Unbalanced hier

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: bayesRecon
 Type: Package
-Date: 2024-05-29
+Date: 2024-08-28
 Title: Probabilistic Reconciliation via Conditioning
-Version: 0.3.0
+Version: 0.3.1
 Authors@R: c(person(given = "Dario",
              family = "Azzimonti",
              role = c("aut","cre"),
@@ -42,7 +42,7 @@ Imports:
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown=TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Suggests: 
     knitr,
     rmarkdown,

NEWS.md

@@ -1,3 +1,9 @@
+# bayesRecon 0.3.1
+
+* IMPORTANT CHANGE IN THE API OF THE `reconc_*` functions: they now require the aggregating matrix A and not the summing matrix S.
+
+* The examples section of the `reconc_TDcond` now contains an example showing how to handle the case of an unbalanced hierarchy. 
+
 # bayesRecon 0.3.0
 
 * Added `reconc_MixCond`, the implementation of Mixed conditioning for the reconciliation of mixed-type hierarchical forecasts.

R/PMF.R

@@ -275,6 +275,16 @@ PMF.bottom_up = function(l_pmf, toll=.TOLL, Rtoll=.RTOLL, return_all=FALSE,
   if (smoothing) l_pmf = lapply(l_pmf, PMF.smoothing, 
                                 alpha=al_smooth, laplace=lap_smooth)
 
+  # In case we have an upper which is a duplicate of a bottom,
+  # the bottom up is simply that bottom.
+  if(length(l_pmf)==1){
+    if (return_all) {
+      return(list(l_pmf))
+    } else {
+      return(l_pmf[[1]])
+    }
+  }
+
   # Doesn't do convolutions sequentially 
   # Instead, for each iteration (while) it creates a new list of vectors 
   # by doing convolution between 1 and 2, 3 and 4, ...

R/hierarchy.R

@@ -293,7 +293,12 @@ get_reconc_matrices <- function(agg_levels, h) {
 
 # Get A from S
 .get_A_from_S <- function(S) {
-  bottom_idxs = which(rowSums(S) == 1)
+  # Bottom rows are those with a single 1; if there are replicated bottom rows,
+  # only one is treated as bottom, the copies will be upper
+  bottom_idxs = which(rowSums(S) == 1 & !duplicated(S))
+  if (length(bottom_idxs) < ncol(S)) {
+    stop("Check S: some bottom rows are missing")
+  }
   upper_idxs = setdiff(1:nrow(S), bottom_idxs)
   A = matrix(S[upper_idxs, ], ncol=ncol(S))
   out = list(A = A,
@@ -331,7 +336,7 @@ get_reconc_matrices <- function(agg_levels, h) {
   for (i in 1:k) {
     for (j in 1:k) {
       if (i < j) {
-        cond1 = A[i,] %*% A[j,] != 0  # Upper i and j have some common descendants
+        cond1 = c(A[i,] %*% A[j,] != 0)  # Upper i and j have some common descendants
         cond2 = any(A[j,] > A[i,])    # Upper j is not a descendant of upper i
         cond3 = any(A[i,] > A[j,])    # Upper i is not a descendant of upper j
         if (cond1 & cond2 & cond3) {
@@ -371,30 +376,40 @@ get_reconc_matrices <- function(agg_levels, h) {
 
   if (!.check_hierarchical(A)) stop("Matrix A is not hierarchical")
 
-  k = nrow(A)
-  m = ncol(A)
+  # First, only keep unique rows of A
+  A_uni = unique(A)
 
-  rows = c()
+  k = nrow(A_uni)
+  m = ncol(A_uni)
+
+  low_rows_A_uni = c()
   for (i in 1:k) {
-    rows = c(rows, i)
+    low_rows_A_uni = c(low_rows_A_uni, i)
     for (j in 1:k) {
       if (i != j) {
         # If upper j is a descendant of upper i, remove i and exit loop
-        if (all(A[j,] <= A[i,])) {
-          rows = rows[-length(rows)] 
+        if (all(A_uni[j,] <= A_uni[i,])) {
+          low_rows_A_uni = low_rows_A_uni[-length(low_rows_A_uni)] 
           break
         }
       }
     }
   }
   # keep all rows except those that have no descendants among the uppers
 
+  # Now, change the indices of the lowest rows to match with A (instead of A_un)
+  # If there are duplicated rows for some lowest rows, only take one copy
+  low_rows_A = (1:nrow(A))[!duplicated(A)][low_rows_A_uni]
+
   # The sum of the rows corresponding to the lowest level should be a vector of 1 
-  if (any(colSums(A[rows,,drop=FALSE])!=1)) {
-    stop("The hierarchy is not balanced")
+  if (any(colSums(A[low_rows_A,,drop=FALSE])!=1)) {
+    unbal_bott = which(colSums(A[low_rows_A,,drop=FALSE])!=1) 
+    err_mess = "It is impossible to find the lowest upper level. Probably the hierarchy is unbalanced, the following bottom should be duplicated (see example): "
+    err_mess = paste0(c(err_mess, unbal_bott), collapse = " ")
+    stop(err_mess)
   }
 
