c7353d0b57
* Run `cargo clippy --fix` * Run `cargo clippy --all-features --fix` * Fix other clippy warnings * cargo fmt Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
102 lines
4.1 KiB
Rust
102 lines
4.1 KiB
Rust
//! # Breast Cancer Wisconsin (Diagnostic) Data Set
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//!
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//! Diagnostic Wisconsin Breast Cancer database
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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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//! | 569 | 30 | No | Classification |
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//!
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//! [Breast Cancer](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) was collected by Dr. William H. Wolberg, W. Nick Street and Olvi L. Mangasarian.
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//! Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and describe characteristics of the cell nuclei present in the image.
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//! 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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//! | Radius (mean of distances from center to points on the perimeter) | Numerical | No |
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//! | Texture (standard deviation of gray-scale values) | Numerical | No |
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//! | Perimeter | Numerical | No |
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//! | Area | Numerical | No |
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//! | Smoothness (local variation in radius lengths) | Numerical | No |
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//! | Compactness (perimeter^2 / area - 1.0) | Numerical | No |
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//! | Concavity (severity of concave portions of the contour) | Numerical | No |
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//! | Concave points (number of concave portions of the contour) | Numerical | No |
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//! | Symmetry | Numerical | No |
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//! | Fractal dimension ("coastline approximation" - 1) | Numerical | No |
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//! | Has cancer | Nominal | Yes |
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//!
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//! The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features.
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//! For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius.
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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) =
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match deserialize_data(std::include_bytes!("breast_cancer.xy")) {
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Err(why) => panic!("Can't deserialize breast_cancer.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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"mean radius", "mean texture", "mean perimeter", "mean area",
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"mean smoothness", "mean compactness", "mean concavity",
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"mean concave points", "mean symmetry", "mean fractal dimension",
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"radius error", "texture error", "perimeter error", "area error",
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"smoothness error", "compactness error", "concavity error",
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"concave points error", "symmetry error",
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"fractal dimension error", "worst radius", "worst texture",
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"worst perimeter", "worst area", "worst smoothness",
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"worst compactness", "worst concavity", "worst concave points",
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"worst symmetry", "worst fractal dimension",
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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!["malignant or benign [0, 1]".to_string()],
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description: "Breast Cancer Wisconsin (Diagnostic) Data Set: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29"
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.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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use super::*;
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// TODO: implement serialization
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// #[test]
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// #[ignore]
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// #[cfg(not(target_arch = "wasm32"))]
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// fn refresh_cancer_dataset() {
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// // run this test to generate breast_cancer.xy file.
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// let dataset = load_dataset();
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// assert!(serialize_data(&dataset, "breast_cancer.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 cancer_dataset() {
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let dataset = load_dataset();
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assert_eq!(
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dataset.data.len(),
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dataset.num_features * dataset.num_samples
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);
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assert_eq!(dataset.target.len(), dataset.num_samples);
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assert_eq!(dataset.num_features, 30);
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assert_eq!(dataset.num_samples, 569);
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}
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}
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