From 9d58deb1ef1fb5ecee5745563699dde5fd25822d Mon Sep 17 00:00:00 2001 From: Tiffany Tang Date: Fri, 22 Sep 2023 16:46:38 -0400 Subject: [PATCH] Vignette updates (#170) * create intro simChef vignette * update comprehensive simChef vignette * add vignette for inputs/outputs cheat sheet * restructure docs folder to accomodate multiple example experiments Close #160 --- .Rbuildignore | 2 +- README.md | 4 +- vignettes/cheat-sheet.Rmd | 79 ++ .../docs/dgps/Linear DGP.md | 23 + .../docs/evaluators/Prediction Accuracy.md | 11 + .../docs/methods/Linear Regression.md | 15 + .../docs/methods/Random Forest.md | 9 + .../Example Experiment/docs/objectives.md | 5 + .../visualizers/Prediction Accuracy Plot.md | 5 + .../docs/dgps/Linear Gaussian DGP.md | 2 +- .../Rejection Prob. (alpha = 0.1).md | 0 .../docs/methods/OLS.md | 2 +- .../docs/objectives.md | 2 +- .../docs/visualizers/Power.md | 0 .../Rejection Prob. (alpha = 0.1) Plot.md | 0 vignettes/figures/api_overview.png | Bin 0 -> 123271 bytes vignettes/figures/eval_experiment.png | Bin 0 -> 655265 bytes vignettes/figures/fit_eval_viz.png | Bin 0 -> 345652 bytes vignettes/figures/fit_experiment.png | Bin 0 -> 1314042 bytes vignettes/figures/run_experiment.png | Bin 0 -> 82783 bytes vignettes/figures/viz_experiment.png | Bin 0 -> 717408 bytes vignettes/references.bib | 79 ++ vignettes/simChef-full.Rmd | 1260 +++++++++++++++++ vignettes/simChef.Rmd | 1057 ++++---------- 24 files changed, 1761 insertions(+), 794 deletions(-) create mode 100644 vignettes/cheat-sheet.Rmd create mode 100644 vignettes/example-docs/Example Experiment/docs/dgps/Linear DGP.md create mode 100644 vignettes/example-docs/Example Experiment/docs/evaluators/Prediction Accuracy.md create mode 100644 vignettes/example-docs/Example Experiment/docs/methods/Linear Regression.md create mode 100644 vignettes/example-docs/Example Experiment/docs/methods/Random Forest.md create mode 100644 vignettes/example-docs/Example Experiment/docs/objectives.md create mode 100644 vignettes/example-docs/Example Experiment/docs/visualizers/Prediction Accuracy Plot.md rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/dgps/Linear Gaussian DGP.md (99%) rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/evaluators/Rejection Prob. (alpha = 0.1).md (100%) rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/methods/OLS.md (99%) rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/objectives.md (98%) rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/visualizers/Power.md (100%) rename vignettes/{ => example-docs/Linear Regression Experiment}/docs/visualizers/Rejection Prob. (alpha = 0.1) Plot.md (100%) create mode 100644 vignettes/figures/api_overview.png create mode 100644 vignettes/figures/eval_experiment.png create mode 100644 vignettes/figures/fit_eval_viz.png create mode 100644 vignettes/figures/fit_experiment.png create mode 100644 vignettes/figures/run_experiment.png create mode 100644 vignettes/figures/viz_experiment.png create mode 100644 vignettes/references.bib create mode 100644 vignettes/simChef-full.Rmd diff --git a/.Rbuildignore b/.Rbuildignore index feebc234..0ac4c9ec 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -6,7 +6,7 @@ ^LICENSE\.md$ ^\.github$ ^vignettes/results$ -^vignettes/docs$ +^vignettes/example-docs$ ^man-roxygen ^.*\.Rproj$ ^\.Rproj\.user$ diff --git a/README.md b/README.md index 317f9b9c..27b7c486 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,8 @@ experiment <- create_experiment() %>% add_dgp(dgp1) %>% add_dgp(dgp2) %>% add_method(method1) %>% + add_evaluator(evaluator1) %>% + add_visualizer(visualizer1) %>% add_vary_across( dgp = dgp1, n = c(100, 1000, 10000) @@ -21,7 +23,7 @@ experiment <- create_experiment() %>% ) results <- experiment %>% - run_experiment() + run_experiment(n_reps = 100) ``` ### Installation diff --git a/vignettes/cheat-sheet.Rmd b/vignettes/cheat-sheet.Rmd new file mode 100644 index 00000000..184b846d --- /dev/null +++ b/vignettes/cheat-sheet.Rmd @@ -0,0 +1,79 @@ +--- +title: "Cheat sheet: Inputs/outputs for user-defined functions" +output: + rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Cheat sheet: Inputs/outputs for user-defined functions} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + # disable R chunk evaluation when using macOS in GH Actions + # see https://github.com/tidyverse/ggplot2/issues/2252#issuecomment-1006713187 + # and https://github.com/lcolladotor/biocthis/issues/27 and + # https://github.com/bodkan/slendr/commit/f0f5ae18452cc9df5d151874e41b0b8fd5f29aa2# + eval = Sys.getenv("RUNNER_OS") != "macOS" +) + +library(simChef) + +set.seed(12345) +``` + +![Overview of inputs and outputs for user-defined functions when running a `simChef` `Experiment`. Dotted boxes denote optional input arguments that are automatically passed through in a `run_experiment()` call if specified.](figures/fit_eval_viz.png){width="100%" #fig:fit-eval-viz-figure} + +### DGP + +- **Inputs** + - Any number of named parameters (e.g., `dgp_param1`, `dgp_param2`, ...) +- **Outputs** + - A list of named elements to be passed onto the method(s) (e.g., `dgp_out1`, `dgp_out2`, ...) + +### Method + +- **Inputs** + - Named parameters matching all names in the list returned by the DGP function (e.g., `dgp_out1`, `dgp_out2`, ...) + - Any number of additional named parameters (e.g., `method_param1`, `method_param2`, ...) +- **Outputs** + - A list of named elements (e.g., `method_out1`, `method_out2`, ...) + +### Evaluator + +- **Inputs** + - `fit_results` (optional): output of `fit_experiment()` + - `vary_params` (optional): character vector of parameter names that are varied across in the experiment + - Any number of additional named parameters (e.g., `eval_param1`, `eval_param2`, ...) +- **Outputs** + - Typically a `tibble` or `data.frame` + +### Visualizer + +- **Inputs** + - `fit_results` (optional): output of `fit_experiment()` + - `eval_results` (optional): output of `evaluate_experiment()` + - `vary_params` (optional): character vector of parameter names that are varied across in the experiment + - Any number of additional named parameters (e.g., `viz_param1`, `viz_param2`, ...) +- **Outputs** + - Typically a plot (e.g., a `ggplot` or `plotly` object). Can also return tables or more generally, any R Markdown snippet (e.g., verbatim text) + +### Outputs of `*_experiment` + +- `fit_results`: output of `fit_experiment()` + - A tibble of results from each (replicate, DGP, Method) combination with the following columns: + - `.rep`: replicate ID + - `.dgp_name`: name of DGP + - `.method_name`: name of Method + - `vary_across` parameter columns (if applicable): value of the specified `vary_across` parameter + - A column corresponding to each named component in the list returned by `Method` (e.g., `method_out1`, `method_out2`, ...) +- `eval_results`: output of `evaluate_experiment()` + - A named list of elements, with one element for each `Evaluator` in the `Experiment` + - The list names correspond to the names given to each `Evaluator` in `create_evaluator(.name = ...)` + - The list elements are exactly the return values from each `Evaluator` function +- `viz_results`: output of `visualize_experiment()` + - A named list of elements, with one element for each `Visualizer` in the `Experiment` + - The list names correspond to the names given to each `Visualizer` in `create_visualizer(.name = ...)` + - The list elements are exactly the return values from each `Visualizer` function diff --git a/vignettes/example-docs/Example Experiment/docs/dgps/Linear DGP.md b/vignettes/example-docs/Example Experiment/docs/dgps/Linear DGP.md new file mode 100644 index 00000000..300646b7 --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/dgps/Linear DGP.md @@ -0,0 +1,23 @@ +We simulate the (test and training) covariate/design matrix $\mathbf{X} \in \mathbb{R}^{n \times p}$ from a standard normal distribution and the response vector $\mathbf{y} \in \mathbb{R}^n$ from a linear model. Specifically, + +\begin{align*} +\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon},\\ +\end{align*} + +where +\begin{align*} +& \mathbf{X}_{ij} \stackrel{iid}{\sim} N\left(0, 1\right) \text{ for all } i = 1, \ldots, n \text{ and } j = 1, \ldots, p, \\ +& \boldsymbol{\epsilon}_i \stackrel{iid}{\sim} N(0, \sigma^2) \text{ for all } i = 1, \ldots, n. +\end{align*} + +**Default Parameters in DGP** + +- Number of training samples: $n_{\text{train}} = 200$ +- Number of test samples: $n_{\text{test}} = 200$ +- Number of features: $p = 2$ +- Amount of noise: $\sigma = 1$ +- Coefficients: $\boldsymbol{\beta} = (1, 0)^\top$ + + + [In practice, documentation of DGPs should answer the questions “what” and “why”. That is, “what” is the DGP, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.] + \ No newline at end of file diff --git a/vignettes/example-docs/Example Experiment/docs/evaluators/Prediction Accuracy.md b/vignettes/example-docs/Example Experiment/docs/evaluators/Prediction Accuracy.md new file mode 100644 index 00000000..b2245f79 --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/evaluators/Prediction Accuracy.md @@ -0,0 +1,11 @@ +Given the true responses $\mathbf{y} \in \mathbb{R}^n$ and predicted responses $\mathbf{\hat{y}} \in \mathbb{R}^n$ from various methods, we evaluate several prediction accuracy metrics, namely: + +- Root Mean Squared Error (RMSE): $\sqrt{\frac{1}{n} || \mathbf{y} - \mathbf{\hat{y}} ||_2^2}$ +- Mean Absolute Error (MAE): $\frac{1}{n} || \mathbf{y} - \mathbf{\hat{y}} ||_1$ +- R-squared ($R^2$): $1 - \frac{|| \mathbf{y} - \mathbf{\hat{y}} ||_2^2}{|| \mathbf{y} - \mathbf{\bar{y}} ||_2^2}$ + +We choose to evaluate both RMSE and MAE as these can convey different messages in the presence of outliers. Further, $R^2$ provides a convenient normalization of RMSE that can often be more easily interpreted. + + + [In practice, documentation of evaluation metrics should answer the questions “what” and “why”. That is, “what” is the metric, and “why” are we using/studying it?] + diff --git a/vignettes/example-docs/Example Experiment/docs/methods/Linear Regression.md b/vignettes/example-docs/Example Experiment/docs/methods/Linear Regression.md new file mode 100644 index 00000000..d67cba00 --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/methods/Linear Regression.md @@ -0,0 +1,15 @@ +Given some training covariate data $\mathbf{X}$ and response vector $\mathbf{y}$, we fit a linear regression model (i.e., ordinary least squares) on $\mathbf{X}$ to predict $\mathbf{y}$ by minimizing the following objective: + +\begin{align*} +\boldsymbol{\hat{\beta}} = \text{argmin}_{\boldsymbol{\beta}} || \mathbf{y} - \mathbf{X} \boldsymbol{\beta} ||_2^2. +\end{align*} + +Then, to make prediction given some test data $\mathbf{X}^{\text{test}}$, we compute: + +\begin{align*} +\boldsymbol{\hat{y}}^{\text{test}} = \mathbf{X}^{\text{test}} \boldsymbol{\hat{\beta}}. +\end{align*} + + + [In practice, documentation of methods should answer the questions “what” and “why”. That is, “what” is the method, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.] + diff --git a/vignettes/example-docs/Example Experiment/docs/methods/Random Forest.md b/vignettes/example-docs/Example Experiment/docs/methods/Random Forest.md new file mode 100644 index 00000000..8e30f0cc --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/methods/Random Forest.md @@ -0,0 +1,9 @@ +Given some training covariate data $\mathbf{X}$ and response vector $\mathbf{y}$, we fit a random forest on $\mathbf{X}$ to predict $\mathbf{y}$. At a high-level, a random forest is an ensemble of classification or regression trees (CART) that are fitted independently of one another on bootstrapped samples of the training data. Further, each CART model is fitted by performing recursive axis-aligned binary splits, where the optimal split at each node is chosen from a random subsample of features to minimize an impurity decrease criterion. + +To make predictions, CART identifies the unique leaf node containing the test data point and predicts the mean response (of training data) in that node. For a random forest, the predictions are averaged across all CARTs in the forest. + +For further details, we refer to [Breiman (2001)](https://link.springer.com/content/pdf/10.1023/A:1010933404324.pdf). + + + [In practice, documentation of methods should answer the questions “what” and “why”. That is, “what” is the method, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.] + diff --git a/vignettes/example-docs/Example Experiment/docs/objectives.md b/vignettes/example-docs/Example Experiment/docs/objectives.md new file mode 100644 index 00000000..938033dc --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/objectives.md @@ -0,0 +1,5 @@ +The objective of this simulation experiment is to provide a basic example on how to use `simChef` and showcase the automated R Markdown-generated documentation. For the sake of illustration, this toy simulation experiment studies the performance of linear regression versus random forests under a linear data-generating process across varying noise levels. + + + [Typically, the objective of the simulation experiment (and this blurb) will be more scientific than instructive and will warrant additional context/background and domain knowledge.] + \ No newline at end of file diff --git a/vignettes/example-docs/Example Experiment/docs/visualizers/Prediction Accuracy Plot.md b/vignettes/example-docs/Example Experiment/docs/visualizers/Prediction Accuracy Plot.md new file mode 100644 index 00000000..9a62b012 --- /dev/null +++ b/vignettes/example-docs/Example Experiment/docs/visualizers/Prediction Accuracy Plot.md @@ -0,0 +1,5 @@ +We plot the prediction accuracy between the true and predicted responses, as measured via RMSE, MAE, and $R^2$, to understand how characteristics of the DGP affect various prediction methods. + + + [In practice, documentation of the plotters should answer the questions “what” and “why”. That is, “what” is the plot, and “why” are we using/studying it?] + diff --git a/vignettes/docs/dgps/Linear Gaussian DGP.md b/vignettes/example-docs/Linear Regression Experiment/docs/dgps/Linear Gaussian DGP.md similarity index 99% rename from vignettes/docs/dgps/Linear Gaussian DGP.md rename to vignettes/example-docs/Linear Regression Experiment/docs/dgps/Linear Gaussian DGP.md index c69d86d0..1c0c1653 100644 --- a/vignettes/docs/dgps/Linear Gaussian DGP.md +++ b/vignettes/example-docs/Linear Regression Experiment/docs/dgps/Linear Gaussian DGP.md @@ -16,4 +16,4 @@ In the Linear Gaussian DGP, we simulate the feature/design matrix $\mathbf{X} \i [In practice, documentation of DGPs should answer the questions “what” and “why”. That is, “what” is the DGP, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.] - + \ No newline at end of file diff --git a/vignettes/docs/evaluators/Rejection Prob. (alpha = 0.1).md b/vignettes/example-docs/Linear Regression Experiment/docs/evaluators/Rejection Prob. (alpha = 0.1).md similarity index 100% rename from vignettes/docs/evaluators/Rejection Prob. (alpha = 0.1).md rename to vignettes/example-docs/Linear Regression Experiment/docs/evaluators/Rejection Prob. (alpha = 0.1).md diff --git a/vignettes/docs/methods/OLS.md b/vignettes/example-docs/Linear Regression Experiment/docs/methods/OLS.md similarity index 99% rename from vignettes/docs/methods/OLS.md rename to vignettes/example-docs/Linear Regression Experiment/docs/methods/OLS.md index 736d6c5f..df369c06 100644 --- a/vignettes/docs/methods/OLS.md +++ b/vignettes/example-docs/Linear Regression Experiment/docs/methods/OLS.md @@ -16,4 +16,4 @@ and then compute the two-sided p-value under the t distribution with $n - p - 1$ [In practice, documentation of methods should answer the questions “what” and “why”. That is, “what” is the method, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.] - + \ No newline at end of file diff --git a/vignettes/docs/objectives.md b/vignettes/example-docs/Linear Regression Experiment/docs/objectives.md similarity index 98% rename from vignettes/docs/objectives.md rename to vignettes/example-docs/Linear Regression Experiment/docs/objectives.md index c0df7601..f8a81810 100644 --- a/vignettes/docs/objectives.md +++ b/vignettes/example-docs/Linear Regression Experiment/docs/objectives.md @@ -2,4 +2,4 @@ The objective of this simulation experiment is to provide a toy example on how t [Typically, the objective of the simulation experiment (and this blurb) will be more scientific than instructive and will warrant additional context/background and domain knowledge.] - + \ No newline at end of file diff --git a/vignettes/docs/visualizers/Power.md b/vignettes/example-docs/Linear Regression Experiment/docs/visualizers/Power.md similarity index 100% rename from vignettes/docs/visualizers/Power.md rename to vignettes/example-docs/Linear Regression Experiment/docs/visualizers/Power.md diff --git a/vignettes/docs/visualizers/Rejection Prob. 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    +*Requirements for DGP function syntax* + +**DGP inputs**: + +- Any number of named parameters. + +**DGP outputs**: + +- A named list with all data necessary to fit the method, evaluate the method, and visualize results. + - Note: This returned list of elements will be automatically passed onto the method function(s) as input arguments of the same name. +
    +:::: + +#### Step 1b. Define method function(s) {-} + +Given the above DGP, suppose we want to investigate the performance of linear regression, specifically the p-values corresponding to the non-intercept coefficients from `summary.lm()`. + +```{r create-method-fun} +lm_fun <- function(X, y, cols = c("X1", "X2")) { + lm_fit <- lm(y ~ X) + pvals_tib <- summary(lm_fit)$coefficients[cols, "Pr(>|t|)"] %>% + setNames(paste(names(.), "p-value")) + return(pvals_tib) +} +``` + +:::: {.infobox} +
    +*Requirements for method function syntax* + +**Method inputs**: + +- Named parameters matching all names in the list returned by the DGP function `linear_dgp_fun` (in this case, `X` and `y`). +- Any number of additional named parameters (in this case, `cols`). + +**Method outputs**: + +- A list (or array-like object) of named elements with all data necessary to evaluate the method and visualize results. +
