From c03fb16a27b15f8b96384dd21e59546e1d2695bc Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Sun, 14 Apr 2024 11:51:04 +0200 Subject: [PATCH 01/36] added pmf functions and reconc_TD functions --- R/PMF.R | 144 +++++++++++++++++++++++++++++++++++++++ R/hierarchy.R | 100 ++++++++++++++++++++++++++- R/prova_pmfs.R | 22 ++++++ R/reconc_TD.R | 180 +++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 444 insertions(+), 2 deletions(-) create mode 100644 R/PMF.R create mode 100644 R/prova_pmfs.R create mode 100644 R/reconc_TD.R diff --git a/R/PMF.R b/R/PMF.R new file mode 100644 index 0000000..eba65ce --- /dev/null +++ b/R/PMF.R @@ -0,0 +1,144 @@ +# A pmf is represented as normalized numeric vector v: +# for each j = 0, ..., M, the probability of j is the value v[[j+1]] + + +### + +# Compute the tempered pmf +# The pmf is raised to the power of 1/temp, and then normalized +# temp must be a positive number +PMF.tempering = function(pmf, temp) { + + if (temp <= 0) stop("temp must be positive") + if (temp == 1) return(pmf) + + temp_pmf = pmf**(1/temp) + return(temp_pmf / sum(temp_pmf)) +} + +# Sample (with replacement) from the probability distribution specified by the pmf +PMF.sample = function(pmf, N_samples) { + s = sample(0:len(pmf), prob = pmf, replace = TRUE, size = N_samples) + return(s) +} + +# Apply smoothing to a pmf to "cover the holes" in the support. +# If there is no hole, it doesn't do anything. +# If the smoothing parameter alpha is not specified, it is set to the min of pmf. +# If laplace is set to TRUE, add alpha to all the points. +# Otherwise, add alpha only to points with zero mass. +PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { + + if (is.null(alpha)) alpha = min(pmf) + + # apply smoothing only if there are holes + if (sum(pmf==0)) { + if (laplace) { pmf = pmf + rep(alpha, length(pmf)) + } else pmf[pmf==0] = alpha + } + + return(pmf) +} + +# Compute convolution between 2 pmfs. Then, for numerical reasons: +# -removes small values at the end of the vector (< Rtoll) +# -set to zero all the values to the left of the support +# -set to zero small values (< toll) +PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { + pmf = convolve(pmf1, rev(pmf2), type="open") + # Look for last value > Rtoll and remove all the elements after it: + last_pos = max(which(pmf > Rtoll)) + pmf = pmf[1:last_pos] + # Set to zero values smaller than toll: + pmf[pmf0)) + m2 = min(which(pmf2>0)) + m = m1 + m2 -1 + if (m>1) pmf[1:(m-1)] = 0 + return(pmf) +} + +# Computes the pmf of the bottom-up distribution analytically +# l_pmf: list of bottom pmfs +# toll and Rtoll: used during convolution (see PMF.conv) +# smoothing: whether to apply smoothing to the bottom pmfs to "cover the holes" +# al_smooth, lap_smooth: smoothing parameters (see PMF.smoothing) +# Returns: +# -the bottom-up pmf, if return_all=FALSE +# -otherwise, a list of lists of pmfs for all the steps of the algorithm; +# they correspond to the variables of the "auxiliary binary tree" +PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, + smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + + # Smoothing to "cover the holes" in the supports of the bottom pmfs + if (smoothing) l_pmf = lapply(l_pmf, PMF.smoothing, + alpha=al_smooth, laplace=lap_smooth) + + # 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, ... + # Then, the new list has length halved (if L is odd, just copy the last element) + # Ends when the list has length 1: contains just 1 vector that is the convolution + # of all the vectors of the list + old_v = l_pmf + l_l_v = list(old_v) # list with all the step-by-step lists of pmf + L = length(old_v) + while (L > 1) { + new_v = c() + for (j in 1:(L%/%2)) { + new_v = c(new_v, list(PMF.conv(old_v[[2*j-1]], old_v[[2*j]], + toll=toll, Rtoll=Rtoll))) + } + if (L%%2 == 1) new_v = c(new_v, list(old_v[[L]])) + old_v = new_v + l_l_v = c(l_l_v, list(old_v)) + L = length(old_v) + } + + if (return_all) { + return(l_l_v) + } else { + return(new_v[[1]]) + } +} + +# Given a vector v_u and a list of bottom pmf l_pmf, +# checks if the elements of v_u are contained in the support of the bottom-up distr +# Returns a vector with the same length of v_u with TRUE if it is contained and FALSE otherwise +PMF.check_support <- function(v_u, l_pmf, toll=1e-16, Rtoll=1e-7, + smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + pmf_u = PMF.bottom_up(l_pmf, Ltoll=Ltoll, Rtoll=Rtoll, return_all=FALSE, + smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth) + # The elements of v_u must be in the support of pmf_u, and the mass must be positive + mask = v_u <= length(pmf_u)+1 & pmf_u[v_u+1] > 0 + return(mask) +} + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/hierarchy.R b/R/hierarchy.R index e253b57..408521f 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -1,4 +1,6 @@ -# Function to find the best hierarchy contained in A +# Function to find the best hierarchy contained in A; +# "best" means that minimizes the number of IS steps required by BUIS. +# It is not the maximal hierarchy contained in A! # Returns a vector with length equal to the number of rows of A # Each entry is 1 if the corresponding row has to be picked, and 0 otherwise .get_hier_rows <- function(A, scale = 196) { @@ -87,7 +89,7 @@ # Function that extract the "best hierarchy rows" from A, and sorts them in # the correct order (i.e. bottom-up) -# Also sorts accordingly the vector v (e.g. of parameters) +# Also sorts accordingly the vectors v, d, it (e.g. of parameters) .get_HG <- function(A, v, d, it) { #get the indices of the "hierarchy rows" of A indices_sol <- .get_hier_rows(A) @@ -319,3 +321,97 @@ get_reconc_matrices <- function(agg_levels, h) { ) return(out) } + +# Check if A is a hierarchical matrix +.check_hierarchical <- function(A) { + + k <- nrow(A) + m <- ncol(A) + + 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 + cond2 = sum(A[i,] - A[j,] >= 0) < m # Upper j is not a descendant of upper i + cond3 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j + if (cond1 & cond2 & cond3) { + return(FALSE) + } + } + } + } + + return(TRUE) + +} + +# Check if A is a hierarchical matrix and if the rows are in bottom-up order +.check_BU_matr <- function(A) { + + k <- nrow(A) + m <- ncol(A) + + 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 + cond2 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j + if (cond1 & cond2) { + return(FALSE) + } + } + } + } + + return(TRUE) +} + +# Find the rows of A corresponding to the lowest level +.lowest_lev <- function(A) { + + if (!.check_hierarchical(A)) stop("Matrix A is not hierarchical") + + k = nrow(A) + m = ncol(A) + + rows = c() + for (i in 1:k) { + rows = c(rows, i) + for (j in 1:k) { + cond1 = A[i,] %*% A[j,] != 0 # Upper i and j have some common descendants + cond2 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j + if (cond1 & cond2) { + rows = rows[-length(rows)] # remove i + } + } + } + # keep all rows except those that satisfy both the above conditions + + return(rows) +} + +# Get the aggregating matrix Au of the sub-hierarchy composed just by the uppers +get_Au <- function(A, lowest_rows=NULL) { + + if (is.null(lowest_rows)) lowest_rows = .lowest_lev(A) + A_ = A[-lowest_rows,] + + 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 + } + } + + return(1*A_u) # to get numerical values instead of TRUE / FALSE +} + + + + + diff --git a/R/prova_pmfs.R b/R/prova_pmfs.R new file mode 100644 index 0000000..e59b9be --- /dev/null +++ b/R/prova_pmfs.R @@ -0,0 +1,22 @@ +dir_path <- dirname(rstudioapi::getSourceEditorContext()$path) +setwd(dir_path) +source("reconc_TD.R") +source("PMF.R") + +pmf1 = sample(1:30, size = 1e3, replace = TRUE) +pmf1 = pmf1 / sum(pmf1) +pmf2 = sample(1:30, size = 1e3, replace = TRUE) +pmf2 = pmf2 / sum(pmf2) + +u = sample(0:1999, size = 1e4, replace = TRUE) + +b = cond_biv_sampling(u, pmf1, pmf2) + + + + + + +outer(u, supp1, `-`) + + diff --git a/R/reconc_TD.R b/R/reconc_TD.R new file mode 100644 index 0000000..ba5ee16 --- /dev/null +++ b/R/reconc_TD.R @@ -0,0 +1,180 @@ +### TODO: +# optimize cond_biv_sampling +# fix TD_sampling +# fix reconc_TD + +# Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u) +# B_1 and B_2 are distributed as pmf1 and pmf2 +# u can be a vector! +cond_biv_sampling = function(u, pmf1, pmf2) { + + # In this way then we iterate over the one with shorter support: + sw = FALSE + if (length(pmf1) > length(pmf2)) { + pmf_ = pmf1 + pmf1 = pmf2 + pmf2 = pmf_ + sw = TRUE + } + + len_u = length(u) + len_supp1 = length(pmf1) + supp1 = 0:(len_supp1-1) + + W1 = t(matrix(pmf1, ncol=len_u, nrow=len_supp1)) + supp2 = outer(u, supp1, `-`) + supp2[supp2<0] = Inf # trick to get NA when we access pmf2 outside the support + W2 = pmf2[supp2+1] # add 1 because support starts from 0 + W2[is.na(W2)] = 0 # set NA to zero + W2 = matrix(W2, nrow = len_u) # back to matrix shape + W = W1 * W2 + W = W / rowSums(W) # normalize + cumW = W %*% upper.tri(diag(len_supp1), diag = TRUE) # cumulative sum on each row + + unif = runif(len_u) + idxs = rowSums(unif > cumW) + 1 + b1 = supp1[idxs] + if (sw) b1 = u - b1 # if we have switched, switch back + + return(list(b1, u-b1)) +} + +# Given a vector u of the upper values +# and a list of pmf (tables) of the bottom distr, +# returns samples (dim: n_bottom x length(u)) +# from the conditional distr of the bottom given the upper values +TD_sampling = function(u, L_tab, Ltoll=1e-16, Rtoll=1e-7, + smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + + l_l_tab = rev(bottom_up_tab(L_tab, toll = toll, Rtoll = Rtoll, return_all = TRUE, + smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth)) + + b_old = matrix(u, nrow = 1) + for (l_tab in l_l_tab[2:length(l_l_tab)]) { + L = length(l_tab) + b_new = matrix(ncol = length(u), nrow = L) + for (j in 1:(L%/%2)) { + b = cond_biv_sampling(b_old[j,], l_tab[[2*j-1]], l_tab[[2*j]]) + b_new[2*j-1,] = b[[1]] + b_new[2*j,] = b[[2]] + } + if (L%%2 == 1) b_new[L,] = b_old[L%/%2 + 1,] + b_old = b_new + } + + return(b_new) +} + +# Reconciliation top-down using (...) +reconc_TD <- function(A, fc_bottom, mu_u, Sigma_u, names_u, + N_samples = 1e3, Ltoll=1e-16, Rtoll=1e-7, + smooth_bottom=TRUE, al_smooth=NULL, lap_smooth=FALSE, + return_samples = FALSE, seed = NULL) { + + set.seed(seed) + + n_b = ncol(A) + n_u = nrow(A) + + lowest_rows = .lowest_lev(A) + A_u = get_Au(A, lowest_rows) + + # Reconcile the upper + rec_gauss_u = rec_gauss(A_u, + mu_u=mu_u[-lowest_rows], Sigma_u=Sigma_u[-lowest_rows, -lowest_rows], + mu_b=mu_u[lowest_rows], Sigma_b=Sigma_u[lowest_rows, lowest_rows], + Sigma_ub=Sigma_u[-lowest_rows, lowest_rows]) + + # Sample from reconciled MVN on the lowest level of the upper + U = mvtnorm::rmvnorm(N_samples, mean=rec_gauss_u$mu_b_tilde, sigma=rec_gauss_u$Sigma_b_tilde) # n_samp x n_lowest_upp + U = round(U) # round to integer + U_js = asplit(U, MARGIN = 2) # split into list of column vectors + + # Prepare lists of upper samples and bottom pmf + n_low_u = length(lowest_rows) + L_tab = lapply(fc_bottom, "[[", "samples") # list of bottom empirical pmf + L_tab_js = list() # list of the lists of bottom pmf relative to the low upp + for (j in lowest_rows) { + Aj = A[j,] + L_tab_js = c(L_tab_js, list(L_tab[as.logical(Aj)])) + } + + # Check that each multivariate sample of U is contained in the support of the bottom-up distr + samp_ok = mapply(check_support, U_js, L_tab_js, + MoreArgs = list(Ltoll=Ltoll, Rtoll=Rtoll, + smoothing=smooth_bottom, al_smooth=al_smooth, lap_smooth=lap_smooth)) + samp_ok = rowSums(samp_ok) == n_low_u + U_js = lapply(U_js, "[", samp_ok) # Only keep the "good" upper samples + print(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the samples ", + "are in the support of the bottom-up distribution")) + + # Get bottom samples via the prob top-down + B = list() + for (j in 1:n_low_u) { + print(j) + B[[j]] = TD_sampling(U_js[[j]], L_tab_js[[j]], + Ltoll=Ltoll, Rtoll=Rtoll, + smoothing=smooth_bottom, al_smooth=al_smooth, lap_smooth=lap_smooth) + } + B = do.call("rbind", B) # dim: n_bottom x n_samples + + U = A %*% B + + rec_bottom = list() + for (i in 1:n_b) { + name = names(fc_bottom)[[i]] + rec_bottom[[name]] = table(B[i,]) + } + rec_upper = list() + for (j in 1:n_u) { + name = names_u[[j]] + rec_upper[[name]] = table(U[j,]) + } + + out = list( + bottom = rec_bottom, + upper = rec_upper, + n_samp = sum(samp_ok) + ) + if (return_samples) { + out$B_samples = B + out$U_samples = U + } + + return(out) +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 14a380e470cf456219bdf4437f6fde41df88672b Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Wed, 17 Apr 2024 15:35:04 +0200 Subject: [PATCH 02/36] optimized code for TD + fixed bugs --- R/PMF.R | 76 +++++++----- R/Rprof.out | 319 ++++++++++++++++++++++++++++++++++++++++++++++++ R/_profile_TD.R | 169 +++++++++++++++++++++++++ R/hierarchy.R | 9 +- R/prova_pmfs.R | 22 ---- R/reconc.R | 2 +- R/reconc_TD.R | 282 ++++++++++++++++++++++++++++-------------- 7 files changed, 730 insertions(+), 149 deletions(-) create mode 100644 R/Rprof.out create mode 100644 R/_profile_TD.R delete mode 100644 R/prova_pmfs.R diff --git a/R/PMF.R b/R/PMF.R index eba65ce..61a25ef 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -1,19 +1,38 @@ # A pmf is represented as normalized numeric vector v: # for each j = 0, ..., M, the probability of j is the value v[[j+1]] - ### -# Compute the tempered pmf -# The pmf is raised to the power of 1/temp, and then normalized -# temp must be a positive number -PMF.tempering = function(pmf, temp) { +# Compute the empirical pmf from a vector of samples +PMF.from_samples = function(v) { + pmf = tabulate(v+1) # the support starts from 0 (tabulate only counts positive integers) + pmf = pmf / sum(pmf) + return(pmf) +} + +# Compute the pmf from a parametric distribution +PMF.from_params = function(params, distr, Rtoll = 1e-7) { - if (temp <= 0) stop("temp must be positive") - if (temp == 1) return(pmf) + if (!(distr %in% c("poisson", "nbinom"))) { + stop(paste0("Input error: distribution ", distr, " is not implemented")) + } - temp_pmf = pmf**(1/temp) - return(temp_pmf / sum(temp_pmf)) + switch( + distr, + "poisson" = { + lambda = params$lambda + M = qpois(1-Rtoll, lambda) + pmf = dpois(0:M, lambda)}, + "nbinom" = { + size = params$size + prob = params$prob + mu = params$mu + M = qnbinom(1-Rtoll, size=size, prob=prob, mu=mu) + pmf = dnbinom(0:M, size=size, prob=prob, mu=mu)}, + ) + + pmf = pmf / sum(pmf) + return(pmf) } # Sample (with replacement) from the probability distribution specified by the pmf @@ -57,6 +76,7 @@ PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { m2 = min(which(pmf2>0)) m = m1 + m2 -1 if (m>1) pmf[1:(m-1)] = 0 + pmf = pmf / sum(pmf) # re-normalize return(pmf) } @@ -107,31 +127,27 @@ PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, # Given a vector v_u and a list of bottom pmf l_pmf, # checks if the elements of v_u are contained in the support of the bottom-up distr # Returns a vector with the same length of v_u with TRUE if it is contained and FALSE otherwise -PMF.check_support <- function(v_u, l_pmf, toll=1e-16, Rtoll=1e-7, - smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { - pmf_u = PMF.bottom_up(l_pmf, Ltoll=Ltoll, Rtoll=Rtoll, return_all=FALSE, +PMF.check_support = function(v_u, l_pmf, toll=1e-16, Rtoll=1e-7, + smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + pmf_u = PMF.bottom_up(l_pmf, toll=toll, Rtoll=Rtoll, return_all=FALSE, smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth) - # The elements of v_u must be in the support of pmf_u, and the mass must be positive - mask = v_u <= length(pmf_u)+1 & pmf_u[v_u+1] > 0 + # The elements of v_u must be in the support of pmf_u + supp_u = which(pmf_u > 0) - 1 + mask = v_u %in% supp_u return(mask) } - - - - - - - - - - - - - - - - +# Compute the tempered pmf +# The pmf is raised to the power of 1/temp, and then normalized +# temp must be a positive number +PMF.tempering = function(pmf, temp) { + + if (temp <= 0) stop("temp must be positive") + if (temp == 1) return(pmf) + + temp_pmf = pmf**(1/temp) + return(temp_pmf / sum(temp_pmf)) +} diff --git a/R/Rprof.out b/R/Rprof.out new file mode 100644 index 0000000..aba2a35 --- /dev/null +++ b/R/Rprof.out @@ -0,0 +1,319 @@ +sample.interval=20000 +"hook" "Rprof" +"unique.default" "unique" 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+".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sum" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"%*%" "reconc_TD" +"%*%" "reconc_TD" +"%*%" "reconc_TD" +"%*%" "reconc_TD" +"%*%" "reconc_TD" +"%*%" "reconc_TD" diff --git a/R/_profile_TD.R b/R/_profile_TD.R new file mode 100644 index 0000000..43408e2 --- /dev/null +++ b/R/_profile_TD.R @@ -0,0 +1,169 @@ +dir_path <- dirname(rstudioapi::getSourceEditorContext()$path) +setwd(dir_path) + +rm( list = ls()); gc(); #clean environment + +source("PMF.R") +source("reconc_TD.R") +source("utils.R") +source("reconc.R") +source("hierarchy.R") +source("shrink_cov.R") + +#### + +get_hier_M5 = function(save_ = FALSE, save_path = "../../../results/") { + + n_b = 3049 + n_u = 11 + n = n_u + n_b + + A = matrix(0, nrow = n_u, ncol = n_b) + A[1,] = 1 # store + A[2,1:565] = 1 # categories + A[3,566:1612] = 1 + A[4,1613:3049] = 1 + A[5,1:416] = 1 # departments + A[6,417:565] = 1 + A[7,566:1097] = 1 + A[8,1098:1612] = 1 + A[9,1613:1828] = 1 + A[10,1829:2226] = 1 + A[11,2227:3049] = 1 + + S = rbind(A, diag(rep(1, n_b))) + + if (save_) { + saveRDS(S, paste0(save_path, "S.rds")) + saveRDS(A, paste0(save_path, "A.rds")) + } + + return(list(A = A, S = S)) +} + +get_gauss_params_bott = function(fc_bottom) { + + mu_bott = c() + sd_bott = c() + for (fc_b in fc_bottom) { + tab = fc_b$samples + vals = as.integer(names(tab)) + mu_b = sum(vals * tab) / sum(tab) + sd_b = (sum((vals-mu_b)**2 * tab) / sum(tab))**0.5 + mu_bott = c(mu_bott, mu_b) + sd_bott = c(sd_bott, sd_b) + } + l = list(mu = mu_bott, sd = sd_bott) + + return(l) +} + +get_gauss_params_upp = function(fc_upper) { + + mu = c() + sd = c() + for (fc in fc_upper) { + mu = c(mu, fc$mu) + sd = c(sd, fc$sigma) + } + + residuals.upper = lapply(fc_upper, "[[", "residuals") + residuals.upper = t(do.call("rbind", residuals.upper)) + Sigma = schaferStrimmer_cov(residuals.upper)$shrink_cov + + l = list(mu = mu, sd = sd, Sigma = Sigma) + return(l) +} + +PMF.from_tab = function(tab) { + supp = names(tab) + pmf = rep(0, max(as.integer(supp))) + for (j in supp) { + pmf[[as.integer(j)+1]] = tab[[j]] + } + pmf = pmf / sum(pmf) + return(pmf) +} + +### + +seed = 42 +h = 1 +STORE = "CA_1" +N_samples = 1e4 +toll=1e-16 +Rtoll=1e-7 +smooth_bottom=TRUE +al_smooth=NULL +lap_smooth=FALSE +n_low_u_rprof = 7 + +CAT = c("HOBBIES", "HOUSEHOLD", "FOODS") +DEPT = c("HOBBIES_1", "HOBBIES_2", "HOUSEHOLD_1", "HOUSEHOLD_2", + "FOODS_1", "FOODS_2", "FOODS_3") +names_u = c(STORE, CAT, DEPT) + +store_path = paste0("../../../Ricerca/Reconciliation/Codici/Mixed/results/", STORE) + +A = get_hier_M5()$A +n_upp = nrow(A) +n_bott = ncol(A) +n = n_bott + n_upp +S = rbind(A, diag(n_bott)) + +str_bott = paste0(store_path,"/h=",h,"/base_fc_bottom.rds") +str_upp = paste0(store_path,"/h=",h,"/base_fc_upper.rds") +fc_bottom = readRDS(str_bott) +tabs_bottom = lapply(fc_bottom, "[[", "samples") +pmf_bottom = lapply(tabs_bottom, PMF.from_tab) + +fc_upper = readRDS(str_upp) +upper_params = get_gauss_params_upp(fc_upper) + +### +# All the upper + +Rprof() +out = reconc_TD(S, pmf_bottom, upper_params, + bottom_in_type = "pmf", N_samples = 1e4, + return_pmf = TRUE, return_samples = TRUE) +Rprof(NULL) +summaryRprof() + +### +# Only one upper + +S_ = S[c(1,12:3060),] +upper_params_ = list() +upper_params_$mu = upper_params$mu[1] +upper_params_$Sigma = upper_params$Sigma[1,1] + +Rprof() +out = reconc_TD(S_, pmf_bottom, upper_params, + bottom_in_type = "pmf", N_samples = 1e4, + return_pmf = FALSE, return_samples = TRUE) +Rprof(NULL) +summaryRprof() + +### +# Many upper, but all "lowest-level" + +S_ = S[c(5:11,12:3060),] +upper_params_ = list() +upper_params_$mu = upper_params$mu[5:11] +upper_params_$Sigma = upper_params$Sigma[5:11,5:11] + +Rprof() +out = reconc_TD(S, pmf_bottom, upper_params, + bottom_in_type = "pmf", N_samples = 1e4, + return_pmf = FALSE, return_samples = TRUE) +Rprof(NULL) +summaryRprof() + + +# Possible speed-up: +# -vectorize .cond_biv_sampling +# Rcpp? + + + diff --git a/R/hierarchy.R b/R/hierarchy.R index 408521f..24307c5 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -391,11 +391,16 @@ get_reconc_matrices <- function(agg_levels, h) { } # Get the aggregating matrix Au of the sub-hierarchy composed just by the uppers -get_Au <- function(A, lowest_rows=NULL) { +.get_Au <- function(A, lowest_rows=NULL) { if (is.null(lowest_rows)) lowest_rows = .lowest_lev(A) - A_ = A[-lowest_rows,] + if (length(lowest_rows) == nrow(A)) { + warning("All the upper are lowest-upper. Return NULL") + return(NULL) + } + + A_ = A[-lowest_rows,] n_bott = ncol(A_) n_upp_u = nrow(A_) n_bott_u = length(lowest_rows) diff --git a/R/prova_pmfs.R b/R/prova_pmfs.R deleted file mode 100644 index e59b9be..0000000 --- a/R/prova_pmfs.R +++ /dev/null @@ -1,22 +0,0 @@ -dir_path <- dirname(rstudioapi::getSourceEditorContext()$path) -setwd(dir_path) -source("reconc_TD.R") -source("PMF.R") - -pmf1 = sample(1:30, size = 1e3, replace = TRUE) -pmf1 = pmf1 / sum(pmf1) -pmf2 = sample(1:30, size = 1e3, replace = TRUE) -pmf2 = pmf2 / sum(pmf2) - -u = sample(0:1999, size = 1e4, replace = TRUE) - -b = cond_biv_sampling(u, pmf1, pmf2) - - - - - - -outer(u, supp1, `-`) - - diff --git a/R/reconc.R b/R/reconc.R index 9659c95..d6f22b0 100644 --- a/R/reconc.R +++ b/R/reconc.R @@ -451,7 +451,7 @@ reconc_BUIS <- function(S, #'# i.e., sample from the reconciled bottoms and multiply by S #'chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition #'Z = matrix(rnorm(n = 2000), nrow = 2) # Sample from standard normal -#'B = chol_decomp %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation +#'B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation #' #'U = S %*% B #'Y_reconc = rbind(U, B) diff --git a/R/reconc_TD.R b/R/reconc_TD.R index ba5ee16..3ddf3d4 100644 --- a/R/reconc_TD.R +++ b/R/reconc_TD.R @@ -1,12 +1,45 @@ -### TODO: -# optimize cond_biv_sampling -# fix TD_sampling -# fix reconc_TD - -# Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u) -# B_1 and B_2 are distributed as pmf1 and pmf2 -# u can be a vector! -cond_biv_sampling = function(u, pmf1, pmf2) { +# # Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u) +# # B_1 and B_2 are distributed as pmf1 and pmf2 +# # u can be a vector! +# .cond_biv_sampling_old = function(u, pmf1, pmf2) { +# +# # In this way then we iterate over the one with shorter support: +# sw = FALSE +# if (length(pmf1) > length(pmf2)) { +# pmf_ = pmf1 +# pmf1 = pmf2 +# pmf2 = pmf_ +# sw = TRUE +# } +# +# len_u = length(u) +# len_supp1 = length(pmf1) +# supp1 = 0:(len_supp1-1) +# +# W1 = t(matrix(pmf1, ncol=len_u, nrow=len_supp1)) +# supp2 = outer(u, supp1, `-`) +# supp2[supp2<0] = Inf # trick to get NA when we access pmf2 outside the support +# W2 = pmf2[supp2+1] # add 1 because support starts from 0 +# W2[is.na(W2)] = 0 # set NA to zero +# W2 = matrix(W2, nrow = len_u) # back to matrix shape +# W = W1 * W2 +# W = W / rowSums(W) # normalize +# cumW = W %*% upper.tri(diag(len_supp1), diag = TRUE) # cumulative sum on each row +# +# unif = runif(len_u) +# idxs = rowSums(unif > cumW) + 1 +# b1 = supp1[idxs] +# if (sw) b1 = u - b1 # if we have switched, switch back +# +# return(list(b1, u-b1)) +# } + +# OPTIMIZED IMPLEMENTATION: LOOP ON DIFFERENT VALUES OF U +# +# Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u), +# where B_1 and B_2 are distributed as pmf1 and pmf2. +# u is a vector +.cond_biv_sampling = function(u, pmf1, pmf2) { # In this way then we iterate over the one with shorter support: sw = FALSE @@ -17,44 +50,46 @@ cond_biv_sampling = function(u, pmf1, pmf2) { sw = TRUE } - len_u = length(u) - len_supp1 = length(pmf1) - supp1 = 0:(len_supp1-1) - - W1 = t(matrix(pmf1, ncol=len_u, nrow=len_supp1)) - supp2 = outer(u, supp1, `-`) - supp2[supp2<0] = Inf # trick to get NA when we access pmf2 outside the support - W2 = pmf2[supp2+1] # add 1 because support starts from 0 - W2[is.na(W2)] = 0 # set NA to zero - W2 = matrix(W2, nrow = len_u) # back to matrix shape - W = W1 * W2 - W = W / rowSums(W) # normalize - cumW = W %*% upper.tri(diag(len_supp1), diag = TRUE) # cumulative sum on each row - - unif = runif(len_u) - idxs = rowSums(unif > cumW) + 1 - b1 = supp1[idxs] + b1 = rep(NA, length(u)) # initialize empty vector + + for (u_uniq in unique(u)) { # loop over different values of u + + len_supp1 = length(pmf1) + supp1 = 0:(len_supp1-1) + p1 = pmf1 + + supp2 = u_uniq - supp1 + supp2[supp2<0] = Inf # trick to get NA when we access pmf2 outside the support + p2 = pmf2[supp2+1] # +1 because support starts from 0, but vector indexing from 1 + p2[is.na(p2)] = 0 # set NA to zero + + p = p1 * p2 + p = p / sum(p) + + u_posit = (u == u_uniq) + b1[u_posit] = sample(supp1, size = sum(u_posit), replace = TRUE, prob = p) + } + if (sw) b1 = u - b1 # if we have switched, switch back return(list(b1, u-b1)) } -# Given a vector u of the upper values -# and a list of pmf (tables) of the bottom distr, -# returns samples (dim: n_bottom x length(u)) -# from the conditional distr of the bottom given the upper values -TD_sampling = function(u, L_tab, Ltoll=1e-16, Rtoll=1e-7, - smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { +# Given a vector u of the upper values and a list of the bottom distr pmfs, +# returns samples (dim: n_bottom x length(u)) from the conditional distr +# of the bottom given the upper values +.TD_sampling = function(u, bott_pmf, toll=1e-16, Rtoll=1e-7, + smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { - l_l_tab = rev(bottom_up_tab(L_tab, toll = toll, Rtoll = Rtoll, return_all = TRUE, + l_l_pmf = rev(PMF.bottom_up(bott_pmf, toll = toll, Rtoll = Rtoll, return_all = TRUE, smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth)) b_old = matrix(u, nrow = 1) - for (l_tab in l_l_tab[2:length(l_l_tab)]) { - L = length(l_tab) + for (l_pmf in l_l_pmf[2:length(l_l_pmf)]) { + L = length(l_pmf) b_new = matrix(ncol = length(u), nrow = L) for (j in 1:(L%/%2)) { - b = cond_biv_sampling(b_old[j,], l_tab[[2*j-1]], l_tab[[2*j]]) + b = .cond_biv_sampling(b_old[j,], l_pmf[[2*j-1]], l_pmf[[2*j]]) b_new[2*j-1,] = b[[1]] b_new[2*j,] = b[[2]] } @@ -65,82 +100,141 @@ TD_sampling = function(u, L_tab, Ltoll=1e-16, Rtoll=1e-7, return(b_new) } +# Sample from a multivariate Gaussian distribution with specified mean and cov. matrix +.MVN_sample = function(n_samples, mu, Sigma) { + n = length(mu) + if (any(dim(Sigma) != c(n,n))) stop("Dimension of mu and Sigma are not compatible!") + + Z = matrix(rnorm(n*n_samples), ncol = n) + Ch = chol(Sigma) + samples = Z %*% Ch + matrix(mu, nrow = n_samples, ncol = n, byrow = TRUE) + return(samples) +} + # Reconciliation top-down using (...) -reconc_TD <- function(A, fc_bottom, mu_u, Sigma_u, names_u, - N_samples = 1e3, Ltoll=1e-16, Rtoll=1e-7, - smooth_bottom=TRUE, al_smooth=NULL, lap_smooth=FALSE, - return_samples = FALSE, seed = NULL) { +# +# TODO: wrap check inputs in a function and reorganize checks of BUIS, etc. +# TODO: add upper_in_type: upper forecasts may not be Gaussian +reconc_TD = function(S, fc_bottom, fc_upper, + bottom_in_type = "pmf", distr = NULL, + N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, + ..., + seed = NULL) { set.seed(seed) - n_b = ncol(A) - n_u = nrow(A) + # Parameters for convolution + # toll=1e-16 + # Rtoll=1e-7 + # smooth_bottom=TRUE + # al_smooth=NULL + # lap_smooth=FALSE + + # Check inputs + .check_S(S) + n_b = ncol(S) # number of bottom TS + n_u = nrow(S) - n_b # number of upper TS + if (length(fc_bottom) != n_b) { + stop("Input error: length of fc_bottom does not match with S") + } + if (!(bottom_in_type %in% c("pmf", "samples", "params"))) { + stop("Input error: bottom_in_type must be either 'pmf', 'samples', or 'params'") + } + if (!(return_pmf | return_samples)) { + stop("Input error: at least one of 'return_pmf' and 'return_samples' must be TRUE") + } + # Get aggr. matrix A and find the "lowest upper" + A = .get_A_from_S(S)$A lowest_rows = .lowest_lev(A) - A_u = get_Au(A, lowest_rows) - - # Reconcile the upper - rec_gauss_u = rec_gauss(A_u, - mu_u=mu_u[-lowest_rows], Sigma_u=Sigma_u[-lowest_rows, -lowest_rows], - mu_b=mu_u[lowest_rows], Sigma_b=Sigma_u[lowest_rows, lowest_rows], - Sigma_ub=Sigma_u[-lowest_rows, lowest_rows]) - - # Sample from reconciled MVN on the lowest level of the upper - U = mvtnorm::rmvnorm(N_samples, mean=rec_gauss_u$mu_b_tilde, sigma=rec_gauss_u$Sigma_b_tilde) # n_samp x n_lowest_upp - U = round(U) # round to integer - U_js = asplit(U, MARGIN = 2) # split into list of column vectors - - # Prepare lists of upper samples and bottom pmf - n_low_u = length(lowest_rows) - L_tab = lapply(fc_bottom, "[[", "samples") # list of bottom empirical pmf - L_tab_js = list() # list of the lists of bottom pmf relative to the low upp + n_u_low = length(lowest_rows) # number of lowest upper + + # Get mean and covariance matrix of the MVN upper base forecasts + mu_u = fc_upper$mu + Sigma_u = fc_upper$Sigma + + ### Get upper samples + if (n_u == n_u_low) { + # If all the upper are lowest-upper, just sample from the base distribution + U = .MVN_sample(N_samples, mu_u, Sigma_u) # (dim: N_samples x n_u_low) + U = round(U) # round to integer + U_js = asplit(U, MARGIN = 2) # split into list of column vectors + + } else { + # Else, analytically reconcile the upper and then sample from the lowest-uppers + + # Check the dimensions of mu and Sigma + if (length(mu_u) != n_u | any(dim(Sigma_u) != c(n_u, n_u))) { + stop("Input error: the dimensions of the upper parameters do not match with S") + } + + # Get the aggregation matrix A_u and the summing matrix S_u for the upper sub-hierarchy + A_u = .get_Au(A, lowest_rows) + S_u = matrix(nrow = n_u, ncol = n_u_low) + S_u[-lowest_rows,] = A_u + S_u[lowest_rows,] = diag(n_u_low) + + # Analytically reconcile the upper + rec_gauss_u = reconc_gaussian(S_u, mu_u, Sigma_u) + + # Sample from reconciled MVN on the lowest level of the upper (dim: N_samples x n_u_low) + U = .MVN_sample(n_samples = N_samples, + mu = rec_gauss_u$bottom_reconciled_mean, + Sigma = rec_gauss_u$bottom_reconciled_covariance) + U = round(U) # round to integer + U_js = asplit(U, MARGIN = 2) # split into list of column vectors + } + + # Prepare list of bottom pmf + if (bottom_in_type == "pmf") { + L_pmf = fc_bottom + } else if (bottom_in_type == "samples") { + L_pmf = lapply(fc_bottom, PMF.from_samples) + } else if (bottom_in_type == "params") { + L_pmf = lapply(fc_bottom, PMF.from_params, distr = distr) + } + + # Prepare list of lists of bottom pmf relative to each lowest upper + L_pmf_js = list() for (j in lowest_rows) { Aj = A[j,] - L_tab_js = c(L_tab_js, list(L_tab[as.logical(Aj)])) + L_pmf_js = c(L_pmf_js, list(L_pmf[as.logical(Aj)])) } - # Check that each multivariate sample of U is contained in the support of the bottom-up distr - samp_ok = mapply(check_support, U_js, L_tab_js, - MoreArgs = list(Ltoll=Ltoll, Rtoll=Rtoll, - smoothing=smooth_bottom, al_smooth=al_smooth, lap_smooth=lap_smooth)) - samp_ok = rowSums(samp_ok) == n_low_u - U_js = lapply(U_js, "[", samp_ok) # Only keep the "good" upper samples - print(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the samples ", + # Check that each multiv. sample of U is contained in the supp of the bottom-up distr + samp_ok = mapply(PMF.check_support, U_js, L_pmf_js, + MoreArgs = list(...)) + samp_ok = rowSums(samp_ok) == n_u_low + # Only keep the "good" upper samples: + U_js = lapply(U_js, "[", samp_ok) + print(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the upper samples ", "are in the support of the bottom-up distribution")) # Get bottom samples via the prob top-down B = list() - for (j in 1:n_low_u) { - print(j) - B[[j]] = TD_sampling(U_js[[j]], L_tab_js[[j]], - Ltoll=Ltoll, Rtoll=Rtoll, - smoothing=smooth_bottom, al_smooth=al_smooth, lap_smooth=lap_smooth) + for (j in 1:n_u_low) { + B[[j]] = .TD_sampling(U_js[[j]], L_pmf_js[[j]], ...) } - B = do.call("rbind", B) # dim: n_bottom x n_samples - - U = A %*% B + B = do.call("rbind", B) # dim: n_bottom x N_samples + U = A %*% B # dim: n_upper x N_samples - rec_bottom = list() - for (i in 1:n_b) { - name = names(fc_bottom)[[i]] - rec_bottom[[name]] = table(B[i,]) + # Prepare output: include the marginal pmfs and/or the samples (depending on "return" inputs) + out = list() + if (return_pmf) { + upper_pmf = lapply(1:n_u, function(i) PMF.from_samples(U[i,])) + bottom_pmf = lapply(1:n_b, function(i) PMF.from_samples(B[i,])) + out$pmf = list( + upper = upper_pmf, + bottom = bottom_pmf + ) } - rec_upper = list() - for (j in 1:n_u) { - name = names_u[[j]] - rec_upper[[name]] = table(U[j,]) - } - - out = list( - bottom = rec_bottom, - upper = rec_upper, - n_samp = sum(samp_ok) - ) if (return_samples) { - out$B_samples = B - out$U_samples = U - } - + out$samples = list( + upper = U, + bottom = B + ) + } + return(out) } From 9d00a9c38ddfda82488ecb5999134dab2e5e2bb1 Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Thu, 18 Apr 2024 18:32:42 +0200 Subject: [PATCH 03/36] reorganized functions in files + added checks of inputs --- R/PMF.R | 13 +- R/Rprof.out | 273 ++++++++++++++++++---------- R/TODO.txt | 11 ++ R/_profile_TD.R | 9 +- R/hierarchy.R | 4 +- R/{reconc.R => reconc_BUIS.R} | 176 +++--------------- R/reconc_MH.R | 2 +- R/{reconc_TD.R => reconc_TDcond.R} | 49 ++--- R/reconc_gaussian.R | 117 ++++++++++++ R/utils.R | 275 +++++++++++++++++++---------- 10 files changed, 538 insertions(+), 391 deletions(-) create mode 100644 R/TODO.txt rename R/{reconc.R => reconc_BUIS.R} (67%) rename R/{reconc_TD.R => reconc_TDcond.R} (82%) create mode 100644 R/reconc_gaussian.R diff --git a/R/PMF.R b/R/PMF.R index 61a25ef..5db2b3d 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -5,8 +5,7 @@ # Compute the empirical pmf from a vector of samples PMF.from_samples = function(v) { - pmf = tabulate(v+1) # the support starts from 0 (tabulate only counts positive integers) - pmf = pmf / sum(pmf) + pmf = tabulate(v+1) / length(v) # the support starts from 0 (tabulate only counts positive integers) return(pmf) } @@ -148,13 +147,3 @@ PMF.tempering = function(pmf, temp) { temp_pmf = pmf**(1/temp) return(temp_pmf / sum(temp_pmf)) } - - - - - - - - - - diff --git a/R/Rprof.out b/R/Rprof.out index aba2a35..0e6073f 100644 --- a/R/Rprof.out +++ b/R/Rprof.out @@ -1,301 +1,342 @@ sample.interval=20000 -"hook" "Rprof" +"f" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "withCallingHandlers" ".rs.callAs" "Rprof" +"as.vector" "unique" "sort" ".check_S" 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+".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".TD_sampling" "reconc_TD" -"sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" @@ -303,9 +344,32 @@ sample.interval=20000 ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" -"sum" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"stopifnot" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"stopifnot" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"...elt" "stopifnot" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"...elt" "stopifnot" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"unique.default" "unique" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"stopifnot" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sum" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" +"rbind" "do.call" "reconc_TD" "rbind" "do.call" "reconc_TD" "rbind" "do.call" "reconc_TD" "rbind" "do.call" "reconc_TD" @@ -317,3 +381,24 @@ sample.interval=20000 "%*%" "reconc_TD" "%*%" "reconc_TD" "%*%" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"as.integer" "tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"as.integer" "tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"as.integer" "tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"as.integer" "tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"max" "tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" +"tabulate" "PMF.from_samples" "FUN" "lapply" "reconc_TD" diff --git a/R/TODO.txt b/R/TODO.txt new file mode 100644 index 0000000..6d283b9 --- /dev/null +++ b/R/TODO.txt @@ -0,0 +1,11 @@ +Finire .check_distr_params e aggiungere ai check (buis, mh, td) +.check_distr: ricontrollare (cambiare nome in check_distr_buis?) +.distr_pmf: riguardare e aggiungere check + +POI: +commento a reconc_TD +test e esempi + +Prima della submission a CRAN: +... solo temporeanei, poi mettere i valori di default nelle funzioni + diff --git a/R/_profile_TD.R b/R/_profile_TD.R index 43408e2..fdece26 100644 --- a/R/_profile_TD.R +++ b/R/_profile_TD.R @@ -120,12 +120,15 @@ pmf_bottom = lapply(tabs_bottom, PMF.from_tab) fc_upper = readRDS(str_upp) upper_params = get_gauss_params_upp(fc_upper) +N_samples = 1e4 + ### # All the upper Rprof() out = reconc_TD(S, pmf_bottom, upper_params, - bottom_in_type = "pmf", N_samples = 1e4, + bottom_in_type = "pmf", N_samples = N_samples, + toll = 1e-15, return_pmf = TRUE, return_samples = TRUE) Rprof(NULL) summaryRprof() @@ -140,7 +143,7 @@ upper_params_$Sigma = upper_params$Sigma[1,1] Rprof() out = reconc_TD(S_, pmf_bottom, upper_params, - bottom_in_type = "pmf", N_samples = 1e4, + bottom_in_type = "pmf", N_samples = N_samples, return_pmf = FALSE, return_samples = TRUE) Rprof(NULL) summaryRprof() @@ -155,7 +158,7 @@ upper_params_$Sigma = upper_params$Sigma[5:11,5:11] Rprof() out = reconc_TD(S, pmf_bottom, upper_params, - bottom_in_type = "pmf", N_samples = 1e4, + bottom_in_type = "pmf", N_samples = N_samples, return_pmf = FALSE, return_samples = TRUE) Rprof(NULL) summaryRprof() diff --git a/R/hierarchy.R b/R/hierarchy.R index 24307c5..47db060 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -322,7 +322,9 @@ get_reconc_matrices <- function(agg_levels, h) { return(out) } -# Check if A is a hierarchical matrix +# Returns TRUE if A is a hierarchy matrix +# (according to Definition 1 in "Find Maximal Hierarchy") +# If this returns TRUE we avoid solving the integer linear programming problem .check_hierarchical <- function(A) { k <- nrow(A) diff --git a/R/reconc.R b/R/reconc_BUIS.R similarity index 67% rename from R/reconc.R rename to R/reconc_BUIS.R index d6f22b0..7c9151a 100644 --- a/R/reconc.R +++ b/R/reconc_BUIS.R @@ -1,40 +1,31 @@ ############################################################################### # Reconciliation with Bottom-Up Importance Sampling (BUIS) +############################################################################### -.distr_sample <- function(params, distr_, n) { - switch( - distr_, - "gaussian" = { - samples = stats::rnorm(n=n, mean = params[[1]], sd = params[[2]]) }, - "poisson" = { - samples = stats::rpois(n=n, lambda = params[[1]]) }, - "nbinom" = { - samples <-if (params[[2]] == 0) { - stop("Parameter size=0 in nbinom, this is not a valid distribution.") - stats::rpois(n=n, lambda = params[[1]]) - } else { - stats::rnbinom(n=n, mu = params[[1]], size = params[[2]]) - } }, - ) - return(samples) -} - -.distr_pmf <- function(x, params, distr_) { - switch( - distr_, - "gaussian" = { - pmf = stats::dnorm(x=x, mean = params[[1]], sd = params[[2]]) }, - "poisson" = { - pmf = stats::dpois(x=x, lambda = params[[1]]) }, - "nbinom" = { - pmf = stats::dnbinom(x=x, mu = params[[1]], size = params[[2]]) }, - ) - return(pmf) +# Checks that there is no bottom continuous variable child of a +# discrete upper variable +.check_hierfamily_rel <- function(sh.res, distr, debug=FALSE) { + for (bi in seq_along(distr[sh.res$bottom_idxs])) { + distr_bottom = distr[sh.res$bottom_idxs][[bi]] + rel_upper_i = sh.res$A[,bi] + rel_distr_upper = unlist(distr[sh.res$upper_idxs])[rel_upper_i == 1] + err_message = "A continuous bottom distribution cannot be child of a discrete one." + if (distr_bottom == "continuous") { + if (sum(rel_distr_upper == "discrete") | sum(rel_distr_upper %in% .DISCR_DISTR)) { + if (debug) { return(-1) } else { stop(err_message) } + } + } + if (distr_bottom %in% .CONT_DISTR) { + if (sum(rel_distr_upper == "discrete") | sum(rel_distr_upper %in% .DISCR_DISTR)) { + if (debug) { return(-1) } else { stop(err_message) } + } + } + } + if (debug) { return(0) } } .emp_pmf <- function(l, density_samples) { - empirical_pmf = sapply(0:max(density_samples), function(i) - sum(density_samples == i) / length(density_samples)) + empirical_pmf = PMF.from_samples(density_samples) w = sapply(l, function(i) empirical_pmf[i + 1]) return(w) } @@ -155,7 +146,8 @@ #' #' 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. If it is a list the i-th element is a string with two possible values: +#' @param distr A string or a list of length n 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'; #' * 'gaussian', 'poisson' or 'nbinom' if `in_type[[i]]`='params'. @@ -257,7 +249,7 @@ reconc_BUIS <- function(S, set.seed(seed) # Ensure that data inputs are valid - .check_input(S, base_forecasts, in_type, distr) + .check_input_BUIS(S, base_forecasts, in_type, distr) if (!is.list(distr)) { distr = rep(list(distr), nrow(S)) } @@ -387,121 +379,3 @@ reconc_BUIS <- function(S, return(out) } - - -#' @title Analytical reconciliation of Gaussian base forecasts -#' -#' @description -#' Closed form computation of the reconciled forecasts in case of Gaussian base forecasts. -#' -#' @param S summing matrix (n x n_bottom). -#' @param base_forecasts.mu a vector containing the means of the base forecasts. -#' @param base_forecasts.Sigma a matrix containing the covariance matrix of the base forecasts. -#' -#' @details -#' The order of the base forecast means and covariance is given by the order of the time series in the summing matrix. -#' -#' The function returns only the reconciled parameters of the bottom variables. -#' The reconciled upper parameters and the reconciled samples for the entire hierarchy can be obtained from the reconciled bottom parameters. -#' See the example section. -#' -#' -#' @return A list containing the bottom reconciled forecasts. The list has the following named elements: -#' -#' * `bottom_reconciled_mean`: reconciled mean for the bottom forecasts; -#' * `bottom_reconciled_covariance`: reconciled covariance for the bottom forecasts. -#' -#' -#' @examples -#' -#'library(bayesRecon) -#' -#'# Create a minimal hierarchy with 2 bottom and 1 upper variable -#'rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) -#'S <- rec_mat$S -#'A <- rec_mat$A -#' -#'#Set the parameters of the Gaussian base forecast distributions -#'mu1 <- 2 -#'mu2 <- 4 -#'muY <- 9 -#'mus <- c(muY,mu1,mu2) -#' -#'sigma1 <- 2 -#'sigma2 <- 2 -#'sigmaY <- 3 -#'sigmas <- c(sigmaY,sigma1,sigma2) -#' -#'Sigma <- diag(sigmas^2) #need to transform into covariance matrix -#'analytic_rec <- reconc_gaussian(S, base_forecasts.mu = mus, -#' base_forecasts.Sigma = Sigma) -#' -#'bottom_mu_reconc <- analytic_rec$bottom_reconciled_mean -#'bottom_Sigma_reconc <- analytic_rec$bottom_reconciled_covariance -#' -#'# Obtain reconciled mu and Sigma for the upper variable -#'upper_mu_reconc <- A %*% bottom_mu_reconc -#'upper_Sigma_reconc <- A %*% bottom_Sigma_reconc %*% t(A) -#' -#'# Obtain reconciled mu and Sigma for the entire hierarchy -#'Y_mu_reconc <- S %*% bottom_mu_reconc -#'Y_Sigma_reconc <- S %*% bottom_Sigma_reconc %*% t(S) # note: singular matrix -#' -#'# Obtain reconciled samples for the entire hierarchy: -#'# i.e., sample from the reconciled bottoms and multiply by S -#'chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition -#'Z = matrix(rnorm(n = 2000), nrow = 2) # Sample from standard normal -#'B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation -#' -#'U = S %*% B -#'Y_reconc = rbind(U, B) -#' -#' @references -#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. -#' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. -#' -#' -#' @seealso [reconc_BUIS()] -#' -#' @export -reconc_gaussian <- function(S, base_forecasts.mu, - base_forecasts.Sigma) { - # Check if S contains only 0s and 1s. - .check_S(S) - hier = .get_A_from_S(S) - A = hier$A - k = nrow(A) #number of upper TS - m = ncol(A) #number of bottom TS - n = length(base_forecasts.mu) #total number of TS - - # Ensure that data inputs are valid - .check_cov(base_forecasts.Sigma) - if (!(nrow(base_forecasts.Sigma) == n)) { - stop("Input error: nrow(base_forecasts.Sigma) != length(base_forecasts.mu)") - } - if (!(k + m == n)) { - stop("Input error: the shape of S is not correct") - } - - Sigma_u = base_forecasts.Sigma[hier$upper_idxs, hier$upper_idxs] - Sigma_b = base_forecasts.Sigma[hier$bottom_idxs, hier$bottom_idxs] - Sigma_ub = matrix(base_forecasts.Sigma[hier$upper_idxs, hier$bottom_idxs], - nrow = length(hier$upper_idxs)) - mu_u = base_forecasts.mu[hier$upper_idxs] - mu_b = base_forecasts.mu[hier$bottom_idxs] - - # Formulation from: - # Zambon, L., et al. "Properties of the reconciled distributions for - # Gaussian and count forecasts." (2023) - Q = Sigma_u - Sigma_ub %*% t(A) - A %*% t(Sigma_ub) + A %*% Sigma_b %*% t(A) - invQ = solve(Q) - mu_b_tilde = mu_b + (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% (A %*% mu_b - mu_u) - Sigma_b_tilde = Sigma_b - (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% t(t(Sigma_ub) - Sigma_b %*% t(A)) - - out = list( - bottom_reconciled_mean = mu_b_tilde, - bottom_reconciled_covariance = Sigma_b_tilde - ) - return(out) -} diff --git a/R/reconc_MH.R b/R/reconc_MH.R index 2a73096..278bb97 100644 --- a/R/reconc_MH.R +++ b/R/reconc_MH.R @@ -94,7 +94,7 @@ reconc_MCMC <- function(S, if (distr == "gaussian") { stop("MCMC for Gaussian distributions is not implemented") } - .check_input(S, base_forecasts, in_type = "params", distr = distr) + .check_input_BUIS(S, base_forecasts, in_type = "params", distr = distr) if (!is.list(distr)) { distr = rep(list(distr), nrow(S)) } diff --git a/R/reconc_TD.R b/R/reconc_TDcond.R similarity index 82% rename from R/reconc_TD.R rename to R/reconc_TDcond.R index 3ddf3d4..5c6653d 100644 --- a/R/reconc_TD.R +++ b/R/reconc_TDcond.R @@ -100,26 +100,18 @@ return(b_new) } -# Sample from a multivariate Gaussian distribution with specified mean and cov. matrix -.MVN_sample = function(n_samples, mu, Sigma) { - n = length(mu) - if (any(dim(Sigma) != c(n,n))) stop("Dimension of mu and Sigma are not compatible!") - - Z = matrix(rnorm(n*n_samples), ncol = n) - Ch = chol(Sigma) - samples = Z %*% Ch + matrix(mu, nrow = n_samples, ncol = n, byrow = TRUE) - return(samples) -} + # Reconciliation top-down using (...) # # TODO: wrap check inputs in a function and reorganize checks of BUIS, etc. +# # TODO: add upper_in_type: upper forecasts may not be Gaussian -reconc_TD = function(S, fc_bottom, fc_upper, +reconc_TDcond = function(S, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, ..., - seed = NULL) { + suppress_warnings = FALSE, seed = NULL) { set.seed(seed) @@ -130,19 +122,14 @@ reconc_TD = function(S, fc_bottom, fc_upper, # al_smooth=NULL # lap_smooth=FALSE + # After testing the convolution parameters: + # remove dots, remove comment above, and set the "best parameters" as default in + # PMF.check_support and .TD_sampling + # Check inputs - .check_S(S) - n_b = ncol(S) # number of bottom TS - n_u = nrow(S) - n_b # number of upper TS - if (length(fc_bottom) != n_b) { - stop("Input error: length of fc_bottom does not match with S") - } - if (!(bottom_in_type %in% c("pmf", "samples", "params"))) { - stop("Input error: bottom_in_type must be either 'pmf', 'samples', or 'params'") - } - if (!(return_pmf | return_samples)) { - stop("Input error: at least one of 'return_pmf' and 'return_samples' must be TRUE") - } + .check_input_TD(S, fc_bottom, fc_upper, + bottom_in_type, distr, + return_pmf, return_samples) # Get aggr. matrix A and find the "lowest upper" A = .get_A_from_S(S)$A @@ -163,11 +150,6 @@ reconc_TD = function(S, fc_bottom, fc_upper, } else { # Else, analytically reconcile the upper and then sample from the lowest-uppers - # Check the dimensions of mu and Sigma - if (length(mu_u) != n_u | any(dim(Sigma_u) != c(n_u, n_u))) { - stop("Input error: the dimensions of the upper parameters do not match with S") - } - # Get the aggregation matrix A_u and the summing matrix S_u for the upper sub-hierarchy A_u = .get_Au(A, lowest_rows) S_u = matrix(nrow = n_u, ncol = n_u_low) @@ -205,10 +187,13 @@ reconc_TD = function(S, fc_bottom, fc_upper, samp_ok = mapply(PMF.check_support, U_js, L_pmf_js, MoreArgs = list(...)) samp_ok = rowSums(samp_ok) == n_u_low - # Only keep the "good" upper samples: + # Only keep the "good" upper samples, and throw a warning if some samples are discarded: U_js = lapply(U_js, "[", samp_ok) - print(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the upper samples ", - "are in the support of the bottom-up distribution")) + if (sum(samp_ok) != N_samples & !suppress_warnings) { + warning(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the upper samples ", + "are in the support of the bottom-up distribution; ", + "the others are discarded.")) + } # Get bottom samples via the prob top-down B = list() diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R new file mode 100644 index 0000000..ce18ed7 --- /dev/null +++ b/R/reconc_gaussian.R @@ -0,0 +1,117 @@ +#' +#' @title Analytical reconciliation of Gaussian base forecasts +#' +#' @description +#' Closed form computation of the reconciled forecasts in case of Gaussian base forecasts. +#' +#' @param S summing matrix (n x n_bottom). +#' @param base_forecasts.mu a vector containing the means of the base forecasts. +#' @param base_forecasts.Sigma a matrix containing the covariance matrix of the base forecasts. +#' +#' @details +#' The order of the base forecast means and covariance is given by the order of the time series in the summing matrix. +#' +#' The function returns only the reconciled parameters of the bottom variables. +#' The reconciled upper parameters and the reconciled samples for the entire hierarchy can be obtained from the reconciled bottom parameters. +#' See the example section. +#' +#' +#' @return A list containing the bottom reconciled forecasts. The list has the following named elements: +#' +#' * `bottom_reconciled_mean`: reconciled mean for the bottom forecasts; +#' * `bottom_reconciled_covariance`: reconciled covariance for the bottom forecasts. +#' +#' +#' @examples +#' +#'library(bayesRecon) +#' +#'# Create a minimal hierarchy with 2 bottom and 1 upper variable +#'rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) +#'S <- rec_mat$S +#'A <- rec_mat$A +#' +#'#Set the parameters of the Gaussian base forecast distributions +#'mu1 <- 2 +#'mu2 <- 4 +#'muY <- 9 +#'mus <- c(muY,mu1,mu2) +#' +#'sigma1 <- 2 +#'sigma2 <- 2 +#'sigmaY <- 3 +#'sigmas <- c(sigmaY,sigma1,sigma2) +#' +#'Sigma <- diag(sigmas^2) #need to transform into covariance matrix +#'analytic_rec <- reconc_gaussian(S, base_forecasts.mu = mus, +#' base_forecasts.Sigma = Sigma) +#' +#'bottom_mu_reconc <- analytic_rec$bottom_reconciled_mean +#'bottom_Sigma_reconc <- analytic_rec$bottom_reconciled_covariance +#' +#'# Obtain reconciled mu and Sigma for the upper variable +#'upper_mu_reconc <- A %*% bottom_mu_reconc +#'upper_Sigma_reconc <- A %*% bottom_Sigma_reconc %*% t(A) +#' +#'# Obtain reconciled mu and Sigma for the entire hierarchy +#'Y_mu_reconc <- S %*% bottom_mu_reconc +#'Y_Sigma_reconc <- S %*% bottom_Sigma_reconc %*% t(S) # note: singular matrix +#' +#'# Obtain reconciled samples for the entire hierarchy: +#'# i.e., sample from the reconciled bottoms and multiply by S +#'chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition +#'Z = matrix(rnorm(n = 2000), nrow = 2) # Sample from standard normal +#'B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation +#' +#'U = S %*% B +#'Y_reconc = rbind(U, B) +#' +#' @references +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +#' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' +#' +#' @seealso [reconc_BUIS()] +#' +#' @export +reconc_gaussian <- function(S, base_forecasts.mu, + base_forecasts.Sigma) { + # Check if S contains only 0s and 1s. + .check_S(S) + hier = .get_A_from_S(S) + A = hier$A + k = nrow(A) #number of upper TS + m = ncol(A) #number of bottom TS + n = length(base_forecasts.mu) #total number of TS + + # Ensure that data inputs are valid + .check_cov(base_forecasts.Sigma, "Sigma") + if (!(nrow(base_forecasts.Sigma) == n)) { + stop("Input error: nrow(base_forecasts.Sigma) != length(base_forecasts.mu)") + } + if (!(k + m == n)) { + stop("Input error: the shape of S is not correct") + } + + Sigma_u = base_forecasts.Sigma[hier$upper_idxs, hier$upper_idxs] + Sigma_b = base_forecasts.Sigma[hier$bottom_idxs, hier$bottom_idxs] + Sigma_ub = matrix(base_forecasts.Sigma[hier$upper_idxs, hier$bottom_idxs], + nrow = length(hier$upper_idxs)) + mu_u = base_forecasts.mu[hier$upper_idxs] + mu_b = base_forecasts.mu[hier$bottom_idxs] + + # Formulation from: + # Zambon, L., et al. "Properties of the reconciled distributions for + # Gaussian and count forecasts." (2023) + Q = Sigma_u - Sigma_ub %*% t(A) - A %*% t(Sigma_ub) + A %*% Sigma_b %*% t(A) + invQ = solve(Q) + mu_b_tilde = mu_b + (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% (A %*% mu_b - mu_u) + Sigma_b_tilde = Sigma_b - (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% t(t(Sigma_ub) - Sigma_b %*% t(A)) + + out = list( + bottom_reconciled_mean = mu_b_tilde, + bottom_reconciled_covariance = Sigma_b_tilde + ) + return(out) +} diff --git a/R/utils.R b/R/utils.R index 4feeb00..9b7ede3 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,7 +1,104 @@ -# Input checks -.DISTR_SET = c("gaussian", "poisson", "nbinom") -.DISTR_SET2 = c("continuous", "discrete") -.check_input <- function(S, base_forecasts, in_type, distr) { +################################################################################ + +.DISTR_TYPES = c("continuous", "discrete") +.DISCR_DISTR = c("poisson", "nbinom") +.CONT_DISTR = c("gaussian") + +################################################################################ +# CHECK INPUT + +# Function to check values allowed in S. +.check_S <- function(S) { + if(!identical(sort(unique(as.vector(S))), c(0,1)) ){ + stop("Input error: S must be a matrix containing only 0s and 1s.") + } +} + +# Check that distr is one of the allowed values, depending on in_type +# (see .DISTR_TYPES, .DISCR_DISTR, .CONT_DISTR) +.check_distr <- function(in_type, distr, i=NULL) { + + add_string = "" + if(!is.null(i)){ + add_string = paste("[[",i,"]]") + } + + if (in_type == "params" & !