https://github.com/boyanangelov/sdmexplain
Explainable Species Distribution Modeling
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.0%) to scientific vocabulary
Keywords
ecology
ecology-modelling
explainable-ml
machine-learning
species-distribution-modelling
Last synced: 5 months ago
·
JSON representation
Repository
Explainable Species Distribution Modeling
Basic Info
- Host: GitHub
- Owner: boyanangelov
- License: other
- Language: R
- Default Branch: master
- Size: 1.25 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
ecology
ecology-modelling
explainable-ml
machine-learning
species-distribution-modelling
Created over 7 years ago
· Last pushed about 6 years ago
Metadata Files
Readme
Contributing
License
Code of conduct
README.Rmd
---
output:
md_document:
variant: markdown_github
fig_width: 7
fig_height: 7
---
```{r echo=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
library(mlr)
library(sdmexplain)
library(dplyr)
```
[](https://zenodo.org/badge/latestdoi/142751918)
# sdmexplain
`sdmexplain` is an R package to make Species Distribution Models more explainable.
## Installation
```
devtools::install_github("boyanangelov/sdmexplain")
```
## Example
Preparing training data.
```{r eval=FALSE}
occ_data_raw <- sdmbench::get_benchmarking_data("Lynx lynx")
occ_data <- occ_data_raw$df_data
occ_data$label <- as.factor(occ_data$label)
coordinates.df <- rbind(occ_data_raw$raster_data$coords_presence,
occ_data_raw$raster_data$background)
occ_data <- cbind(occ_data, coordinates.df)
train_test_split <- rsample::initial_split(occ_data, prop = 0.7)
data.train <- rsample::training(train_test_split)
data.test <- rsample::testing(train_test_split)
train.coords <- dplyr::select(data.train, c("x", "y"))
data.train$x <- NULL
data.train$y <- NULL
test.coords <- dplyr::select(data.test, c("x", "y"))
data.test$x <- NULL
data.test$y <- NULL
```
Training SDM.
```{r eval=FALSE}
task <- makeClassifTask(id = "model", data = data.train, target = "label")
lrn <- makeLearner("classif.lda", predict.type = "prob")
mod <- train(lrn, task)
```
Preparing data for explainability.
```{r eval=FALSE}
explainable_data <- prepare_explainable_data(data.test, mod, test.coords)
```
```{r eval=FALSE}
processed_plots <- process_lime_plots(explainable_data$explanation)
```
Plotting explainable map.
```{r example, eval=FALSE}
plot_explainable_sdm(explainable_data$processed_data,
explainable_data$processed_plots)
```
Cite as: Boyan Angelov. (2018, October 4). boyanangelov/sdmexplain: sdmexplain: An R Package for Making Species Distribution Models More Explainable (Version v0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1445779
Owner
- Name: Boyan Angelov
- Login: boyanangelov
- Kind: user
- Location: Berlin, Germany
- Website: http://boyanangelov.com
- Twitter: thinking_code
- Repositories: 21
- Profile: https://github.com/boyanangelov
Data Strategist | Author | Researcher (Complexity, AI)
GitHub Events
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Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Boyan Angelov | b****v@g****m | 18 |
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Last synced: about 2 years ago
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