    +:::: + +#### Step 1c. Define evaluation function(s) {-} + +To evaluate performance here, one metric (or statistic) of interest could be the rejection probability at some level $\alpha$, which we compute in the following function `reject_prob_fun`. + +```{r create-eval-fun} +reject_prob_fun <- function(fit_results, alpha = 0.05) { + group_vars <- c(".dgp_name", ".method_name") + eval_out <- fit_results %>% + dplyr::group_by(across({{group_vars}})) %>% + dplyr::summarise( + `X1 Reject Prob.` = mean(`X1 p-value` < alpha), + `X2 Reject Prob.` = mean(`X2 p-value` < alpha), + .groups = "keep" + ) + return(eval_out) +} +``` + +:::: {.infobox} +
    +*Requirements for evaluation function syntax* + +**Evaluator inputs**: + +- `fit_results` (optional, but almost always necessary): output of `fit_experiment()`, which automatically gets passed to the evaluation function(s). + - More specifically, `fit_results` is a tibble containing the method outputs of all (replicate, DGP, method) combinations fitted in the experiment. + `fit_results` will have columns named `.rep`, `.dgp_name`, `.method_name`, and any other named arguments that were outputted from the method function (i.e., `lm_fun`). + - Note: This argument must be exactly named `fit_results` or else the evaluation function will not receive the results from the fitted experiment. + - Tip: Many evaluation metrics are most naturally computed across replicates, so it is common to group the `fit_results` by `.dgp_name` and `.method_name` as seen in `reject_prob_fun()` above. In doing so, the rejection probability (across replicates) will be computed for each (DGP, Method) combination separately. However, depending on the goal of the Evaluator function, grouping by `.dgp_name` and `.method_name` might not be necessary. +- `vary_params` (optional): character vector of parameter names that are varied across in the experiment and automatically passed to the evaluation function(s); discussed further in \@ref(example-usage-with-varying-across-component). +- Any number of additional named parameters (in this case, `alpha`). + +**Evaluator outputs**: + +- Typically a `tibble` or `data.frame` with the evaluated metrics or performance of the method. +
    +:::: + +#### Step 1d. Define visualization function(s) {-} + +Lastly, we may want to plot the results from the method fits (stored in `fit_results`) and/or the outputs from our evaluation metrics (stored in `eval_results`). For example, we could plot the power of the hypothesis test as in `power_plot_fun`. + +```{r create-viz-fun} +power_plot_fun <- function(fit_results, col = "X1") { + plt <- ggplot2::ggplot(fit_results) + + ggplot2::aes(x = .data[[paste(col, "p-value")]], + color = as.factor(.method_name)) + + ggplot2::geom_abline(slope = 1, intercept = 0, + color = "darkgray", linetype = "solid", size = 1) + + ggplot2::stat_ecdf(size = 1) + + ggplot2::scale_x_continuous(limits = c(0, 1)) + + ggplot2::labs(x = "t", y = "P( p-value \u2264 t )", + linetype = "", color = "Method") + return(plt) +} +``` + +:::: {.infobox} +
    +*Requirements for visualization function syntax* + +**Visualizer inputs**: + +- `fit_results` (optional): output of `fit_experiment()`, which automatically gets passed to the visualization function(s). + - Note: This argument must be exactly named `fit_results` or else the visualization function will not receive the results from the fitted experiment. + - Tip: Include `fit_results` as an input parameter if the visualization is to be computed using the method outputs. +- `eval_results` (optional): output of `evaluate_experiment()`, which automatically gets passed to the visualization function(s). + - More specifically, `eval_results` is a list of named elements, one element for each Evaluator in the experiment. + - Note: This argument must be exactly named `eval_results` or else the visualization function will not receive the results from the evaluated experiment. + - Tip: Include `eval_results` as an input parameter if the visualization is to be computed using the evaluation outputs. +- `vary_params` (optional): character vector of parameter names that are varied across in the experiment and automatically passed to the visualization function(s); discussed further in \@ref(example-usage-with-varying-across-component). +- Any number of additional named parameters (in this case, `col`). + +**Visualizer outputs**: + +- Typically a plot. We highly recommend using `ggplot` or `plotly`. On rarer occasions, can also return tables or more generally, any R Markdown snippet. +
    +:::: + + +### Step 2. Convert functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects {-} + +Once we have specified the relevant DGP, method, evaluation, and visualization functions, the next step is to convert these functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects for `simChef`. To do so, we simply wrap the functions in `create_dgp()`, `create_method()`, `create_evaluator()`, or `create_visualizer()`, while specifying an intelligible name for the object and any input parameters to pass to the function. + +Each `create_*()` function follows the same syntax and takes in the inputs: + +- `.*_fun`: the function from which to simulate data, fit the method, evaluate metrics, or create a visualization (depending on `*`). +- `.name`: an (intelligible) object name, which will be used in creating the R Markdown report. +- `...`: additional arguments to pass to `.*_fun` above. + +![Overview of the four core components (`DGP()`, `Method()`, `Evaluator()`, `Visualizer()`) in a `simChef` `Experiment`. Using these classes, users can easily build a `simChef` `Experiment` using reusable, custom user-defined functions (i.e., `dgp_fun`, `method_fun`, `eval_fun`, and `viz_fun`). Optional named parameters can be set in these user-defined functions via the `...` arguments in the `create_*()` methods.](figures/api_overview.png){width=100% #fig:api-overview} + +For example, + +```{r create-classes} +## DGPs +linear_dgp <- create_dgp( + .dgp_fun = linear_dgp_fun, .name = "Linear Gaussian DGP", + # additional named parameters to pass to .dgp_fun() + n = 200, beta = c(1, 0), rho = 0, sigma = 1 +) + +## Methods +lm_method <- create_method( + .method_fun = lm_fun, .name = "OLS" + # additional named parameters to pass to .method_fun() +) + +## Evaluators +reject_prob_eval <- create_evaluator( + .eval_fun = reject_prob_fun, .name = "Rejection Prob. (alpha = 0.1)", + # additional named parameters to pass to .eval_fun() + alpha = 0.1 +) + +## Visualizers +power_plot <- create_visualizer( + .viz_fun = power_plot_fun, .name = "Power" + # additional named parameters to pass to .viz_fun() +) +``` + +Some notes on the use of `...` arguments in `create_*()`: + +- `...` arguments in `create_*()` must be named arguments that match those in `.*_fun()`. +- `...` arguments in `create_*()` will override any default arguments set in `.*_fun()`. + - E.g., In `reject_prob_eval`, `alpha = 0.1`, not `0.05`. +- If `...` arguments in `create_*()` are not specified, the default arguments in `.*_fun()` are used. + - E.g., In `lm_method`, `cols = c("X1", "X2")`. + + +### Step 3. Assemble objects into a complete simulation experiment {-} + +At this point, we have created a `DGP()` (`dgp`), `Method()` (`lm_method`), `Evaluator()` (`reject_prob_eval`), and `Visualizer()` (`power_plot`) for our simulation experiment. +The next step is to create a simulation experiment recipe and `add` each component to the recipe via: + +```{r create-experiment} +experiment <- create_experiment(name = "Linear Regression Experiment") %>% + add_dgp(linear_dgp) %>% + add_method(lm_method) %>% + add_evaluator(reject_prob_eval) %>% + add_visualizer(power_plot) +``` + +*Tip*: We can easily see the individual parts of the simulation experiment recipe by printing the experiment. + +```{r print-experiment} +print(experiment) +``` + +*Notes*: + +- Any number of DGPs, Methods, Evaluators, and Visualizers can be added to the simulation experiment recipe. +- Once a DGP, Method, Evaluator, or Visualizer has been added to the simulation experiment recipe, the component can be updated using `update_dgp()`, `update_method()`, `update_evaluator()`, or `update_visualizer()` and removed using `remove_dgp()`, `remove_method()`, `remove_evaluator()`, or `remove_visualizer()`. + + +### Step 4. Document and describe the simulation experiment in text {-} + +A crucial component when running veridical simulations is **documentation**. +We highly encourage practitioners to document: + +- the purpose or objective of the simulation experiment, +- what `DGP(s)`, `Method(s)`, `Evaluator(s)`, and `Visualizer(s)` were used, and +- why these `DGP(s)`, `Method(s)`, `Evaluator(s)`, and `Visualizer(s)` were chosen. + +This can and should be done before even running the simulation experiment. +To facilitate this tedious but important process, we have provided a convenient helper function `init_docs()` to initialize/create a documentation template: + +```{r init-docs} +init_docs(experiment) +``` + +```{r cp-docs, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} +# copy pre-written .md files in vignettes/example-docs to the experiment's docs dir +file.copy( + from = here::here(file.path("vignettes", "example-docs", experiment$name, "docs")), + to = file.path(get_save_dir(experiment)), + recursive = TRUE +) +``` + +This creates a series of blank .md files for the user to fill out with descriptions of the simulation experiment and its recipe components. These blank .md files can be found in the experiment's root results directory under docs/. To find the experiment's root results directory, use `get_save_dir(experiment)`. You can find example .md files corresponding to the current experiment and others [here](https://github.com/Yu-Group/simChef/tree/main/vignettes/example-docs/). + +### Step 5. Run the experiment {-} + +Thus far, we have created and documented the simulation experiment recipe, but we have not generated any results from the experiment. That is, we have only given the simulation experiment instructions on what to do. To run the experiment, say over 10 replicates, we can do so via + +```{r run-experiment} +results <- run_experiment(experiment, n_reps = 10, save = TRUE) +``` + +`run_experiment()` is a convenient wrapper function that: + +1. Fits the experiment (`fit_experiment()`), which fits each `Method` on each `DGP` in the `Experiment`, +2. Evaluates the experiment (`evaluate_experiment()`), which evaluates each `Evaluator` on the fitted results, and +3. Visualizes the experiment (`visualize_experiment()`), which visualizes each `Visualizer` on the fitted and/or evaluated results. + +Consequently, the output of `run_experiment()` is a list of length three: + +```{r run-experiment-str} +str(results, max.level = 2) +``` + +1. `fit_results`: output of `fit_experiment()` + - A tibble of results from each (replicate, DGP, Method) combination with the following columns: + - `.rep`: replicate ID + - `.dgp_name`: name of DGP + - `.method_name`: name of Method + - `vary_across` parameter columns (if applicable): value of the specified `vary_across` parameter + - A column corresponding to each named component in the list returned by `Method` (e.g., `method_out1`, `method_out2`, ...) + + ```{r fit-results-output} + results$fit_results + ``` + +2. `eval_results`: output of `evaluate_experiment()` + - A named list of elements, with one element for each `Evaluator` in the `Experiment` + - The list names correspond to the names given to each `Evaluator` in `create_evaluator(.name = ...)` + - The list elements are exactly the return values from each `Evaluator` function + + ```{r eval-results-output} + results$eval_results + ``` + +3. `viz_results`: output of `visualize_experiment()` + - A named list of elements, with one element for each `Visualizer` in the `Experiment` + - The list names correspond to the names given to each `Visualizer` in `create_visualizer(.name = ...)` + - The list elements are exactly the return values from each `Visualizer` function + + ```{r viz-results-output, fig.width = 6} + results$viz_results + ``` + +Notes: + +- By default, the results are not saved to disk. However, to generate the R Markdown report, we will need to save the results to disk and hence set `save = TRUE` in the call to `run_experiment()` above. +- The experiment can also be run in parallel. For a more detailed walkthrough on how to parallelize the experiment, please see `vignette("parallel")`. + +### Step 6. Visualize results via an automated R Markdown report {-} + +Finally, to visualize and report all results from the simulation experiment, we can render the R Markdown documentation. This will generate a single html document, including the code, documentation (from Step 4), and simulation results. + +```{r render-docs-1, eval = FALSE, warning = FALSE} +render_docs(experiment) +``` + +```{r cp-html-1, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} +# create pkgdown/assets directory, which will be copied to the gh-pages branch +# during the pkgdown workflow (see .github/workflows/pkgdown.yaml) +assets_dir <- here::here("pkgdown/assets") +dir.create(assets_dir, recursive = TRUE) + +# copy html output to pkgdown/assets directory for website +file.copy(from = file.path(get_save_dir(experiment), + paste0(experiment$name, ".html")), + to = file.path(assets_dir, "linear_regression_base_output.html"), + overwrite = TRUE) +``` + +The results can be found [here](../linear_regression_base_output.html). + +Note that if the documentation template has not yet been created for `experiment` (e.g., via `init_docs(experiment)`), then `render_docs()` will automatically create the documentation template for the user to fill out. Again, we highly encourage practitioners to document their simulation experiments in the spirit of transparency and reproducibility. + + +## Example usage with varying across component + +Now, moving slightly beyond the most basic usage of `simChef`, it is often helpful to understand how a method's performance is affected as we vary parameter(s) in the DGP and/or method across different values. + +For instance, what happens to the power as the amount of noise in the linear model increases? + +Using the grammar of `simChef`, we can investigate this question by adding a `vary_across` component to the `Experiment`. In this example, we are varying the `sigma` (i.e,. the noise level) parameter in the Linear Gaussian DGP across four different values (i.e,. 1, 2, 4, and 8) while keeping all other parameters fixed at their baseline value. + +```{r add-vary-across-1} +experiment <- experiment %>% + add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) +``` + +```{r print-vary-across-experiment-1} +print(experiment) +``` + + +:::: {.infobox-blue} +
    +**About `add_vary_across()`** + +More generally, `add_vary_across()` can be used to vary across one or more parameters in a `DGP` or `Method` while keeping all other parameters fixed at their baseline value. The inputs to `add_vary_across` are as follows: + +- `.dgp`, `.method`: Name of the `DGP`/`Method` to vary or the `DGP`/`Method` object itself. + - Note: Only one of `.dgp` or `.method` can be specified at a time. To vary across more than one `DGP` and/or `Method`, use a separate `add_vary_across()` call for each. +- `...`: Any number of named arguments of the form `[param_name] = [param_values]`. + - `[param_name]` should be the name of the argument/parameter in the `DGP`/`Method` function that will be varied. + - `[param_values]` should be a vector or list of values that `[param_name]` will take on, while all other arguments are kept constant at their baseline value (see `DGP$dgp_params` or `Method$method_params` for these baseline values). + +Some notes on varying across multiple parameters: + + - If multiple arguments of `[param_name] = [param_values]` are provided within a call to `add_vary_across()`, the cross-product of parameter values is taken. + - E.g., `add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8), n = c(100, 200, 300))` will result in $4 \times 3 = 12$ distinct configurations of the `DGP`. + - It is possible to vary across both `DGP` and `Method` parameters in an `Experiment`. Here, all DGP-method combinations are fitted. + - E.g., `add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) %>% add_vary_across(.method = "OLS", cols = c("X1", "X2"))` results in 8 DGP-method combinations to be fitted in the `Experiment` (4 variations on the `DGP` $\times$ 2 variations on the `Method`). +