(distr %in% c(.DISCR_DISTR, .CONT_DISTR))) { + stop(paste( + "Input error: if in_type='params', distr", add_string, " must be one of {", + paste(c(.DISCR_DISTR, .CONT_DISTR), collapse = ', '), + "}" + )) + } + if (in_type == "samples" & !(distr %in% .DISTR_TYPES)) { + stop(paste( + "Input error: if in_type='samples', distr", add_string, " must be one of {", + paste(.DISTR_TYPES, collapse = ', '), + "}" + )) + } +} + +# Checks if a matrix is a covariance matrix (i.e. symmetric p.d.) +.check_cov <- function(cov_matrix, Sigma_str) { + # Check if the matrix is square + if (!is.matrix(cov_matrix) || nrow(cov_matrix) != ncol(cov_matrix)) { + stop(paste0(Sigma_str, " is not square")) + } + # Check if the matrix is positive semi-definite + eigen_values <- eigen(cov_matrix, symmetric = TRUE)$values + if (any(eigen_values <= 0)) { + stop(paste0(Sigma_str, " is not positive semi-definite")) + } + # Check if the matrix is symmetric + if (!isSymmetric(cov_matrix)) { + stop("base_forecasts.Sigma not symmetric") + } + # Check if the diagonal elements are non-negative + if (any(diag(cov_matrix) <= 0)) { + stop(paste0(Sigma_str, ": some elements on the diagonal are non-positive")) + } + # If all checks pass, return TRUE + return(TRUE) +} + +# Check the parameters of one of the implemented distribution +.check_distr_params(distr, params) { + if (!(distr %in% c(.DISCR_DISTR, .CONT_DISTR))) { + # Check that the distr is implemented + stop(paste( + "Input error: the distribution must be one of {", + paste(c(.DISCR_DISTR, .CONT_DISTR), collapse = ', '), "}")) + } + switch( + distr, + "gaussian" = { + mean = params[[1]] + sd = params[[2]] + # mean must be a real number + if (length(mean)!=1 | !is.numeric(mean)) { + stop("Input error: the mean of a Gaussian must be a real number") + } + # std must be a positive number + if (length(sd)!=1 || !is.numeric(sd) || sd <= 0) { + stop("Input error: the mean of a Gaussian must be a real number") + } + }, + "poisson" = { + lambda = params[[1]] + # lambda must be a positive number + if (length(lambda)!=1 || !is.numeric(lambda) || lambda <= 0) { + stop("Input error: the mean of a Gaussian must be a real number") + } + }, + "nbinom" = { + # TODO + }, + ) +} + +# Check input for BUIS (and for MH) +.check_input_BUIS <- function(S, base_forecasts, in_type, distr) { .check_S(S) @@ -60,121 +157,105 @@ # - gaussian: 2 parameters, (mu, sd) # - poisson: 1 parameter, (lambda) # - nbinom: 2 parameters, (n, p) - # TODO if distr is a list, check that entries are coherent } - -# Checks if a matrix is a covariance matrix (i.e. symmetric p.d.) -.check_cov <- function(cov_matrix) { - # Check if the matrix is square - if (!is.matrix(cov_matrix) || nrow(cov_matrix) != ncol(cov_matrix)) { - stop("base_forecasts.Sigma not square") +# Check input for TDcond +.check_input_TD = function(S, fc_bottom, fc_upper, + bottom_in_type, distr, + return_pmf, return_samples) { + + .check_S(S) + + n_b = ncol(S) # number of bottom TS + n_u = nrow(S) - n_b # number of upper TS + + if (length(fc_bottom) != n_b) { + stop("Input error: length of fc_bottom does not match with S") } - # Check if the matrix is positive semi-definite - eigen_values <- eigen(cov_matrix, symmetric = TRUE)$values - if (any(eigen_values <= 0)) { - stop("base_forecasts.Sigma not positive semi-definite") + if (!(bottom_in_type %in% c("pmf", "samples", "params"))) { + stop("Input error: bottom_in_type must be either 'pmf', 'samples', or 'params'") } - # Check if the matrix is symmetric - if (!isSymmetric(cov_matrix)) { - stop("base_forecasts.Sigma not symmetric") + if (!(return_pmf | return_samples)) { + stop("Input error: at least one of 'return_pmf' and 'return_samples' must be TRUE") + } + # Check the dimensions of mu and Sigma + if (length(fc_upper$mu) != n_u | any(dim(fc_upper$Sigma) != c(n_u, n_u))) { + stop("Input error: the dimensions of the upper parameters do not match with S") } - # Check if the diagonal elements are non-negative - if (any(diag(cov_matrix) < 0)) { - stop("base_forecasts.Sigma, diagonal elements are non-positive") + # Check that Sigma is a covariance matrix (symmetric positive semi-definite) + .check_cov(fc_upper$Sigma, "Upper covariance matrix") + + # If bottom_in_type is not "params" but distr is specified, throw a warning + if (bottom_in_type %in% c("pmf", "samples") & !is.null(distr)) { + warning(paste0("Since bottom_in_type = '", bottom_in_type, "', the input distr is ignored")) + } + # If bottom_in_type is params, distr must be one of the implemented discrete distr + if (bottom_in_type == "params" & !(distr %in% .DISCR_DISTR)) { + stop(paste0("Input error: distr must be one of {", + paste(DISCR_DISTR, collapse = ', '), "}")) } - # If all checks pass, return TRUE - return(TRUE) } +################################################################################ +# SAMPLE -# Function to check values allowed in S. -.check_S <- function(S) { - if(!identical(sort(unique(as.vector(S))), c(0,1)) ){ - stop("Input error: S must be a matrix containing only 0s and 1s.") - } +# Sample from one of the implemented distributions +.distr_sample <- function(params, distr, n) { + .check_distr_params(distr, params) + switch( + distr, + "gaussian" = { + samples = stats::rnorm(n=n, mean = params[[1]], sd = params[[2]]) }, + "poisson" = { + samples = stats::rpois(n=n, lambda = params[[1]]) }, + "nbinom" = { + samples <-if (params[[2]] == 0) { + stop("Parameter size=0 in nbinom, this is not a valid distribution.") + stats::rpois(n=n, lambda = params[[1]]) + } else { + stats::rnbinom(n=n, mu = params[[1]], size = params[[2]]) + } }, + ) + return(samples) } -# Individual check on the parameter distr -.check_distr <- function(in_type, distr, i=NULL) { +# Sample from a multivariate Gaussian distribution with specified mean and cov. matrix +.MVN_sample = function(n_samples, mu, Sigma) { + n = length(mu) + if (any(dim(Sigma) != c(n,n))) { + stop("Dimension of mu and Sigma are not compatible!") + } + .check_cov <- function(Sigma, "Sigma") - add_string = "" - if(!is.null(i)){ - add_string = paste("[[",i,"]]") - } - - if (in_type == "params" & !(distr %in% .DISTR_SET)) { - stop(paste( - "Input error: if in_type='params', distr", add_string, " must be {", - paste(.DISTR_SET, collapse = ', '), - "}" - )) - } - if (in_type == "samples" & !(distr %in% .DISTR_SET2)) { - stop(paste( - "Input error: if in_type='samples', distr", add_string, " must be {", - paste(.DISTR_SET2, collapse = ', '), - "}" - )) - } + Z = matrix(rnorm(n*n_samples), ncol = n) + Ch = chol(Sigma) + samples = Z %*% Ch + matrix(mu, nrow = n_samples, ncol = n, byrow = TRUE) + return(samples) } -# Returns TRUE if A is a hierarchy matrix -# (according to Definition 1 in "Find Maximal Hierarchy") -# If this returns TRUE we avoid solving the integer linear programming problem -.check_hierarchical <- function(A) { - - k <- nrow(A) - m <- ncol(A) - - 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 - cond2 = sum(A[i,] - A[j,] >= 0) < m # Upper j is not a descendant of upper i - cond3 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j - if (cond1 & cond2 & cond3) { - return(FALSE) - } - } - } - } - - return(TRUE) - -} +################################################################################ +# Miscellaneous -# Checks that there is no bottom continuous variable child of a -# discrete upper variable -.check_hierfamily_rel <- function(sh.res, distr, debug=FALSE) { - for (bi in seq_along(distr[sh.res$bottom_idxs])) { - distr_bottom = distr[sh.res$bottom_idxs][[bi]] - rel_upper_i = sh.res$A[,bi] - rel_distr_upper = unlist(distr[sh.res$upper_idxs])[rel_upper_i == 1] - err_message = "A continuous bottom distribution is child of a discrete one." - if (distr_bottom == .DISTR_SET2[1]) { - if (sum(rel_distr_upper == .DISTR_SET2[2]) | - sum(rel_distr_upper == .DISTR_SET[2]) | sum(rel_distr_upper == .DISTR_SET[3])) { - if (debug) { return(-1) } else { stop(err_message) } - } - } - if (distr_bottom == .DISTR_SET[1]) { - if (sum(rel_distr_upper == .DISTR_SET2[2]) | - sum(rel_distr_upper == .DISTR_SET[2]) | sum(rel_distr_upper == .DISTR_SET[3])) { - if (debug) { return(-1) } else { stop(err_message) } - } - } - } - if (debug) { return(0) } +.distr_pmf <- function(x, params, distr_) { + switch( + distr_, + "gaussian" = { + pmf = stats::dnorm(x=x, mean = params[[1]], sd = params[[2]]) }, + "poisson" = { + pmf = stats::dpois(x=x, lambda = params[[1]]) }, + "nbinom" = { + pmf = stats::dnbinom(x=x, mu = params[[1]], size = params[[2]]) }, + ) + return(pmf) } - -# Misc .shape <- function(m) { print(paste0("(", nrow(m), ",", ncol(m), ")")) } +################################################################################ # Functions for tests + .gen_gaussian <- function(params_file, seed=NULL) { set.seed(seed) params = utils::read.csv(file = params_file, header = FALSE) From f63caba2ec475a63567480f915fbaf1e5eef560c Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Fri, 19 Apr 2024 15:39:06 +0200 Subject: [PATCH 04/36] reorganized checks + minor fix --- R/PMF.R | 33 +++++--- R/utils.R | 247 ++++++++++++++++++++++++++++++------------------------ 2 files changed, 162 insertions(+), 118 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index 5db2b3d..885c068 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -5,31 +5,44 @@ # Compute the empirical pmf from a vector of samples PMF.from_samples = function(v) { - pmf = tabulate(v+1) / length(v) # the support starts from 0 (tabulate only counts positive integers) + .check_discrete_samples(v) + pmf = tabulate(v+1) / length(v) # the support starts from 0 + # Tabulate only counts values above 1: if sum(tabulate(v+1)) > length(v), + # it means that there were negative samples + if (sum(pmf) != 1) { + stop("Input error: same samples are negative") + } return(pmf) } # Compute the pmf from a parametric distribution PMF.from_params = function(params, distr, Rtoll = 1e-7) { - - if (!(distr %in% c("poisson", "nbinom"))) { - stop(paste0("Input error: distribution ", distr, " is not implemented")) + # Check that the distribution is implemented, and that the params are ok + if (!(distr %in% .DISCR_DISTR)) { + stop(paste0("Input error: distr must be one of {", + paste(DISCR_DISTR, collapse = ', '), "}")) } - + .check_distr_params(distr, params) + # Compute the pmf switch( distr, "poisson" = { lambda = params$lambda - M = qpois(1-Rtoll, lambda) - pmf = dpois(0:M, lambda)}, + M = stats::qpois(1-Rtoll, lambda) + pmf = stats::dpois(0:M, lambda)}, "nbinom" = { size = params$size prob = params$prob mu = params$mu - M = qnbinom(1-Rtoll, size=size, prob=prob, mu=mu) - pmf = dnbinom(0:M, size=size, prob=prob, mu=mu)}, + if (!is.null(prob)) { + M = stats::qnbinom(1-Rtoll, size=size, prob=prob) + pmf = stats::dnbinom(0:M, size=size, prob=prob) + } else if (!is.null(mu)) { + M = stats::qnbinom(1-Rtoll, size=size, mu=mu) + pmf = stats::dnbinom(0:M, size=size, mu=mu) + } + }, ) - pmf = pmf / sum(pmf) return(pmf) } diff --git a/R/utils.R b/R/utils.R index 9b7ede3..5d620df 100644 --- a/R/utils.R +++ b/R/utils.R @@ -14,31 +14,6 @@ } } -# Check that distr is one of the allowed values, depending on in_type -# (see .DISTR_TYPES, .DISCR_DISTR, .CONT_DISTR) -.check_distr <- function(in_type, distr, i=NULL) { - - add_string = "" - if(!is.null(i)){ - add_string = paste("[[",i,"]]") - } - - if (in_type == "params" & !(distr %in% c(.DISCR_DISTR, .CONT_DISTR))) { - stop(paste( - "Input error: if in_type='params', distr", add_string, " must be one of {", - paste(c(.DISCR_DISTR, .CONT_DISTR), collapse = ', '), - "}" - )) - } - if (in_type == "samples" & !(distr %in% .DISTR_TYPES)) { - stop(paste( - "Input error: if in_type='samples', distr", add_string, " must be one of {", - paste(.DISTR_TYPES, collapse = ', '), - "}" - )) - } -} - # Checks if a matrix is a covariance matrix (i.e. symmetric p.d.) .check_cov <- function(cov_matrix, Sigma_str) { # Check if the matrix is square @@ -62,105 +37,136 @@ return(TRUE) } -# Check the parameters of one of the implemented distribution -.check_distr_params(distr, params) { +# Checks if the input is a real number +.check_real_number <- function(x) { + return(length(x)==1 & is.numeric(x)) +} + +# Checks if the input is a positive number +.check_positive_number <- function(x) { + return(length(x)==1 && is.numeric(x) && x > 0) +} + +# Check that the distr is implemented +.check_implemented_distr <- function(distr) { if (!(distr %in% c(.DISCR_DISTR, .CONT_DISTR))) { - # Check that the distr is implemented stop(paste( "Input error: the distribution must be one of {", paste(c(.DISCR_DISTR, .CONT_DISTR), collapse = ', '), "}")) } +} + +# Check the parameters of distr +.check_distr_params <- function(distr, params) { + .check_implemented_distr(distr) switch( distr, "gaussian" = { - mean = params[[1]] - sd = params[[2]] - # mean must be a real number - if (length(mean)!=1 | !is.numeric(mean)) { - stop("Input error: the mean of a Gaussian must be a real number") + mean = params$mean + sd = params$sd + if (!.check_real_number(mean)) { + stop("Input error: mean of Gaussian must be a real number") } - # std must be a positive number - if (length(sd)!=1 || !is.numeric(sd) || sd <= 0) { - stop("Input error: the mean of a Gaussian must be a real number") + if (!.check_positive_number(sd)) { + stop("Input error: sd of Gaussian must be a positive number") } }, "poisson" = { - lambda = params[[1]] - # lambda must be a positive number - if (length(lambda)!=1 || !is.numeric(lambda) || lambda <= 0) { - stop("Input error: the mean of a Gaussian must be a real number") + lambda = params$lambda + if (!.check_positive_number(lambda)) { + stop("Input error: lambda of Poisson must be a positive number") } }, "nbinom" = { - # TODO + size = params$size, + prob = params$prob, + mu = params$mu + # Check that size is specified, and that is a positive number + if (is.null(size)) { + stop("Input error: size parameter for the nbinom distribution must be specified") + } + if (!.check_positive_number(size)) { + stop("Input error: size of nbinom must be a positive number") + } + # Check that exactly one of prob, mu is specified + n_prob_mu = !is.null(prob) + !is.null(mu) + if (n_prob_mu == 2) { + stop("Input error: prob and mu for the nbinom distribution are both specified ") + } else if (n_prob_mu == 0) { + stop("Input error: either prob or mu must be specified") + } else { + if (!is.null(prob)) { + if (!.check_positive_number(prob) | prob > 1) { + stop("Input error: prob of nbinom must be positive and <= 1") + } + } else if (!is.null(mu)) { + if (!.check_positive_number(mu)) { + stop("Input error: mu of nbinom must be positive") + } + } + } }, ) +} + +# Check that the samples are discrete +.check_discrete_samples <- function(samples) { + if (!all.equal(x, as.integer(x))) { + stop("Input error: samples are not all discrete") + } } # Check input for BUIS (and for MH) +# base_forecasts, in_type, and distr must be list .check_input_BUIS <- function(S, base_forecasts, in_type, distr) { .check_S(S) - if (!(nrow(S) == length(base_forecasts))) { - stop("Input error: nrow(S) != length(base_forecasts)") + # Check in_type + if (!is.list(in_type)) { + stop("Input error: in_type must be a list") } - - if (is.character(in_type)) { - if (!(in_type %in% c("params", "samples"))) { - stop("Input error: in_type must be either 'samples' or 'params'") - } - flag_list_intype = FALSE - }else if (is.list(in_type)) { - if (!(nrow(S) == length(in_type))) { - stop("Input error: nrow(S) != length(in_type)") - } - for(i in 1:nrow(S)){ - if (!(in_type[[i]] %in% c("params", "samples"))) { - stop("Input error: in_type[[",i,"]] must be either 'samples' or 'params'") - } + if (!(nrow(S) == length(in_type))) { + stop("Input error: nrow(S) != length(in_type)") + } + for(i in 1:nrow(S)){ + if (!(in_type[[i]] %in% c("params", "samples"))) { + stop("Input error: in_type[[",i,"]] must be either 'samples' or 'params'") } - flag_list_intype = TRUE - }else{ - stop("Input error: in_type must be either a string or a list, check documentation") } - if (is.character(distr) & - length(distr) == 1) { - # if distr is a string... - if (flag_list_intype){ - for(i in 1:nrow(S)){ - .check_distr(in_type[[i]], distr) - } - }else{ - .check_distr(in_type, distr) - } - }else if (is.list(distr)) { - if (!(nrow(S) == length(distr))) { - stop("Input error: nrow(S) != length(distr)") - } - for(i in 1:nrow(S)){ - if (flag_list_intype){ - .check_distr(in_type[[i]], distr[[i]],i) - }else{ - .check_distr(in_type, distr[[i]],i) + # Check distr and base forecasts + if (!is.list(distr)) { + stop("Input error: distr must be a list") + } + if (!(nrow(S) == length(distr))) { + stop("Input error: nrow(S) != length(distr)") + } + if (!is.list(base_forecasts)) { + stop("Input error: base_forecasts must be a list") + } + if (!(nrow(S) == length(base_forecasts))) { + stop("Input error: nrow(S) != length(base_forecasts)") + } + for(i in 1:nrow(S)){ + if (in_type[[i]] == "params") { + .check_distr_params(distr[[i]], base_forecasts[[i]]) + } else if (in_type[[i]] == "samples") { + if (!(distr[[i]] %in% .DISTR_TYPES)) { + stop(paste( + "Input error: the distribution must be one of {", + paste(.DISTR_TYPES, collapse = ', '), "}")) } - + if (distr[[i]] == "discrete") + # TODO: check sample size? + } else { + stop("Input error: in_type[[",i,"]] must be either 'samples' or 'params'") } - }else{ - stop("Input error: distr must be either a string or a list, check documentation") } - - # Eventually: - # TODO if in_type=='samples' check sample sizes - # TODO if in_type=='params' check that: - # - gaussian: 2 parameters, (mu, sd) - # - poisson: 1 parameter, (lambda) - # - nbinom: 2 parameters, (n, p) } # Check input for TDcond -.check_input_TD = function(S, fc_bottom, fc_upper, +.check_input_TD <- function(S, fc_bottom, fc_upper, bottom_in_type, distr, return_pmf, return_samples) { @@ -189,10 +195,16 @@ if (bottom_in_type %in% c("pmf", "samples") & !is.null(distr)) { warning(paste0("Since bottom_in_type = '", bottom_in_type, "', the input distr is ignored")) } - # If bottom_in_type is params, distr must be one of the implemented discrete distr - if (bottom_in_type == "params" & !(distr %in% .DISCR_DISTR)) { - stop(paste0("Input error: distr must be one of {", - paste(DISCR_DISTR, collapse = ', '), "}")) + # If bottom_in_type is params, distr must be one of the implemented discrete distr. + # Also, check the parameters + if (bottom_in_type == "params") { + if (!(distr %in% .DISCR_DISTR)) { + stop(paste0("Input error: distr must be one of {", + paste(DISCR_DISTR, collapse = ', '), "}")) + } + for (i in 1:n_b) { + .check_distr_params(distr, fc_bottom[[i]]) + } } } @@ -205,22 +217,28 @@ switch( distr, "gaussian" = { - samples = stats::rnorm(n=n, mean = params[[1]], sd = params[[2]]) }, + mean = params$mean + sd = params$sd + samples = stats::rnorm(n = n, mean = mean, sd = sd) }, "poisson" = { - samples = stats::rpois(n=n, lambda = params[[1]]) }, + lambda = params$lambda + samples = stats::rpois(n = n, lambda = lambda) }, "nbinom" = { - samples <-if (params[[2]] == 0) { - stop("Parameter size=0 in nbinom, this is not a valid distribution.") - stats::rpois(n=n, lambda = params[[1]]) - } else { - stats::rnbinom(n=n, mu = params[[1]], size = params[[2]]) - } }, + size = params$size + prob = params$prob + mu = params$mu + if (!is.null(prob)) { + samples = rnbinom(n = n, size = size, prob = prob) + } else if (!is.null(mu)) { + samples = rnbinom(n = n, size = size, mu = mu) + } + }, ) return(samples) } # Sample from a multivariate Gaussian distribution with specified mean and cov. matrix -.MVN_sample = function(n_samples, mu, Sigma) { +.MVN_sample <- function(n_samples, mu, Sigma) { n = length(mu) if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") @@ -236,15 +254,28 @@ ################################################################################ # Miscellaneous -.distr_pmf <- function(x, params, distr_) { +# Compute the pmf of the distribution specified by distr and params at the points x +.distr_pmf <- function(x, params, distr) { + .check_distr_params(distr, params) switch( - distr_, + distr, "gaussian" = { - pmf = stats::dnorm(x=x, mean = params[[1]], sd = params[[2]]) }, + mean = params$mean + sd = params$sd + pmf = stats::dnorm(x = x, mean = mean, sd = sd) }, "poisson" = { - pmf = stats::dpois(x=x, lambda = params[[1]]) }, + lambda = params$lambda + pmf = stats::dpois(x = x, lambda = lambda) }, "nbinom" = { - pmf = stats::dnbinom(x=x, mu = params[[1]], size = params[[2]]) }, + size = params$size + prob = params$prob + mu = params$mu + if (!is.null(prob)) { + pmf = dnbinom(x = x, size = size, prob = prob) + } else if (!is.null(mu)) { + pmf = dnbinom(x = x, size = size, mu = mu) + } + }, ) return(pmf) } From 14fa245ca6f7b922b70dd42f69e0b6241ad64092 Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Fri, 19 Apr 2024 18:00:49 +0200 Subject: [PATCH 05/36] fixed bugs --- R/PMF.R | 2 +- R/Rprof.out | 759 ++++++++++++++++++++++------------------------ R/TODO.txt | 5 - R/_profile_TD.R | 10 +- R/hierarchy.R | 4 +- R/reconc_BUIS.R | 7 +- R/reconc_MH.R | 4 +- R/reconc_TDcond.R | 14 +- R/utils.R | 27 +- 9 files changed, 392 insertions(+), 440 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index 885c068..22b9a15 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -9,7 +9,7 @@ PMF.from_samples = function(v) { pmf = tabulate(v+1) / length(v) # the support starts from 0 # Tabulate only counts values above 1: if sum(tabulate(v+1)) > length(v), # it means that there were negative samples - if (sum(pmf) != 1) { + if (!isTRUE(all.equal(sum(pmf), 1))) { stop("Input error: same samples are negative") } return(pmf) diff --git a/R/Rprof.out b/R/Rprof.out index 0e6073f..75a110a 100644 --- a/R/Rprof.out +++ b/R/Rprof.out @@ -1,404 +1,357 @@ sample.interval=20000 -"f" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "withCallingHandlers" ".rs.callAs" "Rprof" -"as.vector" "unique" "sort" ".check_S" "reconc_TD" -"unique.default" "unique" "sort" ".check_S" "reconc_TD" -"unique.default" "unique" "sort" ".check_S" "reconc_TD" -"unique.default" "unique" "sort" ".check_S" "reconc_TD" -"unique.default" "unique" "sort" ".check_S" "reconc_TD" -"unique.default" "unique" "sort" ".check_S" "reconc_TD" -"rowSums" "which" ".get_A_from_S" "reconc_TD" -"%*%" ".check_hierarchical" ".lowest_lev" "reconc_TD" -"fft" "fft" "convolve" 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+".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +"sum" "sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +"matrix" ".TD_sampling" "reconc_TDcond" +"matrix" ".TD_sampling" "reconc_TDcond" +"sample.int" "sample" ".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" 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"reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +".cond_biv_sampling" ".TD_sampling" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"rbind" "do.call" "reconc_TDcond" +"%*%" "reconc_TDcond" +"%*%" "reconc_TDcond" +"%*%" "reconc_TDcond" +"%*%" "reconc_TDcond" +"%*%" "reconc_TDcond" diff --git a/R/TODO.txt b/R/TODO.txt index 6d283b9..9aa7973 100644 --- a/R/TODO.txt +++ b/R/TODO.txt @@ -1,8 +1,3 @@ -Finire .check_distr_params e aggiungere ai check (buis, mh, td) -.check_distr: ricontrollare (cambiare nome in check_distr_buis?) -.distr_pmf: riguardare e aggiungere check - -POI: commento a reconc_TD test e esempi diff --git a/R/_profile_TD.R b/R/_profile_TD.R index fdece26..afd4b57 100644 --- a/R/_profile_TD.R +++ b/R/_profile_TD.R @@ -4,9 +4,9 @@ setwd(dir_path) rm( list = ls()); gc(); #clean environment source("PMF.R") -source("reconc_TD.R") +source("reconc_TDcond.R") +source("reconc_gaussian.R") source("utils.R") -source("reconc.R") source("hierarchy.R") source("shrink_cov.R") @@ -126,7 +126,7 @@ N_samples = 1e4 # All the upper Rprof() -out = reconc_TD(S, pmf_bottom, upper_params, +out = reconc_TDcond(S, pmf_bottom, upper_params, bottom_in_type = "pmf", N_samples = N_samples, toll = 1e-15, return_pmf = TRUE, return_samples = TRUE) @@ -142,7 +142,7 @@ upper_params_$mu = upper_params$mu[1] upper_params_$Sigma = upper_params$Sigma[1,1] Rprof() -out = reconc_TD(S_, pmf_bottom, upper_params, +out = reconc_TDcond(S_, pmf_bottom, upper_params_, bottom_in_type = "pmf", N_samples = N_samples, return_pmf = FALSE, return_samples = TRUE) Rprof(NULL) @@ -157,7 +157,7 @@ upper_params_$mu = upper_params$mu[5:11] upper_params_$Sigma = upper_params$Sigma[5:11,5:11] Rprof() -out = reconc_TD(S, pmf_bottom, upper_params, +out = reconc_TDcond(S, pmf_bottom, upper_params, bottom_in_type = "pmf", N_samples = N_samples, return_pmf = FALSE, return_samples = TRUE) Rprof(NULL) diff --git a/R/hierarchy.R b/R/hierarchy.R index 47db060..22970d0 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -87,8 +87,8 @@ return(indices_sol) } -# Function that extract the "best hierarchy rows" from A, and sorts them in -# the correct order (i.e. bottom-up) +# Function that extract the "best hierarchy rows" from A (see .get_hier_rows), +# and sorts them in the correct order (i.e. bottom-up) # Also sorts accordingly the vectors v, d, it (e.g. of parameters) .get_HG <- function(A, v, d, it) { #get the indices of the "hierarchy rows" of A diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 7c9151a..34c8277 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -248,15 +248,16 @@ reconc_BUIS <- function(S, seed = NULL) { set.seed(seed) - # Ensure that data inputs are valid - .check_input_BUIS(S, base_forecasts, in_type, distr) + # Transform distr and in_type into lists if (!is.list(distr)) { distr = rep(list(distr), nrow(S)) } - if (!is.list(in_type)) { in_type = rep(list(in_type), nrow(S)) } + + # Ensure that data inputs are valid + .check_input_BUIS(S, base_forecasts, in_type, distr) # Split bottoms, uppers split_hierarchy.res = .split_hierarchy(S, base_forecasts) diff --git a/R/reconc_MH.R b/R/reconc_MH.R index 278bb97..113a57e 100644 --- a/R/reconc_MH.R +++ b/R/reconc_MH.R @@ -94,10 +94,12 @@ reconc_MCMC <- function(S, if (distr == "gaussian") { stop("MCMC for Gaussian distributions is not implemented") } - .check_input_BUIS(S, base_forecasts, in_type = "params", distr = distr) + # Transform distr into list if (!is.list(distr)) { distr = rep(list(distr), nrow(S)) } + # Check input + .check_input_BUIS(S, base_forecasts, in_type = as.list(rep("params", nrow(S))), distr = distr) n_bottom <- ncol(S) n_ts <- nrow(S) diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 5c6653d..464fe63 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -104,9 +104,6 @@ # Reconciliation top-down using (...) # -# TODO: wrap check inputs in a function and reorganize checks of BUIS, etc. -# -# TODO: add upper_in_type: upper forecasts may not be Gaussian reconc_TDcond = function(S, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, @@ -133,12 +130,14 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, # Get aggr. matrix A and find the "lowest upper" A = .get_A_from_S(S)$A + n_u = nrow(A) + n_b = ncol(A) lowest_rows = .lowest_lev(A) n_u_low = length(lowest_rows) # number of lowest upper # Get mean and covariance matrix of the MVN upper base forecasts mu_u = fc_upper$mu - Sigma_u = fc_upper$Sigma + Sigma_u = as.matrix(fc_upper$Sigma) ### Get upper samples if (n_u == n_u_low) { @@ -242,13 +241,6 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, - - - - - - - diff --git a/R/utils.R b/R/utils.R index 5d620df..f2b57f4 100644 --- a/R/utils.R +++ b/R/utils.R @@ -14,7 +14,7 @@ } } -# Checks if a matrix is a covariance matrix (i.e. symmetric p.d.) +# Check if it is a covariance matrix (i.e. symmetric p.d.) .check_cov <- function(cov_matrix, Sigma_str) { # Check if the matrix is square if (!is.matrix(cov_matrix) || nrow(cov_matrix) != ncol(cov_matrix)) { @@ -78,8 +78,8 @@ } }, "nbinom" = { - size = params$size, - prob = params$prob, + size = params$size + prob = params$prob mu = params$mu # Check that size is specified, and that is a positive number if (is.null(size)) { @@ -111,7 +111,7 @@ # Check that the samples are discrete .check_discrete_samples <- function(samples) { - if (!all.equal(x, as.integer(x))) { + if (!all.equal(samples, as.integer(samples))) { stop("Input error: samples are not all discrete") } } @@ -157,7 +157,9 @@ "Input error: the distribution must be one of {", paste(.DISTR_TYPES, collapse = ', '), "}")) } - if (distr[[i]] == "discrete") + if (distr[[i]] == "discrete") { + .check_discrete_samples(base_forecasts[[i]]) + } # TODO: check sample size? } else { stop("Input error: in_type[[",i,"]] must be either 'samples' or 'params'") @@ -175,15 +177,19 @@ n_b = ncol(S) # number of bottom TS n_u = nrow(S) - n_b # number of upper TS - if (length(fc_bottom) != n_b) { - stop("Input error: length of fc_bottom does not match with S") - } if (!(bottom_in_type %in% c("pmf", "samples", "params"))) { stop("Input error: bottom_in_type must be either 'pmf', 'samples', or 'params'") } if (!(return_pmf | return_samples)) { stop("Input error: at least one of 'return_pmf' and 'return_samples' must be TRUE") } + if (length(fc_bottom) != n_b) { + stop("Input error: length of fc_bottom does not match with S") + } + # If Sigma is a number, transform into a matrix + if (length(fc_upper$Sigma) == 1) { + fc_upper$Sigma = as.matrix(fc_upper$Sigma) + } # Check the dimensions of mu and Sigma if (length(fc_upper$mu) != n_u | any(dim(fc_upper$Sigma) != c(n_u, n_u))) { stop("Input error: the dimensions of the upper parameters do not match with S") @@ -198,6 +204,9 @@ # If bottom_in_type is params, distr must be one of the implemented discrete distr. # Also, check the parameters if (bottom_in_type == "params") { + if (is.null(distr)) { + stop("Input error: if bottom_in_type = 'params', distr must be specified") + } if (!(distr %in% .DISCR_DISTR)) { stop(paste0("Input error: distr must be one of {", paste(DISCR_DISTR, collapse = ', '), "}")) @@ -243,7 +252,7 @@ if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") } - .check_cov <- function(Sigma, "Sigma") + .check_cov(Sigma, "Sigma") Z = matrix(rnorm(n*n_samples), ncol = n) Ch = chol(Sigma) From ff534657639436ae9542c774304ab8750f84c1ac Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Mon, 22 Apr 2024 19:08:38 +0200 Subject: [PATCH 06/36] fixed base_fc params (vector -> list) in documentation, examples, tests, and vignettes --- R/Rprof.out | 3345 ++++++++++++++++++++--- R/TODO.txt | 2 +- R/_profile_TD.R | 5 +- R/hierarchy.R | 2 - R/reconc_BUIS.R | 11 +- R/reconc_MH.R | 9 +- R/reconc_TDcond.R | 44 - tests/testthat/test-examples.R | 16 +- tests/testthat/test-reconc_MCMC.R | 2 +- vignettes/bayesRecon.Rmd | 4 +- vignettes/reconciliation_properties.Rmd | 14 +- 11 files changed, 3022 insertions(+), 432 deletions(-) diff --git a/R/Rprof.out b/R/Rprof.out index 75a110a..27db43c 100644 --- a/R/Rprof.out +++ b/R/Rprof.out @@ -1,357 +1,2990 @@ sample.interval=20000 -"as.vector" "unique" "sort" ".check_S" ".check_input_TD" "reconc_TDcond" -"unique.default" "unique" "sort" ".check_S" ".check_input_TD" "reconc_TDcond" -"unique.default" "unique" "sort" ".check_S" ".check_input_TD" "reconc_TDcond" -"unique.default" "unique" "sort" ".check_S" ".check_input_TD" "reconc_TDcond" -".lowest_lev" "reconc_TDcond" -"tryCatch" "ls" "findLocalsList1" "findLocalsList" "funEnv" "make.functionContext" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"sys.frame" "eval" "match.arg" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" "PMF.bottom_up" "" "mapply" "reconc_TDcond" -"fft" "fft" "convolve" "PMF.conv" 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"?" +"gzfile" "readRDS" "index.search" "help" "eval" "eval" "?" +"browseURL" "print.help_files_with_topic" "" +"browseURL" "print.help_files_with_topic" "" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"parse" "withCallingHandlers" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".rs.tryCatch" ".rs.parsePackageDependencies" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"handleRequireNamespaceCall" "fn" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.parsePackageDependencies" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"ls" "FUN" "lapply" ".rs.objectsOnSearchPath" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" +"lazyLoadDBfetch" "mget" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gzfile" "readRDS" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"grepl" ".rs.extractRCode" +"objects" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"lazyLoadDBfetch" "mget" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeContent" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"grepl" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"Sys.getenv" "utils:::str2logical" "config_val_to_logical" "topic2url" "paste0" "writeLink" "writeBlock" "writeContent" "writeWrapped" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"tryCatchOne" "tryCatchList" "tryCatch" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"file" "readLines" "paste" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"strsplit" "lapply" "strwrap" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"readRDS" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +".Call" ".rs.readSourceDocument" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"lazyLoadDBfetch" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "base::try" "withCallingHandlers" "base::suppressWarnings" +"grepl" ".rs.extractRCode" +"grepl" ".rs.extractRCode" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"grepl" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"gsub" "psub" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "tools:::httpd" "withCallingHandlers" "suppressWarnings" ".rs.getHelp" ".rs.getHelpFromObject" ".rs.getHelpFunction" "impl" ".rs.rpc.get_help" +"grepl" ".rs.extractRCode" +"grepl" ".rs.extractRCode" ".rs.parsePackageDependencies" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"grep" ".rs.extractRCode" +"grepl" ".rs.extractRCode" +"make.callContext" "cmpCall" "cmp" "cmpBuiltinArgs" "h" "tryInline" "cmpCall" "cmp" "cmpSymbolAssign" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "h" "tryInline" "cmpCall" "cmp" "genCode" "cb$putconst" "cmpCallArgs" "cmpCallSymFun" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.onAvailablePackagesStale" ".rs.availablePackages" ".rs.rpc.discover_package_dependencies" +"readRDS" "withCallingHandlers" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".rs.tryCatch" ".rs.onAvailablePackagesReady" "" +".Call" ".rs.enqueClientEvent" "" +"as.name" "handleRequireNamespaceCall" "fn" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.parsePackageDependencies" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"handleColonCall" "fn" ".rs.recursiveWalk" ".rs.recursiveWalk" ".rs.parsePackageDependencies" ".rs.discoverPackageDependencies" ".rs.rpc.discover_package_dependencies" +"ls" "FUN" "lapply" ".rs.objectsOnSearchPath" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" +"h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "h" "tryInline" "cmpCall" "cmp" "genCode" "cb$putconst" "cmpCallArgs" "cmpCallSymFun" "cmpCall" "cmp" "cmpSymbolAssign" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.recordTraceback" "" +"tolower" "nchar" ".rs.startsWith" ".rs.fuzzyMatches" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" +"tolower" "nchar" ".rs.startsWith" ".rs.fuzzyMatches" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" +"file" "read.table" "read.csv" +"substring" ".rs.startsWith" ".rs.fuzzyMatches" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" \ No newline at end of file diff --git a/R/TODO.txt b/R/TODO.txt index 9aa7973..f2ed13c 100644 --- a/R/TODO.txt +++ b/R/TODO.txt @@ -1,5 +1,5 @@ commento a reconc_TD -test e esempi +aggiungere test Prima della submission a CRAN: ... solo temporeanei, poi mettere i valori di default nelle funzioni diff --git a/R/_profile_TD.R b/R/_profile_TD.R index afd4b57..4186c84 100644 --- a/R/_profile_TD.R +++ b/R/_profile_TD.R @@ -120,7 +120,7 @@ pmf_bottom = lapply(tabs_bottom, PMF.from_tab) fc_upper = readRDS(str_upp) upper_params = get_gauss_params_upp(fc_upper) -N_samples = 1e4 +N_samples = 1e5 ### # All the upper @@ -164,9 +164,6 @@ Rprof(NULL) summaryRprof() -# Possible speed-up: -# -vectorize .cond_biv_sampling -# Rcpp? diff --git a/R/hierarchy.R b/R/hierarchy.R index 22970d0..368dd5f 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -323,8 +323,6 @@ get_reconc_matrices <- function(agg_levels, h) { } # Returns TRUE if A is a hierarchy matrix -# (according to Definition 1 in "Find Maximal Hierarchy") -# If this returns TRUE we avoid solving the integer linear programming problem .check_hierarchical <- function(A) { k <- nrow(A) diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 34c8277..a88df59 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -119,11 +119,11 @@ #' #' If `in_type[[i]]`='samples', then `base_forecast[[i]]` is a vector containing samples from the base forecast distribution. #' -#' If `in_type[[i]]`='params', then `base_forecast[[i]]` is a vector containing the estimated: +#' If `in_type[[i]]`='params', then `base_forecast[[i]]` is a list containing the estimated: #' #' * mean and sd for the Gaussian base forecast if `distr[[i]]`='gaussian', see \link[stats]{Normal}; #' * lambda for the Poisson base forecast if `distr[[i]]`='poisson', see \link[stats]{Poisson}; -#' * mu and size for the negative binomial base forecast if `distr[[i]]`='nbinom', see \link[stats]{NegBinomial}. +#' * 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. #' @@ -189,7 +189,7 @@ #' #'base_forecasts = list() #'for (i in 1:nrow(S)) { -#' base_forecasts[[i]] = c(mus[[i]], sigmas[[i]]) +#' base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) #'} #' #' @@ -220,7 +220,7 @@ #' #'base_forecasts <- list() #'for (i in 1:nrow(S)) { -#' base_forecasts[[i]] = lambdas[i] +#' base_forecasts[[i]] = list(lambda = lambdas[i]) #'} #' #'#Sample from the reconciled forecast distribution using the BUIS algorithm @@ -269,7 +269,8 @@ reconc_BUIS <- function(S, .check_hierfamily_rel(split_hierarchy.res, distr) # H, G - is.hier = .check_hierarchical(A) + is.hier = .check_hierarchical(A) + # If A is hierarchical we do not solve the integer linear programming problem if(is.hier) { H = A G = NULL diff --git a/R/reconc_MH.R b/R/reconc_MH.R index 113a57e..adf54cf 100644 --- a/R/reconc_MH.R +++ b/R/reconc_MH.R @@ -6,7 +6,8 @@ #' Uses Markov Chain Monte Carlo algorithm to draw samples from the reconciled #' forecast distribution, which is obtained via conditioning. #' -#' This is a bare-bones implementation of the Metropolis-Hastings algorithm, we suggest the usage of tools to check the convergence. +#' This is a bare-bones implementation of the Metropolis-Hastings algorithm, +#' we suggest the usage of tools to check the convergence. #' The function only works with Poisson or Negative Binomial base forecasts. #' #' The function [reconc_BUIS()] is generally faster on most hierarchies. @@ -25,11 +26,11 @@ #' @details #' #' The parameter `base_forecast` is a list containing n elements. -#' Each element is a vector containing the estimated: +#' Each element is a list containing the estimated: #' #' * mean and sd for the Gaussian base forecast, see \link[stats]{Normal}, if `distr`='gaussian'; #' * lambda for the Poisson base forecast, see \link[stats]{Poisson}, if `distr`='poisson'; -#' * mu and size for the negative binomial base forecast, see \link[stats]{NegBinomial}, if `distr`='nbinom'. +#' * size and prob (or mu) for the negative binomial base forecast, see \link[stats]{NegBinomial}, if `distr`='nbinom'. #' #' The order of the `base_forecast` list is given by the order of the time series in the summing matrix. #' @@ -55,7 +56,7 @@ #' #'base_forecasts = list() #'for (i in 1:nrow(S)) { -#' base_forecasts[[i]] = lambdas[i] +#' base_forecasts[[i]] = list(lambda = lambdas[i]) #'} #' #'#Sample from the reconciled forecast distribution using MCMC diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 464fe63..f8bc284 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -1,41 +1,3 @@ -# # Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u) -# # B_1 and B_2 are distributed as pmf1 and pmf2 -# # u can be a vector! -# .cond_biv_sampling_old = function(u, pmf1, pmf2) { -# -# # In this way then we iterate over the one with shorter support: -# sw = FALSE -# if (length(pmf1) > length(pmf2)) { -# pmf_ = pmf1 -# pmf1 = pmf2 -# pmf2 = pmf_ -# sw = TRUE -# } -# -# len_u = length(u) -# len_supp1 = length(pmf1) -# supp1 = 0:(len_supp1-1) -# -# W1 = t(matrix(pmf1, ncol=len_u, nrow=len_supp1)) -# supp2 = outer(u, supp1, `-`) -# supp2[supp2<0] = Inf # trick to get NA when we access pmf2 outside the support -# W2 = pmf2[supp2+1] # add 1 because support starts from 0 -# W2[is.na(W2)] = 0 # set NA to zero -# W2 = matrix(W2, nrow = len_u) # back to matrix shape -# W = W1 * W2 -# W = W / rowSums(W) # normalize -# cumW = W %*% upper.tri(diag(len_supp1), diag = TRUE) # cumulative sum on each row -# -# unif = runif(len_u) -# idxs = rowSums(unif > cumW) + 1 -# b1 = supp1[idxs] -# if (sw) b1 = u - b1 # if we have switched, switch back -# -# return(list(b1, u-b1)) -# } - -# OPTIMIZED IMPLEMENTATION: LOOP ON DIFFERENT VALUES OF U -# # Sample from the distribution p(B_1, B_2 | B_1 + B_2 = u), # where B_1 and B_2 are distributed as pmf1 and pmf2. # u is a vector @@ -236,12 +198,6 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, - - - - - - diff --git a/tests/testthat/test-examples.R b/tests/testthat/test-examples.R index 5ae3894..363842a 100644 --- a/tests/testthat/test-examples.R +++ b/tests/testthat/test-examples.R @@ -5,7 +5,8 @@ test_that("Monthly, in_type=='params', distr='gaussian'",{ base_forecasts_in = read.csv(file = "dataForTests/Monthly-Gaussian_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(mean = base_forecasts_in[i,1], + sd = base_forecasts_in[i,2]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "gaussian", num_samples = 100000, seed=42) @@ -24,7 +25,8 @@ test_that("Weekly, in_type=='params', distr='gaussian'",{ base_forecasts_in = read.csv(file = "dataForTests/Weekly-Gaussian_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(mean = base_forecasts_in[i,1], + sd = base_forecasts_in[i,2]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "gaussian", num_samples = 100000, seed=42) @@ -42,7 +44,7 @@ test_that("Monthly, in_type=='params', distr='poisson'",{ base_forecasts_in = read.csv(file = "dataForTests/Monthly-Poisson_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(lambda = base_forecasts_in[i,1]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "poisson", num_samples = 100000, seed=42) @@ -56,7 +58,8 @@ test_that("Monthly, in_type=='params', distr='nbinom'",{ base_forecasts_in = read.csv(file = "dataForTests/Monthly-NegBin_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(size = base_forecasts_in[i,2], + mu = base_forecasts_in[i,1]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "nbinom", num_samples = 10000, seed=42) @@ -75,7 +78,8 @@ test_that("Monthly, in_type=='samples', distr='continuous'",{ base_forecasts_in = read.csv(file = "dataForTests/Monthly-Gaussian_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(mean = base_forecasts_in[i,1], + sd = base_forecasts_in[i,2]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "gaussian", num_samples = 100000, seed=42) m = mean(rowMeans(res.buis$reconciled_samples) - rowMeans(res.buis_samples$reconciled_samples)) @@ -92,7 +96,7 @@ test_that("Monthly, in_type=='samples', distr='discrete'",{ base_forecasts_in = read.csv(file = "dataForTests/Monthly-Poisson_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(lambda = base_forecasts_in[i,1]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "poisson", num_samples = 100000, seed=42) m = mean(rowMeans(res.buis$reconciled_samples) - rowMeans(res.buis_samples$reconciled_samples)) diff --git a/tests/testthat/test-reconc_MCMC.R b/tests/testthat/test-reconc_MCMC.R index cbcd5b3..e16fce8 100644 --- a/tests/testthat/test-reconc_MCMC.R +++ b/tests/testthat/test-reconc_MCMC.R @@ -4,7 +4,7 @@ test_that("MCMC Monthly, in_type=='params', distr='poisson'", { base_forecasts_in = read.csv(file = "dataForTests/Monthly-Poisson_basef.csv", header = FALSE) base_forecasts = list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(as.numeric(base_forecasts_in[i,]))[[1]] + base_forecasts[[i]] = list(lambda = base_forecasts_in[i,1]) } res.buis = reconc_BUIS(S, base_forecasts, in_type = "params", distr = "poisson", num_samples = 100000,seed=42) diff --git a/vignettes/bayesRecon.Rmd b/vignettes/bayesRecon.Rmd index 25ff595..d4ccc53 100644 --- a/vignettes/bayesRecon.Rmd +++ b/vignettes/bayesRecon.Rmd @@ -271,8 +271,8 @@ for (level in train.agg) { level.fc <- forecast(model, h = h) # save mean and sd of the gaussian predictive distribution for (i in 1:h) { - fc[[fc.idx]] <- c(level.fc$mean[[i]], - (level.fc$upper[, "95%"][[i]] - level.fc$mean[[i]]) / qnorm(0.975)) + fc[[fc.idx]] <- list(mean = level.fc$mean[[i]], + sd = (level.fc$upper[, "95%"][[i]] - level.fc$mean[[i]]) / qnorm(0.975)) fc.idx <- fc.idx + 1 } level.idx <- level.idx + 1 diff --git a/vignettes/reconciliation_properties.Rmd b/vignettes/reconciliation_properties.Rmd index b9c43bf..76f113b 100644 --- a/vignettes/reconciliation_properties.Rmd +++ b/vignettes/reconciliation_properties.Rmd @@ -109,7 +109,7 @@ for (j in 1:N) { # Base forecasts: base_fc_j <- c() for (i in 1:n) { - base_fc_j[[i]] <- c(base_fc$mu[[j,i]], base_fc$size[[i]]) + base_fc_j[[i]] <- list(size = base_fc$size[[i]], mu = base_fc$mu[[j,i]]) } # Reconcile via importance sampling: @@ -214,8 +214,8 @@ N_samples <- 1e5 ### Example of concordant-shift effect j <- 124 base_fc_j <- c() -for (i in 1:n) base_fc_j[[i]] <- c(extr_mkt_events_basefc$mu[[j,i]], - extr_mkt_events_basefc$size[[i]]) +for (i in 1:n) base_fc_j[[i]] <- list(size = extr_mkt_events_basefc$size[[i]], + mu = extr_mkt_events_basefc$mu[[j,i]]) # Reconcile buis <- reconc_BUIS(S, base_fc_j, "params", "nbinom", num_samples = N_samples, seed = 42) @@ -231,8 +231,8 @@ knitr::kable(matrix(means, nrow=1), col.names = col_names) ### Example of combination effect j <- 1700 base_fc_j <- c() -for (i in 1:n) base_fc_j[[i]] <- c(extr_mkt_events_basefc$mu[[j,i]], - extr_mkt_events_basefc$size[[i]]) +for (i in 1:n) base_fc_j[[i]] <- list(size = extr_mkt_events_basefc$size[[i]], + mu = extr_mkt_events_basefc$mu[[j,i]]) # Reconcile buis <- reconc_BUIS(S, base_fc_j, "params", "nbinom", num_samples = N_samples, seed = 42) @@ -254,8 +254,8 @@ variance is always smaller than the base variance. ```{r} j <- 2308 base_fc_j <- c() -for (i in 1:n) base_fc_j[[i]] <- c(extr_mkt_events_basefc$mu[[j,i]], - extr_mkt_events_basefc$size[[i]]) +for (i in 1:n) base_fc_j[[i]] <- list(size = extr_mkt_events_basefc$size[[i]], + mu = extr_mkt_events_basefc$mu[[j,i]]) # Reconcile buis <- reconc_BUIS(S, base_fc_j, "params", "nbinom", num_samples = N_samples, seed = 42) samples_y <- buis$reconciled_samples From 4512cde619478b224e21ace7e13c6efd95d38a0e Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 23 Apr 2024 12:26:45 +0200 Subject: [PATCH 07/36] fixed bugs --- DESCRIPTION | 2 +- R/PMF.R | 6 +++--- R/Rprof.out | 16 +++++++++++++++- R/reconc_BUIS.R | 4 ++-- R/reconc_MH.R | 4 ++-- R/reconc_gaussian.R | 2 +- R/shrink_cov.R | 5 +++-- R/utils.R | 17 ++++++++--------- README.Rmd | 10 +++++----- man/reconc_BUIS.Rd | 13 +++++++------ man/reconc_MCMC.Rd | 13 +++++++------ man/reconc_gaussian.Rd | 6 +++--- man/schaferStrimmer_cov.Rd | 5 +++-- tests/testthat/test-schaferStrimmer.R | 3 ++- tests/testthat/test-weightswarn.R | 18 +++++++++--------- 15 files changed, 71 insertions(+), 53 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index abd5295..8498eae 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -34,7 +34,7 @@ Imports: Encoding: UTF-8 LazyData: true Roxygen: list(markdown=TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.1 Suggests: knitr, rmarkdown, diff --git a/R/PMF.R b/R/PMF.R index 22b9a15..80f6cbc 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -20,7 +20,7 @@ PMF.from_params = function(params, distr, Rtoll = 1e-7) { # Check that the distribution is implemented, and that the params are ok if (!(distr %in% .DISCR_DISTR)) { stop(paste0("Input error: distr must be one of {", - paste(DISCR_DISTR, collapse = ', '), "}")) + paste(.DISCR_DISTR, collapse = ', '), "}")) } .check_distr_params(distr, params) # Compute the pmf @@ -49,7 +49,7 @@ PMF.from_params = function(params, distr, Rtoll = 1e-7) { # Sample (with replacement) from the probability distribution specified by the pmf PMF.sample = function(pmf, N_samples) { - s = sample(0:len(pmf), prob = pmf, replace = TRUE, size = N_samples) + s = sample(0:length(pmf), prob = pmf, replace = TRUE, size = N_samples) return(s) } @@ -76,7 +76,7 @@ PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { # -set to zero all the values to the left of the support # -set to zero small values (< toll) PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { - pmf = convolve(pmf1, rev(pmf2), type="open") + pmf = stats::convolve(pmf1, rev(pmf2), type="open") # Look for last value > Rtoll and remove all the elements after it: last_pos = max(which(pmf > Rtoll)) pmf = pmf[1:last_pos] diff --git a/R/Rprof.out b/R/Rprof.out index 27db43c..d8706ba 100644 --- a/R/Rprof.out +++ b/R/Rprof.out @@ -2987,4 +2987,18 @@ sample.interval=20000 "gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" "gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" "gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" -"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" \ No newline at end of file +"gzfile" "readRDS" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "FUN" "lapply" "find.package" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "index.search" "help" "eval" "eval" "?" +"gzfile" "readRDS" "lazyLoadDBexec" "tools:::fetchRdDB" "utils:::.getHelpFile" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"gzfile" "read.dcf" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".read_description" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"writeWrapped" "writeBlock" "writeContent" "writeSection" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeContent" "writeSection" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"file" "readLines" "paste" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" +"cmpSym" "cmp" "genCode" "cb$putconst" "cmpCallArgs" "cmpCallSymFun" "cmpCall" "cmp" "cmpBuiltinArgs" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.resolveObjectFromFunctionCall" ".rs.getCompletionsArgument" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" ".rs.rpc.get_completions" +".rs.matchCall" ".rs.getCompletionsArgument" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" ".rs.rpc.get_completions" +"as.list" "mayCallBrowserList" "mayCallBrowser" "mayCallBrowserList" "mayCallBrowser" "mayCallBrowserList" "mayCallBrowser" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.resolveFormalsImpl" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" ".rs.resolveFormals" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" ".rs.rpc.get_completions" +"cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.getCompletionsArgument" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" ".rs.rpc.get_completions" +"file" "read.table" "read.csv" +"ls" "FUN" "lapply" ".rs.objectsOnSearchPath" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index a88df59..634d2c2 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -316,7 +316,7 @@ reconc_BUIS <- function(S, weights = .compute_weights( b = (B %*% c), # (num_samples x 1) - u = unlist(upper_base_forecasts_H[[hi]]), + u = upper_base_forecasts_H[[hi]], in_type_ = in_typeH[[hi]], distr_ = distr_H[[hi]] ) @@ -343,7 +343,7 @@ reconc_BUIS <- function(S, c = G[gi, ] weights = weights * .compute_weights( b = (B %*% c), - u = unlist(upper_base_forecasts_G[[gi]]), + u = upper_base_forecasts_G[[gi]], in_type_ = in_typeG[[gi]], distr_ = distr_G[[gi]] ) diff --git a/R/reconc_MH.R b/R/reconc_MH.R index adf54cf..0f9ffff 100644 --- a/R/reconc_MH.R +++ b/R/reconc_MH.R @@ -60,8 +60,8 @@ #'} #' #'#Sample from the reconciled forecast distribution using MCMC -#'mcmc = reconc_MCMC(S,base_forecasts=lambdas,distr="poisson", -#' num_samples=30000, seed=42) +#'mcmc = reconc_MCMC(S, base_forecasts, distr = "poisson", +#' num_samples = 30000, seed = 42) #'samples_mcmc <- mcmc$reconciled_samples #' #'#Compare the reconciled means with those obtained via BUIS diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R index ce18ed7..f40b28d 100644 --- a/R/reconc_gaussian.R +++ b/R/reconc_gaussian.R @@ -60,7 +60,7 @@ #'# Obtain reconciled samples for the entire hierarchy: #'# i.e., sample from the reconciled bottoms and multiply by S #'chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition -#'Z = matrix(rnorm(n = 2000), nrow = 2) # Sample from standard normal +#'Z = matrix(stats::rnorm(n = 2000), nrow = 2) # Sample from standard normal #'B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation #' #'U = S %*% B diff --git a/R/shrink_cov.R b/R/shrink_cov.R index 2dd1149..8f65b7b 100644 --- a/R/shrink_cov.R +++ b/R/shrink_cov.R @@ -32,8 +32,9 @@ #' #' # Generate samples #' set.seed(42) -#' x <- replicate(nSamples, trueMean) + t(chol_trueSigma)%*%matrix(rnorm(pTrue*nSamples), -#' nrow=pTrue,ncol=nSamples) +#' x <- replicate(nSamples, trueMean) + +#' t(chol_trueSigma)%*%matrix(stats::rnorm(pTrue*nSamples), +#' nrow = pTrue, ncol = nSamples) #' x <- t(x) #' res_shrinkage <- schaferStrimmer_cov(x) #' res_shrinkage$lambda_star # should be 0.01287923 diff --git a/R/utils.R b/R/utils.R index f2b57f4..b2a11d9 100644 --- a/R/utils.R +++ b/R/utils.R @@ -89,10 +89,9 @@ stop("Input error: size of nbinom must be a positive number") } # Check that exactly one of prob, mu is specified - n_prob_mu = !is.null(prob) + !is.null(mu) - if (n_prob_mu == 2) { + if (!is.null(prob) & !is.null(mu)) { stop("Input error: prob and mu for the nbinom distribution are both specified ") - } else if (n_prob_mu == 0) { + } else if (is.null(prob) & is.null(mu)) { stop("Input error: either prob or mu must be specified") } else { if (!is.null(prob)) { @@ -209,7 +208,7 @@ } if (!