update vignettes and website

NEWS.md

@@ -5,6 +5,7 @@
 * burnin parameter
 * multivariate probit model
 * X and Y support (alternative interface)
+* negative binomial distribution
 
 ## Minor changes
 * Improved vignette

R/dnn.R

@@ -34,6 +34,10 @@
 #'
 #' @details
 #'
+#' # Activation functions
+#'
+#' Supported activation functions:  "relu", "leaky_relu", "tanh", "elu", "rrelu", "prelu", "softplus", "celu", "selu", "gelu", "relu6", "sigmoid", "softsign", "hardtanh", "tanhshrink", "softshrink", "hardshrink", "log_sigmoid"
+#'
 #' # Loss functions / Likelihoods
 #'
 #' We support loss functions and likelihoods for different tasks:
@@ -47,6 +51,7 @@
 #' | gaussian | Normal likelihood | Regression, residual error is also estimated (similar to `stats::lm()`)	|
 #' | binomial | Binomial likelihood | Classification/Logistic regression, mortality|
 #' | poisson | Poisson likelihood |Regression, count data, e.g. species abundances|
+#' | nbinom | Negative binomial likelihood | Regression, count data with dispersion parameter |
 #' | mvp | multivariate probit model | joint species distribution model, multi species (presence absence) |
 #'
 #' # Training and convergence of neural networks
@@ -162,12 +167,10 @@
 dnn <- function(formula = NULL,
                 data = NULL,
                 hidden = c(50L, 50L),
-                activation = c("relu", "leaky_relu", "tanh", "elu", "rrelu", "prelu", "softplus",
-                               "celu", "selu", "gelu", "relu6", "sigmoid", "softsign", "hardtanh",
-                               "tanhshrink", "softshrink", "hardshrink", "log_sigmoid"),
+                activation = "selu",
                 bias = TRUE,
                 dropout = 0.0,
-                loss = c("mse", "mae", "softmax", "cross-entropy", "gaussian", "binomial", "poisson", "mvp"),
+                loss = c("mse", "mae", "softmax", "cross-entropy", "gaussian", "binomial", "poisson", "mvp", "nbinom"),
                 validation = 0,
                 lambda = 0.0,
                 alpha = 0.5,
@@ -190,11 +193,9 @@ dnn <- function(formula = NULL,
                 X = NULL,
                 Y = NULL) {
 
-  if(!inherits(activation, "tune")) {
-
-  }
 
   out <- list()
+
   class(out) <- "citodnn"
 
   tuner = check_hyperparameters(hidden = hidden ,
@@ -212,7 +213,20 @@ dnn <- function(formula = NULL,
 
   if(!is.function(loss) & !inherits(loss,"family")){
     loss <- match.arg(loss)
+
+    if((device == "mps") & (loss %in% c("poisson", "nbinom"))) {
+      message("`poisson` or `nbinom` are not yet supported for `device=mps`, switching to `device=cpu`")
+      device = "cpu"
+    }
   }
+
+  if(inherits(loss,"family")) {
+    if((device == "mps") & (loss$family %in% c("poisson", "nbinom"))) {
+      message("`poisson` or `nbinom` are not yet supported for `device=mps`, switching to `device=cpu`")
+      device = "cpu"
+    }
+  }
+
   device_old = device
   device = check_device(device)
   tmp_data = get_X_Y(formula, X, Y, data)
@@ -268,7 +282,7 @@ dnn <- function(formula = NULL,
 
 
   loss_obj <- get_loss(loss, device = device, X = X, Y = Y)
-  if(!is.null(loss_obj$parameter)) loss_obj$parameter <- list(paramter = loss_obj$parameter)
+  if(!is.null(loss_obj$parameter)) loss_obj$parameter <- list(parameter = loss_obj$parameter)
   if(!is.null(custom_parameters)){
     if(!inherits(custom_parameters,"list")){
       warning("custom_parameters has to be list")

R/utils.R

@@ -153,6 +153,46 @@ get_loss <- function(loss, device = "cpu", X = NULL, Y = NULL) {
         Eprob = (exp(logprob-maxlogprob))$mean(dim = 1)
         return((-log(Eprob) - maxlogprob)$mean())
       }
+    } else if(loss == "nbinom") {
+
+      if(is.matrix(Y)) out$parameter = torch::torch_tensor(rep(0.5, ncol(Y)), requires_grad=TRUE, device = device)
+      else out$parameter = torch::torch_tensor(0.5, requires_grad=TRUE, device = device)
+      out$invlink <- function(a) torch::torch_exp(a)
+      out$link <- function(a) log(as.matrix(a))
+      out$parameter_link = function() as.numeric(1.0/(torch::nnf_softplus(out$parameter)+0.0001))
+      out$simulate = function(pred) {
+        theta_tmp = out$parameter_link()
+        probs = 1.0 - theta_tmp/(theta_tmp + pred)
+        total_count = theta_tmp
+
+        if(is.matrix(pred)) {
+          sim = sapply(1:ncol(pred), function(i) {
+            logits = log(probs[,i]) - log1p(-probs[,i])
+            stats::rpois(length(logits), exp(-logits))
+            return( stats::rpois(length(logits), stats::rgamma(length(logits),total_count[i], exp(- logits ))) )
+          })
+        } else {
+          logits = log(probs) - log1p(-probs)
+          stats::rpois(length(pred), exp(-logits))
+          sim = stats::rpois(length(pred), stats::rgamma(length(pred),total_count, exp(- logits )))
+        }
+        return(sim)
+      }
+
+      out$loss = function(pred, true) {
+        eps = 0.0001
+        pred = pred$exp()
+        theta_tmp = 1.0/(torch::nnf_softplus(out$parameter)+eps)
+        probs = torch::torch_clamp(1.0 - theta_tmp/(theta_tmp+pred)+eps, 0.0, 1.0-eps)
+        total_count = theta_tmp
+        value = true
+        logits = torch::torch_log(probs) - torch::torch_log1p(-probs)
+        log_unnormalized_prob <- total_count * torch::torch_log(torch::torch_sigmoid(-logits)) + value * torch::torch_log(torch::torch_sigmoid(logits))
+        log_normalization <- -torch::torch_lgamma(total_count + value) + torch::torch_lgamma(1 + value) + torch::torch_lgamma(total_count)
+        log_normalization <- torch::torch_where(total_count + value == 0, torch::torch_tensor(0, dtype = log_normalization$dtype, device = out$parameter$device), log_normalization)
+        return( - (log_unnormalized_prob - log_normalization)$mean())
+      }
+
     }
     else{
       cat( "unidentified loss \n")