-  return(rows)
+  return(low_rows_A)
 }
 
 
@@ -409,15 +424,13 @@ get_reconc_matrices <- function(agg_levels, h) {
   }
 
   A_ = A[-lowest_rows,,drop=FALSE]
-  n_bott = ncol(A_)
   n_upp_u = nrow(A_)
   n_bott_u = length(lowest_rows)
   A_u = matrix(nrow=n_upp_u, ncol=n_bott_u)
   for (j in 1:n_bott_u) {
     l = lowest_rows[[j]]
-    mask = A[l,]==1
     for (i in 1:n_upp_u) {
-      A_u[i,j] = sum(A_[i, mask]==1) == sum(mask)  # check that is a vector of 1
+      A_u[i,j] = all(A[l,] <= A_[i,])  # check that "lower upper" j is a descendant of "upper upper" i
     }
   }
 

R/reconc_BUIS.R

@@ -62,8 +62,11 @@
 #'
 #' @details
 #'
-#' The parameter `base_forecast` is a list containing n elements where the i-th element depends on
-#' the values of `in_type[[i]]` and `distr[[i]]`.
+#' The parameter `base_forecast` is a list containing n = n_upper + n_bottom elements.
+#' The first n_upper elements of the list are the upper base forecasts, in the order given by the rows of A.
+#' The elements from n_upper+1 until the end of the list are the bottom base forecasts, in the order given by the columns of A.
+#' 
+#' The i-th element depends on the values of `in_type[[i]]` and `distr[[i]]`.
 #'
 #' If `in_type[[i]]`='samples', then `base_forecast[[i]]` is a vector containing samples from the base forecast distribution.
 #'
@@ -74,8 +77,6 @@
 #' * size and prob (or mu) for the negative binomial base forecast if `distr[[i]]`='nbinom', see \link[stats]{NegBinomial}.
 #' 
 #' See the description of the parameters `in_type` and `distr` for more details. 
-#'
-#' The order of the `base_forecast` list is given by the order of the time series in the summing matrix.
 #' 
 #' Warnings are triggered from the Importance Sampling step if:
 #' 
@@ -86,16 +87,16 @@
 #' Note that warnings are an indication that the base forecasts might have issues. 
 #' Please check the base forecasts in case of warnings.
 #'
-#' @param S Summing matrix (n x n_bottom).
+#' @param A aggregation matrix (n_upper x n_bottom).
 #' @param base_forecasts A list containing the base_forecasts, see details.
-#' @param in_type A string or a list of length n. If it is a list the i-th element is a string with two possible values:
+#' @param in_type A string or a list of length n_upper + n_bottom. If it is a list the i-th element is a string with two possible values:
 #'
 #' * 'samples' if the i-th base forecasts are in the form of samples;
 #' * 'params'  if the i-th base forecasts are in the form of estimated parameters.
 #' 
 #' If it `in_type` is a string it is assumed that all base forecasts are of the same type. 
 #'
-#' @param distr A string or a list of length n describing the type of base forecasts. 
+#' @param distr A string or a list of length n_upper + n_bottom describing the type of base forecasts. 
 #' If it is a list the i-th element is a string with two possible values:
 #'
 #' * 'continuous' or 'discrete' if `in_type[[i]]`='samples';
@@ -123,6 +124,7 @@
 #'
 #'# Create a minimal hierarchy with 2 bottom and 1 upper variable
 #'rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2)
+#'A <- rec_mat$A
 #'S <- rec_mat$S
 #'
 #'
@@ -140,21 +142,21 @@
 #'sigmas <- c(sigmaY,sigma1,sigma2)
 #'
 #'base_forecasts = list()
-#'for (i in 1:nrow(S)) {
+#'for (i in 1:length(mus)) {
 #' base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]])
 #'}
 #'
 #'
 #'#Sample from the reconciled forecast distribution using the BUIS algorithm
-#'buis <- reconc_BUIS(S, base_forecasts, in_type="params",
+#'buis <- reconc_BUIS(A, base_forecasts, in_type="params",
 #'                  distr="gaussian", num_samples=100000, seed=42)
 #'
 #'samples_buis <- buis$reconciled_samples
 #'
 #'#In the Gaussian case, the reconciled distribution is still Gaussian and can be
 #'#computed in closed form
 #'Sigma <- diag(sigmas^2)  #transform into covariance matrix
-#'analytic_rec <- reconc_gaussian(S, base_forecasts.mu = mus,
+#'analytic_rec <- reconc_gaussian(A, base_forecasts.mu = mus,
 #'                                 base_forecasts.Sigma = Sigma)
 #'
 #'#Compare the reconciled means obtained analytically and via BUIS
@@ -171,12 +173,12 @@
 #'lambdas <- c(lambdaY,lambda1,lambda2)
 #'
 #'base_forecasts <- list()
-#'for (i in 1:nrow(S)) {
+#'for (i in 1:length(lambdas)) {
 #'  base_forecasts[[i]] = list(lambda = lambdas[i])
 #'}
 #'
 #'#Sample from the reconciled forecast distribution using the BUIS algorithm
-#'buis <- reconc_BUIS(S, base_forecasts, in_type="params",
+#'buis <- reconc_BUIS(A, base_forecasts, in_type="params",
 #'                           distr="poisson", num_samples=100000, seed=42)
 #'samples_buis <- buis$reconciled_samples
 #'
@@ -194,7 +196,7 @@
 #' [reconc_gaussian()]
 #'
 #' @export
-reconc_BUIS <- function(S,
+reconc_BUIS <- function(A,
                    base_forecasts,
                    in_type,
                    distr,
@@ -203,21 +205,33 @@ reconc_BUIS <- function(S,
                    seed = NULL) {
 