(distr %in% .DISCR_DISTR)) { stop(paste0("Input error: distr must be one of {", - paste(DISCR_DISTR, collapse = ', '), "}")) + paste(.DISCR_DISTR, collapse = ', '), "}")) } for (i in 1:n_b) { .check_distr_params(distr, fc_bottom[[i]]) @@ -237,9 +236,9 @@ prob = params$prob mu = params$mu if (!is.null(prob)) { - samples = rnbinom(n = n, size = size, prob = prob) + samples = stats::rnbinom(n = n, size = size, prob = prob) } else if (!is.null(mu)) { - samples = rnbinom(n = n, size = size, mu = mu) + samples = stats::rnbinom(n = n, size = size, mu = mu) } }, ) @@ -254,7 +253,7 @@ } .check_cov(Sigma, "Sigma") - Z = matrix(rnorm(n*n_samples), ncol = n) + Z = matrix(stats::rnorm(n*n_samples), ncol = n) Ch = chol(Sigma) samples = Z %*% Ch + matrix(mu, nrow = n_samples, ncol = n, byrow = TRUE) return(samples) @@ -280,9 +279,9 @@ prob = params$prob mu = params$mu if (!is.null(prob)) { - pmf = dnbinom(x = x, size = size, prob = prob) + pmf = stats::dnbinom(x = x, size = size, prob = prob) } else if (!is.null(mu)) { - pmf = dnbinom(x = x, size = size, mu = mu) + pmf = stats::dnbinom(x = x, size = size, mu = mu) } }, ) diff --git a/README.Rmd b/README.Rmd index 9d3e8cd..22d900f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -92,11 +92,11 @@ lambdas <- c(lambdaY, lambdaS1, lambdaS2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = lambdas[i] + base_forecasts[[i]] = list(lambda = lambdas[i]) } ``` -We recommend using the BUIS algorithm (Zambon et al., 2022) to sample from the reconciled distribution. +We recommend using the BUIS algorithm (Zambon et al., 2024) to sample from the reconciled distribution. ```{r} buis <- reconc_BUIS( @@ -157,7 +157,7 @@ plot( ``` -We also provide a function for sampling using Markov Chain Monte Carlo (Corani et al., 2022). +We also provide a function for sampling using Markov Chain Monte Carlo (Corani et al., 2023). ```{r} mcmc = reconc_MCMC( @@ -192,7 +192,7 @@ sigmas <- c(sigmaY, sigmaS1, sigmaS2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = c(mus[[i]], sigmas[[i]]) + base_forecasts[[i]] = c(mean = mus[[i]], sd = sigmas[[i]]) } ``` @@ -241,7 +241,7 @@ time series*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.04.003) Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. [DOI](https://doi.org/10.1007/s11222-023-10343-y) -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. [DOI](https://doi.org/10.48550/arXiv.2303.15135) +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of the reconciled distributions for Gaussian and count forecasts*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) ## Contributors diff --git a/man/reconc_BUIS.Rd b/man/reconc_BUIS.Rd index 909303f..37181ea 100644 --- a/man/reconc_BUIS.Rd +++ b/man/reconc_BUIS.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/reconc.R +% Please edit documentation in R/reconc_BUIS.R \name{reconc_BUIS} \alias{reconc_BUIS} \title{BUIS for Probabilistic Reconciliation of forecasts via conditioning} @@ -27,7 +27,8 @@ reconc_BUIS( If it \code{in_type} is a string it is assumed that all base forecasts are of the same type.} -\item{distr}{A string or a list of length n describing the type of base forecasts. If it is a list the i-th element is a string with two possible values: +\item{distr}{A string or a list of length n describing the type of base forecasts. +If it is a list the i-th element is a string with two possible values: \itemize{ \item 'continuous' or 'discrete' if \code{in_type[[i]]}='samples'; \item 'gaussian', 'poisson' or 'nbinom' if \code{in_type[[i]]}='params'. @@ -60,11 +61,11 @@ the values of \code{in_type[[i]]} and \code{distr[[i]]}. If \code{in_type[[i]]}='samples', then \code{base_forecast[[i]]} is a vector containing samples from the base forecast distribution. -If \code{in_type[[i]]}='params', then \code{base_forecast[[i]]} is a vector containing the estimated: +If \code{in_type[[i]]}='params', then \code{base_forecast[[i]]} is a list containing the estimated: \itemize{ \item mean and sd for the Gaussian base forecast if \code{distr[[i]]}='gaussian', see \link[stats]{Normal}; \item lambda for the Poisson base forecast if \code{distr[[i]]}='poisson', see \link[stats]{Poisson}; -\item mu and size for the negative binomial base forecast if \code{distr[[i]]}='nbinom', see \link[stats]{NegBinomial}. +\item size and prob (or mu) for the negative binomial base forecast if \code{distr[[i]]}='nbinom', see \link[stats]{NegBinomial}. } See the description of the parameters \code{in_type} and \code{distr} for more details. @@ -104,7 +105,7 @@ sigmas <- c(sigmaY,sigma1,sigma2) base_forecasts = list() for (i in 1:nrow(S)) { -base_forecasts[[i]] = c(mus[[i]], sigmas[[i]]) +base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) } @@ -135,7 +136,7 @@ lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts <- list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = lambdas[i] + base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using the BUIS algorithm diff --git a/man/reconc_MCMC.Rd b/man/reconc_MCMC.Rd index 5fb13e2..14053a2 100644 --- a/man/reconc_MCMC.Rd +++ b/man/reconc_MCMC.Rd @@ -44,18 +44,19 @@ A list containing the reconciled forecasts. The list has the following named ele Uses Markov Chain Monte Carlo algorithm to draw samples from the reconciled forecast distribution, which is obtained via conditioning. -This is a bare-bones implementation of the Metropolis-Hastings algorithm, we suggest the usage of tools to check the convergence. +This is a bare-bones implementation of the Metropolis-Hastings algorithm, +we suggest the usage of tools to check the convergence. The function only works with Poisson or Negative Binomial base forecasts. The function \code{\link[=reconc_BUIS]{reconc_BUIS()}} is generally faster on most hierarchies. } \details{ The parameter \code{base_forecast} is a list containing n elements. -Each element is a vector containing the estimated: +Each element is a list containing the estimated: \itemize{ \item mean and sd for the Gaussian base forecast, see \link[stats]{Normal}, if \code{distr}='gaussian'; \item lambda for the Poisson base forecast, see \link[stats]{Poisson}, if \code{distr}='poisson'; -\item mu and size for the negative binomial base forecast, see \link[stats]{NegBinomial}, if \code{distr}='nbinom'. +\item size and prob (or mu) for the negative binomial base forecast, see \link[stats]{NegBinomial}, if \code{distr}='nbinom'. } The order of the \code{base_forecast} list is given by the order of the time series in the summing matrix. @@ -76,12 +77,12 @@ lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = lambdas[i] + base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using MCMC -mcmc = reconc_MCMC(S,base_forecasts=lambdas,distr="poisson", - num_samples=30000, seed=42) +mcmc = reconc_MCMC(S, base_forecasts, distr = "poisson", + num_samples = 30000, seed = 42) samples_mcmc <- mcmc$reconciled_samples #Compare the reconciled means with those obtained via BUIS diff --git a/man/reconc_gaussian.Rd b/man/reconc_gaussian.Rd index e4f70ee..0bd0961 100644 --- a/man/reconc_gaussian.Rd +++ b/man/reconc_gaussian.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/reconc.R +% Please edit documentation in R/reconc_gaussian.R \name{reconc_gaussian} \alias{reconc_gaussian} \title{Analytical reconciliation of Gaussian base forecasts} @@ -68,8 +68,8 @@ Y_Sigma_reconc <- S \%*\% bottom_Sigma_reconc \%*\% t(S) # note: singular matri # Obtain reconciled samples for the entire hierarchy: # i.e., sample from the reconciled bottoms and multiply by S chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition -Z = matrix(rnorm(n = 2000), nrow = 2) # Sample from standard normal -B = chol_decomp \%*\% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation +Z = matrix(stats::rnorm(n = 2000), nrow = 2) # Sample from standard normal +B = t(chol_decomp) \%*\% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation U = S \%*\% B Y_reconc = rbind(U, B) diff --git a/man/schaferStrimmer_cov.Rd b/man/schaferStrimmer_cov.Rd index 8b37e7b..39b12d7 100644 --- a/man/schaferStrimmer_cov.Rd +++ b/man/schaferStrimmer_cov.Rd @@ -39,8 +39,9 @@ trueMean <- c(0,0) # Generate samples set.seed(42) -x <- replicate(nSamples, trueMean) + t(chol_trueSigma)\%*\%matrix(rnorm(pTrue*nSamples), - nrow=pTrue,ncol=nSamples) +x <- replicate(nSamples, trueMean) + + t(chol_trueSigma)\%*\%matrix(stats::rnorm(pTrue*nSamples), + nrow = pTrue, ncol = nSamples) x <- t(x) res_shrinkage <- schaferStrimmer_cov(x) res_shrinkage$lambda_star # should be 0.01287923 diff --git a/tests/testthat/test-schaferStrimmer.R b/tests/testthat/test-schaferStrimmer.R index 6399bb9..19cad7e 100644 --- a/tests/testthat/test-schaferStrimmer.R +++ b/tests/testthat/test-schaferStrimmer.R @@ -11,7 +11,8 @@ test_that("Test shrinkage estimator", { # we run 100 shrinkage estimators lambdas <- rep(0, 100) for(i in seq(100)){ - rr <- replicate(nSamples, trueMean) + t(chol_trueSigma)%*%matrix(rnorm(pTrue*nSamples), nrow=pTrue,ncol=nSamples) + rr <- replicate(nSamples, trueMean) + + t(chol_trueSigma)%*%matrix(stats::rnorm(pTrue*nSamples), nrow=pTrue,ncol=nSamples) # Estimate mean and covariance from samples mean_est <- rowMeans(rr) Sigma_est <- cov(t(rr)) diff --git a/tests/testthat/test-weightswarn.R b/tests/testthat/test-weightswarn.R index 1724c77..a12e248 100644 --- a/tests/testthat/test-weightswarn.R +++ b/tests/testthat/test-weightswarn.R @@ -3,9 +3,9 @@ test_that("Test effective sample size", { # ----------- n = 200 - b1 = rpois(n=n, lambda = 3) - b2 = rpois(n=n, lambda = 4) - u = rnorm(n=n, mean = 30, sd = 1) + b1 = stats::rpois(n=n, lambda = 3) + b2 = stats::rpois(n=n, lambda = 4) + u = stats::rnorm(n=n, mean = 30, sd = 1) B = cbind(b1,b2) c = matrix(S[1,]) b = (B %*% c) @@ -23,9 +23,9 @@ test_that("Test effective sample size", { # ----------- n = 199 - b1 = rpois(n=n, lambda = 3) - b2 = rpois(n=n, lambda = 4) - u = rnorm(n=n, mean = 30, sd = 1) + b1 = stats::rpois(n=n, lambda = 3) + b2 = stats::rpois(n=n, lambda = 4) + u = stats::rnorm(n=n, mean = 30, sd = 1) B = cbind(b1,b2) c = matrix(S[1,]) b = (B %*% c) @@ -43,9 +43,9 @@ test_that("Test effective sample size", { # ----------- n = 2000 - b1 = rpois(n=n, lambda = 3) - b2 = rpois(n=n, lambda = 4) - u = rnorm(n=n, mean = 18, sd = 1) + b1 = stats::rpois(n=n, lambda = 3) + b2 = stats::rpois(n=n, lambda = 4) + u = stats::rnorm(n=n, mean = 18, sd = 1) B = cbind(b1,b2) c = matrix(S[1,]) b = (B %*% c) From 08bf2e7fa84dff24db7c0ff6919c2208cc197a36 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 23 Apr 2024 16:43:07 +0200 Subject: [PATCH 08/36] added PMF.get_mean and PMF.get_var + minor fix --- R/PMF.R | 20 +++++++++++++++++--- R/TODO.txt | 5 +++-- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index 80f6cbc..7f7be38 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -53,6 +53,20 @@ PMF.sample = function(pmf, N_samples) { return(s) } +# Get the mean of a pmf +PMF.get_mean = function(pmf) { + supp = 0:(length(pmf)-1) + m = pmf %*% supp + return(m) +} + +# Get the variance of a pmf +PMF.get_var = function(pmf) { + supp = 0:(length(pmf)-1) + v = pmf^2 %*% supp - PMF.get_mean(pmf)^2 + return(v) +} + # Apply smoothing to a pmf to "cover the holes" in the support. # If there is no hole, it doesn't do anything. # If the smoothing parameter alpha is not specified, it is set to the min of pmf. @@ -60,7 +74,7 @@ PMF.sample = function(pmf, N_samples) { # Otherwise, add alpha only to points with zero mass. PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { - if (is.null(alpha)) alpha = min(pmf) + if (is.null(alpha)) alpha = min(pmf[pmf!=0]) # apply smoothing only if there are holes if (sum(pmf==0)) { @@ -68,7 +82,7 @@ PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { } else pmf[pmf==0] = alpha } - return(pmf) + return(pmf / sum(pmf)) } # Compute convolution between 2 pmfs. Then, for numerical reasons: @@ -102,7 +116,7 @@ PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { # -otherwise, a list of lists of pmfs for all the steps of the algorithm; # they correspond to the variables of the "auxiliary binary tree" PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, - smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { # Smoothing to "cover the holes" in the supports of the bottom pmfs if (smoothing) l_pmf = lapply(l_pmf, PMF.smoothing, diff --git a/R/TODO.txt b/R/TODO.txt index f2ed13c..ea2747f 100644 --- a/R/TODO.txt +++ b/R/TODO.txt @@ -1,5 +1,6 @@ -commento a reconc_TD -aggiungere test +esperimenti per parametri smoothing +commento a reconc_TD (io o Dario) +aggiungere test (Dario) Prima della submission a CRAN: ... solo temporeanei, poi mettere i valori di default nelle funzioni From 486548c51d2fff60f2733d34c9a2cc6e4579c93a Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Fri, 26 Apr 2024 17:48:17 +0200 Subject: [PATCH 09/36] small fixes and tests for functions of PMF, lowest upper levels, reconc_TDcond, sample funs. --- R/PMF.R | 2 +- R/hierarchy.R | 4 +- tests/testthat/test-PMF.R | 56 +++++++++++++++ tests/testthat/test-hierarchy.R | 14 ++++ tests/testthat/test-reconc_TDcond.R | 69 ++++++++++++++++++ tests/testthat/test-sample_funs.R | 106 ++++++++++++++++++++++++++++ 6 files changed, 249 insertions(+), 2 deletions(-) create mode 100644 tests/testthat/test-PMF.R create mode 100644 tests/testthat/test-reconc_TDcond.R create mode 100644 tests/testthat/test-sample_funs.R diff --git a/R/PMF.R b/R/PMF.R index 7f7be38..2047095 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -63,7 +63,7 @@ PMF.get_mean = function(pmf) { # Get the variance of a pmf PMF.get_var = function(pmf) { supp = 0:(length(pmf)-1) - v = pmf^2 %*% supp - PMF.get_mean(pmf)^2 + v = pmf %*% supp^2 - PMF.get_mean(pmf)^2 return(v) } diff --git a/R/hierarchy.R b/R/hierarchy.R index 368dd5f..7967b31 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -382,6 +382,7 @@ get_reconc_matrices <- function(agg_levels, h) { cond2 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j if (cond1 & cond2) { rows = rows[-length(rows)] # remove i + break } } } @@ -400,7 +401,8 @@ get_reconc_matrices <- function(agg_levels, h) { return(NULL) } - A_ = A[-lowest_rows,] + #A_ = A[-lowest_rows,] + A_ = A[-lowest_rows,,drop=FALSE] n_bott = ncol(A_) n_upp_u = nrow(A_) n_bott_u = length(lowest_rows) diff --git a/tests/testthat/test-PMF.R b/tests/testthat/test-PMF.R new file mode 100644 index 0000000..8061eb9 --- /dev/null +++ b/tests/testthat/test-PMF.R @@ -0,0 +1,56 @@ +test_that("PMF.conv", { + + # Generate a size and a prob each binomial + s1 <- 20; p1 <- 0.6 + s2 <- 30; p2 <- 0.7 + + # Compute the pmf for the two binomials + pmf1 <- dbinom(seq(0,s1),size=s1,prob=p1) + pmf2 <- dbinom(seq(0,s2),size=s2,prob=p2) + + # True mean of the convolution + expected_conv_mean = s1*p1+s2*p2 + expected_conv_var = s1*p1*(1-p1)+s2*p2*(1-p2) + + # Compute the PMF of the convolution + pmf_conv <- PMF.conv(pmf1 = pmf1,pmf2 = pmf2) + + abs(PMF.get_mean(pmf_conv)-expected_conv_mean) + abs(PMF.get_var(pmf_conv)-expected_conv_var) + + # Check if the convolution mean is equal to the truth + expect_lt(abs(PMF.get_mean(pmf_conv)-expected_conv_mean),1e-6) + + # Check if the convolution var is equal to the truth + expect_lt(abs(PMF.get_var(pmf_conv)-expected_conv_var),8e-5) +}) + +test_that("PMF.bottom_up", { + + # Test with 10 bottom + n_bottom <- 10 + + # Create sizes and probs for nbinom bottom distributions + sizes <- c(seq(11,15),seq(19,15)) + probs <- c(rep(0.4,5),rep(0.7,5)) + distr <- "nbinom" + + # Compute true BU params (mean/var) and bottom pmfs + true_bu_mean <- 0 + true_bu_var <- 0 + bott_pmfs <- list() + for(i in seq(n_bottom)){ + true_bu_mean <- true_bu_mean + sizes[i]*(1-probs[i])/probs[i] + true_bu_var <- true_bu_var + sizes[i]*(1-probs[i])/probs[i]^2 + params <- list(size=sizes[i],prob=probs[i]) + bott_pmfs[[i]] <- PMF.from_params(params=params,distr = distr) + } + # Run PMF.bottom_up to get the bottom up pmf + bottom_up_pmf <- PMF.bottom_up(l_pmf=bott_pmfs) + + # Check if true mean is close enough to bottom up pmf mean + expect_lt(abs(PMF.get_mean(bottom_up_pmf)-true_bu_mean)/true_bu_mean,2e-6) + + # Check if true var is close enough to bottom up pmf var + expect_lt(abs(PMF.get_var(bottom_up_pmf)-true_bu_var)/true_bu_var,6e-5) +}) diff --git a/tests/testthat/test-hierarchy.R b/tests/testthat/test-hierarchy.R index fa0e20c..d3b9fb5 100644 --- a/tests/testthat/test-hierarchy.R +++ b/tests/testthat/test-hierarchy.R @@ -12,3 +12,17 @@ test_that("get_reconc_matrices produces expected aggregation and summing matrice diff <- max(abs(expected_rowSumsA-rowSums(out$A)))+max(abs(expected_rowSumsS-rowSums(out$S))) expect_equal(diff, 0) }) + +test_that(".get_Au and .lowest_lev produce expected outcomes", { + + A <- matrix( + data=c(1, 1, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 1, 1),nrow=4,byrow = TRUE) + + + expect_equal(.get_Au(A),matrix(c(1,1,1),ncol=3)) + expect_equal(.lowest_lev(A),c(2,3,4)) + +}) diff --git a/tests/testthat/test-reconc_TDcond.R b/tests/testthat/test-reconc_TDcond.R new file mode 100644 index 0000000..5d4b93c --- /dev/null +++ b/tests/testthat/test-reconc_TDcond.R @@ -0,0 +1,69 @@ +test_that("reconc_TDcond simple example", { + + # Simple example with + # - 12 bottom + # - 10 upper: year, 6 bi-monthly, 3 quarterly + A <- matrix(data=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, + 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1), + nrow=10,byrow = TRUE) + + + S <- rbind(A,diag(nrow=12)) + + # Define means and vars for the forecasts + means <- c(90,31,32,31,33,31,32,62,63,64,rep(15,12)) + vars <- c(20,4,4,4,4,4,4,8,8,8,rep(2,12))^2 + + # create the lists for reconciliation + # upper + fc_upper <- list(mu = means[1:10], + Sigma = diag(vars[1:10])) + + # bottom + fc_bottom <- list() + for(i in seq(ncol(S))){ + fc_bottom[[i]] <-as.integer(.distr_sample(list(mean=means[i+10],sd = vars[i+10]), "gaussian", 2e4)) + fc_bottom[[i]][which(fc_bottom[[i]]<0)] <- 0 # set-negative-to-zero + } + + # reconciliation with TDcond + res.TDcond <- reconc_TDcond(S, fc_bottom, fc_upper, + bottom_in_type = "samples", + N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, + seed = NULL) + + # Compute the reconciliation analytically (everything Gaussian) + fc_bott_gauss <- list(mu = means[11:22], + Sigma = diag(vars[11:22])) + + inv_B <- diag(1/diag(fc_bott_gauss$Sigma)) + inv_U <- diag(1/diag(fc_upper$Sigma)) + At_inv_U_A <- crossprod(A,inv_U)%*%A + # Here we use the reduced A with only the lowest level + Au <- A[.lowest_lev(A),] + inv_A_B_At <- solve(Au%*%tcrossprod(fc_bott_gauss$Sigma,Au)) + + # formulas for the reconciled precision, covariance and mean + bott_reconc_Prec <- inv_B+At_inv_U_A-crossprod(Au,inv_A_B_At)%*%Au + bott_reconc_cov <- solve(bott_reconc_Prec) + bott_reconc_mean <- fc_bott_gauss$mu + tcrossprod(bott_reconc_cov,A)%*%inv_U%*%(fc_upper$mu-A%*%fc_bott_gauss$mu) + + # compute the difference between empirical and analytical + m_diff <- unlist(lapply(res.TDcond$pmf$bottom,PMF.get_mean)) - bott_reconc_mean + + expect_true(all(abs(m_diff/bott_reconc_mean)<8e-3)) + + + # The variances are different + #unlist(lapply(res.TDcond$pmf$bottom,PMF.get_var)) + #diag(bott_reconc_cov) + +}) diff --git a/tests/testthat/test-sample_funs.R b/tests/testthat/test-sample_funs.R new file mode 100644 index 0000000..ac4ef2c --- /dev/null +++ b/tests/testthat/test-sample_funs.R @@ -0,0 +1,106 @@ +test_that("sampling from univariate normal", { + + #for(i in seq(5e4)){ + # Generate 1e4 samples from univariate Gaussian + params <- list(mean=42, sd=1) + distr <- "gaussian" + n <- 1e4 + samples <- .distr_sample(params, distr, n) + + # Compute empirical mean and sd + sam_mean <- mean(samples) + sam_sd <- sd(samples) + + # Check how close empirical values are to the truth + m <- abs(sam_mean-42)/42 + s <- abs(sam_sd-1) + #} + #mean(s<4e-2) + #mean(m<2e-3) + + expect_equal(m < 2e-3, TRUE) + expect_equal(s < 4e-2, TRUE) +}) + +test_that("sampling from univariate nbinom", { + + #m <- seq(5e4) + #for(i in seq(5e4)){ + # Generate 1e4 samples from negative binomial (size, prob) + params <- list(size=12,prob=0.8) + distr <- "nbinom" + n <- 1e4 + samples <- .distr_sample(params, distr, n) + + # Compute empirical mean + sam_mean <- mean(samples) + true_mean <- params$size*(1-params$prob)/params$prob + + # Check how close empirical values are to the truth + m <- abs(sam_mean-true_mean)/true_mean + #} + #mean(m<3e-2) + + expect_equal(m < 3e-2, TRUE) + + #m <- seq(5e4) + #for(i in seq(5e4)){ + # Generate 1e4 samples from negative binomial (size, mu) + params <- list(size=12,mu=true_mean) + distr <- "nbinom" + n <- 1e4 + samples <- .distr_sample(params, distr, n) + + # Compute empirical mean + sam_mean <- mean(samples) + + # Check how close empirical values are to the truth + m <- abs(sam_mean-params$mu)/params$mu + #} + #mean(m<3e-2) + + expect_equal(m < 3e-2, TRUE) +}) + +test_that("sampling from univariate poisson", { + + #m <- seq(5e4) + #for(i in seq(5e4)){ + # Generate 1e4 samples from poisson + params <- list(lambda=10) + distr <- "poisson" + n <- 1e4 + samples <- .distr_sample(params, distr, n) + + # Compute empirical mean + sam_mean <- mean(samples) + + # Check how close empirical values are to the truth + m <- abs(sam_mean-10)/10 + #} + + #mean(m<3e-2) + + expect_equal(m < 3e-2, TRUE) +}) + +test_that("sampling from multivariate normal", { + #m <- matrix(nrow=5e4,ncol=2) + #for(i in seq(5e4)){ + # Generate 1e4 samples from bivariate normal + mu=c(10,10) + Sigma= matrix(c(1,0.7,0.7,1),nrow = 2) + n <- 1e4 + samples <- .MVN_sample(n, mu, Sigma) + + # Compute empirical mean + sam_mean <- colMeans(samples) + + # Check how close empirical values are to the truth + m <- abs(sam_mean-10)/10 + #} + + #all(colMeans(m<8e-3)==1) + + expect_equal(all(m < 8e-3), TRUE) +}) From d257c728309262e3db4c98e6d1c8bc3f76103bbc Mon Sep 17 00:00:00 2001 From: LorenzoZambon Date: Fri, 26 Apr 2024 19:00:22 +0200 Subject: [PATCH 10/36] added check that the hierarchy is balanced --- R/hierarchy.R | 34 ++++++++++++++++++++-------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/R/hierarchy.R b/R/hierarchy.R index 7967b31..3061319 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -331,9 +331,9 @@ 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 - cond2 = sum(A[i,] - A[j,] >= 0) < m # Upper j is not a descendant of upper i - cond3 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j + cond1 = 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) { return(FALSE) } @@ -354,8 +354,8 @@ 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 - cond2 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j + cond1 = A[i,] %*% A[j,] != 0 # Upper i and j have some common descendants + cond2 = any(A[i,] > A[j,]) # Upper i is not a descendant of upper j if (cond1 & cond2) { return(FALSE) } @@ -378,15 +378,16 @@ get_reconc_matrices <- function(agg_levels, h) { for (i in 1:k) { rows = c(rows, i) for (j in 1:k) { - cond1 = A[i,] %*% A[j,] != 0 # Upper i and j have some common descendants - cond2 = sum(A[i,] - A[j,] <= 0) < m # Upper i is not a descendant of upper j - if (cond1 & cond2) { - rows = rows[-length(rows)] # remove i - break + 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)] + break + } } } } - # keep all rows except those that satisfy both the above conditions + # keep all rows except those that have no descendants among the uppers return(rows) } @@ -396,12 +397,16 @@ get_reconc_matrices <- function(agg_levels, h) { if (is.null(lowest_rows)) lowest_rows = .lowest_lev(A) + # The sum of the rows corresponding to the lowest level should be a vector of 1 + if (any(colSums(A[lowest_rows,,drop=FALSE])!=1)) { + stop("The hierarchy is not balanced") + } + if (length(lowest_rows) == nrow(A)) { warning("All the upper are lowest-upper. Return NULL") return(NULL) } - #A_ = A[-lowest_rows,] A_ = A[-lowest_rows,,drop=FALSE] n_bott = ncol(A_) n_upp_u = nrow(A_) @@ -418,7 +423,8 @@ get_reconc_matrices <- function(agg_levels, h) { return(1*A_u) # to get numerical values instead of TRUE / FALSE } - - +# TODO: +# -check that there are no duplicates in the rows of A +# -check that there are no columns of A with all zeros (bottom without any upper) From aaeb566fa7dfb65fb4cbeacefd467ec967afc39c Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Mon, 29 Apr 2024 15:44:18 +0200 Subject: [PATCH 11/36] Fixed several tests, updated .check_S, .lowest_lev functions. The generator for the S and base forecasts for the weekly Gaussian is now in the dataForTests folder. --- .Rbuildignore | 1 + R/Rprof.out | 3004 ----------------- R/TODO.txt | 7 - R/_profile_TD.R | 169 - R/hierarchy.R | 28 +- R/utils.R | 19 +- .../dataForTests/Weekly-Gaussian_S.csv | 196 +- .../dataForTests/Weekly-Gaussian_basef.csv | 196 +- .../dataForTests/generate_weeklyTestData.R | 26 + tests/testthat/test-examples.R | 19 +- tests/testthat/test-hierarchy.R | 28 + tests/testthat/test-reconc_TDcond.R | 7 +- tests/testthat/test-sample_funs.R | 38 +- 13 files changed, 319 insertions(+), 3419 deletions(-) delete mode 100644 R/Rprof.out delete mode 100644 R/TODO.txt delete mode 100644 R/_profile_TD.R create mode 100644 tests/testthat/dataForTests/generate_weeklyTestData.R diff --git a/.Rbuildignore b/.Rbuildignore index 993be48..e3fded5 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,3 +10,4 @@ ^doc$ ^Meta$ ^data-raw$ +^\./tests/testthat/dataForTests/generate_weeklyTestData\.R$ 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"tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" -"gsub" "psub" "htmlify" "utils:::getSrcByte" "isBlankLineRd" "addParaBreaks" "writeLines" "writeLinesUTF8" "of1" "writeBlock" "writeContent" "writeContent" "writeSection" "Rd2HTML" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" -"file" "readLines" "paste" "" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "try" "withCallingHandlers" "" -"cmpSym" "cmp" "genCode" "cb$putconst" "cmpCallArgs" "cmpCallSymFun" "cmpCall" "cmp" "cmpBuiltinArgs" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "h" "tryInline" "cmpCall" "cmp" "genCode" "cmpfun" "doTryCatch" "tryCatchOne" "tryCatchList" "tryCatch" "compiler:::tryCmpfun" ".rs.resolveObjectFromFunctionCall" ".rs.getCompletionsArgument" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" 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".rs.getCompletionsArgument" ".rs.getCompletionsFunction" ".rs.getRCompletions" ".rs.appendCompletionsOptionalElement" ".rs.appendCompletions" ".rs.rpc.get_completions" -"file" "read.table" "read.csv" -"ls" "FUN" "lapply" ".rs.objectsOnSearchPath" ".rs.getCompletionsSearchPath" "Reduce" ".rs.rpc.get_completions" diff --git a/R/TODO.txt b/R/TODO.txt deleted file mode 100644 index ea2747f..0000000 --- a/R/TODO.txt +++ /dev/null @@ -1,7 +0,0 @@ -esperimenti per parametri smoothing -commento a reconc_TD (io o Dario) -aggiungere test (Dario) - -Prima della submission a CRAN: -... solo temporeanei, poi mettere i valori di default nelle funzioni - diff --git a/R/_profile_TD.R b/R/_profile_TD.R deleted file mode 100644 index 4186c84..0000000 --- a/R/_profile_TD.R +++ /dev/null @@ -1,169 +0,0 @@ -dir_path <- dirname(rstudioapi::getSourceEditorContext()$path) -setwd(dir_path) - -rm( list = ls()); gc(); #clean environment - -source("PMF.R") -source("reconc_TDcond.R") -source("reconc_gaussian.R") -source("utils.R") -source("hierarchy.R") -source("shrink_cov.R") - -#### - -get_hier_M5 = function(save_ = FALSE, save_path = "../../../results/") { - - n_b = 3049 - n_u = 11 - n = n_u + n_b - - A = matrix(0, nrow = n_u, ncol = n_b) - A[1,] = 1 # store - A[2,1:565] = 1 # categories - A[3,566:1612] = 1 - A[4,1613:3049] = 1 - A[5,1:416] = 1 # departments - A[6,417:565] = 1 - A[7,566:1097] = 1 - A[8,1098:1612] = 1 - A[9,1613:1828] = 1 - A[10,1829:2226] = 1 - A[11,2227:3049] = 1 - - S = rbind(A, diag(rep(1, n_b))) - - if (save_) { - saveRDS(S, paste0(save_path, "S.rds")) - saveRDS(A, paste0(save_path, "A.rds")) - } - - return(list(A = A, S = S)) -} - -get_gauss_params_bott = function(fc_bottom) { - - mu_bott = c() - sd_bott = c() - for (fc_b in fc_bottom) { - tab = fc_b$samples - vals = as.integer(names(tab)) - mu_b = sum(vals * tab) / sum(tab) - sd_b = (sum((vals-mu_b)**2 * tab) / sum(tab))**0.5 - mu_bott = c(mu_bott, mu_b) - sd_bott = c(sd_bott, sd_b) - } - l = list(mu = mu_bott, sd = sd_bott) - - return(l) -} - -get_gauss_params_upp = function(fc_upper) { - - mu = c() - sd = c() - for (fc in fc_upper) { - mu = c(mu, fc$mu) - sd = c(sd, fc$sigma) - } - - residuals.upper = lapply(fc_upper, "[[", "residuals") - residuals.upper = t(do.call("rbind", residuals.upper)) - Sigma = schaferStrimmer_cov(residuals.upper)$shrink_cov - - l = list(mu = mu, sd = sd, Sigma = Sigma) - return(l) -} - -PMF.from_tab = function(tab) { - supp = names(tab) - pmf = rep(0, max(as.integer(supp))) - for (j in supp) { - pmf[[as.integer(j)+1]] = tab[[j]] - } - pmf = pmf / sum(pmf) - return(pmf) -} - -### - -seed = 42 -h = 1 -STORE = "CA_1" -N_samples = 1e4 -toll=1e-16 -Rtoll=1e-7 -smooth_bottom=TRUE -al_smooth=NULL -lap_smooth=FALSE -n_low_u_rprof = 7 - -CAT = c("HOBBIES", "HOUSEHOLD", "FOODS") -DEPT = c("HOBBIES_1", "HOBBIES_2", "HOUSEHOLD_1", "HOUSEHOLD_2", - "FOODS_1", "FOODS_2", "FOODS_3") -names_u = c(STORE, CAT, DEPT) - -store_path = paste0("../../../Ricerca/Reconciliation/Codici/Mixed/results/", STORE) - -A = get_hier_M5()$A -n_upp = nrow(A) -n_bott = ncol(A) -n = n_bott + n_upp -S = rbind(A, diag(n_bott)) - -str_bott = paste0(store_path,"/h=",h,"/base_fc_bottom.rds") -str_upp = paste0(store_path,"/h=",h,"/base_fc_upper.rds") -fc_bottom = readRDS(str_bott) -tabs_bottom = lapply(fc_bottom, "[[", "samples") -pmf_bottom = lapply(tabs_bottom, PMF.from_tab) - -fc_upper = readRDS(str_upp) -upper_params = get_gauss_params_upp(fc_upper) - -N_samples = 1e5 - -### -# All the upper - -Rprof() -out = reconc_TDcond(S, pmf_bottom, upper_params, - bottom_in_type = "pmf", N_samples = N_samples, - toll = 1e-15, - return_pmf = TRUE, return_samples = TRUE) -Rprof(NULL) -summaryRprof() - -### -# Only one upper - -S_ = S[c(1,12:3060),] -upper_params_ = list() -upper_params_$mu = upper_params$mu[1] -upper_params_$Sigma = upper_params$Sigma[1,1] - -Rprof() -out = reconc_TDcond(S_, pmf_bottom, upper_params_, - bottom_in_type = "pmf", N_samples = N_samples, - return_pmf = FALSE, return_samples = TRUE) -Rprof(NULL) -summaryRprof() - -### -# Many upper, but all "lowest-level" - -S_ = S[c(5:11,12:3060),] -upper_params_ = list() -upper_params_$mu = upper_params$mu[5:11] -upper_params_$Sigma = upper_params$Sigma[5:11,5:11] - -Rprof() -out = reconc_TDcond(S, pmf_bottom, upper_params, - bottom_in_type = "pmf", N_samples = N_samples, - return_pmf = FALSE, return_samples = TRUE) -Rprof(NULL) -summaryRprof() - - - - - diff --git a/R/hierarchy.R b/R/hierarchy.R index 3061319..b86d222 100644 --- a/R/hierarchy.R +++ b/R/hierarchy.R @@ -378,7 +378,7 @@ get_reconc_matrices <- function(agg_levels, h) { for (i in 1:k) { rows = c(rows, i) for (j in 1:k) { - if (i < j) { + 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)] @@ -389,19 +389,20 @@ get_reconc_matrices <- function(agg_levels, h) { } # keep all rows except those that have no descendants among the uppers + # 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") + } + return(rows) } + # Get the aggregating matrix Au of the sub-hierarchy composed just by the uppers .get_Au <- function(A, lowest_rows=NULL) { if (is.null(lowest_rows)) lowest_rows = .lowest_lev(A) - # The sum of the rows corresponding to the lowest level should be a vector of 1 - if (any(colSums(A[lowest_rows,,drop=FALSE])!=1)) { - stop("The hierarchy is not balanced") - } - if (length(lowest_rows) == nrow(A)) { warning("All the upper are lowest-upper. Return NULL") return(NULL) @@ -423,8 +424,17 @@ get_reconc_matrices <- function(agg_levels, h) { return(1*A_u) # to get numerical values instead of TRUE / FALSE } -# TODO: -# -check that there are no duplicates in the rows of A -# -check that there are no columns of A with all zeros (bottom without any upper) +# Check if the rows of A are ordered +.check_ordered_A <- function(A){ + aggregates_sum <- rowSums(A) + ordered_aggreg <- order(aggregates_sum,decreasing = TRUE) + if(all(aggregates_sum == aggregates_sum[ordered_aggreg]) ){ + return(list(value=TRUE)) + }else{ + return(list(value=FALSE, order=ordered_aggreg)) + } + +} + diff --git a/R/utils.R b/R/utils.R index b2a11d9..2b0d17e 100644 --- a/R/utils.R +++ b/R/utils.R @@ -10,8 +10,25 @@ # Function to check values allowed in S. .check_S <- function(S) { if(!identical(sort(unique(as.vector(S))), c(0,1)) ){ - stop("Input error: S must be a matrix containing only 0s and 1s.") + stop("Input error in S: S must be a matrix containing only 0s and 1s.") } + + + if(!all(colSums(S)>1)){ + stop("Input error in S: all bottom level forecasts must aggregate into an upper.") + } + + + if(nrow(unique(S))!=nrow(S)){ + stop("Input error in S: S has a repeated row.") + } + + # Check that each bottom has a corresponding row with with one 1 and the rest 0s. + if(sum(rowSums(S) ==1) != ncol(S)){ + stop("Input error in S: there is at least one bottom that does not have a row with one 1 and the rest 0s.") + } + + } # Check if it is a covariance matrix (i.e. symmetric p.d.) diff --git a/tests/testthat/dataForTests/Weekly-Gaussian_S.csv b/tests/testthat/dataForTests/Weekly-Gaussian_S.csv index 463e342..307348e 100644 --- a/tests/testthat/dataForTests/Weekly-Gaussian_S.csv +++ b/tests/testthat/dataForTests/Weekly-Gaussian_S.csv @@ -1,98 +1,98 @@ 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+8.23530096909963,2 +8.9146638114471,2 +7.76518155820668,2 +7.64859790098853,2 +8.94678115844727,2 +5.11665601166897,2 +7.38615032518283,2 +8.661568693351,2 +8.46365778241307,2 +7.38809811067767,2 +9.3060473841615,2 diff --git a/tests/testthat/dataForTests/generate_weeklyTestData.R b/tests/testthat/dataForTests/generate_weeklyTestData.R new file mode 100644 index 0000000..cd521a6 --- /dev/null +++ b/tests/testthat/dataForTests/generate_weeklyTestData.R @@ -0,0 +1,26 @@ +# Generate the weekly base forecasts parameters +# CHANGE THE WORKING DIRECTORY BEFORE RUNNING +rm(list=ls()) +library(bayesRecon) + +# Generate A matrix for weekly hierarchy +A <- .gen_weekly() +S <- rbind(A, diag(nrow=52,ncol=52))+0e-3 +# Save matrix to file +write.table(S,file="./Weekly-Gaussian_S.csv",row.names = FALSE,sep=',',col.names = FALSE,quote = FALSE) + +# Generate randomly bottom means +set.seed(1) +bottom_means <- runif(52,min=5,max=10) + +# The upper means are generated by +# aggregating the bottom means (according to A) with an epsilon of incoherence +eps = 0.1 +upper_means <- (1+eps)*A%*%bottom_means +upper_means <- as.vector(upper_means) + +# Create the matrix to save to csv +base_forecasts_out <- cbind(c(upper_means,bottom_means),c(rep(3,length(upper_means)),rep(2,length(bottom_means)))) +write.table(base_forecasts_out,file="./Weekly-Gaussian_basef.csv",row.names = FALSE,sep=',',col.names = FALSE,quote = FALSE) + + diff --git a/tests/testthat/test-examples.R b/tests/testthat/test-examples.R index 363842a..161a87d 100644 --- a/tests/testthat/test-examples.R +++ b/tests/testthat/test-examples.R @@ -20,21 +20,22 @@ test_that("Monthly, in_type=='params', distr='gaussian'",{ test_that("Weekly, in_type=='params', distr='gaussian'",{ # Run IS Reconc - S = read.csv(file = "dataForTests/Weekly-Gaussian_S.csv", header = FALSE) - S = as.matrix(S) - base_forecasts_in = read.csv(file = "dataForTests/Weekly-Gaussian_basef.csv", header = FALSE) - base_forecasts = list() + S <- read.csv(file = "dataForTests/Weekly-Gaussian_S.csv", header = FALSE) + S <- as.matrix(S) + mode(S) <- "numeric" + base_forecasts_in <- read.csv(file = "dataForTests/Weekly-Gaussian_basef.csv", header = FALSE) + base_forecasts <- list() for (i in 1:nrow(base_forecasts_in)) { - base_forecasts[[i]] = list(mean = base_forecasts_in[i,1], + base_forecasts[[i]] <- list(mean = base_forecasts_in[i,1], sd = base_forecasts_in[i,2]) } - res.buis = reconc_BUIS(S, base_forecasts, + res.buis <- reconc_BUIS(S, base_forecasts, in_type = "params", distr = "gaussian", num_samples = 100000, seed=42) # Run Gauss Reconc - res.gauss = reconc_gaussian(S, base_forecasts_in[[1]], diag(base_forecasts_in[[2]]^2)) + res.gauss <- reconc_gaussian(S, base_forecasts_in[[1]], diag(base_forecasts_in[[2]]^2)) # Test - b_mask = rowSums(S) == 1 - m = mean(rowMeans(res.buis$reconciled_samples)[b_mask] - as.numeric(res.gauss$bottom_reconciled_mean)) + b_mask <- rowSums(S) == 1 + m <- mean(rowMeans(res.buis$reconciled_samples)[b_mask] - as.numeric(res.gauss$bottom_reconciled_mean)) expect_equal(abs(m) < 2e-2, TRUE) }) diff --git a/tests/testthat/test-hierarchy.R b/tests/testthat/test-hierarchy.R index d3b9fb5..11b1c82 100644 --- a/tests/testthat/test-hierarchy.R +++ b/tests/testthat/test-hierarchy.R @@ -26,3 +26,31 @@ test_that(".get_Au and .lowest_lev produce expected outcomes", { expect_equal(.lowest_lev(A),c(2,3,4)) }) + +test_that(".get_Au behaves identically on A with different row order.",{ + A <- matrix(data=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, + 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1), + nrow=10,byrow = TRUE) + + A1 <- matrix(data=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1), + nrow=10,byrow = TRUE) + + expect_equal(.get_Au(A),.get_Au(A1)) +}) diff --git a/tests/testthat/test-reconc_TDcond.R b/tests/testthat/test-reconc_TDcond.R index 5d4b93c..ddfafc7 100644 --- a/tests/testthat/test-reconc_TDcond.R +++ b/tests/testthat/test-reconc_TDcond.R @@ -23,16 +23,17 @@ test_that("reconc_TDcond simple example", { vars <- c(20,4,4,4,4,4,4,8,8,8,rep(2,12))^2 # create the lists for reconciliation - # upper + ## upper fc_upper <- list(mu = means[1:10], Sigma = diag(vars[1:10])) - # bottom + ## bottom fc_bottom <- list() for(i in seq(ncol(S))){ fc_bottom[[i]] <-as.integer(.distr_sample(list(mean=means[i+10],sd = vars[i+10]), "gaussian", 2e4)) fc_bottom[[i]][which(fc_bottom[[i]]<0)] <- 0 # set-negative-to-zero } + # reconciliation with TDcond res.TDcond <- reconc_TDcond(S, fc_bottom, fc_upper, @@ -41,9 +42,11 @@ test_that("reconc_TDcond simple example", { seed = NULL) # Compute the reconciliation analytically (everything Gaussian) + ## Save bottom forecast parameters fc_bott_gauss <- list(mu = means[11:22], Sigma = diag(vars[11:22])) + # Compute the reconciled precision inv_B <- diag(1/diag(fc_bott_gauss$Sigma)) inv_U <- diag(1/diag(fc_upper$Sigma)) At_inv_U_A <- crossprod(A,inv_U)%*%A diff --git a/tests/testthat/test-sample_funs.R b/tests/testthat/test-sample_funs.R index ac4ef2c..121f9c1 100644 --- a/tests/testthat/test-sample_funs.R +++ b/tests/testthat/test-sample_funs.R @@ -1,6 +1,5 @@ test_that("sampling from univariate normal", { - #for(i in seq(5e4)){ # Generate 1e4 samples from univariate Gaussian params <- list(mean=42, sd=1) distr <- "gaussian" @@ -14,9 +13,6 @@ test_that("sampling from univariate normal", { # Check how close empirical values are to the truth m <- abs(sam_mean-42)/42 s <- abs(sam_sd-1) - #} - #mean(s<4e-2) - #mean(m<2e-3) expect_equal(m < 2e-3, TRUE) expect_equal(s < 4e-2, TRUE) @@ -24,8 +20,6 @@ test_that("sampling from univariate normal", { test_that("sampling from univariate nbinom", { - #m <- seq(5e4) - #for(i in seq(5e4)){ # Generate 1e4 samples from negative binomial (size, prob) params <- list(size=12,prob=0.8) distr <- "nbinom" @@ -38,13 +32,9 @@ test_that("sampling from univariate nbinom", { # Check how close empirical values are to the truth m <- abs(sam_mean-true_mean)/true_mean - #} - #mean(m<3e-2) expect_equal(m < 3e-2, TRUE) - #m <- seq(5e4) - #for(i in seq(5e4)){ # Generate 1e4 samples from negative binomial (size, mu) params <- list(size=12,mu=true_mean) distr <- "nbinom" @@ -56,16 +46,27 @@ test_that("sampling from univariate nbinom", { # Check how close empirical values are to the truth m <- abs(sam_mean-params$mu)/params$mu - #} - #mean(m<3e-2) expect_equal(m < 3e-2, TRUE) + + # Check if it returns an error when all 3 parameters are specified + params <- list(size=12,mu=true_mean,prob=0.8) + distr <- "nbinom" + n <- 1e4 + expect_error(.distr_sample(params, distr, n)) + + # Check if it returns an error when size is not specified + params <- list(mu=true_mean,prob=0.8) + distr <- "nbinom" + n <- 1e4 + expect_error(.distr_sample(params, distr, n)) + + + }) test_that("sampling from univariate poisson", { - #m <- seq(5e4) - #for(i in seq(5e4)){ # Generate 1e4 samples from poisson params <- list(lambda=10) distr <- "poisson" @@ -77,16 +78,12 @@ test_that("sampling from univariate poisson", { # Check how close empirical values are to the truth m <- abs(sam_mean-10)/10 - #} - - #mean(m<3e-2) expect_equal(m < 3e-2, TRUE) }) test_that("sampling from multivariate normal", { - #m <- matrix(nrow=5e4,ncol=2) - #for(i in seq(5e4)){ + # Generate 1e4 samples from bivariate normal mu=c(10,10) Sigma= matrix(c(1,0.7,0.7,1),nrow = 2) @@ -98,9 +95,6 @@ test_that("sampling from multivariate normal", { # Check how close empirical values are to the truth m <- abs(sam_mean-10)/10 - #} - - #all(colMeans(m<8e-3)==1) expect_equal(all(m < 8e-3), TRUE) }) From ddede49ea352f3ff349c711190549ff985984955 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Tue, 30 Apr 2024 17:59:12 +0200 Subject: [PATCH 12/36] changed the structure of the object returned by reconc_TDcond. minor fixes to utils functions. First draft of documentation for reconc_TDcond and PMF functions. --- NAMESPACE | 5 ++ R/PMF.R | 79 +++++++++++++++++- R/reconc_TDcond.R | 122 +++++++++++++++++++++++----- R/utils.R | 8 +- man/PMF.get_mean.Rd | 26 ++++++ man/PMF.get_quantile.Rd | 28 +++++++ man/PMF.get_var.Rd | 26 ++++++ man/PMF.sample.Rd | 28 +++++++ man/reconc_TDcond.Rd | 105 ++++++++++++++++++++++++ tests/testthat/test-PMF.R | 14 ++++ tests/testthat/test-reconc_TDcond.R | 20 ++++- 11 files changed, 429 insertions(+), 32 deletions(-) create mode 100644 man/PMF.get_mean.Rd create mode 100644 man/PMF.get_quantile.Rd create mode 100644 man/PMF.get_var.Rd create mode 100644 man/PMF.sample.Rd create mode 100644 man/reconc_TDcond.Rd diff --git a/NAMESPACE b/NAMESPACE index e2a7930..23b65cf 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,8 +1,13 @@ # Generated by roxygen2: do not edit by hand +export(PMF.get_mean) +export(PMF.get_quantile) +export(PMF.get_var) +export(PMF.sample) export(get_reconc_matrices) export(reconc_BUIS) export(reconc_MCMC) +export(reconc_TDcond) export(reconc_gaussian) export(schaferStrimmer_cov) export(temporal_aggregation) diff --git a/R/PMF.R b/R/PMF.R index 2047095..f9921e8 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -47,26 +47,99 @@ PMF.from_params = function(params, distr, Rtoll = 1e-7) { return(pmf) } -# Sample (with replacement) from the probability distribution specified by the pmf +#' @title Sample from the distribution given as a PMF object +#' +#' @description +#' +#' Samples (with replacement) from the probability distribution specified by `pmf`. +#' +#' @param pmf the PMF object. +#' @param N_samples number of samples. +#' +#' @examples +#' library(bayesRecon) +#' +#' # To do +#' +#' @return `N_samples` drawn from the distribution specified by `pmf`. +#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()] +#' @export PMF.sample = function(pmf, N_samples) { s = sample(0:length(pmf), prob = pmf, replace = TRUE, size = N_samples) return(s) } -# Get the mean of a pmf +#' @title Get the mean of the distribution from a PMF object +#' +#' @description +#' +#' Returns the mean from the PMF specified by `pmf`. +#' +#' @param pmf the PMF object. +#' +#' @examples +#' library(bayesRecon) +#' +#' # To do +#' +#' @return A numerical value for mean of the distribution. +#' @seealso [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] +#' @export PMF.get_mean = function(pmf) { supp = 0:(length(pmf)-1) m = pmf %*% supp return(m) } -# Get the variance of a pmf +#' @title Get the variance of the distribution from a PMF object +#' +#' @description +#' +#' Returns the variance from the PMF specified by `pmf`. +#' +#' @param pmf the PMF object. +#' +#' @examples +#' library(bayesRecon) +#' +#' # To do +#' +#' @return A numerical value for variance. +#' @seealso [PMF.get_mean()], [PMF.get_quantile()], [PMF.sample()] +#' @export PMF.get_var = function(pmf) { supp = 0:(length(pmf)-1) v = pmf %*% supp^2 - PMF.get_mean(pmf)^2 return(v) } + +#' @title Get quantile from a PMF object +#' +#' @description +#' +#' Returns the `p` quantile from the PMF specified by `pmf`. +#' +#' @param pmf the PMF object. +#' @param p the probability of the required quantile. +#' +#' @examples +#' library(bayesRecon) +#' +#' # To do +#' +#' @return A numeric value for the quantile. +#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.sample()] +#' @export +PMF.get_quantile = function(pmf, p) { + if (p <= 0 | p >= 1) { + stop("Input error: probability p must be between 0 and 1") + } + cdf = cumsum(pmf) + x = min(which(cdf >= p)) + return(x-1) +} + # Apply smoothing to a pmf to "cover the holes" in the support. # If there is no hole, it doesn't do anything. # If the smoothing parameter alpha is not specified, it is set to the min of pmf. diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index f8bc284..9766cff 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -64,11 +64,89 @@ -# Reconciliation top-down using (...) -# +#' @title Probabilistic Reconciliation of forecasts via top-down conditioning +#' +#' @description +#' +#' Uses the Top-down conditioning algorithm to draw samples from the reconciled +#' forecast distribution. Reconciliation is performed in two steps: +#' first, the upper base forecasts are reconciled via conditioning, +#' using only the hierarchical constraints between the upper variables; then, +#' the bottom distributions are updated via a probabilistic top-down procedure +#' +#' @details +#' +#' The base (unreconciled) forecasts are passed with the parameters +#' +#' * `fc_bottom`: a list of length n_bottom where each element is either a pmf object (`bottom_in_type='pmf'`), +#' a vector of samples (`bottom_in_type='samples'`) or the parameters (`bottom_in_type='params'`) of the +#' parametric distribution specified in `distr`. +#' * `fc_upper`: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. +#' The list must contain only the named elements `mu` (vector of length n_upper) and `Sigma` (n_upper x n_upper matrix) +#' +#' +#' A PMF object is a numerical vector containing the probability mass function for a forecast. +#' The first element of a PMF vector for the variable X always contains the probability P(X=0) and +#' there is one element for each integer. +#' The last element of the PMF corresponds to the probability of the last value in the support of X. +#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile} for functions that handle PMF objects. +#' +#' +#' A warnings is triggered if the intersection of the support for the reconciled uppers +#' and the support of the bottom-up distribution is too small. In this case only +#' few samples from the reconciled upper are kept. The warning reports the percentage +#' of samples kept. +#' +#' 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). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL +#' @param fc_bottom A list containing the bottom base forecasts, see details. +#' @param fc_upper A list containing the bottom base forecasts, see details. +#' @param bottom_in_type A string with three possible values: +#' +#' * 'pmf' if the bottom base forecasts are in the form of pmf, see details; +#' * 'samples' if the bottom base forecasts are in the form of samples; +#' * 'params' if the bottom base forecasts are in the form of estimated parameters. +#' +#' @param distr A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +#' +#' This is only used if `bottom_in_type=='params'`. +#' +#' @param num_samples Number of samples drawn from the reconciled distribution. +#' @param return_type The return type of the reconciled distributions. A string with three possible values: +#' +#' * 'pmf' returns a list containing reconciled pmf objects; +#' * 'samples' returns a list containing reconciled samples; +#' * 'all' returns a list with both pmf objects and samples. +#' +#' @param ... additional parameters to be passed to the smoothing functions for PMF objects. +#' +#' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples +#' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. +#' @param seed Seed for reproducibility. +#' +#' @return A list containing the reconciled forecasts. The list has the following named elements: +#' +#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, depending on the value of `return_type`; +#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, depending on the value of `return_type`. +#' +#' @examples +#' +#' library(bayesRecon) +#' # TO DO +#' @references +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +#' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' +#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +#' +#' @seealso [reconc_BUIS()] +#' +#' @export reconc_TDcond = function(S, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, - N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, + num_samples = 2e4, return_type = "pmf", ..., suppress_warnings = FALSE, seed = NULL) { @@ -88,7 +166,7 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, # Check inputs .check_input_TD(S, fc_bottom, fc_upper, bottom_in_type, distr, - return_pmf, return_samples) + return_type) # Get aggr. matrix A and find the "lowest upper" A = .get_A_from_S(S)$A @@ -104,7 +182,7 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, ### Get upper samples if (n_u == n_u_low) { # If all the upper are lowest-upper, just sample from the base distribution - U = .MVN_sample(N_samples, mu_u, Sigma_u) # (dim: N_samples x n_u_low) + U = .MVN_sample(num_samples, mu_u, Sigma_u) # (dim: num_samples x n_u_low) U = round(U) # round to integer U_js = asplit(U, MARGIN = 2) # split into list of column vectors @@ -120,8 +198,8 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, # Analytically reconcile the upper rec_gauss_u = reconc_gaussian(S_u, mu_u, Sigma_u) - # Sample from reconciled MVN on the lowest level of the upper (dim: N_samples x n_u_low) - U = .MVN_sample(n_samples = N_samples, + # Sample from reconciled MVN on the lowest level of the upper (dim: num_samples x n_u_low) + U = .MVN_sample(n_samples = num_samples, mu = rec_gauss_u$bottom_reconciled_mean, Sigma = rec_gauss_u$bottom_reconciled_covariance) U = round(U) # round to integer @@ -150,8 +228,8 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, samp_ok = rowSums(samp_ok) == n_u_low # Only keep the "good" upper samples, and throw a warning if some samples are discarded: U_js = lapply(U_js, "[", samp_ok) - if (sum(samp_ok) != N_samples & !suppress_warnings) { - warning(paste0("Only ", round(sum(samp_ok)/N_samples, 3)*100, "% of the upper samples ", + if (sum(samp_ok) != num_samples & !suppress_warnings) { + warning(paste0("Only ", round(sum(samp_ok)/num_samples, 3)*100, "% of the upper samples ", "are in the support of the bottom-up distribution; ", "the others are discarded.")) } @@ -161,24 +239,24 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, for (j in 1:n_u_low) { B[[j]] = .TD_sampling(U_js[[j]], L_pmf_js[[j]], ...) } - B = do.call("rbind", B) # dim: n_bottom x N_samples - U = A %*% B # dim: n_upper x N_samples + B = do.call("rbind", B) # dim: n_bottom x num_samples + U = A %*% B # dim: n_upper x num_samples # Prepare output: include the marginal pmfs and/or the samples (depending on "return" inputs) - out = list() - if (return_pmf) { + out = list(bottom_reconciled=list(), upper_reconciled=list()) + if (return_type %in% c('pmf', 'all')) { upper_pmf = lapply(1:n_u, function(i) PMF.from_samples(U[i,])) bottom_pmf = lapply(1:n_b, function(i) PMF.from_samples(B[i,])) - out$pmf = list( - upper = upper_pmf, - bottom = bottom_pmf - ) + + out$bottom_reconciled$pmf = bottom_pmf + out$upper_reconciled$pmf = upper_pmf + } - if (return_samples) { - out$samples = list( - upper = U, - bottom = B - ) + if (return_type %in% c('samples','all')) { + + out$bottom_reconciled$samples = B + out$upper_reconciled$samples = U + } return(out) diff --git a/R/utils.R b/R/utils.R index 2b0d17e..c8cfc06 100644 --- a/R/utils.R +++ b/R/utils.R @@ -127,7 +127,7 @@ # Check that the samples are discrete .check_discrete_samples <- function(samples) { - if (!all.equal(samples, as.integer(samples))) { + if (!isTRUE(all.equal(samples, as.integer(samples)))) { stop("Input error: samples are not all discrete") } } @@ -186,7 +186,7 @@ # Check input for TDcond .check_input_TD <- function(S, fc_bottom, fc_upper, bottom_in_type, distr, - return_pmf, return_samples) { + return_type) { .check_S(S) @@ -196,8 +196,8 @@ if (!