diff --git a/DESCRIPTION b/DESCRIPTION index 5fdb524d..174bcbb9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,35 +1,35 @@ -Package: loo Type: Package +Package: loo Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models Version: 2.8.0.9000 Date: 2024-07-03 -Authors@R: c(person("Aki", "Vehtari", email = "Aki.Vehtari@aalto.fi", role = c("aut")), - person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = c("cre", "aut")), - person("Måns", "Magnusson", role = c("aut")), - person("Yuling", "Yao", role = c("aut")), - person("Paul-Christian", "Bürkner", role = c("aut")), - person("Topi", "Paananen", role = c("aut")), - person("Andrew", "Gelman", role = c("aut")), - person("Ben", "Goodrich", role = c("ctb")), - person("Juho", "Piironen", role = c("ctb")), - person("Bruno", "Nicenboim", role = c("ctb")), - person("Leevi", "Lindgren", role = c("ctb"))) +Authors@R: c( + person("Aki", "Vehtari", , "Aki.Vehtari@aalto.fi", role = "aut"), + person("Jonah", "Gabry", , "jsg2201@columbia.edu", role = c("cre", "aut")), + person("Måns", "Magnusson", role = "aut"), + person("Yuling", "Yao", role = "aut"), + person("Paul-Christian", "Bürkner", role = "aut"), + person("Topi", "Paananen", role = "aut"), + person("Andrew", "Gelman", role = "aut"), + person("Ben", "Goodrich", role = "ctb"), + person("Juho", "Piironen", role = "ctb"), + person("Bruno", "Nicenboim", role = "ctb"), + person("Leevi", "Lindgren", role = "ctb") + ) Maintainer: Jonah Gabry -URL: https://mc-stan.org/loo/, https://discourse.mc-stan.org -BugReports: https://github.com/stan-dev/loo/issues Description: Efficient approximate leave-one-out cross-validation (LOO) - for Bayesian models fit using Markov chain Monte Carlo, as - described in Vehtari, Gelman, and Gabry (2017) - . - The approximation uses Pareto smoothed importance sampling (PSIS), - a new procedure for regularizing importance weights. - As a byproduct of the calculations, we also obtain approximate - standard errors for estimated predictive errors and for the comparison - of predictive errors between models. The package also provides methods - for using stacking and other model weighting techniques to average - Bayesian predictive distributions. + for Bayesian models fit using Markov chain Monte Carlo, as described + in Vehtari, Gelman, and Gabry (2017) . + The approximation uses Pareto smoothed importance sampling (PSIS), a + new procedure for regularizing importance weights. As a byproduct of + the calculations, we also obtain approximate standard errors for + estimated predictive errors and for the comparison of predictive + errors between models. The package also provides methods for using + stacking and other model weighting techniques to average Bayesian + predictive distributions. License: GPL (>=3) -LazyData: TRUE +URL: https://mc-stan.org/loo/, https://discourse.mc-stan.org +BugReports: https://github.com/stan-dev/loo/issues Depends: R (>= 3.1.2) Imports: @@ -50,8 +50,13 @@ Suggests: rstantools, spdep, testthat (>= 2.1.0) -VignetteBuilder: knitr +VignetteBuilder: + knitr +Config/testthat/edition: 3 +Config/testthat/parallel: true +Config/testthat/start-first: loo_subsampling_cases, loo_subsampling Encoding: UTF-8 -SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc -RoxygenNote: 7.3.2 +LazyData: TRUE Roxygen: list(markdown = TRUE) +RoxygenNote: 7.3.2 +SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc diff --git a/R/effective_sample_sizes.R b/R/effective_sample_sizes.R index 855bef38..360a3098 100644 --- a/R/effective_sample_sizes.R +++ b/R/effective_sample_sizes.R @@ -63,14 +63,14 @@ relative_eff.array <- function(x, ..., cores = getOption("mc.cores", 1)) { S <- prod(dim(x)[1:2]) # posterior sample size = iter * chains if (cores == 1) { - n_eff_vec <- apply(x, 3, ess_rfun) + n_eff_vec <- apply(x, 3, posterior::ess_mean) } else { if (!os_is_windows()) { n_eff_list <- parallel::mclapply( mc.cores = cores, X = seq_len(dim(x)[3]), - FUN = function(i) ess_rfun(x[, , i, drop = TRUE]) + FUN = function(i) posterior::ess_mean(x[, , i, drop = TRUE]) ) } else { cl <- parallel::makePSOCKcluster(cores) @@ -79,7 +79,7 @@ relative_eff.array <- function(x, ..., cores = getOption("mc.cores", 1)) { parallel::parLapply( cl = cl, X = seq_len(dim(x)[3]), - fun = function(i) ess_rfun(x[, , i, drop = TRUE]) + fun = function(i) posterior::ess_mean(x[, , i, drop = TRUE]) ) } n_eff_vec <- unlist(n_eff_list, use.names = FALSE) @@ -192,82 +192,3 @@ psis_n_eff.matrix <- function(w, r_eff = NULL, ...) { } 1 / ss * r_eff } - -#' MCMC effective sample size calculation -#' -#' @noRd -#' @param sims An iterations by chains matrix of draws for a single parameter. -#' In the case of the **loo** package, this will be the **exponentiated** -#' log-likelihood values for the ith observation. -#' @return MCMC effective sample size based on RStan's calculation. -#' -ess_rfun <- function(sims) { - if (is.vector(sims)) dim(sims) <- c(length(sims), 1) - chains <- ncol(sims) - n_samples <- nrow(sims) - acov <- lapply(1:chains, FUN = function(i) posterior::autocovariance(sims[,i])) - acov <- do.call(cbind, acov) - chain_mean <- colMeans(sims) - mean_var <- mean(acov[1,]) * n_samples / (n_samples - 1) - var_plus <- mean_var * (n_samples - 1) / n_samples - if (chains > 1) - var_plus <- var_plus + var(chain_mean) - # Geyer's initial positive sequence - rho_hat_t <- rep.int(0, n_samples) - t <- 0 - rho_hat_even <- 1 - rho_hat_t[t + 1] <- rho_hat_even - rho_hat_odd <- 1 - (mean_var - mean(acov[t + 2, ])) / var_plus - rho_hat_t[t + 2] <- rho_hat_odd - while (t < nrow(acov) - 5 && !is.nan(rho_hat_even + rho_hat_odd) && - (rho_hat_even + rho_hat_odd > 0)) { - t <- t + 2 - rho_hat_even = 1 - (mean_var - mean(acov[t + 1, ])) / var_plus - rho_hat_odd = 1 - (mean_var - mean(acov[t + 2, ])) / var_plus - if ((rho_hat_even + rho_hat_odd) >= 0) { - rho_hat_t[t + 1] <- rho_hat_even - rho_hat_t[t + 2] <- rho_hat_odd - } - } - max_t <- t - # this is used in the improved estimate - if (rho_hat_even>0) - rho_hat_t[max_t + 1] <- rho_hat_even - - # Geyer's initial monotone sequence - t <- 0 - while (t <= max_t - 4) { - t <- t + 2 - if (rho_hat_t[t + 1] + rho_hat_t[t + 2] > - rho_hat_t[t - 1] + rho_hat_t[t]) { - rho_hat_t[t + 1] = (rho_hat_t[t - 1] + rho_hat_t[t]) / 2; - rho_hat_t[t + 2] = rho_hat_t[t + 1]; - } - } - ess <- chains * n_samples - # Geyer's truncated estimate - # tau_hat <- -1 + 2 * sum(rho_hat_t[1:max_t]) - # Improved estimate reduces variance in antithetic case - tau_hat <- -1 + 2 * sum(rho_hat_t[1:max_t]) + rho_hat_t[max_t+1] - # Safety check for negative values and with max ess equal to ess*log10(ess) - tau_hat <- max(tau_hat, 1/log10(ess)) - ess <- ess / tau_hat - ess -} - - -fft_next_good_size <- function(N) { - # Find the optimal next size for the FFT so that - # a minimum number of zeros are padded. - if (N <= 2) - return(2) - while (TRUE) { - m = N - while ((m %% 2) == 0) m = m / 2 - while ((m %% 3) == 0) m = m / 3 - while ((m %% 5) == 0) m = m / 5 - if (m <= 1) - return(N) - N = N + 1 - } -} diff --git a/tests/testthat/_snaps/E_loo.md b/tests/testthat/_snaps/E_loo.md new file mode 100644 index 00000000..db14b331 --- /dev/null +++ b/tests/testthat/_snaps/E_loo.md @@ -0,0 +1,110 @@ +# E_loo.default equal to snapshots + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP5YkyJk2Uw4AAAAOAAAAAT/CCf5d2lYl + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAABXZhbHVlAAQACQAAAAhwYXJl + dG9fawAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+99QfBwyEoAAAAOAAAAAT/CwzqDy8zd + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAABXZhbHVlAAQACQAAAAhwYXJl + dG9fawAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+++XajbmJQAAAAOAAAAAT/CwzqDy8zd + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAABXZhbHVlAAQACQAAAAhwYXJl + dG9fawAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP5Q/UH4+kokAAAAOAAAAAT+pC/0t4hZY + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAABXZhbHVlAAQACQAAAAhwYXJl + dG9fawAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAADv/RdTp/OKVA/lD9Qfj6SiT/0B+XkzCLI + AAAADgAAAAE/qQv9LeIWWAAABAIAAAABAAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAV2 + YWx1ZQAEAAkAAAAIcGFyZXRvX2sAAAD+ + +# E_loo.matrix equal to snapshots + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAAgP5YkyJk2Uw4/qWAe5+slHb+UGxtRYEj6 + v3phLSMHz52/odiRUA18Oz+j5kMZY+OZv6UiBYpZA1c/gOCbIdkALD+aGlgT7KOMv6le704r + 1Mk/lQ5ZQDf3uj+ewlXQr8iOv7Cx0vAv3x8/qBb/vlaNmD+RVknGML4AP5YmXOYjb6E/oOks + rHS+3b+SlEsEisU6v5de/GugPA6/poyaPupPOr+GCwyWu+Dpv3vFGSbfabK/mtljUf0Mlz+K + vNxsu3xMv6N5GBqP57a/jGhAZ3mD+L+mlNu35k+lP3ecrpcfLVC/dzGNuHKBtD+g/BS/OEnN + v4Igw4FPLoM/WQ0Eo+5f+gAAAA4AAAAgP8IJ/l3aViU/pca2RdOM3j+/8rXkBqCrv6pIWFgx + 3Ro/zBDTTM3Ckz+e+pZvzROmP8SF8jCU81Y/2kxN82LpUD+ht5lbUInzP8PjBotEZAk/sBld + 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5tcuLT5aP9i6DzMC02U/1PsEKxeyET+/YoKHfssaP80BWgAOvZg/uJULoaFQPT/I8p50vNhw + P9eBi3nBuZc/x4JCmLKJjj+5nmB9gzl/P9R81IdrKFQ/w+sDN0nZGz+1z55LjIrMP8TX8Aiv + VNQ/zWAJ7qX34gAABAIAAAABAAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAV2YWx1ZQAE + AAkAAAAIcGFyZXRvX2sAAAD+ + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAAgP+++XajbmJQ/8DEU/8Lr5j/vVZ8WRY5+ + P++kqukYCOA/8BCb8nKMzD/vzc6xkMxBP/AVF/p+31Y/8BNphxvXqz/woUx0rrHkP++C95Ag + m9k/8CRmBO/JwD/vr9aTD09nP/Aw1oOFlzI/7+OUeVFCtz/vpJF6/sziP/AHWiqornE/8H4z + /wmU2T/v7IYye0D5P+9+EUnmiwk/7tm0BTqf0j/wLp9qBaS7P+/zccuuIkY/8BoJ2GEYeT/v + cJAOK3HZP/DQ66mN/dI/75Jxr/yrxj/vd3o8ODlDP/ACkeSklNY/77OlzUqqMT/wBtkuWosN + P++WgQC9Cqo/75Hxz1uVVwAAAA4AAAAgP8LDOoPLzN0/vC9PGiC2mD/Dw665YwtSv0/zEf9g + gzs/wrKXeFX04D+6oCb1eqeGP9VkIEZUNbU/2kxN82LpUD+ht5lbUInzP8CPzHjviWW/oC1v + Ox8Noz+3SaHHVF8tv5miuj3t2Pk/yY0i+C8iSD/YnY9OInbSP9Gy/iCM9TY/3teTRr7GQT/l + 5tcuLT5aP9i6DzMC02U/1PsEKxeyET+/YoKHfssaP80BWgAOvZg/uJULoaFQPT/I8p50vNhw + P9eBi3nBuZc/x4JCmLKJjj+5nmB9gzl/P9R81IdrKFQ/w+sDN0nZGz+1z55LjIrMP8TX8Aiv + VNQ/zWAJ7qX34gAABAIAAAABAAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAV2YWx1ZQAE + AAkAAAAIcGFyZXRvX2sAAAD+ + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAAgP5Q/UH4+kok/rJhyoKzLQ7+mS88H3cZO + v4Diw77SBPK/nJmNhpoF3z+eJuXpnME8v6uKXkbg5Hc/iwobiZz0Yz+CcwHscaAbv7nU2Akt + GX+/kI6eu9QpOj+rntL08OAEv7OnDaPGnrk/qZJuk37o6j+khJ9TfTs9v3VOr8Mp0w4/Y4WW + 2hIb6L+5piRwV8y9v51UE9Vh3d6/vDKUx9urWT+Vff+IHf9IP4PB4HUxxjK/rgu97ZVdnD91 + l2lfiMfQv6ABPz28O8C/sFJbkh7yZL+o2Wq0N3S+P4Zn3piChxE/bj6AYsnnNj93X8jkk/2u + v5UhfPojTuu/pJCpz+t4lwAAAA4AAAAgP6kL/S3iFli/rllsqlAP3D+xkRFa2o0Dv6pIWFgx + 3Rq/vbbHaFe7lL+3wSib9eF2P7e2HDVDyo8/2kxN82LpUD+ht5lbUInzv76dY0fV19G/oC1v + Ox8No7+xUxB4TL1Bv7MRS5NxFNQ/uQOl7kpZID/YnY9OInbSP9Gy/iCM9TY/3teTRr7GQT/V + Uo3UFZa8P9i6DzMC02U/1PsEKxeyET+mimNy3QAxP80BWgAOvZg/uEa4KLePjD/I8p50vNhw + v5z9lPVoNWw/vbA2GxXFDz+ISvs8AG4SP9R81IdrKFQ/w+sDN0nZGz+1z55LjIrMP8TX8Aiv + VNQ/taMpWVYn7wAABAIAAAABAAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAV2YWx1ZQAE + AAkAAAAIcGFyZXRvX2sAAAD+ + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAg4AAABAv/RdTp/OKVA/9Afl5MwiyL/z0FnOG0F+ + P/W3tESq+hG/83dZE3d+GD/zW0+Mmka2v/XZilmZkLg/9Anod0Btdb/0f6W0vqw/P/OZiJ83 + G76/9JCX02s4vD/z/c7djgcNv/TDQRow++g/86vcLJmh6L/0wjA4NlBjP/RMb7F0RzK/9HAc + YG8N8D/1mlKBbWwFv/SagAvlYpw/88LV9Rj1xb/0mH86EFjXP/QlJsXLmXy/9FUITMCdhT/0 + YOU3NGbzv/XZiRsmTKE/8/w2ct9kJL/zwk+t/SPSP/Y2Nkb1kwi/9Jccz9A3RT/z1vUQMbyE + v/Szng+3zeY/9MciBArOKL/0f9xw2jLPP/Vga+lxCxC/8tVQDeo7Az/1p5eybgglv/SJCtxH + yEM/9AVPifNnSr/zjLE8SzISP/Ohe1nttQ6/9S6bPIHJgT/0hd4sDJmuv/RsPduiC1E/89vZ + GzKrar/1FZjeT/k2P/S7KA0QeGi/83Y/x5KlBz/ztNqmF3ywv/ZCUvZXPyI/9PI83KF96L/0 + Ml3NWrX1P/Rnu9LXaiq/9WcGWiOq+D/zkg6tBDLov/T2vDTMsMs/9Og0XHuRtr/04y833CW8 + P/NqXO3hEbG/9HEUw/KPQD/1y+CeGQebv/SL1cuvxX8/9SbAzpRiIb/zufzsmkU1P/S0K91V + 63IAAAQCAAAAAQAEAAkAAAADZGltAAAADQAAAAIAAAACAAAAIAAAAP4AAAAOAAAAID+pC/0t + 4hZYv65ZbKpQD9w/sZERWtqNA7+qSFhYMd0av722x2hXu5S/t8Eom/Xhdj+3thw1Q8qPP9pM + TfNi6VA/obeZW1CJ87++nWNH1dfRv6AtbzsfDaO/sVMQeEy9Qb+zEUuTcRTUP7kDpe5KWSA/ + 2J2PTiJ20j/Rsv4gjPU2P97Xk0a+xkE/1VKN1BWWvD/Yug8zAtNlP9T7BCsXshE/popjct0A + MT/NAVoADr2YP7hGuCi3j4w/yPKedLzYcL+c/ZT1aDVsP72wNhsVxQ8/iEr7PABuEj/UfNSH + ayhUP8PrAzdJ2Rs/tc+eS4yKzD/E1/AIr1TUP7WjKVlWJ+8AAAQCAAAAAQAEAAkAAAAFbmFt + ZXMAAAAQAAAAAgAEAAkAAAAFdmFsdWUABAAJAAAACHBhcmV0b19rAAAA/g== + diff --git a/tests/testthat/_snaps/compare.md b/tests/testthat/_snaps/compare.md new file mode 100644 index 00000000..cf27900e --- /dev/null +++ b/tests/testthat/_snaps/compare.md @@ -0,0 +1,56 @@ +# loo_compare returns expected results (2 models) + + WAoAAAACAAQFAAACAwAAAAMOAAAAEAAAAAAAAAAAwBA6U1+cRe4AAAAAAAAAAD+2ake0LxMB + wFTh8N3JQljAVeWWE8MGuUARCD2zEXBfQBEalRIN2T9ACijAYdW5U0AmZ5XrANCKP/H9Zexy + 814/8ZtgnG1nx0Bk4fDdyUJYQGXllhPDBrlAIQg9sxFwX0AhGpUSDdk/AAAEAgAAAAEABAAJ + AAAAA2RpbQAAAA0AAAACAAAAAgAAAAgAAAQCAAAAAQAEAAkAAAAIZGltbmFtZXMAAAATAAAA + AgAAABAAAAACAAQACQAAAAZtb2RlbDEABAAJAAAABm1vZGVsMgAAABAAAAAIAAQACQAAAAll + bHBkX2RpZmYABAAJAAAAB3NlX2RpZmYABAAJAAAACWVscGRfd2FpYwAEAAkAAAAMc2VfZWxw + ZF93YWljAAQACQAAAAZwX3dhaWMABAAJAAAACXNlX3Bfd2FpYwAEAAkAAAAEd2FpYwAEAAkA + AAAHc2Vfd2FpYwAABAIAAAABAAQACQAAAAVjbGFzcwAAABAAAAADAAQACQAAAAtjb21wYXJl + LmxvbwAEAAkAAAAGbWF0cml4AAQACQAAAAVhcnJheQAAAP4= + +# loo_compare returns expected result (3 models) + + WAoAAAACAAQFAAACAwAAAAMOAAAAGAAAAAAAAAAAwBA6U1+cRe7AMA3KkbYEGAAAAAAAAAAA + P7ZqR7QvEwE/y6/t4TTtXsBU4fDdyUJYwFXllhPDBrnAWOVjgjbDYkARCD2zEXBfQBEalRIN + 2T9AEPIF3GigE0AKKMBh1blTQCZnlesA0IpAQcjYUhrdCj/x/WXscvNeP/GbYJxtZ8c/8YDQ + kmfJX0Bk4fDdyUJYQGXllhPDBrlAaOVjgjbDYkAhCD2zEXBfQCEalRIN2T9AIPIF3GigEwAA + BAIAAAABAAQACQAAAANkaW0AAAANAAAAAgAAAAMAAAAIAAAEAgAAAAEABAAJAAAACGRpbW5h + bWVzAAAAEwAAAAIAAAAQAAAAAwAEAAkAAAAGbW9kZWwxAAQACQAAAAZtb2RlbDIABAAJAAAA + Bm1vZGVsMwAAABAAAAAIAAQACQAAAAllbHBkX2RpZmYABAAJAAAAB3NlX2RpZmYABAAJAAAA + CWVscGRfd2FpYwAEAAkAAAAMc2VfZWxwZF93YWljAAQACQAAAAZwX3dhaWMABAAJAAAACXNl + X3Bfd2FpYwAEAAkAAAAEd2FpYwAEAAkAAAAHc2Vfd2FpYwAABAIAAAABAAQACQAAAAVjbGFz + cwAAABAAAAADAAQACQAAAAtjb21wYXJlLmxvbwAEAAkAAAAGbWF0cml4AAQACQAAAAVhcnJh + eQAAAP4= + +# compare returns expected result (2 models) + + Code + comp1 + Output + elpd_diff se + 0.0 0.0 + +--- + + Code + comp2 + Output + elpd_diff se + -4.1 0.1 + +# compare returns expected result (3 models) + + WAoAAAACAAQFAAACAwAAAAMOAAAAGAAAAAAAAAAAwBA6U1+cRe7AMA3KkbYEGAAAAAAAAAAA + P7ZqR7QvEwE/y6/t4TTtXsBU4fDdyUJYwFXllhPDBrnAWOVjgjbDYkARCD2zEXBfQBEalRIN + 2T9AEPIF3GigE0AKKMBh1blTQCZnlesA0IpAQcjYUhrdCj/x/WXscvNeP/GbYJxtZ8c/8YDQ + kmfJX0Bk4fDdyUJYQGXllhPDBrlAaOVjgjbDYkAhCD2zEXBfQCEalRIN2T9AIPIF3GigEwAA + BAIAAAABAAQACQAAAANkaW0AAAANAAAAAgAAAAMAAAAIAAAEAgAAAAEABAAJAAAACGRpbW5h + bWVzAAAAEwAAAAIAAAAQAAAAAwAEAAkAAAACdzEABAAJAAAAAncyAAQACQAAAAJ3MwAAABAA + AAAIAAQACQAAAAllbHBkX2RpZmYABAAJAAAAB3NlX2RpZmYABAAJAAAACWVscGRfd2FpYwAE + AAkAAAAMc2VfZWxwZF93YWljAAQACQAAAAZwX3dhaWMABAAJAAAACXNlX3Bfd2FpYwAEAAkA + AAAEd2FpYwAEAAkAAAAHc2Vfd2FpYwAABAIAAAABAAQACQAAAAVjbGFzcwAAABAAAAAEAAQA + CQAAAAtjb21wYXJlLmxvbwAEAAkAAAAGbWF0cml4AAQACQAAAAVhcnJheQAEAAkAAAAPb2xk + X2NvbXBhcmUubG9vAAAA/g== + diff --git a/tests/testthat/_snaps/crps.md b/tests/testthat/_snaps/crps.md new file mode 100644 index 00000000..8c6b62d3 --- /dev/null +++ b/tests/testthat/_snaps/crps.md @@ -0,0 +1,32 @@ +# crps matches snapshots + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAg4AAAACv+IeiUeluMc/vSY1w5IjJgAABAIAAAAB + AAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAhFc3RpbWF0ZQAEAAkAAAACU0UAAAD+AAAA + DgAAAAq/zdHHAHZD6L/K3ky9mEk4v/B2uF/xc76/38/N1JUUkL/Z4VW5uFHUv/D3XaD33ZO/ + yPJtFZYXlL/RgM+YTul0v+9F9Rv30V+/6Q2h5ndyqgAABAIAAAH/AAAAEAAAAAIABAAJAAAA + CWVzdGltYXRlcwAEAAkAAAAJcG9pbnR3aXNlAAAA/g== + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAg4AAAACv/ELnetrJtw/uJgUmYONYAAABAIAAAAB + AAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAhFc3RpbWF0ZQAEAAkAAAACU0UAAAD+AAAA + DgAAAAq/6MKGUxChmr/pWmzfnhawv/dXJCu4gh2/7/dH2oOKIL/tR104AbV4v/fX3UkYDWm/ + 5+ZWJz0Y3r/qbdDewNjFv/aQu+wUiEO/89yOK7F4DAAABAIAAAH/AAAAEAAAAAIABAAJAAAA + CWVzdGltYXRlcwAEAAkAAAAJcG9pbnR3aXNlAAAA/g== + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAg4AAAACv+IVUtAszNU/vWbB6hSQFgAABAIAAAAB + AAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAhFc3RpbWF0ZQAEAAkAAAACU0UAAAD+AAAA + DgAAAAq/zKEONUJbrL/KuMhrCsFsv/BrsoFFUDu/31EWnT302L/ZnTwwjfZiv/E78Fc0R+6/ + yM8apxh02L/RTacKeM0kv+6ksQycKl+/6bkMJjTk9gAABAIAAAH/AAAAEAAAAAIABAAJAAAA + CWVzdGltYXRlcwAEAAkAAAAJcG9pbnR3aXNlAAAA/g== + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAg4AAAACv/EL1OkLv00/uNvlkZb58AAABAIAAAAB + AAQACQAAAAVuYW1lcwAAABAAAAACAAQACQAAAAhFc3RpbWF0ZQAEAAkAAAACU0UAAAD+AAAA + DgAAAAq/6JdmIec4s7/pNG4XiJ0yv/diwSdPiFi/77pjBRqbxr/tO+gNa/rMv/glDLlafB6/ + 5+SScRdhOb/qdN1KnaG+v/Y6AtSCgWa/9Ca44XM7bgAABAIAAAH/AAAAEAAAAAIABAAJAAAA + CWVzdGltYXRlcwAEAAkAAAAJcG9pbnR3aXNlAAAA/g== + diff --git a/tests/testthat/_snaps/deprecated_extractors.md b/tests/testthat/_snaps/deprecated_extractors.md new file mode 100644 index 00000000..098e688c --- /dev/null +++ b/tests/testthat/_snaps/deprecated_extractors.md @@ -0,0 +1,384 @@ +# extracting estimates by name is deprecated for loo objects + + Code + loo1$elpd_loo + Condition + Warning: + Accessing elpd_loo using '$' is deprecated and will be removed in a future release. Please extract the elpd_loo estimate from the 'estimates' component instead. + Output + [1] -83.58926 + +--- + + Code + loo1$se_elpd_loo + Condition + Warning: + Accessing se_elpd_loo using '$' is deprecated and will be removed in a future release. Please extract the se_elpd_loo estimate from the 'estimates' component instead. + Output + [1] 4.283835 + +--- + + Code + loo1$p_loo + Condition + Warning: + Accessing p_loo using '$' is deprecated and will be removed in a future release. Please extract the p_loo estimate from the 'estimates' component instead. + Output + [1] 3.328834 + +--- + + Code + loo1$se_p_loo + Condition + Warning: + Accessing se_p_loo using '$' is deprecated and will be removed in a future release. Please extract the se_p_loo estimate from the 'estimates' component