    +:::: + +However, when we run the experiment, the results are not quite what we want. +While `fit_results` yields a tibble with 40 rows (= 10 replicates $\times$ 4 `DGP` variations $\times$ 1 `Method`) as desired, the evaluation results in `eval_results` have been summarized/aggregated across all values of `sigma`. + +```{r vary-across-results-1, fig.width = 6} +vary_results <- run_experiment(experiment, n_reps = 10, save = TRUE) +vary_results$fit_results +vary_results$eval_results +vary_results$viz_results +``` + +To see how different values of `sigma` impact the OLS results, we need to modify our `Evaluator` and `Visualizer` functions. +Specifically, in `reject_prob_fun()`, we want to group `fit_results` by `sigma` in addition to `.dgp_name` and `.method_name`. +To do this, we can manually set `group_vars <- c(".dgp_name", ".method_name", "sigma")`. +However, it would be better practice to make use of an input argument named `vary_params` like below. +When we run the experiment (via `run_experiment()`), `vary_params` will be auto-filled by a vector of the parameter names that are varied (i.e., those that have been added via `add_vary_across()`). +In this case, `vary_params` will be auto-filled by `c("sigma")`. +Thus, by using this `vary_params` argument, the `Evaluator` will work more generally, even if other parameters besides `sigma` are varied in the `Experiment`. + +```{r vary-across-evaluator-fun} +reject_prob_fun <- function(fit_results, vary_params = NULL, alpha = 0.05) { + group_vars <- c(".dgp_name", ".method_name", vary_params) # MODIFIED! + eval_out <- fit_results %>% + dplyr::group_by(across({{group_vars}})) %>% + dplyr::summarise( + `X1 Reject Prob.` = mean(`X1 p-value` < alpha), + `X2 Reject Prob.` = mean(`X2 p-value` < alpha), + .groups = "keep" + ) + return(eval_out) +} + +reject_prob_eval <- create_evaluator( + .eval_fun = reject_prob_fun, .name = "Rejection Prob. (alpha = 0.1)", + # additional named parameters to pass to .eval_fun() + alpha = 0.1 +) +``` + +Similarly in `power_plot_fun()`, we need to add `vary_params` as an input argument to plot the results across different values of `sigma`. + +```{r vary-across-visualizer-fun} +power_plot_fun <- function(fit_results, vary_params = NULL, col = "X1") { + plt <- ggplot2::ggplot(fit_results) + + ggplot2::aes(x = .data[[paste(col, "p-value")]], + color = as.factor(.method_name)) + + ggplot2::geom_abline(slope = 1, intercept = 0, + color = "darkgray", linetype = "solid", linewidth = 1) + + ggplot2::stat_ecdf(size = 1) + + ggplot2::scale_x_continuous(limits = c(0, 1)) + + ggplot2::labs(x = "t", y = "P( p-value \u2264 t )", + linetype = "", color = "Method") + # MODIFIED! + if (!is.null(vary_params)) { + plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]]) + } + return(plt) +} + +power_plot <- create_visualizer(.viz_fun = power_plot_fun, .name = "Power") +``` + +Now, we are ready to update our experiment with the corrected `Evaluator` and `Visualizer` and run the experiment via: + +```{r vary-across-results-2, fig.width = 8, fig.height = 6} +vary_results <- experiment %>% + update_evaluator(reject_prob_eval, name = "Rejection Prob. (alpha = 0.1)") %>% + update_visualizer(power_plot, name = "Power") %>% + run_experiment(n_reps = 10, save = TRUE) +vary_results$fit_results +vary_results$eval_results +vary_results$viz_results +``` + +Note here we need to use `update_*` instead of `add_*` since an `Evaluator` named "Rejection Prob. (alpha = 0.1)" and a `Visualizer` named "Power" already exist in the `Experiment`. Using `add_*` will throw an error. + +## More example usages with varying across component + +To further showcase the power of `simChef` and to provide additional examples, let's add another plot (in fact, an interactive plot using `plotly::ggplotly`) to our `Experiment` and run the `Experiment` (a) across various values of the coefficient $\boldsymbol{\beta}_2$ and (b) across the correlation $\rho$ between features in $\mathbf{X}$. + +Notes: + +- The visualizer function below (`reject_prob_plot_fun`) takes in the `Evaluator` results, stored as `eval_results`, and plots the rejection probability for the $\boldsymbol{\beta}_1$ at $\alpha = 0.1$. +- In order to vary the coefficient $\boldsymbol{\beta}_2$, we must specify this through the argument `beta`, which is vector-valued. To plot this vector-valued parameter on the x-axis (or any axis) in our plots, an additional pre-processing step must be performed since these values are not directly compatible with `ggplot`. Here, we leverage a built-in helper function `list_col_to_chr()`, which converts a list-type tibble column to a character-type tibble column that is amenable for plotting (unlike the list-type column). This preprocessing step using `list_col_to_chr()` is included in both `reject_prob_plot_fun()` and our updated version of `power_plot_fun()` below. +- To reduce the computational burden in this vignette, we choose not to vary across multiple parameters simultaneously and instead, choose to vary only a single `DGP` argument in each run of the experiment (hence, the use of `remove_vary_across()` below). + + +```{r add-reject-prob-visualizer} +# create rejection probability plot +reject_prob_plot_fun <- function(eval_results, vary_params = NULL) { + eval_name <- "Rejection Prob. (alpha = 0.1)" + eval_results_df <- eval_results[[eval_name]] + if (is.list(eval_results_df[[vary_params]])) { + # deal with the case when we vary across a parameter that is vector-valued + eval_results_df[[vary_params]] <- list_col_to_chr( + eval_results_df[[vary_params]], name = vary_params, verbatim = TRUE + ) + } + plt <- ggplot2::ggplot(eval_results_df) + + ggplot2::aes(x = .data[[vary_params]], y = `X1 Reject Prob.`, + color = as.factor(.method_name), + fill = as.factor(.method_name)) + + ggplot2::labs(x = vary_params, y = eval_name, + color = "Method", fill = "Method") + + ggplot2::scale_y_continuous(limits = c(0, 1)) + if (is.numeric(eval_results_df[[vary_params]])) { + plt <- plt + + ggplot2::geom_line() + + ggplot2::geom_point(size = 2) + } else { + plt <- plt + + ggplot2::geom_bar(stat = "identity") + } + return(plotly::ggplotly(plt)) +} + +reject_prob_plot <- create_visualizer( + .viz_fun = reject_prob_plot_fun, .name = "Rejection Prob. (alpha = 0.1) Plot" +) + +# update power plot to work for vector-valued vary_params +power_plot_fun <- function(fit_results, vary_params = NULL, col = "X1") { + if (is.list(fit_results[[vary_params]])) { + # deal with the case when we vary across a parameter that is vector-valued + fit_results[[vary_params]] <- list_col_to_chr( + fit_results[[vary_params]], name = vary_params, verbatim = TRUE + ) + } + plt <- ggplot2::ggplot(fit_results) + + ggplot2::aes(x = .data[[paste(col, "p-value")]], + color = as.factor(.method_name)) + + ggplot2::geom_abline(slope = 1, intercept = 0, + color = "darkgray", linetype = "solid", linewidth = 1) + + ggplot2::stat_ecdf(size = 1) + + ggplot2::scale_x_continuous(limits = c(0, 1)) + + ggplot2::labs(x = "t", y = "P( p-value \u2264 t )", + linetype = "", color = "Method") + if (!is.null(vary_params)) { + plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]]) + } + return(plt) +} + +power_plot <- create_visualizer(.viz_fun = power_plot_fun, .name = "Power") + +# update experiment +experiment <- experiment %>% + update_visualizer(power_plot, name = "Power") %>% + add_visualizer(reject_prob_plot) +``` + +```{r run-more-experiments} +# (a) run experiment across varying values of beta_2 +vary_beta2_results <- experiment %>% + remove_vary_across(dgp = "Linear Gaussian DGP") %>% + add_vary_across( + .dgp = "Linear Gaussian DGP", + beta = list( + c(1, 0), + c(1, 0.5), + c(1, 1), + c(1, 1.5), + c(1, 2) + ) + ) %>% + run_experiment(n_reps = 10, save = TRUE) + +# (b) run experiment across varying values of rho (correlation) +vary_cor_results <- experiment %>% + remove_vary_across(dgp = "Linear Gaussian DGP") %>% + add_vary_across(.dgp = "Linear Gaussian DGP", rho = c(0, 0.2, 0.5, 0.9)) %>% + run_experiment(n_reps = 10, save = TRUE) +``` + +Rather than printing all results from these experiment runs here, let's generate the R Markdown report summary: + +```{r render-docs-2, warning = FALSE, message = FALSE} +render_docs(experiment) +``` + +```{r cp-html-2, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} +# copy html output to pkgdown/assets directory for website +file.copy(from = file.path(get_save_dir(experiment), + paste0(experiment$name, ".html")), + to = file.path(assets_dir, "linear_regression_output.html"), + overwrite = TRUE) +``` + +The results can be found [here](../linear_regression_output.html). + +# Simulation experiment templates and library + +In addition to the grammar of simulation experiments, `simChef` also offers a growing built-in library of composable building blocks and templates to meet common simulation needs. + +## Boilerplate code templates + +In particular, we have created boilerplate code templates for running common types of simulation experiments, namely, those focused on prediction (regression and classification), feature selection, and inference. These templates provide (1) a quick starting point with Evaluators and Visualizers that are commonly used for the specified type of simulation experiment and (2) a concrete example of how to get started using functions from the `simChef` library. + +In particular, we have implemented the following templates: + +- `use_prediction_template(type = "regression")` +- `use_prediction_template(type = "classification")` +- `use_feature_selection_template()` +- `use_inference_template()` + +These functions will print out code to the console that can be easily copied and/or run. For example, + +```{r prediction-template} +use_prediction_template(type = "regression") +``` + +For more guidance, we can also include concrete examples of a DGP and Method via: + +```{r prediction-template-with-examples} +use_prediction_template( + type = "regression", include_dgp_example = TRUE, include_method_example = TRUE +) +``` + +A complete list of boilerplate templates can be found [here](https://yu-group.github.io/simChef/reference/index.html#boilerplate-templates). + +## Library of DGPs, Evaluators, and Visualizers + +Beyond these templates, there is also a growing library of built-in evaluation metrics, visualization utilities, and other helper developer functions within `simChef`. +These built-in functions can sometimes greatly reduce the amount of user-written code in Step 1 (from Section \@ref(basic-example-usage)). +For more information on available functions in this built-in library, please see the following: + +- [Evaluator library](https://yu-group.github.io/simChef/reference/index.html#evaluator-library-prediction) +- [Visualizer library](https://yu-group.github.io/simChef/reference/index.html#visualizer-library-prediction) +- [Developer library](https://yu-group.github.io/simChef/reference/index.html#developer-functions) + +A library of DGPs is in the works as part of the [`dgpoix`](https://yu-group.github.io/dgpoix/) R package (currently, in very early development stages). + +# Tools for faster development and computation of simulation experiments + +## Parallelization + +Given the large amount of computation that simulation studies require, one of the main goals of `simChef` is to make it easy to parallelize your simulations. +`simChef` uses the R package [`future`](https://future.futureverse.org/) to distribute simulation replicates across whatever available resources the user specifies. +All you have to do to start running your simulations in parallel is set the `future` plan before calling `run_experiment()`: + +```{r plan-parallelization, eval = FALSE} +n_workers <- availableCores() - 1 +plan(multisession, workers = n_workers) +``` + +For more information on parallelization, please refer to [Computing experimental replicates in parallel](parallel.html). + +## Caching + +To reduce redundant computations, one may want to avoid re-running previously computed and saved components of an `Experiment`. +This can be done by setting the argument `use_cached` to `TRUE` in `run_experiment()`. +More specifically, when an `Experiment` is run with `use_cached = TRUE`, all previously cached results (i.e., those that have been previously saved to disk with `save = TRUE`) are loaded in and checked if their corresponding `Experiment` matches the configurations of the current `Experiment`. +If the cached `Experiment` configurations match that of the current `Experiment`, the cached results are used and only the uncached components of the `Experiment` are run. + +As an example, let us run the following experiment and save its results. + +```{r caching-1} +experiment <- create_experiment(name = "Linear Regression Experiment") %>% + add_dgp(linear_dgp) %>% + add_method(lm_method) %>% + add_evaluator(reject_prob_eval) %>% + add_visualizer(power_plot) %>% + add_visualizer(reject_prob_plot) %>% + add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) + +orig_results <- run_experiment(experiment, n_reps = 10, save = TRUE) +``` + +When setting `use_cached = TRUE`, the following line of code will not generate any new results but will instead read in the saved or cached results from disk. + +```{r caching-2} +cached_results <- run_experiment(experiment, n_reps = 10, use_cached = TRUE) + +all.equal(cached_results, orig_results) +``` + +We can also choose to read in a smaller number of the cached replicates, + +```{r caching-3} +smaller_results <- run_experiment(experiment, n_reps = 5, use_cached = TRUE) + +all.equal( + smaller_results$fit_results, + orig_results$fit_results %>% dplyr::filter(as.numeric(.rep) <= 5) +) +``` + +or increase the number of replicates without re-doing computation for the previously cached replicates. + +```{r caching-4} +larger_results <- run_experiment(experiment, n_reps = 15, use_cached = TRUE) + +all.equal( + larger_results$fit_results %>% dplyr::filter(as.numeric(.rep) <= 10), + orig_results$fit_results +) +``` + +In addition, caching works when adding or modifying components of an `Experiment`. +In the following, we add a new `DGP` to the `Experiment`. +As a result, when we run the `Experiment` with `use_cached = TRUE`, only the replicates involving the new `DGP` are run while the replicates involving the old `DGP` (i.e., the `Linear Gaussian DGP`) are loaded in from the cache. + +```{r caching-5} +new_dgp <- create_dgp( + .dgp_fun = linear_dgp_fun, .name = "Linear Gaussian DGP (large n)", + n = 500, beta = c(1, 0), rho = 0, sigma = 1 +) + +experiment <- experiment %>% + add_dgp(new_dgp) +new_results <- run_experiment(experiment, n_reps = 10, use_cached = TRUE) + +all.equal( + new_results$fit_results %>% dplyr::filter(.dgp_name == "Linear Gaussian DGP"), + orig_results$fit_results +) +``` + +Note that since we did not save the `Experiment` results above, then the following will recompute the replicates corresponding to the new `DGP`. Please set `save = TRUE` in order to cache the results for future use. + +```{r caching-6} +new_results2 <- run_experiment(experiment, n_reps = 10, use_cached = TRUE) +``` + +Some other helpful functions regarding caching include: + +```{r caching-7} +# to load in the cached fit results for an experiment +fit_results <- get_cached_results(experiment, "fit") +# to load in the cached evaluation results for an experiment +eval_results <- get_cached_results(experiment, "eval") +# to load in the cached visualization results for an experiment +viz_results <- get_cached_results(experiment, "viz") +# to load in the cached Experiment object +experiment <- get_cached_results(experiment, "experiment") +# to load in the Experiment parameters corresponding to the cached *_results +cached_exp_params <- get_cached_results(experiment, "experiment_cached_params") + +# to clear the cache +clear_cache(experiment) +``` + +## Checkpointing + +A checkpoint is a periodic snapshot of the simulation results, saved to disk. +Checkpoints help guard against lost progress in the case of unexpected problems, such as node failures when running on a cluster, at the cost of longer simulation running times. +Checkpoints are most useful for long-running simulations where the cost of creating the checkpoints is small relative to the total cost of the simulation. +As a result, users should be careful not to create checkpoints more often than necessary. + +To enable checkpointing, use a positive value for the argument `checkpoint_n_reps` passed to `run_experiment()`. +Below is an artificial example in which we use checkpointing and encounter errors as the simulation progresses past the first checkpoint: + +```{r checkpoint-1, error = TRUE} +counter <- 0 + +# a DGP which returns an error on its 26th call (i.e., when counter hits 26) +checkpoint_dgp_fun <- function() { + counter <<- counter + 1 + if (counter == 26) stop("Unexpected error!") + return("data") +} +checkpoint_dgp <- create_dgp(checkpoint_dgp_fun, .name = "Checkpoint DGP") + +# a toy Method +checkpoint_method_fun <- function(...) return("result") +checkpoint_method <- create_method(checkpoint_method_fun, .name = "Method") + +# create checkpoint experiment +checkpoint_experiment <- create_experiment(name = "checkpoint-exper") %>% + add_dgp(checkpoint_dgp) %>% + add_method(checkpoint_method) + +# run 100 reps, checkpointing every 25 reps, though it should return an error +checkpoint_results <- run_experiment( + checkpoint_experiment, n_reps = 100, checkpoint_n_reps = 25 +) +``` + +Since a full 25 replicates completed before the simulation failed, the first checkpoint was successful. +You can use `get_cached_results(checkpoint_experiment, "fit")` to examine the completed replicates, or simply continue the simulation using the same code you already ran (since the error will not be returned again by design). + +```{r checkpoint-2} +checkpoint_results <- run_experiment( + checkpoint_experiment, n_reps = 100, checkpoint_n_reps = 25 +) +``` + + +## Error debugging + +In the event of an error, `simChef` makes it possible to both retrieve results from completed replicates (before the error occurred) and to gracefully debug errors in user-defined functions. For the sake of demonstration, let us create an artificial example. + +```{r debug-1, error = TRUE} +# create experiment +dgp_fun <- function() return("data") +dgp_fun_err <- function() { stop("Uh oh!") } +dgp <- create_dgp(dgp_fun, .name = "Working DGP") +dgp_err <- create_dgp(dgp_fun_err, .name = "Buggy DGP") +method_fun <- function(x) return("result") +method <- create_method(method_fun, .name = "Method") +buggy_experiment <- create_experiment(name = "debugging-exper") %>% + add_dgp(dgp) %>% + add_dgp(dgp_err) %>% + add_method(method) + +# run experiment though it should return an error +results <- run_experiment(buggy_experiment, n_reps = 2) +``` + +If working interactively, the error can be inspected using the usual call to `rlang::last_error()`. Results that were run and completed before the error can be recovered via `rlang::last_error()$partial_results` and you can find errors that occurred within the simulation loop via `rlang::last_error()$errors`. + +Alternatively, we can wrap the call that ran the error (in this case, `run_experiment()`) in `tryCatch()` as follows: + +```{r debug-2, error = TRUE} +results <- tryCatch( + run_experiment(buggy_experiment, n_reps = 2), + error = identity +) +results +``` + +From this, we can view the results that were completed before we ran into the error via + +```{r debug-3} +results$partial_results +``` + +and extract the error object(s) via + +```{r debug-4} +results$errors +``` + +Note that the error tibble contains the `DGP` and `Method` (if the `DGP` was not the error cause) that were being processed when the error occurred in the `.dgp` and `.method` columns, respectively. +The corresponding input parameters are in the `.dgp_params` and `.method_params` columns (both `NA` here since no parameters were varied in the experiment). +Finally, the captured error itself is in the `.err` column. +Using this, we can easily investigate and reproduce the error and if desired, work directly with the user-specified function that raised the error, e.g., + +```{r debug5, error = TRUE} +# get dgp that ran the error +err_fun <- results$errors$.dgp[[1]]$dgp_fun +# reproduce error via a direct call to the DGP function that raised the error +err_fun() +``` + + +# R Markdown documentation + +One of the key features of `simChef` is the ease of generating an R Markdown report via `render_docs()` as shown below. This automated documentation gathers the scientific details, summary tables, and plots side-by-side with the user's custom source code and parameters for DGPs, methods, evaluation metrics, and visualization procedures. + +```{r rmd-1, eval = FALSE} +render_docs(experiment) +``` + +To be more precise, this call to `render_docs(experiment)` will compile the documentation and results (both evaluation and visualization results) from all saved `Experiments` under the experiment's root results directory (specified by `get_save_dir(experiment)`). + +Equivalently, we can create the same R Markdown report summary by directly specifying the experiment's root results directory. + +```{r rmd-2, eval = FALSE} +render_docs(save_dir = get_save_dir(experiment)) +``` + +## Customizing document options for Evaluators and Visualizers + +There are several options to customize the aesthetics of `Evaluators` and `Visualizers` displayed in the R Markdown report. +These can be set using the `.doc_options` argument in `create_evaluator()` and `create_visualizer()`. +For example, we can customize the height and width of the Power plot via + +```{r rmd-doc-options-1} +power_plot <- create_visualizer( + .viz_fun = power_plot_fun, .name = "Power", + .doc_options = list(height = 10, width = 8) +) +experiment <- experiment %>% + update_visualizer(power_plot, name = "Power") +``` + +and the number of digits in the evaluation table outputs via + +```{r rmd-doc-options-2} +reject_prob_eval <- create_evaluator( + .eval_fun = reject_prob_fun, .name = "Rejection Prob. (alpha = 0.1)", + alpha = 0.1, .doc_options = list(digits = 3) +) +experiment <- experiment %>% + update_evaluator(reject_prob_eval, name = "Rejection Prob. (alpha = 0.1)") +``` + +Alternatively, `set_doc_options()` can be used to update the R Markdown option for an existing object, e.g., + +```{r rmd-doc-options-3} +experiment <- experiment %>% + set_doc_options( + field_name = "visualizer", name = "Power", height = 10, width = 8 + ) +save_experiment(experiment) +``` + +To hide the output of an `Evaluator` or `Visualizer` in the R Markdown report, use `set_doc_options(show = FALSE, ...)