(bottom_in_type %in% c("pmf", "samples", "params"))) { stop("Input error: bottom_in_type must be either 'pmf', 'samples', or 'params'") } - if (!(return_pmf | return_samples)) { - stop("Input error: at least one of 'return_pmf' and 'return_samples' must be TRUE") + if (!(return_type %in% c("pmf", "samples", "all"))) { + stop("Input error: return_type must be either 'pmf', 'samples', or 'all'") } if (length(fc_bottom) != n_b) { stop("Input error: length of fc_bottom does not match with S") diff --git a/man/PMF.get_mean.Rd b/man/PMF.get_mean.Rd new file mode 100644 index 0000000..6aecebb --- /dev/null +++ b/man/PMF.get_mean.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PMF.R +\name{PMF.get_mean} +\alias{PMF.get_mean} +\title{Get the mean of the distribution from a PMF object} +\usage{ +PMF.get_mean(pmf) +} +\arguments{ +\item{pmf}{the PMF object.} +} +\value{ +A numerical value for mean of the distribution. +} +\description{ +Returns the mean from the PMF specified by \code{pmf}. +} +\examples{ +library(bayesRecon) + +# To do + +} +\seealso{ +\code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}} +} diff --git a/man/PMF.get_quantile.Rd b/man/PMF.get_quantile.Rd new file mode 100644 index 0000000..171780d --- /dev/null +++ b/man/PMF.get_quantile.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PMF.R +\name{PMF.get_quantile} +\alias{PMF.get_quantile} +\title{Get quantile from a PMF object} +\usage{ +PMF.get_quantile(pmf, p) +} +\arguments{ +\item{pmf}{the PMF object.} + +\item{p}{the probability of the required quantile.} +} +\value{ +A numeric value for the quantile. +} +\description{ +Returns the \code{p} quantile from the PMF specified by \code{pmf}. +} +\examples{ +library(bayesRecon) + +# To do + +} +\seealso{ +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.sample]{PMF.sample()}} +} diff --git a/man/PMF.get_var.Rd b/man/PMF.get_var.Rd new file mode 100644 index 0000000..38e3b97 --- /dev/null +++ b/man/PMF.get_var.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PMF.R +\name{PMF.get_var} +\alias{PMF.get_var} +\title{Get the variance of the distribution from a PMF object} +\usage{ +PMF.get_var(pmf) +} +\arguments{ +\item{pmf}{the PMF object.} +} +\value{ +A numerical value for variance. +} +\description{ +Returns the variance from the PMF specified by \code{pmf}. +} +\examples{ +library(bayesRecon) + +# To do + +} +\seealso{ +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}} +} diff --git a/man/PMF.sample.Rd b/man/PMF.sample.Rd new file mode 100644 index 0000000..55a4b79 --- /dev/null +++ b/man/PMF.sample.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PMF.R +\name{PMF.sample} +\alias{PMF.sample} +\title{Sample from the distribution given as a PMF object} +\usage{ +PMF.sample(pmf, N_samples) +} +\arguments{ +\item{pmf}{the PMF object.} + +\item{N_samples}{number of samples.} +} +\value{ +\code{N_samples} drawn from the distribution specified by \code{pmf}. +} +\description{ +Samples (with replacement) from the probability distribution specified by \code{pmf}. +} +\examples{ +library(bayesRecon) + +# To do + +} +\seealso{ +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}} +} diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd new file mode 100644 index 0000000..4690c5d --- /dev/null +++ b/man/reconc_TDcond.Rd @@ -0,0 +1,105 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/reconc_TDcond.R +\name{reconc_TDcond} +\alias{reconc_TDcond} +\title{Probabilistic Reconciliation of forecasts via top-down conditioning} +\usage{ +reconc_TDcond( + S, + fc_bottom, + fc_upper, + bottom_in_type = "pmf", + distr = NULL, + num_samples = 20000, + return_type = "pmf", + ..., + suppress_warnings = FALSE, + seed = NULL +) +} +\arguments{ +\item{S}{Summing matrix (n x n_bottom). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL} + +\item{fc_bottom}{A list containing the bottom base forecasts, see details.} + +\item{fc_upper}{A list containing the bottom base forecasts, see details.} + +\item{bottom_in_type}{A string with three possible values: +\itemize{ +\item 'pmf' if the bottom base forecasts are in the form of pmf, see details; +\item 'samples' if the bottom base forecasts are in the form of samples; +\item 'params' if the bottom base forecasts are in the form of estimated parameters. +}} + +\item{distr}{A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). + +This is only used if \code{bottom_in_type=='params'}.} + +\item{num_samples}{Number of samples drawn from the reconciled distribution.} + +\item{return_type}{The return type of the reconciled distributions. A string with three possible values: +\itemize{ +\item 'pmf' returns a list containing reconciled pmf objects; +\item 'samples' returns a list containing reconciled samples; +\item 'all' returns a list with both pmf objects and samples. +}} + +\item{...}{additional parameters to be passed to the smoothing functions for PMF objects.} + +\item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples +are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} + +\item{seed}{Seed for reproducibility.} +} +\value{ +A list containing the reconciled forecasts. The list has the following named elements: +\itemize{ +\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, depending on the value of \code{return_type}; +\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, depending on the value of \code{return_type}. +} +} +\description{ +Uses the Top-down conditioning algorithm to draw samples from the reconciled +forecast distribution. Reconciliation is performed in two steps: +first, the upper base forecasts are reconciled via conditioning, +using only the hierarchical constraints between the upper variables; then, +the bottom distributions are updated via a probabilistic top-down procedure +} +\details{ +The base (unreconciled) forecasts are passed with the parameters +\itemize{ +\item \code{fc_bottom}: a list of length n_bottom where each element is either a pmf object (\code{bottom_in_type='pmf'}), +a vector of samples (\code{bottom_in_type='samples'}) or the parameters (\code{bottom_in_type='params'}) of the +parametric distribution specified in \code{distr}. +\item \code{fc_upper}: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. +The list must contain only the named elements \code{mu} (vector of length n_upper) and \code{Sigma} (n_upper x n_upper matrix) +} + +A PMF object is a numerical vector containing the probability mass function for a forecast. +The first element of a PMF vector for the variable X always contains the probability P(X=0) and +there is one element for each integer. +The last element of the PMF corresponds to the probability of the last value in the support of X. +See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile} for functions that handle PMF objects. + +A warnings is triggered if the intersection of the support for the reconciled uppers +and the support of the bottom-up distribution is too small. In this case only +few samples from the reconciled upper are kept. The warning reports the percentage +of samples kept. + +Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +} +\examples{ + +library(bayesRecon) +# TO DO +} +\references{ +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. + +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +} +\seealso{ +\code{\link[=reconc_BUIS]{reconc_BUIS()}} +} diff --git a/tests/testthat/test-PMF.R b/tests/testthat/test-PMF.R index 8061eb9..c4023fe 100644 --- a/tests/testthat/test-PMF.R +++ b/tests/testthat/test-PMF.R @@ -54,3 +54,17 @@ test_that("PMF.bottom_up", { # Check if true var is close enough to bottom up pmf var expect_lt(abs(PMF.get_var(bottom_up_pmf)-true_bu_var)/true_bu_var,6e-5) }) + +test_that("PMF.quantile",{ + n_samples = 1e5 + size = 10 + prob = 0.6 + p = 0.01 + + x = rnbinom(n_samples, size = size, prob = prob) + pmf = PMF.from_samples(x) + q = PMF.get_quantile(pmf, p) + qq = qnbinom(p, size = size, prob = prob) + + expect_equal(q,qq) +}) diff --git a/tests/testthat/test-reconc_TDcond.R b/tests/testthat/test-reconc_TDcond.R index ddfafc7..2d6cb31 100644 --- a/tests/testthat/test-reconc_TDcond.R +++ b/tests/testthat/test-reconc_TDcond.R @@ -38,8 +38,22 @@ test_that("reconc_TDcond simple example", { # reconciliation with TDcond res.TDcond <- reconc_TDcond(S, fc_bottom, fc_upper, bottom_in_type = "samples", - N_samples = 2e4, return_pmf = TRUE, return_samples = FALSE, - seed = NULL) + num_samples = 2e4, return_type = "pmf", + seed = 42) + + res.TDcond2 <- reconc_TDcond(S, fc_bottom, fc_upper, + bottom_in_type = "samples", + num_samples = 2e4, return_type = "samples", + seed = 42) + + res.TDcond3 <- reconc_TDcond(S, fc_bottom, fc_upper, + bottom_in_type = "samples", + num_samples = 2e4, return_type = "all", + seed = 42) + + # Check if all return_type return identical results + expect_identical(res.TDcond$bottom_reconciled$pmf,res.TDcond3$bottom_reconciled$pmf) + expect_identical(res.TDcond2$bottom_reconciled$samples,res.TDcond3$bottom_reconciled$samples) # Compute the reconciliation analytically (everything Gaussian) ## Save bottom forecast parameters @@ -60,7 +74,7 @@ test_that("reconc_TDcond simple example", { bott_reconc_mean <- fc_bott_gauss$mu + tcrossprod(bott_reconc_cov,A)%*%inv_U%*%(fc_upper$mu-A%*%fc_bott_gauss$mu) # compute the difference between empirical and analytical - m_diff <- unlist(lapply(res.TDcond$pmf$bottom,PMF.get_mean)) - bott_reconc_mean + m_diff <- unlist(lapply(res.TDcond$bottom_reconciled$pmf,PMF.get_mean)) - bott_reconc_mean expect_true(all(abs(m_diff/bott_reconc_mean)<8e-3)) From 2ded7c7c3ea96662e5766dc11cfb7111de85610c Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Thu, 2 May 2024 11:48:55 +0200 Subject: [PATCH 13/36] Updated documentation for PMF and reconc_TDcond, added PMF.summary --- NAMESPACE | 1 + R/PMF.R | 104 ++++++++++++++++++++++++++++++++++++---- R/reconc_TDcond.R | 31 +++++++++++- man/PMF.get_mean.Rd | 13 ++++- man/PMF.get_quantile.Rd | 15 +++++- man/PMF.get_var.Rd | 12 ++++- man/PMF.sample.Rd | 15 +++++- man/PMF.summary.Rd | 37 ++++++++++++++ man/reconc_TDcond.Rd | 31 +++++++++++- 9 files changed, 237 insertions(+), 22 deletions(-) create mode 100644 man/PMF.summary.Rd diff --git a/NAMESPACE b/NAMESPACE index 23b65cf..a492ed6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,6 +4,7 @@ export(PMF.get_mean) export(PMF.get_quantile) export(PMF.get_var) export(PMF.sample) +export(PMF.summary) export(get_reconc_matrices) export(reconc_BUIS) export(reconc_MCMC) diff --git a/R/PMF.R b/R/PMF.R index f9921e8..1439ac6 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -56,16 +56,28 @@ PMF.from_params = function(params, distr, Rtoll = 1e-7) { #' @param pmf the PMF object. #' @param N_samples number of samples. #' +#' @return `N_samples` drawn from the distribution specified by `pmf`. +#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.summary()] +#' #' @examples #' library(bayesRecon) #' -#' # To do +#' # Let's build the pmf of a Binomial distribution with parameters n and p +#' n <- 10 +#' p <- 0.6 +#' pmf_binomial <- apply(matrix(seq(0,n)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) +#' +#' # Draw samples from the PMF object +#' set.seed(1) +#' samples <- PMF.sample(pmf=pmf_binomial,N_samples = 1e4) +#' +#' # Plot the histogram computed with the samples and the true value of the PMF +#' hist(samples,breaks=seq(0,n),freq=FALSE) +#' points(seq(0,n)-0.5,pmf_binomial,pch=16) #' -#' @return `N_samples` drawn from the distribution specified by `pmf`. -#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()] #' @export PMF.sample = function(pmf, N_samples) { - s = sample(0:length(pmf), prob = pmf, replace = TRUE, size = N_samples) + s = sample(0:(length(pmf)-1), prob = pmf, replace = TRUE, size = N_samples) return(s) } @@ -80,10 +92,19 @@ PMF.sample = function(pmf, N_samples) { #' @examples #' library(bayesRecon) #' -#' # To do +#' # Let's build the pmf of a Binomial distribution with parameters n and p +#' n <- 10 +#' p <- 0.6 +#' pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) +#' +#' +#' # The true mean corresponds to n*p +#' true_mean <- n*p +#' mean_from_PMF <- PMF.get_mean(pmf=pmf_binomial) +#' cat("True mean:", true_mean, "\nMean from PMF:", mean_from_PMF) #' #' @return A numerical value for mean of the distribution. -#' @seealso [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] +#' @seealso [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()], [PMF.summary()] #' @export PMF.get_mean = function(pmf) { supp = 0:(length(pmf)-1) @@ -102,10 +123,18 @@ PMF.get_mean = function(pmf) { #' @examples #' library(bayesRecon) #' -#' # To do +#' # Let's build the pmf of a Binomial distribution with parameters n and p +#' n <- 10 +#' p <- 0.6 +#' pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) +#' +#' # The true variance corresponds to n*p*(1-p) +#' true_var <- n*p*(1-p) +#' var_from_PMF <- PMF.get_var(pmf=pmf_binomial) +#' cat("True variance:", true_var, "\nVariance from PMF:", var_from_PMF) #' #' @return A numerical value for variance. -#' @seealso [PMF.get_mean()], [PMF.get_quantile()], [PMF.sample()] +#' @seealso [PMF.get_mean()], [PMF.get_quantile()], [PMF.sample()], [PMF.summary()] #' @export PMF.get_var = function(pmf) { supp = 0:(length(pmf)-1) @@ -126,10 +155,21 @@ PMF.get_var = function(pmf) { #' @examples #' library(bayesRecon) #' -#' # To do +#' # Let's build the pmf of a Binomial distribution with parameters n and p +#' n <- 10 +#' p <- 0.6 +#' pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) +#' +#' # We can obtain the quantile of this PMF with the function PMF.get_quantile() +#' quant_90 <- PMF.get_quantile(pmf=pmf_binomial,p=0.9) +#' +#' # The true median is ceiling(n*p) +#' quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) +#' cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50) +#' #' #' @return A numeric value for the quantile. -#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.sample()] +#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.sample()], [PMF.summary()] #' @export PMF.get_quantile = function(pmf, p) { if (p <= 0 | p >= 1) { @@ -140,6 +180,48 @@ PMF.get_quantile = function(pmf, p) { return(x-1) } + +#' @title Returns summary of a PMF object +#' +#' @description +#' +#' Returns the summary (min,max, IQR, median, mean) of the PMF specified by `pmf`. +#' +#' @param pmf the PMF object. +#' @param toll lowest possible probability mass on the left +#' @param Rtoll lowest possible probability mass on the right +#' +#' +#' @examples +#' library(bayesRecon) +#' +#' # Let's build the pmf of a Binomial distribution with parameters n and p +#' n <- 10 +#' p <- 0.6 +#' pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) +#' +#' # Print the summary of this distribution +#' PMF.summary(pmf=pmf_binomial) +#' +#' +#' @return A summary data.frame +#' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] +#' @export +PMF.summary = function(pmf, toll=1e-16, Rtoll=1e-7) { + # Max is the last position with enough mass + last_pos = max(which(pmf > Rtoll)) + pmf = pmf[1:last_pos] + # Set to zero values smaller than toll: + pmf[pmf0))-1), + `1st Qu.`=PMF.get_quantile(pmf,0.25), + "Median"=PMF.get_quantile(pmf,0.5), + "Mean"=PMF.get_mean(pmf), + `3rd Qu.`=PMF.get_quantile(pmf,0.75), + "Max"=(last_pos-1),check.names = FALSE) + return(all_summaries) +} + # Apply smoothing to a pmf to "cover the holes" in the support. # If there is no hole, it doesn't do anything. # If the smoothing parameter alpha is not specified, it is set to the min of pmf. @@ -158,6 +240,8 @@ PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { return(pmf / sum(pmf)) } + + # Compute convolution between 2 pmfs. Then, for numerical reasons: # -removes small values at the end of the vector (< Rtoll) # -set to zero all the values to the left of the support diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 9766cff..34fa1c0 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -89,7 +89,7 @@ #' The first element of a PMF vector for the variable X always contains the probability P(X=0) and #' there is one element for each integer. #' The last element of the PMF corresponds to the probability of the last value in the support of X. -#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile} for functions that handle PMF objects. +#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. #' #' #' A warnings is triggered if the intersection of the support for the reconciled uppers @@ -133,7 +133,34 @@ #' @examples #' #' library(bayesRecon) -#' # TO DO +#' +#' # Consider a simple hierarchy with two bottom and one upper +#' A <- matrix(c(1,1),nrow=1) +#' S <- rbind(A,diag(nrow=2)) +#' # The bottom forecasts are Poisson with lambda=15 +#' lambda <- 15 +#' n_tot <- 60 +#' fc_bottom <- list() +#' fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +#' fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +#' +#' # The upper forecast is a Normal with mean 40 and std 5 +#' fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) +#' +#' # We can reconcile with reconc_TDcond +#' res.TDcond <- reconc_TDcond(S, fc_bottom, fc_upper) +#' +#' # Note that the bottom distributions are shifted to the right +#' PMF.summary(res.TDcond$bottom_reconciled$pmf[[1]]) +#' PMF.summary(fc_bottom[[1]]) +#' +#' PMF.summary(res.TDcond$bottom_reconciled$pmf[[2]]) +#' PMF.summary(fc_bottom[[2]]) +#' +#' # The upper distribution remains similar +#' PMF.summary(res.TDcond$upper_reconciled$pmf[[1]]) +#' PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) +#' #' @references #' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. #' diff --git a/man/PMF.get_mean.Rd b/man/PMF.get_mean.Rd index 6aecebb..452d609 100644 --- a/man/PMF.get_mean.Rd +++ b/man/PMF.get_mean.Rd @@ -18,9 +18,18 @@ Returns the mean from the PMF specified by \code{pmf}. \examples{ library(bayesRecon) -# To do +# Let's build the pmf of a Binomial distribution with parameters n and p +n <- 10 +p <- 0.6 +pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) + + +# The true mean corresponds to n*p +true_mean <- n*p +mean_from_PMF <- PMF.get_mean(pmf=pmf_binomial) +cat("True mean:", true_mean, "\nMean from PMF:", mean_from_PMF) } \seealso{ -\code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}} +\code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}}, \code{\link[=PMF.summary]{PMF.summary()}} } diff --git a/man/PMF.get_quantile.Rd b/man/PMF.get_quantile.Rd index 171780d..857bc93 100644 --- a/man/PMF.get_quantile.Rd +++ b/man/PMF.get_quantile.Rd @@ -20,9 +20,20 @@ Returns the \code{p} quantile from the PMF specified by \code{pmf}. \examples{ library(bayesRecon) -# To do +# Let's build the pmf of a Binomial distribution with parameters n and p +n <- 10 +p <- 0.6 +pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) + +# We can obtain the quantile of this PMF with the function PMF.get_quantile() +quant_90 <- PMF.get_quantile(pmf=pmf_binomial,p=0.9) + +# The true median is ceiling(n*p) +quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) +cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50) + } \seealso{ -\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.sample]{PMF.sample()}} +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.sample]{PMF.sample()}}, \code{\link[=PMF.summary]{PMF.summary()}} } diff --git a/man/PMF.get_var.Rd b/man/PMF.get_var.Rd index 38e3b97..9422c28 100644 --- a/man/PMF.get_var.Rd +++ b/man/PMF.get_var.Rd @@ -18,9 +18,17 @@ Returns the variance from the PMF specified by \code{pmf}. \examples{ library(bayesRecon) -# To do +# Let's build the pmf of a Binomial distribution with parameters n and p +n <- 10 +p <- 0.6 +pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) + +# The true variance corresponds to n*p*(1-p) +true_var <- n*p*(1-p) +var_from_PMF <- PMF.get_var(pmf=pmf_binomial) +cat("True variance:", true_var, "\nVariance from PMF:", var_from_PMF) } \seealso{ -\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}} +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}}, \code{\link[=PMF.summary]{PMF.summary()}} } diff --git a/man/PMF.sample.Rd b/man/PMF.sample.Rd index 55a4b79..59dec37 100644 --- a/man/PMF.sample.Rd +++ b/man/PMF.sample.Rd @@ -20,9 +20,20 @@ Samples (with replacement) from the probability distribution specified by \code{ \examples{ library(bayesRecon) -# To do +# Let's build the pmf of a Binomial distribution with parameters n and p +n <- 10 +p <- 0.6 +pmf_binomial <- apply(matrix(seq(0,n)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) + +# Draw samples from the PMF object +set.seed(1) +samples <- PMF.sample(pmf=pmf_binomial,N_samples = 1e4) + +# Plot the histogram computed with the samples and the true value of the PMF +hist(samples,breaks=seq(0,n),freq=FALSE) +points(seq(0,n)-0.5,pmf_binomial,pch=16) } \seealso{ -\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}} +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.summary]{PMF.summary()}} } diff --git a/man/PMF.summary.Rd b/man/PMF.summary.Rd new file mode 100644 index 0000000..7f76439 --- /dev/null +++ b/man/PMF.summary.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PMF.R +\name{PMF.summary} +\alias{PMF.summary} +\title{Returns summary of a PMF object} +\usage{ +PMF.summary(pmf, toll = 1e-16, Rtoll = 1e-07) +} +\arguments{ +\item{pmf}{the PMF object.} + +\item{toll}{lowest possible probability mass on the left} + +\item{Rtoll}{lowest possible probability mass on the right} +} +\value{ +A summary data.frame +} +\description{ +Returns the summary (min,max, IQR, median, mean) of the PMF specified by \code{pmf}. +} +\examples{ +library(bayesRecon) + +# Let's build the pmf of a Binomial distribution with parameters n and p +n <- 10 +p <- 0.6 +pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) + +# Print the summary of this distribution +PMF.summary(pmf=pmf_binomial) + + +} +\seealso{ +\code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.sample]{PMF.sample()}} +} diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index 4690c5d..c909091 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -79,7 +79,7 @@ A PMF object is a numerical vector containing the probability mass function for The first element of a PMF vector for the variable X always contains the probability P(X=0) and there is one element for each integer. The last element of the PMF corresponds to the probability of the last value in the support of X. -See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile} for functions that handle PMF objects. +See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. A warnings is triggered if the intersection of the support for the reconciled uppers and the support of the bottom-up distribution is too small. In this case only @@ -91,7 +91,34 @@ Note that warnings are an indication that the base forecasts might have issues. \examples{ library(bayesRecon) -# TO DO + +# Consider a simple hierarchy with two bottom and one upper +A <- matrix(c(1,1),nrow=1) +S <- rbind(A,diag(nrow=2)) +# The bottom forecasts are Poisson with lambda=15 +lambda <- 15 +n_tot <- 60 +fc_bottom <- list() +fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) + +# The upper forecast is a Normal with mean 40 and std 5 +fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) + +# We can reconcile with reconc_TDcond +res.TDcond <- reconc_TDcond(S, fc_bottom, fc_upper) + +# Note that the bottom distributions are shifted to the right +PMF.summary(res.TDcond$bottom_reconciled$pmf[[1]]) +PMF.summary(fc_bottom[[1]]) + +PMF.summary(res.TDcond$bottom_reconciled$pmf[[2]]) +PMF.summary(fc_bottom[[2]]) + +# The upper distribution remains similar +PMF.summary(res.TDcond$upper_reconciled$pmf[[1]]) +PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) + } \references{ Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. From af046dc1866dfaab5b5b2583b289e23964310b69 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Fri, 3 May 2024 17:54:33 +0200 Subject: [PATCH 14/36] Added reconc_mixCond function, MVN_density function, tests, documentation and examples for reconc_mixCond. --- NAMESPACE | 1 + R/reconc_MixCond.R | 204 +++++++++++++++++++++++++++ R/reconc_TDcond.R | 2 +- R/utils.R | 26 ++++ man/reconc_MixCond.Rd | 135 ++++++++++++++++++ man/reconc_TDcond.Rd | 2 +- tests/testthat/test-reconc_MixCond.R | 59 ++++++++ tests/testthat/test-sample_funs.R | 22 +++ 8 files changed, 449 insertions(+), 2 deletions(-) create mode 100644 R/reconc_MixCond.R create mode 100644 man/reconc_MixCond.Rd create mode 100644 tests/testthat/test-reconc_MixCond.R diff --git a/NAMESPACE b/NAMESPACE index a492ed6..0253736 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,6 +8,7 @@ export(PMF.summary) export(get_reconc_matrices) export(reconc_BUIS) export(reconc_MCMC) +export(reconc_MixCond) export(reconc_TDcond) export(reconc_gaussian) export(schaferStrimmer_cov) diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R new file mode 100644 index 0000000..c130318 --- /dev/null +++ b/R/reconc_MixCond.R @@ -0,0 +1,204 @@ +############################################################################### +# Reconciliation with mixed-conditioning (Mix-Cond) +############################################################################### + + + +#' @title Probabilistic Reconciliation of forecasts via mixed conditioning +#' +#' @description +#' +#' Uses the mixed conditioning algorithm to draw samples from the reconciled +#' forecast distribution. +#' +#' @details +#' +#' The base (unreconciled) forecasts are passed with the parameters +#' +#' * `fc_bottom`: a list of length n_bottom where each element is either a pmf object (`bottom_in_type='pmf'`), +#' a vector of samples (`bottom_in_type='samples'`) or the parameters (`bottom_in_type='params'`) of the +#' parametric distribution specified in `distr`. +#' * `fc_upper`: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. +#' The list must contain only the named elements `mu` (vector of length n_upper) and `Sigma` (n_upper x n_upper matrix) +#' +#' +#' A PMF object is a numerical vector containing the probability mass function for a forecast. +#' The first element of a PMF vector for the variable X always contains the probability P(X=0) and +#' there is one element for each integer. +#' The last element of the PMF corresponds to the probability of the last value in the support of X. +#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. +#' +#' +#' A warnings is triggered if the intersection of the support for the reconciled uppers +#' and the support of the bottom-up distribution is too small. In this case only +#' few samples from the reconciled upper are kept. The warning reports the percentage +#' of samples kept. +#' +#' 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). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL +#' @param fc_bottom A list containing the bottom base forecasts, see details. +#' @param fc_upper A list containing the bottom base forecasts, see details. +#' @param bottom_in_type A string with three possible values: +#' +#' * 'pmf' if the bottom base forecasts are in the form of pmf, see details; +#' * 'samples' if the bottom base forecasts are in the form of samples; +#' * 'params' if the bottom base forecasts are in the form of estimated parameters. +#' +#' @param distr A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +#' +#' This is only used if `bottom_in_type=='params'`. +#' +#' @param num_samples Number of samples drawn from the reconciled distribution. +#' @param num_resample Number of importance sampling resamples. +#' @param our_sampler TO BE REMOVED AFTER THE TESTS. +#' @param return_type The return type of the reconciled distributions. A string with three possible values: +#' +#' * 'pmf' returns a list containing reconciled pmf objects; +#' * 'samples' returns a list containing reconciled samples; +#' * 'all' returns a list with both pmf objects and samples. +#' +#' @param ... additional parameters to be passed to the smoothing functions for PMF objects. +#' +#' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples +#' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. +#' @param seed Seed for reproducibility. +#' +#' @return A list containing the reconciled forecasts. The list has the following named elements: +#' +#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, depending on the value of `return_type`; +#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, depending on the value of `return_type`. +#' +#' @examples +#' +#' library(bayesRecon) +#' +#' # Consider a simple hierarchy with two bottom and one upper +#' A <- matrix(c(1,1),nrow=1) +#' S <- rbind(A,diag(nrow=2)) +#' # The bottom forecasts are Poisson with lambda=15 +#' lambda <- 15 +#' n_tot <- 60 +#' fc_bottom <- list() +#' fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +#' fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +#' +#' # The upper forecast is a Normal with mean 40 and std 5 +#' fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) +#' +#' # We can reconcile with reconc_TDcond +#' res.mixCond <- reconc_MixCond(S, fc_bottom, fc_upper) +#' +#' # Note that the bottom distributions are slightly shifted to the right +#' PMF.summary(res.mixCond$bottom_reconciled$pmf[[1]]) +#' PMF.summary(fc_bottom[[1]]) +#' +#' PMF.summary(res.mixCond$bottom_reconciled$pmf[[2]]) +#' PMF.summary(fc_bottom[[2]]) +#' +#' # The upper distribution is slightly shifted to the left +#' PMF.summary(res.mixCond$upper_reconciled$pmf[[1]]) +#' PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]]) +#' +#' @references +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +#' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' +#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +#' +#' @seealso [reconc_TDcond], [reconc_BUIS()] +#' +#' @export +reconc_MixCond = function(S, fc_bottom, fc_upper, + bottom_in_type = "pmf", distr = NULL, + num_samples = 2e4, num_resample = 2e4, + return_type = "pmf", + ..., + suppress_warnings = FALSE, seed = NULL,our_sampler=TRUE) { + + set.seed(seed) + + # Parameters for convolution + # toll=1e-16 + # Rtoll=1e-7 + # smooth_bottom=TRUE + # al_smooth=NULL + # lap_smooth=FALSE + + # After testing the convolution parameters: + # remove dots, remove comment above, and set the "best parameters" as default in + # PMF.check_support and .TD_sampling + + # Check inputs + .check_input_TD(S, fc_bottom, fc_upper, + bottom_in_type, distr, + return_type) + + # Get aggr. matrix A + A = .get_A_from_S(S)$A + n_u = nrow(A) + n_b = ncol(A) + + # Prepare samples from the base bottom distribution + if (bottom_in_type == "pmf") { + B = lapply(fc_bottom, PMF.sample, N_samples=num_samples) + B = do.call("cbind", B) # matrix of bottom samples (N_samples x n_bottom) + } else if (bottom_in_type == "samples") { + B = do.call("cbind", fc_bottom) + } else if (bottom_in_type == "params") { + L_pmf = lapply(fc_bottom, PMF.from_params, distr = distr) + B = lapply(L_pmf, PMF.sample, N_samples=num_samples) + B = do.call("cbind", B) # matrix of bottom samples (N_samples x n_bottom) + } + + + + # Get mean and covariance matrix of the MVN upper base forecasts + mu_u = fc_upper$mu + Sigma_u = as.matrix(fc_upper$Sigma) + + # IS using MVN + U = B %*% t(A) + if(our_sampler){ + weights = .MVN_density(x=U, mu = mu_u, Sigma = Sigma_u) + }else{ + weights = emdbook::dmvnorm(U, mu = mu_u, Sigma = Sigma_u) + } + + + check_weights.res = .check_weigths(weights) + if (check_weights.res$warning & !suppress_warnings) { + warning_msg = check_weights.res$warning_msg + warning(warning_msg) + } + if(!(check_weights.res$warning & (1 %in% check_weights.res$warning_code))){ + B = .resample(B, weights) + } + + ESS = sum(weights)**2/sum(weights**2) + + B = .resample(B, weights, num_resample) + B = t(B) + U = A %*% B + + + # Prepare output: include the marginal pmfs and/or the samples (depending on "return" inputs) + out = list(bottom_reconciled=list(), upper_reconciled=list(), ESS = ESS) + if (return_type %in% c('pmf', 'all')) { + upper_pmf = lapply(1:n_u, function(i) PMF.from_samples(U[i,])) + bottom_pmf = lapply(1:n_b, function(i) PMF.from_samples(B[i,])) + + out$bottom_reconciled$pmf = bottom_pmf + out$upper_reconciled$pmf = upper_pmf + + } + if (return_type %in% c('samples','all')) { + + out$bottom_reconciled$samples = B + out$upper_reconciled$samples = U + + } + + return(out) +} diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 34fa1c0..7aa7f95 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -168,7 +168,7 @@ #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' -#' @seealso [reconc_BUIS()] +#' @seealso [reconc_MixCond], [reconc_BUIS()] #' #' @export reconc_TDcond = function(S, fc_bottom, fc_upper, diff --git a/R/utils.R b/R/utils.R index c8cfc06..9b6abdc 100644 --- a/R/utils.R +++ b/R/utils.R @@ -276,6 +276,32 @@ return(samples) } +# Compute the MVN density +.MVN_density <- function(x, mu, Sigma) { + n = length(mu) + if (any(dim(Sigma) != c(n,n))) { + stop("Dimension of mu and Sigma are not compatible!") + } + .check_cov(Sigma, "Sigma") + + if(is.vector(x)){ + x <- matrix(x, ncol=length(x)) + } + + if (is.matrix(x)) { + mu <- matrix(rep(mu, nrow(x)), ncol = n, byrow = TRUE) + } + + chol_S <- tryCatch(base::chol(Sigma), error = function(e) e) + tmp <- backsolve(chol_S, t(x - mu), transpose = TRUE) + rss <- colSums(tmp^2) + + logval <- -sum(log(diag(chol_S))) - 0.5 * n * log(2 *pi) - 0.5 * rss + + return(exp(logval)) +} + + ################################################################################ # Miscellaneous diff --git a/man/reconc_MixCond.Rd b/man/reconc_MixCond.Rd new file mode 100644 index 0000000..18d74f1 --- /dev/null +++ b/man/reconc_MixCond.Rd @@ -0,0 +1,135 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/reconc_MixCond.R +\name{reconc_MixCond} +\alias{reconc_MixCond} +\title{Probabilistic Reconciliation of forecasts via mixed conditioning} +\usage{ +reconc_MixCond( + S, + fc_bottom, + fc_upper, + bottom_in_type = "pmf", + distr = NULL, + num_samples = 20000, + num_resample = 20000, + return_type = "pmf", + ..., + suppress_warnings = FALSE, + seed = NULL, + our_sampler = TRUE +) +} +\arguments{ +\item{S}{Summing matrix (n x n_bottom). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL} + +\item{fc_bottom}{A list containing the bottom base forecasts, see details.} + +\item{fc_upper}{A list containing the bottom base forecasts, see details.} + +\item{bottom_in_type}{A string with three possible values: +\itemize{ +\item 'pmf' if the bottom base forecasts are in the form of pmf, see details; +\item 'samples' if the bottom base forecasts are in the form of samples; +\item 'params' if the bottom base forecasts are in the form of estimated parameters. +}} + +\item{distr}{A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). + +This is only used if \code{bottom_in_type=='params'}.} + +\item{num_samples}{Number of samples drawn from the reconciled distribution.} + +\item{num_resample}{Number of importance sampling resamples.} + +\item{return_type}{The return type of the reconciled distributions. A string with three possible values: +\itemize{ +\item 'pmf' returns a list containing reconciled pmf objects; +\item 'samples' returns a list containing reconciled samples; +\item 'all' returns a list with both pmf objects and samples. +}} + +\item{...}{additional parameters to be passed to the smoothing functions for PMF objects.} + +\item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples +are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} + +\item{seed}{Seed for reproducibility.} + +\item{our_sampler}{TO BE REMOVED AFTER THE TESTS.} +} +\value{ +A list containing the reconciled forecasts. The list has the following named elements: +\itemize{ +\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, depending on the value of \code{return_type}; +\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, depending on the value of \code{return_type}. +} +} +\description{ +Uses the mixed conditioning algorithm to draw samples from the reconciled +forecast distribution. +} +\details{ +The base (unreconciled) forecasts are passed with the parameters +\itemize{ +\item \code{fc_bottom}: a list of length n_bottom where each element is either a pmf object (\code{bottom_in_type='pmf'}), +a vector of samples (\code{bottom_in_type='samples'}) or the parameters (\code{bottom_in_type='params'}) of the +parametric distribution specified in \code{distr}. +\item \code{fc_upper}: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. +The list must contain only the named elements \code{mu} (vector of length n_upper) and \code{Sigma} (n_upper x n_upper matrix) +} + +A PMF object is a numerical vector containing the probability mass function for a forecast. +The first element of a PMF vector for the variable X always contains the probability P(X=0) and +there is one element for each integer. +The last element of the PMF corresponds to the probability of the last value in the support of X. +See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. + +A warnings is triggered if the intersection of the support for the reconciled uppers +and the support of the bottom-up distribution is too small. In this case only +few samples from the reconciled upper are kept. The warning reports the percentage +of samples kept. + +Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +} +\examples{ + +library(bayesRecon) + +# Consider a simple hierarchy with two bottom and one upper +A <- matrix(c(1,1),nrow=1) +S <- rbind(A,diag(nrow=2)) +# The bottom forecasts are Poisson with lambda=15 +lambda <- 15 +n_tot <- 60 +fc_bottom <- list() +fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) +fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) + +# The upper forecast is a Normal with mean 40 and std 5 +fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) + +# We can reconcile with reconc_TDcond +res.mixCond <- reconc_MixCond(S, fc_bottom, fc_upper) + +# Note that the bottom distributions are slightly shifted to the right +PMF.summary(res.mixCond$bottom_reconciled$pmf[[1]]) +PMF.summary(fc_bottom[[1]]) + +PMF.summary(res.mixCond$bottom_reconciled$pmf[[2]]) +PMF.summary(fc_bottom[[2]]) + +# The upper distribution is slightly shifted to the left +PMF.summary(res.mixCond$upper_reconciled$pmf[[1]]) +PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]]) + +} +\references{ +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. + +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +} +\seealso{ +\link{reconc_TDcond}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} +} diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index c909091..d2e78ea 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -128,5 +128,5 @@ Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ -\code{\link[=reconc_BUIS]{reconc_BUIS()}} +\link{reconc_MixCond}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} } diff --git a/tests/testthat/test-reconc_MixCond.R b/tests/testthat/test-reconc_MixCond.R new file mode 100644 index 0000000..edc95a5 --- /dev/null +++ b/tests/testthat/test-reconc_MixCond.R @@ -0,0 +1,59 @@ +test_that("reconc_MixCond simple example", { + + # Simple example with + # - 12 bottom + # - 10 upper: year, 6 bi-monthly, 3 quarterly + A <- matrix(data=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, + 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1), + nrow=10,byrow = TRUE) + + + S <- rbind(A,diag(nrow=12)) + + # Define means and vars for the forecasts + means <- c(90,62,63,64,31,32,31,33,31,32,rep(15,12)) + vars <- c(20,8,8,8,4,4,4,4,4,4,rep(2,12))^2 + + # create the lists for reconciliation + ## upper + fc_upper <- list(mu = means[1:10], + Sigma = diag(vars[1:10])) + + ## bottom + fc_bottom <- list() + for(i in seq(ncol(S))){ + fc_bottom[[i]] <-as.integer(.distr_sample(list(mean=means[i+10],sd = vars[i+10]), "gaussian", 2e4)) + fc_bottom[[i]][which(fc_bottom[[i]]<0)] <- 0 # set-negative-to-zero + } + + + res.MixCond <- reconc_MixCond(S,fc_bottom,fc_upper,bottom_in_type = "samples",seed=42) + + bott_rec_means <- unlist(lapply(res.MixCond$bottom_reconciled$pmf,PMF.get_mean)) + bott_rec_vars <- unlist(lapply(res.MixCond$bottom_reconciled$pmf,PMF.get_var)) + + + # Create PMF from samples + fc_bottom_pmf <- list() + for(i in seq(ncol(S))){ + fc_bottom_pmf[[i]] <-PMF.from_samples(fc_bottom[[i]]) + } + + # Reconcile from bottom PMF + res.MixCond_pmf <- reconc_MixCond(S,fc_bottom_pmf,fc_upper,seed=42) + + bott_rec_means_pmf <- unlist(lapply(res.MixCond_pmf$bottom_reconciled$pmf,PMF.get_mean)) + bott_rec_vars_pmf <- unlist(lapply(res.MixCond_pmf$bottom_reconciled$pmf,PMF.get_var)) + + expect_equal(bott_rec_means,bott_rec_means_pmf,tolerance = "3e") + expect_equal(bott_rec_vars,bott_rec_vars_pmf,tolerance = "3e") + +}) diff --git a/tests/testthat/test-sample_funs.R b/tests/testthat/test-sample_funs.R index 121f9c1..ff15efd 100644 --- a/tests/testthat/test-sample_funs.R +++ b/tests/testthat/test-sample_funs.R @@ -98,3 +98,25 @@ test_that("sampling from multivariate normal", { expect_equal(all(m < 8e-3), TRUE) }) + +test_that("MVN density works", { + + # Create 3x3 covariance matrix + L <- matrix(0,nrow=3,ncol=3) + L[lower.tri(L,diag=TRUE)] <- c(0.9,0.8,0.5,0.9,0.2,0.6) + Sigma <- L%*%t(L) + + # create mean vector + mu <- c(0,1,-1) + + # matrix where to evaluate the MVN + xx <- matrix(c(0,2,1, + 2,3,4, + 0.5,0.5,0.5, + 0,1,-1), ncol=3,byrow=TRUE) + + res <- .MVN_density(x=xx,mu=mu,Sigma=Sigma) + + true_val <- c(8.742644e-04, 1.375497e-11, 3.739985e-03, 1.306453e-01) + expect_equal(res,true_val,tolerance = "3e") +}) From 10598c7ee929302ff4714b1d44c58d30125336b5 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Thu, 16 May 2024 15:18:49 +0200 Subject: [PATCH 15/36] Minor fixes to reconc_mixCond, reconc_BUIS and PMF. Changed the behaviour of .check_cov in reconc_gaussian. .MVN_density now computes the value in chucked form for computational speed. Some updates to the vignettes references. --- R/PMF.R | 2 +- R/reconc_BUIS.R | 6 ++- R/reconc_MixCond.R | 3 +- R/reconc_gaussian.R | 5 ++- R/utils.R | 68 ++++++++++++++++++++++++------- tests/testthat/test-sample_funs.R | 9 ++++ vignettes/references.bib | 37 +++++++++++++++++ 7 files changed, 110 insertions(+), 20 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index 1439ac6..e23897a 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -311,7 +311,7 @@ PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, # checks if the elements of v_u are contained in the support of the bottom-up distr # Returns a vector with the same length of v_u with TRUE if it is contained and FALSE otherwise PMF.check_support = function(v_u, l_pmf, toll=1e-16, Rtoll=1e-7, - smoothing=FALSE, al_smooth=NULL, lap_smooth=FALSE) { + smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { pmf_u = PMF.bottom_up(l_pmf, toll=toll, Rtoll=Rtoll, return_all=FALSE, smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth) # The elements of v_u must be in the support of pmf_u diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 634d2c2..468f903 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -101,7 +101,11 @@ if (is.na(num_samples)) { num_samples = length(weights) } - tmp_idx = sample(x = 1:num_samples, num_samples, replace = TRUE, prob = weights) + + if(nrow(S_)!=length(weights)) + stop("Error in .resample: nrow(S_) != length(weights)") + + tmp_idx = sample(x = 1:nrow(S_), num_samples, replace = TRUE, prob = weights) return(S_[tmp_idx, ]) } diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index c130318..c0f1a77 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -173,12 +173,11 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, warning(warning_msg) } if(!(check_weights.res$warning & (1 %in% check_weights.res$warning_code))){ - B = .resample(B, weights) + B = .resample(B, weights, num_resample) } ESS = sum(weights)**2/sum(weights**2) - B = .resample(B, weights, num_resample) B = t(B) U = A %*% B diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R index f40b28d..c4baa2a 100644 --- a/R/reconc_gaussian.R +++ b/R/reconc_gaussian.R @@ -86,14 +86,13 @@ reconc_gaussian <- function(S, base_forecasts.mu, n = length(base_forecasts.mu) #total number of TS # Ensure that data inputs are valid - .check_cov(base_forecasts.Sigma, "Sigma") if (!(nrow(base_forecasts.Sigma) == n)) { stop("Input error: nrow(base_forecasts.Sigma) != length(base_forecasts.mu)") } if (!(k + m == n)) { stop("Input error: the shape of S is not correct") } - + .check_cov(base_forecasts.Sigma, "Sigma", pd_check=FALSE) Sigma_u = base_forecasts.Sigma[hier$upper_idxs, hier$upper_idxs] Sigma_b = base_forecasts.Sigma[hier$bottom_idxs, hier$bottom_idxs] Sigma_ub = matrix(base_forecasts.Sigma[hier$upper_idxs, hier$bottom_idxs], @@ -105,6 +104,8 @@ reconc_gaussian <- function(S, base_forecasts.mu, # Zambon, L., et al. "Properties of the reconciled distributions for # Gaussian and count forecasts." (2023) Q = Sigma_u - Sigma_ub %*% t(A) - A %*% t(Sigma_ub) + A %*% Sigma_b %*% t(A) + # we only need to check if Q is p.d. + .check_cov(Q, "Q", pd_check=TRUE) invQ = solve(Q) mu_b_tilde = mu_b + (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% (A %*% mu_b - mu_u) Sigma_b_tilde = Sigma_b - (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% t(t(Sigma_ub) - Sigma_b %*% t(A)) diff --git a/R/utils.R b/R/utils.R index 9b6abdc..97e568b 100644 --- a/R/utils.R +++ b/R/utils.R @@ -32,23 +32,27 @@ } # Check if it is a covariance matrix (i.e. symmetric p.d.) -.check_cov <- function(cov_matrix, Sigma_str) { +.check_cov <- function(cov_matrix, Sigma_str,pd_check=FALSE) { # Check if the matrix is square if (!is.matrix(cov_matrix) || nrow(cov_matrix) != ncol(cov_matrix)) { stop(paste0(Sigma_str, " is not square")) } + # Check if the matrix is positive semi-definite - eigen_values <- eigen(cov_matrix, symmetric = TRUE)$values - if (any(eigen_values <= 0)) { - stop(paste0(Sigma_str, " is not positive semi-definite")) - } - # Check if the matrix is symmetric - if (!isSymmetric(cov_matrix)) { - stop("base_forecasts.Sigma not symmetric") + if(pd_check){ + eigen_values <- eigen(cov_matrix, symmetric = TRUE)$values + if (any(eigen_values <= 0)) { + stop(paste0(Sigma_str, " is not positive semi-definite")) + } + }else{ + # Check if the matrix is symmetric + if (!isSymmetric(cov_matrix)) { + stop(paste0(Sigma_str, " is not symmetric")) + } } # Check if the diagonal elements are non-negative - if (any(diag(cov_matrix) <= 0)) { - stop(paste0(Sigma_str, ": some elements on the diagonal are non-positive")) + if (any(diag(cov_matrix) < 0)) { + stop(paste0(Sigma_str, ": some elements on the diagonal are negative")) } # If all checks pass, return TRUE return(TRUE) @@ -277,7 +281,7 @@ } # Compute the MVN density -.MVN_density <- function(x, mu, Sigma) { +.MVN_density <- function(x, mu, Sigma, max_size_x=5e3, suppress_warnings=TRUE) { n = length(mu) if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") @@ -293,10 +297,46 @@ } chol_S <- tryCatch(base::chol(Sigma), error = function(e) e) - tmp <- backsolve(chol_S, t(x - mu), transpose = TRUE) - rss <- colSums(tmp^2) - logval <- -sum(log(diag(chol_S))) - 0.5 * n * log(2 *pi) - 0.5 * rss + # Constant of the loglikelihood (computed here because it is always the same) + const <- -sum(log(diag(chol_S))) - 0.5 * n * log(2 *pi) + + # This part breaks down the density eval into small chucks, for memory + rows_x <- nrow(x) + if(is.matrix(x) && rows_x > max_size_x){ + + logval <- rep(0, rows_x) + num_backsolves <- rows_x %/% max_size_x + + if(!suppress_warnings){ + warning_msg <- paste0("x has ",rows_x," rows, the density evaluation is broken down into ",num_backsolves," pieces for memory preservation.") + warning(warning_msg) + } + + for(j in seq(num_backsolves)){ + idx_to_select <- (1+(j-1)*max_size_x):((j)*5e3) + tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) + rss <- colSums(tmp^2) + + logval[idx_to_select] <- const - 0.5 * rss + } + + # Last indices + remainder <- rows_x %% max_size_x + if(remainder !=0){ + idx_to_select <- (1+(num_backsolves)*5e3):(remainder+(num_backsolves)*5e3) + tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) + rss <- colSums(tmp^2) + + logval[idx_to_select] <- const - 0.5 * rss + } + + }else{ + tmp <- backsolve(chol_S, t(x - mu), transpose = TRUE) + rss <- colSums(tmp^2) + + logval <- const - 0.5 * rss + } return(exp(logval)) } diff --git a/tests/testthat/test-sample_funs.R b/tests/testthat/test-sample_funs.R index ff15efd..f465270 100644 --- a/tests/testthat/test-sample_funs.R +++ b/tests/testthat/test-sample_funs.R @@ -119,4 +119,13 @@ test_that("MVN density works", { true_val <- c(8.742644e-04, 1.375497e-11, 3.739985e-03, 1.306453e-01) expect_equal(res,true_val,tolerance = "3e") + + # Check if block-evaluation works + xx <- matrix(runif(3*1e4),ncol=3,byrow=TRUE) + + res_chuncks <- .MVN_density(x=xx,mu=mu,Sigma=Sigma) + res_all <- .MVN_density(x=xx,mu=mu,Sigma=Sigma,max_size_x = 1e4) + + expect_equal(res_chuncks,res_all) + }) diff --git a/vignettes/references.bib b/vignettes/references.bib index 3afd588..5e81561 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -185,3 +185,40 @@ @article{agosto2022multivariate year={2022} } +@inproceedings{zambon2024mixed, + title={Probabilistic reconciliation of mixed-type hierarchical time series}, + author={Zambon, Lorenzo and Azzimonti, Dario and Rubattu, Nicolò and Corani, Giorgio}, + booktitle={The 40th Conference on Uncertainty in Artificial Intelligence}, + year={2024}, + url={https://openreview.net/forum?id=KmbmBlrQkr} +} + +@article{MAKRIDAKIS20221325, + title = {{The M5 competition: Background, organization, and implementation}}, + journal = {International Journal of Forecasting}, + volume = {38}, + number = {4}, + pages = {1325-1336}, + year = {2022}, + author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}, + keywords = {Forecasting competitions, M competitions, Accuracy, Uncertainty, Time series, Retail sales forecasting} +} + +@article{svetunkov2023iets, + title={{iETS: State space model for intermittent demand forecasting}}, + author={Svetunkov, Ivan and Boylan, John E}, + journal={International Journal of Production Economics}, + volume={265}, + pages={109013}, + year={2023}, + publisher={Elsevier} +} + + +@Manual{smooth_pkg, + title = {smooth: Forecasting Using State Space Models}, + author = {Ivan Svetunkov}, + year = {2023}, + note = {R package version 4.0.0}, + url = {https://cran.r-project.org/package=smooth}, +} From 832cc5b71db2446850c3d7466fb6428ae8095c66 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Thu, 16 May 2024 19:34:44 +0200 Subject: [PATCH 16/36] Minor fixes to .check_cov and the error handling in MVN_density and MVN_sample. --- R/reconc_gaussian.R | 4 ++-- R/utils.R | 37 ++++++++++++++++++++++++++----------- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R index c4baa2a..e3ab0fa 100644 --- a/R/reconc_gaussian.R +++ b/R/reconc_gaussian.R @@ -92,7 +92,7 @@ reconc_gaussian <- function(S, base_forecasts.mu, if (!(k + m == n)) { stop("Input error: the shape of S is not correct") } - .check_cov(base_forecasts.Sigma, "Sigma", pd_check=FALSE) + .check_cov(base_forecasts.Sigma, "Sigma", pd_check=FALSE, symm_check=TRUE) Sigma_u = base_forecasts.Sigma[hier$upper_idxs, hier$upper_idxs] Sigma_b = base_forecasts.Sigma[hier$bottom_idxs, hier$bottom_idxs] Sigma_ub = matrix(base_forecasts.Sigma[hier$upper_idxs, hier$bottom_idxs], @@ -105,7 +105,7 @@ reconc_gaussian <- function(S, base_forecasts.mu, # Gaussian and count forecasts." (2023) Q = Sigma_u - Sigma_ub %*% t(A) - A %*% t(Sigma_ub) + A %*% Sigma_b %*% t(A) # we only need to check if Q is p.d. - .check_cov(Q, "Q", pd_check=TRUE) + .check_cov(Q, "Q", pd_check=TRUE, symm_check=FALSE) invQ = solve(Q) mu_b_tilde = mu_b + (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% (A %*% mu_b - mu_u) Sigma_b_tilde = Sigma_b - (t(Sigma_ub) - Sigma_b %*% t(A)) %*% invQ %*% t(t(Sigma_ub) - Sigma_b %*% t(A)) diff --git a/R/utils.R b/R/utils.R index 97e568b..2008b69 100644 --- a/R/utils.R +++ b/R/utils.R @@ -32,7 +32,7 @@ } # Check if it is a covariance matrix (i.e. symmetric p.d.) -.check_cov <- function(cov_matrix, Sigma_str,pd_check=FALSE) { +.check_cov <- function(cov_matrix, Sigma_str,pd_check=FALSE,symm_check=FALSE) { # Check if the matrix is square if (!is.matrix(cov_matrix) || nrow(cov_matrix) != ncol(cov_matrix)) { stop(paste0(Sigma_str, " is not square")) @@ -44,7 +44,8 @@ if (any(eigen_values <= 0)) { stop(paste0(Sigma_str, " is not positive semi-definite")) } - }else{ + } + if(symm_check){ # Check if the matrix is symmetric if (!isSymmetric(cov_matrix)) { stop(paste0(Sigma_str, " is not symmetric")) @@ -215,7 +216,7 @@ stop("Input error: the dimensions of the upper parameters do not match with S") } # Check that Sigma is a covariance matrix (symmetric positive semi-definite) - .check_cov(fc_upper$Sigma, "Upper covariance matrix") + .check_cov(fc_upper$Sigma, "Upper covariance matrix", symm_check=TRUE) # If bottom_in_type is not "params" but distr is specified, throw a warning if (bottom_in_type %in% c("pmf", "samples") & !is.null(distr)) { @@ -272,21 +273,26 @@ if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") } - .check_cov(Sigma, "Sigma") + .check_cov(Sigma, "Sigma", pd_check = FALSE, symm_check = FALSE) Z = matrix(stats::rnorm(n*n_samples), ncol = n) - Ch = chol(Sigma) + + Ch <- tryCatch(base::chol(Sigma), + error = function(e) stop(paste0(e,"check the covariance in .MVN_sample, the Cholesky fails."))) + samples = Z %*% Ch + matrix(mu, nrow = n_samples, ncol = n, byrow = TRUE) return(samples) } # Compute the MVN density .MVN_density <- function(x, mu, Sigma, max_size_x=5e3, suppress_warnings=TRUE) { + + # save dimension of mu n = length(mu) if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") } - .check_cov(Sigma, "Sigma") + .check_cov(Sigma, "Sigma", pd_check = FALSE, symm_check = FALSE) if(is.vector(x)){ x <- matrix(x, ncol=length(x)) @@ -296,16 +302,21 @@ mu <- matrix(rep(mu, nrow(x)), ncol = n, byrow = TRUE) } - chol_S <- tryCatch(base::chol(Sigma), error = function(e) e) + # Compute Cholesky of Sigma + chol_S <- tryCatch(base::chol(Sigma), + error = function(e) stop(paste0(e,"check the covariance in .MVN_density, the Cholesky fails."))) # Constant of the loglikelihood (computed here because it is always the same) const <- -sum(log(diag(chol_S))) - 0.5 * n * log(2 *pi) - # This part breaks down the density eval into small chucks, for memory + # This part breaks down the evaluation of the density eval into batches, for memory rows_x <- nrow(x) + if(is.matrix(x) && rows_x > max_size_x){ logval <- rep(0, rows_x) + + # Compute how many batches we need num_backsolves <- rows_x %/% max_size_x if(!suppress_warnings){ @@ -313,18 +324,22 @@ warning(warning_msg) } + for(j in seq(num_backsolves)){ - idx_to_select <- (1+(j-1)*max_size_x):((j)*5e3) + idx_to_select <- (1+(j-1)*max_size_x):((j)*max_size_x) + # Do one backsolve for each batch tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) rss <- colSums(tmp^2) + # Update the logval for those indices logval[idx_to_select] <- const - 0.5 * rss } - # Last indices + # Last indices: if the number of rows of x is not exactly divided by the size of the batches remainder <- rows_x %% max_size_x if(remainder !