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ENH: Add datasets #161

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AtheMathmo merged 5 commits into from
Dec 27, 2016
Merged

ENH: Add datasets #161

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c262ac4
ENH: Add datasets
sinhrks Dec 16, 2016
dcbbd89
add feature gates
sinhrks Dec 18, 2016
367316f
Fix travis tests
sinhrks Dec 19, 2016
7570fe4
Fix lib structure / added comment
sinhrks Dec 27, 2016
ad0ecb0
do not add defaults and tests features
sinhrks Dec 27, 2016
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2 changes: 2 additions & 0 deletions .travis.yml
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Expand Up @@ -11,3 +11,5 @@ script:
- cargo test --verbose
- cargo build --features stats
- cargo test --features stats
- cargo build --features datasets
- cargo test --features datasets
1 change: 1 addition & 0 deletions Cargo.toml
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Expand Up @@ -12,6 +12,7 @@ license = "MIT"

[features]
stats = []
datasets = []

[dependencies]
num = { version = "0.1.35", default-features = false }
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191 changes: 191 additions & 0 deletions src/datasets/iris.rs
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@@ -0,0 +1,191 @@
use rulinalg::matrix::Matrix;
use rulinalg::vector::Vector;

use super::Dataset;

/// Load iris dataset.
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@AtheMathmo AtheMathmo Dec 27, 2016

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This description is great!

