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Merged
Mec-iS merged 3 commits into from
Oct 5, 2026
Merged

Match sklearn sample weights #478

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6490ee0
Fix: behavior of sample weights.
slievens Oct 4, 2026
fa615f2
Merge branch 'smartcorelib:main' into match-sklearn-sample-weights
slievens Oct 5, 2026
f3e7271
Fix failing tests due to change in sample weights
slievens Oct 5, 2026
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21 changes: 15 additions & 6 deletions src/ensemble/base_forest_regressor.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -141,10 +141,16 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
seed: Some(parameters.seed.wrapping_add(tree_idx as u64)), // give each tree its own fixed seed
splitter: parameters.splitter.clone(),
};
// Only use sample weights on base tree if not already applied during bootstrapping
let sample_weights_for_base_tree = if parameters.bootstrap {
None
} else {
sample_weights
};
let tree = BaseTreeRegressor::fit_weak_learner(
x,
y,
sample_weights,
sample_weights_for_base_tree,
samples.clone(),
mtry,
params,
Expand Down Expand Up @@ -476,12 +482,12 @@ mod tests {
let sample_weights: Vec<f64> = (0..20).map(|i| if i < 10 { 1.0 } else { 9.0 }).collect();

let parameters = BaseForestRegressorParameters {
max_depth: Some(1),
max_depth: Some(0),
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 10,
m: None,
keep_samples: true, // keep samples used for each tree, so we can check that they are different
keep_samples: true,
seed: 42,
bootstrap: false, // No bootstrapping
splitter: crate::tree::base_tree_regressor::Splitter::Best,
Expand Down Expand Up @@ -512,7 +518,7 @@ mod tests {

// Use bootstrapping
let parameters = BaseForestRegressorParameters {
max_depth: Some(1),
max_depth: Some(0), // Match sibling test on RandomForestRegressor
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 500, // Use more trees than before to smooth out randomness
Expand All @@ -527,9 +533,12 @@ mod tests {
.expect("Fit should work");
let y_hat = forest.predict(&x).expect("Predict should work");

// weighted mean is (10.0 * 0 + 90*10) / 100 = 9
// with bootstrapping the weight should be close to 9, but not extremely close
for p in y_hat.iter() {
assert!(p > &9.0f64, "expected value well above 9, got {p}");
assert!(
(p - 9.0).abs() < 0.1,
"expected value reasonably close to 9, got {p}"
);
}

// Without weights, the predicted value should be reasonably close to 5
Expand Down
210 changes: 210 additions & 0 deletions src/ensemble/extra_trees_regressor.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -469,4 +469,214 @@ mod tests {
let msg = "'fit' should be called before calling 'predict'";
assert_eq!(yhat.err(), Some(Failed::predict(msg)));
}

mod sklearn_parity {
use super::*;
// sklearn parity tests.
//
// smartcore and numpy use different RNGs.
// However, with many trees, both forests converge to the same bagged
// predictor. Thus we compare the predictions within a tolerance.
//
// Reference: sklearn 1.9.1, numpy 2.4.6. Each reference value is the mean of 10 sklearn
// runs (random_state = 0..10). The tolerance is approximately 4 x the largest standard
// deviation of one sklearn run, for each row.
//
// ```python
// rng = np.random.default_rng(0)
// x = np.round(rng.uniform(-1, 1, (40, 4)), 4)
// y = np.round(x[:, 0] * x[:, 1] + np.sin(3 * x[:, 2]) + 0.1 * rng.normal(size=40), 4)
// x_probe = np.round(rng.uniform(-1, 1, (10, 4)), 4)
// for max_features in [1.0, 2]:
// train, probe = [], []
// for seed in range(N_SEEDS):
// rf = ExtraTreesRegressor(
// n_estimators=N_TREES,
// max_features=max_features,
// max_depth=None,
// min_samples_leaf=5, # so that the predictions are not equal to the training values
// min_samples_split=2,
// bootstrap=False,
// random_state=seed,
// n_jobs=-1,
// ).fit(x, y)
// train.append(rf.predict(x))
// probe.append(rf.predict(x_probe))
// print(f"\nExtra Trees. === max_features={max_features}")
// for name, runs in [("train", train), ("probe", probe)]:
// runs = np.array(runs)
// mean = runs.mean(axis=0)
// max_std = runs.std(axis=0, ddof=1).max()
// max_dev = np.abs(runs - mean).max()
// print(f"{name}: max_std={max_std:.5f} max_dev={max_dev:.5f}")
// print(f"{name}_ref:", rust_vec(mean))
// ```
//