=0){ - idx_to_select <- (1+(num_backsolves)*5e3):(remainder+(num_backsolves)*5e3) + idx_to_select <- (1+(num_backsolves)*max_size_x):(remainder+(num_backsolves)*max_size_x) + # Do backsolve on the remaining indices tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) rss <- colSums(tmp^2) From 121520913dea6b36d6d1c70c8d7680c17d70a690 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Thu, 16 May 2024 22:11:03 +0200 Subject: [PATCH 17/36] removed emdbook dependency --- R/reconc_MixCond.R | 9 ++------- man/reconc_MixCond.Rd | 5 +---- 2 files changed, 3 insertions(+), 11 deletions(-) diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index c0f1a77..4eb901c 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -51,7 +51,6 @@ #' #' @param num_samples Number of samples drawn from the reconciled distribution. #' @param num_resample Number of importance sampling resamples. -#' @param our_sampler TO BE REMOVED AFTER THE TESTS. #' @param return_type The return type of the reconciled distributions. A string with three possible values: #' #' * 'pmf' returns a list containing reconciled pmf objects; @@ -115,7 +114,7 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, num_samples = 2e4, num_resample = 2e4, return_type = "pmf", ..., - suppress_warnings = FALSE, seed = NULL,our_sampler=TRUE) { + suppress_warnings = FALSE, seed = NULL) { set.seed(seed) @@ -160,11 +159,7 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, # IS using MVN U = B %*% t(A) - if(our_sampler){ - weights = .MVN_density(x=U, mu = mu_u, Sigma = Sigma_u) - }else{ - weights = emdbook::dmvnorm(U, mu = mu_u, Sigma = Sigma_u) - } + weights = .MVN_density(x=U, mu = mu_u, Sigma = Sigma_u) check_weights.res = .check_weigths(weights) diff --git a/man/reconc_MixCond.Rd b/man/reconc_MixCond.Rd index 18d74f1..716fcef 100644 --- a/man/reconc_MixCond.Rd +++ b/man/reconc_MixCond.Rd @@ -15,8 +15,7 @@ reconc_MixCond( return_type = "pmf", ..., suppress_warnings = FALSE, - seed = NULL, - our_sampler = TRUE + seed = NULL ) } \arguments{ @@ -54,8 +53,6 @@ This is only used if \code{bottom_in_type=='params'}.} are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} \item{seed}{Seed for reproducibility.} - -\item{our_sampler}{TO BE REMOVED AFTER THE TESTS.} } \value{ A list containing the reconciled forecasts. The list has the following named elements: From 918ff40b363b87977eba7e20d60c9bda67943e86 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Fri, 17 May 2024 18:52:41 +0200 Subject: [PATCH 18/36] fixed seed + reorganized data + fixed MVN_density + other minor fix --- R/M3_example.R | 10 -- R/carparts_example.R | 16 --- R/data.R | 104 ++++++++++++++++++ R/extr_mkt_events.R | 26 ----- R/extr_mkt_events_basefc.R | 27 ----- R/infantMortality.R | 17 --- R/reconc_BUIS.R | 3 +- R/{reconc_MH.R => reconc_MCMC.R} | 2 +- R/reconc_MixCond.R | 2 +- R/reconc_TDcond.R | 2 +- R/utils.R | 26 +++-- ...examples.R => test-reconc_BUIS_gaussian.R} | 0 12 files changed, 124 insertions(+), 111 deletions(-) delete mode 100644 R/M3_example.R delete mode 100644 R/carparts_example.R create mode 100644 R/data.R delete mode 100644 R/extr_mkt_events.R delete mode 100644 R/extr_mkt_events_basefc.R delete mode 100644 R/infantMortality.R rename R/{reconc_MH.R => reconc_MCMC.R} (99%) rename tests/testthat/{test-examples.R => test-reconc_BUIS_gaussian.R} (100%) diff --git a/R/M3_example.R b/R/M3_example.R deleted file mode 100644 index 1acd0d8..0000000 --- a/R/M3_example.R +++ /dev/null @@ -1,10 +0,0 @@ -#' Example of a time series from the M3 forecasting competition -#' -#' A monthly time series, from the M3 forecasting competition ("N1485"). -#' -#' @format -#' List of time series of class \link[stats]{ts}. -#' -#' -#' @source [https://forecasters.org/resources/time-series-data/m3-competition/](https://forecasters.org/resources/time-series-data/m3-competition/) -"M3_example" \ No newline at end of file diff --git a/R/carparts_example.R b/R/carparts_example.R deleted file mode 100644 index 6c70c59..0000000 --- a/R/carparts_example.R +++ /dev/null @@ -1,16 +0,0 @@ -#' Example of a time series from carparts -#' -#' A monthly time series from the `carparts` dataset, 51 observations, Jan 1998 - Mar 2002. -#' -#' @format -#' Univariate time series of class \link[stats]{ts}. -#' -#' @references -#' Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008) Forecasting with exponential -#' smoothing: the state space approach, Springer -#' -#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} -#' -#' @source -#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} -"carparts_example" \ No newline at end of file diff --git a/R/data.R b/R/data.R new file mode 100644 index 0000000..8efa695 --- /dev/null +++ b/R/data.R @@ -0,0 +1,104 @@ +#' Example of a time series from carparts +#' +#' A monthly time series from the `carparts` dataset, 51 observations, Jan 1998 - Mar 2002. +#' +#' @format +#' Univariate time series of class \link[stats]{ts}. +#' +#' @references +#' Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008) Forecasting with exponential +#' smoothing: the state space approach, Springer +#' +#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +#' +#' @source +#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +"carparts_example" + + +#' Infant Mortality grouped time series dataset +#' +#' A yearly grouped time series dataset, from 1901 to 2003, of infant mortality counts (deaths) in Australia; +#' disaggregated by state (see below), and sex (male and female). +#' +#' States: New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia +#' (WA), Northern Territory (NT), Australian Capital Territory (ACT), and Tasmania (TAS). +#' +#' @format +#' List of time series of class \link[stats]{ts}. +#' +#' @source +#' hts package [CRAN](https://cran.r-project.org/package=hts) +#' +#' @references +#' R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579-2589. +"infantMortality" + + +#' Example of a time series from the M3 forecasting competition +#' +#' A monthly time series, from the M3 forecasting competition ("N1485"). +#' +#' @format +#' List of time series of class \link[stats]{ts}. +#' +#' +#' @source [https://forecasters.org/resources/time-series-data/m3-competition/](https://forecasters.org/resources/time-series-data/m3-competition/) +"M3_example" + + +#' Extreme market events dataset +#' +#' Count time series of extreme market events in five economic sectors. +#' The data refer to the trading days between 2004/12/31 and 2018/12/19 (3508 trading days in total). +#' +#' The counts are computed by considering 29 companies included in the Euro Stoxx +#' 50 index and observing if the value of the CDS spread on a given day exceeds +#' the 90-th percentile of its distribution in the last trading year. +#' The companies are divided in the following sectors: Financial (FIN), Information +#' and Communication Technology (ICT), Manufacturing (MFG), Energy (ENG), and Trade (TRD). +#' +#' There are 6 time series: +#' - 5 bottom time series, corresponding to the daily counts for each sector +#' - 1 upper time series, which is the sum of all the bottom (ALL) +#' +#' @format +#' A multivariate time series of class \link[stats]{ts}. +#' +#' @source +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' +#' @references +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' +#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} +"extr_mkt_events" + + +#' Base forecasts for the extreme market events dataset +#' +#' Base forecasts for the `extr_mkt_events` dataset, computed using the model by +#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895}. +#' +#' The predictive distribution for the bottom time series is a multivariate negative +#' binomial with a static vector of dispersion parameters and a time-varying vector +#' of location parameters following a score-driven dynamics. +#' The base forecasts for the upper time series are computed using a univariate version of this model. +#' They are in-sample forecasts: for each training instant, they are computed for +#' time t+1 by conditioning on the counts observed up to time t. +#' +#' @format +#' A list `extr_mkt_events_basefc` containing +#' \describe{ +#' \item{`extr_mkt_events_basefc$mu`}{data frame of the base forecast means, for each day} +#' \item{`extr_mkt_events_basefc$size`}{data frame of the static base forecast size parameters} +#' } +#' +#' @source +#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} +#' +#' @references +#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} +#' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +"extr_mkt_events_basefc" \ No newline at end of file diff --git a/R/extr_mkt_events.R b/R/extr_mkt_events.R deleted file mode 100644 index 71ef9bf..0000000 --- a/R/extr_mkt_events.R +++ /dev/null @@ -1,26 +0,0 @@ -#' Extreme market events dataset -#' -#' Count time series of extreme market events in five economic sectors. -#' The data refer to the trading days between 2004/12/31 and 2018/12/19 (3508 trading days in total). -#' -#' The counts are computed by considering 29 companies included in the Euro Stoxx -#' 50 index and observing if the value of the CDS spread on a given day exceeds -#' the 90-th percentile of its distribution in the last trading year. -#' The companies are divided in the following sectors: Financial (FIN), Information -#' and Communication Technology (ICT), Manufacturing (MFG), Energy (ENG), and Trade (TRD). -#' -#' There are 6 time series: -#' - 5 bottom time series, corresponding to the daily counts for each sector -#' - 1 upper time series, which is the sum of all the bottom (ALL) -#' -#' @format -#' A multivariate time series of class \link[stats]{ts}. -#' -#' @source -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. -#' -#' @references -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. -#' -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} -"extr_mkt_events" diff --git a/R/extr_mkt_events_basefc.R b/R/extr_mkt_events_basefc.R deleted file mode 100644 index 7cbfd7c..0000000 --- a/R/extr_mkt_events_basefc.R +++ /dev/null @@ -1,27 +0,0 @@ -#' Base forecasts for the extreme market events dataset -#' -#' Base forecasts for the `extr_mkt_events` dataset, computed using the model by -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895}. -#' -#' The predictive distribution for the bottom time series is a multivariate negative -#' binomial with a static vector of dispersion parameters and a time-varying vector -#' of location parameters following a score-driven dynamics. -#' The base forecasts for the upper time series are computed using a univariate version of this model. -#' They are in-sample forecasts: for each training instant, they are computed for -#' time t+1 by conditioning on the counts observed up to time t. -#' -#' @format -#' A list `extr_mkt_events_basefc` containing -#' \describe{ -#' \item{`extr_mkt_events_basefc$mu`}{data frame of the base forecast means, for each day} -#' \item{`extr_mkt_events_basefc$size`}{data frame of the static base forecast size parameters} -#' } -#' -#' @source -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} -#' -#' @references -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} -#' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. -"extr_mkt_events_basefc" diff --git a/R/infantMortality.R b/R/infantMortality.R deleted file mode 100644 index 79a0580..0000000 --- a/R/infantMortality.R +++ /dev/null @@ -1,17 +0,0 @@ -#' Infant Mortality grouped time series dataset -#' -#' A yearly grouped time series dataset, from 1901 to 2003, of infant mortality counts (deaths) in Australia; -#' disaggregated by state (see below), and sex (male and female). -#' -#' States: New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia -#' (WA), Northern Territory (NT), Australian Capital Territory (ACT), and Tasmania (TAS). -#' -#' @format -#' List of time series of class \link[stats]{ts}. -#' -#' @source -#' hts package [CRAN](https://cran.r-project.org/package=hts) -#' -#' @references -#' R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579-2589. -"infantMortality" \ No newline at end of file diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 468f903..836f873 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -250,7 +250,8 @@ reconc_BUIS <- function(S, num_samples = 2e4, suppress_warnings = FALSE, seed = NULL) { - set.seed(seed) + + if (!is.null(seed)) set.seed(seed) # Transform distr and in_type into lists if (!is.list(distr)) { diff --git a/R/reconc_MH.R b/R/reconc_MCMC.R similarity index 99% rename from R/reconc_MH.R rename to R/reconc_MCMC.R index 0f9ffff..79a44de 100644 --- a/R/reconc_MH.R +++ b/R/reconc_MCMC.R @@ -89,7 +89,7 @@ reconc_MCMC <- function(S, burn_in = 1000, seed = NULL) { - set.seed(seed) + if (!is.null(seed)) set.seed(seed) # Ensure that data inputs are valid if (distr == "gaussian") { diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index 4eb901c..e61e83c 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -116,7 +116,7 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, ..., suppress_warnings = FALSE, seed = NULL) { - set.seed(seed) + if (!is.null(seed)) set.seed(seed) # Parameters for convolution # toll=1e-16 diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 7aa7f95..3c85d0b 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -177,7 +177,7 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, ..., suppress_warnings = FALSE, seed = NULL) { - set.seed(seed) + if (!is.null(seed)) set.seed(seed) # Parameters for convolution # toll=1e-16 diff --git a/R/utils.R b/R/utils.R index 2008b69..9b0cf25 100644 --- a/R/utils.R +++ b/R/utils.R @@ -289,17 +289,22 @@ # save dimension of mu n = length(mu) + + # Check Sigma if (any(dim(Sigma) != c(n,n))) { stop("Dimension of mu and Sigma are not compatible!") } .check_cov(Sigma, "Sigma", pd_check = FALSE, symm_check = FALSE) + # x must be a matrix with ncol = n (nrow is the number of points to evaluate) + # or a vector with length n (in which case it is transformed into a matrix) if(is.vector(x)){ + if (length(x)!=n) stop("Length of x must be the same of mu") x <- matrix(x, ncol=length(x)) - } - - if (is.matrix(x)) { - mu <- matrix(rep(mu, nrow(x)), ncol = n, byrow = TRUE) + } else if (is.matrix(x)) { + if (ncol(x)!=n) stop("The number of columns of x must be equal to the length of mu") + } else { + stop("x must be either a vector or a matrix") } # Compute Cholesky of Sigma @@ -312,7 +317,7 @@ # This part breaks down the evaluation of the density eval into batches, for memory rows_x <- nrow(x) - if(is.matrix(x) && rows_x > max_size_x){ + if(rows_x > max_size_x){ logval <- rep(0, rows_x) @@ -324,11 +329,10 @@ warning(warning_msg) } - for(j in seq(num_backsolves)){ idx_to_select <- (1+(j-1)*max_size_x):((j)*max_size_x) # Do one backsolve for each batch - tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) + tmp <- backsolve(chol_S, t(x[idx_to_select,]) - mu, transpose = TRUE) rss <- colSums(tmp^2) # Update the logval for those indices @@ -340,14 +344,14 @@ if(remainder !=0){ idx_to_select <- (1+(num_backsolves)*max_size_x):(remainder+(num_backsolves)*max_size_x) # Do backsolve on the remaining indices - tmp <- backsolve(chol_S, t(x[idx_to_select,] - mu[idx_to_select,]), transpose = TRUE) + tmp <- backsolve(chol_S, t(x[idx_to_select,]) - mu, transpose = TRUE) rss <- colSums(tmp^2) logval[idx_to_select] <- const - 0.5 * rss } }else{ - tmp <- backsolve(chol_S, t(x - mu), transpose = TRUE) + tmp <- backsolve(chol_S, t(x) - mu, transpose = TRUE) rss <- colSums(tmp^2) logval <- const - 0.5 * rss @@ -394,7 +398,7 @@ # Functions for tests .gen_gaussian <- function(params_file, seed=NULL) { - set.seed(seed) + if (!is.null(seed)) set.seed(seed) params = utils::read.csv(file = params_file, header = FALSE) out = list() for (i in 1:nrow(params)) { @@ -404,7 +408,7 @@ } .gen_poisson <- function(params_file, seed=NULL) { - set.seed(seed) + if (!is.null(seed)) set.seed(seed) params = utils::read.csv(file = params_file, header = FALSE) out = list() for (i in 1:nrow(params)) { diff --git a/tests/testthat/test-examples.R b/tests/testthat/test-reconc_BUIS_gaussian.R similarity index 100% rename from tests/testthat/test-examples.R rename to tests/testthat/test-reconc_BUIS_gaussian.R From 5aa888d830e31ae7e7a5df111d44924d1aaad262 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 21 May 2024 14:39:35 +0200 Subject: [PATCH 19/36] updated readme, fixed documentation --- R/PMF.R | 34 +++++------ R/reconc_BUIS.R | 65 ++------------------- R/reconc_MixCond.R | 103 ++++++++++++++++++---------------- R/reconc_TDcond.R | 82 ++++++++++++++------------- R/utils.R | 56 ++++++++++++++++++ README.Rmd | 15 +++-- man/M3_example.Rd | 2 +- man/PMF.get_quantile.Rd | 3 - man/PMF.sample.Rd | 2 +- man/PMF.summary.Rd | 8 ++- man/carparts_example.Rd | 2 +- man/extr_mkt_events.Rd | 2 +- man/extr_mkt_events_basefc.Rd | 2 +- man/infantMortality.Rd | 2 +- man/reconc_BUIS.Rd | 9 ++- man/reconc_MCMC.Rd | 2 +- man/reconc_MixCond.Rd | 85 ++++++++++++++++------------ man/reconc_TDcond.Rd | 71 +++++++++++++---------- 18 files changed, 291 insertions(+), 254 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index e23897a..dc83732 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -56,7 +56,7 @@ PMF.from_params = function(params, distr, Rtoll = 1e-7) { #' @param pmf the PMF object. #' @param N_samples number of samples. #' -#' @return `N_samples` drawn from the distribution specified by `pmf`. +#' @return Samples drawn from the distribution specified by `pmf`. #' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.summary()] #' #' @examples @@ -160,9 +160,6 @@ PMF.get_var = function(pmf) { #' p <- 0.6 #' pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) #' -#' # We can obtain the quantile of this PMF with the function PMF.get_quantile() -#' quant_90 <- PMF.get_quantile(pmf=pmf_binomial,p=0.9) -#' #' # The true median is ceiling(n*p) #' quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) #' cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50) @@ -185,12 +182,13 @@ PMF.get_quantile = function(pmf, p) { #' #' @description #' -#' Returns the summary (min,max, IQR, median, mean) of the PMF specified by `pmf`. +#' Returns the summary (min, max, IQR, median, mean) of the PMF specified by `pmf`. #' #' @param pmf the PMF object. -#' @param toll lowest possible probability mass on the left -#' @param Rtoll lowest possible probability mass on the right -#' +#' @param Ltoll used for computing the min of the PMF: the min is the smallest value +#' with probability greater than Ltoll (default: 1e-16) +#' @param Rtoll used for computing the max of the PMF: the max is the largest value +#' with probability greater than Rtoll (default: 1e-7) #' #' @examples #' library(bayesRecon) @@ -208,17 +206,15 @@ PMF.get_quantile = function(pmf, p) { #' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] #' @export PMF.summary = function(pmf, toll=1e-16, Rtoll=1e-7) { - # Max is the last position with enough mass - last_pos = max(which(pmf > Rtoll)) - pmf = pmf[1:last_pos] - # Set to zero values smaller than toll: - pmf[pmf0))-1), - `1st Qu.`=PMF.get_quantile(pmf,0.25), - "Median"=PMF.get_quantile(pmf,0.5), - "Mean"=PMF.get_mean(pmf), - `3rd Qu.`=PMF.get_quantile(pmf,0.75), - "Max"=(last_pos-1),check.names = FALSE) + min_pmf = min(which(pmf > Ltoll)) - 1 + max_pmf = max(which(pmf > Rtoll)) - 1 + all_summaries <- data.frame("Min." = min_pmf, + `1st Qu.` = PMF.get_quantile(pmf,0.25), + "Median" = PMF.get_quantile(pmf,0.5), + "Mean" = PMF.get_mean(pmf), + `3rd Qu.` = PMF.get_quantile(pmf,0.75), + "Max" = max_pmf, + check.names = FALSE) return(all_summaries) } diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 836f873..fc0dccd 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -30,50 +30,6 @@ return(w) } -.check_weigths <- function(w, n_eff_min=200, p_n_eff=0.01) { - warning = FALSE - warning_code = c() - warning_msg = c() - - n = length(w) - n_eff = n - - - # 1. w==0 - if (all(w==0)) { - warning = TRUE - warning_code = c(warning_code, 1) - warning_msg = c(warning_msg, - "Importance Sampling: all the weights are zeros. This is probably caused by a strong incoherence between bottom and upper base forecasts.") - }else{ - - # Effective sample size - n_eff = (sum(w)^2) / sum(w^2) - - # 2. n_eff < threshold - if (n_eff < n_eff_min) { - warning = TRUE - warning_code = c(warning_code, 2) - warning_msg = c(warning_msg, - paste0("Importance Sampling: effective_sample_size= ", round(n_eff,2), " (< ", n_eff_min,").")) - } - - # 3. n_eff < p*n, e.g. p = 0.05 - if (n_eff < p_n_eff*n) { - warning = TRUE - warning_code = c(warning_code, 3) - warning_msg = c(warning_msg, - paste0("Importance Sampling: effective_sample_size= ", round(n_eff,2), " (< ", round(p_n_eff * 100, 2),"%).")) - } - } - res = list(warning = warning, - warning_code = warning_code, - warning_msg = warning_msg, - n_eff = n_eff) - - return(res) -} - .compute_weights <- function(b, u, in_type_, distr_) { if (in_type_ == "samples") { if (distr_ == "discrete") { @@ -97,24 +53,12 @@ return(w) } -.resample <- function(S_, weights, num_samples = NA) { - if (is.na(num_samples)) { - num_samples = length(weights) - } - - if(nrow(S_)!=length(weights)) - stop("Error in .resample: nrow(S_) != length(weights)") - - tmp_idx = sample(x = 1:nrow(S_), num_samples, replace = TRUE, prob = weights) - return(S_[tmp_idx, ]) -} - #' @title BUIS for Probabilistic Reconciliation of forecasts via conditioning #' #' @description #' #' Uses the Bottom-Up Importance Sampling algorithm to draw samples from the reconciled -#' forecast distribution, which is obtained via conditioning. +#' forecast distribution, obtained via conditioning. #' #' @details #' @@ -139,7 +83,8 @@ #' * the effective sample size is < 200; #' * the effective sample size is < 1% of the sample size (`num_samples` if `in_type` is 'params' or the size of the base forecast if if `in_type` is 'samples'). #' -#' Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +#' 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 base_forecasts A list containing the base_forecasts, see details. @@ -158,7 +103,9 @@ #' #' If `distr` is a string it is assumed that all distributions are of the same type. #' -#' @param num_samples Number of samples drawn from the reconciled distribution. +#' @param num_samples Number of samples drawn from the reconciled distribution. +#' This is ignored if `bottom_in_type='samples'`; in this case, the number of reconciled samples is equal to +#' the number of samples of the base forecasts. #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about effective sample size #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. #' @param seed Seed for reproducibility. diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index e61e83c..d17d1f4 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -4,60 +4,66 @@ -#' @title Probabilistic Reconciliation of forecasts via mixed conditioning +#' @title Probabilistic forecast reconciliation of mixed hierarchies via conditioning #' #' @description #' -#' Uses the mixed conditioning algorithm to draw samples from the reconciled -#' forecast distribution. +#' Uses importance sampling to draw samples from the reconciled +#' forecast distribution, obtained via conditioning, in the case of a mixed hierarchy. #' #' @details #' -#' The base (unreconciled) forecasts are passed with the parameters -#' -#' * `fc_bottom`: a list of length n_bottom where each element is either a pmf object (`bottom_in_type='pmf'`), -#' a vector of samples (`bottom_in_type='samples'`) or the parameters (`bottom_in_type='params'`) of the -#' parametric distribution specified in `distr`. -#' * `fc_upper`: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. -#' The list must contain only the named elements `mu` (vector of length n_upper) and `Sigma` (n_upper x n_upper matrix) -#' -#' -#' A PMF object is a numerical vector containing the probability mass function for a forecast. -#' The first element of a PMF vector for the variable X always contains the probability P(X=0) and -#' there is one element for each integer. -#' The last element of the PMF corresponds to the probability of the last value in the support of X. -#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. -#' -#' -#' A warnings is triggered if the intersection of the support for the reconciled uppers -#' and the support of the bottom-up distribution is too small. In this case only -#' few samples from the reconciled upper are kept. The warning reports the percentage -#' of samples kept. -#' -#' 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). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL +#' The base bottom forecasts `fc_bottom` must be a list of length n_bottom, where each element is either +#' * a PMF object (see details below), if `bottom_in_type='pmf'`; +#' * a vector of samples, if `bottom_in_type='samples'`; +#' * a list of parameters, if `bottom_in_type='params'`: +#' * lambda for the Poisson base forecast if `distr`='poisson', see \link[stats]{Poisson}; +#' * size and prob (or mu) for the negative binomial base forecast if `distr`='nbinom', +#' see \link[stats]{NegBinomial}. +#' +#' The base upper forecasts `fc_upper` must be a list containing the parameters of +#' the multivariate Gaussian distribution of the upper forecasts. +#' The list must contain only the named elements `mu` (vector of length n_upper) +#' and `Sigma` (n_upper x n_upper matrix) +#' +#' A PMF object is a numerical vector containing the probability mass function of a discrete distribution. +#' Each element corresponds to the probability of the integers from 0 to the last value of the support. +#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, +#' \link{PMF.summary} for functions that handle PMF objects. +#' +#' Warnings are triggered from the Importance Sampling step if: +#' +#' * weights are all zeros, then the upper forecast is ignored during reconciliation; +#' * the effective sample size is < 200; +#' * the effective sample size is < 1% of the sample size. +#' +#' 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 fc_bottom A list containing the bottom base forecasts, see details. -#' @param fc_upper A list containing the bottom base forecasts, see details. +#' @param fc_upper A list containing the upper base forecasts, see details. #' @param bottom_in_type A string with three possible values: #' #' * 'pmf' if the bottom base forecasts are in the form of pmf, see details; #' * 'samples' if the bottom base forecasts are in the form of samples; #' * 'params' if the bottom base forecasts are in the form of estimated parameters. #' -#' @param distr A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +#' @param distr A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). #' -#' This is only used if `bottom_in_type=='params'`. +#' This is only used if `bottom_in_type='params'`. #' -#' @param num_samples Number of samples drawn from the reconciled distribution. -#' @param num_resample Number of importance sampling resamples. -#' @param return_type The return type of the reconciled distributions. A string with three possible values: +#' @param num_samples Number of samples drawn from the reconciled distribution. #' -#' * 'pmf' returns a list containing reconciled pmf objects; -#' * 'samples' returns a list containing reconciled samples; -#' * 'all' returns a list with both pmf objects and samples. +#' This is ignored if `bottom_in_type='samples'`; in this case, the number of +#' reconciled samples is equal to the number of samples of the base forecasts. +#' +#' @param return_type The return type of the reconciled distributions. +#' A string with three possible values: #' -#' @param ... additional parameters to be passed to the smoothing functions for PMF objects. +#' * 'pmf' returns a list containing the reconciled marginal pmf objects; +#' * 'samples' returns a list containing the reconciled multivariate samples; +#' * 'all' returns a list with both pmf objects and samples. #' #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. @@ -65,8 +71,10 @@ #' #' @return A list containing the reconciled forecasts. The list has the following named elements: #' -#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, depending on the value of `return_type`; -#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, depending on the value of `return_type`. +#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, +#' depending on the value of `return_type`; +#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, +#' depending on the value of `return_type`. #' #' @examples #' @@ -83,9 +91,9 @@ #' fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) #' #' # The upper forecast is a Normal with mean 40 and std 5 -#' fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) +#' fc_upper<- list(mu=40, Sigma=matrix(5^2)) #' -#' # We can reconcile with reconc_TDcond +#' # We can reconcile with reconc_MixCond #' res.mixCond <- reconc_MixCond(S, fc_bottom, fc_upper) #' #' # Note that the bottom distributions are slightly shifted to the right @@ -106,14 +114,12 @@ #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' -#' @seealso [reconc_TDcond], [reconc_BUIS()] +#' @seealso [reconc_TDcond], [reconc_BUIS] #' #' @export reconc_MixCond = function(S, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, - num_samples = 2e4, num_resample = 2e4, - return_type = "pmf", - ..., + num_samples = 2e4, return_type = "pmf", suppress_warnings = FALSE, seed = NULL) { if (!is.null(seed)) set.seed(seed) @@ -145,13 +151,12 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, B = do.call("cbind", B) # matrix of bottom samples (N_samples x n_bottom) } else if (bottom_in_type == "samples") { B = do.call("cbind", fc_bottom) + num_samples = nrow(B) } else if (bottom_in_type == "params") { L_pmf = lapply(fc_bottom, PMF.from_params, distr = distr) B = lapply(L_pmf, PMF.sample, N_samples=num_samples) B = do.call("cbind", B) # matrix of bottom samples (N_samples x n_bottom) - } - - + } # Get mean and covariance matrix of the MVN upper base forecasts mu_u = fc_upper$mu @@ -168,7 +173,7 @@ reconc_MixCond = function(S, fc_bottom, fc_upper, warning(warning_msg) } if(!(check_weights.res$warning & (1 %in% check_weights.res$warning_code))){ - B = .resample(B, weights, num_resample) + B = .resample(B, weights, num_samples) } ESS = sum(weights)**2/sum(weights**2) diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 3c85d0b..55065a6 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -64,62 +64,64 @@ -#' @title Probabilistic Reconciliation of forecasts via top-down conditioning +#' @title Probabilistic forecast reconciliation of mixed hierarchies via top-down conditioning #' #' @description #' -#' Uses the Top-down conditioning algorithm to draw samples from the reconciled +#' Uses the top-down conditioning algorithm to draw samples from the reconciled #' forecast distribution. Reconciliation is performed in two steps: #' first, the upper base forecasts are reconciled via conditioning, #' using only the hierarchical constraints between the upper variables; then, -#' the bottom distributions are updated via a probabilistic top-down procedure +#' the bottom distributions are updated via a probabilistic top-down procedure. #' #' @details #' -#' The base (unreconciled) forecasts are passed with the parameters -#' -#' * `fc_bottom`: a list of length n_bottom where each element is either a pmf object (`bottom_in_type='pmf'`), -#' a vector of samples (`bottom_in_type='samples'`) or the parameters (`bottom_in_type='params'`) of the -#' parametric distribution specified in `distr`. -#' * `fc_upper`: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. -#' The list must contain only the named elements `mu` (vector of length n_upper) and `Sigma` (n_upper x n_upper matrix) -#' -#' -#' A PMF object is a numerical vector containing the probability mass function for a forecast. -#' The first element of a PMF vector for the variable X always contains the probability P(X=0) and -#' there is one element for each integer. -#' The last element of the PMF corresponds to the probability of the last value in the support of X. -#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. +#' The base bottom forecasts `fc_bottom` must be a list of length n_bottom, where each element is either +#' * a PMF object (see details below), if `bottom_in_type='pmf'`; +#' * a vector of samples, if `bottom_in_type='samples'`; +#' * a list of parameters, if `bottom_in_type='params'`: +#' * lambda for the Poisson base forecast if `distr`='poisson', see \link[stats]{Poisson}; +#' * size and prob (or mu) for the negative binomial base forecast if `distr`='nbinom', +#' see \link[stats]{NegBinomial}. #' +#' The base upper forecasts `fc_upper` must be a list containing the parameters of +#' the multivariate Gaussian distribution of the upper forecasts. +#' The list must contain only the named elements `mu` (vector of length n_upper) +#' and `Sigma` (n_upper x n_upper matrix) +#' +#' A PMF object is a numerical vector containing the probability mass function of a discrete distribution. +#' Each element corresponds to the probability of the integers from 0 to the last value of the support. +#' See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, +#' \link{PMF.summary} for functions that handle PMF objects. #' -#' A warnings is triggered if the intersection of the support for the reconciled uppers -#' and the support of the bottom-up distribution is too small. In this case only -#' few samples from the reconciled upper are kept. The warning reports the percentage -#' of samples kept. -#' -#' Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +#' If some of the reconciled upper samples lie outside the support of the bottom-up distribution, +#' those samples are discarded and a warning is triggered. +#' The warning reports the percentage of samples kept. #' -#' @param S Summing matrix (n x n_bottom). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL +#' @param S Summing matrix (n x n_bottom). #' @param fc_bottom A list containing the bottom base forecasts, see details. -#' @param fc_upper A list containing the bottom base forecasts, see details. +#' @param fc_upper A list containing the upper base forecasts, see details. #' @param bottom_in_type A string with three possible values: #' #' * 'pmf' if the bottom base forecasts are in the form of pmf, see details; #' * 'samples' if the bottom base forecasts are in the form of samples; #' * 'params' if the bottom base forecasts are in the form of estimated parameters. #' -#' @param distr A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +#' @param distr A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). #' #' This is only used if `bottom_in_type=='params'`. #' #' @param num_samples Number of samples drawn from the reconciled distribution. -#' @param return_type The return type of the reconciled distributions. A string with three possible values: #' -#' * 'pmf' returns a list containing reconciled pmf objects; -#' * 'samples' returns a list containing reconciled samples; -#' * 'all' returns a list with both pmf objects and samples. +#' This is ignored if `bottom_in_type='samples'`; in this case, the number of +#' reconciled samples is equal to the number of samples of the base forecasts. +#' +#' @param return_type The return type of the reconciled distributions. +#' A string with three possible values: #' -#' @param ... additional parameters to be passed to the smoothing functions for PMF objects. +#' * 'pmf' returns a list containing the reconciled marginal pmf objects; +#' * 'samples' returns a list containing the reconciled multivariate samples; +#' * 'all' returns a list with both pmf objects and samples. #' #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. @@ -127,8 +129,10 @@ #' #' @return A list containing the reconciled forecasts. The list has the following named elements: #' -#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, depending on the value of `return_type`; -#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, depending on the value of `return_type`. +#' * `bottom_reconciled`: a list containing the pmf, the samples (matrix n_bottom x `num_samples`) or both, +#' depending on the value of `return_type`; +#' * `upper_reconciled`: a list containing the pmf, the samples (matrix n_upper x `num_samples`) or both, +#' depending on the value of `return_type`. #' #' @examples #' @@ -168,14 +172,14 @@ #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' -#' @seealso [reconc_MixCond], [reconc_BUIS()] +#' @seealso [reconc_MixCond], [reconc_BUIS] #' #' @export reconc_TDcond = function(S, fc_bottom, fc_upper, - bottom_in_type = "pmf", distr = NULL, - num_samples = 2e4, return_type = "pmf", - ..., - suppress_warnings = FALSE, seed = NULL) { + bottom_in_type = "pmf", distr = NULL, + num_samples = 2e4, return_type = "pmf", + ..., + suppress_warnings = FALSE, seed = NULL) { if (!is.null(seed)) set.seed(seed) @@ -188,7 +192,7 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, # After testing the convolution parameters: # remove dots, remove comment above, and set the "best parameters" as default in - # PMF.check_support and .TD_sampling + # PMF.check_support, .TD_sampling, PMF.summary # Check inputs .check_input_TD(S, fc_bottom, fc_upper, diff --git a/R/utils.R b/R/utils.R index 9b0cf25..b7579d5 100644 --- a/R/utils.R +++ b/R/utils.R @@ -238,6 +238,50 @@ } } +# Check importance sampling weights +.check_weigths <- function(w, n_eff_min=200, p_n_eff=0.01) { + warning = FALSE + warning_code = c() + warning_msg = c() + + n = length(w) + n_eff = n + + # 1. w==0 + if (all(w==0)) { + warning = TRUE + warning_code = c(warning_code, 1) + warning_msg = c(warning_msg, + "Importance Sampling: all the weights are zeros. This is probably caused by a strong incoherence between bottom and upper base forecasts.") + }else{ + + # Effective sample size + n_eff = (sum(w)^2) / sum(w^2) + + # 2. n_eff < threshold + if (n_eff < n_eff_min) { + warning = TRUE + warning_code = c(warning_code, 2) + warning_msg = c(warning_msg, + paste0("Importance Sampling: effective_sample_size= ", round(n_eff,2), " (< ", n_eff_min,").")) + } + + # 3. n_eff < p*n, e.g. p = 0.05 + if (n_eff < p_n_eff*n) { + warning = TRUE + warning_code = c(warning_code, 3) + warning_msg = c(warning_msg, + paste0("Importance Sampling: effective_sample_size= ", round(n_eff,2), " (< ", round(p_n_eff * 100, 2),"%).")) + } + } + res = list(warning = warning, + warning_code = warning_code, + warning_msg = warning_msg, + n_eff = n_eff) + + return(res) +} + ################################################################################ # SAMPLE @@ -360,6 +404,18 @@ return(exp(logval)) } +# Resample from weighted sample +.resample <- function(S_, weights, num_samples = NA) { + if (is.na(num_samples)) { + num_samples = length(weights) + } + + if(nrow(S_)!=length(weights)) + stop("Error in .resample: nrow(S_) != length(weights)") + + tmp_idx = sample(x = 1:nrow(S_), num_samples, replace = TRUE, prob = weights) + return(S_[tmp_idx, ]) +} ################################################################################ # Miscellaneous diff --git a/README.Rmd b/README.Rmd index 22d900f..9a37d61 100644 --- a/README.Rmd +++ b/README.Rmd @@ -26,16 +26,23 @@ knitr::opts_chunk$set( [![License: LGPL (>= 3)](https://img.shields.io/badge/license-LGPL (>= 3)-yellow.svg)](https://www.gnu.org/licences/lgpl-3.0) -The package `bayesRecon` implements probabilistic reconciliation of hierarchical time series forecasts via conditioning. +The package `bayesRecon` implements several methods for probabilistic reconciliation of hierarchical time series forecasts. The main functions are: - * `reconc_gaussian`: implements analytic formulae for the reconciliation of Gaussian base forecasts; - * `reconc_BUIS`: a generic tool for the reconciliation of any probabilistic time series forecast via importance sampling; + * `reconc_gaussian`: reconciliation via conditioning of multivariate Gaussian base forecasts; + this is done analytically; + * `reconc_BUIS`: reconciliation via conditioning of any probabilistic forecast via importance sampling; this is the recommended option for non-Gaussian base forecasts; - * `reconc_MCMC`: a generic tool for the reconciliation of probabilistic count time series forecasts via Markov Chain Monte Carlo. + * `reconc_MCMC`: reconciliation via conditioning of discrete probabilistic forecasts via Markov Chain Monte Carlo; + * `reconc_MixCond`: reconciliation via conditioning of mixed hierarchies, where the upper forecasts are + multivariate Gaussian and the bottom forecasts are discrete distributions; + * `reconc_TDcond`: reconciliation via top-down conditioning of mixed hierarchies, where the upper forecasts are + multivariate Gaussian and the bottom forecasts are discrete distributions. ## News +:boom: [2024-05-24] Added `reconc_MixCond` and `reconc_TDcond`. + :boom: [2023-12-19] Added the vignette "Properties of the reconciled distribution via conditioning". :boom: [2023-08-23] Added the vignette "Probabilistic Reconciliation via Conditioning with bayesRecon". Added the `schaferStrimmer_cov` function. diff --git a/man/M3_example.Rd b/man/M3_example.Rd index b28d993..cdfa103 100644 --- a/man/M3_example.Rd +++ b/man/M3_example.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/M3_example.R +% Please edit documentation in R/data.R \docType{data} \name{M3_example} \alias{M3_example} diff --git a/man/PMF.get_quantile.Rd b/man/PMF.get_quantile.Rd index 857bc93..9058622 100644 --- a/man/PMF.get_quantile.Rd +++ b/man/PMF.get_quantile.Rd @@ -25,9 +25,6 @@ n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) -# We can obtain the quantile of this PMF with the function PMF.get_quantile() -quant_90 <- PMF.get_quantile(pmf=pmf_binomial,p=0.9) - # The true median is ceiling(n*p) quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50) diff --git a/man/PMF.sample.Rd b/man/PMF.sample.Rd index 59dec37..2ba4aa2 100644 --- a/man/PMF.sample.Rd +++ b/man/PMF.sample.Rd @@ -12,7 +12,7 @@ PMF.sample(pmf, N_samples) \item{N_samples}{number of samples.} } \value{ -\code{N_samples} drawn from the distribution specified by \code{pmf}. +Samples drawn from the distribution specified by \code{pmf}. } \description{ Samples (with replacement) from the probability distribution specified by \code{pmf}. diff --git a/man/PMF.summary.Rd b/man/PMF.summary.Rd index 7f76439..1e66812 100644 --- a/man/PMF.summary.Rd +++ b/man/PMF.summary.Rd @@ -9,15 +9,17 @@ PMF.summary(pmf, toll = 1e-16, Rtoll = 1e-07) \arguments{ \item{pmf}{the PMF object.} -\item{toll}{lowest possible probability mass on the left} +\item{Rtoll}{used for computing the max of the PMF: the max is the largest value +with probability greater than Rtoll (default: 1e-7)} -\item{Rtoll}{lowest possible probability mass on the right} +\item{Ltoll}{used for computing the min of the PMF: the min is the smallest value +with probability greater than Ltoll (default: 1e-16)} } \value{ A summary data.frame } \description{ -Returns the summary (min,max, IQR, median, mean) of the PMF specified by \code{pmf}. +Returns the summary (min, max, IQR, median, mean) of the PMF specified by \code{pmf}. } \examples{ library(bayesRecon) diff --git a/man/carparts_example.Rd b/man/carparts_example.Rd index 0b84b5c..1582536 100644 --- a/man/carparts_example.Rd +++ b/man/carparts_example.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/carparts_example.R +% Please edit documentation in R/data.R \docType{data} \name{carparts_example} \alias{carparts_example} diff --git a/man/extr_mkt_events.Rd b/man/extr_mkt_events.Rd index 14c88f1..df68b4b 100644 --- a/man/extr_mkt_events.Rd +++ b/man/extr_mkt_events.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/extr_mkt_events.R +% Please edit documentation in R/data.R \docType{data} \name{extr_mkt_events} \alias{extr_mkt_events} diff --git a/man/extr_mkt_events_basefc.Rd b/man/extr_mkt_events_basefc.Rd index 7de3b76..f7a787a 100644 --- a/man/extr_mkt_events_basefc.Rd +++ b/man/extr_mkt_events_basefc.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/extr_mkt_events_basefc.R +% Please edit documentation in R/data.R \docType{data} \name{extr_mkt_events_basefc} \alias{extr_mkt_events_basefc} diff --git a/man/infantMortality.Rd b/man/infantMortality.Rd index b88bd78..d7634ee 100644 --- a/man/infantMortality.Rd +++ b/man/infantMortality.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/infantMortality.R +% Please edit documentation in R/data.R \docType{data} \name{infantMortality} \alias{infantMortality} diff --git a/man/reconc_BUIS.Rd b/man/reconc_BUIS.Rd index 37181ea..755a13c 100644 --- a/man/reconc_BUIS.Rd +++ b/man/reconc_BUIS.Rd @@ -36,7 +36,9 @@ If it is a list the i-th element is a string with two possible values: If \code{distr} is a string it is assumed that all distributions are of the same type.} -\item{num_samples}{Number of samples drawn from the reconciled distribution.} +\item{num_samples}{Number of samples drawn from the reconciled distribution. +This is ignored if \code{bottom_in_type='samples'}; in this case, the number of reconciled samples is equal to +the number of samples of the base forecasts.} \item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about effective sample size are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} @@ -53,7 +55,7 @@ A list containing the reconciled forecasts. The list has the following named ele } \description{ Uses the Bottom-Up Importance Sampling algorithm to draw samples from the reconciled -forecast distribution, which is obtained via conditioning. +forecast distribution, obtained via conditioning. } \details{ The parameter \code{base_forecast} is a list containing n elements where the i-th element depends on @@ -79,7 +81,8 @@ Warnings are triggered from the Importance Sampling step if: \item the effective sample size is < 1\% of the sample size (\code{num_samples} if \code{in_type} is 'params' or the size of the base forecast if if \code{in_type} is 'samples'). } -Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +Note that warnings are an indication that the base forecasts might have issues. +Please check the base forecasts in case of warnings. } \examples{ diff --git a/man/reconc_MCMC.Rd b/man/reconc_MCMC.Rd index 14053a2..e1cfee7 100644 --- a/man/reconc_MCMC.Rd +++ b/man/reconc_MCMC.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/reconc_MH.R +% Please edit documentation in R/reconc_MCMC.R \name{reconc_MCMC} \alias{reconc_MCMC} \title{MCMC for Probabilistic Reconciliation of forecasts via conditioning} diff --git a/man/reconc_MixCond.Rd b/man/reconc_MixCond.Rd index 716fcef..30f4b48 100644 --- a/man/reconc_MixCond.Rd +++ b/man/reconc_MixCond.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/reconc_MixCond.R \name{reconc_MixCond} \alias{reconc_MixCond} -\title{Probabilistic Reconciliation of forecasts via mixed conditioning} +\title{Probabilistic forecast reconciliation of mixed hierarchies via conditioning} \usage{ reconc_MixCond( S, @@ -11,19 +11,17 @@ reconc_MixCond( bottom_in_type = "pmf", distr = NULL, num_samples = 20000, - num_resample = 20000, return_type = "pmf", - ..., suppress_warnings = FALSE, seed = NULL ) } \arguments{ -\item{S}{Summing matrix (n x n_bottom). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL} +\item{S}{Summing matrix (n x n_bottom).} \item{fc_bottom}{A list containing the bottom base forecasts, see details.} -\item{fc_upper}{A list containing the bottom base forecasts, see details.} +\item{fc_upper}{A list containing the upper base forecasts, see details.} \item{bottom_in_type}{A string with three possible values: \itemize{ @@ -32,23 +30,24 @@ reconc_MixCond( \item 'params' if the bottom base forecasts are in the form of estimated parameters. }} -\item{distr}{A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +\item{distr}{A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). -This is only used if \code{bottom_in_type=='params'}.} +This is only used if \code{bottom_in_type='params'}.} -\item{num_samples}{Number of samples drawn from the reconciled distribution.} +\item{num_samples}{Number of samples drawn from the reconciled distribution. -\item{num_resample}{Number of importance sampling resamples.} +\if{html}{\out{
}}\preformatted{ This is ignored if `bottom_in_type='samples'`; in this case, the number of + reconciled samples is equal to the number of samples of the base forecasts. +}\if{html}{\out{
}}} -\item{return_type}{The return type of the reconciled distributions. A string with three possible values: +\item{return_type}{The return type of the reconciled distributions. +A string with three possible values: \itemize{ -\item 'pmf' returns a list containing reconciled pmf objects; -\item 'samples' returns a list containing reconciled samples; +\item 'pmf' returns a list containing the reconciled marginal pmf objects; +\item 'samples' returns a list containing the reconciled multivariate samples; \item 'all' returns a list with both pmf objects and samples. }} -\item{...}{additional parameters to be passed to the smoothing functions for PMF objects.} - \item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} @@ -57,36 +56,48 @@ are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. \value{ A list containing the reconciled forecasts. The list has the following named elements: \itemize{ -\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, depending on the value of \code{return_type}; -\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, depending on the value of \code{return_type}. +\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, +depending on the value of \code{return_type}; +\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, +depending on the value of \code{return_type}. } } \description{ -Uses the mixed conditioning algorithm to draw samples from the reconciled -forecast distribution. +Uses importance sampling to draw samples from the reconciled +forecast distribution, obtained via conditioning, in the case of a mixed hierarchy. } \details{ -The base (unreconciled) forecasts are passed with the parameters +The base bottom forecasts \code{fc_bottom} must be a list of length n_bottom, where each element is either +\itemize{ +\item a PMF object (see details below), if \code{bottom_in_type='pmf'}; +\item a vector of samples, if \code{bottom_in_type='samples'}; +\item a list of parameters, if \code{bottom_in_type='params'}: \itemize{ -\item \code{fc_bottom}: a list of length n_bottom where each element is either a pmf object (\code{bottom_in_type='pmf'}), -a vector of samples (\code{bottom_in_type='samples'}) or the parameters (\code{bottom_in_type='params'}) of the -parametric distribution specified in \code{distr}. -\item \code{fc_upper}: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. -The list must contain only the named elements \code{mu} (vector of length n_upper) and \code{Sigma} (n_upper x n_upper matrix) +\item lambda for the Poisson base forecast if \code{distr}='poisson', see \link[stats]{Poisson}; +\item size and prob (or mu) for the negative binomial base forecast if \code{distr}='nbinom', +see \link[stats]{NegBinomial}. +} } -A PMF object is a numerical vector containing the probability mass function for a forecast. -The first element of a PMF vector for the variable X always contains the probability P(X=0) and -there is one element for each integer. -The last element of the PMF corresponds to the probability of the last value in the support of X. -See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. +The base upper forecasts \code{fc_upper} must be a list containing the parameters of +the multivariate Gaussian distribution of the upper forecasts. +The list must contain only the named elements \code{mu} (vector of length n_upper) +and \code{Sigma} (n_upper x n_upper matrix) -A warnings is triggered if the intersection of the support for the reconciled uppers -and the support of the bottom-up distribution is too small. In this case only -few samples from the reconciled upper are kept. The warning reports the percentage -of samples kept. +A PMF object is a numerical vector containing the probability mass function of a discrete distribution. +Each element corresponds to the probability of the integers from 0 to the last value of the support. +See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, +\link{PMF.summary} for functions that handle PMF objects. + +Warnings are triggered from the Importance Sampling step if: +\itemize{ +\item weights are all zeros, then the upper forecast is ignored during reconciliation; +\item the effective sample size is < 200; +\item the effective sample size is < 1\% of the sample size. +} -Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +Note that warnings are an indication that the base forecasts might have issues. +Please check the base forecasts in case of warnings. } \examples{ @@ -103,9 +114,9 @@ fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,la fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) # The upper forecast is a Normal with mean 40 and std 5 -fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) +fc_upper<- list(mu=40, Sigma=matrix(5^2)) -# We can reconcile with reconc_TDcond +# We can reconcile with reconc_MixCond res.mixCond <- reconc_MixCond(S, fc_bottom, fc_upper) # Note that the bottom distributions are slightly shifted to the right @@ -128,5 +139,5 @@ Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ -\link{reconc_TDcond}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} +\link{reconc_TDcond}, \link{reconc_BUIS} } diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index d2e78ea..46d6685 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/reconc_TDcond.R \name{reconc_TDcond} \alias{reconc_TDcond} -\title{Probabilistic Reconciliation of forecasts via top-down conditioning} +\title{Probabilistic forecast reconciliation of mixed hierarchies via top-down conditioning} \usage{ reconc_TDcond( S, @@ -18,11 +18,11 @@ reconc_TDcond( ) } \arguments{ -\item{S}{Summing matrix (n x n_bottom). return_pmf = TRUE, return_samples = FALSE, suppress_warnings = FALSE, seed = NULL} +\item{S}{Summing matrix (n x n_bottom).} \item{fc_bottom}{A list containing the bottom base forecasts, see details.} -\item{fc_upper}{A list containing the bottom base forecasts, see details.} +\item{fc_upper}{A list containing the upper base forecasts, see details.} \item{bottom_in_type}{A string with three possible values: \itemize{ @@ -31,21 +31,24 @@ reconc_TDcond( \item 'params' if the bottom base forecasts are in the form of estimated parameters. }} -\item{distr}{A string describing the type of bottom base forecasts ('gaussian', 'poisson' or 'nbinom'). +\item{distr}{A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). This is only used if \code{bottom_in_type=='params'}.} -\item{num_samples}{Number of samples drawn from the reconciled distribution.} +\item{num_samples}{Number of samples drawn from the reconciled distribution. -\item{return_type}{The return type of the reconciled distributions. A string with three possible values: +\if{html}{\out{
