sklears_feature_selection/genetic_optimization/
predictiveperformanceobjective_traits.rs

1//! # PredictivePerformanceObjective - Trait Implementations
2//!
3//! This module contains trait implementations for `PredictivePerformanceObjective`.
4//!
5//! ## Implemented Traits
6//!
7//! - `ObjectiveFunction`
8//!
9//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
10
11use super::functions::*;
12use super::types::*;
13use scirs2_core::ndarray::{Array1, Array2};
14use sklears_core::error::{Result as SklResult, SklearsError};
15
16impl ObjectiveFunction for PredictivePerformanceObjective {
17    fn evaluate(
18        &self,
19        x: &Array2<f64>,
20        y_classif: Option<&Array1<i32>>,
21        y_regression: Option<&Array1<f64>>,
22        feature_mask: &[bool],
23    ) -> SklResult<f64> {
24        let selected_indices: Vec<usize> = feature_mask
25            .iter()
26            .enumerate()
27            .filter_map(|(i, &selected)| if selected { Some(i) } else { None })
28            .collect();
29        if selected_indices.is_empty() {
30            return Ok(0.0);
31        }
32        let mut total_performance = 0.0;
33        if let Some(y) = y_classif {
34            for &idx in &selected_indices {
35                let feature = x.column(idx).to_owned();
36                let y_float = y.mapv(|v| v as f64);
37                let correlation = compute_pearson_correlation(&feature, &y_float).abs();
38                total_performance += correlation;
39            }
40        } else if let Some(y) = y_regression {
41            for &idx in &selected_indices {
42                let feature = x.column(idx).to_owned();
43                let correlation = compute_pearson_correlation(&feature, y).abs();
44                total_performance += correlation;
45            }
46        } else {
47            return Err(SklearsError::InvalidInput(
48                "Need either classification or regression target".to_string(),
49            ));
50        }
51        Ok(total_performance / selected_indices.len() as f64)
52    }
53    fn name(&self) -> &str {
54        "predictive_performance"
55    }
56    fn clone_box(&self) -> Box<dyn ObjectiveFunction> {
57        Box::new(self.clone())
58    }
59}

sklears_feature_selection/genetic_optimization/predictiveperformanceobjective_traits.rs

sklears_feature_selection/genetic_optimization/
predictiveperformanceobjective_traits.rs