` or `create_*(.doc_show = FALSE, ...)`. +Note that if document options are set after running the experiment, the experiment must be manually saved via `save_experiment(experiment)` in order for these changes to appear in the R Markdown output. + +## Customizing aesthetics of R Markdown documentation + +There are additional customization options that can be set in `render_docs()` including the order in which results of `Evaluators` and `Visualizers` are displayed (via the `eval_order` and `viz_order` arguments) and the R Markdown output type or theme. For example, + +```{r rmd-custom-options-1, eval = FALSE} +# use html_document instead of vthemes::vmodern (default) +render_docs(experiment, output_format = rmarkdown::html_document()) + +# add custom css style +render_docs(experiment, output_options = list(css = "path/to/file.css")) +``` + +To modify the R Markdown file directly, set `write_rmd = TRUE` in `render_docs()`: + +```{r rmd-custom-options-2, eval = FALSE} +render_docs(experiment, write_rmd = TRUE) +``` + +This will write/save the raw R Markdown file used to generate the automated `simChef` documentation to your disk. +The file can be found in the experiment's root results directory (specified by `get_save_dir(experiment)`) and can be directly modified to your liking. + + +# Additional notes and usage + +## The Experiment R6 class + +It is important to note that the `Experiment` class is an `R6`. +With this, we need to be careful about clones versus pointers. +In the following, it may look like the `new_experiment` object has a `vary_across` component while the `old_experiment` object does not have a `vary_across` component. +However, due to the use of the `R6` class, when `old_experiment` is piped into `add_vary_across()`, this is in itself modifying `old_experiment`, and `new_experiment` is simply pointing to this modified `old_experiment`. + +```{r notes-1} +old_experiment <- create_experiment(name = "Old Experiment") %>% + add_dgp(linear_dgp) %>% + add_method(lm_method) %>% + add_evaluator(reject_prob_eval) %>% + add_visualizer(power_plot) +old_experiment + +new_experiment <- old_experiment %>% + add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) +old_experiment + +all.equal(new_experiment, old_experiment) +data.table::address(new_experiment) == data.table::address(old_experiment) +``` + +## Creating an experiment from another experiment + +To modify `new_experiment` without changing `old_experiment`, we need to create `new_experiment` as a *clone* of the `old_experiment` as follows: + +```{r notes-2} +new_experiment <- create_experiment( + name = "I am a clone", clone_from = old_experiment +) + +data.table::address(new_experiment) == data.table::address(old_experiment) +new_experiment +``` + +When creating an `Experiment` from a clone, we are making a deep clone of the parent experiment's `DGPs`, `Methods`, `Evaluators`, and `Visualizers`, but not the `vary_across` component. +Thus, if we want a `vary_across` component in `new_experiment`, we must add this `vary_across` component ourselves: + +```{r notes-3} +new_experiment <- new_experiment %>% + add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) +``` + +We can also add/update DGPs, Methods, Evaluators, and Visualizers to the cloned experiment without modifying the parent `old_experiment`. + +```{r notes-4} +# add DGP +new_dgp <- create_dgp( + .dgp_fun = linear_dgp_fun, .name = "Linear Gaussian DGP (large n)", + n = 500, beta = c(1, 0), rho = 0, sigma = 1 +) +new_experiment <- new_experiment %>% + add_dgp(new_dgp) + +new_experiment +old_experiment +``` + + +## Additional handling of the experiment + +`run_experiment()` is the easiest and most concise way of running the simulation experiment from start to finish. +However, when debugging and developing the simulation experiment, it may be helpful to run only parts of the experiment so as to not repeat time-consuming, redundant computations. +As discussed in Section \@ref(a-tidy-grammar-for-simulation-experiments), `run_experiment()` is a wrapper around `fit_experiment()`, `evaluate_experiment()`, and `visualize_experiment()`. +Although generally not necessary, sometimes it can be helpful to call each of these functions separately: + +```{r handling-1} +fit_results <- fit_experiment(experiment, n_reps = 10) +eval_results <- evaluate_experiment(experiment, fit_results) +viz_results <- visualize_experiment(experiment, fit_results, eval_results) +``` + +Similarly, although it is not usually necessary, there may be some instances (e.g., for exploratory data analysis or further diagnosis of the experiment results), in which one would like to generate small samples of data from the `DGPs` in the experiment via + +```{r handling-2} +data_list <- generate_data(experiment, n_reps = 1) +str(data_list) +``` + +It may also be helpful to work with individual `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` objects outside of running an `Experiment`. This can be done by leveraging the verbs and grammar laid out in Section \@ref(a-tidy-grammar-for-simulation-experiments). The API is as follows: + +```{r handling-3, eval = FALSE} +data_list <- DGP$generate() +method_results <- Method$fit(data_list = data_list) +eval_results <- Evaluator$evaluate( + fit_results = fit_results, + vary_params = vary_params +) +viz_results <- Visualizer$visualize( + fit_results = fit_results, + eval_results = eval_results, + vary_params = vary_params +) +``` + +Here, `fit_results` is the output of `fit_experiment()`, `eval_results` is the output of `evaluate_experiment()`, and `vary_params` is a character vector of the parameter names that are being varied in the experiment. + +Other helpful methods for handling the experiment include the `get_*` family of methods, i.e., + +```{r handling-4} +get_dgps(experiment) +get_methods(experiment) +get_evaluators(experiment) +get_visualizers(experiment) +get_vary_across(experiment) +``` + +```{r handling-5, results = "hide"} +get_save_dir(experiment) +#> [1] "./results/Linear Regression Experiment" +``` + +## Saving results + +Since the R Markdown report heavily relies on the results file structure to organize the report summary, it may be helpful to understand the default saving structure utilized in `simChef`. + +By default, the experiment's root results directory is ./results/\{EXPERIMENT_NAME\}. To change the root results directory, one can specify the desired directory via the `save_dir` argument in `create_experiment()` or use the `set_save_dir()` helper function. + +All results from `run_experiment(experiment, ..., save = TRUE)` without a `vary_across` component are then saved under the root results directory. If `experiment` has a `vary_across` component, then the results of `run_experiment(experiment, ..., save = TRUE)` are saved under ./\{ROOT_RESULTS_DIR\}/\{DGP_OR_METHOD_NAME\}/Varying \{PARAM_NAME\}/. + +Here's the directory tree of the "Linear Regression Experiment" example: + +```{r file-str, eval = FALSE} +fs::dir_tree(get_save_dir(experiment)) +#> results +#> ├── Linear Gaussian DGP +#> │ ├── Varying beta +#> │ │ ├── eval_results.rds +#> │ │ ├── experiment.rds +#> │ │ ├── experiment_cached_params.rds +#> │ │ ├── fit_results.rds +#> │ │ └── viz_results.rds +#> │ ├── Varying rho +#> │ │ ├── eval_results.rds +#> │ │ ├── experiment.rds +#> │ │ ├── experiment_cached_params.rds +#> │ │ ├── fit_results.rds +#> │ │ └── viz_results.rds +#> │ └── Varying sigma +#> │ ├── eval_results.rds +#> │ ├── experiment.rds +#> │ ├── fit_results.rds +#> │ └── viz_results.rds +#> ├── Linear Regression Experiment.html +#> ├── docs +#> │ ├── dgps +#> │ │ └── Linear Gaussian DGP.md +#> │ ├── evaluators +#> │ │ └── Rejection Prob. (alpha = 0.1).md +#> │ ├── methods +#> │ │ └── OLS.md +#> │ ├── objectives.md +#> │ └── visualizers +#> │ ├── Power.md +#> │ └── Rejection Prob. (alpha = 0.1) Plot.md +#> ├── eval_results.rds +#> ├── experiment.rds +#> ├── experiment_cached_params.rds +#> ├── fit_results.rds +#> └── viz_results.rds +``` + +## Simulation directory template + +To help with onboarding and setting up your simulation study, we have provided a helper function `create_sim()`. + +```{r create-sim, eval = FALSE} +create_sim(path = "path/to/sim/dir") +``` + +`create_sim()` initializes a ready-to-use directory for your `simChef` simulation study. +For more information and other best practices to improve reproducibility and replicability of your simulation study, please checkout [Setting up your simulation study](simulation-directory-setup.html). + +# References diff --git a/vignettes/simChef.Rmd b/vignettes/simChef.Rmd index 8d8af517..8c987198 100644 --- a/vignettes/simChef.Rmd +++ b/vignettes/simChef.Rmd @@ -1,11 +1,15 @@ --- title: "Getting started with simChef" output: - rmarkdown::html_vignette + bookdown::html_document2: + base_format: rmarkdown::html_vignette + toc: true vignette: > - %\VignetteIndexEntry{simChef Vignette} + %\VignetteIndexEntry{Getting started with simChef} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} +pkgdown: + as_is: true --- ```{r include = FALSE} @@ -24,7 +28,7 @@ library(simChef) set.seed(12345) # remove old cached results to start fresh -for (fname in list.files(file.path("results", "Linear Regression Experiment"), +for (fname in list.files(file.path("results", "Example Experiment"), pattern = ".rds", recursive = T, full.names = T)) { file.remove(fname) } @@ -32,298 +36,196 @@ for (fname in list.files(file.path("results", "Linear Regression Experiment"), # Overview -The goal of `simChef` is to seamlessly and efficiently run simulation experiments using a simple grammar. The results of these simulation experiments can also be conveniently viewed in an organized and interactive browser (or html file) (e.g., [here](../linear_regression_output.html)). The basic usage of `simChef` can be summarized as follows: - -1. Create individual parts of the simulation experiment recipe - a. Create DGP(s) (data-generating processes) via `create_dgp()` - ```{r overview1} - dgp <- create_dgp( - function(n = 100, p = 10, noise_max = 5) { - noise_level <- runif(1, max = noise_max) - X <- matrix(rnorm(n*p), nrow = n) - y <- X %*% rnorm(p, sd = 2) + rnorm(n, sd = noise_level) - # dgp's should return a named list - return(list(X = X, y = y, noise_level = noise_level)) - } - ) - dgp - ``` - b. Create Method(s) via `create_method()` - ```{r overview2} - method <- create_method( - # method function args include the names in the dgp output list (`X` and `y` here) - function(X, y, ...) { - # by concatenating ... with the list output, we can pass through other - # outputs from the dgp to later stages of the simulation (evaluation and - # visualization) - return(c(list(fit = lm(y ~ X)), ...)) - } - ) - method - ``` - c. Create Evaluator(s) via `create_evaluator()` - ```{r overview3} - evaluator <- create_evaluator( - # the main computational loop of the simulation will return a `tibble::tibble` - # which is passed as 'fit_results' to our evaluation functions - function(fit_results) { - # calculate R-squared - fit_results %>% - dplyr::mutate( - rsq = sapply(fit, function(.fit) summary(.fit)$r.squared) - ) - } - ) - evaluator - ``` - d. Create Visualizer(s) via `create_visualizer()` - ```{r overview4} - visualizer <- create_visualizer( - # you can use 'fit_results' as well here by adding it as an argument - function(eval_results) { - require(ggplot2) - # return a plot r-squared vs noise level - ggplot(aes(x = noise_level, y = rsq), data = eval_results[[1]]) + - geom_point() + - geom_smooth() - } - ) - visualizer - ``` -2. Assemble these recipe parts into a complete simulation experiment, e.g.: - ```{r overview5} - experiment <- create_experiment(name = "Experiment") %>% - add_dgp(dgp, name = "DGP1") %>% - add_method(method, name = "Method1") %>% - add_evaluator(evaluator, name = "Evaluator1") %>% - add_visualizer(visualizer, name = "Visualizer1") - experiment - ``` -3. Document and describe the simulation experiment in text - ```{r eval = FALSE} - init_docs(experiment) - ``` -4. Run the experiment - ```{r eval = FALSE} - results <- run_experiment(experiment, n_reps = 100, save = T) - # or alternatively, `results <- experiment$run(n_reps = 100, save = T)` - ``` -5. Visualize results via an automated R Markdown report - ```{r eval = FALSE} - render_docs(experiment) - ``` - -We will next go through a toy example simulation using linear regression. - -# Basic usage - -## Create individual parts of the simulation experiment recipe - -To begin, we first need to create the individual parts of the simulation experiment recipe. There are four main components/classes and verbs in the simulation experiment: - -1. **DGP** (data-generating process): the data-generating processes from which to **generate** data -2. **Method**: the methods (or models) to **fit** in the experiment -3. **Evaluator**: the evaluation metrics used to **evaluate** the methods performance -4. **Visualizer**: the visualization functions used to **visualize** outputs from the method fits or evaluation results (can be tables, plots, or even R Markdown snippets to display) - -To create a `DGP()`, `Method()`, `Evaluator()`, or `Visualizer()`, we can respectively use the `create_dgp()`, `create_method()`, `create_evaluator()`, or `create_visualizer()` functions. Each `create_*()` function follows the same syntax and takes in the inputs: - -- `.*_fun`: the first input is a function from which to simulate data, fit the method, evaluate the metrics, or create a visualization (depending on `*`). -- `.name`: (optional) name of the component. Can be specified either here in `create_*()` or when creating the experiment as we will see later. -- `...