}}\preformatted{ This is ignored if `bottom_in_type='samples'`; in this case, the number of + reconciled samples is equal to the number of samples of the base forecasts. +}\if{html}{\out{
}}} + +\item{return_type}{The return type of the reconciled distributions. +A string with three possible values: \itemize{ -\item 'pmf' returns a list containing reconciled pmf objects; -\item 'samples' returns a list containing reconciled samples; +\item 'pmf' returns a list containing the reconciled marginal pmf objects; +\item 'samples' returns a list containing the reconciled multivariate samples; \item 'all' returns a list with both pmf objects and samples. }} -\item{...}{additional parameters to be passed to the smoothing functions for PMF objects.} - \item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} @@ -54,39 +57,45 @@ are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. \value{ A list containing the reconciled forecasts. The list has the following named elements: \itemize{ -\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, depending on the value of \code{return_type}; -\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, depending on the value of \code{return_type}. +\item \code{bottom_reconciled}: a list containing the pmf, the samples (matrix n_bottom x \code{num_samples}) or both, +depending on the value of \code{return_type}; +\item \code{upper_reconciled}: a list containing the pmf, the samples (matrix n_upper x \code{num_samples}) or both, +depending on the value of \code{return_type}. } } \description{ -Uses the Top-down conditioning algorithm to draw samples from the reconciled +Uses the top-down conditioning algorithm to draw samples from the reconciled forecast distribution. Reconciliation is performed in two steps: first, the upper base forecasts are reconciled via conditioning, using only the hierarchical constraints between the upper variables; then, -the bottom distributions are updated via a probabilistic top-down procedure +the bottom distributions are updated via a probabilistic top-down procedure. } \details{ -The base (unreconciled) forecasts are passed with the parameters +The base bottom forecasts \code{fc_bottom} must be a list of length n_bottom, where each element is either \itemize{ -\item \code{fc_bottom}: a list of length n_bottom where each element is either a pmf object (\code{bottom_in_type='pmf'}), -a vector of samples (\code{bottom_in_type='samples'}) or the parameters (\code{bottom_in_type='params'}) of the -parametric distribution specified in \code{distr}. -\item \code{fc_upper}: a list containing the parameters of the multivariate Gaussian distribution of the upper forecasts. -The list must contain only the named elements \code{mu} (vector of length n_upper) and \code{Sigma} (n_upper x n_upper matrix) +\item a PMF object (see details below), if \code{bottom_in_type='pmf'}; +\item a vector of samples, if \code{bottom_in_type='samples'}; +\item a list of parameters, if \code{bottom_in_type='params'}: +\itemize{ +\item lambda for the Poisson base forecast if \code{distr}='poisson', see \link[stats]{Poisson}; +\item size and prob (or mu) for the negative binomial base forecast if \code{distr}='nbinom', +see \link[stats]{NegBinomial}. +} } -A PMF object is a numerical vector containing the probability mass function for a forecast. -The first element of a PMF vector for the variable X always contains the probability P(X=0) and -there is one element for each integer. -The last element of the PMF corresponds to the probability of the last value in the support of X. -See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, \link{PMF.summary} for functions that handle PMF objects. +The base upper forecasts \code{fc_upper} must be a list containing the parameters of +the multivariate Gaussian distribution of the upper forecasts. +The list must contain only the named elements \code{mu} (vector of length n_upper) +and \code{Sigma} (n_upper x n_upper matrix) -A warnings is triggered if the intersection of the support for the reconciled uppers -and the support of the bottom-up distribution is too small. In this case only -few samples from the reconciled upper are kept. The warning reports the percentage -of samples kept. +A PMF object is a numerical vector containing the probability mass function of a discrete distribution. +Each element corresponds to the probability of the integers from 0 to the last value of the support. +See also \link{PMF.get_mean}, \link{PMF.get_var}, \link{PMF.sample}, \link{PMF.get_quantile}, +\link{PMF.summary} for functions that handle PMF objects. -Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings. +If some of the reconciled upper samples lie outside the support of the bottom-up distribution, +those samples are discarded and a warning is triggered. +The warning reports the percentage of samples kept. } \examples{ @@ -128,5 +137,5 @@ Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ -\link{reconc_MixCond}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} +\link{reconc_MixCond}, \link{reconc_BUIS} } From 68f8e0657d3745bb379be6a7b194cb02027ba559 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 21 May 2024 16:46:52 +0200 Subject: [PATCH 20/36] fixed bugs + minor fix --- R/PMF.R | 2 +- R/reconc_BUIS.R | 1 + R/reconc_MixCond.R | 3 +- R/reconc_TDcond.R | 5 +-- R/utils.R | 3 ++ README.Rmd | 4 +-- README.md | 40 ++++++++++++++--------- man/PMF.summary.Rd | 8 ++--- man/figures/README-unnamed-chunk-6-1.png | Bin 24483 -> 4754 bytes man/figures/README-unnamed-chunk-7-1.png | Bin 27861 -> 4501 bytes man/reconc_MixCond.Rd | 8 ++--- man/reconc_TDcond.Rd | 10 +++--- 12 files changed, 47 insertions(+), 37 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index dc83732..d0ba099 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -205,7 +205,7 @@ PMF.get_quantile = function(pmf, p) { #' @return A summary data.frame #' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] #' @export -PMF.summary = function(pmf, toll=1e-16, Rtoll=1e-7) { +PMF.summary = function(pmf, Ltoll=1e-16, Rtoll=1e-7) { min_pmf = min(which(pmf > Ltoll)) - 1 max_pmf = max(which(pmf > Rtoll)) - 1 all_summaries <- data.frame("Min." = min_pmf, diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index fc0dccd..d8940ca 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -106,6 +106,7 @@ #' @param num_samples Number of samples drawn from the reconciled distribution. #' This is ignored if `bottom_in_type='samples'`; in this case, the number of reconciled samples is equal to #' the number of samples of the base forecasts. +#' #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about effective sample size #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. #' @param seed Seed for reproducibility. diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index d17d1f4..92c2d8e 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -54,7 +54,6 @@ #' This is only used if `bottom_in_type='params'`. #' #' @param num_samples Number of samples drawn from the reconciled distribution. -#' #' This is ignored if `bottom_in_type='samples'`; in this case, the number of #' reconciled samples is equal to the number of samples of the base forecasts. #' @@ -114,7 +113,7 @@ #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' -#' @seealso [reconc_TDcond], [reconc_BUIS] +#' @seealso [reconc_TDcond()], [reconc_BUIS()] #' #' @export reconc_MixCond = function(S, fc_bottom, fc_upper, diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 55065a6..f1d8fc3 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -112,7 +112,6 @@ #' This is only used if `bottom_in_type=='params'`. #' #' @param num_samples Number of samples drawn from the reconciled distribution. -#' #' This is ignored if `bottom_in_type='samples'`; in this case, the number of #' reconciled samples is equal to the number of samples of the base forecasts. #' @@ -123,6 +122,8 @@ #' * 'samples' returns a list containing the reconciled multivariate samples; #' * 'all' returns a list with both pmf objects and samples. #' +#' @param ... additional parameters for pmf convolution +#' #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. #' @param seed Seed for reproducibility. @@ -172,7 +173,7 @@ #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' -#' @seealso [reconc_MixCond], [reconc_BUIS] +#' @seealso [reconc_MixCond()], [reconc_BUIS()] #' #' @export reconc_TDcond = function(S, fc_bottom, fc_upper, diff --git a/R/utils.R b/R/utils.R index b7579d5..c9944af 100644 --- a/R/utils.R +++ b/R/utils.R @@ -81,6 +81,9 @@ # Check the parameters of distr .check_distr_params <- function(distr, params) { .check_implemented_distr(distr) + if (!is.list(params)) { + stop("Input error: the parameters of the distribution must be given as a list.") + } switch( distr, "gaussian" = { diff --git a/README.Rmd b/README.Rmd index 9a37d61..b66c499 100644 --- a/README.Rmd +++ b/README.Rmd @@ -199,7 +199,7 @@ sigmas <- c(sigmaY, sigmaS1, sigmaS2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = c(mean = mus[[i]], sd = sigmas[[i]]) + base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) } ``` @@ -218,7 +218,7 @@ samples_buis <- buis$reconciled_samples buis_means <- rowMeans(samples_buis) ``` -In the base forecasts are Gaussian, the reconciled distribution is still Gaussian and can be computed in closed form: +If the base forecasts are Gaussian, the reconciled distribution is still Gaussian and can be computed in closed form: ```{r} Sigma <- diag(sigmas ^ 2) #transform into covariance matrix diff --git a/README.md b/README.md index 44d789f..2f4daaf 100644 --- a/README.md +++ b/README.md @@ -18,21 +18,29 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h 3)](https://img.shields.io/badge/license-LGPL%20(%3E=%203)-yellow.svg)](https://www.gnu.org/licences/lgpl-3.0) -The package `bayesRecon` implements probabilistic reconciliation of -hierarchical time series forecasts via conditioning. +The package `bayesRecon` implements several methods for probabilistic +reconciliation of hierarchical time series forecasts. The main functions are: -- `reconc_gaussian`: implements analytic formulae for the reconciliation - of Gaussian base forecasts; -- `reconc_BUIS`: a generic tool for the reconciliation of any - probabilistic time series forecast via importance sampling; this is - the recommended option for non-Gaussian base forecasts; -- `reconc_MCMC`: a generic tool for the reconciliation of probabilistic - count time series forecasts via Markov Chain Monte Carlo. +- `reconc_gaussian`: reconciliation via conditioning of multivariate + Gaussian base forecasts; this is done analytically; +- `reconc_BUIS`: reconciliation via conditioning of any probabilistic + forecast via importance sampling; this is the recommended option for + non-Gaussian base forecasts; +- `reconc_MCMC`: reconciliation via conditioning of discrete + probabilistic forecasts via Markov Chain Monte Carlo; +- `reconc_MixCond`: reconciliation via conditioning of mixed + hierarchies, where the upper forecasts are multivariate Gaussian and + the bottom forecasts are discrete distributions; +- `reconc_TDcond`: reconciliation via top-down conditioning of mixed + hierarchies, where the upper forecasts are multivariate Gaussian and + the bottom forecasts are discrete distributions. ## News +:boom: \[2024-05-24\] Added `reconc_MixCond` and `reconc_TDcond`. + :boom: \[2023-12-19\] Added the vignette “Properties of the reconciled distribution via conditioning”. @@ -97,11 +105,11 @@ lambdas <- c(lambdaY, lambdaS1, lambdaS2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = lambdas[i] + base_forecasts[[i]] = list(lambda = lambdas[i]) } ``` -We recommend using the BUIS algorithm (Zambon et al., 2022) to sample +We recommend using the BUIS algorithm (Zambon et al., 2024) to sample from the reconciled distribution. ``` r @@ -175,7 +183,7 @@ plot( We also provide a function for sampling using Markov Chain Monte Carlo -(Corani et al., 2022). +(Corani et al., 2023). ``` r mcmc = reconc_MCMC( @@ -210,7 +218,7 @@ sigmas <- c(sigmaY, sigmaS1, sigmaS2) base_forecasts = list() for (i in 1:nrow(S)) { - base_forecasts[[i]] = c(mus[[i]], sigmas[[i]]) + base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) } ``` @@ -229,7 +237,7 @@ samples_buis <- buis$reconciled_samples buis_means <- rowMeans(samples_buis) ``` -In the base forecasts are Gaussian, the reconciled distribution is still +If the base forecasts are Gaussian, the reconciled distribution is still Gaussian and can be computed in closed form: ``` r @@ -263,9 +271,9 @@ Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. [DOI](https://doi.org/10.1007/s11222-023-10343-y) -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of the reconciled distributions for Gaussian and count forecasts*. -[DOI](https://doi.org/10.48550/arXiv.2303.15135) +[DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) ## Contributors diff --git a/man/PMF.summary.Rd b/man/PMF.summary.Rd index 1e66812..14a02bf 100644 --- a/man/PMF.summary.Rd +++ b/man/PMF.summary.Rd @@ -4,16 +4,16 @@ \alias{PMF.summary} \title{Returns summary of a PMF object} \usage{ -PMF.summary(pmf, toll = 1e-16, Rtoll = 1e-07) +PMF.summary(pmf, Ltoll = 1e-16, Rtoll = 1e-07) } \arguments{ \item{pmf}{the PMF object.} -\item{Rtoll}{used for computing the max of the PMF: the max is the largest value -with probability greater than Rtoll (default: 1e-7)} - \item{Ltoll}{used for computing the min of the PMF: the min is the smallest value with probability greater than Ltoll (default: 1e-16)} + +\item{Rtoll}{used for computing the max of the PMF: the max is the largest value +with probability greater than Rtoll (default: 1e-7)} } \value{ A summary 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b/man/reconc_MixCond.Rd index 30f4b48..5670b12 100644 --- a/man/reconc_MixCond.Rd +++ b/man/reconc_MixCond.Rd @@ -35,10 +35,8 @@ reconc_MixCond( This is only used if \code{bottom_in_type='params'}.} \item{num_samples}{Number of samples drawn from the reconciled distribution. - -\if{html}{\out{
}}\preformatted{ This is ignored if `bottom_in_type='samples'`; in this case, the number of - reconciled samples is equal to the number of samples of the base forecasts. -}\if{html}{\out{
}}} +This is ignored if \code{bottom_in_type='samples'}; in this case, the number of +reconciled samples is equal to the number of samples of the base forecasts.} \item{return_type}{The return type of the reconciled distributions. A string with three possible values: @@ -139,5 +137,5 @@ Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ -\link{reconc_TDcond}, \link{reconc_BUIS} +\code{\link[=reconc_TDcond]{reconc_TDcond()}}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} } diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index 46d6685..cab2f74 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -36,10 +36,8 @@ reconc_TDcond( This is only used if \code{bottom_in_type=='params'}.} \item{num_samples}{Number of samples drawn from the reconciled distribution. - -\if{html}{\out{
}}\preformatted{ This is ignored if `bottom_in_type='samples'`; in this case, the number of - reconciled samples is equal to the number of samples of the base forecasts. -}\if{html}{\out{
}}} +This is ignored if \code{bottom_in_type='samples'}; in this case, the number of +reconciled samples is equal to the number of samples of the base forecasts.} \item{return_type}{The return type of the reconciled distributions. A string with three possible values: @@ -49,6 +47,8 @@ A string with three possible values: \item 'all' returns a list with both pmf objects and samples. }} +\item{...}{additional parameters for pmf convolution} + \item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} @@ -137,5 +137,5 @@ Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ -\link{reconc_MixCond}, \link{reconc_BUIS} +\code{\link[=reconc_MixCond]{reconc_MixCond()}}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} } From 7802bbe7ab68f3a6825cd7363efeb0b7394f5735 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 21 May 2024 19:11:34 +0200 Subject: [PATCH 21/36] updated bayesRecon-package + fixed references --- R/bayesRecon-package.R | 51 +++++++++++++++++++++++++++++------------- R/reconc_BUIS.R | 4 +++- R/reconc_MCMC.R | 5 +++-- R/reconc_MixCond.R | 12 +++++++--- R/reconc_TDcond.R | 14 ++++++++---- R/reconc_gaussian.R | 9 +++++--- README.Rmd | 23 ++++++++++++------- 7 files changed, 82 insertions(+), 36 deletions(-) diff --git a/R/bayesRecon-package.R b/R/bayesRecon-package.R index a62ba47..865f6f3 100644 --- a/R/bayesRecon-package.R +++ b/R/bayesRecon-package.R @@ -4,28 +4,49 @@ #' #' @section Main functions: #' -#' The package implements reconciliation via conditioning for probabilistic forecasts of hierarchical time series. The main functions are -#' -#' * [reconc_gaussian()]: analytical reconciliation of Gaussian base forecasts; -#' * [reconc_BUIS()]: reconciliation of any probabilistic base forecast via importance sampling; -#' this is the recommended option for non-Gaussian base forecasts; -#' * [reconc_MCMC()]: reconciliation of probabilistic discrete base forecasts via Markov Chain Monte Carlo. -#' +#' The package implements reconciliation via conditioning for probabilistic forecasts +#' of hierarchical time series. The main functions are: +#' +#' * [reconc_gaussian()]: reconciliation via conditioning of multivariate Gaussian +#' base forecasts; this is done analytically; +#' * [reconc_BUIS()]: reconciliation via conditioning of any probabilistic forecast +#' via importance sampling; this is the recommended option for non-Gaussian base forecasts; +#' * [reconc_MCMC()]: reconciliation via conditioning of discrete probabilistic +#' forecasts via Markov Chain Monte Carlo; +#' * [reconc_MixCond()]: reconciliation via conditioning of mixed hierarchies, where +#' the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions; +#' * [reconc_TDcond()]: reconciliation via top-down conditioning of mixed hierarchies, where +#' the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions. +#' #' @section Utility functions: #' #' * [temporal_aggregation()]: temporal aggregation of a given time series object of class \link[stats]{ts}; -#' * [get_reconc_matrices()]: aggregation and summing matrices for a temporal hierarchy of time series from user-selected list of aggregation levels. +#' * [get_reconc_matrices()]: aggregation and summing matrices for a temporal hierarchy +#' of time series from user-selected list of aggregation levels; +#' * [schaferStrimmer_cov()]: computes the Schäfer-Strimmer shrinkage estimator for the covariance matrix; +#' * [PMF.get_mean(), PMF.get_var(), PMF.get_quantile(), PMF.summary(), PMF.sample()]: +#' functions for handling PMF objects. #' #' @references -#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. -#' -#' Corani, G., Azzimonti, D., Rubattu, N. (2023). *Probabilistic reconciliation of count time series*. \doi{10.1016/j.ijforecast.2023.04.003}. -#' -#' Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. \doi{10.1007/s11222-023-10343-y}. -#' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +#' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' \doi{10.1007/978-3-030-67664-3_13}. +#' +#' Corani, G., Azzimonti, D., Rubattu, N. (2023). +#' *Probabilistic reconciliation of count time series*. +#' \doi{10.1016/j.ijforecast.2023.04.003}. +#' +#' Zambon, L., Azzimonti, D. & Corani, G. (2024). +#' *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +#' \doi{10.1007/s11222-023-10343-y}. #' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' +#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +#' *Probabilistic reconciliation of mixed-type hierarchical time series*. +#' The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' #' @keywords internal "_PACKAGE" diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index d8940ca..7003aed 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -184,7 +184,9 @@ #'print(rowMeans(samples_buis)) #' #' @references -#' Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. \doi{10.1007/s11222-023-10343-y}. +#' Zambon, L., Azzimonti, D. & Corani, G. (2024). +#' *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +#' \doi{10.1007/s11222-023-10343-y}. #' #' #' @seealso diff --git a/R/reconc_MCMC.R b/R/reconc_MCMC.R index 79a44de..4bdaf82 100644 --- a/R/reconc_MCMC.R +++ b/R/reconc_MCMC.R @@ -73,8 +73,9 @@ #'print(rowMeans(samples_buis)) #' #' @references -#' Corani, G., Azzimonti, D., Rubattu, N. (2023). *Probabilistic reconciliation of count time series*. \doi{10.1016/j.ijforecast.2023.04.003}. -#' +#' Corani, G., Azzimonti, D., Rubattu, N. (2023). +#' *Probabilistic reconciliation of count time series*. +#' \doi{10.1016/j.ijforecast.2023.04.003}. #' #' @seealso #' [reconc_BUIS()] diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index 92c2d8e..e831aa0 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -107,11 +107,17 @@ #' PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]]) #' #' @references -#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +#' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' \doi{10.1007/978-3-030-67664-3_13}. #' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' -#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +#' *Probabilistic reconciliation of mixed-type hierarchical time series*. +#' The 40th Conference on Uncertainty in Artificial Intelligence, accepted. #' #' @seealso [reconc_TDcond()], [reconc_BUIS()] #' diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index f1d8fc3..27c50cb 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -167,12 +167,18 @@ #' PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) #' #' @references -#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +#' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' \doi{10.1007/978-3-030-67664-3_13}. #' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. -#' -#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series* The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' +#' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +#' *Probabilistic reconciliation of mixed-type hierarchical time series*. +#' The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +#' #' @seealso [reconc_MixCond()], [reconc_BUIS()] #' #' @export diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R index e3ab0fa..8cbc940 100644 --- a/R/reconc_gaussian.R +++ b/R/reconc_gaussian.R @@ -67,10 +67,13 @@ #'Y_reconc = rbind(U, B) #' #' @references -#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. -#' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +#' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' \doi{10.1007/978-3-030-67664-3_13}. #' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' @seealso [reconc_BUIS()] #' diff --git a/README.Rmd b/README.Rmd index b66c499..d3f5913 100644 --- a/README.Rmd +++ b/README.Rmd @@ -35,8 +35,8 @@ The main functions are: * `reconc_BUIS`: reconciliation via conditioning of any probabilistic forecast via importance sampling; this is the recommended option for non-Gaussian base forecasts; * `reconc_MCMC`: reconciliation via conditioning of discrete probabilistic forecasts via Markov Chain Monte Carlo; - * `reconc_MixCond`: reconciliation via conditioning of mixed hierarchies, where the upper forecasts are - multivariate Gaussian and the bottom forecasts are discrete distributions; + * `reconc_MixCond`: reconciliation via conditioning of mixed hierarchies, where + the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions; * `reconc_TDcond`: reconciliation via top-down conditioning of mixed hierarchies, where the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions. @@ -240,15 +240,22 @@ while the reconciled means obtained via BUIS are ## References -Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule*. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. [DOI](https://doi.org/10.1007/978-3-030-67664-3_13) +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +*Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule*. [DOI](https://doi.org/10.1007/978-3-030-67664-3_13) -Corani, G., Azzimonti, D., Rubattu, N. (2023). *Probabilistic reconciliation of count -time series*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.04.003) +Corani, G., Azzimonti, D., Rubattu, N. (2023). +*Probabilistic reconciliation of count time series*. +[DOI](https://doi.org/10.1016/j.ijforecast.2023.04.003) -Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts -for real-valued and count time series*. [DOI](https://doi.org/10.1007/s11222-023-10343-y) +Zambon, L., Azzimonti, D. & Corani, G. (2024). +*Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. [DOI](https://doi.org/10.1007/s11222-023-10343-y) -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of the reconciled distributions for Gaussian and count forecasts*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +*Properties of the reconciled distributions for Gaussian and count forecasts*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +*Probabilistic reconciliation of mixed-type hierarchical time series*. +The 40th Conference on Uncertainty in Artificial Intelligence, accepted. ## Contributors From 2ca7de8de39605b5372fccc413dfa8c7b52881c6 Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Wed, 22 May 2024 16:07:04 +0200 Subject: [PATCH 22/36] fixed references, updated description, minor fixes --- DESCRIPTION | 10 ++++++- R/bayesRecon-package.R | 8 ++++-- R/data.R | 50 ++++++++++++++++++++++++++--------- R/reconc_BUIS.R | 1 + R/reconc_MCMC.R | 3 ++- R/reconc_MixCond.R | 2 ++ R/reconc_TDcond.R | 2 ++ R/reconc_gaussian.R | 2 ++ README.Rmd | 15 ++++++++--- README.md | 15 +++++++---- man/bayesRecon-package.Rd | 49 ++++++++++++++++++++++++++-------- man/carparts_example.Rd | 13 ++++++--- man/extr_mkt_events.Rd | 14 +++++++--- man/extr_mkt_events_basefc.Rd | 17 +++++++++--- man/infantMortality.Rd | 4 ++- man/reconc_BUIS.Rd | 5 +++- man/reconc_MCMC.Rd | 5 +++- man/reconc_MixCond.Rd | 18 +++++++++---- man/reconc_TDcond.Rd | 18 +++++++++---- man/reconc_gaussian.Rd | 10 +++++-- vignettes/references.bib | 22 ++++++++------- 21 files changed, 210 insertions(+), 73 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8498eae..2b58eed 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -24,7 +24,15 @@ Authors@R: c(person(given = "Dario", email = "giorgio.corani@idsia.ch", comment = c(ORCID = "0000-0002-1541-8384"))) Maintainer: Dario Azzimonti -Description: Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2022) , Bottom-Up Importance Sampling (Zambon et al., 2022) . +Description: Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. + The available methods include analytical Gaussian reconciliation (Corani et al., 2021) + , + MCMC reconciliation of count time series (Corani et al., 2024) + , + Bottom-Up Importance Sampling (Zambon et al., 2024) + , + methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) + (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted). License: LGPL (>= 3) Depends: R (>= 4.1.0) Imports: diff --git a/R/bayesRecon-package.R b/R/bayesRecon-package.R index 865f6f3..880590e 100644 --- a/R/bayesRecon-package.R +++ b/R/bayesRecon-package.R @@ -24,24 +24,28 @@ #' * [get_reconc_matrices()]: aggregation and summing matrices for a temporal hierarchy #' of time series from user-selected list of aggregation levels; #' * [schaferStrimmer_cov()]: computes the Schäfer-Strimmer shrinkage estimator for the covariance matrix; -#' * [PMF.get_mean(), PMF.get_var(), PMF.get_quantile(), PMF.summary(), PMF.sample()]: +#' * [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.summary()], [PMF.sample()]: #' functions for handling PMF objects. #' #' @references #' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). #' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. #' \doi{10.1007/978-3-030-67664-3_13}. #' -#' Corani, G., Azzimonti, D., Rubattu, N. (2023). +#' Corani, G., Azzimonti, D., Rubattu, N. (2024). #' *Probabilistic reconciliation of count time series*. +#' International Journal of Forecasting 40 (2), 457-469. #' \doi{10.1016/j.ijforecast.2023.04.003}. #' #' Zambon, L., Azzimonti, D. & Corani, G. (2024). #' *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +#' Statistics and Computing 34 (1), 21. #' \doi{10.1007/s11222-023-10343-y}. #' #' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). #' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). #' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). diff --git a/R/data.R b/R/data.R index 8efa695..12f13fb 100644 --- a/R/data.R +++ b/R/data.R @@ -6,13 +6,18 @@ #' Univariate time series of class \link[stats]{ts}. #' #' @references -#' Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008) Forecasting with exponential -#' smoothing: the state space approach, Springer +#' Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008). +#' Forecasting with exponential smoothing: the state space approach. +#' Springer Science & Business Media. #' -#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +#' Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R., & Montero-Manso, P. (2020). +#' Car Parts Dataset (without Missing Values) (Version 2) +#' \doi{10.5281/zenodo.4656021} #' #' @source -#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +#' Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R.J., & Montero-Manso, P. (2020). +#' Car Parts Dataset (without Missing Values) (Version 2) +#' \doi{10.5281/zenodo.4656021} "carparts_example" @@ -31,7 +36,9 @@ #' hts package [CRAN](https://cran.r-project.org/package=hts) #' #' @references -#' R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579-2589. +#' Hyndman, R.J., Ahmed, R.A., Athanasopoulos, G., Shang, H.L. (2011). +#' Optimal combination forecasts for hierarchical time series. +#' Computational Statistics and Data Analysis, 55(9), 2579-2589. "infantMortality" @@ -66,19 +73,29 @@ #' A multivariate time series of class \link[stats]{ts}. #' #' @source -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' @references -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). +#' \doi{10.1016/j.ijforecast.2023.12.004}. #' -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} +#' Agosto, A. (2022). +#' *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. +#' \doi{10.2139/ssrn.4119895} "extr_mkt_events" #' Base forecasts for the extreme market events dataset #' #' Base forecasts for the `extr_mkt_events` dataset, computed using the model by -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895}. +#' Agosto, A. (2022). +#' *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. +#' \doi{10.2139/ssrn.4119895}. #' #' The predictive distribution for the bottom time series is a multivariate negative #' binomial with a static vector of dispersion parameters and a time-varying vector @@ -95,10 +112,17 @@ #' } #' #' @source -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} +#' Agosto, A. (2022). +#' *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. +#' \doi{10.2139/ssrn.4119895} #' #' @references -#' Agosto, A. (2022). *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. \doi{10.2139/ssrn.4119895} -#' -#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). *Properties of the reconciled distributions for Gaussian and count forecasts*. \doi{10.48550/arXiv.2303.15135}. +#' Agosto, A. (2022). +#' *Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion*. +#' \doi{10.2139/ssrn.4119895} +#' +#' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +#' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). +#' \doi{10.1016/j.ijforecast.2023.12.004}. "extr_mkt_events_basefc" \ No newline at end of file diff --git a/R/reconc_BUIS.R b/R/reconc_BUIS.R index 7003aed..4c7b495 100644 --- a/R/reconc_BUIS.R +++ b/R/reconc_BUIS.R @@ -186,6 +186,7 @@ #' @references #' Zambon, L., Azzimonti, D. & Corani, G. (2024). #' *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +#' Statistics and Computing 34 (1), 21. #' \doi{10.1007/s11222-023-10343-y}. #' #' diff --git a/R/reconc_MCMC.R b/R/reconc_MCMC.R index 4bdaf82..7fc7c24 100644 --- a/R/reconc_MCMC.R +++ b/R/reconc_MCMC.R @@ -73,8 +73,9 @@ #'print(rowMeans(samples_buis)) #' #' @references -#' Corani, G., Azzimonti, D., Rubattu, N. (2023). +#' Corani, G., Azzimonti, D., Rubattu, N. (2024). #' *Probabilistic reconciliation of count time series*. +#' International Journal of Forecasting 40 (2), 457-469. #' \doi{10.1016/j.ijforecast.2023.04.003}. #' #' @seealso diff --git a/R/reconc_MixCond.R b/R/reconc_MixCond.R index e831aa0..dbccc65 100644 --- a/R/reconc_MixCond.R +++ b/R/reconc_MixCond.R @@ -109,10 +109,12 @@ #' @references #' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). #' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. #' \doi{10.1007/978-3-030-67664-3_13}. #' #' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). #' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). #' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 27c50cb..537f416 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -169,10 +169,12 @@ #' @references #' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). #' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. #' \doi{10.1007/978-3-030-67664-3_13}. #' #' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). #' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). #' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). diff --git a/R/reconc_gaussian.R b/R/reconc_gaussian.R index 8cbc940..a9f1fe3 100644 --- a/R/reconc_gaussian.R +++ b/R/reconc_gaussian.R @@ -69,10 +69,12 @@ #' @references #' Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). #' *Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule*. +#' ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. #' \doi{10.1007/978-3-030-67664-3_13}. #' #' Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). #' *Properties of the reconciled distributions for Gaussian and count forecasts*. +#' International Journal of Forecasting (in press). #' \doi{10.1016/j.ijforecast.2023.12.004}. #' #' @seealso [reconc_BUIS()] diff --git a/README.Rmd b/README.Rmd index d3f5913..6f3002b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -241,17 +241,24 @@ while the reconciled means obtained via BUIS are ## References Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). -*Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule*. [DOI](https://doi.org/10.1007/978-3-030-67664-3_13) +*Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule*. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. +[DOI](https://doi.org/10.1007/978-3-030-67664-3_13) -Corani, G., Azzimonti, D., Rubattu, N. (2023). +Corani, G., Azzimonti, D., Rubattu, N. (2024). *Probabilistic reconciliation of count time series*. +International Journal of Forecasting 40 (2), 457-469. [DOI](https://doi.org/10.1016/j.ijforecast.2023.04.003) Zambon, L., Azzimonti, D. & Corani, G. (2024). -*Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. [DOI](https://doi.org/10.1007/s11222-023-10343-y) +*Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +Statistics and Computing 34 (1), 21. +[DOI](https://doi.org/10.1007/s11222-023-10343-y) Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). -*Properties of the reconciled distributions for Gaussian and count forecasts*. [DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) +*Properties of the reconciled distributions for Gaussian and count forecasts*. +International Journal of Forecasting (in press). +[DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). *Probabilistic reconciliation of mixed-type hierarchical time series*. diff --git a/README.md b/README.md index 2f4daaf..8a75905 100644 --- a/README.md +++ b/README.md @@ -258,23 +258,28 @@ the reconciled means obtained via BUIS are 7.41, 2.71, 4.71. Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). *Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule*. -In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine -Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture -Notes in Computer Science(), vol 12459. Springer, Cham. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. [DOI](https://doi.org/10.1007/978-3-030-67664-3_13) -Corani, G., Azzimonti, D., Rubattu, N. (2023). *Probabilistic -reconciliation of count time series*. +Corani, G., Azzimonti, D., Rubattu, N. (2024). *Probabilistic +reconciliation of count time series*. International Journal of +Forecasting 40 (2), 457-469. [DOI](https://doi.org/10.1016/j.ijforecast.2023.04.003) Zambon, L., Azzimonti, D. & Corani, G. (2024). *Efficient probabilistic reconciliation of forecasts for real-valued and count time series*. +Statistics and Computing 34 (1), 21. [DOI](https://doi.org/10.1007/s11222-023-10343-y) Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of the reconciled distributions for Gaussian and count forecasts*. +International Journal of Forecasting (in press). [DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004) +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +*Probabilistic reconciliation of mixed-type hierarchical time series*. +The 40th Conference on Uncertainty in Artificial Intelligence, accepted. + ## Contributors diff --git a/man/bayesRecon-package.Rd b/man/bayesRecon-package.Rd index f2b1a79..80d73eb 100644 --- a/man/bayesRecon-package.Rd +++ b/man/bayesRecon-package.Rd @@ -6,7 +6,7 @@ \alias{bayesRecon-package} \title{bayesRecon: Probabilistic Reconciliation via Conditioning} \description{ -Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) \doi{10.1007/978-3-030-67664-3_13}, MCMC reconciliation of count time series (Corani et al., 2022) \doi{10.48550/arXiv.2207.09322}, Bottom-Up Importance Sampling (Zambon et al., 2022) \doi{10.48550/arXiv.2210.02286}. +Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) \doi{10.1007/978-3-030-67664-3_13}, MCMC reconciliation of count time series (Corani et al., 2024) \doi{10.1016/j.ijforecast.2023.04.003}, Bottom-Up Importance Sampling (Zambon et al., 2024) \doi{10.1007/s11222-023-10343-y}, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted). } \section{Learn more}{ @@ -17,12 +17,19 @@ To learn more about \code{bayesRecon}, start with the vignettes: \code{browseVig \section{Main functions}{ -The package implements reconciliation via conditioning for probabilistic forecasts of hierarchical time series. The main functions are +The package implements reconciliation via conditioning for probabilistic forecasts +of hierarchical time series. The main functions are: \itemize{ -\item \code{\link[=reconc_gaussian]{reconc_gaussian()}}: analytical reconciliation of Gaussian base forecasts; -\item \code{\link[=reconc_BUIS]{reconc_BUIS()}}: reconciliation of any probabilistic base forecast via importance sampling; -this is the recommended option for non-Gaussian base forecasts; -\item \code{\link[=reconc_MCMC]{reconc_MCMC()}}: reconciliation of probabilistic discrete base forecasts via Markov Chain Monte Carlo. +\item \code{\link[=reconc_gaussian]{reconc_gaussian()}}: reconciliation via conditioning of multivariate Gaussian +base forecasts; this is done analytically; +\item \code{\link[=reconc_BUIS]{reconc_BUIS()}}: reconciliation via conditioning of any probabilistic forecast +via importance sampling; this is the recommended option for non-Gaussian base forecasts; +\item \code{\link[=reconc_MCMC]{reconc_MCMC()}}: reconciliation via conditioning of discrete probabilistic +forecasts via Markov Chain Monte Carlo; +\item \code{\link[=reconc_MixCond]{reconc_MixCond()}}: reconciliation via conditioning of mixed hierarchies, where +the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions; +\item \code{\link[=reconc_TDcond]{reconc_TDcond()}}: reconciliation via top-down conditioning of mixed hierarchies, where +the upper forecasts are multivariate Gaussian and the bottom forecasts are discrete distributions. } } @@ -30,18 +37,38 @@ this is the recommended option for non-Gaussian base forecasts; \itemize{ \item \code{\link[=temporal_aggregation]{temporal_aggregation()}}: temporal aggregation of a given time series object of class \link[stats]{ts}; -\item \code{\link[=get_reconc_matrices]{get_reconc_matrices()}}: aggregation and summing matrices for a temporal hierarchy of time series from user-selected list of aggregation levels. +\item \code{\link[=get_reconc_matrices]{get_reconc_matrices()}}: aggregation and summing matrices for a temporal hierarchy +of time series from user-selected list of aggregation levels; +\item \code{\link[=schaferStrimmer_cov]{schaferStrimmer_cov()}}: computes the Schäfer-Strimmer shrinkage estimator for the covariance matrix; +\item \code{\link[=PMF.get_mean]{PMF.get_mean()}}, \code{\link[=PMF.get_var]{PMF.get_var()}}, \code{\link[=PMF.get_quantile]{PMF.get_quantile()}}, \code{\link[=PMF.summary]{PMF.summary()}}, \code{\link[=PMF.sample]{PMF.sample()}}: +functions for handling PMF objects. } } \references{ -Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +\emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. +\doi{10.1007/978-3-030-67664-3_13}. -Corani, G., Azzimonti, D., Rubattu, N. (2023). \emph{Probabilistic reconciliation of count time series}. \doi{10.1016/j.ijforecast.2023.04.003}. +Corani, G., Azzimonti, D., Rubattu, N. (2024). +\emph{Probabilistic reconciliation of count time series}. +International Journal of Forecasting 40 (2), 457-469. +\doi{10.1016/j.ijforecast.2023.04.003}. -Zambon, L., Azzimonti, D. & Corani, G. (2024). \emph{Efficient probabilistic reconciliation of forecasts for real-valued and count time series}. \doi{10.1007/s11222-023-10343-y}. +Zambon, L., Azzimonti, D. & Corani, G. (2024). +\emph{Efficient probabilistic reconciliation of forecasts for real-valued and count time series}. +Statistics and Computing 34 (1), 21. +\doi{10.1007/s11222-023-10343-y}. -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +\emph{Probabilistic reconciliation of mixed-type hierarchical time series}. +The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \author{ \strong{Maintainer}: Dario Azzimonti \email{dario.azzimonti@gmail.com} (\href{https://orcid.org/0000-0001-5080-3061}{ORCID}) diff --git a/man/carparts_example.Rd b/man/carparts_example.Rd index 1582536..8c01289 100644 --- a/man/carparts_example.Rd +++ b/man/carparts_example.Rd @@ -8,7 +8,9 @@ Univariate time series of class \link[stats]{ts}. } \source{ -Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R.J., & Montero-Manso, P. (2020). +Car Parts Dataset (without Missing Values) (Version 2) +\doi{10.5281/zenodo.4656021} } \usage{ carparts_example @@ -17,9 +19,12 @@ carparts_example A monthly time series from the \code{carparts} dataset, 51 observations, Jan 1998 - Mar 2002. } \references{ -Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008) Forecasting with exponential -smoothing: the state space approach, Springer +Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008). +Forecasting with exponential smoothing: the state space approach. +Springer Science & Business Media. -Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021} +Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R., & Montero-Manso, P. (2020). +Car Parts Dataset (without Missing Values) (Version 2) +\doi{10.5281/zenodo.4656021} } \keyword{datasets} diff --git a/man/extr_mkt_events.Rd b/man/extr_mkt_events.Rd index df68b4b..5567e07 100644 --- a/man/extr_mkt_events.Rd +++ b/man/extr_mkt_events.Rd @@ -8,7 +8,10 @@ A multivariate time series of class \link[stats]{ts}. } \source{ -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. } \usage{ extr_mkt_events @@ -31,8 +34,13 @@ There are 6 time series: } } \references{ -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. -Agosto, A. (2022). \emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. \doi{10.2139/ssrn.4119895} +Agosto, A. (2022). +\emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. +\doi{10.2139/ssrn.4119895} } \keyword{datasets} diff --git a/man/extr_mkt_events_basefc.Rd b/man/extr_mkt_events_basefc.Rd index f7a787a..a608b54 100644 --- a/man/extr_mkt_events_basefc.Rd +++ b/man/extr_mkt_events_basefc.Rd @@ -12,14 +12,18 @@ A list \code{extr_mkt_events_basefc} containing } } \source{ -Agosto, A. (2022). \emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. \doi{10.2139/ssrn.4119895} +Agosto, A. (2022). +\emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. +\doi{10.2139/ssrn.4119895} } \usage{ extr_mkt_events_basefc } \description{ Base forecasts for the \code{extr_mkt_events} dataset, computed using the model by -Agosto, A. (2022). \emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. \doi{10.2139/ssrn.4119895}. +Agosto, A. (2022). +\emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. +\doi{10.2139/ssrn.4119895}. } \details{ The predictive distribution for the bottom time series is a multivariate negative @@ -30,8 +34,13 @@ They are in-sample forecasts: for each training instant, they are computed for time t+1 by conditioning on the counts observed up to time t. } \references{ -Agosto, A. (2022). \emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. \doi{10.2139/ssrn.4119895} +Agosto, A. (2022). +\emph{Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion}. +\doi{10.2139/ssrn.4119895} -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. } \keyword{datasets} diff --git a/man/infantMortality.Rd b/man/infantMortality.Rd index d7634ee..990a8fa 100644 --- a/man/infantMortality.Rd +++ b/man/infantMortality.Rd @@ -22,6 +22,8 @@ States: New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (WA), Northern Territory (NT), Australian Capital Territory (ACT), and Tasmania (TAS). } \references{ -R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579-2589. +Hyndman, R.J., Ahmed, R.A., Athanasopoulos, G., Shang, H.L. (2011). +Optimal combination forecasts for hierarchical time series. +Computational Statistics and Data Analysis, 55(9), 2579-2589. } \keyword{datasets} diff --git a/man/reconc_BUIS.Rd b/man/reconc_BUIS.Rd index 755a13c..751282b 100644 --- a/man/reconc_BUIS.Rd +++ b/man/reconc_BUIS.Rd @@ -152,7 +152,10 @@ print(rowMeans(samples_buis)) } \references{ -Zambon, L., Azzimonti, D. & Corani, G. (2024). \emph{Efficient probabilistic reconciliation of forecasts for real-valued and count time series}. \doi{10.1007/s11222-023-10343-y}. +Zambon, L., Azzimonti, D. & Corani, G. (2024). +\emph{Efficient probabilistic reconciliation of forecasts for real-valued and count time series}. +Statistics and Computing 34 (1), 21. +\doi{10.1007/s11222-023-10343-y}. } \seealso{ \code{\link[=reconc_gaussian]{reconc_gaussian()}} diff --git a/man/reconc_MCMC.Rd b/man/reconc_MCMC.Rd index e1cfee7..8efd524 100644 --- a/man/reconc_MCMC.Rd +++ b/man/reconc_MCMC.Rd @@ -95,7 +95,10 @@ print(rowMeans(samples_buis)) } \references{ -Corani, G., Azzimonti, D., Rubattu, N. (2023). \emph{Probabilistic reconciliation of count time series}. \doi{10.1016/j.ijforecast.2023.04.003}. +Corani, G., Azzimonti, D., Rubattu, N. (2024). +\emph{Probabilistic reconciliation of count time series}. +International Journal of Forecasting 40 (2), 457-469. +\doi{10.1016/j.ijforecast.2023.04.003}. } \seealso{ \code{\link[=reconc_BUIS]{reconc_BUIS()}} diff --git a/man/reconc_MixCond.Rd b/man/reconc_MixCond.Rd index 5670b12..af0d9dd 100644 --- a/man/reconc_MixCond.Rd +++ b/man/reconc_MixCond.Rd @@ -130,11 +130,19 @@ PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]]) } \references{ -Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. - -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. - -Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +\emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. +\doi{10.1007/978-3-030-67664-3_13}. + +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +\emph{Probabilistic reconciliation of mixed-type hierarchical time series}. +The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ \code{\link[=reconc_TDcond]{reconc_TDcond()}}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index cab2f74..2da9f8a 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -130,11 +130,19 @@ PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) } \references{ -Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. - -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. - -Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). \emph{Probabilistic reconciliation of mixed-type hierarchical time series} The 40th Conference on Uncertainty in Artificial Intelligence, accepted. +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +\emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. +\doi{10.1007/978-3-030-67664-3_13}. + +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. + +Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). +\emph{Probabilistic reconciliation of mixed-type hierarchical time series}. +The 40th Conference on Uncertainty in Artificial Intelligence, accepted. } \seealso{ \code{\link[=reconc_MixCond]{reconc_MixCond()}}, \code{\link[=reconc_BUIS]{reconc_BUIS()}} diff --git a/man/reconc_gaussian.Rd b/man/reconc_gaussian.Rd index 0bd0961..0cf16f1 100644 --- a/man/reconc_gaussian.Rd +++ b/man/reconc_gaussian.Rd @@ -76,9 +76,15 @@ Y_reconc = rbind(U, B) } \references{ -Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). \emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. \doi{10.1007/978-3-030-67664-3_13}. +Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). +\emph{Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule}. +ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. +\doi{10.1007/978-3-030-67664-3_13}. -Zambon, L., Agosto, A., Giudici, P., Corani, G. (2023). \emph{Properties of the reconciled distributions for Gaussian and count forecasts}. \doi{10.48550/arXiv.2303.15135}. +Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). +\emph{Properties of the reconciled distributions for Gaussian and count forecasts}. +International Journal of Forecasting (in press). +\doi{10.1016/j.ijforecast.2023.12.004}. } \seealso{ \code{\link[=reconc_BUIS]{reconc_BUIS()}} diff --git a/vignettes/references.bib b/vignettes/references.bib index 5e81561..d751f4d 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -148,15 +148,12 @@ @article{zambon2022efficient -@article{zambon2023properties, - title={Properties of the reconciled distributions for Gaussian and count forecasts}, - author={Zambon, Lorenzo and Agosto, Arianna and Giudici, Paolo and Corani, Giorgio}, - journal={arXiv preprint arXiv:2303.15135}, - year={2023}, - eprint={2303.15135}, - archivePrefix={arXiv}, - primaryClass={stat.AP}, - doi={10.48550/arXiv.2303.15135} +@article{zambon2024properties, +title = {Properties of the reconciled distributions for Gaussian and count forecasts}, +journal = {International Journal of Forecasting}, +year = {2024}, +issn = {0169-2070}, +author = {Lorenzo Zambon and Arianna Agosto and Paolo Giudici and Giorgio Corani} } @@ -164,7 +161,12 @@ @article{corani2023probabilistic title={Probabilistic reconciliation of count time series}, author={Corani, Giorgio and Azzimonti, Dario and Rubattu, Nicol{\`o}}, journal={International Journal of Forecasting}, - year={2023}, + volume = {40}, + number = {2}, + pages = {457-469}, + year = {2024}, + issn = {0169-2070}, + doi = {https://doi.org/10.1016/j.ijforecast.2023.04.003}, publisher={Elsevier} } From 34b1540afd5d9701c21d88cc01207672dde1269d Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Thu, 23 May 2024 11:52:12 +0200 Subject: [PATCH 23/36] changed parameters for PMF convolution and smoothing + fixed warning TDcond --- R/PMF.R | 20 ++++++++++---------- R/reconc_TDcond.R | 27 +++++++-------------------- R/utils.R | 9 +++++++++ man/PMF.summary.Rd | 6 +++--- man/reconc_TDcond.Rd | 3 --- 5 files changed, 29 insertions(+), 36 deletions(-) diff --git a/R/PMF.R b/R/PMF.R index d0ba099..2995d79 100644 --- a/R/PMF.R +++ b/R/PMF.R @@ -16,7 +16,7 @@ PMF.from_samples = function(v) { } # Compute the pmf from a parametric distribution -PMF.from_params = function(params, distr, Rtoll = 1e-7) { +PMF.from_params = function(params, distr, Rtoll = .RTOLL) { # Check that the distribution is implemented, and that the params are ok if (!