---
title: "Avoiding model refits in leave-one-out cross-validation with moment matching"
author: "Topi Paananen, Paul Bürkner, Aki Vehtari and Jonah Gabry"
date: "`r Sys.Date()`"
output:
  html_vignette:
    toc: yes
params:
  EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true")
---
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%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{Avoiding model refits in leave-one-out cross-validation with moment matching}
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```{r, child="children/SETTINGS-knitr.txt"}
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```{r, child="children/SEE-ONLINE.txt", eval = if (isTRUE(exists("params"))) !params$EVAL else TRUE}
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# Introduction

This vignette demonstrates how to improve the Monte Carlo sampling accuracy of
leave-one-out cross-validation with the __loo__ package and Stan. The __loo__
package automatically monitors the sampling accuracy using Pareto $k$
diagnostics for each observation. Here, we present a method for quickly
improving the accuracy when the Pareto diagnostics indicate problems. This is
done by performing some additional computations using the existing posterior
sample. If successful, this will decrease the Pareto $k$ values, making the
model assessment more reliable. __loo__ also stores the original Pareto $k$
values with the name `influence_pareto_k` which are not changed. They can be
used as a diagnostic of how much each observation influences the posterior
distribution. 

The methodology presented is based on the paper

* Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2020).
Implicitly Adaptive Importance Sampling.
[arXiv preprint arXiv:1906.08850](https://arxiv.org/abs/1906.08850).

More information about the Pareto $k$ diagnostics is given in the following
papers

* Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).

* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). 
Pareto smoothed importance sampling. 
[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).

# Example: Eradication of Roaches

We will use the same example as in the vignette
[_Using the loo package (version >= 2.0.0)_](https://mc-stan.org/loo/articles/loo2-example.html).
See the demo for a description of the problem and data. We will use the same Poisson
regression model as in the case study.

## Coding the Stan model

Here is the Stan code for fitting the Poisson regression model, which 
we will use for modeling the number of roaches.

```{r stancode}
stancode <- "
data {
  int<lower=1> K;
  int<lower=1> N;
  matrix[N,K] x;
  int y[N];
  vector[N] offset;

  real beta_prior_scale;
  real alpha_prior_scale;
}
parameters {
  vector[K] beta;
  real intercept;
}
model {
  y ~ poisson(exp(x * beta + intercept + offset));
  beta ~ normal(0,beta_prior_scale);
  intercept ~ normal(0,alpha_prior_scale);
}
generated quantities {
  vector[N] log_lik;
  for (n in 1:N)
    log_lik[n] = poisson_lpmf(y[n] | exp(x[n] * beta + intercept + offset[n]));
}
"
```

Following the usual approach recommended in 
[_Writing Stan programs for use with the loo package_](http://mc-stan.org/loo/articles/loo2-with-rstan.html), 
we compute the log-likelihood for each observation in the 
`generated quantities` block of the Stan program.


## Setup

In addition to __loo__, we load the __rstan__ package for fitting the model,
and the __rstanarm__ package for the data.

```{r setup, message=FALSE}
library("rstan")
library("loo")
seed <- 9547
set.seed(seed)
```

## Fitting the model with RStan

Next we fit the model in Stan using the __rstan__ package:

```{r modelfit, message=FALSE}
# Prepare data
data(roaches, package = "rstanarm")
roaches$roach1 <- sqrt(roaches$roach1)
y <- roaches$y
x <- roaches[,c("roach1", "treatment", "senior")]
offset <- log(roaches[,"exposure2"])
n <- dim(x)[1]
k <- dim(x)[2]

standata <- list(N = n, K = k, x = as.matrix(x), y = y, offset = offset, beta_prior_scale = 2.5, alpha_prior_scale = 5.0)

# Compile
stanmodel <- stan_model(model_code = stancode)

# Fit model
fit <- sampling(stanmodel, data = standata, seed = seed, refresh = 0)
print(fit, pars = "beta")
```

Let us now evaluate the predictive performance of the model using `loo()`.

```{r loo1}
loo1 <- loo(fit)
loo1
```

The `loo()` function output warnings that there are some observations which are
highly influential, and thus the accuracy of importance sampling is compromised
as indicated by the large Pareto $k$ diagnostic values (> 0.7). As discussed in
the vignette
[_Using the loo package (version >= 2.0.0)_](https://mc-stan.org/loo/articles/loo2-example.html),
this may be an indication of model misspecification. Despite that, it is still
beneficial to be able to evaluate the predictive performance of the model
accurately.

## Moment matching correction for importance sampling

To improve the accuracy of the `loo()` result above, we could perform
leave-one-out cross-validation by explicitly leaving out single observations and
refitting the model using MCMC repeatedly. However, the Pareto $k$ diagnostics
indicate that there are 19 observations which are problematic. This would
require 19 model refits which may require a lot of computation time.

Instead of refitting with MCMC, we can perform a faster moment matching
correction to the importance sampling for the problematic observations. This can
be done with the `loo_moment_match()` function in the __loo__ package, which
takes our existing `loo` object as input and modifies it. The moment matching
requires some evaluations of the model posterior density. For models fitted with
__rstan__, this can be conveniently done by using the existing `stanfit` object.

First, we show how the moment matching can be used for a model fitted using
__rstan__. It only requires setting the argument `moment_match` to `TRUE` in the
`loo()` function. Optionally, you can also set the argument `k_threshold` which
determines the Pareto $k$ threshold, above which moment matching is used. By
default, it operates on all observations whose Pareto $k$ value is larger than
0.7.

```{r loo_moment_match}
# available in rstan >= 2.21
loo2 <- loo(fit, moment_match = TRUE)
loo2
```

After the moment matching, all observations have the diagnostic
Pareto $k$ less than 0.7, meaning that the estimates are now reliable.
The total `elpd_loo` estimate also changed from `-5457.8` to `-5478.5`, showing
that before moment matching, `loo()` overestimated the predictive
performance of the model.

