foxhunt/ml/tests/dropout_scheduler_tests.rs

//! Tests for adaptive dropout scheduling (Wave 26 P1.6)
//!
//! Tests verify that dropout rate decreases linearly from initial to final
//! over a specified number of training steps.

use ml::dqn::network::{DropoutScheduler, QNetwork, QNetworkConfig};

#[test]
fn test_dropout_scheduler_creation() {
    let scheduler = DropoutScheduler::new(0.5, 0.1, 10000);
    assert_eq!(scheduler.get_rate(), 0.5, "Initial rate should be 0.5");
}

#[test]
fn test_dropout_scheduler_linear_decay() {
    let mut scheduler = DropoutScheduler::new(0.5, 0.1, 10000);

    // At step 0, should be at initial rate
    assert_eq!(scheduler.get_rate(), 0.5);

    // At 25% progress (2500 steps)
    scheduler.step(2500);
    let rate_25 = scheduler.get_rate();
    assert!(
        (rate_25 - 0.4).abs() < 1e-6,
        "At 25% progress, rate should be ~0.4, got {}",
        rate_25
    );

    // At 50% progress (5000 steps)
    scheduler.step(2500); // Total 5000 steps
    let rate_50 = scheduler.get_rate();
    assert!(
        (rate_50 - 0.3).abs() < 1e-6,
        "At 50% progress, rate should be ~0.3, got {}",
        rate_50
    );

    // At 75% progress (7500 steps)
    scheduler.step(2500); // Total 7500 steps
    let rate_75 = scheduler.get_rate();
    assert!(
        (rate_75 - 0.2).abs() < 1e-6,
        "At 75% progress, rate should be ~0.2, got {}",
        rate_75
    );

    // At 100% progress (10000 steps)
    scheduler.step(2500); // Total 10000 steps
    assert_eq!(scheduler.get_rate(), 0.1, "Final rate should be 0.1");

    // Beyond decay steps, should stay at final rate
    scheduler.step(5000); // Total 15000 steps
    assert_eq!(
        scheduler.get_rate(),
        0.1,
        "Rate should stay at final after decay_steps"
    );
}

#[test]
fn test_dropout_scheduler_step_increment() {
    let mut scheduler = DropoutScheduler::new(0.8, 0.2, 1000);

    // Step by single increments
    for _ in 0..500 {
        scheduler.step(1);
    }
    let rate_half = scheduler.get_rate();
    assert!(
        (rate_half - 0.5).abs() < 1e-6,
        "At halfway point, should be midpoint of range: {}",
        rate_half
    );
}

#[test]
fn test_dropout_scheduler_current_step_tracking() {
    let mut scheduler = DropoutScheduler::new(0.5, 0.1, 10000);
    assert_eq!(scheduler.current_step(), 0);

    scheduler.step(100);
    assert_eq!(scheduler.current_step(), 100);

    scheduler.step(200);
    assert_eq!(scheduler.current_step(), 300);
}

#[test]
fn test_qnetwork_config_with_dropout_schedule() {
    let config = QNetworkConfig {
        state_dim: 10,
        num_actions: 3,
        hidden_dims: vec![64, 32],
        dropout_schedule: Some((0.5, 0.1, 10000)), // (initial, final, steps)
        ..Default::default()
    };

    assert!(config.dropout_schedule.is_some());
    let (initial, final_rate, steps) = config.dropout_schedule.unwrap();
    assert_eq!(initial, 0.5);
    assert_eq!(final_rate, 0.1);
    assert_eq!(steps, 10000);
}

#[test]
fn test_qnetwork_with_adaptive_dropout() -> anyhow::Result<()> {
    let config = QNetworkConfig {
        state_dim: 4,
        num_actions: 3,
        hidden_dims: vec![8],
        dropout_schedule: Some((0.5, 0.1, 1000)),
        ..Default::default()
    };

    let network = QNetwork::new(config)?;
    let state = vec![1.0, 0.5, -0.5, 0.0];

    // Initial dropout rate should be 0.5
    let initial_rate = network.get_dropout_rate();
    assert!(
        (initial_rate - 0.5).abs() < 1e-6,
        "Initial dropout rate should be 0.5"
    );

    // Perform forward passes to advance training steps
    for _ in 0..500 {
        network.forward(&state)?;
    }

    // After 500 steps (50% progress), rate should be ~0.3
    let mid_rate = network.get_dropout_rate();
    assert!(
        mid_rate < 0.5 && mid_rate > 0.1,
        "Mid-training dropout rate should be between initial and final: {}",
        mid_rate
    );

    Ok(())
}

#[test]
fn test_dropout_scheduler_zero_decay_steps() {
    // Edge case: if decay_steps is 0, should immediately be at final rate
    let scheduler = DropoutScheduler::new(0.5, 0.1, 0);
    assert_eq!(
        scheduler.get_rate(),
        0.1,
        "With 0 decay steps, should be at final rate"
    );
}

#[test]
fn test_dropout_scheduler_same_initial_and_final() {
    // Edge case: if initial == final, rate should stay constant
    let mut scheduler = DropoutScheduler::new(0.3, 0.3, 1000);
    assert_eq!(scheduler.get_rate(), 0.3);

    scheduler.step(500);
    assert_eq!(scheduler.get_rate(), 0.3, "Rate should stay constant");

    scheduler.step(500);
    assert_eq!(scheduler.get_rate(), 0.3, "Rate should stay constant");
}

#[test]
fn test_dropout_scheduler_realistic_training_schedule() {
    // Test realistic schedule: 0.5 -> 0.05 over 100k steps
    let mut scheduler = DropoutScheduler::new(0.5, 0.05, 100_000);

    // Early training (10k steps): high dropout for regularization
    scheduler.step(10_000);
    let early_rate = scheduler.get_rate();
    assert!(
        early_rate > 0.4,
        "Early training should have high dropout: {}",
        early_rate
    );

    // Mid training (50k steps): moderate dropout
    scheduler.step(40_000); // Total 50k
    let mid_rate = scheduler.get_rate();
    assert!(
        (mid_rate - 0.275).abs() < 0.01,
        "Mid training should have moderate dropout: {}",
        mid_rate
    );

    // Late training (100k steps): low dropout for fine-tuning
    scheduler.step(50_000); // Total 100k
    let late_rate = scheduler.get_rate();
    assert_eq!(late_rate, 0.05, "Late training should have low dropout");
}