(distr %in% .DISCR_DISTR)) { stop(paste0("Input error: distr must be one of {", @@ -186,9 +186,9 @@ PMF.get_quantile = function(pmf, p) { #' #' @param pmf the PMF object. #' @param Ltoll used for computing the min of the PMF: the min is the smallest value -#' with probability greater than Ltoll (default: 1e-16) +#' with probability greater than Ltoll (default: 1e-15) #' @param Rtoll used for computing the max of the PMF: the max is the largest value -#' with probability greater than Rtoll (default: 1e-7) +#' with probability greater than Rtoll (default: 1e-9) #' #' @examples #' library(bayesRecon) @@ -205,7 +205,7 @@ PMF.get_quantile = function(pmf, p) { #' @return A summary data.frame #' @seealso [PMF.get_mean()], [PMF.get_var()], [PMF.get_quantile()], [PMF.sample()] #' @export -PMF.summary = function(pmf, Ltoll=1e-16, Rtoll=1e-7) { +PMF.summary = function(pmf, Ltoll=.TOLL, Rtoll=.RTOLL) { min_pmf = min(which(pmf > Ltoll)) - 1 max_pmf = max(which(pmf > Rtoll)) - 1 all_summaries <- data.frame("Min." = min_pmf, @@ -223,7 +223,7 @@ PMF.summary = function(pmf, Ltoll=1e-16, Rtoll=1e-7) { # If the smoothing parameter alpha is not specified, it is set to the min of pmf. # If laplace is set to TRUE, add alpha to all the points. # Otherwise, add alpha only to points with zero mass. -PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { +PMF.smoothing = function(pmf, alpha = 1e-9, laplace=FALSE) { if (is.null(alpha)) alpha = min(pmf[pmf!=0]) @@ -242,7 +242,7 @@ PMF.smoothing = function(pmf, alpha = NULL, laplace=FALSE) { # -removes small values at the end of the vector (< Rtoll) # -set to zero all the values to the left of the support # -set to zero small values (< toll) -PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { +PMF.conv = function(pmf1, pmf2, toll=.TOLL, Rtoll=.RTOLL) { pmf = stats::convolve(pmf1, rev(pmf2), type="open") # Look for last value > Rtoll and remove all the elements after it: last_pos = max(which(pmf > Rtoll)) @@ -268,8 +268,8 @@ PMF.conv = function(pmf1, pmf2, toll=1e-16, Rtoll=1e-7) { # -the bottom-up pmf, if return_all=FALSE # -otherwise, a list of lists of pmfs for all the steps of the algorithm; # they correspond to the variables of the "auxiliary binary tree" -PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, - smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { +PMF.bottom_up = function(l_pmf, toll=.TOLL, Rtoll=.RTOLL, return_all=FALSE, + smoothing=TRUE, al_smooth=.ALPHA_SMOOTHING, lap_smooth=.LAP_SMOOTHING) { # Smoothing to "cover the holes" in the supports of the bottom pmfs if (smoothing) l_pmf = lapply(l_pmf, PMF.smoothing, @@ -306,8 +306,8 @@ PMF.bottom_up = function(l_pmf, toll=1e-16, Rtoll=1e-7, return_all=FALSE, # Given a vector v_u and a list of bottom pmf l_pmf, # checks if the elements of v_u are contained in the support of the bottom-up distr # Returns a vector with the same length of v_u with TRUE if it is contained and FALSE otherwise -PMF.check_support = function(v_u, l_pmf, toll=1e-16, Rtoll=1e-7, - smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { +PMF.check_support = function(v_u, l_pmf, toll=.TOLL, Rtoll=.RTOLL, + smoothing=TRUE, al_smooth=.ALPHA_SMOOTHING, lap_smooth=.LAP_SMOOTHING) { pmf_u = PMF.bottom_up(l_pmf, toll=toll, Rtoll=Rtoll, return_all=FALSE, smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth) # The elements of v_u must be in the support of pmf_u diff --git a/R/reconc_TDcond.R b/R/reconc_TDcond.R index 537f416..2553e9b 100644 --- a/R/reconc_TDcond.R +++ b/R/reconc_TDcond.R @@ -40,8 +40,9 @@ # Given a vector u of the upper values and a list of the bottom distr pmfs, # returns samples (dim: n_bottom x length(u)) from the conditional distr # of the bottom given the upper values -.TD_sampling = function(u, bott_pmf, toll=1e-16, Rtoll=1e-7, - smoothing=TRUE, al_smooth=NULL, lap_smooth=FALSE) { +.TD_sampling = function(u, bott_pmf, + toll=.TOLL, Rtoll=.RTOLL, smoothing=TRUE, + al_smooth=.ALPHA_SMOOTHING, lap_smooth=.LAP_SMOOTHING) { l_l_pmf = rev(PMF.bottom_up(bott_pmf, toll = toll, Rtoll = Rtoll, return_all = TRUE, smoothing=smoothing, al_smooth=al_smooth, lap_smooth=lap_smooth)) @@ -122,8 +123,6 @@ #' * 'samples' returns a list containing the reconciled multivariate samples; #' * 'all' returns a list with both pmf objects and samples. #' -#' @param ... additional parameters for pmf convolution -#' #' @param suppress_warnings Logical. If \code{TRUE}, no warnings about samples #' are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details. #' @param seed Seed for reproducibility. @@ -187,22 +186,10 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, num_samples = 2e4, return_type = "pmf", - ..., suppress_warnings = FALSE, seed = NULL) { if (!is.null(seed)) set.seed(seed) - # Parameters for convolution - # toll=1e-16 - # Rtoll=1e-7 - # smooth_bottom=TRUE - # al_smooth=NULL - # lap_smooth=FALSE - - # After testing the convolution parameters: - # remove dots, remove comment above, and set the "best parameters" as default in - # PMF.check_support, .TD_sampling, PMF.summary - # Check inputs .check_input_TD(S, fc_bottom, fc_upper, bottom_in_type, distr, @@ -263,13 +250,13 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, } # Check that each multiv. sample of U is contained in the supp of the bottom-up distr - samp_ok = mapply(PMF.check_support, U_js, L_pmf_js, - MoreArgs = list(...)) + samp_ok = mapply(PMF.check_support, U_js, L_pmf_js) samp_ok = rowSums(samp_ok) == n_u_low # Only keep the "good" upper samples, and throw a warning if some samples are discarded: U_js = lapply(U_js, "[", samp_ok) if (sum(samp_ok) != num_samples & !suppress_warnings) { - warning(paste0("Only ", round(sum(samp_ok)/num_samples, 3)*100, "% of the upper samples ", + # We round down to the nearest decimal + warning(paste0("Only ", floor(sum(samp_ok)/num_samples*1000)/10, "% of the upper samples ", "are in the support of the bottom-up distribution; ", "the others are discarded.")) } @@ -277,7 +264,7 @@ reconc_TDcond = function(S, fc_bottom, fc_upper, # Get bottom samples via the prob top-down B = list() for (j in 1:n_u_low) { - B[[j]] = .TD_sampling(U_js[[j]], L_pmf_js[[j]], ...) + B[[j]] = .TD_sampling(U_js[[j]], L_pmf_js[[j]]) } B = do.call("rbind", B) # dim: n_bottom x num_samples U = A %*% B # dim: n_upper x num_samples diff --git a/R/utils.R b/R/utils.R index c9944af..53418e6 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,9 +1,18 @@ ################################################################################ +# IMPLEMENTED DISTRIBUTIONS .DISTR_TYPES = c("continuous", "discrete") .DISCR_DISTR = c("poisson", "nbinom") .CONT_DISTR = c("gaussian") +################################################################################ +# PARAMETERS FOR PMF CONVOLUTION AND SMOOTHING + +.TOLL = 1e-15 +.RTOLL = 1e-9 +.ALPHA_SMOOTHING = 1e-9 +.LAP_SMOOTHING = FALSE + ################################################################################ # CHECK INPUT diff --git a/man/PMF.summary.Rd b/man/PMF.summary.Rd index 14a02bf..de31220 100644 --- a/man/PMF.summary.Rd +++ b/man/PMF.summary.Rd @@ -4,16 +4,16 @@ \alias{PMF.summary} \title{Returns summary of a PMF object} \usage{ -PMF.summary(pmf, Ltoll = 1e-16, Rtoll = 1e-07) +PMF.summary(pmf, Ltoll = .TOLL, Rtoll = .RTOLL) } \arguments{ \item{pmf}{the PMF object.} \item{Ltoll}{used for computing the min of the PMF: the min is the smallest value -with probability greater than Ltoll (default: 1e-16)} +with probability greater than Ltoll (default: 1e-15)} \item{Rtoll}{used for computing the max of the PMF: the max is the largest value -with probability greater than Rtoll (default: 1e-7)} +with probability greater than Rtoll (default: 1e-9)} } \value{ A summary data.frame diff --git a/man/reconc_TDcond.Rd b/man/reconc_TDcond.Rd index 2da9f8a..eb2cafb 100644 --- a/man/reconc_TDcond.Rd +++ b/man/reconc_TDcond.Rd @@ -12,7 +12,6 @@ reconc_TDcond( distr = NULL, num_samples = 20000, return_type = "pmf", - ..., suppress_warnings = FALSE, seed = NULL ) @@ -47,8 +46,6 @@ A string with three possible values: \item 'all' returns a list with both pmf objects and samples. }} -\item{...}{additional parameters for pmf convolution} - \item{suppress_warnings}{Logical. If \code{TRUE}, no warnings about samples are triggered. If \code{FALSE}, warnings are generated. Default is \code{FALSE}. See Details.} From ee84adb304b5fbb7866a8dce71f6112a02680250 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Thu, 23 May 2024 12:59:33 +0200 Subject: [PATCH 24/36] First version of the mixed type reconciliation vignette --- R/data.R | 36 ++- data-raw/M5_utils.R | 74 +++++ data-raw/M5data_forecasts.R | 154 +++++++++++ data/M5_CA1_basefc.rda | Bin 0 -> 299534 bytes man/M5_CA1_basefc.Rd | 48 ++++ vignettes/img/M5store_hier.png | Bin 0 -> 140264 bytes vignettes/mixed_reconciliation.Rmd | 427 +++++++++++++++++++++++++++++ 7 files changed, 738 insertions(+), 1 deletion(-) create mode 100644 data-raw/M5_utils.R create mode 100644 data-raw/M5data_forecasts.R create mode 100644 data/M5_CA1_basefc.rda create mode 100644 man/M5_CA1_basefc.Rd create mode 100644 vignettes/img/M5store_hier.png create mode 100644 vignettes/mixed_reconciliation.Rmd diff --git a/R/data.R b/R/data.R index 12f13fb..d3667de 100644 --- a/R/data.R +++ b/R/data.R @@ -125,4 +125,38 @@ #' *Properties of the reconciled distributions for Gaussian and count forecasts*. #' International Journal of Forecasting (in press). #' \doi{10.1016/j.ijforecast.2023.12.004}. -"extr_mkt_events_basefc" \ No newline at end of file +"extr_mkt_events_basefc" + + +#' Example of hierarchical forecasts for a store from the M5 competition +#' +#' This dataset contains forecasts for the hierarchy of time series related to the store `CA_1` from the M5 competition. +#' +#' The store `CA_1` contains 3049 item level time series and 11 aggregate time series: +#' * Store level aggregation (`CA_1`) +#' * Category level aggregations (`HOBBIES`, `HOUSEHOLD`, `FOODS`) +#' * Department level aggregations (`HOBBIES_1`, `HOBBIES_2`, `HOUSEHOLD_1`, `HOUSEHOLD_2`, `FOODS_1`, `FOODS_2`, `FOODS_3`) +#' +#' Forecasts are generated with the function \link[smooth]{forecast} and the model \link[smooth]{adam} from the package `smooth`. +#' * The models for the bottom time series are selected with multiplicative Gamma error term (`MNN`); +#' * The models for the upper time series (`AXZ`) is selected with Gaussian additive error term, seasonality selected based on information criterion. +#' +#' The raw data was downloaded with the package \link[m5]{m5-package}. +#' +#' @format +#' A list containing: +#' * `upper`: a list of 11 elements each representing an aggregation level. Each element contains: `mu`, `sigma` the mean and standard deviation of the Gaussian forecast, `actual` the actual value, `residuals` the residuals of the model used to estimate forecasts covariance. +#' * `lower`: a list of 3049 elements each representing a forecast for each item. Each element contains `pmf` the probability mass function of the item level forecast, `actual` the actual value. +#' * `A`: the aggregation matrix for A. +#' * `S`: the S matrix for the hierarchy. +#' * `Q_u`: scaling factors for computing MASE on the upper forecasts. +#' * `Q_b`: scaling factors for computing MASE on the bottom forecasts. +#' +#' @references +#' Joachimiak K (2022). *m5: 'M5 Forecasting' Challenges Data*. R package version 0.1.1, . +#' +#' Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). *The M5 Accuracy competition: Results, findings and conclusions.* International Journal of Forecasting 38(4) 1346-1364. \doi{10.1016/j.ijforecast.2021.10.009} +#' +#' @source +#' Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). *The M5 Accuracy competition: Results, findings and conclusions.* International Journal of Forecasting 38(4) 1346-1364. \doi{10.1016/j.ijforecast.2021.10.009} +"M5_CA1_basefc" \ No newline at end of file diff --git a/data-raw/M5_utils.R b/data-raw/M5_utils.R new file mode 100644 index 0000000..199c950 --- /dev/null +++ b/data-raw/M5_utils.R @@ -0,0 +1,74 @@ +get_hier_M5 = function(save_ = FALSE, save_path = "../../../results/") { + + n_b = 3049 + n_u = 11 + n = n_u + n_b + + A = matrix(0, nrow = n_u, ncol = n_b) + A[1,] = 1 # store + A[2,1:565] = 1 # categories + A[3,566:1612] = 1 + A[4,1613:3049] = 1 + A[5,1:416] = 1 # departments + A[6,417:565] = 1 + A[7,566:1097] = 1 + A[8,1098:1612] = 1 + A[9,1613:1828] = 1 + A[10,1829:2226] = 1 + A[11,2227:3049] = 1 + + S = rbind(A, diag(rep(1, n_b))) + + if (save_) { + saveRDS(S, paste0(save_path, "S.rds")) + saveRDS(A, paste0(save_path, "A.rds")) + } + + return(list(A = A, S = S)) +} + +get_upp_ts = function(dset.store.train, dset.store.test, lev, + name, h, len = 1941) { + + if (!(lev %in% c("store_id", "cat_id", "dept_id"))) stop("Wrong lev name") + + df1 = dset.store.train[dset.store.train[[lev]] == name] + df2 = dset.store.test[dset.store.test[[lev]] == name] + st = which(colnames(df1)=="d_1") + + serie = cbind( + df1[,st:(st+len-1)], + df2[,st:(st+h-1)] ) + data.train = serie[,1:(len+h-1)] + data.test = serie[,(len+h):(len+h)] + train.agg = colSums(data.train) + test.agg = colSums(data.test) + return(list(train = train.agg, test = test.agg)) +} + +get_bott_ts = function(dset.store.train, dset.store.test, + item_id, h, len = 1941) { + + df1 = dset.store.train[dset.store.train$item_id == item.id] + df2 = dset.store.test[dset.store.test$item_id == item.id] + st = which(colnames(df1)=="d_1") + + serie = c(df1[,st:(st+len-1)], df2[,st:(st+h-1)] ) + train = as.numeric(serie)[1:(len+h-1)] + test = as.numeric(serie)[[len+h]] + return(list(train = train, test = test)) +} + +model_upper = function(data, mod = "AXX") { + model = auto.adam(data, mod, lags = c(7), distribution = "dnorm", + occurrence = "none") + # uses: orders = list(ar = c(3, 3), i = c(2, 1), ma = c(3, 3), select = TRUE) + return(model) +} + +model_bottom = function(data, model_str = "MNN", occ_str = "auto", distr = c("dgamma")) { + # distr = c("dnorm","dlaplace","ds","dgnorm", "dlnorm", "dinvgauss", "dgamma") + model = adam(data, model_str, lags = c(7), + occurrence = occ_str, distribution = distr) + return(model) +} \ No newline at end of file diff --git a/data-raw/M5data_forecasts.R b/data-raw/M5data_forecasts.R new file mode 100644 index 0000000..c73c98a --- /dev/null +++ b/data-raw/M5data_forecasts.R @@ -0,0 +1,154 @@ +rm(list = ls()) +library(m5) +library(smooth) + + +source("./data-raw/M5_utils.R") + +################################## + +seed=42 + +set.seed(seed) +STORE="CA_1" +n_samples_b = 2e4 +round_up = FALSE + +path_m5_data <- "./data-raw/M5_data" +m5_forecast_path <- "./data-raw/M5_for_data/" + +m5::m5_download(path_m5_data) + +dset = m5::m5_get_raw_evaluation(path = path_m5_data) +names(dset) = c("sales_train_evaluation", "sales_test_evaluation", 'sell_prices', + "calendar","weights_evaluation") +dset.store.train = dset$sales_train_evaluation +dset.store.train = dset.store.train[dset.store.train$store_id == STORE] +dset.store.test = dset$sales_test_evaluation +dset.store.test = dset.store.test[dset.store.test$store_id == STORE] + +CAT = c("HOBBIES", "HOUSEHOLD", "FOODS") +DEPT = c("HOBBIES_1", "HOBBIES_2", "HOUSEHOLD_1", "HOUSEHOLD_2", + "FOODS_1", "FOODS_2", "FOODS_3") + +store_path = paste0(m5_forecast_path, STORE) +len = 1941 + +############### +### Base forecasts upper time series +print("Computing base forecasts of upper time series...") + +h=1 + +if (!dir.exists(store_path)) dir.create(store_path,recursive = TRUE) + +str_base_fc = paste0(store_path,"/CA_1_h_",h,"_base_fc.rds") + + +M5_CA1_basefc = list() + +train_u <- list() + +# Store +ts.agg = get_upp_ts(dset.store.train, dset.store.test, "store_id", + STORE, h = h, len = len) +train.agg = ts.agg$train +test.agg = ts.agg$test + +model = model_upper(train.agg) +fc.model = forecast(model, h = 1) +M5_CA1_basefc$upper[[STORE]] = list( + mu = fc.model$mean, + sigma = model$scale, + actual = test.agg, + residuals = model$residuals +) +train_u[[STORE]] <- train.agg + +# Category +for (cat.id in CAT) { + ts.agg = get_upp_ts(dset.store.train, dset.store.test, "cat_id", + cat.id, h = h, len = len) + train.agg = ts.agg$train + test.agg = ts.agg$test + + model = model_upper(train.agg) + fc.model = forecast(model, h = 1) + M5_CA1_basefc$upper[[cat.id]] = list( + mu = fc.model$mean, + sigma = model$scale, + actual = test.agg, + residuals = model$residuals + ) + train_u[[cat.id]] <- train.agg +} + +# Department +for (dept.id in DEPT) { + ts.agg = get_upp_ts(dset.store.train, dset.store.test, "dept_id", + dept.id, h = h, len = len) + train.agg = ts.agg$train + test.agg = ts.agg$test + + model = model_upper(train.agg) + fc.model = forecast(model, h = 1) + M5_CA1_basefc$upper[[dept.id]] = list( + mu = fc.model$mean, + sigma = model$scale, + actual = test.agg, + residuals = model$residuals + ) + train_u[[dept.id]] <- train.agg +} + + +saveRDS(M5_CA1_basefc, str_base_fc) + + +train_b <- list() + +for (item.id in unique(dset.store.train$item_id)) { + + bts = get_bott_ts(dset.store.train, dset.store.test, + item_id, h, len = 1941) + train = bts$train + test = bts$test + + model = model_bottom(train, model_str = "MNN", + occ_str = "auto", #"odds-ratio", + distr = "dgamma") + fc.model = forecast(model, h = 1, interval="simulated", + scenarios=TRUE, nsim = n_samples_b) + + # round to integer (up or to the closest integer, depending on round_up) + samples = if (round_up) ceiling(fc.model$scenarios[1,]) else round(fc.model$scenarios[1,]) + samples[samples<0] = 0 # set negative to zero + samples <- as.integer(samples) + pmf = PMF.from_samples(samples) # empirical pmf + + M5_CA1_basefc$bottom[[item.id]] = list( + pmf = pmf, + actual = test + #residuals = model$residuals + ) + train_b[[item.id]] <- train +} + + +hier = get_hier_M5(save_ = FALSE) + +M5_CA1_basefc$A <- hier$A +M5_CA1_basefc$S <- hier$S + + +Q_u = unlist(lapply(train_u, function(x) mean(abs(x[-1] - x[-length(x)])))) +Q_b = unlist(lapply(train_b, function(x) mean(abs(x[-1] - x[-length(x)])))) + +M5_CA1_basefc$Q_u <- Q_u +M5_CA1_basefc$Q_b <- Q_b + + +usethis::use_data(M5_CA1_basefc, overwrite = TRUE) + +unlink("./data-raw/M5_for_data/",recursive=TRUE) +unlink("./data-raw/M5_data/",recursive=TRUE) diff --git a/data/M5_CA1_basefc.rda b/data/M5_CA1_basefc.rda new file mode 100644 index 0000000000000000000000000000000000000000..de875ba81111c2997dc7176545dc3b3ebf98e04f GIT binary patch literal 299534 zcmafabx<5l@aS^f!Qt*C*a3$-1PCs{ox_6@oB+X{Kya5JA-KC8?h>3J!7T|AToMS* z%lE5#uipFX_15h4bkFQm_jb*8b@xge+6wbX88B%Zn3*}IqCRmw{eR&nLy$NC1Z$wk 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elements each representing an aggregation level. Each element contains: \code{mu}, \code{sigma} the mean and standard deviation of the Gaussian forecast, \code{actual} the actual value, \code{residuals} the residuals of the model used to estimate forecasts covariance. +\item \code{lower}: a list of 3049 elements each representing a forecast for each item. Each element contains \code{pmf} the probability mass function of the item level forecast, \code{actual} the actual value. +\item \code{A}: the aggregation matrix for A. +\item \code{S}: the S matrix for the hierarchy. +\item \code{Q_u}: scaling factors for computing MASE on the upper forecasts. +\item \code{Q_b}: scaling factors for computing MASE on the bottom forecasts. +} +} +\source{ +Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). \emph{The M5 Accuracy competition: Results, findings and conclusions.} International Journal of Forecasting 38(4) 1346-1364. \doi{10.1016/j.ijforecast.2021.10.009} +} +\usage{ +M5_CA1_basefc +} +\description{ +This dataset contains forecasts for the hierarchy of time series related to the store \code{CA_1} from the M5 competition. +} +\details{ +The store \code{CA_1} contains 3049 item level time series and 11 aggregate time series: +\itemize{ +\item Store level aggregation (\code{CA_1}) +\item Category level aggregations (\code{HOBBIES}, \code{HOUSEHOLD}, \code{FOODS}) +\item Department level aggregations (\code{HOBBIES_1}, \code{HOBBIES_2}, \code{HOUSEHOLD_1}, \code{HOUSEHOLD_2}, \code{FOODS_1}, \code{FOODS_2}, \code{FOODS_3}) +} + +Forecasts are generated with the function \link[smooth]{forecast} and the model \link[smooth]{adam} from the package \code{smooth}. +\itemize{ +\item The models for the bottom time series are selected with multiplicative Gamma error term (\code{MNN}); +\item The models for the upper time series (\code{AXZ}) is selected with Gaussian additive error term, seasonality selected based on information criterion. +} + +The raw data was downloaded with the package \link[m5]{m5-package}. +} +\references{ +Joachimiak K (2022). \emph{m5: 'M5 Forecasting' Challenges Data}. R package version 0.1.1, \url{https://CRAN.R-project.org/package=m5}. + +Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). \emph{The M5 Accuracy competition: Results, findings and conclusions.} International Journal of Forecasting 38(4) 1346-1364. \doi{10.1016/j.ijforecast.2021.10.009} +} +\keyword{datasets} diff --git a/vignettes/img/M5store_hier.png b/vignettes/img/M5store_hier.png new file mode 100644 index 0000000000000000000000000000000000000000..026a1c12d69b852bce9bc04d9be98c51c9ec322b GIT binary patch literal 140264 zcmeFacRbbo|38i>T2w~%$SOM_`;?GmL{`R;k?g(4ajueVQOGPiGqTswKqX}Fk&*1Z z9lp<(ab3KB$NTfS{`>v$>2@o}c|GT2-XD+W3f6EY=tH!6;drhZk#5VeuV=><|7T zdV87slx^xgrYqdnCmp+;bts}oYkMW}`IiW<@sSy5E@&&C$+=pGkc9NOM7Pvm+JzB6JuNw#8DrT0 z@$-^HbVEtWbq^tgdd7qNu3P0ZteXP;_db?o$R^UZ@c0-XAMiPUs`U7f786a=hZ!Au8(RJM7|y|i(GwnospzUWbTsnwf5g+T}6qLx8cIGQ?7>ghCX(|F1R?qU);d8 zzS|tdR3*N4i#JY+{4~kq4k>KOi;Ds>XP@A_cEY~G_=U*4J~~l;R(QJtUfV5;&D((6 z&S7a!%iZAl>rO2tHP$*wrHf%a^OjbtzUxxvyP{fA<^f^5=t90q@#ELV0~aiWN~Vq@?~Na>c;UU%@DD8pkrJ1ZeBE=Na4PDfr&OB*9D9zj7tF7B&b zSFdt_796(D7IqJvI4o>0?GCa(&MiY*eH&vdJ7Y@=I%wR7dY1Nfq6`esM1TL;U8kXw 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ZM_eyF?(&oKp>_;F;OXk;vd$@?2>@~(BiR4| literal 0 HcmV?d00001 diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd new file mode 100644 index 0000000..c1e51d2 --- /dev/null +++ b/vignettes/mixed_reconciliation.Rmd @@ -0,0 +1,427 @@ +--- +title: "Reconciliation of M5 hierarchy with mixed-type forecasts" +author: "Lorenzo Zambon, Dario Azzimonti, Nicolò Rubattu, Giorgio Corani" +date: "2024-05-22" +lang: "en" +bibliography: references.bib +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Reconciliation of M5 hierarchy with mixed-type forecasts} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(bayesRecon) +``` + +# Introduction + +This vignette partially reproduces the results of the paper *Probabilistic reconciliation of mixed-type hierarchical time series* [@zambon2024mixed], +accepted for publication at the 40th Conference on Uncertainty in Artificial Intelligence. +We replicate the reconciliation of the one-step ahead forecasts of one store from the time series hierarchy of the M5 competition [@MAKRIDAKIS20221325]. Sec. 5 of the paper presents the results of the experiment conducted on 10 different stores. + +# Data and forecasts + +The M5 competition [@MAKRIDAKIS20221325] contains daily time series of sales data referring to 10 different stores. The hierarchy of each store has the same structure: 3049 bottom time series (single items) and 11 upper time series, obtained by aggregating the items by department, product category, and store. The figure below shows the hierarchy of a store. + +```{r out.width = '100%', echo = FALSE} +knitr::include_graphics("img/M5store_hier.png") +``` + +We focus here on the store ``CA_1''. We computed the one-step ahead ($h=1$) forecasts for each bottom and upper time series, i.e. base forecasts, by using iETS [@svetunkov2023iets], available from the R package smooth [@smooth_pkg]. + +The base forecasts are stored in the data `M5_CA1_basefc`, available with this package. + +```{r} +# Hierarchy composed by 3060 time series: 3049 bottom and 11 upper +n_b <- 3049 +n_u <- 11 +n <- n_b + n_u + +# Save matrix A and S +A <- M5_CA1_basefc$A +S <- M5_CA1_basefc$S + +# Base forecasts: +base_fc_upper <- M5_CA1_basefc$upper +base_fc_bottom <- M5_CA1_basefc$bottom +``` + +# Gaussian reconciliations + +We start by assuming that all forecasts are Gaussian and perform a Gaussian forecast reconciliation +with the function `bayesRecon::reconc_gaussian`. + +The method `Gauss` considers the bottom forecasts as independent and the upper forecasts are a multivariate Gaussian. + + +```{r} +# We will save all results in the list rec_fc +rec_fc = list( + Gauss = list(), + Mixed_cond = list(), + TD_cond = list() + ) + +# Extract Gaussian parameters from the upper forecasts +mu_u = c() +sd_u = c() +for (fc in base_fc_upper) { + mu_u = c(mu_u, fc$mu) + sd_u = c(sd_u, fc$sigma) +} +residuals.upper = lapply(base_fc_upper, "[[", "residuals") +residuals.upper = t(do.call("rbind", residuals.upper)) +Sigma_u = schaferStrimmer_cov(residuals.upper)$shrink_cov + + +# Extract parameters for bottom forecasts +mu_b = c() +sd_b = c() +for (fc_b in base_fc_bottom) { + pmf = fc_b$pmf + mu_bott = PMF.get_mean(pmf) + sd_bott = PMF.get_var(pmf)**0.5 + mu_b = c(mu_bott, mu_b) + sd_b = c(sd_bott, sd_b) +} +Sigma_b = diag(sd_b**2) + +### Gaussian reconciliation ### + +base_forecasts.mu <- c(mu_u,mu_b) +base_forecasts.Sigma <- diag(c(sd_u**2,sd_b**2)) + +# Gauss +base_forecasts.Sigma_corr <- base_forecasts.Sigma +base_forecasts.Sigma_corr[1:11,1:11] <- Sigma_u + +start <- Sys.time() +gauss = reconc_gaussian(S, base_forecasts.mu, + base_forecasts.Sigma_corr) +stop <- Sys.time() + +rec_fc$Gauss = list(mu_b = gauss$bottom_reconciled_mean, + Sigma_b = gauss$bottom_reconciled_covariance, + mu_u = A %*% gauss$bottom_reconciled_mean, + Sigma_u = A %*% gauss$bottom_reconciled_covariance %*% t(A)) + + +Gauss_time_print <- round(difftime(stop, start, units = "secs"), 2) +cat("Computational time for Gaussian reconciliation: ", + Gauss_time_print, "s") +Gauss_time <- as.double(Gauss_time_print) +``` + + +# Reconciliation with mixed-cond + +We now reconcile the forecasts with the algorithm Mix-cond described in [@zambon2024mixed], Sec. 4. The algorithm is implemented in the function \link{reconc_MixCond}. + +```{r} +### Mixed correlated +seed <- 1 +N_samples_IS <- 5e4 + +fc_upper_4rec <- list(mu=mu_u, Sigma=Sigma_u) +fc_bottom_4rec <- lapply(base_fc_bottom,function(x) x$pmf) + +start <- Sys.time() +mix_cond = reconc_MixCond(S, fc_bottom_4rec, fc_upper_4rec, bottom_in_type = "pmf", + num_samples = N_samples_IS, return_type = "pmf", seed = seed) +stop <- Sys.time() + + +rec_fc$Mixed_cond = list( + bottom = mix_cond$bottom_reconciled$pmf, + upper = mix_cond$upper_reconciled$pmf, + ESS = mix_cond$ESS) + +MixCond_time_print <- round(difftime(stop, start, units = "secs"), 2) +cat("Computational time for Mix-cond reconciliation: ", + MixCond_time_print, "s") +MixCond_time <- as.double(MixCond_time_print) +``` + +As discussed in [@zambon2024mixed], Sec. 4, Mix-cond performs poorly in high dimensions. While we have not computed the metrics for this reconciliation yet, we see that the implementation returns a warning regarding the effective sample size of the importance sampling algorithm. This is a sign that Mix-cond is not handling well the high incoherence between the joint bottom-up and the upper base forecasts, see also [@zambon2024mixed], fig. 3, for a graphical example. + +# Reconciliation with TD-cond + +In [@zambon2024mixed], Sec. 5, TD-cond is proposed as a reliable alternative in high dimensions. This algorithm is implemented in the function \link{reconc_TDcond}. + +```{r} +### TD-cond +N_samples_TD <- 1e4 + +start <- Sys.time() +td = reconc_TDcond(S, fc_bottom_4rec, fc_upper_4rec, + bottom_in_type = "pmf", num_samples = N_samples_TD, + return_type = "pmf", seed = seed) +stop <- Sys.time() + +rec_fc$TD_cond = list( + bottom = td$bottom_reconciled$pmf, + upper = td$upper_reconciled$pmf) + +TDCond_time_print <- round(difftime(stop, start, units = "secs"), 2) +cat("Computational time for TD-cond reconciliation: ", + TDCond_time_print, "s") +TDCond_time <- as.double(TDCond_time_print) +``` + +The algorithm TD-cond still returns a warning regarding the high incoherence between the joint bottom-up and the upper base forecasts. We will see that this warning does not impact the performances of TD-cond. + +# Comparison + +For each time series in the hierarchy, we compute the following three metrics for each method: + +- MASE: Mean Absolute Scaled Error + +- MIS: Mean Interval Score + +- RPS: Ranked Probability Score + +```{r} +# Parameters for computing the metrics +alpha<- 0.9 # MIS alpha +jitt <- 1e-9 # jitter for numerical stability +n_samp_gtrunc <- 1e4 # number of truncated normal samples + + +# Save actual values +actuals_u = unlist(lapply(base_fc_upper, "[[", "actual")) +actuals_b = unlist(lapply(base_fc_bottom, "[[", "actual")) +actuals = c(actuals_u, actuals_b) + +# Scaling factor for computing MASE +Q_u <- M5_CA1_basefc$Q_u +Q_b <- M5_CA1_basefc$Q_b +Q <- c(Q_u, Q_b) + +# Initialize list to save the results +mase = list("base" = matrix(nrow = n, ncol = 1), + "Gauss" = matrix(nrow = n, ncol = 1), + "Mix-cond" = matrix(nrow = n, ncol = 1), + "TD-cond" = matrix(nrow = n, ncol = 1)) +mis = mase +rps = mase +``` + +```{r,include=FALSE} +AE_pmf = function(pmf, actual) { + return(abs(PMF.get_quantile(pmf,p=0.5) - actual)) +} + +IS_pmf = function(pmf, actual, alpha=0.1) { + u = PMF.get_quantile(pmf, p=1-(alpha/2)) + l = PMF.get_quantile(pmf, p=alpha/2) + return(u - l + (2/alpha)*(l - actual)*(actual < l) + + (2/alpha)*(actual - u)*(actual > u) ) +} + +CRPS_pmf = function(pmf, actual) { + v = pmf + cdf = cumsum(v) / sum(v) + M = length(cdf) + if (actual >= M) { # if actuals is outside the supp of tab, add ones to the end of the cdf + cdf = c(cdf, rep(1, (actual-M+1))) + M = length(cdf) + } + cdf_act = (0:(M-1)) >= actual + crps_ = sum((cdf - cdf_act)**2) + return(crps_) +} + +MIS_gauss = function(mus, sds, actuals, alpha=0.1, trunc=FALSE) { + z = qnorm(1-(alpha/2)) + u = mus + z * sds + l = mus - z * sds + if (trunc) { + l[l<0] = 0 + u[u<0] = 0 + } + return(u - l + (2/alpha)*(l - actuals)*(actuals < l) + + (2/alpha)*(actuals - u)*(actuals > u) ) +} + +# MIS_samples = function(samples, actual, alpha=0.1) { +# if (nrow(samples)!=length(actual)) stop(paste0(samples, " must be of shape length(", actual, ") x n_samples")) +# u = apply(samples, 1, function(x) quantile(x, 1-(alpha/2))) +# l = apply(samples, 1, function(x) quantile(x, alpha/2)) +# return(u - l + (2/alpha)*(l - actual)*(actual < l) + +# (2/alpha)*(actual - u)*(actual > u) ) +# } + +skill.score <- function(ref, met) { + s = (2 * (ref - met) / (ref + met)) * 100 + s[is.na(s)] = 0 + return (s) +} +``` + + + + +```{r} +### BASE forecasts ### +# Upper +mase$base[1:n_u,1] = abs(mu_u - actuals_u) / Q_u +mis$base[1:n_u,1] = MIS_gauss(mu_u, sd_u, actuals_u, alpha) +rps$base[1:n_u,1] = scoringRules::crps(actuals_u, "norm", mean=mu_u, sd=sd_u) + +# Bottom +pmfs = lapply(base_fc_bottom, "[[", "pmf") +mase$base[(n_u+1):n,1] = mapply(AE_pmf, pmfs, actuals_b) / Q_b +mis$base[(n_u+1):n,1] = mapply(IS_pmf, pmfs, actuals_b, + MoreArgs = list(alpha=alpha)) + +rps$base[(n_u+1):n,1] = mapply(CRPS_pmf, pmfs, actuals_b) +``` + +```{r} +# Metrics for Gauss reconciliations +mu = c(rec_fc$Gauss$mu_u, rec_fc$Gauss$mu_b) +sd = c(diag(rec_fc$Gauss$Sigma_u)**0.5, diag(rec_fc$Gauss$Sigma_b)**0.5) +sd = sd + jitt +mase$Gauss[,1] = abs(mu - actuals) / Q +mis$Gauss[,1] = MIS_gauss(mu, sd, actuals, alpha) +rps$Gauss[,1] = scoringRules::crps(actuals, "norm", mean=mu, sd=sd) +``` + +```{r,eval=FALSE,include=FALSE} +# Metrics for Gauss-truncated reconciliations +# Bottom +mu_b = rec_fc$Gauss$mu_b +mu_b_trunc = mu_b +mu_b_trunc[mu_b_trunc<0] = 0 +sd_b = diag(rec_fc$Gauss$Sigma_b)**0.5 + +mase$`Gauss-T`[(n_u+1):n,1] = abs(mu_b_trunc - actuals_b) / Q_b +mis$`Gauss-T`[(n_u+1):n,1] = MIS_gauss(mu_b_trunc, sd_b, actuals_b, alpha, trunc=TRUE) +Sigma_b = rec_fc$Gauss$Sigma_b +Sigma_b = Sigma_b + diag(jitt, nrow(Sigma_b)) +samples_b = t(mvtnorm::rmvnorm(n_samp_gtrunc, mean = mu_b, + sigma = Sigma_b, method = "chol")) +samples_b[samples_b<0] = 0 +rps$`Gauss-T`[(n_u+1):n,1] = scoringRules::crps_sample(actuals_b, samples_b) +# Upper +samples_u = A %*% samples_b +rm(samples_b) +medians_u = apply(samples_u, 1, median) +mase$`Gauss-T`[1:n_u,1] = abs(medians_u - actuals_u) / Q_u +mis$`Gauss-T`[1:n_u,1] = MIS_samples(samples_u, actuals_u, alpha) +rps$`Gauss-T`[1:n_u,1] = scoringRules::crps_sample(actuals_u, samples_u) +``` + +```{r} +# Metrics for Mix-cond reconciliation +pmfs = c(rec_fc$Mixed_cond$upper, rec_fc$Mixed_cond$bottom) +mase$`Mix-cond`[,1] = mapply(AE_pmf, pmfs, actuals) / Q +mis$`Mix-cond`[,1] = mapply(IS_pmf, pmfs, actuals, + MoreArgs = list(alpha=alpha)) +rps$`Mix-cond`[,1] = mapply(CRPS_pmf, pmfs, actuals) +``` + +```{r} +# Metrics for TD-cond reconciliation +pmfs = c(rec_fc$TD_cond$upper, rec_fc$TD_cond$bottom) +mase$`TD-cond`[,1] = mapply(AE_pmf, pmfs, actuals) / Q +mis$`TD-cond`[,1] = mapply(IS_pmf, pmfs, actuals, + MoreArgs = list(alpha=alpha)) +rps$`TD-cond`[,1] = mapply(CRPS_pmf, pmfs, actuals) +``` + + +## Skill scores +We report the improvement over the base forecasts using the skill score values and averaging them across experiments. +For instance, the skill score of Gauss on RPS is: + +$$ \text{Skill}_{\%}\,(\text{RPS, }Gauss) = 100 \cdot +\frac{\text{RPS}(base) - \text{RPS}(Gauss)} +{(\text{RPS}(base) + \text{RPS}(Gauss))/2}$$ + + + +```{r} +scores = list( + mase = mase, + mis = mis, + rps = rps + ) + +ref_met="base" +methods_ = names(mase) +methods_ = setdiff(methods_, ref_met) +metrics = names(scores) + +skill_scores = list() +for (m in metrics) { + skill_scores[[m]] = list() + for (met in methods_) { + skill_scores[[m]][["upper"]][[met]] = skill.score(scores[[m]][[ref_met]][1:11], + scores[[m]][[met]][1:11]) + skill_scores[[m]][["bottom"]][[met]] = skill.score(scores[[m]][[ref_met]][12:3060], + scores[[m]][[met]][12:3060]) + } +} +``` + +## Boxplots + +Finally, we show a comparison of the skill scores for each method divided in upper and bottom level. + +```{r,fig.width=10,fig.height=5} +custom_colors = c("#a8a8e4", + "#a9c7e4", + "#aae4df") + +# Boxplots of MASE skill scores +par(mfrow = c(1, 2)) +boxplot(skill_scores$mase$upper,main="MASE upper variables",col=custom_colors) +abline(h=0,lty=3) +boxplot(skill_scores$mase$bottom,main= "MASE bottom variables",col=custom_colors) +abline(h=0,lty=3) +``` +```{r,eval=TRUE,include=FALSE} +par(mfrow = c(1, 1)) +``` + + +```{r,fig.width=10,fig.height=5} +# Boxplots of MIS skill scores +par(mfrow = c(1, 2)) +boxplot(skill_scores$mis$upper,main="MIS upper variables",col=custom_colors) +abline(h=0,lty=3) +boxplot(skill_scores$mis$bottom,main= "MIS bottom variables",col=custom_colors) +abline(h=0,lty=3) +``` +```{r,eval=TRUE,include=FALSE} +par(mfrow = c(1, 1)) +``` + +```{r,fig.width=10,fig.height=5} +# Boxplots of RPS skill scores +par(mfrow = c(1, 2)) +boxplot(skill_scores$rps$upper,main="RPS upper variables",col=custom_colors) +abline(h=0,lty=3) +boxplot(skill_scores$rps$bottom,main= "RPS bottom variables",col=custom_colors) +abline(h=0,lty=3) +``` +```{r,eval=TRUE,include=FALSE} +par(mfrow = c(1, 1)) +``` + +# Full reproducibility + +The full experiment described in [@zambon2024mixed] can be reproduced by using the code available **here**. + +# References +

From aea8d466c57dc6713bac61f78ece57e49f8ba4eb Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Thu, 23 May 2024 17:57:45 +0200 Subject: [PATCH 25/36] fixed bugs in vignette --- vignettes/mixed_reconciliation.Rmd | 371 +++++++++++++---------------- 1 file changed, 162 insertions(+), 209 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index c1e51d2..858a01d 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -26,17 +26,22 @@ library(bayesRecon) This vignette partially reproduces the results of the paper *Probabilistic reconciliation of mixed-type hierarchical time series* [@zambon2024mixed], accepted for publication at the 40th Conference on Uncertainty in Artificial Intelligence. -We replicate the reconciliation of the one-step ahead forecasts of one store from the time series hierarchy of the M5 competition [@MAKRIDAKIS20221325]. Sec. 5 of the paper presents the results of the experiment conducted on 10 different stores. +We replicate the reconciliation of the one-step ahead forecasts of one store from the +time series hierarchy of the M5 competition [@MAKRIDAKIS20221325]. +Sec. 5 of the paper presents the results of the experiments conducted on 10 different stores. # Data and forecasts -The M5 competition [@MAKRIDAKIS20221325] contains daily time series of sales data referring to 10 different stores. The hierarchy of each store has the same structure: 3049 bottom time series (single items) and 11 upper time series, obtained by aggregating the items by department, product category, and store. The figure below shows the hierarchy of a store. +The M5 competition [@MAKRIDAKIS20221325] contains daily time series of sales data referring to 10 different stores. +The hierarchy of each store has the same structure: 3049 bottom time series (single items) and 11 upper time series, +obtained by aggregating the items by department, product category, and store. The figure below shows the hierarchy of a store. ```{r out.width = '100%', echo = FALSE} knitr::include_graphics("img/M5store_hier.png") ``` -We focus here on the store ``CA_1''. We computed the one-step ahead ($h=1$) forecasts for each bottom and upper time series, i.e. base forecasts, by using iETS [@svetunkov2023iets], available from the R package smooth [@smooth_pkg]. +We focus here on the store "CA_1". We computed the one-step ahead $(h=1)$ forecasts for each bottom and upper time series, +i.e. the base forecasts, by using iETS [@svetunkov2023iets], available from the R package smooth [@smooth_pkg]. The base forecasts are stored in the data `M5_CA1_basefc`, available with this package. @@ -53,135 +58,124 @@ S <- M5_CA1_basefc$S # Base forecasts: base_fc_upper <- M5_CA1_basefc$upper base_fc_bottom <- M5_CA1_basefc$bottom + +# We will save all the results in the list rec_fc +rec_fc <- list( + Gauss = list(), + Mixed_cond = list(), + TD_cond = list() + ) ``` -# Gaussian reconciliations +# Gaussian reconciliation We start by assuming that all forecasts are Gaussian and perform a Gaussian forecast reconciliation -with the function `bayesRecon::reconc_gaussian`. +with the function `bayesRecon::reconc_gaussian`. We refer to this method as `Gauss`. -The method `Gauss` considers the bottom forecasts as independent and the upper forecasts are a multivariate Gaussian. +The upper base forecasts are assumed to be a multivariate Gaussian, whose covariance matrix is estimated from the +in-sample residuals, while the bottom base forecasts are assumed to be independent. ```{r} -# We will save all results in the list rec_fc -rec_fc = list( - Gauss = list(), - Mixed_cond = list(), - TD_cond = list() - ) - -# Extract Gaussian parameters from the upper forecasts -mu_u = c() -sd_u = c() -for (fc in base_fc_upper) { - mu_u = c(mu_u, fc$mu) - sd_u = c(sd_u, fc$sigma) -} -residuals.upper = lapply(base_fc_upper, "[[", "residuals") -residuals.upper = t(do.call("rbind", residuals.upper)) -Sigma_u = schaferStrimmer_cov(residuals.upper)$shrink_cov +# Parameters of the upper base forecast distributions +mu_u <- unlist(lapply(base_fc_upper, "[[", "mu")) # upper means +# Compute the (shrinked) covariance matrix of the residuals +residuals.upper <- lapply(base_fc_upper, "[[", "residuals") +residuals.upper <- t(do.call("rbind", residuals.upper)) +Sigma_u <- schaferStrimmer_cov(residuals.upper)$shrink_cov - -# Extract parameters for bottom forecasts -mu_b = c() -sd_b = c() +# Parameters of the bottom base forecast distributions +mu_b <- c() +sd_b <- c() for (fc_b in base_fc_bottom) { - pmf = fc_b$pmf - mu_bott = PMF.get_mean(pmf) - sd_bott = PMF.get_var(pmf)**0.5 - mu_b = c(mu_bott, mu_b) - sd_b = c(sd_bott, sd_b) + pmf <- fc_b$pmf + mu_b <- c(mu_b, PMF.get_mean(pmf)) + sd_b <- c(sd_b, PMF.get_var(pmf)**0.5) } -Sigma_b = diag(sd_b**2) - -### Gaussian reconciliation ### +Sigma_b <- diag(sd_b**2) +# Mean and covariance matrix of the base forecasts base_forecasts.mu <- c(mu_u,mu_b) -base_forecasts.Sigma <- diag(c(sd_u**2,sd_b**2)) - -# Gauss -base_forecasts.Sigma_corr <- base_forecasts.Sigma -base_forecasts.Sigma_corr[1:11,1:11] <- Sigma_u - +base_forecasts.Sigma <- matrix(0, nrow = n, ncol = n) +base_forecasts.Sigma[1:n_u,1:n_u] <- Sigma_u +base_forecasts.Sigma[(n_u+1):n,(n_u+1):n] <- Sigma_b + +# Gaussian reconciliation start <- Sys.time() -gauss = reconc_gaussian(S, base_forecasts.mu, - base_forecasts.Sigma_corr) +gauss <- reconc_gaussian(S, base_forecasts.mu, base_forecasts.Sigma) stop <- Sys.time() -rec_fc$Gauss = list(mu_b = gauss$bottom_reconciled_mean, - Sigma_b = gauss$bottom_reconciled_covariance, - mu_u = A %*% gauss$bottom_reconciled_mean, - Sigma_u = A %*% gauss$bottom_reconciled_covariance %*% t(A)) - +rec_fc$Gauss <- list(mu_b = gauss$bottom_reconciled_mean, + Sigma_b = gauss$bottom_reconciled_covariance, + mu_u = A %*% gauss$bottom_reconciled_mean, + Sigma_u = A %*% gauss$bottom_reconciled_covariance %*% t(A)) -Gauss_time_print <- round(difftime(stop, start, units = "secs"), 2) -cat("Computational time for Gaussian reconciliation: ", - Gauss_time_print, "s") -Gauss_time <- as.double(Gauss_time_print) +Gauss_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) +cat("Computational time for Gaussian reconciliation: ", Gauss_time, "s") ``` # Reconciliation with mixed-cond -We now reconcile the forecasts with the algorithm Mix-cond described in [@zambon2024mixed], Sec. 4. The algorithm is implemented in the function \link{reconc_MixCond}. +We now reconcile the forecasts with the algorithm Mix-cond described in [@zambon2024mixed], Sec. 4. +The algorithm is implemented in the function \link{reconc_MixCond}. ```{r} -### Mixed correlated seed <- 1 N_samples_IS <- 5e4 +# Base forecasts fc_upper_4rec <- list(mu=mu_u, Sigma=Sigma_u) -fc_bottom_4rec <- lapply(base_fc_bottom,function(x) x$pmf) +fc_bottom_4rec <- lapply(base_fc_bottom, "[[", "pmf") # list of PMFs +# MixCond reconciliation start <- Sys.time() -mix_cond = reconc_MixCond(S, fc_bottom_4rec, fc_upper_4rec, bottom_in_type = "pmf", - num_samples = N_samples_IS, return_type = "pmf", seed = seed) +mix_cond <- reconc_MixCond(S, fc_bottom_4rec, fc_upper_4rec, bottom_in_type = "pmf", + num_samples = N_samples_IS, return_type = "pmf", seed = seed) stop <- Sys.time() - -rec_fc$Mixed_cond = list( +rec_fc$Mixed_cond <- list( bottom = mix_cond$bottom_reconciled$pmf, - upper = mix_cond$upper_reconciled$pmf, - ESS = mix_cond$ESS) + upper = mix_cond$upper_reconciled$pmf, + ESS = mix_cond$ESS + ) -MixCond_time_print <- round(difftime(stop, start, units = "secs"), 2) -cat("Computational time for Mix-cond reconciliation: ", - MixCond_time_print, "s") -MixCond_time <- as.double(MixCond_time_print) +MixCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) +cat("Computational time for Mix-cond reconciliation: ", MixCond_time, "s") ``` As discussed in [@zambon2024mixed], Sec. 4, Mix-cond performs poorly in high dimensions. While we have not computed the metrics for this reconciliation yet, we see that the implementation returns a warning regarding the effective sample size of the importance sampling algorithm. This is a sign that Mix-cond is not handling well the high incoherence between the joint bottom-up and the upper base forecasts, see also [@zambon2024mixed], fig. 3, for a graphical example. # Reconciliation with TD-cond -In [@zambon2024mixed], Sec. 5, TD-cond is proposed as a reliable alternative in high dimensions. This algorithm is implemented in the function \link{reconc_TDcond}. +In [@zambon2024mixed], Sec. 5, TD-cond is proposed as a reliable alternative in high dimensions. +This algorithm is implemented in the function \link{reconc_TDcond}. ```{r} -### TD-cond N_samples_TD <- 1e4 +# TDcond reconciliation start <- Sys.time() -td = reconc_TDcond(S, fc_bottom_4rec, fc_upper_4rec, +td <- reconc_TDcond(S, fc_bottom_4rec, fc_upper_4rec, bottom_in_type = "pmf", num_samples = N_samples_TD, return_type = "pmf", seed = seed) stop <- Sys.time() -rec_fc$TD_cond = list( +rec_fc$TD_cond <- list( bottom = td$bottom_reconciled$pmf, - upper = td$upper_reconciled$pmf) + upper = td$upper_reconciled$pmf + ) -TDCond_time_print <- round(difftime(stop, start, units = "secs"), 2) -cat("Computational time for TD-cond reconciliation: ", - TDCond_time_print, "s") -TDCond_time <- as.double(TDCond_time_print) +TDCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) +cat("Computational time for TD-cond reconciliation: ", TDCond_time, "s") ``` -The algorithm TD-cond still returns a warning regarding the high incoherence between the joint bottom-up and the upper base forecasts. We will see that this warning does not impact the performances of TD-cond. +The algorithm TD-cond still returns a warning regarding the high incoherence between the joint bottom-up and the upper base forecasts. +We will see that this warning does not impact the performances of TD-cond. # Comparison -For each time series in the hierarchy, we compute the following three metrics for each method: +For each time series in the hierarchy, we compute the following scores for each method: - MASE: Mean Absolute Scaled Error @@ -190,80 +184,69 @@ For each time series in the hierarchy, we compute the following three metrics fo - RPS: Ranked Probability Score ```{r} -# Parameters for computing the metrics -alpha<- 0.9 # MIS alpha -jitt <- 1e-9 # jitter for numerical stability -n_samp_gtrunc <- 1e4 # number of truncated normal samples - +# Parameters for computing the scores +alpha <- 0.1 # MIS uses 90% coverage intervals +jitt <- 1e-9 # jitter for numerical stability # Save actual values -actuals_u = unlist(lapply(base_fc_upper, "[[", "actual")) -actuals_b = unlist(lapply(base_fc_bottom, "[[", "actual")) -actuals = c(actuals_u, actuals_b) +actuals_u <- unlist(lapply(base_fc_upper, "[[", "actual")) +actuals_b <- unlist(lapply(base_fc_bottom, "[[", "actual")) +actuals <- c(actuals_u, actuals_b) # Scaling factor for computing MASE Q_u <- M5_CA1_basefc$Q_u Q_b <- M5_CA1_basefc$Q_b Q <- c(Q_u, Q_b) -# Initialize list to save the results -mase = list("base" = matrix(nrow = n, ncol = 1), - "Gauss" = matrix(nrow = n, ncol = 1), - "Mix-cond" = matrix(nrow = n, ncol = 1), - "TD-cond" = matrix(nrow = n, ncol = 1)) -mis = mase -rps = mase +# Initialize lists to save the results +mase <- list() +mis <- list() +rps <- list() ``` ```{r,include=FALSE} -AE_pmf = function(pmf, actual) { +# Functions for computing the scores of a PMF +AE_pmf <- function(pmf, actual) { return(abs(PMF.get_quantile(pmf,p=0.5) - actual)) } - -IS_pmf = function(pmf, actual, alpha=0.1) { - u = PMF.get_quantile(pmf, p=1-(alpha/2)) - l = PMF.get_quantile(pmf, p=alpha/2) +IS_pmf <- function(pmf, actual, alpha) { + u <- PMF.get_quantile(pmf, p=1-(alpha/2)) + l <- PMF.get_quantile(pmf, p=alpha/2) return(u - l + (2/alpha)*(l - actual)*(actual < l) + - (2/alpha)*(actual - u)*(actual > u) ) + (2/alpha)*(actual - u)*(actual > u) ) } - -CRPS_pmf = function(pmf, actual) { - v = pmf - cdf = cumsum(v) / sum(v) - M = length(cdf) - if (actual >= M) { # if actuals is outside the supp of tab, add ones to the end of the cdf - cdf = c(cdf, rep(1, (actual-M+1))) - M = length(cdf) +RPS_pmf <- function(pmf, actual) { + cdf <- cumsum(pmf) / sum(pmf) + M <- length(cdf) + # if actual is outside the supp of pmf, add ones to the end of the cdf: + if (actual >= M) { + cdf <- c(cdf, rep(1, (actual-M+1))) + M <- length(cdf) } - cdf_act = (0:(M-1)) >= actual - crps_ = sum((cdf - cdf_act)**2) + cdf_act <- (0:(M-1)) >= actual # unit step function in actual + crps_ <- sum((cdf - cdf_act)**2) return(crps_) } -MIS_gauss = function(mus, sds, actuals, alpha=0.1, trunc=FALSE) { - z = qnorm(1-(alpha/2)) - u = mus + z * sds - l = mus - z * sds +# Function for computing the MIS of (possibly truncated) Gaussian forecasts +MIS_gauss <- function(mus, sds, actuals, alpha, trunc=FALSE) { + z <- qnorm(1-(alpha/2)) + u <- mus + z * sds + l <- mus - z * sds + # If it is truncated, set negative quantiles to zero if (trunc) { - l[l<0] = 0 - u[u<0] = 0 + l[l<0] <- 0 + u[u<0] <- 0 } return(u - l + (2/alpha)*(l - actuals)*(actuals < l) + - (2/alpha)*(actuals - u)*(actuals > u) ) + (2/alpha)*(actuals - u)*(actuals > u) ) } -# MIS_samples = function(samples, actual, alpha=0.1) { -# if (nrow(samples)!