   if (!is.null(seed)) set.seed(seed)
+
+  n_upper = nrow(A)
+  n_bottom = ncol(A)
+  n_tot <- length(base_forecasts)
 
   # Transform distr and in_type into lists
   if (!is.list(distr)) {
-    distr = rep(list(distr), nrow(S))
+    distr = rep(list(distr), n_tot)
   }
   if (!is.list(in_type)) {
-    in_type = rep(list(in_type), nrow(S))
+    in_type = rep(list(in_type), n_tot)
   }
 
   # Ensure that data inputs are valid
-  .check_input_BUIS(S, base_forecasts, in_type, distr)
+  .check_input_BUIS(A, base_forecasts, in_type, distr)
 
   # Split bottoms, uppers
-  split_hierarchy.res = .split_hierarchy(S, base_forecasts)
-  A = split_hierarchy.res$A
+  # the first nrow(A) elements of base_forecasts are upper
+  # the second ncol(A) elements of base_forecasts are lower
+
+  split_hierarchy.res = list(
+    A = A,
+    upper = base_forecasts[1:nrow(A)],
+    bottom = base_forecasts[(nrow(A)+1):n_tot],
+    upper_idxs = 1:nrow(A),
+    bottom_idxs = (nrow(A)+1):n_tot
+  )
   upper_base_forecasts = split_hierarchy.res$upper
   bottom_base_forecasts = split_hierarchy.res$bottom
 
@@ -249,8 +263,7 @@ reconc_BUIS <- function(S,
   }
 
   # Reconciliation using BUIS
-  n_upper = nrow(A)
-  n_bottom = ncol(A)
+
   # 1. Bottom samples
   B = list()
   in_type_bottom = in_type[split_hierarchy.res$bottom_idxs]
@@ -276,13 +289,13 @@ reconc_BUIS <- function(S,
       in_type_ = in_typeH[[hi]],
       distr_ = distr_H[[hi]]
     )
-    check_weights.res = .check_weigths(weights)
+    check_weights.res = .check_weights(weights)
     if (check_weights.res$warning & !suppress_warnings) {
       warning_msg = check_weights.res$warning_msg
       # add information to the warning message
-      upper_fromS_i = which(lapply(seq_len(nrow(S)), function(i) sum(abs(S[i,] - c))) == 0)
+      upper_fromA_i = which(lapply(seq_len(nrow(A)), function(i) sum(abs(A[i,] - c))) == 0)
       for (wmsg in warning_msg) {
-        wmsg = paste(wmsg, paste0("Check the upper forecast at index: ", upper_fromS_i,"."))
+        wmsg = paste(wmsg, paste0("Check the upper forecast at index: ", upper_fromA_i,"."))
         warning(wmsg)
       }
     }
@@ -304,18 +317,18 @@ reconc_BUIS <- function(S,
         distr_ = distr_G[[gi]]
       )
     }
-    check_weights.res = .check_weigths(weights)
+    check_weights.res = .check_weights(weights)
     if (check_weights.res$warning & !suppress_warnings) {
       warning_msg = check_weights.res$warning_msg
       # add information to the warning message
-      upper_fromS_i = c()
+      upper_fromA_i = c()
       for (gi in 1:nrow(G)) {
         c = G[gi, ]
-        upper_fromS_i = c(upper_fromS_i,
-                          which(lapply(seq_len(nrow(S)), function(i) sum(abs(S[i,] - c))) == 0))
+        upper_fromA_i = c(upper_fromA_i,
+                          which(lapply(seq_len(nrow(A)), function(i) sum(abs(A[i,] - c))) == 0))
       }
       for (wmsg in warning_msg) {
-        wmsg = paste(wmsg, paste0("Check the upper forecasts at index: ", paste0("{",paste(upper_fromS_i, collapse = ","), "}.")))
+        wmsg = paste(wmsg, paste0("Check the upper forecasts at index: ", paste0("{",paste(upper_fromA_i, collapse = ","), "}.")))
         warning(wmsg)
       }
     }