SDMtune

Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.

https://github.com/consbiol-unibern/sdmtune

Keywords

Repository

Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.

README.Rmd

---
output:
  github_document
bibliography: ./vignettes/SDMtune.bib
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "docs/reference/figures/README-"
)
```

# SDMtune 

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**SDMtune** provides a user-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the RStudio viewer pane during their execution.
Visit the [package website](https://consbiol-unibern.github.io/SDMtune/) and learn how to use **SDMtune** starting from the first article [Prepare data for the analysis](https://consbiol-unibern.github.io/SDMtune/articles/prepare-data.html).

## Installation

You can install the latest release version from CRAN:

```{r cran-installation, eval = FALSE}
install.packages("SDMtune")
```

Or the development version from GitHub:

```{r gh-installation, eval = FALSE}
devtools::install_github("ConsBiol-unibern/SDMtune")
```

## Hyperparameters tuning & real-time charts

**SDMtune** implements three functions for hyperparameters tuning:

* `gridSearch`: runs all the possible combinations of predefined hyperparameters' values;
* `randomSearch`: randomly selects a fraction of the possible combinations of predefined hyperparameters' values;
* `optimizeModel`: uses a *genetic algorithm* that aims to optimize the given evaluation metric by combining the predefined hyperparameters' values.

When the amount of hyperparameters' combinations is high, the computation time necessary to train all the defined models could be very long. The function `optimizeModel` offers a valid alternative that reduces computation time thanks to an implemented *genetic algorithm*. This function seeks the best combination of hyperparameters reaching a near optimal or optimal solution in a reduced amount of time compared to `gridSearch`. The following code shows an example using a simulated dataset. First a model is trained using the **Maxnet** algorithm implemented in the `maxnet` package with default hyperparameters' values. After the model is trained, both the `gridSearch` and `optimizeModel` functions are executed to compare the execution time and model performance evaluated with the AUC metric. If the following code is not clear, please check the articles in the [website](https://consbiol-unibern.github.io/SDMtune/).

```{r example, eval=FALSE}
library(SDMtune)

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE, seed = 25)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a Maxnet model
model <- train(method = "Maxnet", data = train)

# Define the hyperparameters to test
h <- list(reg = seq(0.1, 3, 0.1), fc = c("lq", "lh", "lqp", "lqph", "lqpht"))

# Test all the possible combinations with gridSearch
gs <- gridSearch(model, hypers = h, metric = "auc", test = test)
head(gs@results[order(-gs@results$test_AUC), ])  # Best combinations

# Use the genetic algorithm instead with optimizeModel
om <- optimizeModel(model, hypers = h, metric = "auc", test = test, seed = 4)
head(om@results)  # Best combinations
```

During the execution of "tuning" and "variable selection" functions, real-time charts displaying training and validation metrics are displayed in the RStudio viewer pane (below is a screencast of the previous executed `optimizeModel` function).



## Speed test

In the following example we train a **Maxent** model:
 
 
```{r train Maxent, eval=FALSE}
# Train a Maxent model
sdmtune_model <- train(method = "Maxent", data = data)
```

We compare the execution time of the `predict` function between **SDMtune** that uses its own algorithm and **dismo** [@Hijmans2017] that calls the MaxEnt Java software [@Phillips2006]. We first convert the object `sdmtune_model` in a object that is accepted by **dismo**:
```{r sdmtune2maxent, eval=FALSE}
maxent_model <- SDMmodel2MaxEnt(sdmtune_model)
```

Next is a function used below to test if the results are equal, with a tolerance of `1e-7`:
```{r check, eval=FALSE}
my_check <- function(values) {
  return(all.equal(values[[1]], values[[2]], tolerance = 1e-7))
}
```

Now we test the execution time using the **microbenckmark** package:
```{r bench, eval=FALSE}
bench <- microbenchmark::microbenchmark(
  SDMtune = predict(sdmtune_model, data = data, type = "cloglog"),
  dismo = predict(maxent_model, data@data),
  check = my_check
)
```

and plot the output:
```{r plot bench, eval=FALSE}
library(ggplot2)
ggplot(bench, aes(x = expr, y = time/1000000, fill = expr)) +
  geom_boxplot() +
  labs(fill = "", x = "Package", y = "time (milliseconds)") +
  theme_minimal()
```


## Set working environment

To train a **Maxent** model using the Java implementation you need that:

* the **Java JDK** software is installed
* the package **rJava** is installed

You can check the version of MaxEnt used by `dismo` with the following command:

```{r maxent version, eval=FALSE}
dismo::maxent()
```

The MaxEnt `jar` file used by `dismo` is located in the folder returned by the following command:

```{r dismo folder, eval=FALSE}
system.file(package="dismo")
```

In case you want to upgrade to a newer version of MaxEnt (if available), download the file **maxent.jar** [here](https://biodiversityinformatics.amnh.org/open_source/maxent/) and replace the file already present in the previous folder.

The function `checkMaxentInstallation` checks that Java JDK and rJava are installed, and that the file maxent.jar is in the correct folder.

```{r check Maxent installation, eval=FALSE}
checkMaxentInstallation()
```

If everything is correctly configured for `dismo`, the command `dismo::maxent()` will return the new MaxEnt version.

## Code of conduct

Please note that this project follows a [Contributor Code of Conduct](https://consbiol-unibern.github.io/SDMtune/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

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