foxhunt/ml/tests/ppo_hyperopt_validation_test.rs

//! PPO Hyperopt Validation Test
//!
//! Validates PPO hyperparameter optimization pipeline end-to-end.
//! Runs 5 trials of PSO optimization and verifies convergence,
//! result structure, and parameter bounds.
//!
//! Run manually:
//!   SQLX_OFFLINE=true cargo test -p ml --test ppo_hyperopt_validation_test -- --ignored --nocapture
//!
//! Expected runtime: 15-30 minutes (GPU), 45-90 minutes (CPU)

#![allow(unused_crate_dependencies)]

use anyhow::{Context, Result};
use std::path::PathBuf;

use ml::hyperopt::adapters::ppo::PPOTrainer;
use ml::hyperopt::ArgminOptimizer;

/// Locate the real training data directory.
///
/// Returns `Ok(path)` if the directory exists, `Err` otherwise so the test
/// can skip gracefully without marking as failed.
fn get_data_dir() -> Result<PathBuf> {
    let workspace_root = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
        .parent()
        .context("Failed to resolve workspace root")?
        .to_path_buf();

    let data_dir = workspace_root.join("test_data/real/databento/ml_training_small");
    if !data_dir.exists() {
        anyhow::bail!(
            "Training data not found at {}. \
             This test requires real Databento data to run.",
            data_dir.display()
        );
    }
    Ok(data_dir)
}

#[test]
#[ignore]
fn test_ppo_hyperopt_5_trials() -> Result<()> {
    // --- Setup ---
    let data_dir = match get_data_dir() {
        Ok(dir) => dir,
        Err(e) => {
            eprintln!("Skipping test: {e}");
            return Ok(());
        }
    };

    let start = std::time::Instant::now();
    let num_trials: usize = 5;
    let n_initial: usize = 2;
    let episodes_per_trial: usize = 10;

    // --- Create trainer and optimizer ---
    let trainer = PPOTrainer::new(&data_dir, episodes_per_trial)
        .context("Failed to create PPOTrainer")?;

    let optimizer = ArgminOptimizer::builder()
        .max_trials(num_trials)
        .n_initial(n_initial)
        .n_particles(3) // small swarm for test speed
        .seed(42)
        .build();

    // --- Run optimization ---
    let result = optimizer
        .optimize(trainer)
        .context("Hyperopt optimization failed")?;

    let elapsed = start.elapsed();

    // --- Assertions ---

    // 1. All trials should have completed (at least n_initial; PSO may add more)
    let trials_completed = result.all_trials.len();
    assert!(
        trials_completed >= n_initial,
        "Expected at least {n_initial} trials, got {trials_completed}"
    );

    // 2. Best objective should be finite (not NaN or Inf)
    assert!(
        result.best_objective.is_finite(),
        "Best objective is not finite: {}",
        result.best_objective
    );

    // 3. Every trial objective should be finite with positive duration
    for trial in &result.all_trials {
        assert!(
            trial.objective.is_finite(),
            "Trial {} has non-finite objective: {}",
            trial.trial_num,
            trial.objective
        );
        assert!(
            trial.duration_secs > 0.0,
            "Trial {} has non-positive duration: {}",
            trial.trial_num,
            trial.duration_secs
        );
    }

    // 4. Best objective should be <= first trial (optimizer should not regress)
    if let Some(first_trial) = result.all_trials.first() {
        assert!(
            result.best_objective <= first_trial.objective,
            "Optimizer regressed: best={} > first={}",
            result.best_objective,
            first_trial.objective
        );
    }

    // 5. Convergence plot should have entries matching trial count
    assert_eq!(
        result.convergence_plot_data.len(),
        trials_completed,
        "Convergence plot entries ({}) should match trial count ({trials_completed})",
        result.convergence_plot_data.len()
    );

    // 6. Convergence plot should be monotonically non-increasing
    for window in result.convergence_plot_data.windows(2) {
        let (_, prev_best) = window[0];
        let (_, curr_best) = window[1];
        assert!(
            curr_best <= prev_best + f64::EPSILON,
            "Convergence plot is not monotonically non-increasing: {prev_best} -> {curr_best}"
        );
    }

    // 7. Best policy learning rate should be in sane range
    let best_policy_lr = result.best_params.policy_learning_rate;
    assert!(
        best_policy_lr > 1e-7 && best_policy_lr < 1.0,
        "Best policy learning rate out of sane range: {best_policy_lr}"
    );

    // 8. Best value learning rate should be in sane range
    let best_value_lr = result.best_params.value_learning_rate;
    assert!(
        best_value_lr > 1e-7 && best_value_lr < 1.0,
        "Best value learning rate out of sane range: {best_value_lr}"
    );

    // 9. Clip epsilon should be within configured bounds
    let best_clip = result.best_params.clip_epsilon;
    assert!(
        best_clip >= 0.1 && best_clip <= 0.3,
        "Best clip epsilon out of bounds [0.1, 0.3]: {best_clip}"
    );

    // 10. Value loss coefficient should be within configured bounds
    let best_vlc = result.best_params.value_loss_coeff;
    assert!(
        best_vlc >= 0.5 && best_vlc <= 2.0,
        "Best value loss coeff out of bounds [0.5, 2.0]: {best_vlc}"
    );

    // 11. Entropy coefficient should be in sane range
    let best_entropy = result.best_params.entropy_coeff;
    assert!(
        best_entropy > 1e-5 && best_entropy < 1.0,
        "Best entropy coeff out of sane range: {best_entropy}"
    );

    // --- Report ---
    println!("\n{}", "=".repeat(70));
    println!("  PPO HYPEROPT REPORT");
    println!("{}", "=".repeat(70));
    println!("  Trials completed : {trials_completed}");
    println!("  Best objective   : {:.6}", result.best_objective);
    println!(
        "  Best policy LR   : {:.2e}",
        result.best_params.policy_learning_rate
    );
    println!(
        "  Best value LR    : {:.2e}",
        result.best_params.value_learning_rate
    );
    println!(
        "  Best clip eps     : {:.4}",
        result.best_params.clip_epsilon
    );
    println!(
        "  Best value coeff  : {:.4}",
        result.best_params.value_loss_coeff
    );
    println!(
        "  Best entropy coeff: {:.6}",
        result.best_params.entropy_coeff
    );
    println!("  Total time       : {:.1}s", elapsed.as_secs_f64());
    println!(
        "  Avg time/trial   : {:.1}s",
        elapsed.as_secs_f64() / trials_completed as f64
    );
    println!("{}", "-".repeat(70));
    println!("  TRIAL HISTORY");
    println!("{}", "-".repeat(70));
    for trial in &result.all_trials {
        println!(
            "    Trial {:>2} | obj: {:>10.4} | time: {:>6.1}s | plr: {:.2e} | vlr: {:.2e} | clip: {:.3}",
            trial.trial_num,
            trial.objective,
            trial.duration_secs,
            trial.params.policy_learning_rate,
            trial.params.value_learning_rate,
            trial.params.clip_epsilon,
        );
    }
    println!("{}", "-".repeat(70));
    println!("  CONVERGENCE");
    println!("{}", "-".repeat(70));
    for (trial_num, best_so_far) in &result.convergence_plot_data {
        println!(
            "    Trial {:>2} | best so far: {:>10.4}",
            trial_num, best_so_far
        );
    }
    println!("{}", "=".repeat(70));

    Ok(())
}