fn sklearn_parity_train_data() -> (DenseMatrix<f64>, Vec<f64>) {
let x = DenseMatrix::from_2d_array(&[
&[0.2739, -0.4604, -0.9181, -0.9669],
&[0.6265, 0.8255, 0.2133, 0.459],
&[0.0872, 0.8701, 0.6317, -0.9945],
&[0.7148, -0.9328, 0.4593, -0.6487],
&[0.7264, 0.0829, -0.4006, -0.1546],
&[-0.9434, -0.7514, 0.3412, 0.2944],
&[0.2308, -0.2326, 0.9944, 0.9617],
&[0.3711, 0.3009, 0.3769, -0.2222],
&[-0.7298, 0.443, 0.0507, -0.3795],
&[-0.0283, 0.779, 0.8681, -0.2844],
&[0.1431, -0.3563, 0.1886, -0.3242],
&[-0.2168, 0.7805, -0.5457, 0.2464],
&[-0.832, 0.6653, 0.5742, -0.5213],
&[0.753, -0.8829, -0.3278, -0.6994],
&[-0.0993, 0.5926, -0.5387, -0.896],
&[-0.1909, -0.603, -0.8185, 0.1607],
&[-0.4026, 0.344, -0.601, 0.8842],
&[-0.2698, -0.789, 0.2582, 0.8543],
&[-0.1192, 0.9092, -0.0002, -0.1495],
&[0.2404, 0.9902, 0.8979, -0.0799],
&[0.5155, -0.0052, 0.0586, 0.5716],
&[-0.1707, 0.469, 0.4223, 0.8641],
&[-0.7701, 0.458, 0.8548, 0.9359],
&[-0.9706, 0.7273, 0.9624, 0.9144],
&[-0.7025, 0.9453, 0.7799, 0.6447],
&[-0.04, -0.5353, 0.6038, 0.8471],
&[-0.4677, 0.0779, -0.1145, 0.862],
&[-0.919, 0.464, 0.2287, -0.9433],
&[0.4384, -0.968, 0.5159, 0.0255],
&[0.8582, -0.8678, 0.6826, -0.8666],
&[-0.3114, -0.1394, 0.9321, 0.1245],
&[-0.4823, -0.5166, 0.7762, -0.5483],
&[-0.7509, -0.4233, 0.1722, 0.1082],
&[0.6194, 0.121, -0.4232, -0.1742],
&[0.6362, 0.253, 0.9182, -0.2612],
&[0.1052, 0.1878, 0.6966, -0.7091],
&[-0.187, 0.8199, -0.9139, 0.6454],
&[-0.1692, 0.6596, -0.9801, -0.2699],
&[-0.8427, 0.3052, -0.4523, 0.4053],
&[0.8876, -0.7464, 0.7296, -0.8811],
])
.unwrap();
let y = vec![
-0.5144, 0.9957, 0.7839, 0.3660, -0.9022, 1.5099, 0.0804, 1.1980, -0.1768, 0.4984,
0.3364, -1.0023, 0.5267, -1.3905, -1.0531, -0.4267, -1.0746, 0.9736, -0.1242,
0.5237, 0.2751, 0.6806, 0.1690, -0.4747, -0.0497, 1.0539, -0.3933, 0.1634, 0.6273,
0.0960, 0.5208, 1.0106, 0.7643, -1.0745, 0.4076, 0.9968, -0.5477, -0.3399, -1.0701,
0.0243,
];
(x, y)
}

fn sklearn_parity_probe_data() -> DenseMatrix<f64> {
DenseMatrix::from_2d_array(&[
&[-0.3606, -0.625, 0.3451, -0.6098],
&[0.1554, 0.2045, 0.9248, -0.8555],
&[-0.0001, 0.4882, -0.6455, -0.2239],
&[-0.8742, 0.4518, -0.8245, -0.2098],
&[0.747, -0.0554, 0.8252, 0.5318],
&[0.8306, -0.7452, -0.8529, -0.8593],
&[0.7377, 0.2681, -0.0069, -0.6729],
&[0.3475, -0.364, 0.4218, -0.0793],
&[0.0149, 0.5793, -0.8145, 0.1575],
&[-0.6055, 0.6163, -0.0223, 0.9774],
])
.unwrap()
}

