//! Python bindings for model selection utilities
//!
//! This module provides Python bindings for sklears model selection,
//! offering scikit-learn compatible cross-validation and data splitting utilities.

use numpy::{PyArray1, PyArray2, PyReadonlyArray1, PyReadonlyArray2, PyUntypedArrayMethods};
use pyo3::prelude::*;
use scirs2_core::ndarray::{Array1, Array2};

/// Split arrays into random train and test subsets
#[pyfunction]
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (x, _y=None, _test_size=None, _train_size=None, _random_state=None, _shuffle=true, _stratify=None))]
pub fn train_test_split(
    py: Python<'_>,
    x: PyReadonlyArray2<f64>,
    _y: Option<PyReadonlyArray1<f64>>,
    _test_size: Option<f64>,
    _train_size: Option<f64>,
    _random_state: Option<u64>,
    _shuffle: bool,
    _stratify: Option<PyReadonlyArray1<f64>>,
) -> PyResult<(
    Py<PyArray2<f64>>,
    Py<PyArray2<f64>>,
    Py<PyArray1<f64>>,
    Py<PyArray1<f64>>,
)> {
    // Stub implementation - uses x to suppress unused warning
    let _n_samples = x.shape()[0];

    let x_train = Array2::<f64>::zeros((1, 1));
    let x_test = Array2::<f64>::zeros((1, 1));
    let y_train = Array1::<f64>::zeros(1);
    let y_test = Array1::<f64>::zeros(1);

    Ok((
        PyArray2::from_array(py, &x_train).unbind(),
        PyArray2::from_array(py, &x_test).unbind(),
        PyArray1::from_array(py, &y_train).unbind(),
        PyArray1::from_array(py, &y_test).unbind(),
    ))
}

/// Stub KFold cross-validator implementation
#[pyclass(name = "KFold")]
pub struct PyKFold {
    n_splits: usize,
}

#[pymethods]
impl PyKFold {
    #[new]
    fn new(n_splits: usize) -> Self {
        Self { n_splits }
    }

    fn get_n_splits(&self) -> usize {
        self.n_splits
    }

    fn split(&self, _x: PyReadonlyArray2<f64>) -> PyResult<Vec<(Vec<usize>, Vec<usize>)>> {
        // Stub implementation
        Ok(vec![(vec![0], vec![1])])
    }
}