`: additional arguments get passed into the `*_fun` above. - -### Create data-generating process (DGP) - -As a toy DGP example, let us create a function to simulate a random Gaussian data matrix $\mathbf{X}$ of size $n \times 2$ and a linear response vector $\mathbf{y}$ of size $n \times 1$, where - -\begin{gather*} -\mathbf{X} \sim N\left(\mathbf{0}, \begin{pmatrix} 1 & \rho \\ \rho & 1 \end{pmatrix}\right), \\ -\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon},\\ -\boldsymbol{\epsilon} \sim N(\mathbf{0}, \sigma^2 \mathbf{I}_n) -\end{gather*} - -```{r basics1} -dgp_fun <- function(n, beta, rho, sigma) { - cov_mat <- matrix(c(1, rho, rho, 1), byrow = T, nrow = 2, ncol = 2) - X <- MASS::mvrnorm(n = n, mu = rep(0, 2), Sigma = cov_mat) - y <- X %*% beta + rnorm(n, sd = sigma) - return(list(X = X, y = y)) +Welcome to `simChef`! Our goal is to empower data scientists to focus their attention toward scientific best practices by removing the administrative burdens of simulation design. Using `simChef`, practitioners can seamlessly and efficiently run simulation experiments using an intuitive [tidy +grammar](https://design.tidyverse.org/). The core features of `simChef` include: + +1. A tidyverse-inspired grammar of data-driven simulation experiments +2. A growing built-in library of composable building blocks for evaluation metrics and visualization utilities +3. Flexible and seamless integration with distributed computation, caching, checkpointing, and debugging tools +4. Automated generation of an R Markdown document to easily navigate, visualize, and interpret simulation results (see example [here](../example_experiment.html)) + +To highlight the ease of running simulation studies using `simChef`, we will begin by showcasing a basic example usage in Section \@ref(example-usage). Then, in Section \@ref(writing-your-own-simulation-experiment), we will provide additional details on how to create your own simulation experiment using `simChef`. + +# Example usage + +> **Simulation Example:** Suppose, for concreteness, that we would like to evaluate the prediction accuracy of two methods (linear regression and random forests) under a linear data-generating process across different signal-to-noise ratios. + +At its core, `simChef` breaks down a simulation experiment into four components: + +- `DGP()`: the data-generating processes (DGPs) from which to *generate* data +- `Method()`: the methods (or models) to *fit* on the data in the experiment +- `Evaluator()`: the evaluation metrics used to *evaluate* the methods' performance +- `Visualizer()`: the visualization procedures used to *visualize* outputs from the method fits or evaluation results (can be tables, plots, or even R Markdown snippets to display) + +Using these components, users can easily run a simulation experiment in six simple steps. Below, we summarize these steps and provide code for our running example. + +## Step 1. Define DGP, method, evaluation, and visualization functions of interest. {-} + +Recall that in our simulation example, we would like to study two methods (linear regression and random forests) under a linear DGP. We code this DGP (`linear_dgp_fun`) and methods (`linear_reg_fun` and `rf_fun`) via custom functions below. + +```{r dgp-funs} +#' Linear Data-Generating Process +#' +#' @description Generate training and test data according to the classical +#' linear regression model: y = X*beta + noise. +#' +#' @param n_train Number of training samples. +#' @param n_test Number of training samples. +#' @param p Number of features. +#' @param beta Coefficient vector in linear regression function. +#' @param noise_sd Standard deviation of additive noise term. +linear_dgp_fun <- function(n_train, n_test, p, beta, noise_sd) { + n <- n_train + n_test + X <- matrix(rnorm(n * p), nrow = n, ncol = p) + y <- X %*% beta + rnorm(n, sd = noise_sd) + data_list <- list( + X_train = X[1:n_train, , drop = FALSE], + y_train = y[1:n_train], + X_test = X[(n_train + 1):n, , drop = FALSE], + y_test = y[(n_train + 1):n] + ) + return(data_list) } ``` -We can create an object of class `DGP()` from this function via - -```{r basics2} -dgp <- create_dgp(.dgp_fun = dgp_fun, .name = "Linear Gaussian DGP", - # additional named parameters to pass to dgp_fun() - n = 200, beta = c(1, 0), rho = 0, sigma = 1) -``` - -Note that the additional arguments in `create_dgp()` must be named arguments that match those in `dgp_fun()`. - -### Create method - -Given the above DGP, suppose we want to investigate the performance of linear regression, specifically the p-values that are outputted for the non-intercept coefficients from `summary.lm()`. - -```{r basics3} -lm_fun <- function(X, y, cols = c("X1", "X2")) { - lm_fit <- lm(y ~ X) - pvals <- summary(lm_fit)$coefficients[cols, "Pr(>|t|)"] %>% - setNames(paste(names(.), "p-value")) - return(pvals) +```{r method-funs} +#' Linear Regression Method +#' +#' @param X_train Training data matrix. +#' @param y_train Training response vector. +#' @param X_test Test data matrix. +#' @param y_test Test response vector. +linear_reg_fun <- function(X_train, y_train, X_test, y_test) { + train_df <- dplyr::bind_cols(data.frame(X_train), y = y_train) + fit <- lm(y ~ ., data = train_df) + predictions <- predict(fit, data.frame(X_test)) + return(list(predictions = predictions, y_test = y_test)) } -``` - -We can create an object of class `Method()` from this function via - -```{r basics4} -lm_method <- create_method(.method_fun = lm_fun) -``` - -A couple notes here: -- The inputs into the method function `lm_fun()` should include the named outputs from the DGP function `dgp_fun()` (in this case, `X` and `y`), as the data generated from the DGP will be automatically passed into the Method. -- Additional arguments can be passed into `create_method()` as was done previously in `create_dgp()`. However, this is not necessary in this case if we are happy with the default argument for `cols` in `lm_fun()`. - -### Create evaluator - -To evaluate the performance of linear regression, one metric (or statistic) of interest could be the rejection probability at some level $\alpha$, which we compute in the following function. - -```{r basics5} -reject_prob_fun <- function(fit_results, alpha = 0.05) { - group_vars <- c(".dgp_name", ".method_name") - eval_out <- fit_results %>% - dplyr::group_by(across({{group_vars}})) %>% - dplyr::summarise( - `X1 Reject Prob.` = mean(`X1 p-value` < alpha), - `X2 Reject Prob.` = mean(`X2 p-value` < alpha) - ) - return(eval_out) +#' Random Forest Method +#' +#' @param X_train Training data matrix. +#' @param y_train Training response vector. +#' @param X_test Test data matrix. +#' @param y_test Test response vector. +#' @param ... Additional arguments to pass to `ranger::ranger()` +rf_fun <- function(X_train, y_train, X_test, y_test, ...) { + train_df <- dplyr::bind_cols(data.frame(X_train), y = y_train) + fit <- ranger::ranger(y ~ ., data = train_df, ...) + predictions <- predict(fit, data.frame(X_test))$predictions + return(list(predictions = predictions, y_test = y_test)) } ``` -We can create an object of class `Evaluator()` from this function via - -```{r basics6} -reject_prob_eval <- create_evaluator(.eval_fun = reject_prob_fun, alpha = 0.1) -``` - -There are a few key points to keep in mind when writing a custom Evaluator function such as `reject_prob_fun()`. +We can similarly write custom functions to evaluate the prediction accuracy of these methods and to visualize results. However, `simChef` also provides a built-in library of helper functions for common evaluation and visualization needs. We leverage this library for convenience here. In particular, for prediction tasks, we will use the functions `simChef::summarize_pred_err()` and `simChef::plot_pred_err()` to evaluate and plot the prediction error between the observed and predicted responses, respectively. For details, see `? simChef::summarize_pred_err` and `? simChef::plot_pred_err`. -- First, the Evaluator function should almost always take in the named argument `fit_results`. `fit_results` is a placeholder for the tibble that will be outputted by `Experiment$fit()` and automatically passed in when the experiment is fitted. Note that this argument must be exactly named `fit_results` or else the Evaluator function will not receive the results from `Experiment$fit()`. We will see `Experiment$fit()` in action later, but for now, we can think of `fit_results` as a tibble containing the results of all (replicate, DGP, method) combinations fitted in the experiment. Naturally, `fit_results` will have columns named `.rep`, `.dgp_name`, `.method_name`, and any other named arguments that were outputted from the method function (i.e., `lm_fun`). -- Many evaluation metrics are most naturally computed across replicates, so it is common to group the `fit_results` by `.dgp_name` and `.method_name` as seen in `reject_prob_fun()` above. In doing so, the rejection probability (across replicates) will be computed for each (DGP, Method) combination separately. However, depending on the goal of the Evaluator function, grouping by `.dgp_name` and `.method_name` might not be necessary. +*Note*: Most of the user-written code is encapsulated by Step 1. From here, there is minimal coding on the user end as we turn to leverage the `simChef` grammar for experiments. -As before, additional arguments can be passed into `create_evaluator()`, and in this case, we have overwritten the default value of `alpha` with `alpha = 0.1`. +## Step 2. Convert functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects. {-} -### Create visualizer +Once we have specified the relevant DGP, method, evaluation, and visualization functions, the next step is to convert these functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects (`R6`) for `simChef`. To do so, we simply wrap the functions in `create_dgp()`, `create_method()`, `create_evaluator()`, or `create_visualizer()`, while specifying (a) an intelligible name for the object and (b) any input parameters to pass to the function. For example, -Lastly, we may want to plot the results from the `Method` fits (stored in `fit_results`) and/or the outputs from our `Evaluators` (stored in `eval_results`). For example, we could plot the power of the hypothesis test. +```{r create-classes} +## DGPs +linear_dgp <- create_dgp( + .dgp_fun = linear_dgp_fun, .name = "Linear DGP", + # additional named parameters to pass to .dgp_fun() + n_train = 200, n_test = 200, p = 2, beta = c(1, 0), noise_sd = 1 +) -```{r basics7} -power_plot_fun <- function(fit_results, col = "X1") { - plt <- ggplot2::ggplot(fit_results) + - ggplot2::aes(x = .data[[paste(col, "p-value")]], - color = as.factor(.method_name)) + - ggplot2::geom_abline(slope = 1, intercept = 0, - color = "darkgray", linetype = "solid", size = 1) + - ggplot2::stat_ecdf(size = 1) + - ggplot2::scale_x_continuous(limits = c(0, 1)) + - ggplot2::labs(x = "t", y = "P( p-value \u2264 t )", - linetype = "", color = "Method") - return(plt) -} -``` +## Methods +linear_reg <- create_method( + .method_fun = linear_reg_fun, .name = "Linear Regression" + # additional named parameters to pass to .method_fun() +) +rf <- create_method( + .method_fun = rf_fun, .name = "Random Forest", + # additional named parameters to pass to .method_fun() + num.threads = 1 +) -We can create an object of class `Visualizer()` from this function via +## Evaluators +pred_err <- create_evaluator( + .eval_fun = summarize_pred_err, .name = 'Prediction Accuracy', + # additional named parameters to pass to .eval_fun() + truth_col = "y_test", estimate_col = "predictions" +) -```{r basics8} -power_plot <- create_visualizer(.viz_fun = power_plot_fun) +## Visualizers +pred_err_plot <- create_visualizer( + .viz_fun = plot_pred_err, .name = 'Prediction Accuracy Plot', + # additional named parameters to pass to .viz_fun() + eval_name = 'Prediction Accuracy' +) ``` -Like the custom `Evaluator` functions, custom `Visualizer` functions such as `power_plot_fun()` can take in the argument `fit_results` if we want to construct a plot from the method fit outputs. If we want to construct a visualization using the output of our `Evaluators` (e.g., the output from `reject_prob_fun()`), the `Visualizer` function can also take the argument `eval_results` as input. Note again that the arguments must be exactly named `fit_results` and `eval_results` in order to work properly. Beyond creating plots, `Visualizers` can return tables and more generally, any R Markdown snippet containing results. - -As with the other `create_*` functions, additional arguments that need to pass into the custom `Visualizer` function can be passed into `create_visualizer()`. - -## Assemble simulation experiment recipe +## Step 3. Assemble recipe parts into a complete simulation experiment. {-} -At this point, we have created a `DGP` (`dgp`), `Method` (`lm_method`), `Evaluator` (`reject_prob_eval`), and `Visualizer` (`power_plot`). The next step is to create the simulation experiment recipe and `add` each component to the recipe via +Thus far, we have created the many individual components (i.e., `DGP(s)`, `Method(s)`, `Evaluator(s)`, and `Visualizer(s)`) for our simulation experiment. We next assemble or `add` these components together to create a complete simulation experiment recipe via: -```{r basics9} -experiment <- create_experiment(name = "Linear Regression Experiment") %>% - add_dgp(dgp, name = "Linear Gaussian DGP") %>% - add_method(lm_method, name = "OLS") %>% - add_evaluator(reject_prob_eval, name = "Rejection Prob. (alpha = 0.1)") %>% - add_visualizer(power_plot, name = "Power") +```{r experiment-recipe} +experiment <- create_experiment(name = "Example Experiment") %>% + add_dgp(linear_dgp) %>% + add_method(linear_reg) %>% + add_method(rf) %>% + add_evaluator(pred_err) %>% + add_visualizer(pred_err_plot) ``` -Any number of DGPs, Methods, Evaluators, and Visualizers can be added to the simulation experiment recipe, but each added element to the experiment should have an intelligible name as this will be used in creating the R Markdown results report. +We can also vary across one or more parameters in the `DGP(s)` and/or `Method(s)` by adding a `vary_across` component to the simulation experiment recipe. In the running simulation example, we would like to vary across the amount of noise (`noise_sd`) in the underlying DGP, which can be done as follows: -We can easily see the individual parts of the simulation experiment recipe by printing the experiment. - -```{r basics10} -print(experiment) +```{r vary-across} +experiment <- experiment %>% + add_vary_across(.dgp = "Linear DGP", noise_sd = c(0.1, 0.5, 1, 2)) ``` -Once a DGP, Method, Evaluator, or Visualizer has been added to the simulation experiment recipe, the component can be updated using `update_dgp()`, `update_method()`, `update_evaluator()`, or `update_visualizer()` and removed using `remove_dgp()`, `remove_method()`, `remove_evaluator()`, or `remove_visualizer()`. +*Tip*: To see a high-level summary of the simulation experiment recipe, it can be helpful to print the experiment. -## Document the simulation experiment - -A crucial component when running veridical simulations is **documentation**. We highly encourage practitioners to document the purpose or objective of the simulation experiment, what DGPs, Methods, Evaluators, and Visualizers were used, and why these DGPs, Methods, Evaluators, and Visualizers were chosen. This can and should be done before even running the simulation experiment. To facilitate this tedious but important process, we can easily create a documentation template to fill out using - -```{r} -init_docs(experiment) +```{r print-experiment} +print(experiment) ``` -```{r cp_docs, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} -# copy pre-written .md files in vignettes/docs to the experiment's docs dir -file.copy(from = here::here("vignettes/docs"), - to = file.path(experiment$get_save_dir()), - recursive = TRUE) -``` +## Step 4. Document and describe the simulation experiment in text. {-} -This creates a series of blank .md files for the user to fill out with descriptions of the simulation experiment and its recipe components. These blank .md files can be found in the experiment's root results directory under docs/. To find the experiment's root results directory, use `experiment$get_save_dir()`. You can find example .md files corresponding to the current experiment [here](https://github.com/Yu-Group/simChef/tree/main/vignettes/docs). +A crucial component when running veridical simulations is **documentation**. We highly encourage practitioners to document: -## Run the simulation experiment +- the purpose or objective of the simulation experiment, +- what `DGP(s)`, `Method(s)`, `Evaluator(s)`, and `Visualizer(s)` were used, and +- why these `DGP(s)`, `Method(s)`, `Evaluator(s)`, and `Visualizer(s)` were chosen. -Thus far, we have created and documented the simulation experiment recipe, but we have not generated any results from the experiment. That is, we have only given the simulation experiment instructions on what to do. To run the experiment, say over 10 replicates, we can do so via +This can and should be done before even running the simulation experiment. To facilitate this tedious but important process, we can easily initialize a documentation template to fill out using -```{r basics11} -results <- experiment$run(n_reps = 10, save = TRUE) +```{r init-docs} +init_docs(experiment) ``` -or alternatively, - -```{r eval = FALSE} -results <- experiment %>% - run_experiment(n_reps = 10, save = TRUE) +```{r cp-docs, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} +# copy pre-written .md files in vignettes/example-docs/ to the experiment's docs dir +file.copy( + from = here::here(file.path("vignettes", "example-docs", experiment$name, "docs")), + to = file.path(experiment$get_save_dir()), + recursive = TRUE +) +file.remove(file.path(experiment$get_save_dir(), "experiment.rds")) ``` -The output of `experiment$run()` is a list of length three: +## Step 5. Run the experiment. {-} -- `fit_results`: output of `Method` fits (see `experiment$fit()`) -- `eval_results`: output of `Evaluators` (see `experiment$evaluate()`) -- `viz_results`: output of `Visualizers` (see `experiment$visualize()`) +At this point, we have created and documented the simulation experiment recipe, but we have not generated any results from the experiment. That is, we have only given the simulation experiment instructions on what to do. To run the experiment, say over 100 replicates, we can do so via -```{r basics12} -str(results, max.level = 2) -results$fit_results -results$eval_results -results$viz_results +```{r run-experiment} +results <- run_experiment(experiment, n_reps = 100, save = TRUE) ``` -By default, the results are not saved to disk. However, to generate the R Markdown report, we will need to save the results to disk and hence set `save = TRUE` above. - -The experiment can also be run in parallel. For a more detailed walkthrough on how to parallelize the experiment, please see `vignette("parallel")`. - -## Visualize results +## Step 6. Visualize results via an automated R Markdown report. {-} -Finally, we can easily visualize all results from the simulation experiment in an html file (generated using R Markdown) or browser. +Finally, to visualize and report all results from the simulation experiment, we can render the documentation. This will generate a single html document, including the code, documentation (from Step 4), and simulation results. -```{r render_docs1, eval = FALSE} +```{r render-docs, message = FALSE, warning = FALSE} render_docs(experiment) ``` -```{r cp_html1, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} +```{r cp-html, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} # create pkgdown/assets directory, which will be copied to the gh-pages branch # during the pkgdown workflow (see .github/workflows/pkgdown.yaml) assets_dir <- here::here("pkgdown/assets") @@ -332,579 +234,156 @@ dir.create(assets_dir, recursive = TRUE) # copy html output to pkgdown/assets directory for website file.copy(from = file.path(experiment$get_save_dir(), paste0(experiment$name, ".html")), - to = file.path(assets_dir, "linear_regression_base_output.html"), + to = file.path(assets_dir, "example_experiment.html"), overwrite = TRUE) ``` -The results can be found [here](../linear_regression_base_output.html). +The rendered document corresponding to this simulation example can be found [here](../example_experiment.html), and voila! Simulation example complete! -Note that if the documentation template has not yet been created for `experiment` (e.g., via `init_docs(experiment)`), then `render_docs()` will automatically create the documentation template for the user to fill out. Again, we highly encourage practitioners to document their simulation experiments in the spirit of transparency and reproducibility. +In the next section, we will provide additional details necessary for you to create your own simulation experiment. -## Running an experiment across varying