https://github.com/shogun-toolbox/shogun-rust
Shogun for Rust
https://github.com/shogun-toolbox/shogun-rust
machine-learning rust shogun
Last synced: 2 months ago
JSON representation
Shogun for Rust
- Host: GitHub
- URL: https://github.com/shogun-toolbox/shogun-rust
- Owner: shogun-toolbox
- Created: 2020-05-26T15:29:31.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-01T07:20:09.000Z (over 5 years ago)
- Last Synced: 2025-04-14T10:53:23.061Z (6 months ago)
- Topics: machine-learning, rust, shogun
- Language: Rust
- Size: 42 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# shogun-rust
This is a Rust crate with bindings to the [Shogun](https://github.com/shogun-toolbox/shogun) machine learning framework.
Note: this crate is in very early development and only supports a very limited part of the Shogun library.
Note: this is just a Rust wrapper for the shogun C++ library so the internals/API are not very Rust-like.
More information about the design can be found [here](https://gf712.github.io/programming/2020/05/28/shogun-rust.html).
# Build
Assumes you have shogun-static installed locally, as well as spdlog. If not found CMake will throw an error.
To build simply:
```bash
cargo build
```
And then from another crate:
```rust
extern crate shogun;
```
# Example
## Basic API
```rust
use shogun::shogun::{Kernel, Version};
fn main() {
let version = Version::new();
println!("Shogun version {}", version.main_version().unwrap());
// shogun-rust supports Shogun's factory functions
let k = match Kernel::new("GaussianKernel") {
Ok(obj) => obj,
Err(msg) => {
panic!("No can do: {}", msg);
},
};
// also supports put
match k.put("log_width", &1.0) {
Err(msg) => println!("Failed to put value."),
_ => (),
}
// and get
match k.get("log_width") {
Ok(value) => match value.downcast_ref::() {
Some(fvalue) => println!("GaussianKernel::log_width: {}", fvalue),
None => println!("GaussianKernel::log_width not of type f64"),
},
Err(msg) => panic!("{}", msg),
}
}
```
## Training a Random Forest
```rust
let f_feats_train = File::read_csv("classifier_4class_2d_linear_features_train.dat".to_string())?;
let f_feats_test = File::read_csv("classifier_4class_2d_linear_features_test.dat".to_string())?;
let f_labels_train = File::read_csv("classifier_4class_2d_linear_labels_train.dat".to_string())?;
let f_labels_test = File::read_csv("classifier_4class_2d_linear_labels_test.dat".to_string())?;
let features_train = Features::from_file(&f_feats_train)?;
let features_test = Features::from_file(&f_feats_test)?;
let labels_train = Labels::from_file(&f_labels_train)?;
let labels_test = Labels::from_file(&f_labels_test)?;
let mut rand_forest = Machine::new("RandomForest")?;
let m_vote = CombinationRule::new("MajorityVote")?;
rand_forest.put("labels", &labels_train)?;
rand_forest.put("num_bags", &100)?;
rand_forest.put("combination_rule", &m_vote)?;
rand_forest.put("seed", &1)?;
rand_forest.train(&features_train)?;
let predictions = rand_forest.apply(&features_test)?;
let acc = Evaluation::new("MulticlassAccuracy")?;
rand_forest.put("oob_evaluation_metric", &acc)?;
let accuracy = acc.evaluate(&predictions, &labels_test)?;
println!("Model accuracy: {}", accuracy);
```