///
/// The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
///
/// ## Attribute Information
///
/// ### Data
///
/// ``Matrix<f64>`` contains following columns.
///
/// - sepal length in cm
/// - sepal width in cm
/// - petal length in cm
/// - petal width in cm
///
/// ### Target
///
/// ``Vector<usize>`` contains numbers corresponding to iris species:
///
/// - ``0``: Iris Setosa
/// - ``1``: Iris Versicolour
/// - ``2``: Iris Virginica
///
/// Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
/// Irvine, CA: University of California, School of Information and Computer Science.
pub fn load() -> Dataset<Matrix<f64>, Vector<usize>> {
let data: Matrix<f64> = matrix![5.1, 3.5, 1.4, 0.2;
4.9, 3.0, 1.4, 0.2;
4.7, 3.2, 1.3, 0.2;
4.6, 3.1, 1.5, 0.2;
5.0, 3.6, 1.4, 0.2;
5.4, 3.9, 1.7, 0.4;
4.6, 3.4, 1.4, 0.3;
5.0, 3.4, 1.5, 0.2;
4.4, 2.9, 1.4, 0.2;
4.9, 3.1, 1.5, 0.1;
5.4, 3.7, 1.5, 0.2;
4.8, 3.4, 1.6, 0.2;
4.8, 3.0, 1.4, 0.1;
4.3, 3.0, 1.1, 0.1;
5.8, 4.0, 1.2, 0.2;
5.7, 4.4, 1.5, 0.4;
5.4, 3.9, 1.3, 0.4;
5.1, 3.5, 1.4, 0.3;
5.7, 3.8, 1.7, 0.3;
5.1, 3.8, 1.5, 0.3;
5.4, 3.4, 1.7, 0.2;
5.1, 3.7, 1.5, 0.4;
4.6, 3.6, 1.0, 0.2;
5.1, 3.3, 1.7, 0.5;
4.8, 3.4, 1.9, 0.2;
5.0, 3.0, 1.6, 0.2;
5.0, 3.4, 1.6, 0.4;
5.2, 3.5, 1.5, 0.2;
5.2, 3.4, 1.4, 0.2;
4.7, 3.2, 1.6, 0.2;
4.8, 3.1, 1.6, 0.2;
5.4, 3.4, 1.5, 0.4;
5.2, 4.1, 1.5, 0.1;
5.5, 4.2, 1.4, 0.2;
4.9, 3.1, 1.5, 0.1;
5.0, 3.2, 1.2, 0.2;
5.5, 3.5, 1.3, 0.2;
4.9, 3.1, 1.5, 0.1;
4.4, 3.0, 1.3, 0.2;
5.1, 3.4, 1.5, 0.2;
5.0, 3.5, 1.3, 0.3;
4.5, 2.3, 1.3, 0.3;
4.4, 3.2, 1.3, 0.2;
5.0, 3.5, 1.6, 0.6;
5.1, 3.8, 1.9, 0.4;
4.8, 3.0, 1.4, 0.3;
5.1, 3.8, 1.6, 0.2;
4.6, 3.2, 1.4, 0.2;
5.3, 3.7, 1.5, 0.2;
5.0, 3.3, 1.4, 0.2;
7.0, 3.2, 4.7, 1.4;
6.4, 3.2, 4.5, 1.5;
6.9, 3.1, 4.9, 1.5;
5.5, 2.3, 4.0, 1.3;
6.5, 2.8, 4.6, 1.5;
5.7, 2.8, 4.5, 1.3;
6.3, 3.3, 4.7, 1.6;
4.9, 2.4, 3.3, 1.0;
6.6, 2.9, 4.6, 1.3;
5.2, 2.7, 3.9, 1.4;
5.0, 2.0, 3.5, 1.0;
5.9, 3.0, 4.2, 1.5;
6.0, 2.2, 4.0, 1.0;
6.1, 2.9, 4.7, 1.4;
5.6, 2.9, 3.6, 1.3;
6.7, 3.1, 4.4, 1.4;
5.6, 3.0, 4.5, 1.5;
5.8, 2.7, 4.1, 1.0;
6.2, 2.2, 4.5, 1.5;
5.6, 2.5, 3.9, 1.1;
5.9, 3.2, 4.8, 1.8;
6.1, 2.8, 4.0, 1.3;
6.3, 2.5, 4.9, 1.5;
6.1, 2.8, 4.7, 1.2;
6.4, 2.9, 4.3, 1.3;
6.6, 3.0, 4.4, 1.4;
6.8, 2.8, 4.8, 1.4;
6.7, 3.0, 5.0, 1.7;
6.0, 2.9, 4.5, 1.5;
5.7, 2.6, 3.5, 1.0;
5.5, 2.4, 3.8, 1.1;
5.5, 2.4, 3.7, 1.0;
5.8, 2.7, 3.9, 1.2;
6.0, 2.7, 5.1, 1.6;
5.4, 3.0, 4.5, 1.5;
6.0, 3.4, 4.5, 1.6;
6.7, 3.1, 4.7, 1.5;
6.3, 2.3, 4.4, 1.3;
5.6, 3.0, 4.1, 1.3;
5.5, 2.5, 4.0, 1.3;
5.5, 2.6, 4.4, 1.2;
6.1, 3.0, 4.6, 1.4;
5.8, 2.6, 4.0, 1.2;
5.0, 2.3, 3.3, 1.0;
5.6, 2.7, 4.2, 1.3;
5.7, 3.0, 4.2, 1.2;
5.7, 2.9, 4.2, 1.3;
6.2, 2.9, 4.3, 1.3;
5.1, 2.5, 3.0, 1.1;
5.7, 2.8, 4.1, 1.3;
6.3, 3.3, 6.0, 2.5;
5.8, 2.7, 5.1, 1.9;
7.1, 3.0, 5.9, 2.1;
6.3, 2.9, 5.6, 1.8;
6.5, 3.0, 5.8, 2.2;
7.6, 3.0, 6.6, 2.1;
4.9, 2.5, 4.5, 1.7;
7.3, 2.9, 6.3, 1.8;
6.7, 2.5, 5.8, 1.8;
7.2, 3.6, 6.1, 2.5;
6.5, 3.2, 5.1, 2.0;
6.4, 2.7, 5.3, 1.9;
6.8, 3.0, 5.5, 2.1;
5.7, 2.5, 5.0, 2.0;
5.8, 2.8, 5.1, 2.4;
6.4, 3.2, 5.3, 2.3;
6.5, 3.0, 5.5, 1.8;
7.7, 3.8, 6.7, 2.2;
7.7, 2.6, 6.9, 2.3;
6.0, 2.2, 5.0, 1.5;
6.9, 3.2, 5.7, 2.3;
5.6, 2.8, 4.9, 2.0;
7.7, 2.8, 6.7, 2.0;
6.3, 2.7, 4.9, 1.8;
6.7, 3.3, 5.7, 2.1;
7.2, 3.2, 6.0, 1.8;
6.2, 2.8, 4.8, 1.8;
6.1, 3.0, 4.9, 1.8;
6.4, 2.8, 5.6, 2.1;
7.2, 3.0, 5.8, 1.6;
7.4, 2.8, 6.1, 1.9;
7.9, 3.8, 6.4, 2.0;
6.4, 2.8, 5.6, 2.2;
6.3, 2.8, 5.1, 1.5;
6.1, 2.6, 5.6, 1.4;
7.7, 3.0, 6.1, 2.3;
6.3, 3.4, 5.6, 2.4;
6.4, 3.1, 5.5, 1.8;
6.0, 3.0, 4.8, 1.8;
6.9, 3.1, 5.4, 2.1;
6.7, 3.1, 5.6, 2.4;
6.9, 3.1, 5.1, 2.3;
5.8, 2.7, 5.1, 1.9;
6.8, 3.2, 5.9, 2.3;
6.7, 3.3, 5.7, 2.5;
6.7, 3.0, 5.2, 2.3;
6.3, 2.5, 5.0, 1.9;
6.5, 3.0, 5.2, 2.0;
6.2, 3.4, 5.4, 2.3;
5.9, 3.0, 5.1, 1.8];
let target: Vec<usize> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2];