parameters in the DGP/Method +# Writing your own simulation experiment -Now, going slightly beyond the most basic usage of `simChef`, it is often helpful to understand how a method's performance is affected as we vary a single parameter in the DGP across different values. For instance, what happens to the power as the amount of noise in the linear model increases? +When it comes to writing your own simulation experiment using `simChef`, the main task is writing the necessary DGP, method, evaluation, and visualization functions for the experiment (i.e,. step 1 above). +Once these functions are appropriately written/specified, `simChef` takes care of the rest, with steps 2-6 requiring very little effort from the user. -Using the simple grammar of `simChef`, we can investigate this question by adding a `vary_across` component to the simulation experiment. -```{r vary1} -experiment <- experiment %>% - add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) -``` +Thus, focusing on step 1, it is helpful to understand the inputs and outputs that are necessary for writing each user-defined function. To do so, let us walk through what happens when we run a `simChef` `Experiment` and highlight the necessary inputs and outputs to these functions as we go. -```{r vary2} -print(experiment) -``` +## Overivew of running an experiment {-} -In `add_vary_across()`, the `dgp` argument is the name of the DGP to vary or the DGP object itself. All subsequent arguments should be of the form `[param_name] = [param_values]`, where `param_name` is the name of the argument/parameter in the `DGP` function that will be varied, and `param_values` is a list or atomic vector of values that `param_name` will take and vary across while all other arguments are kept constant at their base value (see `dgp$dgp_params`). Here, we are varying the `sigma` parameter (i.e., the SD of the additive noise term) over values of 1, 2, 4, and 8 within the Linear Gaussian DGP. (Note: we can also vary across parameters in a `Method` by inputting the `method` argument instead of the `dgp` argument in `add_vary_across()`.) +At the highest level, running an experiment (via `run_experiment()`) consists of three main steps (see Figure \@ref(fig:run-experiment-figure)): -However, when we run the experiment, the results are not quite what we expect. The results are summarized/aggregated across all values of `sigma`. +1. **Fit** each `Method` on each `DGP` (and for each of the varying parameter configurations specified in `vary_across`) via `fit_experiment()`. +2. **Evaluate** the experiment according to each `Evaluator` via `evaluate_experiment()`. +3. **Visualize** the experiment according to each `Visualizer` via `visualize_experiment()`. -```{r vary3} -vary_results <- experiment$run(n_reps = 10, save = TRUE) -vary_results$eval_results -vary_results$viz_results -``` +`run_experiment()` is simply a wrapper around these three functions: `fit_experiment()`, `evaluate_experiment()`, and `visualize_experiment()`. -To see how different values of `sigma` affect the experiment results, we need to modify our `Evaluator` and `Visualizer` functions. Specifically, in `reject_prob_eval`, we want to group `fit_results` by `sigma` in addition to `.dgp_name` and `.method_name`. To do this, we need to add a `vary_params` argument to our custom `Evaluator` function. When we run the experiment, `vary_params` will be auto-filled by a vector of the parameter names that are varied (i.e., those that have been added via `add_vary_across()`). In this case, `vary_params` will be auto-filled by `c("sigma")`. - -```{r vary4} -reject_prob_fun <- function(fit_results, vary_params = NULL, alpha = 0.05) { - group_vars <- c(".dgp_name", ".method_name", vary_params) - eval_out <- fit_results %>% - dplyr::group_by(across({{group_vars}})) %>% - dplyr::summarise( - `X1 Reject Prob.` = mean(`X1 p-value` < alpha), - `X2 Reject Prob.` = mean(`X2 p-value` < alpha) - ) - return(eval_out) -} -reject_prob_eval <- create_evaluator(.eval_fun = reject_prob_fun, alpha = 0.1) -``` +![Overview of running a `simChef` `Experiment`. The `Experiment` class handles relationships among the four classes: `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()`. Experiments may have multiple `DGPs` and `Methods`, which are combined across the Cartesian product of their varying parameters (represented by *). Once computed, each `Evaluator` and `Visualizer` takes in the fitted simulation replicates, while `Visualizer` additionally receives evaluation summaries.](figures/run_experiment.png){width=100% #fig:run-experiment-figure} -Similarly in `power_plot_fun`, we need to add a `vary_params` argument to plot the results across different values of `sigma`. - -```{r vary5} -power_plot_fun <- function(fit_results, vary_params = NULL, col = "X1") { - - if (!is.null(vary_params)) { - # deal with the case when we vary across a parameter that is vector-valued - if (is.list(fit_results[[vary_params]])) { - fit_results[[vary_params]] <- list_col_to_chr(fit_results[[vary_params]], - name = vary_params, - verbatim = TRUE) - } - } - - plt <- ggplot2::ggplot(fit_results) + - ggplot2::aes(x = .data[[paste(col, "p-value")]], - color = as.factor(.method_name)) + - ggplot2::geom_abline(slope = 1, intercept = 0, - color = "darkgray", linetype = "solid", linewidth = 1) + - ggplot2::stat_ecdf(size = 1) + - ggplot2::scale_x_continuous(limits = c(0, 1)) + - ggplot2::labs(x = "t", y = "P( p-value \u2264 t )", - linetype = "", color = "Method") - if (!is.null(vary_params)) { - plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]]) - } - return(plt) -} -power_plot <- create_visualizer(.viz_fun = power_plot_fun) +In what follows, we will discuss fitting, evaluating, and visualizing the experiment, each in turn. -``` +## Fitting an experiment {-} -Here, we have also added a pre-processing step to deal with the potential case when we vary across a *list* of parameter values. This pre-processing step uses a helper function, `list_col_to_chr()`, to convert a list-type tibble column to a character-type tibble column that is amenable for plotting (unlike the list-type column). +When fitting an experiment, each `Method` is fit on each `DGP` (for each specified `vary_across` parameter configuration). As such, the outputs of the user-defined `DGP` functions should match input arguments to the user-defined `Method` functions. More specifically, -Now, we are ready to update our experiment and run the experiment via -```{r vary6} -vary_results <- experiment %>% - update_evaluator(reject_prob_eval, name = "Rejection Prob. (alpha = 0.1)") %>% - update_visualizer(power_plot, name = "Power") %>% - run_experiment(n_reps = 10, save = TRUE) -vary_results$eval_results -vary_results$viz_results -``` +- **DGP inputs**: + - Any number of named parameters (e.g., `dgp_param1`, `dgp_param2`, ...) +- **DGP outputs**: + - A list of named elements to be passed onto the method(s) (e.g., `dgp_out1`, `dgp_out2`, ...) +- **Method inputs**: + - Named parameters matching all names in the list returned by the DGP function (e.g., `dgp_out1`, `dgp_out2`, ...) + - Any number of additional named parameters (e.g., `method_param1`, `method_param2`, ...) +- **Method outputs**: + - A list of named elements (e.g., `method_out1`, `method_out2`, ...) + [Note: this output list should contain all information necessary to evaluate and visualize the method's performance] + +Finally, `fit_experiment()` returns the fitted results across all experimental replicates in a `tibble`. This `tibble` contains: -Note here we need to use `update_*` instead of `add_*` since an `Evaluator` named "Rejection Prob. (alpha = 0.1)" and a `Visualizer` named "Power" already exist in the `Experiment`. Using `add_*` will throw an error. - -For fun, let's add another plot (in fact, an interactive plot using `plotly::ggplotly`) to our `Experiment` and run the `Experiment` across various values of the coefficient $\boldsymbol{\beta}_2$ and the correlation $\rho$ between features in $\mathbf{X}$. (Note: the visualizer function below (`reject_prob_plot_fun`) takes in the `Evaluator` results, stored as `eval_results`, and plots the rejection probability for the $\boldsymbol{\beta}_1$ at $\alpha = 0.1$.) - -```{r vary7} -# create rejection probability plot -reject_prob_plot_fun <- function(eval_results, vary_params = NULL, - alpha = 0.05) { - eval_results <- eval_results$`Rejection Prob. (alpha = 0.1)` - if (is.list(eval_results[[vary_params]])) { - # deal with the case when we vary across a parameter that is vector-valued - eval_results[[vary_params]] <- list_col_to_chr(eval_results[[vary_params]], - name = vary_params, - verbatim = TRUE) - } - plt <- ggplot2::ggplot(eval_results) + - ggplot2::aes(x = .data[[vary_params]], y = `X1 Reject Prob.`, - color = as.factor(.method_name), - fill = as.factor(.method_name)) + - ggplot2::labs(x = vary_params, - y = sprintf("Rejection Probability (alpha = %s)", alpha), - color = "Method", fill = "Method") + - ggplot2::scale_y_continuous(limits = c(0, 1)) - if (is.numeric(eval_results[[vary_params]])) { - plt <- plt + - ggplot2::geom_line() + - ggplot2::geom_point(size = 2) - } else { - plt <- plt + - ggplot2::geom_bar(stat = "identity") - } - return(plotly::ggplotly(plt)) -} -reject_prob_plot <- create_visualizer(.viz_fun = reject_prob_plot_fun, alpha = 0.1) +- Identifier columns + - `.rep`: replicate ID + - `.dgp_name`: name of DGP + - `.method_name`: name of Method + - `vary_across` parameter columns (if applicable): value of the specified `vary_across` parameter +- A column corresponding to each named component in the list returned by the `Method` (e.g., `method_out1`, `method_out2`, ...) + +We summarize these inputs and outputs in Figure \@ref(fig:fit-experiment-figure). -experiment <- experiment %>% - add_visualizer(reject_prob_plot, name = "Rejection Prob. (alpha = 0.1) Plot") - -# run experiment across values of beta_2 -vary_beta2_results <- experiment %>% - remove_vary_across(dgp = "Linear Gaussian DGP") %>% - add_vary_across(.dgp = "Linear Gaussian DGP", - beta = list(c(1, 0), - c(1, 0.5), - c(1, 1), - c(1, 1.5), - c(1, 2))) %>% - run_experiment(n_reps = 10, save = TRUE) - -# run experiment across values of rho (correlation) -vary_cor_results <- experiment %>% - remove_vary_across(dgp = "Linear Gaussian DGP") %>% - add_vary_across(.dgp = "Linear Gaussian DGP", - rho = c(0, 0.2, 0.5, 0.9)) %>% - run_experiment(n_reps = 10, save = TRUE) -``` -Now when generating the R Markdown report summary for an `Experiment`, the R Markdown will compile results (both evaluation and visualization results) from all saved `Experiments` under the root results directory `experiment$get_save_dir()`. Since the results from the many `vary_across` runs above are all saved under the original `experiment`'s results directory, then the following will include all of the above results in a single document. +![Overview of fitting a `simChef` `Experiment`. Inputs and outputs for user-defined functions are also provided.](figures/fit_experiment.png){width=100% #fig:fit-experiment-figure} -```{r render_docs2, warning = FALSE} -render_docs(experiment) -``` +## Evaluating an experiment {-} -```{r cp_html2, echo = FALSE, warning = FALSE, message = FALSE, results = "hide"} -# copy html output to pkgdown/assets directory for website -file.copy(from = file.path(experiment$get_save_dir(), - paste0(experiment$name, ".html")), - to = file.path(assets_dir, "linear_regression_output.html"), - overwrite = TRUE) -``` - -Equivalently, we can create the same R Markdown report summary by directly specifying the experiment's root results directory. +Next, when evaluating an experiment, the output of `fit_experiment()` (i.e., `fit_results` from Figure \@ref(fig:fit-experiment-figure)) as well as a character vector of the `vary_across` parameter names (i.e., `vary_params`) are automatically passed to each `Evaluator` function. +Typically, an `Evaluator` takes the `fit_results` tibble in as input, evaluates some metric(s) on each row or group of rows (e.g., summarizing across replicates), and returns the resulting `tibble` or `data.frame`. +We summarize the relevant inputs and outputs below and in Figure \@ref(fig:eval-experiment-figure). -```{r eval = FALSE} -render_docs(save_dir = experiment$get_save_dir()) -``` - - -The results can be found [here](../linear_regression_output.html). +- **Evaluator inputs**: + - `fit_results` (optional): output of `fit_experiment()` + - `vary_params` (optional): character vector of parameter names that are varied across in the experiment + - Any number of additional named parameters (e.g., `eval_param1`, `eval_param2`, ...) +- **Evaluator outputs**: + - Typically a `tibble` or `data.frame` + +The output of `evaluate_experiment` is a named list of evaluation result tibbles, one component for each `Evaluator` in the experiment. Here, the list names correspond to the names given to each `Evaluator` in `create_evaluator(.name = ...)`, and the list elements are exactly the return value from the `Evaluator` function. -In addition to showing all results under the root results directory, `render_docs()` will automatically generate a blank documentation template for every `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` found in any one of the `Experiments` under the root results directory. If one would like to generate the documentation template but not create the R Markdown report, see `init_docs()`. +![Overview of evaluating a `simChef` `Experiment`. Inputs and outputs for user-defined functions are also provided. Dotted boxes denote optional input arguments.](figures/eval_experiment.png){width=100% #fig:eval-experiment-figure} -# Simulation Experiment Templates +## Visualizing an experiment {-} -We have also provided boilerplate code templates for running common *types* of simulation experiments, namely, those focused on prediction (regression and classification), feature selection, or inference. These templates provide (1) a quick starting point with Evaluators and Visualizers that are commonly used for the specified type of simulation experiment and (2) a concrete example of how to get started using functions from the `simChef` library. +In the last step, when visualizing an experiment, the output of `fit_experiment()` and `evaluate_experiment` (`fit_results` and `eval_results`, respectively) as well as `vary_params` are automatically passed to each `Visualizer` function. +Typically, a `Visualizer` takes in the `fit_results` and/or `eval_results` and constructs a plot to visualize the performance of various methods (perhaps, across the varying parameter specified by `vary_params`). +We summarize the relevant inputs and outputs below and in Figure \@ref(fig:viz-experiment-figure). -Currently, we have implemented the following templates: +- **Visualizer inputs**: + - `fit_results` (optional): output of `fit_experiment()` + - `eval_results` (optional): output of `evaluate_experiment()` + - `vary_params` (optional): character vector of parameter names that are varied across in the experiment + - Any number of additional named parameters (e.g., `viz_param1`, `viz_param2`, ...) +- **Visualizer outputs**: + - Typically a plot (e.g., a `ggplot` or `plotly` object) + +The output of `visualize_experiment` is a named list of visualizations or plots, one component for each `Visualizer` in the experiment. Here, the list names correspond to the names given to each `Visualizer` in `create_visualizer(.name = ...)`, and the list elements are exactly the return value from the `Visualizer` function. -- `use_prediction_template(type = "regression")` -- `use_prediction_template(type = "classification")` -- `use_feature_selection_template()` -- `use_inference_template()` +![Overview of visualizing a `simChef` `Experiment`. Inputs and outputs for user-defined functions are also provided. Dotted boxes denote optional input arguments.](figures/viz_experiment.png){width=100% #fig:viz-experiment-figure} -These functions will print out code to the console that can be easily copied and/or run. For example, - -```{r templates1} -use_prediction_template(type = "regression") -``` +## Putting it all together {-} -For more guidance, we can also include concrete examples of a DGP and Method via: - -```{r templates2} -use_prediction_template(type = "regression", - include_dgp_example = TRUE, - include_method_example = TRUE) -``` - -# Additional notes and usage - -## The Experiment R6 Class - -It is important to note that the `Experiment()` class is an `R6`. With this, we need to be careful about clones versus pointers. In the following, it may look like the `vary_experiment` object has a `vary_across` component while the `experiment` object does not have a `vary_across` component. However, when `experiment` is piped into `add_vary_across()`, this is in itself modifying `experiment`, and `vary_experiment` is simply pointing to this modified `experiment`. - -```{r notes1} -experiment <- experiment %>% - remove_vary_across(dgp = "Linear Gaussian DGP") -experiment -vary_experiment <- experiment %>% - add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) -all.equal(vary_experiment, experiment) -data.table::address(experiment) == data.table::address(vary_experiment) -experiment -``` - -## Creating an experiment from another experiment - -To modify `vary_experiment` without making changes to `experiment`, we need to create `vary_experiment` as a new experiment by explicitly *cloning* from the old experiment `experiment`. - -```{r notes2} -vary_experiment <- create_experiment(name = "I am a clone", - clone_from = experiment) -data.table::address(experiment) == data.table::address(vary_experiment) -``` - -```{r notes3} -vary_experiment -``` - -When creating an `Experiment` from a clone, we are making a deep clone of the parent experiment's `DGPs`, `Methods`, `Evaluators`, and `Visualizers`, but not the `vary_across` component. We thus need to add a `vary_across` component to `vary_experiment` using `add_vary_across()`. - -```{r notes4} -# this works without an error -vary_experiment <- vary_experiment %>% - add_vary_across(.dgp = "Linear Gaussian DGP", sigma = c(1, 2, 4, 8)) -``` - -We can also add/update DGPs, Methods, Evaluators, and Visualizers to the cloned experiment without modifying the parent `experiment`. - -```{r notes5} -# add DGP -dgp_new <- create_dgp(.dgp_fun = dgp_fun, .name = "Linear Gaussian DGP (large n)", - n = 500, beta = c(1, 