphi) / self.n_basis 169 | KL = ( 170 | qphi * (torch.log(qphi + 1e-16) - torch.log(pi)) 171 | ).sum() 172 | return KL 173 | 174 | elif self.inference == "non-collapsed": 175 | qDir = Dirichlet(concentration=self.qalpha_z) 176 | pDir = Dirichlet(concentration=self.alpha_z) 177 | 178 | # KL(q(pi) || p(pi)) 179 | KL_Dir = torch.distributions.kl_divergence(qDir, pDir) 180 | 181 | # E[log q(phi) - log p(phi | pi)] under q(pi)q(phi) 182 | qpi = qDir.rsample() 183 | qphi = self.get_phi() 184 | 185 | # KL categorical 186 | KL_Cat = ( 187 | qphi * (torch.log(qphi + 1e-16) - torch.log(qpi[None, :])) 188 | ).sum() 189 | return KL_Dir + KL_Cat 190 | 191 | def forward(self, z): 192 | if self.likelihood == "Bernoulli": 193 | pred = self.get_basis(z) 194 | dropout_prob = None 195 | theta = None 196 | 197 | elif self.likelihood == "Gaussian": 198 | pred = self.get_basis(z) 199 | dropout_prob = None 200 | theta = None 201 | 202 | elif self.likelihood == "NB": 203 | pred = softplus(self.get_basis(z)) 204 | theta = softplus(self.nb_theta) 205 | dropout_prob = None 206 | 207 | elif self.likelihood == "ZINB": 208 | pred = softplus(self.get_basis(z)) 209 | dropout_prob = self.dropout_decoder(z) 210 | theta = softplus(self.nb_theta) 211 | 212 | return pred, dropout_prob, theta 213 | 214 | def loglik(self, y_obs, y_pred, dropout_prob_logit, theta): 215 | 216 | if self.likelihood == "Gaussian": 217 | 218 | sigma = 1e-4 + softplus(self.noise_sd) 219 | p_data = torch.distributions.normal.Normal(loc=y_pred, scale=sigma[:, :, None]) 220 | log_p = p_data.log_prob(y_obs[:, :, None]) 221 | 222 | elif self.likelihood == "NB": 223 | 224 | log_p = NB_log_prob(y_obs[:, :, None], mu=y_pred, theta=theta[:, :, None]) 225 | 226 | elif self.likelihood == "ZINB": 227 | 228 | # [batch_size, output_dim, n_basis] 229 | log_p = ZINB_log_prob(y_obs[:, :, None], y_pred, theta[:, :, None], dropout_prob_logit[:, :, None]) 230 | 231 | if self.likelihood == "Bernoulli": 232 | 233 | log_p = -F.binary_cross_entropy_with_logits(y_pred, y_obs[:, :, None].repeat(1, 1, self.n_basis), reduction='none') 234 | 235 | phi = self.get_phi() 236 | 237 | loglik = (phi * log_p).sum() 238 | 239 | return loglik 240 | 241 | def loss(self, y_obs, y_pred, dropout_prob_logit, theta, batch_scale=1.0, beta=1.0): 242 | 243 | prior_loss = 0.0 244 | 245 | if self.translation_invariance: 246 | prior_loss += self.get_delta().pow(2).sum() 247 | 248 | if self.scale_invariance: 249 | lambdas = self.get_lambda() 250 | prior_loss -= Gamma(torch.ones_like(lambdas), torch.ones_like(lambdas)).log_prob(lambdas).sum() 251 | 252 | loglik = batch_scale * self.loglik(y_obs, y_pred, dropout_prob_logit, theta) 253 | 254 | decoder_loss = - loglik + beta * self.KL_phi() + prior_loss 255 | 256 | return decoder_loss 257 | 258 | def get_similarity_matrix(self): 259 | with torch.no_grad(): 260 | mat = torch.mm(self.get_phi(), self.get_phi().t()) 261 | return mat 262 | 263 | -------------------------------------------------------------------------------- /toy_data/toy.csv: -------------------------------------------------------------------------------- 1 | feature1,feature2,feature3,feature4,feature5,feature6,feature7,feature8,feature9,feature10,feature11,feature12,feature13,feature14,feature15,feature16,feature17,feature18,feature19,feature20,feature21,feature22,feature23,feature24,feature25,feature26,feature27,feature28,feature29,feature30,feature31,feature32,feature33,feature34,feature35,feature36,feature37,feature38,feature39,feature40 2 | -0.31913,-0.01744,-0.09281,0.03695,-0.04538,0.24511,-0.16483,-0.09763,-0.06621,0.05141,-0.00124,0.15467,0.3226,0.08446,0.14619,0.04975,-0.1992,0.08192,0.0618,-0.11969,0.02624,0.37481,0.24866,0.59506,0.5069,0.67347,0.89976,0.76913,0.98915,1.33984,0.30467,0.21023,0.88065,0.80321,0.96179,1.50897,0.91293,1.14287,1.52256,1.69237 3 | 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