foxhunt/ml/tests/ppo_params_minibatch_test.rs

//! Test suite for PPOParams minibatch_size field
//!
//! This test file verifies that PPOParams struct includes the minibatch_size field
//! and properly handles serialization, deserialization, and VRAM-bounded validation.

use ml::hyperopt::adapters::ppo::PPOParams;

#[test]
fn test_ppo_params_has_minibatch_size_field() {
    let params = PPOParams::default();
    assert_eq!(params.minibatch_size, 128);
}

#[test]
fn test_minibatch_size_serialization() {
    let params = PPOParams {
        policy_learning_rate: 1e-6,
        value_learning_rate: 0.001,
        clip_epsilon: 0.2,
        value_loss_coeff: 1.0,
        entropy_coeff: 0.01,
        minibatch_size: 128,
    };

    let json = serde_json::to_string(&params).unwrap();
    let deserialized: PPOParams = serde_json::from_str(&json).unwrap();
    assert_eq!(deserialized.minibatch_size, 128);
}

#[test]
fn test_minibatch_size_vram_bounds() {
    // Minibatch size should be within VRAM limits (trainers/ppo.rs:185)
    let params = PPOParams::default();
    assert!(params.minibatch_size >= 64);
    assert!(params.minibatch_size <= 230);
}

#[test]
fn test_minibatch_size_custom_values() {
    // Test creation with various minibatch sizes
    let test_sizes = vec![64, 100, 128, 150, 200, 230];

    for size in test_sizes {
        let params = PPOParams {
            policy_learning_rate: 1e-5,
            value_learning_rate: 1e-4,
            clip_epsilon: 0.2,
            value_loss_coeff: 1.0,
            entropy_coeff: 0.05,
            minibatch_size: size,
        };

        assert_eq!(params.minibatch_size, size);
        assert!(
            params.minibatch_size >= 64,
            "Size {} is below minimum",
            size
        );
        assert!(
            params.minibatch_size <= 230,
            "Size {} exceeds VRAM limit",
            size
        );
    }
}