0), rho = 0, sigma = 1) -vary_experiment <- vary_experiment %>% - add_dgp(dgp_new, "Linear Gaussian DGP (large n)") -vary_experiment -experiment -``` +As alluded to previously, once the `DGP`, `Method`, `Evaluator`, and `Visualizer` functions have been defined, the hard work has been done. For convenience, we provide below a template that can be used to run an experiment, assuming that `dgp_fun`, `method_fun`, `evaluator_fun`, and `visualizer_fun` have been appropriately defined. -## Using cached results from an experiment - -To reduce computation, one may want to avoid re-running previously computed and saved components of an Experiment. This can be done by setting the argument `use_cached` to `TRUE` in `run_experiment()`. More specifically, when an Experiment is run with `use_cached = TRUE`, all previously cached results (i.e., those that have been previously saved to disk with `save = TRUE`) are loaded in and checked if their corresponding Experiment matches the configurations of the current Experiment. If the cached Experiment configurations indeed match that of the current Experiment, the cached results are used and only the uncached components of the Experiment are run. - -As an example, let us run the following experiment and save its results. - -```{r caching1} -orig_results <- experiment$run(n_reps = 10, save = TRUE) -``` - -When setting `use_cached = TRUE`, the following line of code will not generate any new results but will instead simply read in the saved or cached results from disk. - -```{r caching2} -cached_results <- experiment$run(n_reps = 10, use_cached = TRUE) -all.equal(orig_results, cached_results) -``` - -We can also choose to read in a smaller number of the cached replicates - -```{r caching3} -smaller_results <- experiment$run(n_reps = 5, use_cached = TRUE) -max(as.numeric(smaller_results$fit_results$.rep)) -``` - -or increase the number of replicates without re-doing computation for the previously cached replicates - -```{r caching4} -larger_results <- experiment$run(n_reps = 15, use_cached = TRUE) -all.equal(orig_results$fit_results, - larger_results$fit_results %>% dplyr::filter(as.numeric(.rep) <= 10)) -``` - -In addition, caching works intuitively when adding or modifying components of an Experiment. In the following, we add a new DGP to the Experiment, so when we run the Experiment with `use_cached = TRUE`, only the replicates involving the new DGP are run while the replicates involving the old DGP (i.e., the `Linear Gaussian DGP`) are loaded in from the cache. - -```{r caching5} -experiment <- experiment %>% add_dgp(dgp = dgp_new, name = "New DGP") -new_results <- experiment$run(n_reps = 10, use_cached = TRUE) -all.equal(new_results$fit_results %>% dplyr::filter(.dgp_name == "Linear Gaussian DGP"), - orig_results$fit_results) -``` - -Note that since we did not save the Experiment results above, then the following will re-run the replicates corresponding to the new DGP as above. Please set `save = TRUE` in order to cache the results for future use. - -```{r caching6} -new_results2 <- experiment$run(n_reps = 10, use_cached = TRUE) -identical(new_results$fit_results %>% dplyr::filter(.dgp_name == "New DGP"), - new_results2$fit_results %>% dplyr::filter(.dgp_name == "New DGP")) -``` - -Some helpful functions regarding caching include: - -```{r caching7} -# to load in the cached fit results for an experiment -fit_results <- get_cached_results(experiment, "fit") -# to load in the cached evaluation results for an experiment -eval_results <- get_cached_results(experiment, "eval") -# to load in the cached visualization results for an experiment -viz_results <- get_cached_results(experiment, "viz") -# to load in the cached Experiment object -experiment <- get_cached_results(experiment, "experiment") -# to load in the Experiment parameters corresponding to the cached *_results -cached_exp_params <- get_cached_results(experiment, "experiment_cached_params") - -# to clear the cache -clear_cache(experiment) -``` - -## Using checkpoints within an experiment - -A checkpoint is a periodic snapshot of the simulation results, saved to disk. -Checkpoints help guard against lost progress in the case of unexpected problems, -such as node failures when running on a cluster, at the cost of longer -simulation running times. Checkpoints are most useful for long-running -simulations where the cost of creating the checkpoints is small relative to the -total cost of the simulation. As a result, users should be careful not to create -checkpoints more often than necessary. - -To enable checkpointing, use a positive value for the argument -`checkpoint_n_reps` passed to `Experiment$run()` or `Experiment$fit()`. Below is -an example simulation run in which we use checkpointing and encounter errors as -the simulation progresses past the first checkpoint: - -```{r, eval=FALSE} -# create a new experiment -experiment <- create_experiment(name = "checkpoint-exper") %>% - ... # add dgps, methods, etc. - -# run 100 reps of the experiment, checkpointing every 25 reps -experiment$fit(n_reps = 100, checkpoint_n_reps = 25) -#> Fitting checkpoint-exper... -#> 25 reps completed (totals: 25/100) | time taken: 0.060645 minutes -#> Saving fit results checkpoint... -#> Fit results saved | time taken: 0.080968 seconds - -# the simulation fails here! -``` - -Since a full 25 replicates completed before the simulation failed, the first -checkpoint was successful. You can use `get_cached_results(experiment, "fit")` -to examine the completed replicates, or simply continue the simulation using the -same code you already ran: - -```{r, eval=FALSE} -experiment$fit(n_reps = 100, checkpoint_n_reps = 25) -#> Reading in cached fit results... -#> Fitting checkpoint-exper... -#> 25 reps completed (totals: 50/100) | time taken: 0.066513 minutes -#> Saving fit results checkpoint... -#> Fit results saved | time taken: 0.133003 seconds -#> 25 reps completed (totals: 75/100) | time taken: 0.125143 minutes -#> Saving fit results checkpoint... -#> Fit results saved | time taken: 0.107203 seconds -#> 25 reps completed (totals: 100/100) | time taken: 0.184656 minutes -#> Saving fit results... -#> Fit results saved | time taken: 0.124632 seconds -#> ============================== -``` - - -## Setting global plotting theme for built-in `Visualizers` - -By default, all `Visualizers` in the built-in `simChef` library use the default `ggplot` theme when plotting. To modify this plotting theme (for all built-in `Visualizers` and any `Visualizer` built upon `plot_eval_constructor()`), one can change the `simChef.plot_theme` global option. - -In particular, to use the [vthemes](https://yu-group.github.io/vthemes/) plotting theme: - -```{r eval = FALSE} -options(simChef.plot_theme = "vthemes") # to use the vthemes plotting theme -``` - -More generally, one can specify any `ggplot::theme()` object. - -```{r eval = FALSE} -options(simChef.plot_theme = ggplot2::theme_minimal()) -``` - - -## Additional R Markdown notes and options - -There are several customizable options regarding the aesthetics of Evaluators and Visualizers displayed in the Rmd report. These can be modified using the `doc_options` argument in `create_evaluator()` and `create_visualizer()`. For example, we can customize the height and width of the Power plot via -```{r rmd_notes1} -power_plot <- create_visualizer(.viz_fun = power_plot_fun, - .doc_options = list(height = 10, width = 8)) -experiment <- experiment %>% - update_visualizer(power_plot, "Power") -``` - -and the number of digits in the evaluation table outputs via -```{r rmd_notes2} -reject_prob_eval <- create_evaluator(.eval_fun = reject_prob_fun, alpha = 0.1, - .doc_options = list(digits = 3)) -experiment <- experiment %>% - update_evaluator(reject_prob_eval, "Rejection Prob. (alpha = 0.1)") -``` - -Alternatively, `set_doc_options()` can be used to update the R Markdown option for an existing object, rather than having to recreate the `Evaluator()` or `Visualizer()` from scratch, e.g., -```{r rmd_notes3} -experiment <- experiment %>% - set_doc_options(field_name = "visualizer", name = "Power", show = TRUE, - height = 10, width = 8) -save_experiment(experiment) -``` - -To hide the output of an `Evaluator()` or `Visualizer()` in the R Markdown report, set `show = FALSE` in `set_doc_options()` or `create_*`. -Note that if document options are set after running the experiment, the experiment must be manually saved via `save_experiment(experiment)` in order for these changes to appear in the R Markdown output. - -There are additional customization options that can be set in `render_docs()` including the order in which results of Evaluators and Visualizers are displayed (via the `eval_order` and `viz_order` arguments) and the Rmd output type or theme. For example, - -```{r eval = FALSE} -# use html_document instead of vthemes::vmodern (default) -render_docs(experiment, - output_format = rmarkdown::html_document()) - -# add custom css style -render_docs(experiment, - output_options = list(css = "path/to/file.css")) -``` - -## Saving results - -Since the R Markdown report heavily relies on the results file structure to organize the report summary, it may be helpful to understand the default saving structure utilized in `simChef`. - -By default, the root results directory is ./results/\{EXPERIMENT_NAME\}. If the experiment was created by cloning another experiment, then the default results directory is the same as the parent experiment. To change the root results directory, one can specify the desired directory via the `save_dir` argument in `create_experiment()` or use the `set_save_dir()` helper function. - -All results from `experiment$run(..., save = TRUE)` without a `vary_across` component are then saved under the root results directory. If `experiment` has a `vary_across` component, then the results of `experiment$run(..., save = TRUE)` are saved under ./\{ROOT_RESULTS_DIR\}/\{DGP_OR_METHOD_NAME\}/Varying \{PARAM_NAME\}/. - -Here's the directory tree of the "Linear Regression Experiment" example: - -```{r, eval = FALSE} -fs::dir_tree(get_save_dir(experiment)) -#> results -#> ├── Linear Gaussian DGP -#> │ ├── Varying beta -#> │ │ ├── eval_results.rds -#> │ │ ├── experiment.rds -#> │ │ ├── experiment_cached_params.rds -#> │ │ ├── fit_results.rds -#> │ │ └── viz_results.rds -#> │ ├── Varying rho -#> │ │ ├── eval_results.rds -#> │ │ ├── experiment.rds -#> │ │ ├── experiment_cached_params.rds -#> │ │ ├── fit_results.rds -#> │ │ └── viz_results.rds -#> │ └── Varying sigma -#> │ ├── eval_results.rds -#> │ ├── experiment.rds -#> │ ├── fit_results.rds -#> │ └── viz_results.rds -#> ├── Linear Regression Experiment.html -#> ├── docs -#> │ ├── dgps -#> │ │ └── Linear Gaussian DGP.md -#> │ ├── evaluators -#> │ │ └── Rejection Prob. (alpha = 0.1).md -#> │ ├── methods -#> │ │ └── OLS.md -#> │ ├── objectives.md -#> │ └── visualizers -#> │ ├── Power.md -#> │ └── Rejection Prob. (alpha = 0.1) Plot.md -#> ├── eval_results.rds -#> ├── experiment.rds -#> ├── experiment_cached_params.rds -#> ├── fit_results.rds -#> └── viz_results.rds -``` - -## Additional handling of the experiment - -`Experiment$run()` is the easiest and most concise way of running the simulation experiment from start to finish. However, when debugging and developing the simulation experiment, it may be helpful to run only parts of the experiment so as to not repeat time-consuming, redundant computations. We split up the experimental run into the following three parts: - -1. `experiment$fit()`: fit the Method(s) in the experiment on multiple replicates of the DGP(s) and return the results from the fits -2. `experiment$evaluate()`: evaluate the experiment through the Evaluator(s) and return the evaluation results -3. `experiment$visualize()`: create visualizations of the fit/evaluation results from the experiment using the Visualizer(s) and return the visualization results - -```{r handling1} -fit_results <- experiment$fit(n_reps = 10) -eval_results <- experiment$evaluate(fit_results) -viz_results <- experiment$visualize(fit_results, eval_results) -``` - -or alternatively, - -```{r eval = FALSE} -fit_results <- fit_experiment(experiment, n_reps = 10) -eval_results <- evaluate_experiment(experiment, fit_results) -viz_results <- visualize_experiment(experiment, fit_results, eval_results) -``` - -`Experiment$run()` is simply a wrapper around these three functions: `fit()`, `evaluate()`, and `visualize()`. - -Thus far, we have neither stored nor returned any data from the `DGPs` since these can be large objects that require high memory loads when `n_reps` is large. However, one can generate small samples of data from the `DGPs` in the experiment via - -```{r handling2} -data_list <- experiment$generate_data(n_reps = 1) -str(data_list) -``` - -or alternatively, - -```{r eval = FALSE} -data_list <- generate_data(experiment, n_reps = 1) -``` - -This might be helpful for exploratory data analysis or further diagnosis of the experiment results. - -Other helpful methods for handling the experiment include the `get_*` family of methods, i.e., -```{r handling3} -get_dgps(experiment) # or `experiment$get_dgps()` -get_methods(experiment) # or `experiment$get_methods()` -get_evaluators(experiment) # or `experiment$get_evaluators()` -get_visualizers(experiment) # or `experiment$get_visualizers()` -get_vary_across(experiment) # or `experiment$get_vary_across()` -``` +```{r experiment-template, eval = FALSE} +## Step 2: Create DGP, Method, Evaluator, Visualizer class objects +dgp <- create_dgp( + .dgp_fun = dgp_fun, .name = "DGP Name", + # additional named parameters to pass to .dgp_fun() + dgp_param1 = XXX, dgp_param2 = XXX, ... +) +method <- create_method( + .method_fun = method_fun, .name = "Method Name", + # additional named parameters to pass to .method_fun() + method_param1 = XXX, method_param2 = XXX, ... +) +evaluator <- create_evaluator( + .eval_fun = evaluator_fun, .name = "Evaluator Name", + # additional named parameters to pass to .eval_fun() + eval_param1 = XXX, eval_param2 = XXX, ... +) +visualizer <- create_visualizer( + .viz_fun = visualizer_fun, .name = "Visualizer Name", + # additional named parameters to pass to .viz_fun() + viz_param1 = XXX, viz_param2 = XXX, ... +) -```{r handling4, results = "hide"} -get_save_dir(experiment) # or `experiment$get_save_dir()` -#> [1] "./results/Linear Regression Experiment" -``` +## Step 3: Assemble experiment recipe +experiment <- create_experiment(name = "Experiment Name") %>% + add_dgp(dgp) %>% + add_method(method) %>% + add_evaluator(evaluator) %>% + add_visualizer(visualizer) %>% + # optionally vary across DGP parameter(s) + add_vary_across(.dgp = "DGP Name", dgp_param1 = list(XXX, XXX, ...)) %>% + # optionally vary across Method parameter(s) + add_vary_across(.method = "Method Name", method_param1 = list(XXX, XXX, ...)) + +## Step 4: Document experiment recipe +init_docs(experiment) -## Error debugging - -In the event of an error, `simChef` makes it possible to both retrieve results from completed replicates (before the error occurred) and to gracefully debug errors in user-defined functions. For the sake of demonstration, let us create an artificial example. - -```{r debug1, error = TRUE} -# create experiment -dgp_fun <- function() return("data") -dgp_fun_err <- function() { stop("Uh oh!") } -dgp <- create_dgp(dgp_fun) -dgp_err <- create_dgp(dgp_fun_err) -method_fun <- function(x) return("result") -method <- create_method(method_fun) -experiment <- create_experiment( - dgp_list = list("Working DGP" = dgp, "Buggy DGP" = dgp_err), - method_list = list("Method" = method) -) +## Step 5: Run experiment +results <- run_experiment(experiment, n_reps = 100, save = TRUE) -# run experiment though it should return an error -results <- run_experiment(experiment, n_reps = 2) +## Step 6: Render experiment documentation and results +render_docs(experiment) ``` -If working interactively, the error can be inspected using the usual call to `rlang::last_error()`. Results that were run and completed before the error can be recovered via `rlang::last_error()$partial_results` and you can find errors that occurred within the simulation loop via `rlang::last_error()$errors`. -Alternatively, we can wrap the call that ran the error (in this case, `run_experiment()`) in `tryCatch()` as follows: -```{r debug2, error = TRUE} -results <- tryCatch(run_experiment(experiment, n_reps = 2), - error = identity) -results -``` +# Additional Resources -From this, we can view the results that were completed before we ran into the error via -```{r debug3} -results$partial_results -``` -and extract the error object(s) via -```{r debug4} -results$errors -``` +For more information on `simChef` and additional features, check out: -Note that the error tibble contains the `DGP` and `Method` (if the `DGP` was not the error cause) that were being processed when the error occurred in the `.dgp` and `.method` columns, respectively. The corresponding input parameters are in the `.dgp_params` and `.method_params` columns (both `NA` here since no parameters were varied in the experiment). Finally, the captured error itself is in the `.err` column. Using this, we can easily investigate and reproduce the error and if desired, work directly with the user-specified function that raised the error, e.g., +- [simChef culinary school](https://jpdunc23.github.io/simChef-culinary-school/slides/slides.html) (tutorial slides) +- [Computing experimental replicates in parallel](parallel.html) (vignette) +- [Setting up your simulation study](simulation-directory-setup.html) (vignette) +- [Cheat sheet: Inputs/outputs for user-defined functions](cheat-sheet.Rmd) (vignette) +- [simChef: A comprehensive guide](simChef-full.html) (vignette) for information on caching, checkpointing, error debugging, common simulation experiment templates, and more +- [Case Study #1](https://github.com/PhilBoileau/simChef-case-study) using `simChef` to develop a new flexible approach for predictive biomarker discovery with rendered documentation [here](https://philboileau.github.io/simChef-case-study/results/empirical-fdr-comparison/empirical-fdr-comparison.html) -```{r debug5, error = TRUE} -# get dgp that ran the error -err_fun <- results$errors$.dgp[[1]]$dgp_fun -# reproduce error via a direct call to the DGP function that raised the error -err_fun() -``` +Additional `simChef` examples/case studies and built-in library functions are still to come. Stay tuned!

