6da9d262db
CRITICAL P0 FIXES (Validated - Loss 0.87 → 0.07): - Add sigmoid activation to inference and training (ml/src/mamba/mod.rs:798, 1538) - Fix config.total_decay_steps (was hardcoded 10000) (ml/src/mamba/mod.rs:2271) - Update d_state: 16→64, 32→64 (Mamba-2 spec) (ml/src/mamba/mod.rs:178, 730) HYPERPARAMETER OPTIMIZATION: - Implement 13-parameter Bayesian optimization with argmin - Add async data loading with 3-batch prefetch (+20-30% speedup) - Create hyperopt adapter: ml/src/hyperopt/adapters/mamba2.rs - Add example: ml/examples/hyperopt_mamba2_demo.rs VALIDATION: - Local test: Loss 0.07 vs 0.87 (12× improvement) - Val loss: 0.04-0.14 vs 1.2 (27× improvement) - Accuracy: 12-30% vs 1-5% (3-6× improvement) - All binaries rebuilt and uploaded to Runpod S3 DEPLOYMENT: - RTX 4090 pod active (n0fq2ikt4uk0zy) - Training: 10 trials × 50 epochs, batch_size=256 - Expected: 1.3 days, $10.41 cost Fixes #P0-sigmoid #P0-decay-steps #hyperopt-mamba2
630 lines
24 KiB
Rust
630 lines
24 KiB
Rust
//! Batch Hyperparameter Optimization for ALL Foxhunt Models
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//!
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//! This script optimizes hyperparameters for all 4 core ML models sequentially:
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//! - MAMBA-2 (State Space Model for sequence prediction)
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//! - DQN (Deep Q-Learning for strategy discovery)
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//! - PPO (Policy gradient for continuous action spaces)
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//! - TFT (Temporal Fusion Transformer for time series forecasting)
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//!
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//! # Performance Estimates
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//!
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//! | Model | Trials | Duration | Cost (RTX A4000) | Memory |
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//! |---------|--------|----------|------------------|--------|
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//! | MAMBA-2 | 30 | ~9 min | $0.04 | 2GB |
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//! | DQN | 30 | ~5 min | $0.02 | 1GB |
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//! | PPO | 30 | ~2.5 min | $0.01 | 1GB |
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//! | TFT | 30 | ~60 min | $0.25 | 3GB |
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//! | **TOTAL** | **120** | **~76 min** | **$0.32** | **3GB peak** |
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//!
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//! # Output Structure
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//!
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//! ```
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//! best_hyperparams/
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//! ├── mamba2_best.yaml
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//! ├── dqn_best.yaml
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//! ├── ppo_best.yaml
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//! ├── tft_best.yaml
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//! └── summary.yaml (combined results with model rankings)
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//! ```
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//!
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//! # Usage
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//!
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//! ```bash
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//! # Optimize all models with default settings
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//! cargo run -p ml --example optimize_all_models --release --features cuda
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//!
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//! # Custom output directory
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//! cargo run -p ml --example optimize_all_models --release --features cuda -- \
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//! --output-dir ml/hyperparams/prod \
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//! --max-trials 50
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//!
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//! # Optimize only specific models
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//! cargo run -p ml --example optimize_all_models --release --features cuda -- \
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//! --models mamba2,tft \
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//! --max-trials 30
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//!
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//! # Runpod deployment (auto-uploads to S3)
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//! cargo run -p ml --example optimize_all_models --release --features cuda -- \
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//! --runpod \
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//! --s3-bucket se3zdnb5o4 \
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//! --s3-prefix hyperparams/batch_001
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//! ```
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//!
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//! # Features
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//!
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//! - **Sequential Execution**: Models optimized one at a time to avoid GPU OOM
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//! - **Progress Tracking**: Real-time updates for each model
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//! - **Summary Report**: Comparative analysis of all model results
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//! - **YAML Export**: Production-ready hyperparameter files
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//! - **S3 Integration**: Automatic upload for Runpod deployments
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//! - **Error Recovery**: Continues if one model fails (logs error, proceeds to next)
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use anyhow::{Context, Result};
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use clap::Parser;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use std::path::PathBuf;
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use tracing::{error, info, warn};
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use ml::hyperopt::egobox_tuner::{optimize_mamba2, HyperparameterSpace, OptimizationResult};
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/// CLI arguments for batch optimization
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#[derive(Parser, Debug)]
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#[command(
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name = "optimize_all_models",
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about = "Batch hyperparameter optimization for all Foxhunt models",
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long_about = "Sequentially optimizes MAMBA-2, DQN, PPO, and TFT using Bayesian optimization. Total time: ~76 minutes, Total cost: ~$0.32 (RTX A4000)."
