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use crate::error::EvaluatorError;
use crate::evaluator::*;
use crate::float_trait::Float;
use crate::time_series::TimeSeries;
#[derive(Clone, Debug)]
pub struct FeatureExtractor<T: Float> {
info: EvaluatorInfo,
features: VecFE<T>,
}
impl<T> FeatureExtractor<T>
where
T: Float,
{
pub fn new(features: VecFE<T>) -> Self {
let info = EvaluatorInfo {
size: features.iter().map(|x| x.size_hint()).sum(),
min_ts_length: features
.iter()
.map(|x| x.min_ts_length())
.max()
.unwrap_or(0),
t_required: features.iter().any(|x| x.is_t_required()),
m_required: features.iter().any(|x| x.is_m_required()),
w_required: features.iter().any(|x| x.is_w_required()),
sorting_required: features.iter().any(|x| x.is_sorting_required()),
};
Self { info, features }
}
pub fn clone_features(&self) -> VecFE<T> {
self.features.clone()
}
pub fn add_feature(&mut self, feature: Box<dyn FeatureEvaluator<T>>) {
self.features.push(feature);
}
}
impl<T> FeatureEvaluator<T> for FeatureExtractor<T>
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
let mut vec = Vec::with_capacity(self.size_hint());
for x in self.features.iter() {
vec.extend(x.eval(ts)?);
}
Ok(vec)
}
fn eval_or_fill(&self, ts: &mut TimeSeries<T>, fill_value: T) -> Vec<T> {
self.features
.iter()
.flat_map(|x| x.eval_or_fill(ts, fill_value))
.collect()
}
fn get_info(&self) -> &EvaluatorInfo {
&self.info
}
fn get_names(&self) -> Vec<&str> {
self.features.iter().flat_map(|x| x.get_names()).collect()
}
fn get_descriptions(&self) -> Vec<&str> {
self.features
.iter()
.flat_map(|x| x.get_descriptions())
.collect()
}
}