#![allow(clippy::approx_constant)]
use approx::assert_abs_diff_eq;
use rtx_preprocessing::{PolynomialFeatures, PreprocessingError, Transformer};
use rtx_tensor::{Device, Tensor};

#[test]
fn test_polynomial_features_creation() {
    let transformer = PolynomialFeatures::new();
    assert!(!transformer.is_fitted());
    assert_eq!(transformer.degree(), 2);
    assert!(transformer.include_bias());
    assert!(transformer.interaction_only());

    let transformer = PolynomialFeatures::with_params(3, false, false);
    assert_eq!(transformer.degree(), 3);
    assert!(!transformer.include_bias());
    assert!(!transformer.interaction_only());
}

#[test]
fn test_polynomial_features_invalid_degree() {
    let result = std::panic::catch_unwind(|| PolynomialFeatures::with_params(0, true, true));
    assert!(result.is_err());
}

#[test]
fn test_polynomial_features_fit_simple() {
    let data = Tensor::from_slice(&[1.0, 2.0, 3.0, 4.0], &[2, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::new();

    let result = transformer.fit(&data);
    assert!(result.is_ok());
    assert!(transformer.is_fitted());
}

#[test]
fn test_polynomial_features_transform_degree2() {
    let data = Tensor::from_slice(&[2.0, 3.0], &[1, 2], &Device::cpu()).unwrap(); // [x1=2, x2=3]
    let mut transformer = PolynomialFeatures::with_params(2, true, false);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    let values = poly_features
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // Expected features: [1, x1, x2, x1^2, x1*x2, x2^2] = [1, 2, 3, 4, 6, 9]
    let expected = vec![1.0, 2.0, 3.0, 4.0, 6.0, 9.0];
    assert_eq!(values.len(), expected.len());

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_polynomial_features_transform_no_bias() {
    let data = Tensor::from_slice(&[2.0, 3.0], &[1, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(2, false, false);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    let values = poly_features
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // Expected features: [x1, x2, x1^2, x1*x2, x2^2] = [2, 3, 4, 6, 9]
    let expected = vec![2.0, 3.0, 4.0, 6.0, 9.0];
    assert_eq!(values.len(), expected.len());

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_polynomial_features_interaction_only() {
    let data = Tensor::from_slice(&[2.0, 3.0], &[1, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(2, true, true);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    let values = poly_features
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // Expected features: [1, x1, x2, x1*x2] = [1, 2, 3, 6] (no x1^2, x2^2)
    let expected = vec![1.0, 2.0, 3.0, 6.0];
    assert_eq!(values.len(), expected.len());

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_polynomial_features_degree3() {
    let data = Tensor::from_slice(&[2.0, 3.0], &[1, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(3, false, false);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    let values = poly_features
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // Should include terms up to degree 3: x1, x2, x1^2, x1*x2, x2^2, x1^3, x1^2*x2, x1*x2^2, x2^3
    assert!(values.len() >= 9); // At least 9 features for degree 3 with 2 input features

    // Check some specific values
    assert_abs_diff_eq!(values[0], 2.0, epsilon = 1e-5); // x1
    assert_abs_diff_eq!(values[1], 3.0, epsilon = 1e-5); // x2

    // Check for x1^3 = 8 and x2^3 = 27 somewhere in the features
    assert!(values.iter().any(|&v| (v - 8.0).abs() < 1e-10));
    assert!(values.iter().any(|&v| (v - 27.0).abs() < 1e-10));
}

#[test]
fn test_polynomial_features_multiple_samples() {
    let data = Tensor::from_slice(&[1.0, 2.0, 3.0, 4.0], &[2, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(2, true, false);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    assert_eq!(poly_features.shape()[0], 2); // Still 2 samples
    assert!(poly_features.shape()[1] > 2); // More features than input
}

#[test]
fn test_polynomial_features_single_feature() {
    let data = Tensor::from_slice(&[2.0, 3.0], &[2, 1], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(3, true, false);

    transformer.fit(&data).unwrap();
    let poly_features = transformer.transform(&data).unwrap();

    assert_eq!(poly_features.shape()[0], 2); // 2 samples
    assert_eq!(poly_features.shape()[1], 4); // [1, x, x^2, x^3]

    let values = poly_features
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // First sample: x=2 -> [1, 2, 4, 8]
    assert_abs_diff_eq!(values[0], 1.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[1], 2.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[2], 4.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[3], 8.0, epsilon = 1e-5);

    // Second sample: x=3 -> [1, 3, 9, 27]
    assert_abs_diff_eq!(values[4], 1.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[5], 3.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[6], 9.0, epsilon = 1e-5);
    assert_abs_diff_eq!(values[7], 27.0, epsilon = 1e-5);
}

#[test]
fn test_polynomial_features_get_feature_names() {
    let data = Tensor::from_slice(&[1.0, 2.0], &[1, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::with_params(2, true, false);

    transformer.fit(&data).unwrap();
    let feature_names = transformer.get_feature_names();

    // Should have names like ["1", "x0", "x1", "x0^2", "x0 x1", "x1^2"]
    assert!(feature_names.len() >= 6);
    assert!(feature_names.contains(&"1".to_string()));
    assert!(feature_names.contains(&"x0".to_string()));
    assert!(feature_names.contains(&"x1".to_string()));
}

#[test]
fn test_polynomial_features_reset() {
    let data = Tensor::from_slice(&[1.0, 2.0], &[1, 2], &Device::cpu()).unwrap();
    let mut transformer = PolynomialFeatures::new();

    transformer.fit(&data).unwrap();
    assert!(transformer.is_fitted());

    transformer.reset();
    assert!(!transformer.is_fitted());
}