Dataset{ data: data,
target: Vector::new(target) }
}
25 changes: 25 additions & 0 deletions src/datasets/mod.rs
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use std::fmt::Debug;

/// Module for iris dataset.
pub mod iris;

/// Dataset container
#[derive(Clone, Debug)]
pub struct Dataset<D, T> where D: Clone + Debug, T: Clone + Debug {
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@AtheMathmo AtheMathmo Dec 17, 2016

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I think this makes sense for now. We might want to be more strict in future if we want to be generic over DataSets. However, this is something that I don't think we will ever want to do.


data: D,
target: T
}

impl<D, T> Dataset<D, T> where D: Clone + Debug, T: Clone + Debug {

/// Returns explanatory variable (features)
pub fn data(&self) -> &D {
&self.data
}

/// Returns objective variable (target)
pub fn target(&self) -> &T {
&self.target
}
}
4 changes: 4 additions & 0 deletions src/lib.rs
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Expand Up @@ -221,3 +221,7 @@ pub mod analysis {
pub mod cross_validation;
pub mod score;
}

#[cfg(feature = "datasets")]
/// Module for datasets.
pub mod datasets;
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@AtheMathmo AtheMathmo Dec 17, 2016

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We should feature gate this. My thinking is that if we have a few datasets users will not want to download all of this data by default.

To do this:

18 changes: 18 additions & 0 deletions tests/datasets.rs
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extern crate rusty_machine as rm;


#[cfg(datasets)]
mod test {

use rm::datasets::iris;
use rm::linalg::BaseMatrix;

#[test]
fn test_iris() {
let dt = iris::load_();
assert_eq!(dt.data().rows(), 150);
assert_eq!(dt.data().cols(), 4);

assert_eq!(dt.target().size(), 150);
}
}
5 changes: 4 additions & 1 deletion tests/lib.rs
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Expand Up @@ -10,4 +10,7 @@ pub mod learning {
pub mod optim {
mod grad_desc;
}
}
}

#[cfg(datasets)]
pub mod datasets;