//! Property-based tests for clustering algorithm Python bindings
//!
//! This module contains property-based tests to ensure the robustness
//! and correctness of clustering algorithm implementations.

use proptest::prelude::*;
use scirs2_autograd::ndarray::{Array1, Array2};
use scirs2_core::random::thread_rng;
use std::collections::HashSet;

#[allow(non_snake_case)]
#[cfg(test)]
mod clustering_properties {
    use super::*;

    // Property: Cluster labels should be valid integers within expected range
    proptest! {
        #[test]
        fn test_cluster_label_validity(
            n_samples in 10..100usize,
            n_clusters in 2..10usize,
        ) {
            prop_assume!(n_clusters <= n_samples);

            // Generate random labels as if from a clustering algorithm
            let mut rng = thread_rng();
            let labels: Vec<i32> = (0..n_samples)
                .map(|_| (rng.random::<f64>() * n_clusters as f64).floor() as i32)
                .collect();

            // All labels should be non-negative
            prop_assert!(labels.iter().all(|&label| label >= 0));

            // Labels should not exceed the number of clusters
            prop_assert!(labels.iter().all(|&label| label < n_clusters as i32));

            // Should have the correct number of labels
            prop_assert_eq!(labels.len(), n_samples);
        }
    }

    // Property: Distance calculations should satisfy metric properties
    proptest! {
        #[test]
        fn test_euclidean_distance_properties(
            n_features in 1..10usize,
        ) {
            let mut rng = thread_rng();
            let point_a = Array1::from_shape_fn(n_features, |_| rng.random::<f64>());
            let point_b = Array1::from_shape_fn(n_features, |_| rng.random::<f64>());
            let point_c = Array1::from_shape_fn(n_features, |_| rng.random::<f64>());

            // Calculate Euclidean distances
            let dist_ab = (&point_a - &point_b).mapv(|x| x * x).sum().sqrt();
            let dist_ba = (&point_b - &point_a).mapv(|x| x * x).sum().sqrt();
            let dist_aa = (&point_a - &point_a).mapv(|x| x * x).sum().sqrt();
            let dist_ac = (&point_a - &point_c).mapv(|x| x * x).sum().sqrt();
            let dist_bc = (&point_b - &point_c).mapv(|x| x * x).sum().sqrt();

            // Test metric properties

            // 1. Non-negativity: d(a,b) >= 0
            prop_assert!(dist_ab >= 0.0);

            // 2. Identity: d(a,a) = 0
            prop_assert!((dist_aa).abs() < 1e-10);

            // 3. Symmetry: d(a,b) = d(b,a)
            prop_assert!((dist_ab - dist_ba).abs() < 1e-10);

            // 4. Triangle inequality: d(a,c) <= d(a,b) + d(b,c)
            prop_assert!(dist_ac <= dist_ab + dist_bc + 1e-10); // Allow for floating point errors
        }
    }

    // Property: K-means cluster assignments should be stable for identical inputs
    proptest! {
        #[test]
        fn test_kmeans_determinism(
            n_samples in 10..50usize,
            n_features in 2..5usize,
            n_clusters in 2..5usize,
        ) {
            prop_assume!(n_clusters <= n_samples);

            let mut rng = thread_rng();
            let data = Array2::from_shape_fn((n_samples, n_features), |_| rng.random::<f64>());

            // Generate cluster centers
            let centers = Array2::from_shape_fn((n_clusters, n_features), |_| rng.random::<f64>());

            // Assign each point to closest cluster (simplified K-means assignment)
            let mut assignments1 = Vec::new();
            let mut assignments2 = Vec::new();

            for i in 0..n_samples {
                let point = data.row(i);
                let mut closest_cluster = 0;
                let mut min_distance = f64::INFINITY;

                for j in 0..n_clusters {
                    let center = centers.row(j);
                    let distance = (&point.to_owned() - &center.to_owned()).mapv(|x| x * x).sum().sqrt();
                    if distance < min_distance {
                        min_distance = distance;
                        closest_cluster = j;
                    }
                }
                assignments1.push(closest_cluster as i32);
                assignments2.push(closest_cluster as i32);
            }

