ndarray-glm 0.0.9

Performs regression for generalized linear models using IRLS on data stored in arrays.
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//! testing regularization

use anyhow::Result;
use approx::assert_abs_diff_eq;
use ndarray::{array, Array1, Array2};
use ndarray_glm::{standardize, Linear, Logistic, ModelBuilder};

#[test]
/// Test that the intercept is not affected by regularization when the dependent
/// data is centered. This is only strictly true for linear regression.
fn same_lin_intercept() -> Result<()> {
    let y_data: Array1<f64> = array![0.3, 0.5, 0.8, 0.2];
    let x_data: Array2<f64> = array![[1.5, 0.6], [2.1, 0.8], [1.2, 0.7], [1.6, 0.3]];
    // standardize the data
    let x_data = standardize(x_data);

    let lin_model = ModelBuilder::<Linear>::data(&y_data, &x_data).build()?;
    let lin_fit = lin_model.fit()?;
    let lin_model_reg = ModelBuilder::<Linear>::data(&y_data, &x_data).build()?;
    // use a pretty large regularization term to make sure the effect is pronounced
    let lin_fit_reg = lin_model_reg.fit_options().l2_reg(1.0).fit()?;
    dbg!(&lin_fit.result);
    dbg!(&lin_fit_reg.result);
    // Ensure that the intercept terms are equal
    assert_abs_diff_eq!(
        lin_fit.result[0],
        lin_fit_reg.result[0],
        epsilon = 2.0 * f64::EPSILON
    );

    Ok(())
}

#[test]
/// Test the smoothed lasso regression on underconstrained data
fn lasso_smooth_underconstrained() -> Result<()> {
    let y_data: Array1<bool> = array![true, false, true];
    let x_data: Array2<f64> = array![[0.1, 1.5, 8.0], [-0.1, 1.0, -12.0], [0.2, 0.5, 9.5]];
    let model = ModelBuilder::<Logistic>::data(&y_data, &x_data).build()?;
    // The smoothing parameter needs to be relatively large in order to work
    let fit = model.fit_options().l1_smooth_reg(1.0, 1e-2).fit()?;
    dbg!(fit.result);
    let like = fit.model_like;
    // make sure the likelihood isn't NaN
    assert!(like.is_normal());
    Ok(())
}