instead. + Output + [1] 1.152103 + +--- + + Code + loo1$looic + Condition + Warning: + Accessing looic using '$' is deprecated and will be removed in a future release. Please extract the looic estimate from the 'estimates' component instead. + Output + [1] 167.1785 + +--- + + Code + loo1$se_looic + Condition + Warning: + Accessing se_looic using '$' is deprecated and will be removed in a future release. Please extract the se_looic estimate from the 'estimates' component instead. + Output + [1] 8.567671 + +--- + + Code + loo1["elpd_loo"] + Condition + Warning: + Accessing elpd_loo using '[' is deprecated and will be removed in a future release. Please extract the elpd_loo estimate from the 'estimates' component instead. + Output + $elpd_loo + [1] -83.58926 + + +--- + + Code + loo1["se_elpd_loo"] + Condition + Warning: + Accessing se_elpd_loo using '[' is deprecated and will be removed in a future release. Please extract the se_elpd_loo estimate from the 'estimates' component instead. + Output + $se_elpd_loo + [1] 4.283835 + + +--- + + Code + loo1["p_loo"] + Condition + Warning: + Accessing p_loo using '[' is deprecated and will be removed in a future release. Please extract the p_loo estimate from the 'estimates' component instead. + Output + $p_loo + [1] 3.328834 + + +--- + + Code + loo1["se_p_loo"] + Condition + Warning: + Accessing se_p_loo using '[' is deprecated and will be removed in a future release. Please extract the se_p_loo estimate from the 'estimates' component instead. + Output + $se_p_loo + [1] 1.152103 + + +--- + + Code + loo1["looic"] + Condition + Warning: + Accessing looic using '[' is deprecated and will be removed in a future release. Please extract the looic estimate from the 'estimates' component instead. + Output + $looic + [1] 167.1785 + + +--- + + Code + loo1["se_looic"] + Condition + Warning: + Accessing se_looic using '[' is deprecated and will be removed in a future release. Please extract the se_looic estimate from the 'estimates' component instead. + Output + $se_looic + [1] 8.567671 + + +--- + + Code + loo1[["elpd_loo"]] + Condition + Warning: + Accessing elpd_loo using '[[' is deprecated and will be removed in a future release. Please extract the elpd_loo estimate from the 'estimates' component instead. + Output + [1] -83.58926 + +--- + + Code + loo1[["se_elpd_loo"]] + Condition + Warning: + Accessing se_elpd_loo using '[[' is deprecated and will be removed in a future release. Please extract the se_elpd_loo estimate from the 'estimates' component instead. + Output + [1] 4.283835 + +--- + + Code + loo1[["p_loo"]] + Condition + Warning: + Accessing p_loo using '[[' is deprecated and will be removed in a future release. Please extract the p_loo estimate from the 'estimates' component instead. + Output + [1] 3.328834 + +--- + + Code + loo1[["se_p_loo"]] + Condition + Warning: + Accessing se_p_loo using '[[' is deprecated and will be removed in a future release. Please extract the se_p_loo estimate from the 'estimates' component instead. + Output + [1] 1.152103 + +--- + + Code + loo1[["looic"]] + Condition + Warning: + Accessing looic using '[[' is deprecated and will be removed in a future release. Please extract the looic estimate from the 'estimates' component instead. + Output + [1] 167.1785 + +--- + + Code + loo1[["se_looic"]] + Condition + Warning: + Accessing se_looic using '[[' is deprecated and will be removed in a future release. Please extract the se_looic estimate from the 'estimates' component instead. + Output + [1] 8.567671 + +# extracting estimates by name is deprecated for waic objects + + Code + waic1$elpd_waic + Condition + Warning: + Accessing elpd_waic using '$' is deprecated and will be removed in a future release. Please extract the elpd_waic estimate from the 'estimates' component instead. + Output + [1] -83.53033 + +--- + + Code + waic1$se_elpd_waic + Condition + Warning: + Accessing se_elpd_waic using '$' is deprecated and will be removed in a future release. Please extract the se_elpd_waic estimate from the 'estimates' component instead. + Output + [1] 4.258048 + +--- + + Code + waic1$p_waic + Condition + Warning: + Accessing p_waic using '$' is deprecated and will be removed in a future release. Please extract the p_waic estimate from the 'estimates' component instead. + Output + [1] 3.269898 + +--- + + Code + waic1$se_p_waic + Condition + Warning: + Accessing se_p_waic using '$' is deprecated and will be removed in a future release. Please extract the se_p_waic estimate from the 'estimates' component instead. + Output + [1] 1.124365 + +--- + + Code + waic1$waic + Condition + Warning: + Accessing waic using '$' is deprecated and will be removed in a future release. Please extract the waic estimate from the 'estimates' component instead. + Output + [1] 167.0607 + +--- + + Code + waic1$se_waic + Condition + Warning: + Accessing se_waic using '$' is deprecated and will be removed in a future release. Please extract the se_waic estimate from the 'estimates' component instead. + Output + [1] 8.516096 + +--- + + Code + waic1["elpd_waic"] + Condition + Warning: + Accessing elpd_waic using '[' is deprecated and will be removed in a future release. Please extract the elpd_waic estimate from the 'estimates' component instead. + Output + $elpd_waic + [1] -83.53033 + + +--- + + Code + waic1["se_elpd_waic"] + Condition + Warning: + Accessing se_elpd_waic using '[' is deprecated and will be removed in a future release. Please extract the se_elpd_waic estimate from the 'estimates' component instead. + Output + $se_elpd_waic + [1] 4.258048 + + +--- + + Code + waic1["p_waic"] + Condition + Warning: + Accessing p_waic using '[' is deprecated and will be removed in a future release. Please extract the p_waic estimate from the 'estimates' component instead. + Output + $p_waic + [1] 3.269898 + + +--- + + Code + waic1["se_p_waic"] + Condition + Warning: + Accessing se_p_waic using '[' is deprecated and will be removed in a future release. Please extract the se_p_waic estimate from the 'estimates' component instead. + Output + $se_p_waic + [1] 1.124365 + + +--- + + Code + waic1["waic"] + Condition + Warning: + Accessing waic using '[' is deprecated and will be removed in a future release. Please extract the waic estimate from the 'estimates' component instead. + Output + $waic + [1] 167.0607 + + +--- + + Code + waic1["se_waic"] + Condition + Warning: + Accessing se_waic using '[' is deprecated and will be removed in a future release. Please extract the se_waic estimate from the 'estimates' component instead. + Output + $se_waic + [1] 8.516096 + + +--- + + Code + waic1[["elpd_waic"]] + Condition + Warning: + Accessing elpd_waic using '[[' is deprecated and will be removed in a future release. Please extract the elpd_waic estimate from the 'estimates' component instead. + Output + [1] -83.53033 + +--- + + Code + waic1[["se_elpd_waic"]] + Condition + Warning: + Accessing se_elpd_waic using '[[' is deprecated and will be removed in a future release. Please extract the se_elpd_waic estimate from the 'estimates' component instead. + Output + [1] 4.258048 + +--- + + Code + waic1[["p_waic"]] + Condition + Warning: + Accessing p_waic using '[[' is deprecated and will be removed in a future release. Please extract the p_waic estimate from the 'estimates' component instead. + Output + [1] 3.269898 + +--- + + Code + waic1[["se_p_waic"]] + Condition + Warning: + Accessing se_p_waic using '[[' is deprecated and will be removed in a future release. Please extract the se_p_waic estimate from the 'estimates' component instead. + Output + [1] 1.124365 + +--- + + Code + waic1[["waic"]] + Condition + Warning: + Accessing waic using '[[' is deprecated and will be removed in a future release. Please extract the waic estimate from the 'estimates' component instead. + Output + [1] 167.0607 + +--- + + Code + waic1[["se_waic"]] + Condition + Warning: + Accessing se_waic using '[[' is deprecated and will be removed in a future release. Please extract the se_waic estimate from the 'estimates' component instead. + Output + [1] 8.516096 + diff --git a/tests/testthat/_snaps/gpdfit.md b/tests/testthat/_snaps/gpdfit.md new file mode 100644 index 00000000..348f3512 --- /dev/null +++ b/tests/testthat/_snaps/gpdfit.md @@ -0,0 +1,15 @@ +# gpdfit returns correct result + + WAoAAAACAAQFAAACAwAAAAIOAAAAAj+cD4qKVTgaP/BK8xJCK3sAAAQCAAAAAQAEAAkAAAAF + bmFtZXMAAAAQAAAAAgAEAAkAAAABawAEAAkAAAAFc2lnbWEAAAD+ + +--- + + WAoAAAACAAQFAAACAwAAAAIOAAAAAj+yA4g2tkbvP/BK8xJCK3sAAAQCAAAAAQAEAAkAAAAF + bmFtZXMAAAAQAAAAAgAEAAkAAAABawAEAAkAAAAFc2lnbWEAAAD+ + +--- + + WAoAAAACAAQFAAACAwAAAAIOAAAAAj+yA5BUlFrHP/BK8oexSVIAAAQCAAAAAQAEAAkAAAAF + bmFtZXMAAAAQAAAAAgAEAAkAAAABawAEAAkAAAAFc2lnbWEAAAD+ + diff --git a/tests/testthat/_snaps/loo_and_waic.md b/tests/testthat/_snaps/loo_and_waic.md new file mode 100644 index 00000000..018b6d98 --- /dev/null +++ b/tests/testthat/_snaps/loo_and_waic.md @@ -0,0 +1,109 @@ +# loo, waic and elpd results haven't changed + + WAoAAAACAAQFAAACAwAAAAMTAAAACgAAAg4AAAAGwFTltnf8GWZACqFzqDCa8kBk5bZ3/Blm + QBEipbqh/pI/8m8Dok+RiEAhIqW6of6SAAAEAgAAAAEABAAJAAAAA2RpbQAAAA0AAAACAAAA + AwAAAAIAAAQCAAAAAQAEAAkAAAAIZGltbmFtZXMAAAATAAAAAgAAABAAAAADAAQACQAAAAhl + bHBkX2xvbwAEAAkAAAAFcF9sb28ABAAJAAAABWxvb2ljAAAAEAAAAAIABAAJAAAACEVzdGlt + YXRlAAQACQAAAAJTRQAAAP4AAAIOAAAAoMAC+fJnaLHwwAEP7abtCtDAArJiAQQUDsABa6HW + xx3swAC19bRGOqbAAOVsk57EhMAHXE6oz8X6wAhOMwo4HzjAAxWJEz7wYMAAvCuQLdoawAEz + +EIq/LDAARqnVc5fZMAAuMi8GPlmwAJBKzhjQ9zAAe5YKyVuTsADlXoqYwIGwBIRlu/H12/A + E/WagapcWsACpJaoNucYwBLYLgxdc6fAA7rSrp1oIMAEf9XEej+SwAa4oZCQGv7ABf5oQger + 6sADXWMppHVuwAEJEltQ9nLAAOaxVhD86sAB7SPP8uDQwAnDIjSfxULABAp5a+6r9sAFJHaV + 7SH8wAEph84QBEw/eQDDaV43ET9xp+BQBCmTP3qfwKytXZ0/ctyOXeTxzz9xqi9N9aR4P3IZ + d1RCmrs/hDyfFGZAlj+GNSCYztY2P3e6iLhToUY/cX4fUwwY4j9yNONK++tcP3MW2+Teir8/ + chrQtYH1VT94S/hkJOXvP4KGRiL5AqY/jR2tM+V2kj+xFwrjXj1kP6T55+1SS6g/h0J5DiSH + Lz+l12L3soNYP36Eg9WZBwg/fE5degjr7j+CgjOtLdigP4MAE38XAjM/efuEjSOY2z938dyR + 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log_lik_i_test, + unconstrain_pars_test, log_prob_upars_test, log_lik_i_upars_test, max_iters = 1, + k_thres = 0.5, split = TRUE, cov = TRUE, cores = 1) + Condition + Warning: + The maximum number of moment matching iterations ('max_iters' argument) + was reached. + Increasing the value may improve accuracy. + Warning: + Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details. + Output + + Computed from 4000 by 30 log-likelihood matrix. + + Estimate SE + elpd_loo -74.0 18.8 + p_loo 11.7 11.1 + looic 148.0 37.6 + ------ + MCSE of elpd_loo is NA. + MCSE and ESS estimates assume independent draws (r_eff=1). + + Pareto k diagnostic values: + Count Pct. Min. ESS + (-Inf, 0.7] (good) 29 96.7% 3758 + (0.7, 1] (bad) 1 3.3% + (1, Inf) (very bad) 0 0.0% + See help('pareto-k-diagnostic') for details. + +# loo_moment_match.default works + + WAoAAAACAAQFAAACAwAAAAMTAAAACgAAAg4AAAAGwFJQKLlLGUdAJeeelRE5/EBiUCi5SxlH + QDIH7c01qv5AJJ/C+xNRK0BCB+3NNar+AAAEAgAAAAEABAAJAAAAA2RpbQAAAA0AAAACAAAA + AwAAAAIAAAQCAAAAAQAEAAkAAAAIZGltbmFtZXMAAAATAAAAAgAAABAAAAADAAQACQAAAAhl + bHBkX2xvbwAEAAkAAAAFcF9sb28ABAAJAAAABWxvb2ljAAAAEAAAAAIABAAJAAAACEVzdGlt + YXRlAAQACQAAAAJTRQAAAP4AAAIOAAAAlsAz2SCKL/OPv/uoQkpWQUi//t6gM8RRaL/7ME6e + 7mn4v/slBOxuIMC//9jJ8p3XcL/7L6Fev0U4wAA2/FFJKpi//ScFgWbouL/8Pocg7R74v/0p + qq1wTGC/+x8Kh2qjyL/7JFoKTeIwv/soK3EUBUC//KBEQBPCGMAAKtiBefwUv/s4omTXOVjA + A0qKEEuTHL/7hlp0sBHYv/xVS8d60Ki//yJy272SKL/7oVoD6+JIv/7iRaQt0cC//VaEo30D + QL/85OxxwadowAHqd1LUWNy/+9Uw/+PdqL/7IYmDCMJQv/+TLUcS2KC//UsYUnCS0D/ADGFe + xkQGP2Gfcpi2J8Y/Y+tB1l78/T9hmK3v3QgHP2GaKR7p1QA/ZQkD2NQUNz9hp3Fk+oG7P2Wd + 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1qHAMkFd+IMlVMAlmhwBr+u2wCrsfq/IqmLAMCZhaI8Ej8Azru6dRrUKwCKk8XdB6XrAJTC6 + h4rGnMAmaq0Zsj9BwCRTGur9Wn7AKY3wcEFYGMAl6SqJoPaCwCdT3hgPXebAKUcD0VViXMAy + XmUM58tmwCi+3+DPBgnAJYPIAtRdEMAjqNuY33MdwDPaX7VI+vTAKZAnYmqK0cAk9J5BvIFf + wC6tMmYaL2bAI5UGBra55MAtm7iKuVWkwC7LKTJRFiHAGXU3OUNcIMAoWQyni1xKwCUUyPHo + Y3/AJ1uvQftfW8Am1zbR0XdmwCiGEvlWsO/AL1pZUMU8EAAAAA4AAAABP+71T2guFhoAAAQC + AAAAAQAEAAkAAAAFbmFtZXMAAAAQAAAABAAEAAkAAAADbHdpAAQACQAAAARsd2ZpAAQACQAA + AAhsb2dfbGlraQAEAAkAAAAHcl9lZmZfaQAAAP4= + diff --git a/tests/testthat/_snaps/loo_predictive_metric.md b/tests/testthat/_snaps/loo_predictive_metric.md new file mode 100644 index 00000000..c1c820a1 --- /dev/null +++ b/tests/testthat/_snaps/loo_predictive_metric.md @@ -0,0 +1,60 @@ +# loo_predictive_metric is equal to snapshot + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+b6DHwJlO4AAAAOAAAAAT+0VQV434B0 + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP/c/cc5N6ckAAAAOAAAAAT/C02/w8SU2 + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+r8D5W4BOIAAAAOAAAAAT+07COl+XlU + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP/qvkDt3DvwAAAAOAAAAAT/BcF2FCRTJ + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+bBWsjIsVUAAAAOAAAAAT/BpKrAKkv4 + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABQAZBDZS19iwAAAAOAAAAAT/dFfIrJ59T + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP9gAAAAAAAAAAAAOAAAAAT+16K3SNqWP + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP9QAAAAAAAAAAAAOAAAAAT+0+ea7xOyz + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP9aw32sN9rEAAAAOAAAAAT+uapXy8Hyq + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + +--- + + WAoAAAACAAQFAAACAwAAAAITAAAAAgAAAA4AAAABP+AAAAAAAAAAAAAOAAAAAQAAAAAAAAAA + AAAEAgAAAAEABAAJAAAABW5hbWVzAAAAEAAAAAIABAAJAAAACGVzdGltYXRlAAQACQAAAAJz + ZQAAAP4= + diff --git a/tests/testthat/_snaps/loo_subsampling.md b/tests/testthat/_snaps/loo_subsampling.md new file mode 100644 index 00000000..cfaf8025 --- /dev/null +++ b/tests/testthat/_snaps/loo_subsampling.md @@ -0,0 +1,20 @@ +# loo_compare_subsample + + Code + lcss <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2, lss3)) + Condition + Warning: + Different subsamples in 'model3' and 'model2'. Naive diff SE is used. + Warning: + Different subsamples in 'model3' and 'model1'. Naive diff SE is used. + +--- + + Code + lcssapi <- loo_compare(lss1, lss2, lss3) + Condition + Warning: + Different subsamples in 'model3' and 'model2'. Naive diff SE is used. + Warning: + Different subsamples in 'model3' and 'model1'. Naive diff SE is used. + diff --git a/tests/testthat/_snaps/model_weighting.md b/tests/testthat/_snaps/model_weighting.md new file mode 100644 index 00000000..0ed937d0 --- /dev/null +++ b/tests/testthat/_snaps/model_weighting.md @@ -0,0 +1,8 @@ +# loo_model_weights (stacking and pseudo-BMA) gives expected result + + WAoAAAACAAQFAAACAwAAAAAOAAAAAz/KEXngFjO6P+l7oXTIUDU+YzIi3AAAAA== + +--- + + WAoAAAACAAQFAAACAwAAAAAOAAAAAz+xA6UGtqDFP+3eFS5zKzY/J2MLYAsc4w== + diff --git a/tests/testthat/_snaps/print_plot.md b/tests/testthat/_snaps/print_plot.md new file mode 100644 index 00000000..17742c92 --- /dev/null +++ b/tests/testthat/_snaps/print_plot.md @@ -0,0 +1,4 @@ +# mcse_loo extractor gives correct value + + WAoAAAACAAQFAAACAwAAAAAOAAAAAT+2J8YDcP5s + diff --git a/tests/testthat/_snaps/psis.md b/tests/testthat/_snaps/psis.md new file mode 100644 index 00000000..9836dc49 --- /dev/null +++ b/tests/testthat/_snaps/psis.md @@ -0,0 +1,4801 @@ +# psis results haven't changed + + 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7WeuRVdLxz/thpx9PZyZP+x8NVYA5gc/7bgj+feDuD/tVJY/ijqGP+vk6tOVjqc/7oFMRnLC + Az/tFUhNscONP+71S5Zdfro/7NXCRLX3pz/oSat+OPKuP+rzPc6+kK4/7rBYQ1wZPj/h+KYW + Rg06P/BP+OJ7Frg/6GyNUDLmvD/v8/5XaLpuP+3Om11xZMc/7CaGH4VipT/tF1NNe4n9P+1m + F6TWIdc/7s96p0MNpT/ndID3UIhJP+lAQASVPCI/5qSPlaOkhD/s9t8Xe5GFP+zJwNIA1oY/ + 7It81CYJlj/uynC4minDAAAEAgAAAAEABAAJAAAABGRpbXMAAAANAAAAAgAAA+gAAAAgAAAE + AgAAAAEABAAJAAAABm1ldGhvZAAAABAAAAABAAQACQAAAARwc2lzAAAEAgAAAAEABAAJAAAA + BWNsYXNzAAAAEAAAAAMABAAJAAAABHBzaXMABAAJAAAAE2ltcG9ydGFuY2Vfc2FtcGxpbmcA + BAAJAAAABGxpc3QAAAD+ + +# psis throws correct errors and warnings + + Code + psis(-LLarr[1:5, , ]) + Condition + Warning: + Not enough tail samples to fit the generalized Pareto distribution in some or all columns of matrix of log importance ratios. Skipping the following columns: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... [22 more not printed]. + Warning: + Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details. + Output + Computed from 10 by 32 log-weights matrix. + MCSE and ESS estimates assume independent draws (r_eff=1). + Pareto k diagnostic values: + Count Pct. Min. ESS + (-Inf, 0] (good) 0 0.0% + (0, 1] (bad) 0 0.0% + (1, Inf) (very bad) 32 100.0% + See help('pareto-k-diagnostic') for details. + diff --git a/tests/testthat/_snaps/psislw.md b/tests/testthat/_snaps/psislw.md new file mode 100644 index 00000000..94daa306 --- /dev/null +++ b/tests/testthat/_snaps/psislw.md @@ -0,0 +1,14 @@ +# psislw handles special cases, throws appropriate errors/warnings + + Code + psis <- psislw(x[, 1], wcp = 0.01) + Condition + Warning in `psislw()`: + 'psislw' is deprecated. + Use 'psis' instead. + See help("Deprecated") + Warning: + All tail values are the same. Weights are truncated but not smoothed. + Warning: + Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details. + diff --git a/tests/testthat/_snaps/relative_eff.md b/tests/testthat/_snaps/relative_eff.md new file mode 100644 index 00000000..42687550 --- /dev/null +++ b/tests/testthat/_snaps/relative_eff.md @@ -0,0 +1,9 @@ +# relative_eff results haven't changed + + WAoAAAACAAQFAAACAwAAAAAOAAAAID/uIiOKmu2kP+6GCkXEpTM/70Lb4e15Oj/tZ65FV0vH + P+2GnH09nJk/7Hw1VgDmBz/tuCP594O4P+1Ulj+KOoY/6+Tq05WOpz/ugUxGcsIDP+0VSE2x + w40/7vVLll1+uj/s1cJEtfenP+hJq3448q4/6vM9zr6Qrj/usFhDXBk+P+H4phZGDTo/8E/4 + 4nsWuD/obI1QMua8P+/z/ldoum4/7c6bXXFkxz/sJoYfhWKlP+0XU017if0/7WYXpNYh1z/u + z3qnQw2lP+d0gPdQiEk/6UBABJU8Ij/mpI+Vo6SEP+z23xd7kYU/7MnA0gDWhj/si3zUJgmW + P+7KcLiaKcM= + diff --git a/tests/testthat/reference-results/E_loo_default_mean.rds b/tests/testthat/reference-results/E_loo_default_mean.rds