Simulation study for the paper "Statistical Foundations of Prior-Data Fitted Networks" (ICML 2023)

pfn-sim-study.R

# required libraries
library("reticulate")
library("tidyverse")
library("RANN")
library("qrng")
library("readr")
library("ggthemes")


## Python setup -------------------------------------------

use_python("/usr/local/bin/python")  # may need modification

# python library in env: https://github.com/automl/TabPFN
interface <- import("tabpfn.scripts.transformer_prediction_interface")
pfn <- interface$TabPFNClassifier(device = "cuda")


## Simulation/evaluation logic -----------------------------

# true model
p_0 <- function(x) 0.5 + 0.5 * sin(rowSums(as.matrix(x)))

# fitted model
p_hat <- function(x, fit) {
  p <- fit$predict(x, return_winning_probability = TRUE)
  (1 - p[[2]]) * (p[[1]] == 0) + p[[2]] * p[[1]]
}

# localized version
localized_pfn_predict <- function(x_test, x_train, y_train) {
  d <- NCOL(x_train)
  n_train <- NROW(x_train)
  x_test <- matrix(x_test, ncol = d)
  n_test <- NROW(x_test)
  
  # find k_n nearest neighbors
  p_test <- numeric(n_test)
  k_opt <- round(n_train * min((n_train / 500)^(-d/(d + 4)), 1))
  nns <- RANN::nn2(x_train, x_test, k = k_opt)

  # predict each test sample
  for (i in 1:n_test) {
    i_sel <- nns$nn.idx[i, ]
    pfn_fit <- pfn$fit(x_train[i_sel, , drop = FALSE], y_train[i_sel],
                       overwrite_warning = TRUE)
    p_test[i] <- p_hat(x_test[i, , drop = FALSE], pfn_fit)
  }
  p_test
}

# one round of simulation/evaluation
do_one <- function(seed, n, x_test) {
  print(paste0("n = ", n, " seed = ", seed))
  set.seed(seed)
  
  x_train <- replicate(5, rnorm(n))
  y_train <- rbinom(n, 1, p_0(x_train))
  pfn <- pfn$fit(x_train, y_train, overwrite_warning = TRUE)
  
  err_tab <- p_0(x_test) - p_hat(x_test, pfn)
  err_ltab <- if (n > 1000) {
    p_0(x_test) - localized_pfn_predict(x_test, x_train, y_train)
  } else {
    err_tab
  }
  
  data.frame(error = err_tab, i = 1:nrow(x_test), method = "tab") |>
    rbind(data.frame(error = err_ltab, i = 1:nrow(x_test), method = "ltab")) |>
    mutate(seed = seed, n = n, true = rep(p_0(x_test), 2))
}


## run study ---------------------------------------

x_test <- qnorm(qrng::ghalton(500, 5))
scenarios <- crossing(seed = 1:500, n = c(200, 500, 1000, 2000, 4000))
results <- map2_dfr(scenarios$seed, scenarios$n, ~ do_one(.x, .y, x_test))
readr::write_csv(results, "sim-results.csv")
# results <- readr::read_csv("sim-results.csv")


## analyze results --------------------------------

bias <- group_by(results, n, method, i) |>
  summarize(val = mean(error)^2, se = var(error) / length(error)) |>
  group_by(n, method) |>
  summarize(val = mean(val), se = mean(se) / sqrt(length(se)))
variance <- group_by(results, n, method, i) |>
  summarize(val = var(error), se = var(error) / sqrt(0.5 * length(error)))|>
  group_by(n, method) |>
  summarize(val = mean(val), se = mean(se) / sqrt(length(se)))

df <- bind_rows(
  `average squared bias` = bias,
  `average variance` = variance,
  .id = "type"
)

df |>
  mutate(
    method = factor(method, c("tab", "ltab")) |> 
      fct_recode(`TabPFN` = "tab", `TabPFN + Localization` = "ltab") 
  ) |>
  ggplot(aes(n, val, color = method)) +
  facet_wrap(~type, scales = "free") +
  geom_point() +
  geom_line() +
  geom_errorbar(
    aes(ymin = val - 1.96 * se, ymax = val + 1.96 * se),
    width = 1,
    position = position_dodge(0.05)
  ) +
  theme_minimal() +
  ylab("") +
  expand_limits(y = 0, x = 0) +
  scale_color_manual(values = rev(ggthemes::hc_pal()(2))) +
  theme(legend.position = "bottom")
ggsave("bias-variance.pdf", width = 8, height = 4)

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