imbalanced_ensemble/
balanced_random_forest.rs

1// imbalanced-ensemble/src/balanced_random_forest.rs
2use imbalanced_core::traits::*;
3use ndarray::{Array1, Array2, ArrayView1, ArrayView2};
4use std::sync::Arc;
5
6/// Balanced Random Forest for imbalanced classification
7pub struct BalancedRandomForest {
8    /// Number of trees in the forest
9    _n_estimators: usize,
10    /// Maximum depth of trees
11    max_depth: Option<usize>,
12    /// Resampling strategy for each bootstrap
13    _resampler: Arc<dyn ResamplingStrategy<Input = (), Output = (Array2<f64>, Array1<i32>), Config = ()>>,
14}
15
16impl BalancedRandomForest {
17    /// Create a new balanced random forest
18    pub fn new(n_estimators: usize) -> Self {
19        Self {
20            _n_estimators: n_estimators,
21            max_depth: None,
22            _resampler: Arc::new(DummyResampler),
23        }
24    }
25    
26    /// Set maximum depth of trees
27    pub fn with_max_depth(mut self, max_depth: usize) -> Self {
28        self.max_depth = Some(max_depth);
29        self
30    }
31}
32
33/// Dummy resampler for compilation
34struct DummyResampler;
35
36impl ResamplingStrategy for DummyResampler {
37    type Input = ();
38    type Output = (Array2<f64>, Array1<i32>);
39    type Config = ();
40    
41    fn resample(
42        &self,
43        x: ArrayView2<f64>,
44        y: ArrayView1<i32>,
45        _config: &Self::Config,
46    ) -> Result<(Array2<f64>, Array1<i32>), ResamplingError> {
47        // Just return original data for now
48        Ok((x.to_owned(), y.to_owned()))
49    }
50}
imbalanced_ensemble/balanced_random_forest.rs

imbalanced_ensemble/
balanced_random_forest.rs