deleted file mode 100644 index d9dabe0c..00000000 Binary files a/tests/testthat/reference-results/E_loo_default_mean.rds and /dev/null differ diff --git a/tests/testthat/reference-results/E_loo_default_quantile_10_50_90.rds b/tests/testthat/reference-results/E_loo_default_quantile_10_50_90.rds deleted file mode 100644 index abf2bc12..00000000 Binary files a/tests/testthat/reference-results/E_loo_default_quantile_10_50_90.rds and /dev/null differ diff --git a/tests/testthat/reference-results/E_loo_default_quantile_50.rds b/tests/testthat/reference-results/E_loo_default_quantile_50.rds deleted file mode 100644 index eb26d540..00000000 Binary files a/tests/testthat/reference-results/E_loo_default_quantile_50.rds and /dev/null differ diff --git a/tests/testthat/reference-results/E_loo_default_sd.rds b/tests/testthat/reference-results/E_loo_default_sd.rds deleted file mode 100644 index 0de0402a..00000000 Binary files a/tests/testthat/reference-results/E_loo_default_sd.rds and /dev/null differ diff --git a/tests/testthat/reference-results/E_loo_default_var.rds b/tests/testthat/reference-results/E_loo_default_var.rds deleted file mode 100644 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b/tests/testthat/reference-results/waic.rds deleted file mode 100644 index bde2954a..00000000 Binary files a/tests/testthat/reference-results/waic.rds and /dev/null differ diff --git a/tests/testthat/test_0_helpers.R b/tests/testthat/test_0_helpers.R index 09bfb06a..d9523b21 100644 --- a/tests/testthat/test_0_helpers.R +++ b/tests/testthat/test_0_helpers.R @@ -1,5 +1,4 @@ library(loo) -context("helper functions and example data") LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() diff --git a/tests/testthat/test_E_loo.R b/tests/testthat/test_E_loo.R index 940fb929..cfbacf1e 100644 --- a/tests/testthat/test_E_loo.R +++ b/tests/testthat/test_E_loo.R @@ -1,7 +1,5 @@ library(loo) -context("E_loo") - LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() LLvec <- LLmat[, 1] @@ -96,20 +94,20 @@ test_that("E_loo return types correct for default/vector method", { expect_length(E_test_quant_vec2$pareto_k, 1) }) -test_that("E_loo.default equal to reference", { - expect_equal_to_reference(E_test_mean_vec, test_path("reference-results/E_loo_default_mean.rds")) - expect_equal_to_reference(E_test_var_vec, test_path("reference-results/E_loo_default_var.rds")) - expect_equal_to_reference(E_test_sd_vec, test_path("reference-results/E_loo_default_sd.rds")) - expect_equal_to_reference(E_test_quant_vec, test_path("reference-results/E_loo_default_quantile_50.rds")) - expect_equal_to_reference(E_test_quant_vec2, test_path("reference-results/E_loo_default_quantile_10_50_90.rds")) +test_that("E_loo.default equal to snapshots", { + expect_snapshot_value(E_test_mean_vec, style = "serialize") + expect_snapshot_value(E_test_var_vec, style = "serialize") + expect_snapshot_value(E_test_sd_vec, style = "serialize") + expect_snapshot_value(E_test_quant_vec, style = "serialize") + expect_snapshot_value(E_test_quant_vec2, style = "serialize") }) -test_that("E_loo.matrix equal to reference", { - expect_equal_to_reference(E_test_mean, test_path("reference-results/E_loo_matrix_mean.rds")) - expect_equal_to_reference(E_test_var, test_path("reference-results/E_loo_matrix_var.rds")) - expect_equal_to_reference(E_test_sd, test_path("reference-results/E_loo_matrix_sd.rds")) - expect_equal_to_reference(E_test_quant, test_path("reference-results/E_loo_matrix_quantile_50.rds")) - expect_equal_to_reference(E_test_quant2, test_path("reference-results/E_loo_matrix_quantile_10_90.rds")) +test_that("E_loo.matrix equal to snapshots", { + expect_snapshot_value(E_test_mean, style = "serialize") + expect_snapshot_value(E_test_var, style = "serialize") + expect_snapshot_value(E_test_sd, style = "serialize") + expect_snapshot_value(E_test_quant, style = "serialize") + expect_snapshot_value(E_test_quant2, style = "serialize") }) test_that("E_loo throws correct errors and warnings", { diff --git a/tests/testthat/test_compare.R b/tests/testthat/test_compare.R index 8835a530..e70a25f9 100644 --- a/tests/testthat/test_compare.R +++ b/tests/testthat/test_compare.R @@ -1,18 +1,15 @@ library(loo) set.seed(123) -SW <- suppressWarnings - -context("compare models") LLarr <- example_loglik_array() LLarr2 <- array(rnorm(prod(dim(LLarr)), c(LLarr), 0.5), dim = dim(LLarr)) LLarr3 <- array(rnorm(prod(dim(LLarr)), c(LLarr), 1), dim = dim(LLarr)) -w1 <- SW(waic(LLarr)) -w2 <- SW(waic(LLarr2)) +w1 <- suppressWarnings(waic(LLarr)) +w2 <- suppressWarnings(waic(LLarr2)) test_that("loo_compare throws appropriate errors", { - w3 <- SW(waic(LLarr[,, -1])) - w4 <- SW(waic(LLarr[,, -(1:2)])) + w3 <- suppressWarnings(waic(LLarr[,, -1])) + w4 <- suppressWarnings(waic(LLarr[,, -(1:2)])) expect_error(loo_compare(2, 3), "must be a list if not a 'loo' object") expect_error(loo_compare(w1, w2, x = list(w1, w2)), @@ -41,11 +38,11 @@ test_that("loo_compare throws appropriate warnings", { expect_warning(loo_compare(w3, w4), "Not all models have the same y variable") set.seed(123) - w_list <- lapply(1:25, function(x) SW(waic(LLarr + rnorm(1, 0, 0.1)))) + w_list <- lapply(1:25, function(x) suppressWarnings(waic(LLarr + rnorm(1, 0, 0.1)))) expect_warning(loo_compare(w_list), "Difference in performance potentially due to chance") - w_list_short <- lapply(1:4, function(x) SW(waic(LLarr + rnorm(1, 0, 0.1)))) + w_list_short <- lapply(1:4, function(x) suppressWarnings(waic(LLarr + rnorm(1, 0, 0.1)))) expect_no_warning(loo_compare(w_list_short)) }) @@ -62,24 +59,22 @@ test_that("loo_compare returns expected results (2 models)", { expect_equal(colnames(comp1), comp_colnames) expect_equal(rownames(comp1), c("model1", "model2")) expect_output(print(comp1), "elpd_diff") - expect_equivalent(comp1[1:2,1], c(0, 0)) - expect_equivalent(comp1[1:2,2], c(0, 0)) + expect_equal(comp1[1:2,1], c(0, 0), ignore_attr = TRUE) + expect_equal(comp1[1:2,2], c(0, 0), ignore_attr = TRUE) comp2 <- loo_compare(w1, w2) expect_s3_class(comp2, "compare.loo") - - # expect_equal_to_reference(comp2, "reference-results/loo_compare_two_models.rds") - comp2_ref <- readRDS(test_path("reference-results/loo_compare_two_models.rds")) - expect_equivalent(comp2, comp2_ref) expect_equal(colnames(comp2), comp_colnames) - + + expect_snapshot_value(comp2, style = "serialize") + # specifying objects via ... and via arg x gives equal results expect_equal(comp2, loo_compare(x = list(w1, w2))) }) test_that("loo_compare returns expected result (3 models)", { - w3 <- SW(waic(LLarr3)) + w3 <- suppressWarnings(waic(LLarr3)) comp1 <- loo_compare(w1, w2, w3) expect_equal(colnames(comp1), comp_colnames) @@ -88,13 +83,11 @@ test_that("loo_compare returns expected result (3 models)", { expect_s3_class(comp1, "compare.loo") expect_s3_class(comp1, "matrix") - # expect_equal_to_reference(comp1, "reference-results/loo_compare_three_models.rds") - comp1_ref <- readRDS(test_path("reference-results/loo_compare_three_models.rds")) - expect_equivalent(comp1, comp1_ref) + expect_snapshot_value(comp1, style = "serialize") # specifying objects via '...' gives equivalent results (equal # except rownames) to using 'x' argument - expect_equivalent(comp1, loo_compare(x = list(w1, w2, w3))) + expect_equal(comp1, loo_compare(x = list(w1, w2, w3)), ignore_attr = TRUE) }) # Tests for deprecated compare() ------------------------------------------ @@ -105,23 +98,23 @@ test_that("compare throws deprecation warnings", { }) test_that("compare returns expected result (2 models)", { - comp1 <- expect_warning(loo::compare(w1, w1), "Deprecated") - expect_output(print(comp1), "elpd_diff") + expect_warning(comp1 <- loo::compare(w1, w1), "Deprecated") + expect_snapshot(comp1) expect_equal(comp1[1:2], c(elpd_diff = 0, se = 0)) - comp2 <- expect_warning(loo::compare(w1, w2), "Deprecated") - # expect_equal_to_reference(comp2, "reference-results/compare_two_models.rds") + expect_warning(comp2 <- loo::compare(w1, w2), "Deprecated") + expect_snapshot(comp2) expect_named(comp2, c("elpd_diff", "se")) expect_s3_class(comp2, "compare.loo") # specifying objects via ... and via arg x gives equal results - comp_via_list <- expect_warning(loo::compare(x = list(w1, w2)), "Deprecated") + expect_warning(comp_via_list <- loo::compare(x = list(w1, w2)), "Deprecated") expect_equal(comp2, comp_via_list) }) test_that("compare returns expected result (3 models)", { - w3 <- SW(waic(LLarr3)) - comp1 <- expect_warning(loo::compare(w1, w2, w3), "Deprecated") + w3 <- suppressWarnings(waic(LLarr3)) + expect_warning(comp1 <- loo::compare(w1, w2, w3), "Deprecated") expect_equal( colnames(comp1), @@ -133,12 +126,12 @@ test_that("compare returns expected result (3 models)", { expect_equal(comp1[1,1], 0) expect_s3_class(comp1, "compare.loo") expect_s3_class(comp1, "matrix") - # expect_equal_to_reference(comp1, "reference-results/compare_three_models.rds") + expect_snapshot_value(comp1, style = "serialize") # specifying objects via '...' gives equivalent results (equal # except rownames) to using 'x' argument - comp_via_list <- expect_warning(loo::compare(x = list(w1, w2, w3)), "Deprecated") - expect_equivalent(comp1, comp_via_list) + expect_warning(comp_via_list <- loo::compare(x = list(w1, w2, w3)), "Deprecated") + expect_equal(comp1, comp_via_list, ignore_attr = TRUE) }) test_that("compare throws appropriate errors", { @@ -151,7 +144,7 @@ test_that("compare throws appropriate errors", { expect_error(suppressWarnings(loo::compare(x = list(w1))), "requires at least two models") - w3 <- SW(waic(LLarr2[,,-1])) + w3 <- suppressWarnings(waic(LLarr2[,,-1])) expect_error(suppressWarnings(loo::compare(x = list(w1, w3))), "same number of data points") expect_error(suppressWarnings(loo::compare(x = list(w1, w2, w3))), diff --git a/tests/testthat/test_crps.R b/tests/testthat/test_crps.R index 9c1f50cd..ce523476 100644 --- a/tests/testthat/test_crps.R +++ b/tests/testthat/test_crps.R @@ -24,11 +24,11 @@ test_that("crps computation is correct", { expect_equal(.crps_fun(pi, pi^2, scale = TRUE), -pi^2/pi - 0.5 * log(pi)) }) -test_that("crps matches references", { - expect_equal_to_reference(with_seed(1, crps(x1, x2, y)), 'reference-results/crps.rds') - expect_equal_to_reference(with_seed(1, scrps(x1, x2, y)), 'reference-results/scrps.rds') - expect_equal_to_reference(with_seed(1, loo_crps(x1, x2, y, ll)), 'reference-results/loo_crps.rds') - expect_equal_to_reference(with_seed(1, loo_scrps(x1, x2, y, ll)), 'reference-results/loo_scrps.rds') +test_that("crps matches snapshots", { + expect_snapshot_value(with_seed(1, crps(x1, x2, y)), style = "serialize") + expect_snapshot_value(with_seed(1, scrps(x1, x2, y)), style = "serialize") + expect_snapshot_value(with_seed(1, loo_crps(x1, x2, y, ll)), style = "serialize") + expect_snapshot_value(with_seed(1, loo_scrps(x1, x2, y, ll)), style = "serialize") }) test_that("input validation throws correct errors", { diff --git a/tests/testthat/test_deprecated_extractors.R b/tests/testthat/test_deprecated_extractors.R index 39433d21..6f27767e 100644 --- a/tests/testthat/test_deprecated_extractors.R +++ b/tests/testthat/test_deprecated_extractors.R @@ -2,173 +2,201 @@ library(loo) options(mc.cores = 1) set.seed(123) -context("Depracted extractors") - LLarr <- example_loglik_array() r_eff_arr <- relative_eff(exp(LLarr)) loo1 <- suppressWarnings(loo(LLarr, r_eff = r_eff_arr)) waic1 <- suppressWarnings(waic(LLarr)) - -expect_warning_fixed <- function(object, regexp = NULL) { - expect_warning(object, regexp = regexp, fixed = TRUE) -} - test_that("extracting estimates by name is deprecated for loo objects", { # $ method + expect_snapshot(loo1$elpd_loo) expect_equal( - expect_warning_fixed(loo1$elpd_loo, "elpd_loo using '$' is deprecated"), + suppressWarnings(loo1$elpd_loo), loo1$estimates["elpd_loo", "Estimate"] ) + expect_snapshot(loo1$se_elpd_loo) expect_equal( - expect_warning_fixed(loo1$se_elpd_loo, "se_elpd_loo using '$' is deprecated"), + suppressWarnings(loo1$se_elpd_loo), loo1$estimates["elpd_loo", "SE"] ) + expect_snapshot(loo1$p_loo) expect_equal( - expect_warning_fixed(loo1$p_loo, "p_loo using '$' is deprecated"), + suppressWarnings(loo1$p_loo), loo1$estimates["p_loo", "Estimate"] ) + expect_snapshot(loo1$se_p_loo) expect_equal( - expect_warning_fixed(loo1$se_p_loo, "se_p_loo using '$' is deprecated"), + suppressWarnings(loo1$se_p_loo), loo1$estimates["p_loo", "SE"] ) + expect_snapshot(loo1$looic) expect_equal( - expect_warning_fixed(loo1$looic, "looic using '$' is deprecated"), + suppressWarnings(loo1$looic), loo1$estimates["looic", "Estimate"] ) + expect_snapshot(loo1$se_looic) expect_equal( - expect_warning_fixed(loo1$se_looic, "se_looic using '$' is deprecated"), + suppressWarnings(loo1$se_looic), loo1$estimates["looic", "SE"] ) # [ method + expect_snapshot(loo1["elpd_loo"]) expect_equal( - expect_warning_fixed(loo1["elpd_loo"], "elpd_loo using '[' is deprecated")[[1]], + suppressWarnings(loo1["elpd_loo"][[1]]), loo1$estimates["elpd_loo", "Estimate"] ) + expect_snapshot(loo1["se_elpd_loo"]) expect_equal( - expect_warning_fixed(loo1["se_elpd_loo"], "se_elpd_loo using '[' is deprecated")[[1]], + suppressWarnings(loo1["se_elpd_loo"][[1]]), loo1$estimates["elpd_loo", "SE"] ) + expect_snapshot(loo1["p_loo"]) expect_equal( - expect_warning_fixed(loo1["p_loo"], "p_loo using '[' is deprecated")[[1]], + suppressWarnings(loo1["p_loo"][[1]]), loo1$estimates["p_loo", "Estimate"] ) + expect_snapshot(loo1["se_p_loo"]) expect_equal( - expect_warning_fixed(loo1["se_p_loo"], "se_p_loo using '[' is deprecated")[[1]], + suppressWarnings(loo1["se_p_loo"][[1]]), loo1$estimates["p_loo", "SE"] ) + expect_snapshot(loo1["looic"]) expect_equal( - expect_warning_fixed(loo1["looic"], "looic using '[' is deprecated")[[1]], + suppressWarnings(loo1["looic"][[1]]), loo1$estimates["looic", "Estimate"] ) + expect_snapshot(loo1["se_looic"]) expect_equal( - expect_warning_fixed(loo1["se_looic"], "se_looic using '[' is deprecated")[[1]], + suppressWarnings(loo1["se_looic"][[1]]), loo1$estimates["looic", "SE"] ) - # [[ method + expect_snapshot(loo1[["elpd_loo"]]) expect_equal( - expect_warning_fixed(loo1[["elpd_loo"]], "elpd_loo using '[[' is deprecated"), + suppressWarnings(loo1[["elpd_loo"]]), loo1$estimates["elpd_loo", "Estimate"] ) + expect_snapshot(loo1[["se_elpd_loo"]]) expect_equal( - expect_warning_fixed(loo1[["se_elpd_loo"]], "se_elpd_loo using '[[' is deprecated"), + suppressWarnings(loo1[["se_elpd_loo"]]), loo1$estimates["elpd_loo", "SE"] ) + expect_snapshot(loo1[["p_loo"]]) expect_equal( - expect_warning_fixed(loo1[["p_loo"]], "p_loo using '[[' is deprecated"), + suppressWarnings(loo1[["p_loo"]]), loo1$estimates["p_loo", "Estimate"] ) + expect_snapshot(loo1[["se_p_loo"]]) expect_equal( - expect_warning_fixed(loo1[["se_p_loo"]], "se_p_loo using '[[' is deprecated"), + suppressWarnings(loo1[["se_p_loo"]]), loo1$estimates["p_loo", "SE"] ) + expect_snapshot(loo1[["looic"]]) expect_equal( - expect_warning_fixed(loo1[["looic"]], "looic using '[[' is deprecated"), + suppressWarnings(loo1[["looic"]]), loo1$estimates["looic", "Estimate"] ) + expect_snapshot(loo1[["se_looic"]]) expect_equal( - expect_warning_fixed(loo1[["se_looic"]], "se_looic using '[[' is deprecated"), + suppressWarnings(loo1[["se_looic"]]), loo1$estimates["looic", "SE"] ) }) test_that("extracting estimates by name is deprecated for waic objects", { + expect_snapshot(waic1$elpd_waic) expect_equal( - expect_warning_fixed(waic1$elpd_waic, "elpd_waic using '$' is deprecated"), + suppressWarnings(waic1$elpd_waic), waic1$estimates["elpd_waic", "Estimate"] ) + expect_snapshot(waic1$se_elpd_waic) expect_equal( - expect_warning_fixed(waic1$se_elpd_waic, "se_elpd_waic using '$' is deprecated"), + suppressWarnings(waic1$se_elpd_waic), waic1$estimates["elpd_waic", "SE"] ) + expect_snapshot(waic1$p_waic) expect_equal( - expect_warning_fixed(waic1$p_waic, "p_waic using '$' is deprecated"), + suppressWarnings(waic1$p_waic), waic1$estimates["p_waic", "Estimate"] ) + expect_snapshot(waic1$se_p_waic) expect_equal( - expect_warning_fixed(waic1$se_p_waic, "se_p_waic using '$' is deprecated"), + suppressWarnings(waic1$se_p_waic), waic1$estimates["p_waic", "SE"] ) + expect_snapshot(waic1$waic) expect_equal( - expect_warning_fixed(waic1$waic, "waic using '$' is deprecated"), + suppressWarnings(waic1$waic), waic1$estimates["waic", "Estimate"] ) + expect_snapshot(waic1$se_waic) expect_equal( - expect_warning_fixed(waic1$se_waic, "se_waic using '$' is deprecated"), + suppressWarnings(waic1$se_waic), waic1$estimates["waic", "SE"] ) - + # [ method + expect_snapshot(waic1["elpd_waic"]) expect_equal( - expect_warning_fixed(waic1["elpd_waic"], "elpd_waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["elpd_waic"][[1]]), waic1$estimates["elpd_waic", "Estimate"] ) + expect_snapshot(waic1["se_elpd_waic"]) expect_equal( - expect_warning_fixed(waic1["se_elpd_waic"], "se_elpd_waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["se_elpd_waic"][[1]]), waic1$estimates["elpd_waic", "SE"] ) + expect_snapshot(waic1["p_waic"]) expect_equal( - expect_warning_fixed(waic1["p_waic"], "p_waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["p_waic"][[1]]), waic1$estimates["p_waic", "Estimate"] ) + expect_snapshot(waic1["se_p_waic"]) expect_equal( - expect_warning_fixed(waic1["se_p_waic"], "se_p_waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["se_p_waic"][[1]]), waic1$estimates["p_waic", "SE"] ) + expect_snapshot(waic1["waic"]) expect_equal( - expect_warning_fixed(waic1["waic"], "waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["waic"][[1]]), waic1$estimates["waic", "Estimate"] ) + expect_snapshot(waic1["se_waic"]) expect_equal( - expect_warning_fixed(waic1["se_waic"], "se_waic using '[' is