            // Assignments should be identical for identical inputs
            prop_assert_eq!(assignments1, assignments2);
        }
    }

    // Property: Number of unique clusters should not exceed the specified number
    proptest! {
        #[test]
        fn test_cluster_count_validity(
            n_samples in 10..100usize,
            n_clusters in 2..10usize,
        ) {
            prop_assume!(n_clusters <= n_samples);

            let mut rng = thread_rng();
            let labels: Vec<i32> = (0..n_samples)
                .map(|_| (rng.random::<f64>() * n_clusters as f64).floor() as i32)
                .collect();

            let unique_labels: HashSet<i32> = labels.into_iter().collect();

            // Number of unique clusters should not exceed n_clusters
            prop_assert!(unique_labels.len() <= n_clusters);

            // All unique labels should be non-negative
            prop_assert!(unique_labels.iter().all(|&label| label >= 0));
        }
    }

    // Property: Cluster centers should be meaningful for their assigned points
    proptest! {
        #[test]
        fn test_cluster_center_properties(
            n_features in 1..5usize,
            cluster_size in 5..20usize,
        ) {
            let mut rng = thread_rng();

            // Generate points around a center
            let true_center = Array1::from_shape_fn(n_features, |_| rng.random::<f64>());
            let mut points = Vec::new();

            for _ in 0..cluster_size {
                let noise = Array1::from_shape_fn(n_features, |_| (rng.random::<f64>() - 0.5) * 0.1);
                let point = &true_center + &noise;
                points.push(point);
            }

            // Calculate empirical center (mean of points)
            let mut empirical_center = Array1::zeros(n_features);
            for point in &points {
                empirical_center = &empirical_center + point;
            }
            empirical_center /= cluster_size as f64;

            // Empirical center should be close to true center
            let center_distance = (&empirical_center - &true_center).mapv(|x: f64| -> f64 { x * x }).sum().sqrt();
            prop_assert!(center_distance < 1.0); // Should be reasonably close given small noise
        }
    }

    // Property: DBSCAN noise points should be labeled as -1
    proptest! {
        #[test]
        fn test_dbscan_noise_labeling(
            n_samples in 10..50usize,
            noise_ratio in 0.0f64..0.5,
        ) {
            // Simulate DBSCAN labels with some noise points
            let _rng = thread_rng();
            let n_noise = (n_samples as f64 * noise_ratio) as usize;
            let n_clustered = n_samples - n_noise;

            let mut labels = Vec::new();

            // Add clustered points (labels 0, 1, 2, ...)
            for i in 0..n_clustered {
                labels.push((i % 3) as i32); // Distribute among 3 clusters
            }

            // Add noise points (label -1)
            for _ in 0..n_noise {
                labels.push(-1);
            }

            let noise_count = labels.iter().filter(|&&label| label == -1).count();
            let cluster_count = labels.iter().filter(|&&label| label >= 0).count();

            prop_assert_eq!(noise_count, n_noise);
            prop_assert_eq!(cluster_count, n_clustered);
            prop_assert_eq!(noise_count + cluster_count, n_samples);
        }
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod clustering_validation_properties {
    use super::*;

    // Property: Input data should have consistent dimensions
    proptest! {
        #[test]
        fn test_clustering_input_validation(
            n_samples in 5..100usize,
            n_features in 1..10usize,
        ) {
            let mut rng = thread_rng();
            let data = Array2::from_shape_fn((n_samples, n_features), |_| rng.random::<f64>());

            // Basic consistency checks
            prop_assert_eq!(data.nrows(), n_samples);
            prop_assert_eq!(data.ncols(), n_features);
            prop_assert!(data.iter().all(|&val| val.is_finite()));
        }
    }

    // Property: Invalid cluster numbers should be handled appropriately
    proptest! {
        #[test]
        fn test_invalid_cluster_parameters(
            n_samples in 5..20usize,
            invalid_n_clusters in 0usize..1,
        ) {
            // Test that invalid cluster numbers (0 or negative) are detected
            // In practice, these should cause validation errors
            prop_assert!(invalid_n_clusters < 1);
            prop_assert!(n_samples >= 5); // Ensure we have sufficient samples

            // The actual clustering algorithm should reject invalid_n_clusters
        }
    }
}
