use crate::evaluator::*;
macro_const! {
const DOC: &str = r#"
Measure of the variability amplitude
$$
\frac{\sigma_m^2 - \langle \delta^2 \rangle}{\langle m \rangle^2},
$$
where $\langle \delta^2 \rangle$ is the mean of squared error, $\sigma_m$ is the magnitude
standard deviation. Note that this definition differs from
[Sánchez et al. 2017](https://doi.org/10.3847/1538-4357/aa9188)
- Depends on: **magnitude**, **error**
- Minimum number of observations: **2**
- Number of features: **1**
Sánchez et al. 2017 [DOI:10.3847/1538-4357/aa9188](https://doi.org/10.3847/1538-4357/aa9188)
"#;
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct ExcessVariance {}
lazy_info!(
EXCESS_VARIANCE_INFO,
ExcessVariance,
size: 1,
min_ts_length: 2,
t_required: false,
m_required: true,
w_required: true,
sorting_required: false,
);
impl ExcessVariance {
pub fn new() -> Self {
Self {}
}
pub fn doc() -> &'static str {
DOC
}
}
impl FeatureNamesDescriptionsTrait for ExcessVariance {
fn get_names(&self) -> Vec<&str> {
vec!["excess_variance"]
}
fn get_descriptions(&self) -> Vec<&str> {
vec!["variability amplitude (excess of magnitude variability over typical error)"]
}
}
impl<T> FeatureEvaluator<T> for ExcessVariance
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
let mean_error2 = ts.w.sample.fold(T::zero(), |sum, w| sum + w.recip()) / ts.lenf();
Ok(vec![
(ts.m.get_std2() - mean_error2) / ts.m.get_mean().powi(2),
])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::tests::*;
check_feature!(ExcessVariance);
feature_test!(
mean,
[ExcessVariance::new()],
[0.41846885813148793],
[0.0; 9],
[1.0_f32, 1.0, 1.0, 1.0, 5.0, 6.0, 6.0, 6.0, 7.0],
[1.0, 0.5, 1.0, 2.0, 0.5, 2.0, 1.0, 1.0, 0.5],
);
}