openml-rust
A rust interface to OpenML.
The aim of this crate is to give rust code access to Machine Learning data hosted by OpenML. Thus, Machine Learning algorithms developed in Rust can be easily applied to state-of-the-art data sets and their performance compared to existing implementations in a reproducable way.
Example
= task.run(|train, test| { // train classifier let nbc: NaiveBayesClassifier
= train .map(|(x, y)| (x, y)) .collect(); // test classifier let y_out: Vec<_> = test .map(|x| nbc.predict(x)) .collect(); Box::new(y_out.into_iter()) }); println!("Classification Accuracy: {}", result.result()); }">
extern crate openml; use openml::prelude::*; use openml::{PredictiveAccuracy, SupervisedClassification}; use openml::baseline::NaiveBayesClassifier; fn main() { // Load "Supervised Classification on iris" task (https://www.openml.org/t/59) let task = SupervisedClassification::from_openml(59).unwrap(); println!("Task: {}", task.name()); // run the task let result: PredictiveAccuracy<_> = task.run(|train, test| { // train classifier let nbc: NaiveBayesClassifier<u8> = train .map(|(x, y)| (x, y)) .collect(); // test classifier let y_out: Vec<_> = test .map(|x| nbc.predict(x)) .collect(); Box::new(y_out.into_iter()) }); println!("Classification Accuracy: {}", result.result()); }
Goals
- get data sets
- get tasks
- Runtime check panics if the wrong task type is loaded (
SupervisedRegressionattempts to load a Clustering Task)
- Runtime check panics if the wrong task type is loaded (
- get split sets
- task types
- Supervised Classification
- Supervised Regression
- Learning Curve
- Clustering
- run tasks
- runner takes a closure where the user defines learning and prediction
- make openml.org optional (manual construction of tasks)
Future Maybe-Goals
- flow support
- run support
- full OpenML API support
- authentication
- more tasks
- Supervised Datastream Classification
- Machine Learning Challenge
- Survival Analysis
- Subgroup Discovery
Non-Goals
- implementations of machine learning algorithms
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