deprecated")[[1]], + suppressWarnings(waic1["se_waic"][[1]]), waic1$estimates["waic", "SE"] ) - + # [[ method + expect_snapshot(waic1[["elpd_waic"]]) expect_equal( - expect_warning_fixed(waic1[["elpd_waic"]], "elpd_waic using '[[' is deprecated"), + suppressWarnings(waic1[["elpd_waic"]]), waic1$estimates["elpd_waic", "Estimate"] ) + expect_snapshot(waic1[["se_elpd_waic"]]) expect_equal( - expect_warning_fixed(waic1[["se_elpd_waic"]], "se_elpd_waic using '[[' is deprecated"), + suppressWarnings(waic1[["se_elpd_waic"]]), waic1$estimates["elpd_waic", "SE"] ) + expect_snapshot(waic1[["p_waic"]]) expect_equal( - expect_warning_fixed(waic1[["p_waic"]], "p_waic using '[[' is deprecated"), + suppressWarnings(waic1[["p_waic"]]), waic1$estimates["p_waic", "Estimate"] ) + expect_snapshot(waic1[["se_p_waic"]]) expect_equal( - expect_warning_fixed(waic1[["se_p_waic"]], "se_p_waic using '[[' is deprecated"), + suppressWarnings(waic1[["se_p_waic"]]), waic1$estimates["p_waic", "SE"] ) + expect_snapshot(waic1[["waic"]]) expect_equal( - expect_warning_fixed(waic1[["waic"]], "waic using '[[' is deprecated"), + suppressWarnings(waic1[["waic"]]), waic1$estimates["waic", "Estimate"] ) + expect_snapshot(waic1[["se_waic"]]) expect_equal( - expect_warning_fixed(waic1[["se_waic"]], "se_waic using '[[' is deprecated"), + suppressWarnings(waic1[["se_waic"]]), waic1$estimates["waic", "SE"] ) }) diff --git a/tests/testthat/test_extract_log_lik.R b/tests/testthat/test_extract_log_lik.R index 1c2b7efa..bd55033e 100644 --- a/tests/testthat/test_extract_log_lik.R +++ b/tests/testthat/test_extract_log_lik.R @@ -1,6 +1,5 @@ library(loo) -context("extract_log_lik") test_that("extract_log_lik throws appropriate errors", { x1 <- rnorm(100) expect_error(extract_log_lik(x1), regexp = "Not a stanfit object") diff --git a/tests/testthat/test_gpdfit.R b/tests/testthat/test_gpdfit.R index 97c57555..a1b1c902 100644 --- a/tests/testthat/test_gpdfit.R +++ b/tests/testthat/test_gpdfit.R @@ -1,18 +1,16 @@ library(loo) -context("generalized pareto") - test_that("gpdfit returns correct result", { set.seed(123) x <- rexp(100) gpdfit_val_old <- unlist(gpdfit(x, wip=FALSE, min_grid_pts = 80)) - expect_equal_to_reference(gpdfit_val_old, "reference-results/gpdfit_old.rds") + expect_snapshot_value(gpdfit_val_old, style = "serialize") gpdfit_val_wip <- unlist(gpdfit(x, wip=TRUE, min_grid_pts = 80)) - expect_equal_to_reference(gpdfit_val_wip, "reference-results/gpdfit.rds") + expect_snapshot_value(gpdfit_val_wip, style = "serialize") gpdfit_val_wip_default_grid <- unlist(gpdfit(x, wip=TRUE)) - expect_equal_to_reference(gpdfit_val_wip_default_grid, "reference-results/gpdfit_default_grid.rds") + expect_snapshot_value(gpdfit_val_wip_default_grid, style = "serialize") }) test_that("qgpd returns the correct result ", { diff --git a/tests/testthat/test_kfold_helpers.R b/tests/testthat/test_kfold_helpers.R index c613cafa..c35b192e 100644 --- a/tests/testthat/test_kfold_helpers.R +++ b/tests/testthat/test_kfold_helpers.R @@ -1,8 +1,6 @@ library(loo) set.seed(14014) -context("kfold helper functions") - test_that("kfold_split_random works", { fold_rand <- kfold_split_random(10, 100) expect_length(fold_rand, 100) diff --git a/tests/testthat/test_loo_and_waic.R b/tests/testthat/test_loo_and_waic.R index d43de50e..663d9858 100644 --- a/tests/testthat/test_loo_and_waic.R +++ b/tests/testthat/test_loo_and_waic.R @@ -2,8 +2,6 @@ library(loo) options(mc.cores = 1) set.seed(123) -context("loo, waic and elpd") - LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() LLvec <- LLmat[, 1] @@ -24,9 +22,9 @@ test_that("using loo.cores is deprecated", { }) test_that("loo, waic and elpd results haven't changed", { - expect_equal_to_reference(loo1, "reference-results/loo.rds") - expect_equal_to_reference(waic1, "reference-results/waic.rds") - expect_equal_to_reference(elpd1, "reference-results/elpd.rds") + expect_snapshot_value(loo1, style = "serialize") + expect_snapshot_value(waic1, style = "serialize") + expect_snapshot_value(elpd1, style = "serialize") }) test_that("loo with cores=1 and cores=2 gives same results", { @@ -117,7 +115,7 @@ test_that("loo.array and loo.matrix give same result", { # the mcse_elpd_loo columns won't be identical because we use sampling expect_identical(loo1$pointwise[, -2], l2$pointwise[, -2]) - expect_equal(loo1$pointwise[, 2], l2$pointwise[, 2], tol = 0.005) + expect_equal(loo1$pointwise[, 2], l2$pointwise[, 2], tolerance = 0.005) }) test_that("loo.array runs with multiple cores", { diff --git a/tests/testthat/test_loo_approximate_posterior.R b/tests/testthat/test_loo_approximate_posterior.R index e3adf791..f241ba2f 100644 --- a/tests/testthat/test_loo_approximate_posterior.R +++ b/tests/testthat/test_loo_approximate_posterior.R @@ -1,7 +1,5 @@ suppressPackageStartupMessages(library(loo)) -context("loo_approximate_posterior") - # Create test data # Checked by Mans M and Paul B 24th of June 2019 set.seed(123) @@ -58,8 +56,8 @@ test_that("loo_approximate_posterior.array works as loo_approximate_posterior.ma ll_array[,2,] <- ll[(S/2 + 1):S,] # Assert that they are ok - expect_equivalent(ll_array[1:2,1,1:2], ll[1:2,1:2]) - expect_equivalent(ll_array[1:2,2,1:2], ll[(S/2+1):((S/2)+2),1:2]) + expect_equal(ll_array[1:2,1,1:2], ll[1:2,1:2], ignore_attr = TRUE) + expect_equal(ll_array[1:2,2,1:2], ll[(S/2+1):((S/2)+2),1:2], ignore_attr = TRUE) # Compute aploo expect_silent(aploo1 <- loo_approximate_posterior.matrix(x = ll, log_p = log_p, log_g = log_g)) diff --git a/tests/testthat/test_loo_moment_matching.R b/tests/testthat/test_loo_moment_matching.R index ae67b20b..039c3b35 100644 --- a/tests/testthat/test_loo_moment_matching.R +++ b/tests/testthat/test_loo_moment_matching.R @@ -1,8 +1,6 @@ library(loo) options(mc.cores = 1) - -context("moment matching") set.seed(123) S <- 4000 @@ -159,11 +157,11 @@ test_that("loo_moment_match.default warnings work", { k_thres = 100, split = TRUE, cov = TRUE, cores = 1)) - expect_warning(loo_moment_match(x, loo_manual, post_draws_test, log_lik_i_test, + expect_snapshot(loo_moment_match(x, loo_manual, post_draws_test, log_lik_i_test, unconstrain_pars_test, log_prob_upars_test, log_lik_i_upars_test, max_iters = 1, k_thres = 0.5, split = TRUE, - cov = TRUE, cores = 1), "The maximum number of moment matching iterations") + cov = TRUE, cores = 1)) expect_error(loo_moment_match(x, loo_manual_tis, post_draws_test, log_lik_i_test, unconstrain_pars_test, log_prob_upars_test, @@ -196,7 +194,7 @@ test_that("loo_moment_match.default works", { expect_equal(loo_moment_match_object$pointwise[,"influence_pareto_k"],loo_manual$pointwise[,"influence_pareto_k"]) expect_equal(loo_moment_match_object$pointwise[,"influence_pareto_k"],loo_manual$diagnostics$pareto_k) - expect_equal_to_reference(loo_moment_match_object, "reference-results/moment_match_loo_1.rds") + expect_snapshot_value(loo_moment_match_object, style = "serialize") loo_moment_match_object2 <- suppressWarnings(loo_moment_match(x, loo_manual, post_draws_test, log_lik_i_test, unconstrain_pars_test, log_prob_upars_test, @@ -204,7 +202,7 @@ test_that("loo_moment_match.default works", { k_thres = 0.5, split = FALSE, cov = TRUE, cores = 1)) - expect_equal_to_reference(loo_moment_match_object2, "reference-results/moment_match_loo_2.rds") + expect_snapshot_value(loo_moment_match_object2, style = "serialize") loo_moment_match_object3 <- suppressWarnings(loo_moment_match(x, loo_manual, post_draws_test, log_lik_i_test, unconstrain_pars_test, log_prob_upars_test, @@ -212,7 +210,7 @@ test_that("loo_moment_match.default works", { k_thres = 0.5, split = TRUE, cov = TRUE, cores = 1)) - expect_equal_to_reference(loo_moment_match_object3, "reference-results/moment_match_loo_3.rds") + expect_snapshot_value(loo_moment_match_object3, style = "serialize") loo_moment_match_object4 <- suppressWarnings(loo_moment_match(x, loo_manual, post_draws_test, log_lik_i_test, unconstrain_pars_test, log_prob_upars_test, @@ -262,7 +260,7 @@ test_that("variance and covariance transformations work", { k_thres = 0.0, split = FALSE, cov = TRUE, cores = 1)) - expect_equal_to_reference(loo_moment_match_object, "reference-results/moment_match_var_and_cov.rds") + expect_snapshot_value(loo_moment_match_object, style = "serialize") }) @@ -284,7 +282,7 @@ test_that("loo_moment_match.default works with multiple cores", { cov = TRUE, cores = 2)) expect_equal(loo_moment_match_manual3$diagnostics$pareto_k, loo_moment_match_manual4$diagnostics$pareto_k) - expect_equal(loo_moment_match_manual3$diagnostics$n_eff, loo_moment_match_manual4$diagnostics$n_eff) + expect_equal(loo_moment_match_manual3$diagnostics$n_eff, loo_moment_match_manual4$diagnostics$n_eff, tolerance = 5e-4) expect_equal(loo_moment_match_manual3$estimates, loo_moment_match_manual4$estimates) @@ -315,8 +313,7 @@ test_that("loo_moment_match_split works", { log_prob_upars = log_prob_upars_test, log_lik_i_upars = log_lik_i_upars_test, cores = 1, r_eff_i = 1, is_method = "psis") - expect_equal_to_reference(split2, "reference-results/moment_match_split.rds") - + expect_snapshot_value(split2, style = "serialize") }) test_that("passing arguments works", { diff --git a/tests/testthat/test_loo_predictive_metric.R b/tests/testthat/test_loo_predictive_metric.R index cabf7b36..3d796249 100644 --- a/tests/testthat/test_loo_predictive_metric.R +++ b/tests/testthat/test_loo_predictive_metric.R @@ -78,17 +78,17 @@ test_that('loo_predictive_metric return types are correct', { expect_named(bacc_quant, c('estimate', 'se')) }) -test_that('loo_predictive_metric is equal to reference', { - expect_equal_to_reference(mae_mean, 'reference-results/loo_predictive_metric_mae_mean.rds') - expect_equal_to_reference(mae_quant, 'reference-results/loo_predictive_metric_mae_quant.rds') - expect_equal_to_reference(rmse_mean, 'reference-results/loo_predictive_metric_rmse_mean.rds') - expect_equal_to_reference(rmse_quant, 'reference-results/loo_predictive_metric_rmse_quant.rds') - expect_equal_to_reference(mse_mean, 'reference-results/loo_predictive_metric_mse_mean.rds') - expect_equal_to_reference(mse_quant, 'reference-results/loo_predictive_metric_mse_quant.rds') - expect_equal_to_reference(acc_mean, 'reference-results/loo_predictive_metric_acc_mean.rds') - expect_equal_to_reference(acc_quant, 'reference-results/loo_predictive_metric_acc_quant.rds') - expect_equal_to_reference(bacc_mean, 'reference-results/loo_predictive_metric_bacc_mean.rds') - expect_equal_to_reference(bacc_quant, 'reference-results/loo_predictive_metric_bacc_quant.rds') +test_that('loo_predictive_metric is equal to snapshot', { + expect_snapshot_value(mae_mean, style = "serialize") + expect_snapshot_value(mae_quant, style = "serialize") + expect_snapshot_value(rmse_mean, style = "serialize") + expect_snapshot_value(rmse_quant, style = "serialize") + expect_snapshot_value(mse_mean, style = "serialize") + expect_snapshot_value(mse_quant, style = "serialize") + expect_snapshot_value(acc_mean, style = "serialize") + expect_snapshot_value(acc_quant, style = "serialize") + expect_snapshot_value(bacc_mean, style = "serialize") + expect_snapshot_value(bacc_quant, style = "serialize") }) test_that('MAE computation is correct', { diff --git a/tests/testthat/test_loo_subsampling.R b/tests/testthat/test_loo_subsampling.R index 64032d0a..3bd38dee 100644 --- a/tests/testthat/test_loo_subsampling.R +++ b/tests/testthat/test_loo_subsampling.R @@ -1,8 +1,6 @@ library(loo) options(mc.cores = 1) -context("loo_subsampling") - test_that("overall loo_subampling works as expected (compared with loo) for diff_est", { set.seed(123) N <- 1000; K <- 10; S <- 1000; a0 <- 3; b0 <- 2 @@ -25,8 +23,8 @@ test_that("overall loo_subampling works as expected (compared with loo) for diff expect_s3_class(loo_ss, "psis_loo_ss") # Check consistency - expect_equivalent(loo_ss$pointwise[, "elpd_loo_approx"], - loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]]) + expect_equal(loo_ss$pointwise[, "elpd_loo_approx"], + loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]], ignore_attr = TRUE) # Expect values z <- 2 @@ -37,15 +35,15 @@ test_that("overall loo_subampling works as expected (compared with loo) for diff expect_lte(loo_ss$estimates["looic", "Estimate"] - z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) expect_gte(loo_ss$estimates["looic", "Estimate"] + z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) - expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tol = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tolerance = 0.00000001)) # Test that observations works as expected expect_message(loo_ss2 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = obs_idx(loo_ss), loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) - expect_equal(loo_ss2$estimates, loo_ss$estimates, tol = 0.00000001) + expect_equal(loo_ss2$estimates, loo_ss$estimates, tolerance = 0.00000001) expect_silent(loo_ss2 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = loo_ss, loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) - expect_equal(loo_ss2$estimates, loo_ss$estimates, tol = 0.00000001) + expect_equal(loo_ss2$estimates, loo_ss$estimates, tolerance = 0.00000001) # Test lpd expect_silent(loo_ss_lpd <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 500, loo_approximation = "lpd", r_eff = rep(1, nrow(fake_data)))) @@ -58,9 +56,9 @@ test_that("overall loo_subampling works as expected (compared with loo) for diff expect_lte(loo_ss_lpd$estimates["looic", "Estimate"] - z * loo_ss_lpd$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) expect_gte(loo_ss_lpd$estimates["looic", "Estimate"] + z * loo_ss_lpd$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) - expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss_lpd$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss_lpd$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss_lpd$estimates["looic", "Estimate"], tol = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss_lpd$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss_lpd$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss_lpd$estimates["looic", "Estimate"], tolerance = 0.00000001)) expect_silent(loo_ss_lpd10 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 500, loo_approximation = "lpd", loo_approximation_draws = 10, r_eff = rep(1, nrow(fake_data)))) expect_s3_class(loo_ss_lpd10, "psis_loo_ss") @@ -73,9 +71,9 @@ test_that("overall loo_subampling works as expected (compared with loo) for diff expect_lte(loo_ss_lpd10$estimates["looic", "Estimate"] - z * loo_ss_lpd10$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) expect_gte(loo_ss_lpd10$estimates["looic", "Estimate"] + z * loo_ss_lpd10$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) - expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss_lpd10$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss_lpd10$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss_lpd10$estimates["looic", "Estimate"], tol = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss_lpd10$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss_lpd10$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss_lpd10$estimates["looic", "Estimate"], tolerance = 0.00000001)) # Test conversion of objects expect_silent(true_loo_2 <- loo:::as.psis_loo.psis_loo(true_loo)) @@ -107,9 +105,9 @@ test_that("loo with subsampling of all observations works as ordinary loo.", { expect_s3_class(loo_ss, "psis_loo_ss") expect_error(loo_ss <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 1001, loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) - expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001) - expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tol = 0.00000001) - expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tol = 0.00000001) + expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001) + expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001) + expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tolerance = 0.00000001) expect_equal(dim(true_loo),dim(loo_ss)) expect_equal(true_loo$diagnostics, loo_ss$diagnostics) @@ -132,7 +130,7 @@ test_that("overall loo_subsample works with diff_srs as expected (compared with expect_silent(true_loo <- loo(x = llfun_test, draws = fake_posterior, data = fake_data, r_eff = rep(1, nrow(fake_data)))) expect_silent(loo_ss <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 200, loo_approximation = "plpd", estimator = "diff_srs", r_eff = rep(1, nrow(fake_data)))) - expect_equal(true_loo$estimates[1,1], loo_ss$estimates[1,1], tol = 0.1) + expect_equal(true_loo$estimates[1,1], loo_ss$estimates[1,1], tolerance = 0.1) }) @@ -159,8 +157,8 @@ test_that("Test the srs estimator with 'none' approximation", { # Check consistency - expect_equivalent(loo_ss$pointwise[, "elpd_loo_approx"], - loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]]) + expect_equal(loo_ss$pointwise[, "elpd_loo_approx"], + loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]], ignore_attr = TRUE) # Expect values z <- 2 @@ -171,9 +169,9 @@ test_that("Test the srs estimator with 'none' approximation", { expect_lte(loo_ss$estimates["looic", "Estimate"] - z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) expect_gte(loo_ss$estimates["looic", "Estimate"] + z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) - expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tol = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tolerance = 0.00000001)) }) @@ -209,11 +207,11 @@ test_that("Test the Hansen-Hurwitz estimator", { # Check consistency - expect_equivalent(loo_ss$pointwise[, "elpd_loo_approx"], - loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]]) + expect_equal(loo_ss$pointwise[, "elpd_loo_approx"], + loo_ss$loo_subsampling$elpd_loo_approx[loo_ss$pointwise[, "idx"]], ignore_attr = TRUE) # Check consistency - expect_equivalent(loo_ss_max$pointwise[, "elpd_loo_approx"], - loo_ss_max$loo_subsampling$elpd_loo_approx[loo_ss_max$pointwise[, "idx"]]) + expect_equal(loo_ss_max$pointwise[, "elpd_loo_approx"], + loo_ss_max$loo_subsampling$elpd_loo_approx[loo_ss_max$pointwise[, "idx"]], ignore_attr = TRUE) # Expect values z <- 2 @@ -224,9 +222,9 @@ test_that("Test the Hansen-Hurwitz estimator", { expect_lte(loo_ss$estimates["looic", "Estimate"] - z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) expect_gte(loo_ss$estimates["looic", "Estimate"] + z * loo_ss$estimates["looic", "subsampling SE"], true_loo$estimates["looic", "Estimate"]) - expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tol = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(true_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tolerance = 0.00000001)) expect_lte(loo_ss_max$estimates["elpd_loo", "Estimate"] - z * loo_ss_max$estimates["elpd_loo", "subsampling SE"], true_loo$estimates["elpd_loo", "Estimate"]) expect_gte(loo_ss_max$estimates["elpd_loo", "Estimate"] + z * loo_ss_max$estimates["elpd_loo", "subsampling SE"], true_loo$estimates["elpd_loo", "Estimate"]) @@ -283,7 +281,7 @@ test_that("update.psis_loo_ss works as expected (compared with loo)", { expect_silent(loo_ss4 <- update(object = loo_ss, draws = fake_posterior, data = fake_data, observations = 1000, r_eff = rep(1, nrow(fake_data)))) expect_equal(loo_ss4$estimates[,1], true_loo$estimates[,1]) - expect_equal(loo_ss4$estimates[,2], true_loo$estimates[,2], tol = 0.001) + expect_equal(loo_ss4$estimates[,2], true_loo$estimates[,2], tolerance = 0.001) expect_silent(loo_ss5 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 1000, loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) @@ -325,678 +323,6 @@ test_that("update.psis_loo_ss works as expected (compared with loo)", { }) - - - - - - -context("loo_subsampling_approximations") - -geterate_test_elpd_dataset <- function() { - N <- 10; K <- 10; S <- 1000; a0 <- 3; b0 <- 2 - p <- 0.7 - y <- rbinom(N, size = K, prob = p) - a <- a0 + sum(y); b <- b0 + N * K - sum(y) - fake_posterior <- draws <- as.matrix(rbeta(S, a, b)) - fake_data <- data.frame(y,K) - rm(N, K, S, a0, b0, p, y, a, b) - - list(fake_posterior = fake_posterior, fake_data = fake_data) -} - -test_elpd_loo_approximation <- function(cores) { - set.seed(123) - test_data <- geterate_test_elpd_dataset() - fake_posterior <- test_data$fake_posterior - fake_data <- test_data$fake_data - - llfun_test <- function(data_i, draws) { - dbinom(data_i$y, size = data_i$K, prob = draws, log = TRUE) - } - - # Compute plpd approximation - expect_silent(pi_vals <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "plpd", cores = cores)) - # Compute it manually - point <- mean(fake_posterior) - llik <- dbinom(fake_data$y, size = fake_data$K, prob = point, log = TRUE) - abs_lliks <- abs(llik) - man_elpd_loo_approximation <- abs_lliks/sum(abs_lliks) - expect_equal(abs(pi_vals)/sum(abs(pi_vals)), man_elpd_loo_approximation, tol = 0.00001) - - # Compute lpd approximation - expect_silent(pi_vals <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "lpd", cores = cores)) - # Compute it manually - llik <- numeric(10) - for(i in seq_along(fake_data$y)){ - llik[i] <- loo:::logMeanExp(dbinom(fake_data$y[i], size = fake_data$K, prob = fake_posterior, log = TRUE)) - } - abs_lliks <- abs(llik) - man_approx_loo_variable <- abs_lliks/sum(abs_lliks) - expect_equal(abs(pi_vals)/sum(abs(pi_vals)), man_approx_loo_variable, tol = 0.00001) - - # Compute waic approximation - expect_silent(pi_vals_waic <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", cores = cores)) - expect_true(all(pi_vals > pi_vals_waic)) - expect_true(sum(pi_vals) - sum(pi_vals_waic) < 1) - - # Compute tis approximation - expect_silent(pi_vals_tis <- loo:::elpd_loo_approximation(.llfun = llfun_test, - data = fake_data, - draws = fake_posterior, - loo_approximation = "tis", - loo_approximation_draws = 100, - cores = cores)) - expect_true(all(pi_vals > pi_vals_tis)) - expect_true(sum(pi_vals) - sum(pi_vals_tis) < 1) -} - -test_that("elpd_loo_approximation works as expected", { - test_elpd_loo_approximation(1) -}) - -test_that("elpd_loo_approximation with multiple cores", { - test_elpd_loo_approximation(2) -}) - -test_that("Test loo_approximation_draws", { - - - set.seed(123) - N <- 1000; K <- 10; S <- 1000; a0 <- 3; b0 <- 2 - p <- 0.7 - y <- rbinom(N, size = K, prob = p) - a <- a0 + sum(y); b <- b0 + N * K - sum(y) - fake_posterior <- draws <- as.matrix(rbeta(S, a, b)) - fake_data <- data.frame(y,K) - rm(N, K, S, a0, b0, p, y, a, b) - llfun_test <- function(data_i, draws) { - dbinom(data_i$y, size = data_i$K, prob = draws, log = TRUE) - } - - expect_silent(res1 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) - expect_silent(res2 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", loo_approximation_draws = 10, cores = 1)) - expect_silent(res3 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior[1:10*100,], loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) - expect_silent(res4 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior[1:10*100,, drop = FALSE], loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) - expect_failure(expect_equal(res1, res3)) - expect_equal(res2, res3) - - expect_silent(loo_ss1 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) - expect_silent(loo_ss2 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 10, r_eff = rep(1, nrow(fake_data)))) - expect_silent(loo_ss3 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 31, r_eff = rep(1, nrow(fake_data)))) - expect_error(loo_ss4 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 3100, r_eff = rep(1, nrow(fake_data)))) - - expect_equal(names(loo_ss1$loo_subsampling), c("elpd_loo_approx", "loo_approximation", "loo_approximation_draws", "estimator", ".llfun", ".llgrad", ".llhess", "data_dim", "ndraws")) - expect_null(loo_ss1$loo_subsampling$loo_approximation_draws) - expect_equal(loo_ss2$loo_subsampling$loo_approximation_draws, 10L) - expect_equal(loo_ss3$loo_subsampling$loo_approximation_draws, 31L) - -}) - - - -test_that("waic using delta method and gradient", { - - - if (FALSE){ - # Code to generate testdata - saved and loaded to avoid dependency of mvtnorm - set.seed(123) - N <- 400; beta <- c(1,2); X_full <- matrix(rep(1,N), ncol = 1); X_full <- cbind(X_full, runif(N)); S <- 1000 - y_full <- rnorm(n = N, mean = X_full%*%beta, sd = 1) - X <- X_full; y <- y_full - Lambda_0 <- diag(length(beta)); mu_0 <- c(0,0) - b_hat <- solve(t(X)%*%X)%*%t(X)%*%y - mu_n <- solve(t(X)%*%X)%*%(t(X)%*%X%*%b_hat + Lambda_0%*%mu_0) - Lambda_n <- t(X)%*%X + Lambda_0 - # Uncomment row below when running. Commented out to remove CHECK warnings - # fake_posterior <- mvtnorm::rmvnorm(n = S, mean = mu_n, sigma = solve(Lambda_n)) - colnames(fake_posterior) <- c("a", "b") - fake_data <- data.frame(y, X) - save(fake_posterior, fake_data, file = test_path("data-for-tests/normal_reg_waic_test_example.rda")) - } else { - load(file = test_path("data-for-tests/normal_reg_waic_test_example.rda")) - } - - .llfun <- function(data_i, draws) { - # data_i: ith row of fdata (fake_data[i,, drop=FALSE]) - # draws: entire fake_posterior matrix - dnorm(data_i$y, mean = draws[, c("a", "b")] %*% t(as.matrix(data_i[, c("X1", "X2")])), sd = 1, log = TRUE) - } - - .llgrad <- function(data_i, draws) { - x_i <- data_i[, "X2"] - gr <- cbind(data_i$y - draws[,"a"] - draws[,"b"]*x_i, - (data_i$y - draws[,"a"] - draws[,"b"]*x_i) * x_i) - colnames(gr) <- c("a", "b") - gr - } - - fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) - - expect_silent(approx_loo_waic <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic")) - expect_silent(approx_loo_waic_delta <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad", .llgrad = .llgrad)) - expect_silent(approx_loo_waic_delta_diag <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad_marginal", .llgrad = .llgrad)) - - # Test that the approaches should not deviate too much - diff_waic_delta <- mean(approx_loo_waic - approx_loo_waic_delta) - diff_waic_delta_diag <- mean(approx_loo_waic - approx_loo_waic_delta_diag) - expect_equal(approx_loo_waic,approx_loo_waic_delta_diag, tol = 0.1) - expect_equal(approx_loo_waic,approx_loo_waic_delta, tol = 0.01) - - # Test usage in subsampling_loo - expect_silent(loo_ss_waic <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic", observations = 50, llgrad = .llgrad)) - expect_silent(loo_ss_waic_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50, llgrad = .llgrad)) - expect_silent(loo_ss_waic_delta_marginal <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad_marginal", observations = 50, llgrad = .llgrad)) - expect_silent(loo_ss_plpd <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "plpd", observations = 50, llgrad = .llgrad)) - expect_error(loo_ss_waic_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50)) -}) - -test_that("waic using delta 2nd order method", { - - - if (FALSE){ - # Code to generate testdata - saved and loaded to avoid dependency of MCMCPack - set.seed(123) - N <- 100; beta <- c(1,2); X_full <- matrix(rep(1,N), ncol = 1); X_full <- cbind(X_full, runif(N)); S <- 1000 - y_full <- rnorm(n = N, mean = X_full%*%beta, sd = 0.5) - X <- X_full; y <- y_full - # Uncomment row below when running. Commented out to remove CHECK warnings - # fake_posterior <- MCMCpack::MCMCregress(y~x, data = data.frame(y = y,x=X[,2]), thin = 10, mcmc = 10000) # Because Im lazy - fake_posterior <- as.matrix(fake_posterior) - fake_posterior[,"sigma2"] <- sqrt(fake_posterior[,"sigma2"]) - colnames(fake_posterior) <- c("a", "b", "sigma") - fake_data <- data.frame(y, X) - save(fake_posterior, fake_data, file = test_path("data-for-tests/normal_reg_waic_test_example2.rda"), compression_level = 9) - } else { - load(file = test_path("data-for-tests/normal_reg_waic_test_example2.rda")) - } - - .llfun <- function(data_i, draws) { - # data_i: ith row of fdata (data_i <- fake_data[i,, drop=FALSE]) - # draws: entire fake_posterior matrix - dnorm(data_i$y, mean = draws[, c("a", "b")] %*% t(as.matrix(data_i[, c("X1", "X2")])), sd = draws[, c("sigma")], log = TRUE) - } - - .llgrad <- function(data_i, draws) { - sigma <- draws[,"sigma"] - sigma2 <- sigma^2 - b <- draws[,"b"] - a <- draws[,"a"] - x_i <- unlist(data_i[, c("X1", "X2")]) - e <- (data_i$y - draws[,"a"] * x_i[1] - draws[,"b"] * x_i[2]) - - gr <- cbind(e * x_i[1] / sigma2, - e * x_i[2] / sigma2, - - 1 / sigma + e^2 / (sigma2 * sigma)) - colnames(gr) <- c("a", "b", "sigma") - gr - } - - .llhess <- function(data_i, draws) { - hess_array <- array(0, dim = c(ncol(draws), ncol(draws), nrow(draws)), dimnames = list(colnames(draws),colnames(draws),NULL)) - sigma <- draws[,"sigma"] - sigma2 <- sigma^2 - sigma3 <- sigma2*sigma - b <- draws[,"b"] - a <- draws[,"a"] - x_i <- unlist(data_i[, c("X1", "X2")]) - e <- (data_i$y - draws[,"a"] * x_i[1] - draws[,"b"] * x_i[2]) - - hess_array[1,1,] <- - x_i[1]^2 / sigma2 - hess_array[1,2,] <- hess_array[2,1,] <- - x_i[1] * x_i[2] / sigma2 - hess_array[2,2,] <- - x_i[2]^2 / sigma2 - hess_array[3,1,] <- hess_array[1,3,] <- -2 * x_i[1] * e / sigma3 - hess_array[3,2,] <- hess_array[2,3,] <- -2 * x_i[2] * e / sigma3 - hess_array[3,3,] <- 1 / sigma2 - 3 * e^2 / (sigma2^2) - hess_array - } - - #data <- fake_data - fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) - #draws <- fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) - - expect_silent(approx_loo_waic <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic")) - expect_silent(approx_loo_waic_delta <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad", .llgrad = .llgrad)) - expect_silent(approx_loo_waic_delta2 <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_hess", .llgrad = .llgrad, .llhess = .llhess)) - - # Test that the approaches should not deviate too much - expect_equal(approx_loo_waic,approx_loo_waic_delta2, tol = 0.01) - expect_equal(approx_loo_waic,approx_loo_waic_delta, tol = 0.01) - - expect_silent(test_loo_ss_waic <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic", observations = 50, llgrad = .llgrad)) - expect_error(test_loo_ss_delta2 <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_hess", observations = 50, llgrad = .llgrad)) - expect_silent(test_loo_ss_delta2 <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_hess", observations = 50, llgrad = .llgrad, llhess = .llhess)) - expect_silent(test_loo_ss_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50, llgrad = .llgrad)) - expect_silent(test_loo_ss_point <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "plpd", observations = 50, llgrad = .llgrad)) -}) - - - - - - - -context("loo_subsampling_estimation") - -test_that("whhest works as expected", { - - - N <- 100 - m <- 10 - z <- rep(1/N, m) - y <- 1:10 - m_i <- rep(1,m) - expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - expect_equal(whe$y_hat_ppz, 550) - man_var <- (sum((whe$y_hat_ppz - y/z)^2)/(m-1))/m - expect_equal(whe$v_hat_y_ppz, man_var) - z <- 1:10/(sum(1:10)*10) - expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - expect_equal(whe$y_hat_ppz, 550) - expect_equal(whe$v_hat_y_ppz, 0) - - # School book example - # https://newonlinecourses.science.psu.edu/stat506/node/15/ - z <- c(650/15650, 2840/15650, 3200/15650) - y <- c(420, 1785, 2198) - m_i <- c(1,1,1) - N <- 10 - expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - expect_equal(round(whe$y_hat_ppz, 2), 10232.75, tol = 0) - expect_equal(whe$v_hat_y_ppz, 73125.74, tol = 0.01) - # Double check that it is rounding error - man_var_round <- (sum((round(y/z,2) - 10232.75)^2)) * (1/2) * (1/3) - expect_equal(man_var_round, 73125.74, tol = 0.001) - man_var_exact <- (sum((y/z - 10232.75)^2)) * (1/2) * (1/3) - expect_equal(whe$v_hat_y_ppz, man_var_exact, tol = 0.001) - - # Add test for variance estimation - N <- 100 - m <- 10 - y <- rep(1:10, 1) - true_var <- var(rep(y, 10)) * (99) - z <- rep(1/N, m) - m_i <- rep(100000, m) - expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - expect_equal(true_var, whe$hat_v_y_ppz, tol = 0.01) - - # Add tests for m_i - N <- 100 - y <- rep(1:10, 2) - m <- length(y) - z <- rep(1/N, m) - m_i <- rep(1,m) - expect_silent(whe1 <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - y <- rep(1:10) - m <- length(y) - z <- rep(1/N, m) - m_i <- rep(2,m) - expect_silent(whe2 <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) - expect_equal(whe1$y_hat_ppz, whe2$y_hat_ppz) - expect_equal(whe1$v_hat_y_ppz, whe2$v_hat_y_ppz) - expect_equal(whe1$hat_v_y_ppz, whe1$hat_v_y_ppz) - -}) - - -test_that("srs_diff_est works as expected", { - - - set.seed(1234) - N <- 1000 - y_true <- 1:N - sigma_hat_true <- sqrt(N * sum((y_true - mean(y_true))^2) / length(y_true)) - y_approx <- rnorm(N, y_true, 0.1) - m <- 100 - sigma_hat <- y_hat <- se_y_hat <- numeric(10000) - for(i in 1:10000){ - y_idx <- sample(1:N, size = m) - y <- y_true[y_idx] - res <- loo:::srs_diff_est(y_approx, y, y_idx) - y_hat[i] <- res$y_hat - se_y_hat[i] <- sqrt(res$v_y_hat) - sigma_hat[i] <- sqrt(res$hat_v_y) - } - expect_equal(mean(y_hat), sum(y_true), tol = 0.1) - - in_ki <- y_hat + 2 * se_y_hat > sum(y_true) & y_hat - 2*se_y_hat < sum(y_true) - expect_equal(mean(in_ki), 0.95, tol = 0.01) - - # Should be unbiased - expect_equal(mean(sigma_hat), sigma_hat_true, tol = 0.1) - - m <- N - y_idx <- sample(1:N, size = m) - y <- y_true[y_idx] - res <- loo:::srs_diff_est(y_approx, y, y_idx) - expect_equal(res$y_hat, 500500, tol = 0.0001) - expect_equal(res$v_y_hat, 0, tol = 0.0001) - expect_equal(sqrt(res$hat_v_y), sigma_hat_true, tol = 0.1) - -}) - -test_that("srs_est works as expected", { - - - set.seed(1234) - # Cochran 1976 example Table 2.2 - - y <- c(rep(42,23),rep(41,4), 36, 32, 29, 27, 27, 23, 19, 16, 16, 15, 15, 14, 11, 10, 9, 7, 6, 6, 6, 5, 5, 4, 3) - expect_equal(sum(y), 1471) - approx_loo <- rep(0L, 676) - expect_equal(sum(y^2), 54497) - res <- loo:::srs_est(y = y, approx_loo) - expect_equal(res$y_hat, 19888, tol = 0.0001) - expect_equal(res$v_y_hat, 676^2*229*(1-0.074)/50, tol = 0.0001) - expect_equal(res$hat_v_y, 676 * var(y), tol = 0.0001) - - # Simulation example - set.seed(1234) - N <- 1000 - y_true <- 1:N - sigma_hat_true <- sqrt(N * sum((y_true - mean(y_true))^2) / length(y_true)) - - m <- 100 - y_hat <- se_y_hat <- sigma_hat <- numeric(10000) - for(i in 1:10000){ - y_idx <- sample(1:N, size = m) - y <- y_true[y_idx] - res <- loo:::srs_est(y = y, y_approx = y_true) - y_hat[i] <- res$y_hat - se_y_hat[i] <- sqrt(res$v_y_hat) - sigma_hat[i] <- sqrt(res$hat_v_y) - } - expect_equal(mean(y_hat), sum(y_true), tol = 0.1) - - in_ki <- y_hat + 2*se_y_hat > sum(y_true) & y_hat - 2*se_y_hat < sum(y_true) - expect_equal(mean(in_ki), 0.95, tol = 0.01) - - # Should be unbiased - expect_equal(mean(sigma_hat), sigma_hat_true, tol = 0.1) - - m <- N - y_idx <- sample(1:N, size = m) - y <- y_true[y_idx] - res <- loo:::srs_est(y, y_true) - expect_equal(res$y_hat, 500500, tol = 0.0001) - expect_equal(res$v_y_hat, 0, tol = 0.0001) - -}) - - - - -context("loo_subsampling cases") - -test_that("Test loo_subsampling and loo_approx with radon data", { - skip_on_cran() # avoid going over time limit for tests - - load(test_path("data-for-tests/test_radon_laplace_loo.rda")) - # Rename to spot variable leaking errors - llfun_test <- llfun - log_p_test <- log_p - log_g_test <- log_q - draws_test <- draws - data_test <- data - rm(llfun, log_p,log_q, draws, data) - - set.seed(134) - expect_silent(full_loo <- loo(llfun_test, draws = draws_test, data = data_test, r_eff = rep(1, nrow(data_test)))) - expect_s3_class(full_loo, "psis_loo") - - set.seed(134) - expect_silent(loo_ss <- loo_subsample(x = llfun_test, draws = draws_test, data = data_test, observations = 200, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) - expect_s3_class(loo_ss, "psis_loo_ss") - - set.seed(134) - expect_silent(loo_ap_ss <- loo_subsample(x = llfun_test, draws = draws_test, data = data_test, log_p = log_p_test, log_g = log_g_test, observations = 200, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) - expect_s3_class(loo_ap_ss, "psis_loo_ss") - expect_s3_class(loo_ap_ss, "psis_loo_ap") - - expect_silent(loo_ap_ss_full <- loo_subsample(x = llfun_test, log_p = log_p_test, log_g = log_g_test, draws = draws_test, data = data_test, observations = NULL, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) - expect_failure(expect_s3_class(loo_ap_ss_full, "psis_loo_ss")) - expect_s3_class(loo_ap_ss_full, "psis_loo_ap") - - # Expect similar results - z <- 2 - expect_lte(loo_ss$estimates["elpd_loo", "Estimate"] - z * loo_ss$estimates["elpd_loo", "subsampling SE"], full_loo$estimates["elpd_loo", "Estimate"]) - expect_gte(loo_ss$estimates["elpd_loo", "Estimate"] + z * loo_ss$estimates["elpd_loo", "subsampling SE"], full_loo$estimates["elpd_loo", "Estimate"]) - expect_lte(loo_ss$estimates["p_loo", "Estimate"] - z * loo_ss$estimates["p_loo", "subsampling SE"], full_loo$estimates["p_loo", "Estimate"]) - expect_gte(loo_ss$estimates["p_loo", "Estimate"] + z * loo_ss$estimates["p_loo", "subsampling SE"], full_loo$estimates["p_loo", "Estimate"]) - expect_lte(loo_ss$estimates["looic", "Estimate"] - z * loo_ss$estimates["looic", "subsampling SE"], full_loo$estimates["looic", "Estimate"]) - expect_gte(loo_ss$estimates["looic", "Estimate"] + z * loo_ss$estimates["looic", "subsampling SE"], full_loo$estimates["looic", "Estimate"]) - - expect_failure(expect_equal(full_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(full_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(full_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tol = 0.00000001)) - - z <- 2 - expect_lte(loo_ap_ss$estimates["elpd_loo", "Estimate"] - z * loo_ap_ss$estimates["elpd_loo", "subsampling SE"], loo_ap_ss_full$estimates["elpd_loo", "Estimate"]) - expect_gte(loo_ap_ss$estimates["elpd_loo", "Estimate"] + z * loo_ap_ss$estimates["elpd_loo", "subsampling SE"], loo_ap_ss_full$estimates["elpd_loo", "Estimate"]) - expect_lte(loo_ap_ss$estimates["p_loo", "Estimate"] - z * loo_ap_ss$estimates["p_loo", "subsampling SE"], loo_ap_ss_full$estimates["p_loo", "Estimate"]) - expect_gte(loo_ap_ss$estimates["p_loo", "Estimate"] + z * loo_ap_ss$estimates["p_loo", "subsampling SE"], loo_ap_ss_full$estimates["p_loo", "Estimate"]) - expect_lte(loo_ap_ss$estimates["looic", "Estimate"] - z * loo_ap_ss$estimates["looic", "subsampling SE"], loo_ap_ss_full$estimates["looic", "Estimate"]) - expect_gte(loo_ap_ss$estimates["looic", "Estimate"] + z * loo_ap_ss$estimates["looic", "subsampling SE"], loo_ap_ss_full$estimates["looic", "Estimate"]) - - expect_failure(expect_equal(loo_ap_ss_full$estimates["elpd_loo", "Estimate"], loo_ap_ss$estimates["elpd_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(loo_ap_ss_full$estimates["p_loo", "Estimate"], loo_ap_ss$estimates["p_loo", "Estimate"], tol = 0.00000001)) - expect_failure(expect_equal(loo_ap_ss_full$estimates["looic", "Estimate"], loo_ap_ss$estimates["looic", "Estimate"], tol = 0.00000001)) - - # Correct printout - expect_failure(expect_output(print(full_loo), "Posterior approximation correction used\\.")) - expect_failure(expect_output(print(full_loo), "subsampled log-likelihood\nvalues")) - - expect_failure(expect_output(print(loo_ss), "Posterior approximation correction used\\.")) - expect_output(print(loo_ss), "subsampled log-likelihood\nvalues") - - expect_output(print(loo_ap_ss), "Posterior approximation correction used\\.") - expect_output(print(loo_ap_ss), "subsampled log-likelihood\nvalues") - - expect_output(print(loo_ap_ss_full), "Posterior approximation correction used\\.") - expect_failure(expect_output(print(loo_ap_ss_full), "subsampled log-likelihood\nvalues")) - - # Test conversion of objects - expect_silent(loo_ap_full <- loo:::as.psis_loo.psis_loo(loo_ap_ss_full)) - expect_s3_class(loo_ap_full, "psis_loo_ap") - expect_silent(loo_ap_full_ss <- loo:::as.psis_loo_ss.psis_loo(loo_ap_full)) - expect_s3_class(loo_ap_full_ss, "psis_loo_ss") - expect_s3_class(loo_ap_full_ss, "psis_loo_ap") - expect_silent(loo_ap_full2 <- loo:::as.psis_loo.psis_loo_ss(loo_ap_full_ss)) - expect_s3_class(loo_ap_full2, "psis_loo_ap") - expect_failure(expect_s3_class(loo_ap_full2, "psis_loo_ss")) - expect_equal(loo_ap_full2,loo_ap_full) - - # Test update - set.seed(4712) - expect_silent(loo_ss2 <- update(loo_ss, draws = draws_test, data = data_test, observations = 1000, r_eff = rep(1, nrow(data_test)))) - expect_gt(dim(loo_ss2)[2], dim(loo_ss)[2]) - expect_gt(dim(loo_ss2$pointwise)[1], dim(loo_ss$pointwise)[1]) - expect_equal(nobs(loo_ss), 200) - expect_equal(nobs(loo_ss2), 1000) - for(i in 1:nrow(loo_ss2$estimates)) { - expect_lt(loo_ss2$estimates[i, "subsampling SE"], - loo_ss$estimates[i, "subsampling SE"]) - } - - set.seed(4712) - expect_silent(loo_ap_ss2 <- update(object = loo_ap_ss, draws = draws_test, data = data_test, observations = 2000)) - expect_gt(dim(loo_ap_ss2)[2], dim(loo_ap_ss)[2]) - expect_gt(dim(loo_ap_ss2$pointwise)[1], dim(loo_ap_ss$pointwise)[1]) - expect_equal(nobs(loo_ap_ss), 200) - expect_equal(nobs(loo_ap_ss2), 2000) - for(i in 1:nrow(loo_ap_ss2$estimates)) { - expect_lt(loo_ap_ss2$estimates[i, "subsampling SE"], - loo_ap_ss$estimates[i, "subsampling SE"]) - } - - expect_equal(round(full_loo$estimates), round(loo_ap_ss_full$estimates)) - expect_failure(expect_equal(full_loo$estimates, loo_ap_ss_full$estimates)) - expect_equal(dim(full_loo), dim(loo_ap_ss_full)) - expect_s3_class(loo_ap_ss_full, "psis_loo_ap") - -}) - - -test_that("Test the vignette", { - skip_on_cran() - - - # NOTE - # If any of these test fails, the vignette probably needs to be updated - - if (FALSE) { - # Generate vignette test case - library("rstan") - stan_code <- " - data { - int N; // number of data points - int P; // number of predictors (including intercept) - matrix[N,P] X; // predictors (including 1s for intercept) - int y[N]; // binary outcome - } - parameters { - vector[P] beta; - } - model { - beta ~ normal(0, 1); - y ~ bernoulli_logit(X * beta); - } - " - # logistic <- function(x) {1 / (1 + exp(-x))} - # logit <- function(x) {log(x) - log(1-x)} - llfun_logistic <- function(data_i, draws) { - x_i <- as.matrix(data_i[, which(grepl(colnames(data_i), pattern = "X")), drop=FALSE]) - y_pred <- draws %*% t(x_i) - dbinom(x = data_i$y, size = 1, prob = 1 / (1 + exp(-y_pred)), log = TRUE) - } - - # Prepare data - url <- "http://stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat" - wells <- read.table(url) - wells$dist100 <- with(wells, dist / 100) - X <- model.matrix(~ dist100 + arsenic, wells) - standata <- list(y = wells$switch, X = X, N = nrow(X), P = ncol(X)) - - # Fit model - set.seed(4711) - fit_1 <- stan(model_code = stan_code, data = standata, seed = 4711) - print(fit_1, pars = "beta") - - parameter_draws <- extract(fit_1)$beta - stan_df <- as.data.frame(standata) - loo_i(1, llfun_logistic, data = stan_df, draws = parameter_draws) - - sm <- stan_model(model_code = stan_code) - set.seed(4711) - fit_laplace <- optimizing(sm, data = standata, draws = 2000, seed = 42) - parameter_draws_laplace <- fit_laplace$theta_tilde - log_p <- fit_laplace$log_p # The log density of the posterior - log_g <- fit_laplace$log_g # The log density of the approximation - - # For comparisons - standata$X[, "arsenic"] <- log(standata$X[, "arsenic"]) - stan_df2 <- as.data.frame(standata) - set.seed(4711) - fit_2 <- stan(fit = fit_1, data = standata, seed = 4711) - parameter_draws_2 <- extract(fit_2)$beta - - save(llfun_logistic, - stan_df, stan_df2, - parameter_draws, parameter_draws_laplace, parameter_draws_2, - log_p, log_g, - file = test_path("data-for-tests/loo_subsample_vignette.rda"), compression_level = 9) - - } else { - load(test_path("data-for-tests/loo_subsample_vignette.rda")) - } - - set.seed(4711) - expect_no_warning(looss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100)) - expect_output(print(looss_1), "Computed from 4000 by 100 subsampled log-likelihood") - expect_output(print(looss_1), "values from 3020 total observations.") - expect_output(print(looss_1), "MCSE and ESS estimates assume independent draws") - expect_output(print(looss_1), "elpd_loo -1968.5 15.6 0.3") - expect_output(print(looss_1), "p_loo 3.1 0.1 0.4") - expect_s3_class(looss_1, c("psis_loo_ss", "psis_loo", "loo")) - - set.seed(4711) - expect_no_warning(looss_1b <- update(looss_1, draws = parameter_draws, data = stan_df, observations = 200)) - expect_output(print(looss_1b), "Computed from 4000 by 200 subsampled log-likelihood") - expect_output(print(looss_1b), "values from 3020 total observations.") - expect_output(print(looss_1b), "MCSE and ESS estimates assume independent draws") - expect_output(print(looss_1b), "elpd_loo -1968.3 15.6 0.2") - expect_output(print(looss_1b), "p_loo 3.2 0.1 0.4") - expect_s3_class(looss_1b, c("psis_loo_ss", "psis_loo", "loo")) - - set.seed(4711) - expect_no_warning(looss_2 <- loo_subsample(x = llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100, estimator = "hh_pps", loo_approximation = "lpd", loo_approximation_draws = 100)) - expect_output(print(looss_2), "Computed from 4000 by 100 subsampled log-likelihood") - expect_output(print(looss_2), "values from 3020 total observations.") - expect_output(print(looss_2), "MCSE and ESS estimates assume independent draws") - # Currently failing - # expect_output(print(looss_2), "elpd_loo -1968.9 15.4 0.5") - # expect_output(print(looss_2), "p_loo 3.5 0.2 0.5") - expect_s3_class(looss_2, c("psis_loo_ss", "psis_loo", "loo")) - - set.seed(4711) - expect_no_warning(aploo_1 <- loo_approximate_posterior(llfun_logistic, draws = parameter_draws_laplace, data = stan_df, log_p = log_p, log_g = log_g)) - expect_output(print(aploo_1), "Computed from 2000 by 3020 log-likelihood matrix") - expect_output(print(aploo_1), "MCSE and ESS estimates assume independent draws") - expect_output(print(aploo_1), "elpd_loo -1968.4 15.6") - expect_output(print(aploo_1), "p_loo 3.2 0.2") - expect_output(print(aploo_1), "Posterior approximation correction used.") - expect_output(print(aploo_1), "All Pareto k estimates are good") - expect_equal(length(pareto_k_ids(aploo_1,threshold=0.5)), 31) - expect_s3_class(aploo_1, c("psis_loo_ap", "psis_loo", "loo")) - - set.seed(4711) - expect_no_warning(looapss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws_laplace, data = stan_df, log_p = log_p, log_g = log_g, observations = 100)) - expect_output(print(looapss_1), "Computed from 2000 by 100 subsampled log-likelihood") - expect_output(print(looapss_1), "MCSE and ESS estimates assume independent draws") - expect_output(print(looapss_1), "values from 3020 total observations.") - expect_output(print(looapss_1), "elpd_loo -1968.2 15.6 0.4") - expect_output(print(looapss_1), "p_loo 2.9 0.1 0.5") - expect_output(print(looapss_1), "All Pareto k estimates are good") - expect_equal(length(pareto_k_ids(looapss_1,threshold=0.5)), 3) - - # Loo compare - set.seed(4711) - expect_no_warning(looss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100)) - set.seed(4712) - expect_no_warning(looss_2 <- loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = 100)) - expect_output(print(looss_2), "Computed from 4000 by 100 subsampled log-likelihood") - expect_output(print(looss_2), "MCSE and ESS estimates assume independent draws") - expect_output(print(looss_2), "values from 3020 total observations.") - expect_output(print(looss_2), "elpd_loo -1952.0 16.2 0.2") - expect_output(print(looss_2), "p_loo 2.6 0.1 0.3") - - expect_warning(comp <- loo_compare(looss_1, looss_2), "Different subsamples in 'model2' and 'model1'. Naive diff SE is used.") - expect_output(print(comp), "model1 16.5 22.5 0.4") - - set.seed(4712) - expect_no_warning(looss_2_m <- loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = looss_1)) - expect_message(looss_2_m <- suppressWarnings(loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = obs_idx(looss_1))), - "Simple random sampling with replacement assumed.") - - expect_silent(comp <- loo_compare(looss_1, looss_2_m)) - expect_output(print(comp), "model1 16.1 4.4 0.1") - - set.seed(4712) - expect_no_warning(looss_1 <- update(looss_1, draws = parameter_draws, data = stan_df, observations = 200)) - expect_no_warning(looss_2_m <- update(looss_2_m, draws = parameter_draws_2, data = stan_df2, observations = looss_1)) - expect_silent(comp2 <- loo_compare(looss_1, looss_2_m)) - expect_output(print(comp2), "model1 16.3 4.4 0.1") - - expect_no_warning(looss_2_full <- loo(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2)) - expect_message(comp3 <- loo_compare(x = list(looss_1, looss_2_full)), - "Estimated elpd_diff using observations included in loo calculations for all models.") - expect_output(print(comp3), "model1 16.5 4.4 0.3") - -}) - - -context("loo_compare_subsample") - test_that("loo_compare_subsample", { skip_on_cran() # to get under cran check time limit @@ -1047,7 +373,7 @@ test_that("loo_compare_subsample", { expect_silent(lss3o1 <- loo_subsample(llfun_test, data = fake_data3, draws = fake_posterior3, observations = lss1, r_eff = rep(1, N))) expect_silent(lss2hh <- loo_subsample(llfun_test, data = fake_data2, draws = fake_posterior2, observations = 100, estimator = "hh_pps", r_eff = rep(1, N))) - expect_warning(lcss <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2, lss3))) + expect_snapshot(lcss <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2, lss3))) expect_warning(lcss2 <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2, lss3o1))) expect_silent(lcsso <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2o1, lss3o1))) expect_warning(lcssohh <- loo:::loo_compare.psis_loo_ss_list(x = list(lss1, lss2hh, lss3o1))) @@ -1070,7 +396,7 @@ test_that("loo_compare_subsample", { expect_silent(lcss2m <- loo:::loo_compare.psis_loo_ss_list(x = list(lss2o1, lss3o1))) expect_equal(unname(lcss2m[,]), unname(lcsso[1:2,])) - expect_warning(lcssapi <- loo_compare(lss1, lss2, lss3)) + expect_snapshot(lcssapi <- loo_compare(lss1, lss2, lss3)) expect_equal(lcssapi, lcss) expect_warning(lcssohhapi <- loo_compare(lss1, lss2hh, lss3o1)) expect_equal(lcssohhapi, lcssohh) @@ -1079,8 +405,6 @@ test_that("loo_compare_subsample", { }) -context("subsample with tis, sis") - test_that("Test 'tis' and 'sis'", { skip_on_cran() diff --git a/tests/testthat/test_loo_subsampling_approximations.R b/tests/testthat/test_loo_subsampling_approximations.R new file mode 100644 index 00000000..3f25ab2c --- /dev/null +++ b/tests/testthat/test_loo_subsampling_approximations.R @@ -0,0 +1,382 @@ +library(loo) +options(mc.cores = 1) + +generate_test_elpd_dataset <- function() { + N <- 10; K <- 10; S <- 1000; a0 <- 3; b0 <- 2 + p <- 0.7 + y <- rbinom(N, size = K, prob = p) + a <- a0 + sum(y); b <- b0 + N * K - sum(y) + fake_posterior <- draws <- as.matrix(rbeta(S, a, b)) + fake_data <- data.frame(y,K) + rm(N, K, S, a0, b0, p, y, a, b) + + list(fake_posterior = fake_posterior, fake_data = fake_data) +} + +test_elpd_loo_approximation <- function(cores) { + set.seed(123) + test_data <- generate_test_elpd_dataset() + fake_posterior <- test_data$fake_posterior + fake_data <- test_data$fake_data + + llfun_test <- function(data_i, draws) { + dbinom(data_i$y, size = data_i$K, prob = draws, log = TRUE) + } + + # Compute plpd approximation + expect_silent(pi_vals <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "plpd", cores = cores)) + # Compute it manually + point <- mean(fake_posterior) + llik <- dbinom(fake_data$y, size = fake_data$K, prob = point, log = TRUE) + abs_lliks <- abs(llik) + man_elpd_loo_approximation <- abs_lliks/sum(abs_lliks) + expect_equal(abs(pi_vals)/sum(abs(pi_vals)), man_elpd_loo_approximation, tolerance = 0.00001) + + # Compute lpd approximation + expect_silent(pi_vals <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "lpd", cores = cores)) + # Compute it manually + llik <- numeric(10) + for(i in seq_along(fake_data$y)){ + llik[i] <- loo:::logMeanExp(dbinom(fake_data$y[i], size = fake_data$K, prob = fake_posterior, log = TRUE)) + } + abs_lliks <- abs(llik) + man_approx_loo_variable <- abs_lliks/sum(abs_lliks) + expect_equal(abs(pi_vals)/sum(abs(pi_vals)), man_approx_loo_variable, tolerance = 0.00001) + + # Compute waic approximation + expect_silent(pi_vals_waic <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", cores = cores)) + expect_true(all(pi_vals > pi_vals_waic)) + expect_true(sum(pi_vals) - sum(pi_vals_waic) < 1) + + # Compute tis approximation + expect_silent(pi_vals_tis <- loo:::elpd_loo_approximation(.llfun = llfun_test, + data = fake_data, + draws = fake_posterior, + loo_approximation = "tis", + loo_approximation_draws = 100, + cores = cores)) + expect_true(all(pi_vals > pi_vals_tis)) + expect_true(sum(pi_vals) - sum(pi_vals_tis) < 1) +} + +test_that("elpd_loo_approximation works as expected", { + test_elpd_loo_approximation(1) +}) + +test_that("elpd_loo_approximation with multiple cores", { + test_elpd_loo_approximation(2) +}) + +test_that("Test loo_approximation_draws", { + set.seed(123) + N <- 1000; K <- 10; S <- 1000; a0 <- 3; b0 <- 2 + p <- 0.7 + y <- rbinom(N, size = K, prob = p) + a <- a0 + sum(y); b <- b0 + N * K - sum(y) + fake_posterior <- draws <- as.matrix(rbeta(S, a, b)) + fake_data <- data.frame(y,K) + rm(N, K, S, a0, b0, p, y, a, b) + llfun_test <- function(data_i, draws) { + dbinom(data_i$y, size = data_i$K, prob = draws, log = TRUE) + } + + expect_silent(res1 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) + expect_silent(res2 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior, loo_approximation = "waic", loo_approximation_draws = 10, cores = 1)) + expect_silent(res3 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior[1:10*100,], loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) + expect_silent(res4 <- loo:::elpd_loo_approximation(.llfun = llfun_test, data = fake_data, draws = fake_posterior[1:10*100,, drop = FALSE], loo_approximation = "waic", loo_approximation_draws = NULL, cores = 1)) + expect_failure(expect_equal(res1, res3)) + expect_equal(res2, res3) + + expect_silent(loo_ss1 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", r_eff = rep(1, nrow(fake_data)))) + expect_silent(loo_ss2 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 10, r_eff = rep(1, nrow(fake_data)))) + expect_silent(loo_ss3 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 31, r_eff = rep(1, nrow(fake_data)))) + expect_error(loo_ss4 <- loo_subsample(x = llfun_test, draws = fake_posterior, data = fake_data, observations = 100, loo_approximation = "plpd", loo_approximation_draws = 3100, r_eff = rep(1, nrow(fake_data)))) + + expect_equal(names(loo_ss1$loo_subsampling), c("elpd_loo_approx", "loo_approximation", "loo_approximation_draws", "estimator", ".llfun", ".llgrad", ".llhess", "data_dim", "ndraws")) + expect_null(loo_ss1$loo_subsampling$loo_approximation_draws) + expect_equal(loo_ss2$loo_subsampling$loo_approximation_draws, 10L) + expect_equal(loo_ss3$loo_subsampling$loo_approximation_draws, 31L) +}) + + + +test_that("waic using delta method and gradient", { + + + if (FALSE){ + # Code to generate testdata - saved and loaded to avoid dependency of mvtnorm + set.seed(123) + N <- 400; beta <- c(1,2); X_full <- matrix(rep(1,N), ncol = 1); X_full <- cbind(X_full, runif(N)); S <- 1000 + y_full <- rnorm(n = N, mean = X_full%*%beta, sd = 1) + X <- X_full; y <- y_full + Lambda_0 <- diag(length(beta)); mu_0 <- c(0,0) + b_hat <- solve(t(X)%*%X)%*%t(X)%*%y + mu_n <- solve(t(X)%*%X)%*%(t(X)%*%X%*%b_hat + Lambda_0%*%mu_0) + Lambda_n <- t(X)%*%X + Lambda_0 + # Uncomment row below when running. Commented out to remove CHECK warnings + # fake_posterior <- mvtnorm::rmvnorm(n = S, mean = mu_n, sigma = solve(Lambda_n)) + colnames(fake_posterior) <- c("a", "b") + fake_data <- data.frame(y, X) + save(fake_posterior, fake_data, file = test_path("data-for-tests/normal_reg_waic_test_example.rda")) + } else { + load(file = test_path("data-for-tests/normal_reg_waic_test_example.rda")) + } + + .llfun <- function(data_i, draws) { + # data_i: ith row of fdata (fake_data[i,, drop=FALSE]) + # draws: entire fake_posterior matrix + dnorm(data_i$y, mean = draws[, c("a", "b")] %*% t(as.matrix(data_i[, c("X1", "X2")])), sd = 1, log = TRUE) + } + + .llgrad <- function(data_i, draws) { + x_i <- data_i[, "X2"] + gr <- cbind(data_i$y - draws[,"a"] - draws[,"b"]*x_i, + (data_i$y - draws[,"a"] - draws[,"b"]*x_i) * x_i) + colnames(gr) <- c("a", "b") + gr + } + + fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) + + expect_silent(approx_loo_waic <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic")) + expect_silent(approx_loo_waic_delta <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad", .llgrad = .llgrad)) + expect_silent(approx_loo_waic_delta_diag <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad_marginal", .llgrad = .llgrad)) + + # Test that the approaches should not deviate too much + diff_waic_delta <- mean(approx_loo_waic - approx_loo_waic_delta) + diff_waic_delta_diag <- mean(approx_loo_waic - approx_loo_waic_delta_diag) + expect_equal(approx_loo_waic,approx_loo_waic_delta_diag, tolerance = 0.1) + expect_equal(approx_loo_waic,approx_loo_waic_delta, tolerance = 0.01) + + # Test usage in subsampling_loo + expect_silent(loo_ss_waic <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic", observations = 50, llgrad = .llgrad)) + expect_silent(loo_ss_waic_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50, llgrad = .llgrad)) + expect_silent(loo_ss_waic_delta_marginal <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad_marginal", observations = 50, llgrad = .llgrad)) + expect_silent(loo_ss_plpd <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "plpd", observations = 50, llgrad = .llgrad)) + expect_error(loo_ss_waic_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50)) +}) + +test_that("waic using delta 2nd order method", { + + + if (FALSE){ + # Code to generate testdata - saved and loaded to avoid dependency of MCMCPack + set.seed(123) + N <- 100; beta <- c(1,2); X_full <- matrix(rep(1,N), ncol = 1); X_full <- cbind(X_full, runif(N)); S <- 1000 + y_full <- rnorm(n = N, mean = X_full%*%beta, sd = 0.5) + X <- X_full; y <- y_full + # Uncomment row below when running. Commented out to remove CHECK warnings + # fake_posterior <- MCMCpack::MCMCregress(y~x, data = data.frame(y = y,x=X[,2]), thin = 10, mcmc = 10000) # Because Im lazy + fake_posterior <- as.matrix(fake_posterior) + fake_posterior[,"sigma2"] <- sqrt(fake_posterior[,"sigma2"]) + colnames(fake_posterior) <- c("a", "b", "sigma") + fake_data <- data.frame(y, X) + save(fake_posterior, fake_data, file = test_path("data-for-tests/normal_reg_waic_test_example2.rda"), compression_level = 9) + } else { + load(file = test_path("data-for-tests/normal_reg_waic_test_example2.rda")) + } + + .llfun <- function(data_i, draws) { + # data_i: ith row of fdata (data_i <- fake_data[i,, drop=FALSE]) + # draws: entire fake_posterior matrix + dnorm(data_i$y, mean = draws[, c("a", "b")] %*% t(as.matrix(data_i[, c("X1", "X2")])), sd = draws[, c("sigma")], log = TRUE) + } + + .llgrad <- function(data_i, draws) { + sigma <- draws[,"sigma"] + sigma2 <- sigma^2 + b <- draws[,"b"] + a <- draws[,"a"] + x_i <- unlist(data_i[, c("X1", "X2")]) + e <- (data_i$y - draws[,"a"] * x_i[1] - draws[,"b"] * x_i[2]) + + gr <- cbind(e * x_i[1] / sigma2, + e * x_i[2] / sigma2, + - 1 / sigma + e^2 / (sigma2 * sigma)) + colnames(gr) <- c("a", "b", "sigma") + gr + } + + .llhess <- function(data_i, draws) { + hess_array <- array(0, dim = c(ncol(draws), ncol(draws), nrow(draws)), dimnames = list(colnames(draws),colnames(draws),NULL)) + sigma <- draws[,"sigma"] + sigma2 <- sigma^2 + sigma3 <- sigma2*sigma + b <- draws[,"b"] + a <- draws[,"a"] + x_i <- unlist(data_i[, c("X1", "X2")]) + e <- (data_i$y - draws[,"a"] * x_i[1] - draws[,"b"] * x_i[2]) + + hess_array[1,1,] <- - x_i[1]^2 / sigma2 + hess_array[1,2,] <- hess_array[2,1,] <- - x_i[1] * x_i[2] / sigma2 + hess_array[2,2,] <- - x_i[2]^2 / sigma2 + hess_array[3,1,] <- hess_array[1,3,] <- -2 * x_i[1] * e / sigma3 + hess_array[3,2,] <- hess_array[2,3,] <- -2 * x_i[2] * e / sigma3 + hess_array[3,3,] <- 1 / sigma2 - 3 * e^2 / (sigma2^2) + hess_array + } + + #data <- fake_data + fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) + #draws <- fake_posterior <- cbind(fake_posterior, runif(nrow(fake_posterior))) + + expect_silent(approx_loo_waic <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic")) + expect_silent(approx_loo_waic_delta <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_grad", .llgrad = .llgrad)) + expect_silent(approx_loo_waic_delta2 <- loo:::elpd_loo_approximation(.llfun, data = fake_data, draws = fake_posterior, cores = 1, loo_approximation = "waic_hess", .llgrad = .llgrad, .llhess = .llhess)) + + # Test that the approaches should not deviate too much + expect_equal(approx_loo_waic,approx_loo_waic_delta2, tolerance = 0.01) + expect_equal(approx_loo_waic,approx_loo_waic_delta, tolerance = 0.01) + + expect_silent(test_loo_ss_waic <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic", observations = 50, llgrad = .llgrad)) + expect_error(test_loo_ss_delta2 <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_hess", observations = 50, llgrad = .llgrad)) + expect_silent(test_loo_ss_delta2 <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_hess", observations = 50, llgrad = .llgrad, llhess = .llhess)) + expect_silent(test_loo_ss_delta <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "waic_grad", observations = 50, llgrad = .llgrad)) + expect_silent(test_loo_ss_point <- loo_subsample(x = .llfun, data = fake_data, draws = fake_posterior, cores = 1, r_eff = rep(1, nrow(fake_data)), loo_approximation = "plpd", observations = 50, llgrad = .llgrad)) +}) + + +test_that("whhest works as expected", { + + + N <- 100 + m <- 10 + z <- rep(1/N, m) + y <- 1:10 + m_i <- rep(1,m) + expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + expect_equal(whe$y_hat_ppz, 550) + man_var <- (sum((whe$y_hat_ppz - y/z)^2)/(m-1))/m + expect_equal(whe$v_hat_y_ppz, man_var) + z <- 1:10/(sum(1:10)*10) + expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + expect_equal(whe$y_hat_ppz, 550) + expect_equal(whe$v_hat_y_ppz, 0) + + # School book example + # https://newonlinecourses.science.psu.edu/stat506/node/15/ + z <- c(650/15650, 2840/15650, 3200/15650) + y <- c(420, 1785, 2198) + m_i <- c(1,1,1) + N <- 10 + expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + expect_equal(round(whe$y_hat_ppz, 2), 10232.75, tolerance = 0) + expect_equal(whe$v_hat_y_ppz, 73125.74, tolerance = 0.01) + # Double check that it is rounding error + man_var_round <- (sum((round(y/z,2) - 10232.75)^2)) * (1/2) * (1/3) + expect_equal(man_var_round, 73125.74, tolerance = 0.001) + man_var_exact <- (sum((y/z - 10232.75)^2)) * (1/2) * (1/3) + expect_equal(whe$v_hat_y_ppz, man_var_exact, tolerance = 0.001) + + # Add test for variance estimation + N <- 100 + m <- 10 + y <- rep(1:10, 1) + true_var <- var(rep(y, 10)) * (99) + z <- rep(1/N, m) + m_i <- rep(100000, m) + expect_silent(whe <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + expect_equal(true_var, whe$hat_v_y_ppz, tolerance = 0.01) + + # Add tests for m_i + N <- 100 + y <- rep(1:10, 2) + m <- length(y) + z <- rep(1/N, m) + m_i <- rep(1,m) + expect_silent(whe1 <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + y <- rep(1:10) + m <- length(y) + z <- rep(1/N, m) + m_i <- rep(2,m) + expect_silent(whe2 <- loo:::whhest(z = z, m_i = m_i, y = y, N = N)) + expect_equal(whe1$y_hat_ppz, whe2$y_hat_ppz) + expect_equal(whe1$v_hat_y_ppz, whe2$v_hat_y_ppz) + expect_equal(whe1$hat_v_y_ppz, whe1$hat_v_y_ppz) + +}) + + +test_that("srs_diff_est works as expected", { + + + set.seed(1234) + N <- 1000 + y_true <- 1:N + sigma_hat_true <- sqrt(N * sum((y_true - mean(y_true))^2) / length(y_true)) + y_approx <- rnorm(N, y_true, 0.1) + m <- 100 + sigma_hat <- y_hat <- se_y_hat <- numeric(10000) + for(i in 1:10000){ + y_idx <- sample(1:N, size = m) + y <- y_true[y_idx] + res <- loo:::srs_diff_est(y_approx, y, y_idx) + y_hat[i] <- res$y_hat + se_y_hat[i] <- sqrt(res$v_y_hat) + sigma_hat[i] <- sqrt(res$hat_v_y) + } + expect_equal(mean(y_hat), sum(y_true), tolerance = 0.1) + + in_ki <- y_hat + 2 * se_y_hat > sum(y_true) & y_hat - 2*se_y_hat < sum(y_true) + expect_equal(mean(in_ki), 0.95, tolerance = 0.01) + + # Should be unbiased + expect_equal(mean(sigma_hat), sigma_hat_true, tolerance = 0.1) + + m <- N + y_idx <- sample(1:N, size = m) + y <- y_true[y_idx] + res <- loo:::srs_diff_est(y_approx, y, y_idx) + expect_equal(res$y_hat, 500500, tolerance = 0.0001) + expect_equal(res$v_y_hat, 0, tolerance = 0.0001) + expect_equal(sqrt(res$hat_v_y), sigma_hat_true, tolerance = 0.1) + +}) + +test_that("srs_est works as expected", { + + + set.seed(1234) + # Cochran 1976 example Table 2.2 + + y <- c(rep(42,23),rep(41,4), 36, 32, 29, 27, 27, 23, 19, 16, 16, 15, 15, 14, 11, 10, 9, 7, 6, 6, 6, 5, 5, 4, 3) + expect_equal(sum(y), 1471) + approx_loo <- rep(0L, 676) + expect_equal(sum(y^2), 54497) + res <- loo:::srs_est(y = y, approx_loo) + expect_equal(res$y_hat, 19888, tolerance = 0.0001) + expect_equal(res$v_y_hat, 676^2*229*(1-0.074)/50, tolerance = 0.0001) + expect_equal(res$hat_v_y, 676 * var(y), tolerance = 0.0001) + + # Simulation example + set.seed(1234) + N <- 1000 + y_true <- 1:N + sigma_hat_true <- sqrt(N * sum((y_true - mean(y_true))^2) / length(y_true)) + + m <- 100 + y_hat <- se_y_hat <- sigma_hat <- numeric(10000) + for(i in 1:10000){ + y_idx <- sample(1:N, size = m) + y <- y_true[y_idx] + res <- loo:::srs_est(y = y, y_approx = y_true) + y_hat[i] <- res$y_hat + se_y_hat[i] <- sqrt(res$v_y_hat) + sigma_hat[i] <- sqrt(res$hat_v_y) + } + expect_equal(mean(y_hat), sum(y_true), tolerance = 0.1) + + in_ki <- y_hat + 2*se_y_hat > sum(y_true) & y_hat - 2*se_y_hat < sum(y_true) + expect_equal(mean(in_ki), 0.95, tolerance = 0.01) + + # Should be unbiased + expect_equal(mean(sigma_hat), sigma_hat_true, tolerance = 0.1) + + m <- N + y_idx <- sample(1:N, size = m) + y <- y_true[y_idx] + res <- loo:::srs_est(y, y_true) + expect_equal(res$y_hat, 500500, tolerance = 0.0001) + expect_equal(res$v_y_hat, 0, tolerance = 0.0001) + +}) diff --git a/tests/testthat/test_loo_subsampling_cases.R b/tests/testthat/test_loo_subsampling_cases.R new file mode 100644 index 00000000..26f517d2 --- /dev/null +++ b/tests/testthat/test_loo_subsampling_cases.R @@ -0,0 +1,268 @@ +library(loo) +options(mc.cores = 1) + +test_that("Test loo_subsampling and loo_approx with radon data", { + skip_on_cran() # avoid going over time limit for tests + + load(test_path("data-for-tests/test_radon_laplace_loo.rda")) + # Rename to spot variable leaking errors + llfun_test <- llfun + log_p_test <- log_p + log_g_test <- log_q + draws_test <- draws + data_test <- data + rm(llfun, log_p,log_q, draws, data) + + set.seed(134) + expect_silent(full_loo <- loo(llfun_test, draws = draws_test, data = data_test, r_eff = rep(1, nrow(data_test)))) + expect_s3_class(full_loo, "psis_loo") + + set.seed(134) + expect_silent(loo_ss <- loo_subsample(x = llfun_test, draws = draws_test, data = data_test, observations = 200, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) + expect_s3_class(loo_ss, "psis_loo_ss") + + set.seed(134) + expect_silent(loo_ap_ss <- loo_subsample(x = llfun_test, draws = draws_test, data = data_test, log_p = log_p_test, log_g = log_g_test, observations = 200, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) + expect_s3_class(loo_ap_ss, "psis_loo_ss") + expect_s3_class(loo_ap_ss, "psis_loo_ap") + + expect_silent(loo_ap_ss_full <- loo_subsample(x = llfun_test, log_p = log_p_test, log_g = log_g_test, draws = draws_test, data = data_test, observations = NULL, loo_approximation = "plpd", r_eff = rep(1, nrow(data_test)))) + expect_failure(expect_s3_class(loo_ap_ss_full, "psis_loo_ss")) + expect_s3_class(loo_ap_ss_full, "psis_loo_ap") + + # Expect similar results + z <- 2 + expect_lte(loo_ss$estimates["elpd_loo", "Estimate"] - z * loo_ss$estimates["elpd_loo", "subsampling SE"], full_loo$estimates["elpd_loo", "Estimate"]) + expect_gte(loo_ss$estimates["elpd_loo", "Estimate"] + z * loo_ss$estimates["elpd_loo", "subsampling SE"], full_loo$estimates["elpd_loo", "Estimate"]) + expect_lte(loo_ss$estimates["p_loo", "Estimate"] - z * loo_ss$estimates["p_loo", "subsampling SE"], full_loo$estimates["p_loo", "Estimate"]) + expect_gte(loo_ss$estimates["p_loo", "Estimate"] + z * loo_ss$estimates["p_loo", "subsampling SE"], full_loo$estimates["p_loo", "Estimate"]) + expect_lte(loo_ss$estimates["looic", "Estimate"] - z * loo_ss$estimates["looic", "subsampling SE"], full_loo$estimates["looic", "Estimate"]) + expect_gte(loo_ss$estimates["looic", "Estimate"] + z * loo_ss$estimates["looic", "subsampling SE"], full_loo$estimates["looic", "Estimate"]) + + expect_failure(expect_equal(full_loo$estimates["elpd_loo", "Estimate"], loo_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(full_loo$estimates["p_loo", "Estimate"], loo_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(full_loo$estimates["looic", "Estimate"], loo_ss$estimates["looic", "Estimate"], tolerance = 0.00000001)) + + z <- 2 + expect_lte(loo_ap_ss$estimates["elpd_loo", "Estimate"] - z * loo_ap_ss$estimates["elpd_loo", "subsampling SE"], loo_ap_ss_full$estimates["elpd_loo", "Estimate"]) + expect_gte(loo_ap_ss$estimates["elpd_loo", "Estimate"] + z * loo_ap_ss$estimates["elpd_loo", "subsampling SE"], loo_ap_ss_full$estimates["elpd_loo", "Estimate"]) + expect_lte(loo_ap_ss$estimates["p_loo", "Estimate"] - z * loo_ap_ss$estimates["p_loo", "subsampling SE"], loo_ap_ss_full$estimates["p_loo", "Estimate"]) + expect_gte(loo_ap_ss$estimates["p_loo", "Estimate"] + z * loo_ap_ss$estimates["p_loo", "subsampling SE"], loo_ap_ss_full$estimates["p_loo", "Estimate"]) + expect_lte(loo_ap_ss$estimates["looic", "Estimate"] - z * loo_ap_ss$estimates["looic", "subsampling SE"], loo_ap_ss_full$estimates["looic", "Estimate"]) + expect_gte(loo_ap_ss$estimates["looic", "Estimate"] + z * loo_ap_ss$estimates["looic", "subsampling SE"], loo_ap_ss_full$estimates["looic", "Estimate"]) + + expect_failure(expect_equal(loo_ap_ss_full$estimates["elpd_loo", "Estimate"], loo_ap_ss$estimates["elpd_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(loo_ap_ss_full$estimates["p_loo", "Estimate"], loo_ap_ss$estimates["p_loo", "Estimate"], tolerance = 0.00000001)) + expect_failure(expect_equal(loo_ap_ss_full$estimates["looic", "Estimate"], loo_ap_ss$estimates["looic", "Estimate"], tolerance = 0.00000001)) + + # Correct printout + expect_failure(expect_output(print(full_loo), "Posterior approximation correction used\\.")) + expect_failure(expect_output(print(full_loo), "subsampled log-likelihood\nvalues")) + + expect_failure(expect_output(print(loo_ss), "Posterior approximation correction used\\.")) + expect_output(print(loo_ss), "subsampled log-likelihood\nvalues") + + expect_output(print(loo_ap_ss), "Posterior approximation correction used\\.") + expect_output(print(loo_ap_ss), "subsampled log-likelihood\nvalues") + + expect_output(print(loo_ap_ss_full), "Posterior approximation correction used\\.") + expect_failure(expect_output(print(loo_ap_ss_full), "subsampled log-likelihood\nvalues")) + + # Test conversion of objects + expect_silent(loo_ap_full <- loo:::as.psis_loo.psis_loo(loo_ap_ss_full)) + expect_s3_class(loo_ap_full, "psis_loo_ap") + expect_silent(loo_ap_full_ss <- loo:::as.psis_loo_ss.psis_loo(loo_ap_full)) + expect_s3_class(loo_ap_full_ss, "psis_loo_ss") + expect_s3_class(loo_ap_full_ss, "psis_loo_ap") + expect_silent(loo_ap_full2 <- loo:::as.psis_loo.psis_loo_ss(loo_ap_full_ss)) + expect_s3_class(loo_ap_full2, "psis_loo_ap") + expect_failure(expect_s3_class(loo_ap_full2, "psis_loo_ss")) + expect_equal(loo_ap_full2,loo_ap_full) + + # Test update + set.seed(4712) + expect_silent(loo_ss2 <- update(loo_ss, draws = draws_test, data = data_test, observations = 1000, r_eff = rep(1, nrow(data_test)))) + expect_gt(dim(loo_ss2)[2], dim(loo_ss)[2]) + expect_gt(dim(loo_ss2$pointwise)[1], dim(loo_ss$pointwise)[1]) + expect_equal(nobs(loo_ss), 200) + expect_equal(nobs(loo_ss2), 1000) + for(i in 1:nrow(loo_ss2$estimates)) { + expect_lt(loo_ss2$estimates[i, "subsampling SE"], + loo_ss$estimates[i, "subsampling SE"]) + } + + set.seed(4712) + expect_silent(loo_ap_ss2 <- update(object = loo_ap_ss, draws = draws_test, data = data_test, observations = 2000)) + expect_gt(dim(loo_ap_ss2)[2], dim(loo_ap_ss)[2]) + expect_gt(dim(loo_ap_ss2$pointwise)[1], dim(loo_ap_ss$pointwise)[1]) + expect_equal(nobs(loo_ap_ss), 200) + expect_equal(nobs(loo_ap_ss2), 2000) + for(i in 1:nrow(loo_ap_ss2$estimates)) { + expect_lt(loo_ap_ss2$estimates[i, "subsampling SE"], + loo_ap_ss$estimates[i, "subsampling SE"]) + } + + expect_equal(round(full_loo$estimates), round(loo_ap_ss_full$estimates)) + expect_failure(expect_equal(full_loo$estimates, loo_ap_ss_full$estimates)) + expect_equal(dim(full_loo), dim(loo_ap_ss_full)) + expect_s3_class(loo_ap_ss_full, "psis_loo_ap") + +}) + + +test_that("Test the vignette", { + skip_on_cran() + + + # NOTE + # If any of these test fails, the vignette probably needs to be updated + + if (FALSE) { + # Generate vignette test case + library("rstan") + stan_code <- " + data { + int N; // number of data points + int P; // number of predictors (including intercept) + matrix[N,P] X; // predictors (including 1s for intercept) + int y[N]; // binary outcome + } + parameters { + vector[P] beta; + } + model { + beta ~ normal(0, 1); + y ~ bernoulli_logit(X * beta); + } + " + # logistic <- function(x) {1 / (1 + exp(-x))} + # logit <- function(x) {log(x) - log(1-x)} + llfun_logistic <- function(data_i, draws) { + x_i <- as.matrix(data_i[, which(grepl(colnames(data_i), pattern = "X")), drop=FALSE]) + y_pred <- draws %*% t(x_i) + dbinom(x = data_i$y, size = 1, prob = 1 / (1 + exp(-y_pred)), log = TRUE) + } + + # Prepare data + url <- "http://stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat" + wells <- read.table(url) + wells$dist100 <- with(wells, dist / 100) + X <- model.matrix(~ dist100 + arsenic, wells) + standata <- list(y = wells$switch, X = X, N = nrow(X), P = ncol(X)) + + # Fit model + set.seed(4711) + fit_1 <- stan(model_code = stan_code, data = standata, seed = 4711) + print(fit_1, pars = "beta") + + parameter_draws <- extract(fit_1)$beta + stan_df <- as.data.frame(standata) + loo_i(1, llfun_logistic, data = stan_df, draws = parameter_draws) + + sm <- stan_model(model_code = stan_code) + set.seed(4711) + fit_laplace <- optimizing(sm, data = standata, draws = 2000, seed = 42) + parameter_draws_laplace <- fit_laplace$theta_tilde + log_p <- fit_laplace$log_p # The log density of the posterior + log_g <- fit_laplace$log_g # The log density of the approximation + + # For comparisons + standata$X[, "arsenic"] <- log(standata$X[, "arsenic"]) + stan_df2 <- as.data.frame(standata) + set.seed(4711) + fit_2 <- stan(fit = fit_1, data = standata, seed = 4711) + parameter_draws_2 <- extract(fit_2)$beta + + save(llfun_logistic, + stan_df, stan_df2, + parameter_draws, parameter_draws_laplace, parameter_draws_2, + log_p, log_g, + file = test_path("data-for-tests/loo_subsample_vignette.rda"), compression_level = 9) + + } else { + load(test_path("data-for-tests/loo_subsample_vignette.rda")) + } + + set.seed(4711) + expect_no_warning(looss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100)) + expect_output(print(looss_1), "Computed from 4000 by 100 subsampled log-likelihood") + expect_output(print(looss_1), "values from 3020 total observations.") + expect_output(print(looss_1), "MCSE and ESS estimates assume independent draws") + expect_output(print(looss_1), "elpd_loo -1968.5 15.6 0.3") + expect_output(print(looss_1), "p_loo 3.1 0.1 0.4") + expect_s3_class(looss_1, c("psis_loo_ss", "psis_loo", "loo")) + + set.seed(4711) + expect_no_warning(looss_1b <- update(looss_1, draws = parameter_draws, data = stan_df, observations = 200)) + expect_output(print(looss_1b), "Computed from 4000 by 200 subsampled log-likelihood") + expect_output(print(looss_1b), "values from 3020 total observations.") + expect_output(print(looss_1b), "MCSE and ESS estimates assume independent draws") + expect_output(print(looss_1b), "elpd_loo -1968.3 15.6 0.2") + expect_output(print(looss_1b), "p_loo 3.2 0.1 0.4") + expect_s3_class(looss_1b, c("psis_loo_ss", "psis_loo", "loo")) + + set.seed(4711) + expect_no_warning(looss_2 <- loo_subsample(x = llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100, estimator = "hh_pps", loo_approximation = "lpd", loo_approximation_draws = 100)) + expect_output(print(looss_2), "Computed from 4000 by 100 subsampled log-likelihood") + expect_output(print(looss_2), "values from 3020 total observations.") + expect_output(print(looss_2), "MCSE and ESS estimates assume independent draws") + # Currently failing + # expect_output(print(looss_2), "elpd_loo -1968.9 15.4 0.5") + # expect_output(print(looss_2), "p_loo 3.5 0.2 0.5") + expect_s3_class(looss_2, c("psis_loo_ss", "psis_loo", "loo")) + + set.seed(4711) + expect_no_warning(aploo_1 <- loo_approximate_posterior(llfun_logistic, draws = parameter_draws_laplace, data = stan_df, log_p = log_p, log_g = log_g)) + expect_output(print(aploo_1), "Computed from 2000 by 3020 log-likelihood matrix") + expect_output(print(aploo_1), "MCSE and ESS estimates assume independent draws") + expect_output(print(aploo_1), "elpd_loo -1968.4 15.6") + expect_output(print(aploo_1), "p_loo 3.2 0.2") + expect_output(print(aploo_1), "Posterior approximation correction used.") + expect_output(print(aploo_1), "All Pareto k estimates are good") + expect_equal(length(pareto_k_ids(aploo_1,threshold=0.5)), 31) + expect_s3_class(aploo_1, c("psis_loo_ap", "psis_loo", "loo")) + + set.seed(4711) + expect_no_warning(looapss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws_laplace, data = stan_df, log_p = log_p, log_g = log_g, observations = 100)) + expect_output(print(looapss_1), "Computed from 2000 by 100 subsampled log-likelihood") + expect_output(print(looapss_1), "MCSE and ESS estimates assume independent draws") + expect_output(print(looapss_1), "values from 3020 total observations.") + expect_output(print(looapss_1), "elpd_loo -1968.2 15.6 0.4") + expect_output(print(looapss_1), "p_loo 2.9 0.1 0.5") + expect_output(print(looapss_1), "All Pareto k estimates are good") + expect_equal(length(pareto_k_ids(looapss_1,threshold=0.5)), 3) + + # Loo compare + set.seed(4711) + expect_no_warning(looss_1 <- loo_subsample(llfun_logistic, draws = parameter_draws, data = stan_df, observations = 100)) + set.seed(4712) + expect_no_warning(looss_2 <- loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = 100)) + expect_output(print(looss_2), "Computed from 4000 by 100 subsampled log-likelihood") + expect_output(print(looss_2), "MCSE and ESS estimates assume independent draws") + expect_output(print(looss_2), "values from 3020 total observations.") + expect_output(print(looss_2), "elpd_loo -1952.0 16.2 0.2") + expect_output(print(looss_2), "p_loo 2.6 0.1 0.3") + + expect_warning(comp <- loo_compare(looss_1, looss_2), "Different subsamples in 'model2' and 'model1'. Naive diff SE is used.") + expect_output(print(comp), "model1 16.5 22.5 0.4") + + set.seed(4712) + expect_no_warning(looss_2_m <- loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = looss_1)) + expect_message(looss_2_m <- suppressWarnings(loo_subsample(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2, observations = obs_idx(looss_1))), + "Simple random sampling with replacement assumed.") + + expect_silent(comp <- loo_compare(looss_1, looss_2_m)) + expect_output(print(comp), "model1 16.1 4.4 0.1") + + set.seed(4712) + expect_no_warning(looss_1 <- update(looss_1, draws = parameter_draws, data = stan_df, observations = 200)) + expect_no_warning(looss_2_m <- update(looss_2_m, draws = parameter_draws_2, data = stan_df2, observations = looss_1)) + expect_silent(comp2 <- loo_compare(looss_1, looss_2_m)) + expect_output(print(comp2), "model1 16.3 4.4 0.1") + + expect_no_warning(looss_2_full <- loo(x = llfun_logistic, draws = parameter_draws_2, data = stan_df2)) + expect_message(comp3 <- loo_compare(x = list(looss_1, looss_2_full)), + "Estimated elpd_diff using observations included in loo calculations for all models.") + expect_output(print(comp3), "model1 16.5 4.4 0.3") + +}) diff --git a/tests/testthat/test_model_weighting.R b/tests/testthat/test_model_weighting.R index fb7c0e7c..6397d7b3 100644 --- a/tests/testthat/test_model_weighting.R +++ b/tests/testthat/test_model_weighting.R @@ -1,7 +1,5 @@ library(loo) -context("loo_model_weights") - # generate fake data set.seed(123) y<-rnorm(50,0,1) @@ -22,7 +20,7 @@ loo_list <- lapply(1:length(ll_list), function(j) { loo(ll_list[[j]], r_eff = r_eff_list[[j]]) }) -tol <- 0.01 # absoulte tolerance of weights +tol <- 0.01 # absolute tolerance of weights test_that("loo_model_weights throws correct errors and warnings", { expect_error(loo_model_weights(log_lik1), "list of matrices or a list of 'psis_loo' objects") @@ -58,21 +56,19 @@ test_that("loo_model_weights (stacking and pseudo-BMA) gives expected result", { expect_s3_class(w1, "stacking_weights") expect_length(w1, 3) expect_named(w1, paste0("model" ,c(1:3))) - expect_equal_to_reference(as.numeric(w1), "reference-results/model_weights_stacking.rds", - tolerance = tol, scale=1) + expect_snapshot_value(as.numeric(w1), style = "serialize") expect_output(print(w1), "Method: stacking") - + w1_b <- loo_model_weights(loo_list) expect_identical(w1, w1_b) - + w2 <- loo_model_weights(ll_list, r_eff_list=r_eff_list, - method = "pseudobma", BB = TRUE) - expect_type(w2, "double") - expect_s3_class(w2, "pseudobma_bb_weights") - expect_length(w2, 3) - expect_named(w2, paste0("model", c(1:3))) - expect_equal_to_reference(as.numeric(w2), "reference-results/model_weights_pseudobma.rds", - tolerance = tol, scale=1) + method = "pseudobma", BB = TRUE) + expect_type(w2, "double") + expect_s3_class(w2, "pseudobma_bb_weights") + expect_length(w2, 3) + expect_named(w2, paste0("model", c(1:3))) + expect_snapshot_value(as.numeric(w2), style = "serialize") expect_output(print(w2), "Method: pseudo-BMA+") w3 <- loo_model_weights(ll_list, r_eff_list=r_eff_list, @@ -81,7 +77,7 @@ test_that("loo_model_weights (stacking and pseudo-BMA) gives expected result", { expect_length(w3, 3) expect_named(w3, paste0("model" ,c(1:3))) expect_equal(as.numeric(w3), c(5.365279e-05, 9.999436e-01, 2.707028e-06), - tolerance = tol, scale = 1) + tolerance = tol) expect_output(print(w3), "Method: pseudo-BMA") w3_b <- loo_model_weights(loo_list, method = "pseudobma", BB = FALSE) diff --git a/tests/testthat/test_pointwise.R b/tests/testthat/test_pointwise.R index 237304c3..5707692c 100644 --- a/tests/testthat/test_pointwise.R +++ b/tests/testthat/test_pointwise.R @@ -1,7 +1,5 @@ library(loo) -context("pointwise convenience function") - loo1 <- suppressWarnings(loo(example_loglik_matrix())) test_that("pointwise throws the right errors", { diff --git a/tests/testthat/test_print_plot.R b/tests/testthat/test_print_plot.R index a81b4af4..d59b52fe 100644 --- a/tests/testthat/test_print_plot.R +++ b/tests/testthat/test_print_plot.R @@ -1,8 +1,6 @@ library(loo) set.seed(1414) -context("print, plot, diagnostics") - LLarr <- example_loglik_array() waic1 <- suppressWarnings(waic(LLarr)) loo1 <- suppressWarnings(loo(LLarr)) @@ -149,7 +147,7 @@ test_that("psis_n_eff_values extractor works", { test_that("mcse_loo extractor gives correct value", { mcse <- mcse_loo(loo1) expect_type(mcse, "double") - expect_equal_to_reference(mcse, "reference-results/mcse_loo.rds") + expect_snapshot_value(mcse, style = "serialize") }) test_that("mcse_loo returns NA when it should", { diff --git a/tests/testthat/test_psis.R b/tests/testthat/test_psis.R index ef4b14cd..246eae04 100644 --- a/tests/testthat/test_psis.R +++ b/tests/testthat/test_psis.R @@ -3,8 +3,6 @@ options(mc.cores=1) options(loo.cores=NULL) set.seed(123) -context("psis") - LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() LLvec <- LLmat[, 1] @@ -14,7 +12,7 @@ r_eff_vec <- relative_eff(exp(LLvec), chain_id = chain_id) psis1 <- psis(log_ratios = -LLarr, r_eff = r_eff_arr) test_that("psis results haven't changed", { - expect_equal_to_reference(psis1, "reference-results/psis.rds") + expect_snapshot_value(psis1, style = "serialize") }) test_that("psis returns object with correct structure", { @@ -78,10 +76,7 @@ test_that("psis throws correct errors and warnings", { expect_error(psis(-LLarr, r_eff = r_eff_arr), "mix NA and not NA values") # tail length warnings - expect_warning( - psis(-LLarr[1:5,, ]), - "Not enough tail samples to fit the generalized Pareto distribution" - ) + expect_snapshot(psis(-LLarr[1:5,, ])) # no NAs or non-finite values allowed LLmat[1,1] <- NA @@ -147,7 +142,7 @@ test_that("psis_n_eff methods works properly", { test_that("do_psis_i throws warning if all tail values the same", { xx <- c(1,2,3,4,4,4,4,4,4,4,4) - val <- expect_warning(do_psis_i(xx, tail_len_i = 6), "all tail values are the same") + expect_warning(val <- do_psis_i(xx, tail_len_i = 6), "all tail values are the same") expect_equal(val$pareto_k, Inf) }) diff --git a/tests/testthat/test_psis_approximate_posterior.R b/tests/testthat/test_psis_approximate_posterior.R index 0529d0f4..c4d3f5e5 100644 --- a/tests/testthat/test_psis_approximate_posterior.R +++ b/tests/testthat/test_psis_approximate_posterior.R @@ -1,7 +1,5 @@ library(loo) -context("psis_approximate_posterior") - load(test_path("data-for-tests/test_data_psis_approximate_posterior.rda")) test_that("Laplace approximation, independent posterior", { diff --git a/tests/testthat/test_psislw.R b/tests/testthat/test_psislw.R index fb339751..c65b1f9b 100644 --- a/tests/testthat/test_psislw.R +++ b/tests/testthat/test_psislw.R @@ -1,8 +1,6 @@ library(loo) SW <- suppressWarnings -context("psislw") - set.seed(123) x <- matrix(rnorm(5000), 100, 50) @@ -16,18 +14,9 @@ test_that("psislw throws deprecation warning", { test_that("psislw handles special cases, throws appropriate errors/warnings", { - expect_warning( - psis <- psislw(x[, 1], wcp = 0.01), - regexp = "All tail values are the same. Weights are truncated but not smoothed" - ) + expect_snapshot(psis <- psislw(x[, 1], wcp = 0.01)) expect_true(is.infinite(psis$pareto_k)) - expect_warning( - psislw(x[, 1], wcp = 0.01), - regexp = "Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details", - fixed = TRUE - ) - expect_error( expect_deprecated(psislw(wcp = 0.2)), regexp = "'lw' or 'llfun' and 'llargs' must be specified" diff --git a/tests/testthat/test_relative_eff.R b/tests/testthat/test_relative_eff.R index 800c08b8..6def1df5 100644 --- a/tests/testthat/test_relative_eff.R +++ b/tests/testthat/test_relative_eff.R @@ -2,13 +2,11 @@ library(loo) options(mc.cores = 1) set.seed(123) -context("relative_eff methods") - LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() test_that("relative_eff results haven't changed", { - expect_equal_to_reference(relative_eff(exp(LLarr)), "reference-results/relative_eff.rds") + expect_snapshot_value(relative_eff(exp(LLarr)), style = "serialize") }) test_that("relative_eff is equal to ESS / S", { @@ -16,7 +14,7 @@ test_that("relative_eff is equal to ESS / S", { ess <- r_eff <- rep(NA, dims[3]) for (j in 1:dims[3]) { r_eff[j] <- relative_eff(LLarr[,,1, drop=FALSE]) - ess[j] <- ess_rfun(LLarr[,,1]) + ess[j] <- posterior::ess_mean(LLarr[,,1]) } S <- prod(dim(LLarr)[1:2]) expect_equal(r_eff, ess / S) diff --git a/tests/testthat/test_tisis.R b/tests/testthat/test_tisis.R index 5d693a37..0d41c953 100644 --- a/tests/testthat/test_tisis.R +++ b/tests/testthat/test_tisis.R @@ -3,8 +3,6 @@ options(mc.cores=1) options(loo.cores=NULL) set.seed(123) -context("tis and is") - LLarr <- example_loglik_array() LLmat <- example_loglik_matrix() LLvec <- LLmat[, 1] @@ -143,20 +141,20 @@ test_that("explict test of values for 'sis' and 'tis'", { lw <- 1:16 expect_silent(tis_true <- tis(log_ratios = lw, r_eff = NA)) expect_equal(as.vector(weights(tis_true, log = TRUE, normalize = FALSE)), - c(-14.0723, -13.0723, -12.0723, -11.0723, -10.0723, -9.0723, -8.0723, -7.0723, -6.0723, -5.0723, -4.0723, -3.0723, -2.0723, -1.0723, -0.0723, 0.) + 15.07238, tol = 0.001) + c(-14.0723, -13.0723, -12.0723, -11.0723, -10.0723, -9.0723, -8.0723, -7.0723, -6.0723, -5.0723, -4.0723, -3.0723, -2.0723, -1.0723, -0.0723, 0.) + 15.07238, tolerance = 0.001) expect_silent(is_true <- sis(log_ratios = lw, r_eff = NA)) expect_equal(as.vector(weights(is_true, log = TRUE, normalize = FALSE)), - lw, tol = 0.00001) + lw, tolerance = 0.00001) lw <- c(0.7609420, 1.3894140, 0.4158346, 2.5307927, 4.3379119, 2.4159240, 2.2462172, 0.8057697, 0.9333107, 1.5599302) expect_silent(tis_true <- tis(log_ratios = lw, r_eff = NA)) expect_equal(as.vector(weights(tis_true, log = TRUE, normalize = FALSE)), c(-2.931, -2.303, -3.276, -1.161, 0, -1.276, -1.446, -2.886, -2.759, -2.132) + 3.692668, - tol = 0.001) + tolerance = 0.001) expect_silent(is_true <- sis(log_ratios = lw, r_eff = NA)) expect_equal(as.vector(weights(is_true, log = TRUE, normalize = FALSE)), - lw, tol = 0.00001) + lw, tolerance = 0.00001) })