cfbd45bfc0
* Improve features * Add wasm32-wasi as a target * Update .github/workflows/ci.yml Co-authored-by: morenol <22335041+morenol@users.noreply.github.com>
86 lines
2.6 KiB
Rust
86 lines
2.6 KiB
Rust
//! # The Iris flower dataset
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//!
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//! | Number of Instances | Number of Attributes | Missing Values? | Associated Tasks: |
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//! |-|-|-|-|
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//! | 150 | 4 | No | Classification |
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//!
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//! [Fisher's Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris) is a multivariate dataset that was published in 1936 by Ronald Fisher.
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//! This multivariate dataset is frequently used to demonstrate various machine learning algorithms. The dataset has following attributes:
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//!
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//! | Predictor | Data Type | Target? |
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//! |-|-|-|
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//! | Sepal length | Numerical | No |
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//! | Sepal width | Numerical | No |
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//! | Petal length | Numerical | No |
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//! | Petal width | Numerical | No |
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//! | Class | Nominal | Yes |
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//!
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use crate::dataset::deserialize_data;
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use crate::dataset::Dataset;
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/// Get dataset
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pub fn load_dataset() -> Dataset<f32, u32> {
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let (x, y, num_samples, num_features): (Vec<f32>, Vec<u32>, usize, usize) =
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match deserialize_data(std::include_bytes!("iris.xy")) {
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Err(why) => panic!("Can't deserialize iris.xy. {}", why),
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Ok((x, y, num_samples, num_features)) => (
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x,
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y.into_iter().map(|x| x as u32).collect(),
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num_samples,
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num_features,
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),
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};
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Dataset {
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data: x,
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target: y,
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num_samples,
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num_features,
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feature_names: vec![
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"sepal length (cm)",
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"sepal width (cm)",
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"petal length (cm)",
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"petal width (cm)",
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]
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.iter()
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.map(|s| s.to_string())
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.collect(),
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target_names: vec!["setosa", "versicolor", "virginica"]
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.iter()
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.map(|s| s.to_string())
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.collect(),
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description: "Iris dataset: https://archive.ics.uci.edu/ml/datasets/iris".to_string(),
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}
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}
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#[cfg(test)]
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mod tests {
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// #[cfg(not(target_arch = "wasm32"))]
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// use super::super::*;
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use super::*;
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// TODO: fix serialization
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// #[cfg(not(target_arch = "wasm32"))]
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// #[test]
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// #[ignore]
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// fn refresh_iris_dataset() {
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// // run this test to generate iris.xy file.
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// let dataset = load_dataset();
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// assert!(serialize_data(&dataset, "iris.xy").is_ok());
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// }
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#[cfg_attr(
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all(target_arch = "wasm32", not(target_os = "wasi")),
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wasm_bindgen_test::wasm_bindgen_test
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)]
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#[test]
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fn iris_dataset() {
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let dataset = load_dataset();
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assert_eq!(dataset.data.len(), 50 * 3 * 4);
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assert_eq!(dataset.target.len(), 50 * 3);
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assert_eq!(dataset.num_features, 4);
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assert_eq!(dataset.num_samples, 50 * 3);
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}
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}
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