sklears_python/
model_selection.rs

1//! Python bindings for model selection utilities
2//!
3//! This module provides Python bindings for sklears model selection,
4//! offering scikit-learn compatible cross-validation and data splitting utilities.
5
6use numpy::{PyArray1, PyArray2, PyReadonlyArray1, PyReadonlyArray2, PyUntypedArrayMethods};
7use pyo3::prelude::*;
8use scirs2_core::ndarray::{Array1, Array2};
9
10/// Split arrays into random train and test subsets
11#[pyfunction]
12#[allow(clippy::too_many_arguments)]
13#[pyo3(signature = (x, _y=None, _test_size=None, _train_size=None, _random_state=None, _shuffle=true, _stratify=None))]
14pub fn train_test_split(
15    py: Python<'_>,
16    x: PyReadonlyArray2<f64>,
17    _y: Option<PyReadonlyArray1<f64>>,
18    _test_size: Option<f64>,
19    _train_size: Option<f64>,
20    _random_state: Option<u64>,
21    _shuffle: bool,
22    _stratify: Option<PyReadonlyArray1<f64>>,
23) -> PyResult<(
24    Py<PyArray2<f64>>,
25    Py<PyArray2<f64>>,
26    Py<PyArray1<f64>>,
27    Py<PyArray1<f64>>,
28)> {
29    // Stub implementation - uses x to suppress unused warning
30    let _n_samples = x.shape()[0];
31
32    let x_train = Array2::<f64>::zeros((1, 1));
33    let x_test = Array2::<f64>::zeros((1, 1));
34    let y_train = Array1::<f64>::zeros(1);
35    let y_test = Array1::<f64>::zeros(1);
36
37    Ok((
38        PyArray2::from_array(py, &x_train).unbind(),
39        PyArray2::from_array(py, &x_test).unbind(),
40        PyArray1::from_array(py, &y_train).unbind(),
41        PyArray1::from_array(py, &y_test).unbind(),
42    ))
43}
44
45/// Stub KFold cross-validator implementation
46#[pyclass(name = "KFold")]
47pub struct PyKFold {
48    n_splits: usize,
49}
50
51#[pymethods]
52impl PyKFold {
53    #[new]
54    fn new(n_splits: usize) -> Self {
55        Self { n_splits }
56    }
57
58    fn get_n_splits(&self) -> usize {
59        self.n_splits
60    }
61
62    fn split(&self, _x: PyReadonlyArray2<f64>) -> PyResult<Vec<(Vec<usize>, Vec<usize>)>> {
63        // Stub implementation
64        Ok(vec![(vec![0], vec![1])])
65    }
66}
sklears_python/model_selection.rs

sklears_python/
model_selection.rs