fn sklearn_sample_weights() -> Vec<f64> {
(0..40).into_iter().map(|i| ((i % 4) + 1) as f64).collect()
}

fn assert_close_to_sklearn(actual: &[f64], expected: &[f64], tol: f64, label: &str) {
assert_eq!(actual.len(), expected.len(), "{label}: length");
for (i, (a, e)) in actual.iter().zip(expected.iter()).enumerate() {
assert!(
(a - e).abs() <= tol,
"{label}, row {i}: smartcore {a}, sklearn {e}, tol {tol}"
);
}
}

/// Fits the extra trees regressor with 2000 trees and compares the predictions with sklearn.
fn check_sklearn_parity(
m: usize,
train_ref: &[f64],
probe_ref: &[f64],
tol: f64,
sample_weights: Option<&[f64]>,
) {
let (x, y) = sklearn_parity_train_data();
let x_probe = sklearn_parity_probe_data();

// Use `min_samples_leaf`= 5 to prevent the tree from predicting all training examples correct
let parameters = ExtraTreesRegressorParameters::default()
.with_n_trees(2000)
.with_m(m)
.with_min_samples_leaf(5)
.with_min_samples_split(2)
.with_keep_samples(true)
.with_seed(42);
let forest = match sample_weights {
None => ExtraTreesRegressor::fit(&x, &y, parameters).unwrap(),
Some(sample_weights) => {
ExtraTreesRegressor::fit_with_weights(&x, &y, sample_weights, parameters)
.unwrap()
}
};

let y_hat: Vec<f64> = forest.predict(&x).unwrap();
assert_close_to_sklearn(&y_hat, train_ref, tol, "train");

let y_hat_probe: Vec<f64> = forest.predict(&x_probe).unwrap();
assert_close_to_sklearn(&y_hat_probe, probe_ref, tol, "probe");
}

#[test]
fn sklearn_parity_all_features() {
/*
* train: max_std=0.01081 max_dev=0.02053
* probe: max_std=0.01127 max_dev=0.02510
*/
let train_ref = [
-0.514400, 0.350098, 0.500618, 0.370298, -0.582169, 0.630296, 0.362747, 0.528070,
0.027928, 0.409103, 0.376290, -0.672295, 0.408925, -0.406530, -0.656713, -0.433254,
-0.656863, 0.572460, -0.005012, 0.398251, 0.134855, 0.457975, 0.232323, 0.203198,
0.255614, 0.580654, -0.171093, 0.266703, 0.538009, 0.339515, 0.425172, 0.512873,
0.452168, -0.597154, 0.382057, 0.522851, -0.636264, -0.652577, -0.614220, 0.327194,
];
let probe_ref = [
0.538464, 0.434785, -0.641271, -0.638714, 0.324906, -0.499588, -0.042825, 0.545153,
-0.637955, -0.073064,
];
check_sklearn_parity(4, &train_ref, &probe_ref, 0.045, None);
}

#[test]
fn sklearn_parity_with_weights() {
/*
* train: max_std=0.01332 max_dev=0.02559
* probe: max_std=0.01166 max_dev=0.01867
*/
let train_ref = [
-0.494622, 0.335181, 0.532771, 0.398018, -0.535612, 0.545873, 0.337625, 0.588692,
0.030596, 0.431604, 0.376213, -0.686286, 0.400965, -0.400551, -0.658719, -0.481374,
-0.649386, 0.477041, -0.017966, 0.424634, 0.126455, 0.419039, 0.196019, 0.148182,
0.238049, 0.512727, -0.195074, 0.255755, 0.513608, 0.384085, 0.438159, 0.544918,
0.374897, -0.552449, 0.433162, 0.583000, -0.636756, -0.643531, -0.627507, 0.372639,
];

let probe_ref = [
0.530162, 0.489443, -0.611812, -0.626195, 0.352540, -0.502707, 0.001104, 0.551536,
-0.621779, -0.120032,
];

check_sklearn_parity(
4,
&train_ref,
&probe_ref,
0.05,
Some(&sklearn_sample_weights()),
);
}
}
}
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