The updated Pareto $k$ values stored in
`loo2$diagnostics$pareto_k` are considered algorithmic diagnostic values that
indicate the sampling accuracy. The original Pareto $k$ values are
stored in `loo2$pointwise[,"influence_pareto_k"]` and these are not modified
by the moment matching. These can be considered as diagnostics
for how big influence each observation has on the posterior distribution.
In addition to the Pareto $k$ diagnostics, moment matching also updates
the effective sample size estimates. 

# Using `loo_moment_match()` directly

The moment matching can also be performed by explicitly calling the function
`loo_moment_match()`. This enables its use also for models that are not using
__rstan__ or another package with built-in support for `loo_moment_match()`. To
use `loo_moment_match()`, the user must give the model object `x`, the `loo`
object, and 5 helper functions as arguments to `loo_moment_match()`. The helper
functions are

* `post_draws`	
    + A function the takes `x` as the first argument and returns a matrix of
    posterior draws of the model parameters, `pars`.
* `log_lik_i`	
    + A function that takes `x` and `i` and returns a matrix
    (one column per chain) or a vector (all chains stacked) of log-likeliood
    draws of the ith observation based on the model `x`. If the draws are
    obtained using MCMC, the matrix with MCMC chains separated is preferred.
* `unconstrain_pars`	
    + A function that takes arguments `x` and `pars`, and returns posterior
    draws on the unconstrained space based on the posterior draws on the
    constrained space passed via `pars`.
* `log_prob_upars`	
    + A function that takes arguments `x` and `upars`, and returns a matrix of
    log-posterior density values of the unconstrained posterior draws passed
    via `upars`.
* `log_lik_i_upars`	
    + A function that takes arguments `x`, `upars`, and `i` and returns a
    vector of log-likelihood draws of the `i`th observation based on the
    unconstrained posterior draws passed via `upars`.

Next, we show how the helper functions look like for RStan objects, and show an
example of using `loo_moment_match()` directly. For stanfit objects from 
__rstan__ objects, the functions look like this:

```{r stanfitfuns}
# create a named list of draws for use with rstan methods
.rstan_relist <- function(x, skeleton) {
  out <- utils::relist(x, skeleton)
  for (i in seq_along(skeleton)) {
    dim(out[[i]]) <- dim(skeleton[[i]])
  }
  out
}

# rstan helper function to get dims of parameters right
.create_skeleton <- function(pars, dims) {
  out <- lapply(seq_along(pars), function(i) {
    len_dims <- length(dims[[i]])
    if (len_dims < 1) return(0)
    return(array(0, dim = dims[[i]]))
  })
  names(out) <- pars
  out
}

# extract original posterior draws
post_draws_stanfit <- function(x, ...) {
  as.matrix(x)
}

# compute a matrix of log-likelihood values for the ith observation
# matrix contains information about the number of MCMC chains
log_lik_i_stanfit <- function(x, i, parameter_name = "log_lik", ...) {
  loo::extract_log_lik(x, parameter_name, merge_chains = FALSE)[, , i]
}

# transform parameters to the unconstraint space
unconstrain_pars_stanfit <- function(x, pars, ...) {
  skeleton <- .create_skeleton(x@sim$pars_oi, x@par_dims[x@sim$pars_oi])
  upars <- apply(pars, 1, FUN = function(theta) {
    rstan::unconstrain_pars(x, .rstan_relist(theta, skeleton))
  })
  # for one parameter models
  if (is.null(dim(upars))) {
    dim(upars) <- c(1, length(upars))
  }
  t(upars)
}

# compute log_prob for each posterior draws on the unconstrained space
log_prob_upars_stanfit <- function(x, upars, ...) {
  apply(upars, 1, rstan::log_prob, object = x,
        adjust_transform = TRUE, gradient = FALSE)
}

# compute log_lik values based on the unconstrained parameters
log_lik_i_upars_stanfit <- function(x, upars, i, parameter_name = "log_lik",
                                  ...) {
  S <- nrow(upars)
  out <- numeric(S)
  for (s in seq_len(S)) {
    out[s] <- rstan::constrain_pars(x, upars = upars[s, ])[[parameter_name]][i]
  }
  out
}
```

Using these function, we can call `loo_moment_match()` to update the existing
`loo` object.

```{r loo_moment_match.default, message=FALSE}
loo3 <- loo::loo_moment_match.default(
  x = fit,
  loo = loo1,
  post_draws = post_draws_stanfit,
  log_lik_i = log_lik_i_stanfit,
  unconstrain_pars = unconstrain_pars_stanfit,
  log_prob_upars = log_prob_upars_stanfit,
  log_lik_i_upars = log_lik_i_upars_stanfit
)
loo3
```

As expected, the result is identical to the previous result of
`loo2 <- loo(fit, moment_match = TRUE)`.

# References

Gelman, A., and Hill, J. (2007).  *Data Analysis Using Regression and Multilevel Hierarchical Models.*  Cambridge University Press.

Stan Development Team (2020) _RStan: the R interface to Stan, Version 2.21.1_   https://mc-stan.org

Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2021).
Implicitly adaptive importance sampling. _Statistics and Computing_, 31, 16.
\doi:10.1007/s11222-020-09982-2. arXiv preprint arXiv:1906.08850.

Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