=length(actual)) stop(paste0(samples, " must be of shape length(", actual, ") x n_samples")) -# u = apply(samples, 1, function(x) quantile(x, 1-(alpha/2))) -# l = apply(samples, 1, function(x) quantile(x, alpha/2)) -# return(u - l + (2/alpha)*(l - actual)*(actual < l) + -# (2/alpha)*(actual - u)*(actual > u) ) -# } - +# Function for computing the skill score skill.score <- function(ref, met) { - s = (2 * (ref - met) / (ref + met)) * 100 - s[is.na(s)] = 0 - return (s) + s <- (2 * (ref - met) / (ref + met)) * 100 + s[is.na(s)] <- 0 # if both numerator and denominator are 0, set skill score to 0 + return(s) } ``` @@ -271,72 +254,38 @@ skill.score <- function(ref, met) { ```{r} -### BASE forecasts ### +### Compute scores for the base forecasts # Upper -mase$base[1:n_u,1] = abs(mu_u - actuals_u) / Q_u -mis$base[1:n_u,1] = MIS_gauss(mu_u, sd_u, actuals_u, alpha) -rps$base[1:n_u,1] = scoringRules::crps(actuals_u, "norm", mean=mu_u, sd=sd_u) - +mu_u <- unlist(lapply(base_fc_upper, "[[", "mu")) +sd_u <- unlist(lapply(base_fc_upper, "[[", "sigma")) +mase$base[1:n_u] <- abs(mu_u - actuals_u) / Q_u +mis$base[1:n_u] <- MIS_gauss(mu_u, sd_u, actuals_u, alpha) +rps$base[1:n_u] <- scoringRules::crps(actuals_u, "norm", mean=mu_u, sd=sd_u) # Bottom pmfs = lapply(base_fc_bottom, "[[", "pmf") -mase$base[(n_u+1):n,1] = mapply(AE_pmf, pmfs, actuals_b) / Q_b -mis$base[(n_u+1):n,1] = mapply(IS_pmf, pmfs, actuals_b, - MoreArgs = list(alpha=alpha)) - -rps$base[(n_u+1):n,1] = mapply(CRPS_pmf, pmfs, actuals_b) -``` - -```{r} -# Metrics for Gauss reconciliations -mu = c(rec_fc$Gauss$mu_u, rec_fc$Gauss$mu_b) -sd = c(diag(rec_fc$Gauss$Sigma_u)**0.5, diag(rec_fc$Gauss$Sigma_b)**0.5) -sd = sd + jitt -mase$Gauss[,1] = abs(mu - actuals) / Q -mis$Gauss[,1] = MIS_gauss(mu, sd, actuals, alpha) -rps$Gauss[,1] = scoringRules::crps(actuals, "norm", mean=mu, sd=sd) -``` - -```{r,eval=FALSE,include=FALSE} -# Metrics for Gauss-truncated reconciliations -# Bottom -mu_b = rec_fc$Gauss$mu_b -mu_b_trunc = mu_b -mu_b_trunc[mu_b_trunc<0] = 0 -sd_b = diag(rec_fc$Gauss$Sigma_b)**0.5 - -mase$`Gauss-T`[(n_u+1):n,1] = abs(mu_b_trunc - actuals_b) / Q_b -mis$`Gauss-T`[(n_u+1):n,1] = MIS_gauss(mu_b_trunc, sd_b, actuals_b, alpha, trunc=TRUE) -Sigma_b = rec_fc$Gauss$Sigma_b -Sigma_b = Sigma_b + diag(jitt, nrow(Sigma_b)) -samples_b = t(mvtnorm::rmvnorm(n_samp_gtrunc, mean = mu_b, - sigma = Sigma_b, method = "chol")) -samples_b[samples_b<0] = 0 -rps$`Gauss-T`[(n_u+1):n,1] = scoringRules::crps_sample(actuals_b, samples_b) -# Upper -samples_u = A %*% samples_b -rm(samples_b) -medians_u = apply(samples_u, 1, median) -mase$`Gauss-T`[1:n_u,1] = abs(medians_u - actuals_u) / Q_u -mis$`Gauss-T`[1:n_u,1] = MIS_samples(samples_u, actuals_u, alpha) -rps$`Gauss-T`[1:n_u,1] = scoringRules::crps_sample(actuals_u, samples_u) -``` - -```{r} -# Metrics for Mix-cond reconciliation -pmfs = c(rec_fc$Mixed_cond$upper, rec_fc$Mixed_cond$bottom) -mase$`Mix-cond`[,1] = mapply(AE_pmf, pmfs, actuals) / Q -mis$`Mix-cond`[,1] = mapply(IS_pmf, pmfs, actuals, - MoreArgs = list(alpha=alpha)) -rps$`Mix-cond`[,1] = mapply(CRPS_pmf, pmfs, actuals) -``` - -```{r} -# Metrics for TD-cond reconciliation -pmfs = c(rec_fc$TD_cond$upper, rec_fc$TD_cond$bottom) -mase$`TD-cond`[,1] = mapply(AE_pmf, pmfs, actuals) / Q -mis$`TD-cond`[,1] = mapply(IS_pmf, pmfs, actuals, - MoreArgs = list(alpha=alpha)) -rps$`TD-cond`[,1] = mapply(CRPS_pmf, pmfs, actuals) +mase$base[(n_u+1):n] <- mapply(AE_pmf, pmfs, actuals_b) / Q_b +mis$base[(n_u+1):n] <- mapply(IS_pmf, pmfs, actuals_b, MoreArgs = list(alpha=alpha)) +rps$base[(n_u+1):n] <- mapply(RPS_pmf, pmfs, actuals_b) + +### Compute scores for Gauss reconciliation +mu <- c(rec_fc$Gauss$mu_u, rec_fc$Gauss$mu_b) +sd <- c(diag(rec_fc$Gauss$Sigma_u), diag(rec_fc$Gauss$Sigma_b))**0.5 +sd <- sd + jitt +mase$Gauss <- abs(mu - actuals) / Q +mis$Gauss <- MIS_gauss(mu, sd, actuals, alpha) +rps$Gauss <- scoringRules::crps(actuals, "norm", mean=mu, sd=sd) + +### Compute scores for Mix-cond reconciliation +pmfs <- c(rec_fc$Mixed_cond$upper, rec_fc$Mixed_cond$bottom) +mase$MixCond <- mapply(AE_pmf, pmfs, actuals) / Q +mis$MixCond <- mapply(IS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) +rps$MixCond <- mapply(RPS_pmf, pmfs, actuals) + +### Compute scores for TD-cond reconciliation +pmfs <- c(rec_fc$TD_cond$upper, rec_fc$TD_cond$bottom) +mase$TDcond <- mapply(AE_pmf, pmfs, actuals) / Q +mis$TDcond <- mapply(IS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) +rps$TDcond <- mapply(RPS_pmf, pmfs, actuals) ``` @@ -349,27 +298,25 @@ $$ \text{Skill}_{\%}\,(\text{RPS, }Gauss) = 100 \cdot {(\text{RPS}(base) + \text{RPS}(Gauss))/2}$$ - ```{r} -scores = list( +scores <- list( mase = mase, mis = mis, rps = rps ) +scores_ = names(scores) -ref_met="base" -methods_ = names(mase) -methods_ = setdiff(methods_, ref_met) -metrics = names(scores) - -skill_scores = list() -for (m in metrics) { - skill_scores[[m]] = list() +ref_met <- "base" +methods_ <- c("Gauss", "MixCond", "TDcond") + +skill_scores <- list() +for (s in scores_) { + skill_scores[[s]] <- list() for (met in methods_) { - skill_scores[[m]][["upper"]][[met]] = skill.score(scores[[m]][[ref_met]][1:11], - scores[[m]][[met]][1:11]) - skill_scores[[m]][["bottom"]][[met]] = skill.score(scores[[m]][[ref_met]][12:3060], - scores[[m]][[met]][12:3060]) + skill_scores[[s]][["upper"]][[met]] <- skill.score(scores[[s]][[ref_met]][1:n_u], + scores[[s]][[met]][1:n_u]) + skill_scores[[s]][["bottom"]][[met]] <- skill.score(scores[[s]][[ref_met]][(n_u+1):n], + scores[[s]][[met]][(n_u+1):n]) } } ``` @@ -379,15 +326,17 @@ for (m in metrics) { Finally, we show a comparison of the skill scores for each method divided in upper and bottom level. ```{r,fig.width=10,fig.height=5} -custom_colors = c("#a8a8e4", +custom_colors <- c("#a8a8e4", "#a9c7e4", "#aae4df") # Boxplots of MASE skill scores par(mfrow = c(1, 2)) -boxplot(skill_scores$mase$upper,main="MASE upper variables",col=custom_colors) +boxplot(skill_scores$mase$upper, main = "MASE upper time series", + col = custom_colors, ylim = c(-200,50)) abline(h=0,lty=3) -boxplot(skill_scores$mase$bottom,main= "MASE bottom variables",col=custom_colors) +boxplot(skill_scores$mase$bottom, main = "MASE bottom time series", + col = custom_colors, ylim = c(-200,50)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} @@ -398,9 +347,11 @@ par(mfrow = c(1, 1)) ```{r,fig.width=10,fig.height=5} # Boxplots of MIS skill scores par(mfrow = c(1, 2)) -boxplot(skill_scores$mis$upper,main="MIS upper variables",col=custom_colors) +boxplot(skill_scores$mis$upper, main = "MIS upper time series", + col = custom_colors, ylim = c(-150,50)) abline(h=0,lty=3) -boxplot(skill_scores$mis$bottom,main= "MIS bottom variables",col=custom_colors) +boxplot(skill_scores$mis$bottom, main = "MIS bottom time series", + col = custom_colors, ylim = c(-150,50)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} @@ -410,9 +361,11 @@ par(mfrow = c(1, 1)) ```{r,fig.width=10,fig.height=5} # Boxplots of RPS skill scores par(mfrow = c(1, 2)) -boxplot(skill_scores$rps$upper,main="RPS upper variables",col=custom_colors) +boxplot(skill_scores$rps$upper, main = "RPS upper time series", + col = custom_colors, ylim = c(-120,50)) abline(h=0,lty=3) -boxplot(skill_scores$rps$bottom,main= "RPS bottom variables",col=custom_colors) +boxplot(skill_scores$rps$bottom, main = "RPS bottom time series", + col = custom_colors, ylim = c(-120,50)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} From 0be45f9f9517404c43d4649c2fe31660f6b22a40 Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Fri, 24 May 2024 10:28:40 +0200 Subject: [PATCH 26/36] Small fix to the structure of M5_CA1_basefc dataset. --- data-raw/CA_1_h_1_base_fc.rds | Bin 0 -> 456316 bytes data-raw/M5data_forecasts.R | 196 ++++++++++++++++++---------------- data/M5_CA1_basefc.rda | Bin 299534 -> 299774 bytes 3 files changed, 103 insertions(+), 93 deletions(-) create mode 100644 data-raw/CA_1_h_1_base_fc.rds diff --git a/data-raw/CA_1_h_1_base_fc.rds b/data-raw/CA_1_h_1_base_fc.rds new file mode 100644 index 0000000000000000000000000000000000000000..f0b59151372d1089c32d82cb3d50eff55182af15 GIT binary patch literal 456316 zcmV(pK=8jGiwFP!000001GM~kR88^Q|Br7(CBuo3G@J&CrqeuVxSHoW&2ydRS*LlV 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a/data-raw/M5data_forecasts.R +++ b/data-raw/M5data_forecasts.R @@ -15,7 +15,7 @@ n_samples_b = 2e4 round_up = FALSE path_m5_data <- "./data-raw/M5_data" -m5_forecast_path <- "./data-raw/M5_for_data/" +m5_forecast_path <- "./data-raw/" m5::m5_download(path_m5_data) @@ -31,124 +31,134 @@ CAT = c("HOBBIES", "HOUSEHOLD", "FOODS") DEPT = c("HOBBIES_1", "HOBBIES_2", "HOUSEHOLD_1", "HOUSEHOLD_2", "FOODS_1", "FOODS_2", "FOODS_3") -store_path = paste0(m5_forecast_path, STORE) +store_path = paste0(m5_forecast_path) len = 1941 +h=1 ############### ### Base forecasts upper time series -print("Computing base forecasts of upper time series...") - -h=1 if (!dir.exists(store_path)) dir.create(store_path,recursive = TRUE) str_base_fc = paste0(store_path,"/CA_1_h_",h,"_base_fc.rds") - -M5_CA1_basefc = list() - -train_u <- list() - -# Store -ts.agg = get_upp_ts(dset.store.train, dset.store.test, "store_id", - STORE, h = h, len = len) -train.agg = ts.agg$train -test.agg = ts.agg$test - -model = model_upper(train.agg) -fc.model = forecast(model, h = 1) -M5_CA1_basefc$upper[[STORE]] = list( - mu = fc.model$mean, - sigma = model$scale, - actual = test.agg, - residuals = model$residuals -) -train_u[[STORE]] <- train.agg - -# Category -for (cat.id in CAT) { - ts.agg = get_upp_ts(dset.store.train, dset.store.test, "cat_id", - cat.id, h = h, len = len) - train.agg = ts.agg$train - test.agg = ts.agg$test +if(file.exists(str_base_fc)){ + print("Loading base forecasts of upper time series...") + M5_CA1_basefc <- readRDS(str_base_fc) +}else{ - model = model_upper(train.agg) - fc.model = forecast(model, h = 1) - M5_CA1_basefc$upper[[cat.id]] = list( - mu = fc.model$mean, - sigma = model$scale, - actual = test.agg, - residuals = model$residuals - ) - train_u[[cat.id]] <- train.agg -} - -# Department -for (dept.id in DEPT) { - ts.agg = get_upp_ts(dset.store.train, dset.store.test, "dept_id", - dept.id, h = h, len = len) + print("Computing base forecasts of upper time series...") + + + + M5_CA1_basefc = list() + + train_u <- list() + + # Store + ts.agg = get_upp_ts(dset.store.train, dset.store.test, "store_id", + STORE, h = h, len = len) train.agg = ts.agg$train test.agg = ts.agg$test model = model_upper(train.agg) fc.model = forecast(model, h = 1) - M5_CA1_basefc$upper[[dept.id]] = list( - mu = fc.model$mean, + M5_CA1_basefc$upper[[STORE]] = list( + mu = as.numeric(fc.model$mean), sigma = model$scale, actual = test.agg, residuals = model$residuals ) - train_u[[dept.id]] <- train.agg -} - - -saveRDS(M5_CA1_basefc, str_base_fc) - - -train_b <- list() - -for (item.id in unique(dset.store.train$item_id)) { + train_u[[STORE]] <- train.agg - bts = get_bott_ts(dset.store.train, dset.store.test, - item_id, h, len = 1941) - train = bts$train - test = bts$test + # Category + for (cat.id in CAT) { + ts.agg = get_upp_ts(dset.store.train, dset.store.test, "cat_id", + cat.id, h = h, len = len) + train.agg = ts.agg$train + test.agg = ts.agg$test + + model = model_upper(train.agg) + fc.model = forecast(model, h = 1) + M5_CA1_basefc$upper[[cat.id]] = list( + mu = as.numeric(fc.model$mean), + sigma = model$scale, + actual = test.agg, + residuals = model$residuals + ) + train_u[[cat.id]] <- train.agg + } - model = model_bottom(train, model_str = "MNN", - occ_str = "auto", #"odds-ratio", - distr = "dgamma") - fc.model = forecast(model, h = 1, interval="simulated", - scenarios=TRUE, nsim = n_samples_b) + # Department + for (dept.id in DEPT) { + ts.agg = get_upp_ts(dset.store.train, dset.store.test, "dept_id", + dept.id, h = h, len = len) + train.agg = ts.agg$train + test.agg = ts.agg$test + + model = model_upper(train.agg) + fc.model = forecast(model, h = 1) + M5_CA1_basefc$upper[[dept.id]] = list( + mu = as.numeric(fc.model$mean), + sigma = model$scale, + actual = test.agg, + residuals = model$residuals + ) + train_u[[dept.id]] <- train.agg + } - # round to integer (up or to the closest integer, depending on round_up) - samples = if (round_up) ceiling(fc.model$scenarios[1,]) else round(fc.model$scenarios[1,]) - samples[samples<0] = 0 # set negative to zero - samples <- as.integer(samples) - pmf = PMF.from_samples(samples) # empirical pmf - M5_CA1_basefc$bottom[[item.id]] = list( - pmf = pmf, - actual = test - #residuals = model$residuals - ) - train_b[[item.id]] <- train + saveRDS(M5_CA1_basefc, str_base_fc) + + + train_b <- list() + + for (item.id in unique(dset.store.train$item_id)) { + + print(paste0("Doing ",item.id,"...")) + bts = get_bott_ts(dset.store.train, dset.store.test, + item_id, h, len = 1941) + train = bts$train + test = bts$test + + model = model_bottom(train, model_str = "MNN", + occ_str = "auto", #"odds-ratio", + distr = "dgamma") + fc.model = forecast(model, h = 1, interval="simulated", + scenarios=TRUE, nsim = n_samples_b) + + # round to integer (up or to the closest integer, depending on round_up) + samples = if (round_up) ceiling(fc.model$scenarios[1,]) else round(fc.model$scenarios[1,]) + samples[samples<0] = 0 # set negative to zero + samples <- as.integer(samples) + pmf = PMF.from_samples(samples) # empirical pmf + + M5_CA1_basefc$bottom[[item.id]] = list( + pmf = pmf, + actual = test + #residuals = model$residuals + ) + train_b[[item.id]] <- train + print("Done.\n") + } + + + hier = get_hier_M5(save_ = FALSE) + + M5_CA1_basefc$A <- hier$A + M5_CA1_basefc$S <- hier$S + + + Q_u = unlist(lapply(train_u, function(x) mean(abs(x[-1] - x[-length(x)])))) + Q_b = unlist(lapply(train_b, function(x) mean(abs(x[-1] - x[-length(x)])))) + + M5_CA1_basefc$Q_u <- Q_u + M5_CA1_basefc$Q_b <- Q_b + + saveRDS(M5_CA1_basefc, str_base_fc) } -hier = get_hier_M5(save_ = FALSE) - -M5_CA1_basefc$A <- hier$A -M5_CA1_basefc$S <- hier$S - - -Q_u = unlist(lapply(train_u, function(x) mean(abs(x[-1] - x[-length(x)])))) -Q_b = unlist(lapply(train_b, function(x) mean(abs(x[-1] - x[-length(x)])))) - -M5_CA1_basefc$Q_u <- Q_u -M5_CA1_basefc$Q_b <- Q_b - - usethis::use_data(M5_CA1_basefc, overwrite = 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z!`XK}H@V}@aOfQEpBw7^ch~kN_gz<{=&@jl80fRV{f{%$eNQIb%2w(w616DdCO&Jn;BIO@efYkQ?|#p$=j(HQ zC*pr2i#nB1`q%SszjxeQa;!d7I#{OFt!XCoU!T`;+Fg4&-pOTaNZ$+a_WJ*c&G()w zhd6W;qEw%4!p-IQc>edQM#^c5(oTW@*mz3+$hpXB1_+0b}! zrwZUV)p~y;&5m07Pka8aiPz@k&b6J5lXrcy*EsPLTcd2u4V!Vro$Ed$(D3mba6B(< z-aE&J=G^`Ibt|mPhocp@dDSf5yWcoD56k-=&&q$^-l}gW<2(z@aW_+`CylKv-mA;_ zz8oIoi*i=d>DtrDhuz&;R?s~URYms0nv!V%c=1!)RAoeBu&g3#^=l|A>e2*l;>jRX zMtT%8cI-{z9;8qMJA8Y5=vf-5y#e?IP#?fmKnEB~-qn->N~(`NK&qj06BmyQLdAUd fDo^4G;vg1#^`5G&A_s*x|Ha&qP81{=0E(*ssL@|R From 1ebc6fd25a14ce5ca28450065698d55e881c3c0e Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Fri, 24 May 2024 17:35:16 +0200 Subject: [PATCH 27/36] Fixes to the mixed reconciliation vignette, minor fix to reconciliation properties vignette. --- vignettes/mixed_reconciliation.Rmd | 156 +++++++++++++++++------- vignettes/reconciliation_properties.Rmd | 4 +- 2 files changed, 113 insertions(+), 47 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index 858a01d..f568108 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -28,7 +28,7 @@ This vignette partially reproduces the results of the paper *Probabilistic recon accepted for publication at the 40th Conference on Uncertainty in Artificial Intelligence. We replicate the reconciliation of the one-step ahead forecasts of one store from the time series hierarchy of the M5 competition [@MAKRIDAKIS20221325]. -Sec. 5 of the paper presents the results of the experiments conducted on 10 different stores. +Sec. 5 of the paper presents the results of the experiments conducted on 10 different stores replicated 14 times with rolling one-step ahead forecasts. # Data and forecasts @@ -43,7 +43,7 @@ knitr::include_graphics("img/M5store_hier.png") We focus here on the store "CA_1". We computed the one-step ahead $(h=1)$ forecasts for each bottom and upper time series, i.e. the base forecasts, by using iETS [@svetunkov2023iets], available from the R package smooth [@smooth_pkg]. -The base forecasts are stored in the data `M5_CA1_basefc`, available with this package. +The base forecasts are stored in the data `M5_CA1_basefc`, available with this package. ```{r} # Hierarchy composed by 3060 time series: 3049 bottom and 11 upper @@ -51,11 +51,11 @@ n_b <- 3049 n_u <- 11 n <- n_b + n_u -# Save matrix A and S +# Load matrix A and S A <- M5_CA1_basefc$A S <- M5_CA1_basefc$S -# Base forecasts: +# Load base forecasts: base_fc_upper <- M5_CA1_basefc$upper base_fc_bottom <- M5_CA1_basefc$bottom @@ -70,10 +70,9 @@ rec_fc <- list( # Gaussian reconciliation We start by assuming that all forecasts are Gaussian and perform a Gaussian forecast reconciliation -with the function `bayesRecon::reconc_gaussian`. We refer to this method as `Gauss`. +with the function `reconc_gaussian()`. We refer to this method as `Gauss`. Note that this assumption is not correct because the the bottom base forecasts are not Gaussian. -The upper base forecasts are assumed to be a multivariate Gaussian, whose covariance matrix is estimated from the -in-sample residuals, while the bottom base forecasts are assumed to be independent. +The upper base forecasts are assumed to be a multivariate Gaussian, whose covariance matrix is estimated from the in-sample residuals, while the bottom base forecasts are assumed to be independent. ```{r} @@ -99,7 +98,16 @@ base_forecasts.mu <- c(mu_u,mu_b) base_forecasts.Sigma <- matrix(0, nrow = n, ncol = n) base_forecasts.Sigma[1:n_u,1:n_u] <- Sigma_u base_forecasts.Sigma[(n_u+1):n,(n_u+1):n] <- Sigma_b - +``` + +The forecast reconciliation is performed with the function `reconc_gaussian()` which takes as input + +* the matrix `S`, the base forecast means; +* `base_forecasts.mu` and the base forecast covariance `base_forecasts.Sigma`. + +The function returns the reconciled mean and covariance for the bottom time series. + +```{r} # Gaussian reconciliation start <- Sys.time() gauss <- reconc_gaussian(S, base_forecasts.mu, base_forecasts.Sigma) @@ -115,10 +123,17 @@ cat("Computational time for Gaussian reconciliation: ", Gauss_time, "s") ``` -# Reconciliation with mixed-cond +# Reconciliation with mixed-conditioning We now reconcile the forecasts with the algorithm Mix-cond described in [@zambon2024mixed], Sec. 4. -The algorithm is implemented in the function \link{reconc_MixCond}. +The algorithm is implemented in the function `reconc_MixCond()`. The function takes as input + +* the `S` matrix; +* the probability mass functions of each bottom base forecast, stored in the list `fc_bottom_4rec`; +* the parameters of the multivariate Gaussian distribution for the upper variables, `fc_upper_4rec`; +* additional function parameters, among those note that `num_samples` specifies the number of samples used in the internal importance sampling (IS) algorithm. The parameter `return_type` can be changed to `samples` or `all` to obtain the IS samples. + +The function returns the reconciled forecasts in the form of probability mass functions for both the upper and bottom time series. ```{r} seed <- 1 @@ -144,12 +159,12 @@ MixCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) cat("Computational time for Mix-cond reconciliation: ", MixCond_time, "s") ``` -As discussed in [@zambon2024mixed], Sec. 4, Mix-cond performs poorly in high dimensions. While we have not computed the metrics for this reconciliation yet, we see that the implementation returns a warning regarding the effective sample size of the importance sampling algorithm. This is a sign that Mix-cond is not handling well the high incoherence between the joint bottom-up and the upper base forecasts, see also [@zambon2024mixed], fig. 3, for a graphical example. +As discussed in [@zambon2024mixed], Sec. 4, Mix-cond performs poorly in high dimensions and it does not handle well high incoherence between the joint bottom-up and the upper base forecasts, see also [@zambon2024mixed], fig. 3, for a graphical example. -# Reconciliation with TD-cond +# Reconciliation with top down conditioning In [@zambon2024mixed], Sec. 5, TD-cond is proposed as a reliable alternative in high dimensions. -This algorithm is implemented in the function \link{reconc_TDcond}. +This algorithm is implemented in the function `reconc_TDcond()`. The function takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. ```{r} N_samples_TD <- 1e4 @@ -160,7 +175,11 @@ td <- reconc_TDcond(S, fc_bottom_4rec, fc_upper_4rec, bottom_in_type = "pmf", num_samples = N_samples_TD, return_type = "pmf", seed = seed) stop <- Sys.time() +``` +The algorithm TD-cond returns a warning regarding the incoherence between the joint bottom-up and the upper base forecasts. +We will see that this warning does not impact the performances of TD-cond. +```{r} rec_fc$TD_cond <- list( bottom = td$bottom_reconciled$pmf, upper = td$upper_reconciled$pmf @@ -170,9 +189,7 @@ TDCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) cat("Computational time for TD-cond reconciliation: ", TDCond_time, "s") ``` -The algorithm TD-cond still returns a warning regarding the high incoherence between the joint bottom-up and the upper base forecasts. -We will see that this warning does not impact the performances of TD-cond. - + # Comparison For each time series in the hierarchy, we compute the following scores for each method: @@ -204,12 +221,21 @@ mis <- list() rps <- list() ``` +The following functions are used for computing the scores: + +* `AE_pmf`: compute the absolute error from a PMF; +* `MIS_pmf`: compute interval score from a PMF; +* `RPS_pmf`: compute RPS from a PMF; +* `MIS_gauss`: compute MIS for a Gaussian distribution. + +The implementation of these functions is available in the source code of the vignette but not shown here. + ```{r,include=FALSE} # Functions for computing the scores of a PMF AE_pmf <- function(pmf, actual) { return(abs(PMF.get_quantile(pmf,p=0.5) - actual)) } -IS_pmf <- function(pmf, actual, alpha) { +MIS_pmf <- function(pmf, actual, alpha) { u <- PMF.get_quantile(pmf, p=1-(alpha/2)) l <- PMF.get_quantile(pmf, p=alpha/2) return(u - l + (2/alpha)*(l - actual)*(actual < l) + @@ -241,20 +267,13 @@ MIS_gauss <- function(mus, sds, actuals, alpha, trunc=FALSE) { return(u - l + (2/alpha)*(l - actuals)*(actuals < l) + (2/alpha)*(actuals - u)*(actuals > u) ) } - -# Function for computing the skill score -skill.score <- function(ref, met) { - s <- (2 * (ref - met) / (ref + met)) * 100 - s[is.na(s)] <- 0 # if both numerator and denominator are 0, set skill score to 0 - return(s) -} ``` ```{r} -### Compute scores for the base forecasts +# Compute scores for the base forecasts # Upper mu_u <- unlist(lapply(base_fc_upper, "[[", "mu")) sd_u <- unlist(lapply(base_fc_upper, "[[", "sigma")) @@ -264,10 +283,10 @@ rps$base[1:n_u] <- scoringRules::crps(actuals_u, "norm", mean=mu_u, sd=sd_u) # Bottom pmfs = lapply(base_fc_bottom, "[[", "pmf") mase$base[(n_u+1):n] <- mapply(AE_pmf, pmfs, actuals_b) / Q_b -mis$base[(n_u+1):n] <- mapply(IS_pmf, pmfs, actuals_b, MoreArgs = list(alpha=alpha)) +mis$base[(n_u+1):n] <- mapply(MIS_pmf, pmfs, actuals_b, MoreArgs = list(alpha=alpha)) rps$base[(n_u+1):n] <- mapply(RPS_pmf, pmfs, actuals_b) -### Compute scores for Gauss reconciliation +# Compute scores for Gauss reconciliation mu <- c(rec_fc$Gauss$mu_u, rec_fc$Gauss$mu_b) sd <- c(diag(rec_fc$Gauss$Sigma_u), diag(rec_fc$Gauss$Sigma_b))**0.5 sd <- sd + jitt @@ -275,16 +294,16 @@ mase$Gauss <- abs(mu - actuals) / Q mis$Gauss <- MIS_gauss(mu, sd, actuals, alpha) rps$Gauss <- scoringRules::crps(actuals, "norm", mean=mu, sd=sd) -### Compute scores for Mix-cond reconciliation +# Compute scores for Mix-cond reconciliation pmfs <- c(rec_fc$Mixed_cond$upper, rec_fc$Mixed_cond$bottom) mase$MixCond <- mapply(AE_pmf, pmfs, actuals) / Q -mis$MixCond <- mapply(IS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) +mis$MixCond <- mapply(MIS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) rps$MixCond <- mapply(RPS_pmf, pmfs, actuals) -### Compute scores for TD-cond reconciliation +# Compute scores for TD-cond reconciliation pmfs <- c(rec_fc$TD_cond$upper, rec_fc$TD_cond$bottom) mase$TDcond <- mapply(AE_pmf, pmfs, actuals) / Q -mis$TDcond <- mapply(IS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) +mis$TDcond <- mapply(MIS_pmf, pmfs, actuals, MoreArgs = list(alpha=alpha)) rps$TDcond <- mapply(RPS_pmf, pmfs, actuals) ``` @@ -297,6 +316,17 @@ $$ \text{Skill}_{\%}\,(\text{RPS, }Gauss) = 100 \cdot \frac{\text{RPS}(base) - \text{RPS}(Gauss)} {(\text{RPS}(base) + \text{RPS}(Gauss))/2}$$ +This formula is implemented in the function `skill.score`, available in the source code of the vignette but not shown here. + +```{r,include=FALSE} +# Function for computing the skill score +skill.score <- function(ref, met) { + s <- (2 * (ref - met) / (ref + met)) * 100 + s[is.na(s)] <- 0 # if both numerator and denominator are 0, set skill score to 0 + return(s) +} +``` + ```{r} scores <- list( @@ -309,6 +339,7 @@ scores_ = names(scores) ref_met <- "base" methods_ <- c("Gauss", "MixCond", "TDcond") +# For each score and method we compute the skill score with respect to the base forecasts skill_scores <- list() for (s in scores_) { skill_scores[[s]] <- list() @@ -321,56 +352,91 @@ for (s in scores_) { } ``` +We report in the tables below the mean values for each skill score. + + +```{r} +mean_skill_scores <- list() + +for (s in scores_) { + mean_skill_scores[[s]] <- rbind(data.frame(lapply(skill_scores[[s]][["upper"]], mean)), + data.frame(lapply(skill_scores[[s]][["bottom"]], mean)) + ) + rownames(mean_skill_scores[[s]]) <- c("upper","bottom") +} +``` + +```{r} +knitr::kable(mean_skill_scores$mase,digits = 2,caption = "Mean skill score on MASE.",align = 'lccc') +``` +The mean MASE skill score is positive only for the TD-cond reconciliation. Both Mix-cond and Gauss achieve scores lower than the base forecasts, even if Mix-cond degrades less the base forecasts compared to Gauss. + +```{r} +knitr::kable(mean_skill_scores$mis,digits = 2,caption = "Mean skill score on MIS.") +``` +The mean MIS score of TD-cond is slightly above that of the base forecasts. Mix-cond achieves slightly higher scores than the base forecasts only on the bottom variables. Gauss strongly degrades the base forecasts according to this metric. + +```{r} +knitr::kable(mean_skill_scores$rps,digits = 2,caption = "Mean skill score on RPS.") +``` +The mean RPS skill score for TD-cond is positive for both upper and bottom time series. Mix-cond slightly improves the base forecasts on the bottom variables, however it degrades the upper base forecasts. Gauss strongly degrades both upper and bottom base forecasts. + +The computational time required for the Gaussian reconciliation is `r Gauss_time` seconds, Mix-cond requires `r MixCond_time` seconds and TD-cond requires `r TDCond_time` seconds. + ## Boxplots -Finally, we show a comparison of the skill scores for each method divided in upper and bottom level. +Finally, we show the boxplots of the skill scores for each method divided in upper and bottom level. -```{r,fig.width=10,fig.height=5} +```{r,fig.width=7,fig.height=8} custom_colors <- c("#a8a8e4", "#a9c7e4", "#aae4df") # Boxplots of MASE skill scores -par(mfrow = c(1, 2)) +par(mfrow = c(2, 1)) boxplot(skill_scores$mase$upper, main = "MASE upper time series", - col = custom_colors, ylim = c(-200,50)) + col = custom_colors, ylim = c(-80,80)) abline(h=0,lty=3) boxplot(skill_scores$mase$bottom, main = "MASE bottom time series", - col = custom_colors, ylim = c(-200,50)) + col = custom_colors, ylim = c(-200,200)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` +Both Mix-cond and TD-cond do not improve the bottom MASE over the base forecasts (boxplot flattened on the value zero), however TD-cond provides a slight improvement over the upper base forecast (boxplot over the zero line). - -```{r,fig.width=10,fig.height=5} +```{r,fig.width=7,fig.height=8} # Boxplots of MIS skill scores -par(mfrow = c(1, 2)) +par(mfrow = c(2, 1)) boxplot(skill_scores$mis$upper, main = "MIS upper time series", - col = custom_colors, ylim = c(-150,50)) + col = custom_colors, ylim = c(-150,150)) abline(h=0,lty=3) boxplot(skill_scores$mis$bottom, main = "MIS bottom time series", - col = custom_colors, ylim = c(-150,50)) + col = custom_colors, ylim = c(-200,200)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` +Both Mix-cond and TD-cond do not improve nor degrade the bottom base forecasts in MIS score as shown by the small boxplots centered around zero. On the upper variables instead only TD-cond does not degrade the MIS score of the base forecasts. + -```{r,fig.width=10,fig.height=5} +```{r,fig.width=7,fig.height=8} # Boxplots of RPS skill scores -par(mfrow = c(1, 2)) +par(mfrow = c(2,1)) boxplot(skill_scores$rps$upper, main = "RPS upper time series", - col = custom_colors, ylim = c(-120,50)) + col = custom_colors, ylim = c(-80,80)) abline(h=0,lty=3) boxplot(skill_scores$rps$bottom, main = "RPS bottom time series", - col = custom_colors, ylim = c(-120,50)) + col = custom_colors, ylim = c(-200,200)) abline(h=0,lty=3) ``` ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` +According to RPS, TD-cond does not degrade the bottom base forecasts and improves the upper base forecasts. On the other hand both Gauss and Mix-cond strongly degrade the upper base forecasts. + # Full reproducibility diff --git a/vignettes/reconciliation_properties.Rmd b/vignettes/reconciliation_properties.Rmd index 76f113b..9e5f222 100644 --- a/vignettes/reconciliation_properties.Rmd +++ b/vignettes/reconciliation_properties.Rmd @@ -21,9 +21,9 @@ knitr::opts_chunk$set( # Introduction -This vignette reproduces the results of the paper *Properties of the reconciled distributions for Gaussian and count forecasts* [@zambon2023properties], +This vignette reproduces the results of the paper *Properties of the reconciled distributions for Gaussian and count forecasts* [@zambon2024properties], accepted for publication in the International Journal of Forecasting. -We replicate here the main experiment of the paper (see sec. 3, @zambon2023properties) where we reconcile base forecasts constituted by negative binomial distributions. +We replicate here the main experiment of the paper (see sec. 3, @zambon2024properties) where we reconcile base forecasts constituted by negative binomial distributions. 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diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index f568108..5dc59aa 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -24,28 +24,29 @@ library(bayesRecon) # Introduction -This vignette partially reproduces the results of the paper *Probabilistic reconciliation of mixed-type hierarchical time series* [@zambon2024mixed], -accepted for publication at the 40th Conference on Uncertainty in Artificial Intelligence. -We replicate the reconciliation of the one-step ahead forecasts of one store from the -time series hierarchy of the M5 competition [@MAKRIDAKIS20221325]. -Sec. 5 of the paper presents the results of the experiments conducted on 10 different stores replicated 14 times with rolling one-step ahead forecasts. +This vignette partially reproduces the results of *Probabilistic reconciliation of mixed-type hierarchical time series* [@zambon2024mixed], +published at UAI 2024 (the 40th Conference on Uncertainty in Artificial Intelligence). -# Data and forecasts +In particular, we replicate the reconciliation of the one-step ahead $(h=1)$ forecasts of one store of the M5 competition [@MAKRIDAKIS20221325]. +Sec. 5 of the paper presents the results for 10 stores, each reconciled +14 times using rolling one-step ahead forecasts. -The M5 competition [@MAKRIDAKIS20221325] contains daily time series of sales data referring to 10 different stores. -The hierarchy of each store has the same structure: 3049 bottom time series (single items) and 11 upper time series, -obtained by aggregating the items by department, product category, and store. The figure below shows the hierarchy of a store. + +# Data and base forecasts + +The M5 competition [@MAKRIDAKIS20221325] is about daily time series of sales data referring to 10 different stores. +Each store has the same hierarchy: 3049 bottom time series (single items) and 11 upper time series, obtained by aggregating the items by department, product category, and store; see the below. ```{r out.width = '100%', echo = FALSE} knitr::include_graphics("img/M5store_hier.png") ``` -We focus here on the store "CA_1". We computed the one-step ahead $(h=1)$ forecasts for each bottom and upper time series, -i.e. the base forecasts, by using iETS [@svetunkov2023iets], available from the R package smooth [@smooth_pkg]. +We reproduce the results of store "CA_1". The base forecasts $(h=1)$ of bottom and upper time series are stored `M5_CA1_basefc`, available as data in the package. +The base forecast are computed using iETS [@svetunkov2023iets], implemented by the R package smooth [@smooth_pkg]. -The base forecasts are stored in the data `M5_CA1_basefc`, available with this package. ```{r} +data("M5_CA1_basefc") # Hierarchy composed by 3060 time series: 3049 bottom and 11 upper n_b <- 3049 n_u <- 11 @@ -69,11 +70,11 @@ rec_fc <- list( # Gaussian reconciliation -We start by assuming that all forecasts are Gaussian and perform a Gaussian forecast reconciliation -with the function `reconc_gaussian()`. We refer to this method as `Gauss`. Note that this assumption is not correct because the the bottom base forecasts are not Gaussian. - -The upper base forecasts are assumed to be a multivariate Gaussian, whose covariance matrix is estimated from the in-sample residuals, while the bottom base forecasts are assumed to be independent. +We perform first the Gaussian reconciliation (`Gauss`). +It assumes all forecasts to be Gaussian, even though the bottom base forecasts are not Gaussian. +We assume the upper base forecasts to be a multivariate Gaussian, estimating their covariance matrix from the in-sample residuals. We assume instead the bottom base forecasts to be independent. +**giustificare why** ```{r} # Parameters of the upper base forecast distributions @@ -100,10 +101,11 @@ base_forecasts.Sigma[1:n_u,1:n_u] <- Sigma_u base_forecasts.Sigma[(n_u+1):n,(n_u+1):n] <- Sigma_b ``` -The forecast reconciliation is performed with the function `reconc_gaussian()` which takes as input +We reconcile the forecast using the function `reconc_gaussian()` which takes as input: -* the matrix `S`, the base forecast means; -* `base_forecasts.mu` and the base forecast covariance `base_forecasts.Sigma`. +* the summing matrix `S`; +* the means of the base forecast, `base_forecasts.mu`; +* the covariance of the base forecast, `base_forecasts.Sigma`. The function returns the reconciled mean and covariance for the bottom time series. From 456ab76ce951dc803fc62a18aa174a1dc59e0ec6 Mon Sep 17 00:00:00 2001 From: gcorani Date: Sat, 25 May 2024 17:42:57 +0200 Subject: [PATCH 30/36] minor fixes to text --- vignettes/bayesRecon_cache/html/__packages | 1 + 1 file changed, 1 insertion(+) create mode 100644 vignettes/bayesRecon_cache/html/__packages diff --git a/vignettes/bayesRecon_cache/html/__packages b/vignettes/bayesRecon_cache/html/__packages new file mode 100644 index 0000000..0b55a29 --- /dev/null +++ b/vignettes/bayesRecon_cache/html/__packages @@ -0,0 +1 @@ +bayesRecon From 598071c91dff12b35d56247f148a2492101bbc5c Mon Sep 17 00:00:00 2001 From: gcorani Date: Sat, 25 May 2024 18:02:00 +0200 Subject: [PATCH 31/36] minor edit to the text --- vignettes/mixed_reconciliation.Rmd | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index 5dc59aa..db545f6 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -73,8 +73,8 @@ rec_fc <- list( We perform first the Gaussian reconciliation (`Gauss`). It assumes all forecasts to be Gaussian, even though the bottom base forecasts are not Gaussian. -We assume the upper base forecasts to be a multivariate Gaussian, estimating their covariance matrix from the in-sample residuals. We assume instead the bottom base forecasts to be independent. -**giustificare why** +We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussian. + ```{r} # Parameters of the upper base forecast distributions @@ -121,18 +121,19 @@ rec_fc$Gauss <- list(mu_b = gauss$bottom_reconciled_mean, Sigma_u = A %*% gauss$bottom_reconciled_covariance %*% t(A)) Gauss_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) -cat("Computational time for Gaussian reconciliation: ", Gauss_time, "s") +cat("Time taken by Gaussian reconciliation: ", Gauss_time, "s") ``` # Reconciliation with mixed-conditioning -We now reconcile the forecasts with the algorithm Mix-cond described in [@zambon2024mixed], Sec. 4. +We now reconcile the forecasts using the mixed-conditioning +approach of [@zambon2024mixed], Sec. 4. The algorithm is implemented in the function `reconc_MixCond()`. The function takes as input -* the `S` matrix; -* the probability mass functions of each bottom base forecast, stored in the list `fc_bottom_4rec`; -* the parameters of the multivariate Gaussian distribution for the upper variables, `fc_upper_4rec`; +* the summing matrix `S`; +* the probability mass functions of the bottom base forecast, stored in the list `fc_bottom_4rec`; +* the parameters of the multivariate Gaussian distribution for the upper variables, `fc_upper_4rec`. * additional function parameters, among those note that `num_samples` specifies the number of samples used in the internal importance sampling (IS) algorithm. The parameter `return_type` can be changed to `samples` or `all` to obtain the IS samples. The function returns the reconciled forecasts in the form of probability mass functions for both the upper and bottom time series. From 64c17796aff596f089b898d3daab4c6a36526e41 Mon Sep 17 00:00:00 2001 From: gcorani Date: Mon, 27 May 2024 11:02:44 +0200 Subject: [PATCH 32/36] minor edit to the text --- vignettes/mixed_reconciliation.Rmd | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index db545f6..0e40543 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -162,12 +162,14 @@ MixCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) cat("Computational time for Mix-cond reconciliation: ", MixCond_time, "s") ``` -As discussed in [@zambon2024mixed], Sec. 4, Mix-cond performs poorly in high dimensions and it does not handle well high incoherence between the joint bottom-up and the upper base forecasts, see also [@zambon2024mixed], fig. 3, for a graphical example. - -# Reconciliation with top down conditioning - -In [@zambon2024mixed], Sec. 5, TD-cond is proposed as a reliable alternative in high dimensions. -This algorithm is implemented in the function `reconc_TDcond()`. The function takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. +As discussed in [@zambon2024mixed], Sec. 4, conditioning with mixed variables performs poorly in high dimensions. +This is because the bottom-up distribution, built by assuming the bottom forecast to be independent, is untenable +in high dimensions. +Moreover, forecast for count time series are usually biased and their sum tend to be strongly biased; see [@zambon2024mixed], fig. 3, for a graphical example. + +# Top down conditioning +For this reason, we introduced top down conditioning (TD-cond; see [@zambon2024mixed], Sec. 5) as a more reliable approach for reconciliation with mixed variables in high dimensions. +The algorithm is implemented by the function `reconc_TDcond()`; it takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. ```{r} N_samples_TD <- 1e4 From 8bb1f5ec71c46d0bd82dfae60b74f377962932ac Mon Sep 17 00:00:00 2001 From: gcorani Date: Mon, 27 May 2024 12:03:30 +0200 Subject: [PATCH 33/36] minor edit to the text --- vignettes/mixed_reconciliation.Rmd | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index 0e40543..b212e79 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -46,7 +46,6 @@ The base forecast are computed using iETS [@svetunkov2023iets], implemented by ```{r} -data("M5_CA1_basefc") # Hierarchy composed by 3060 time series: 3049 bottom and 11 upper n_b <- 3049 n_u <- 11 @@ -70,10 +69,10 @@ rec_fc <- list( # Gaussian reconciliation -We perform first the Gaussian reconciliation (`Gauss`). +We perform Gaussian reconciliation (`Gauss`) first. It assumes all forecasts to be Gaussian, even though the bottom base forecasts are not Gaussian. -We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussian. +We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussian. ```{r} @@ -168,7 +167,7 @@ in high dimensions. Moreover, forecast for count time series are usually biased and their sum tend to be strongly biased; see [@zambon2024mixed], fig. 3, for a graphical example. # Top down conditioning -For this reason, we introduced top down conditioning (TD-cond; see [@zambon2024mixed], Sec. 5) as a more reliable approach for reconciliation with mixed variables in high dimensions. +Top down conditioning (TD-cond; see [@zambon2024mixed], Sec. 5) is a more reliable approach for reconciling mixed variables in high dimensions. The algorithm is implemented by the function `reconc_TDcond()`; it takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. ```{r} From 6eff3d32dffacda28d60d2e77888fcaeb08dbdea Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Mon, 27 May 2024 17:32:30 +0200 Subject: [PATCH 34/36] Minor changes to references for the vignettes --- vignettes/mixed_reconciliation.Rmd | 2 +- vignettes/references.bib | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index b212e79..87f6e84 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -69,7 +69,7 @@ rec_fc <- list( # Gaussian reconciliation -We perform Gaussian reconciliation (`Gauss`) first. +We perform Gaussian reconciliation (`Gauss`, [@corani2021probabilistic]) first. It assumes all forecasts to be Gaussian, even though the bottom base forecasts are not Gaussian. We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussian. diff --git a/vignettes/references.bib b/vignettes/references.bib index d751f4d..9aa27e4 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -130,7 +130,8 @@ @InProceedings{corani2021probabilistic booktitle="Machine Learning and Knowledge Discovery in Databases", year="2021", publisher="Springer International Publishing", - pages="211--226" + pages="211--226", + doi="10.1007/978-3-030-67664-3_13" } @article{zambon2022efficient, @@ -191,8 +192,7 @@ @inproceedings{zambon2024mixed title={Probabilistic reconciliation of mixed-type hierarchical time series}, author={Zambon, Lorenzo and Azzimonti, Dario and Rubattu, Nicolò and Corani, Giorgio}, booktitle={The 40th Conference on Uncertainty in Artificial Intelligence}, - year={2024}, - url={https://openreview.net/forum?id=KmbmBlrQkr} + year={2024} } @article{MAKRIDAKIS20221325, @@ -203,7 +203,8 @@ @article{MAKRIDAKIS20221325 pages = {1325-1336}, year = {2022}, author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}, - keywords = {Forecasting competitions, M competitions, Accuracy, Uncertainty, Time series, Retail sales forecasting} + keywords = {Forecasting competitions, M competitions, Accuracy, Uncertainty, Time series, Retail sales forecasting}, + doi = {10.1016/j.ijforecast.2021.07.007} } @article{svetunkov2023iets, @@ -213,7 +214,8 @@ @article{svetunkov2023iets volume={265}, pages={109013}, year={2023}, - publisher={Elsevier} + publisher={Elsevier}, + doi={10.1016/j.ijpe.2023.109013} } From 3c0233e0d3179cb7ba75c87a638b1b2fdae58ddb Mon Sep 17 00:00:00 2001 From: Zambon Lorenzo Gianmaria Date: Tue, 28 May 2024 17:17:13 +0200 Subject: [PATCH 35/36] minor fix vignette and utils --- R/utils.R | 2 +- vignettes/mixed_reconciliation.Rmd | 57 ++++++++++++++++-------------- 2 files changed, 31 insertions(+), 28 deletions(-) diff --git a/R/utils.R b/R/utils.R index 53418e6..f99cab0 100644 --- a/R/utils.R +++ b/R/utils.R @@ -144,7 +144,7 @@ # Check that the samples are discrete .check_discrete_samples <- function(samples) { - if (!isTRUE(all.equal(samples, as.integer(samples)))) { + if (!isTRUE(all.equal(unname(samples), as.integer(samples)))) { stop("Input error: samples are not all discrete") } } diff --git a/vignettes/mixed_reconciliation.Rmd b/vignettes/mixed_reconciliation.Rmd index 87f6e84..7e4e9dc 100644 --- a/vignettes/mixed_reconciliation.Rmd +++ b/vignettes/mixed_reconciliation.Rmd @@ -27,22 +27,22 @@ library(bayesRecon) This vignette partially reproduces the results of *Probabilistic reconciliation of mixed-type hierarchical time series* [@zambon2024mixed], published at UAI 2024 (the 40th Conference on Uncertainty in Artificial Intelligence). -In particular, we replicate the reconciliation of the one-step ahead $(h=1)$ forecasts of one store of the M5 competition [@MAKRIDAKIS20221325]. -Sec. 5 of the paper presents the results for 10 stores, each reconciled +In particular, we replicate the reconciliation of the one-step ahead (h=1) forecasts of one store of the M5 competition [@MAKRIDAKIS20221325]. +Sect. 5 of the paper presents the results for 10 stores, each reconciled 14 times using rolling one-step ahead forecasts. # Data and base forecasts The M5 competition [@MAKRIDAKIS20221325] is about daily time series of sales data referring to 10 different stores. -Each store has the same hierarchy: 3049 bottom time series (single items) and 11 upper time series, obtained by aggregating the items by department, product category, and store; see the below. +Each store has the same hierarchy: 3049 bottom time series (single items) and 11 upper time series, obtained by aggregating the items by department, product category, and store; see the figure below. ```{r out.width = '100%', echo = FALSE} knitr::include_graphics("img/M5store_hier.png") ``` -We reproduce the results of store "CA_1". The base forecasts $(h=1)$ of bottom and upper time series are stored `M5_CA1_basefc`, available as data in the package. -The base forecast are computed using iETS [@svetunkov2023iets], implemented by the R package smooth [@smooth_pkg]. +We reproduce the results of the store "CA_1". The base forecasts (for h=1) of the bottom and upper time series are stored in `M5_CA1_basefc`, available as data in the package. +The base forecast are computed using ADAM [@svetunkov2023iets], implemented in the R package smooth [@smooth_pkg]. ```{r} @@ -69,10 +69,10 @@ rec_fc <- list( # Gaussian reconciliation -We perform Gaussian reconciliation (`Gauss`, [@corani2021probabilistic]) first. +We first perform Gaussian reconciliation (`Gauss`, @corani2021probabilistic). It assumes all forecasts to be Gaussian, even though the bottom base forecasts are not Gaussian. -We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussian. +We assume the upper base forecasts to be a multivariate Gaussian and we estimate their covariance matrix from the in-sample residuals. We assume also the bottom base forecasts to be independent Gaussians. ```{r} @@ -100,7 +100,7 @@ base_forecasts.Sigma[1:n_u,1:n_u] <- Sigma_u base_forecasts.Sigma[(n_u+1):n,(n_u+1):n] <- Sigma_b ``` -We reconcile the forecast using the function `reconc_gaussian()` which takes as input: +We reconcile using the function `reconc_gaussian()`, which takes as input: * the summing matrix `S`; * the means of the base forecast, `base_forecasts.mu`; @@ -127,15 +127,15 @@ cat("Time taken by Gaussian reconciliation: ", Gauss_time, "s") # Reconciliation with mixed-conditioning We now reconcile the forecasts using the mixed-conditioning -approach of [@zambon2024mixed], Sec. 4. -The algorithm is implemented in the function `reconc_MixCond()`. The function takes as input +approach of @zambon2024mixed, Sect. 3. +The algorithm is implemented in the function `reconc_MixCond()`. The function takes as input: * the summing matrix `S`; -* the probability mass functions of the bottom base forecast, stored in the list `fc_bottom_4rec`; -* the parameters of the multivariate Gaussian distribution for the upper variables, `fc_upper_4rec`. -* additional function parameters, among those note that `num_samples` specifies the number of samples used in the internal importance sampling (IS) algorithm. The parameter `return_type` can be changed to `samples` or `all` to obtain the IS samples. +* the probability mass functions of the bottom base forecasts, stored in the list `fc_bottom_4rec`; +* the parameters of the multivariate Gaussian distribution for the upper variables, `fc_upper_4rec`; +* additional function parameters; among those note that `num_samples` specifies the number of samples used in the internal importance sampling (IS) algorithm. -The function returns the reconciled forecasts in the form of probability mass functions for both the upper and bottom time series. +The function returns the reconciled forecasts in the form of probability mass functions for both the upper and bottom time series. The function parameter `return_type` can be changed to `samples` or `all` to obtain the IS samples. ```{r} seed <- 1 @@ -161,14 +161,14 @@ MixCond_time <- as.double(round(difftime(stop, start, units = "secs"), 2)) cat("Computational time for Mix-cond reconciliation: ", MixCond_time, "s") ``` -As discussed in [@zambon2024mixed], Sec. 4, conditioning with mixed variables performs poorly in high dimensions. -This is because the bottom-up distribution, built by assuming the bottom forecast to be independent, is untenable +As discussed in @zambon2024mixed, Sect. 3, conditioning with mixed variables performs poorly in high dimensions. +This is because the bottom-up distribution, built by assuming the bottom forecasts to be independent, is untenable in high dimensions. -Moreover, forecast for count time series are usually biased and their sum tend to be strongly biased; see [@zambon2024mixed], fig. 3, for a graphical example. +Moreover, forecasts for count time series are usually biased and their sum tends to be strongly biased; see @zambon2024mixed, Fig. 3, for a graphical example. # Top down conditioning -Top down conditioning (TD-cond; see [@zambon2024mixed], Sec. 5) is a more reliable approach for reconciling mixed variables in high dimensions. -The algorithm is implemented by the function `reconc_TDcond()`; it takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. +Top down conditioning (TD-cond; see @zambon2024mixed, Sect. 4) is a more reliable approach for reconciling mixed variables in high dimensions. +The algorithm is implemented in the function `reconc_TDcond()`; it takes the same arguments as `reconc_MixCond()` and returns reconciled forecasts in the same format. ```{r} N_samples_TD <- 1e4 @@ -180,7 +180,7 @@ td <- reconc_TDcond(S, fc_bottom_4rec, fc_upper_4rec, return_type = "pmf", seed = seed) stop <- Sys.time() ``` -The algorithm TD-cond returns a warning regarding the incoherence between the joint bottom-up and the upper base forecasts. +The algorithm TD-cond raises a warning regarding the incoherence between the joint bottom-up and the upper base forecasts. We will see that this warning does not impact the performances of TD-cond. ```{r} @@ -196,6 +196,8 @@ cat("Computational time for TD-cond reconciliation: ", TDCond_time, "s") # Comparison +The computational time required for the Gaussian reconciliation is `r Gauss_time` seconds, Mix-cond requires `r MixCond_time` seconds and TD-cond requires `r TDCond_time` seconds. + For each time series in the hierarchy, we compute the following scores for each method: - MASE: Mean Absolute Scaled Error @@ -227,9 +229,9 @@ rps <- list() The following functions are used for computing the scores: -* `AE_pmf`: compute the absolute error from a PMF; -* `MIS_pmf`: compute interval score from a PMF; -* `RPS_pmf`: compute RPS from a PMF; +* `AE_pmf`: compute the absolute error for a PMF; +* `MIS_pmf`: compute interval score for a PMF; +* `RPS_pmf`: compute RPS for a PMF; * `MIS_gauss`: compute MIS for a Gaussian distribution. The implementation of these functions is available in the source code of the vignette but not shown here. @@ -385,11 +387,9 @@ knitr::kable(mean_skill_scores$rps,digits = 2,caption = "Mean skill score on RPS ``` The mean RPS skill score for TD-cond is positive for both upper and bottom time series. Mix-cond slightly improves the base forecasts on the bottom variables, however it degrades the upper base forecasts. Gauss strongly degrades both upper and bottom base forecasts. -The computational time required for the Gaussian reconciliation is `r Gauss_time` seconds, Mix-cond requires `r MixCond_time` seconds and TD-cond requires `r TDCond_time` seconds. - ## Boxplots -Finally, we show the boxplots of the skill scores for each method divided in upper and bottom level. +Finally, we show the boxplots of the skill scores for each method divided in upper and bottom levels. ```{r,fig.width=7,fig.height=8} custom_colors <- c("#a8a8e4", @@ -408,7 +408,8 @@ abline(h=0,lty=3) ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` -Both Mix-cond and TD-cond do not improve the bottom MASE over the base forecasts (boxplot flattened on the value zero), however TD-cond provides a slight improvement over the upper base forecast (boxplot over the zero line). + +Both Mix-cond and TD-cond do not improve the bottom MASE over the base forecasts (boxplot flattened on the value zero), however TD-cond provides a slight improvement over the upper base forecasts (boxplot over the zero line). ```{r,fig.width=7,fig.height=8} # Boxplots of MIS skill scores @@ -423,6 +424,7 @@ abline(h=0,lty=3) ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` + Both Mix-cond and TD-cond do not improve nor degrade the bottom base forecasts in MIS score as shown by the small boxplots centered around zero. On the upper variables instead only TD-cond does not degrade the MIS score of the base forecasts. @@ -439,6 +441,7 @@ abline(h=0,lty=3) ```{r,eval=TRUE,include=FALSE} par(mfrow = c(1, 1)) ``` + According to RPS, TD-cond does not degrade the bottom base forecasts and improves the upper base forecasts. On the other hand both Gauss and Mix-cond strongly degrade the upper base forecasts. From b9a0a30bd46a234bd9621207756002dca627fb6d Mon Sep 17 00:00:00 2001 From: Dario Azzimonti Date: Wed, 29 May 2024 17:15:25 +0200 Subject: [PATCH 36/36] Final fixes to vignette, bumped package version, updated ReadMe, DESCRIPTION and NEWS. --- DESCRIPTION | 4 ++-- NEWS.md | 16 ++++++++++++++-- README.Rmd | 2 +- README.md | 3 ++- man/figures/README-unnamed-chunk-6-1.png | Bin 4754 -> 24483 bytes man/figures/README-unnamed-chunk-7-1.png | Bin 4501 -> 27861 bytes vignettes/mixed_reconciliation.Rmd | 7 ++++--- 7 files changed, 23 insertions(+), 9 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2b58eed..4e08c4a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: bayesRecon Type: Package -Date: 2023-12-19 +Date: 2024-05-29 Title: Probabilistic Reconciliation via Conditioning -Version: 0.2.0 +Version: 0.3.0 Authors@R: c(person(given = "Dario", family = "Azzimonti", role = c("aut","cre"), diff --git a/NEWS.md b/NEWS.md index 4cb856a..d1cbfdd 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,8 +1,20 @@ +# bayesRecon 0.3.0 + +* Added `reconc_MixCond`, the implementation of Mixed conditioning for the reconciliation of mixed-type hierarchical forecasts. + +* Added `reconc_TDcond`, the implementation of Top-down conditioning for the reconciliation of mixed-type hierarchical forecasts. + +* Vignette "Reconciliation of M5 hierarchy with mixed-type forecasts". + +* Added functions `PMF.get_mean`, `PMF.get_quantile`, `PMF.get_var`, `PMF.sample`, `PMF.summary` that return point estimates and samples from a probability mass function object. + +* Added the dataset `M5_CA1_basefc` which contains the base forecasts for the store "CA1" of the hierarchical time series in the M5 competition. + # bayesRecon 0.2.0 -* Vignette "Properties of the reconciled distribution via conditioning" +* Vignette "Properties of the reconciled distribution via conditioning". -* Added option in `reconc_BUIS` to pass some base forecast as parameters and some as samples +* Added option in `reconc_BUIS` to pass some base forecast as parameters and some as samples. * Added option in `reconc_BUIS` to input a list of distributions instead of a string. diff --git a/README.Rmd b/README.Rmd index 6f3002b..759620f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -41,7 +41,7 @@ The main functions are: multivariate Gaussian and the bottom forecasts are discrete distributions. ## News -:boom: [2024-05-24] Added `reconc_MixCond` and `reconc_TDcond`. +:boom: [2024-05-29] Added `reconc_MixCond` and `reconc_TDcond` and the vignette "Reconciliation of M5 hierarchy with mixed-type forecasts". :boom: [2023-12-19] Added the vignette "Properties of the reconciled distribution via conditioning". diff --git a/README.md b/README.md index 8a75905..ea2b080 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,8 @@ The main functions are: ## News -:boom: \[2024-05-24\] Added `reconc_MixCond` and `reconc_TDcond`. +:boom: \[2024-05-29\] Added `reconc_MixCond` and `reconc_TDcond` and the +vignette 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