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ZcP&IoO1=A}-7mn8(G}Cn`IqiI{XYXZPmBNn literal 0 HcmV?d00001 diff --git a/vignettes/references.bib b/vignettes/references.bib new file mode 100644 index 00000000..7716bbbc --- /dev/null +++ b/vignettes/references.bib @@ -0,0 +1,79 @@ + +@article{yu-veridical-2020, + title = {Veridical data science}, + volume = {117}, + issn = {0027-8424, 1091-6490}, + url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1901326117}, + doi = {10.1073/pnas.1901326117}, + abstract = {Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.}, + language = {en}, + number = {8}, + urldate = {2021-09-04}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Yu, Bin and Kumbier, Karl}, + month = feb, + year = {2020}, + pages = {3920--3929}, + file = {Yu and Kumbier - 2020 - Veridical data science.pdf:/home/james/Zotero/storage/2IMU9L5Q/Yu and Kumbier - 2020 - Veridical data science.pdf:application/pdf}, +} + +@article{lang-batchtools-2017, + title = {batchtools: {Tools} for {R} to work on batch systems}, + volume = {2}, + issn = {2475-9066}, + shorttitle = {batchtools}, + url = {https://joss.theoj.org/papers/10.21105/joss.00135}, + doi = {10.21105/joss.00135}, + abstract = {Lang et al, (2017), batchtools: Tools for R to work on batch systems, Journal of Open Source Software, 2(10), 135, doi:10.21105/joss.00135}, + language = {en}, + number = {10}, + urldate = {2023-04-20}, + journal = {Journal of Open Source Software}, + author = {Lang, Michel and Bischl, Bernd and Surmann, Dirk}, + month = feb, + year = {2017}, + pages = {135}, + file = {Full Text PDF:/home/james/Zotero/storage/PPA2UTP7/Lang et al. - 2017 - batchtools Tools for R to work on batch systems.pdf:application/pdf}, +} + +@article{wickham-welcome-2019, + title = {Welcome to the {Tidyverse}}, + volume = {4}, + issn = {2475-9066}, + url = {https://joss.theoj.org/papers/10.21105/joss.01686}, + doi = {10.21105/joss.01686}, + abstract = {Wickham et al., (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686}, + language = {en}, + number = {43}, + urldate = {2023-04-20}, + journal = {Journal of Open Source Software}, + author = {Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang, Winston and McGowan, Lucy D'Agostino and François, Romain and Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester, Jim and Kuhn, Max and Pedersen, Thomas Lin and Miller, Evan and Bache, Stephan Milton and Müller, Kirill and Ooms, Jeroen and Robinson, David and Seidel, Dana Paige and Spinu, Vitalie and Takahashi, Kohske and Vaughan, Davis and Wilke, Claus and Woo, Kara and Yutani, Hiroaki}, + month = nov, + year = {2019}, + pages = {1686}, + file = {Full Text PDF:/home/james/Zotero/storage/RKZ8IGRN/Wickham et al. - 2019 - Welcome to the Tidyverse.pdf:application/pdf}, +} + +@article{bengtsson-unifying-2021, + title = {A {Unifying} {Framework} for {Parallel} and {Distributed} {Processing} in {R} using {Futures}}, + volume = {13}, + issn = {2073-4859}, + url = {https://journal.r-project.org/archive/2021/RJ-2021-048/index.html}, + doi = {10.32614/RJ-2021-048}, + abstract = {A future is a programming construct designed for concurrent and asynchronous evaluation of code, making it particularly useful for parallel processing. The future package implements the Future API for programming with futures in R. This minimal API provides sufficient constructs for implementing parallel versions of well-established, high-level map-reduce APIs. The future ecosystem supports exception handling, output and condition relaying, parallel random number generation, and automatic identification of globals lowering the threshold to parallelize code. The Future API bridges parallel frontends with parallel backends, following the philosophy that end-users are the ones who choose the parallel backend while the developer focuses on what to parallelize. A variety of backends exist, and third-party contributions meeting the specifications, which ensure that the same code works on all backends, are automatically supported. The future framework solves several problems not addressed by other parallel frameworks in R.}, + language = {en}, + number = {2}, + urldate = {2023-04-20}, + journal = {The R Journal}, + author = {Bengtsson, Henrik}, + year = {2021}, + pages = {208}, + file = {Bengtsson - 2021 - A Unifying Framework for Parallel and Distributed .pdf:/home/james/Zotero/storage/KEV9JT2I/Bengtsson - 2021 - A Unifying Framework for Parallel and Distributed .pdf:application/pdf}, +} + +@Manual{chang-r6-2022, + title = {R6: Encapsulated Classes with Reference Semantics}, + author = {Winston Chang}, + year = {2022}, + note = {https://r6.r-lib.org, https://github.com/r-lib/R6/}, +} diff --git a/vignettes/simChef-full.Rmd b/vignettes/simChef-full.Rmd new file mode 100644 index 00000000..44e499c6 --- /dev/null +++ b/vignettes/simChef-full.Rmd @@ -0,0 +1,1260 @@ +--- +title: "simChef: A comprehensive guide" +output: + bookdown::html_document2: + base_format: rmarkdown::html_vignette + toc: true +vignette: > + %\VignetteIndexEntry{simChef: A comprehensive guide} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +pkgdown: + as_is: true +bibliography: references.bib +--- + +```{r include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.align = "center", + # disable R chunk evaluation when using macOS in GH Actions + # see https://github.com/tidyverse/ggplot2/issues/2252#issuecomment-1006713187 + # and https://github.com/lcolladotor/biocthis/issues/27 and + # https://github.com/bodkan/slendr/commit/f0f5ae18452cc9df5d151874e41b0b8fd5f29aa2# + eval = Sys.getenv("RUNNER_OS") != "macOS" +) + +library(simChef) + +set.seed(12345) + +# remove old cached results to start fresh +exp_names <- c( + "Linear Regression Experiment", "checkpoint-exper", "debugging-exper" +) +for (exp_name in exp_names) { + fnames <- list.files( + file.path("results", exp_name), pattern = ".rds", + recursive = TRUE, full.names = TRUE + ) + for (fname in fnames) { + file.remove(fname) + } +} +``` + +```{css echo = FALSE} +.infobox { + padding: .5em 1em 0em 1.5em; + border: 2px solid orange; + border-radius: 10px; + background: #f5f5f5 5px center/3em no-repeat; + margin-bottom: 1em; +} + +.infobox-blue { + padding: .5em 1em 0em 1.5em; + border: 2px solid cornflowerblue; + border-radius: 10px; + background: #f5f5f5 5px center/3em no-repeat; + margin-bottom: 1em; +} + +details { + margin-bottom: unset +} +``` + + +# Overview {-} + +The goal of `simChef` is to seamlessly and efficiently run simulation experiments using a simple grammar. The results of these simulation experiments can also be conveniently viewed in an organized and interactive browser (or html file) (e.g., [here](../linear_regression_output.html)). The basic usage of `simChef` can be summarized as follows: + +1. **Define data-generation, method, evaluation, and visualization functions of interest.** + ```{r overview-define, eval = FALSE} + ## Define data-generating process (DGP) function(s) + dgp_fun <- function(n = 100, p = 10, noise_sd = 1) { + X <- matrix(rnorm(n*p), nrow = n) + betas <- rnorm(p, sd = 2) + noise <- rnorm(n, sd = noise_sd) + y <- X %*% betas + noise + # dgp function should return a named list + return(list(X = X, y = y, noise_sd = noise_sd)) + } + + ## Define method function(s) + # method function args include the names in the dgp output list (`X` and `y` here) + method_fun <- function(X, y, ...) { + fit <- lm(y ~ X) + # by concatenating ... with the list output, we can easily pass through + # other outputs from the dgp to later stages of the simulation + # (evaluation and visualization) + return(list(fit = fit, ...)) + } + + ## Define evaluation function(s) + # the main computational loop of the simulation will return a `tibble::tibble` + # which is passed as 'fit_results' to our evaluation functions + evaluator_fun <- function(fit_results) { + # calculate R-squared + fit_results %>% + dplyr::mutate( + rsq = sapply(fit, function(.fit) summary(.fit)$r.squared) + ) + } + + ## d. Define visualization function(s) + visualizer_fun <- function(eval_results) { + require(ggplot2) + # return a plot r-squared vs noise level + ggplot(aes(x = noise_sd, y = rsq), data = eval_results[[1]]) + + geom_point() + + geom_smooth() + } + ``` +2. **Convert functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects.** + ```{r overview-class, eval = FALSE} + dgp <- create_dgp(.dgp_fun = dgp_fun, .name = "DGP1") + method <- create_method(.method_fun = method_fun, .name = "Method1") + evaluator <- create_evaluator(.eval_fun = evaluator_fun, .name = "Evaluator1") + visualizer <- create_visualizer(.viz_fun = visualizer_fun, .name = "Visualizer1") + ``` +3. **Assemble these recipe parts into a complete simulation experiment.** + ```{r overview-experiment, eval = FALSE} + experiment <- create_experiment(name = "Experiment") %>% + add_dgp(dgp) %>% + add_method(method) %>% + add_evaluator(evaluator) %>% + add_visualizer(visualizer) + experiment + ``` +4. **Document and describe the simulation experiment in text.** + ```{r overview-init-docs, eval = FALSE} + init_docs(experiment) + ``` +5. **Run the experiment.** + ```{r overview-run, eval = FALSE} + results <- run_experiment(experiment, n_reps = 100, save = TRUE) + ``` +6. **Visualize results via an automated R Markdown report.** + ```{r overview-render-docs, eval = FALSE} + render_docs(experiment) + ``` + +In this vignette, we will provide an overview of the core features of `simChef`: + +1. A tidyverse-inspired grammar of data-driven simulation experiments +2. A growing built-in library of composable building blocks for evaluation metrics and visualization utilities +3. Flexible and seamless integration with distributed computation, caching, checkpointing, and debugging tools +4. Automated generation of an R Markdown document to easily navigate, visualize, and interpret simulation results (see example [here](../linear_regression_output.html)) + +Note: Before proceeding through this guide, we recommend first checking out our introductory vignette: [Getting started with simChef](simChef.html). + +# A tidy grammar for simulation experiments + +Inspired by the tidyverse [@wickham-welcome-2019], `simChef` develops an intuitive grammar for running simulation studies. +To this end, `simChef` conceptualizes a simulation experiment into a set of core nouns, each with a corresponding verb (or verbs). + +More specifically, `simChef` first breaks down a simulation experiment into four modular components (or nouns), each implemented as an `R6` class object [@chang-r6-2022]. +Further, each of these classes is associated with a particular action or verb (italicized below). + +1. **DGP**: the data-generating process from which to *generate* data. +2. **Method**: the methods (or models) to *fit* in the experiment. +3. **Evaluator**: the evaluation metrics used to *evaluate* the methods' performance. +4. **Visualizer**: the visualization procedure used to *visualize* outputs from the method fits or evaluation results (can be tables, plots, or even R Markdown snippets to display). + +There is also a fifth and final noun: + +5. **Experiment**: an `R6` class which unites the four components above (DGP(s), Method(s), Evaluator(s), and Visualizer(s)) and stores references to these objects along with the DGP and Method parameters that should be varied in the study. There are four verbs associated with an Experiment: + a. *Fit* the Experiment: each Method in the Experiment is fit on each DGP in the Experiment. + b. *Evaluate* the Experiment: each Evaluator in the Experiment is evaluated on the fitted experiment results. + c. *Visualize* the Experiment: each Visualizer in the Experiment is visualized using the fitted and/or evaluated experiment results. + d. *Run* the Experiment: wrapper to fit, evaluate, and visualize the Experiment all at once. + +## Basic example usage + +Leveraging this grammar, we can easily use `simChef` to run a simulation experiment in six steps. + +1. Define DGP, method, evaluation, and visualization functions of interest. +2. Convert functions into `DGP()`, `Method()`, `Evaluator()`, and `Visualizer()` class objects. +3. Assemble objects into a complete simulation experiment. +4. Document and describe the simulation experiment in text. +5. Run the experiment. +6. Visualize results via an automated R Markdown report. + +> For concreteness, suppose that we would like to run an experiment, studying the performance of linear regression under a linear Gaussian data-generating process. + +### Step 1. Define DGP, method, evaluation, and visualization functions of interest {-} + +To begin, we first need to define the individual parts of the simulation experiment, namely, the data-generation, method, evaluation, and visualization functions under study. +We also need to write these functions such that they "play nicely" with one another under the `simChef` API. +We will make note of these requirements as we go, but for an introductory overview, please refer to the *Writing your own simulation experiment* section in [Getting started with simChef](simChef.html) or our [Cheat sheet: Inputs/outputs for user-defined functions](cheat-sheet.html). + +*Note*: Step 1 requires the most user-written code. After this step, there is minimal coding on the user end as we turn to leverage the `simChef` grammar for experiments. + +#### Step 1a. Define data-generating process (DGP) function(s) {-} + +As a toy DGP example, let us create a function to simulate a random Gaussian data matrix $\mathbf{X}$ of size $n \times 2$ and a linear response vector $\mathbf{y}$ of size $n \times 1$, where + +\begin{gather*} +\mathbf{X} \sim N\left(\mathbf{0}, \begin{pmatrix} 1 & \rho \\ \rho & 1 \end{pmatrix}\right), \\ +\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon},\\ +\boldsymbol{\epsilon} \sim N(\mathbf{0}, \sigma^2 \mathbf{I}_n) +\end{gather*} + +```{r create-dgp-fun} +linear_dgp_fun <- function(n, beta, rho, sigma) { + cov_mat <- matrix(c(1, rho, rho, 1), byrow = T, nrow = 2, ncol = 2) + X <- MASS::mvrnorm(n = n, mu = rep(0, 2), Sigma = cov_mat) + y <- X %*% beta + rnorm(n, sd = sigma) + return(list(X = X, y = y)) +} +``` + +:::: {.infobox} +