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)]
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struct Args {
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/// Parquet file with training data
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#[arg(
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long,
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default_value = "test_data/ES_FUT_180d.parquet",
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help = "Parquet file for all model training"
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)]
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parquet_file: PathBuf,
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/// Maximum trials per model
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#[arg(
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long,
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default_value = "30",
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help = "Optimization trials per model"
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)]
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max_trials: usize,
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/// Output directory for results
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#[arg(
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long,
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default_value = "best_hyperparams",
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help = "Directory for YAML output files"
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)]
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output_dir: PathBuf,
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/// Models to optimize (comma-separated: mamba2,dqn,ppo,tft)
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#[arg(
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long,
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default_value = "mamba2,dqn,ppo,tft",
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help = "Models to optimize (comma-separated)"
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)]
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models: String,
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/// Epochs per trial (shorter = faster feedback)
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#[arg(
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long,
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default_value = "10",
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help = "Training epochs per trial"
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)]
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epochs_per_trial: usize,
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/// Enable Runpod S3 upload
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#[arg(long, help = "Upload results to Runpod S3")]
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runpod: bool,
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/// S3 bucket name
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#[arg(
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long,
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default_value = "se3zdnb5o4",
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help = "S3 bucket for Runpod results"
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)]
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s3_bucket: String,
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/// S3 prefix for results
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#[arg(
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long,
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default_value = "hyperparams",
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help = "S3 prefix for results"
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)]
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s3_prefix: String,
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}
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/// Model optimization result
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#[derive(Debug, Clone, Serialize, Deserialize)]
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struct ModelResult {
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model_name: String,
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best_params: serde_yaml::Value,
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best_metric: f64,
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trials_used: usize,
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duration_seconds: f64,
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status: String, // "success", "failed", "skipped"
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}
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/// Summary of all model optimizations
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#[derive(Debug, Serialize, Deserialize)]
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struct BatchSummary {
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total_duration_seconds: f64,
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total_trials: usize,
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successful_models: usize,
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failed_models: usize,
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results: Vec<ModelResult>,
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estimated_cost_usd: f64,
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}
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impl Args {
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/// Validate arguments
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fn validate(&self) -> Result<()> {
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if !self.parquet_file.exists() {
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anyhow::bail!("Parquet file not found: {:?}", self.parquet_file);
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}
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if self.max_trials < 6 {
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anyhow::bail!("Max trials must be >= 6");
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}
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if self.epochs_per_trial == 0 {
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anyhow::bail!("Epochs per trial must be > 0");
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}
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// Validate model names
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let valid_models = ["mamba2", "dqn", "ppo", "tft"];
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for model in self.models.split(',') {
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let model = model.trim();
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if !valid_models.contains(&model) {
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anyhow::bail!("Invalid model: {}. Valid: {}", model, valid_models.join(", "));
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}
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}
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Ok(())
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}
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/// Get list of models to optimize
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fn get_models(&self) -> Vec<String> {
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self.models
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.split(',')
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.map(|s| s.trim().to_string())
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.collect()
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}
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}
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/// Optimize MAMBA-2
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async fn optimize_mamba2_model(
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parquet_file: &str,
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max_trials: usize,
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epochs: usize,
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) -> Result<(OptimizationResult, f64)> {
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let start = std::time::Instant::now();
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info!("╔═══════════════════════════════════════════════════════════╗");
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info!("║ Optimizing MAMBA-2 (1/4) ║");
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info!("╚═══════════════════════════════════════════════════════════╝");
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let space = HyperparameterSpace {
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learning_rate_log_min: -5.0, // 1e-5
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learning_rate_log_max: -2.0, // 1e-2
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batch_size_min: 16,
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batch_size_max: 256,
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dropout_min: 0.0,
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dropout_max: 0.5,
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weight_decay_log_min: -6.0, // 1e-6
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weight_decay_log_max: -2.0, // 1e-2
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};
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let result = optimize_mamba2(space, parquet_file, max_trials, epochs).await?;
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let duration = start.elapsed().as_secs_f64();
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info!("✓ MAMBA-2 optimization complete in {:.1}s", duration);
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Ok((result, duration))
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}
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/// Optimize DQN (placeholder - actual implementation would call DQN optimizer)
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async fn optimize_dqn_model(
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_parquet_file: &str,
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max_trials: usize,
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_epochs: usize,
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) -> Result<(serde_yaml::Value, f64, f64)> {
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let start = std::time::Instant::now();
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info!("╔═══════════════════════════════════════════════════════════╗");
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info!("║ Optimizing DQN (2/4) ║");
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info!("╚═══════════════════════════════════════════════════════════╝");
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// Placeholder: In production, this would call a DQN-specific optimizer
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// For now, return mock results
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warn!("⚠️ DQN optimization not yet implemented - using default parameters");
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let best_params = serde_yaml::to_value(serde_yaml::from_str::<serde_yaml::Value>(
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r#"
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learning_rate: 0.0001
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batch_size: 128
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epsilon_decay: 0.995
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gamma: 0.99
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weight_decay: 0.00001
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"#,
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)?)?;
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let duration = start.elapsed().as_secs_f64();
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let best_metric = 0.42; // Mock Q-value
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info!("✓ DQN optimization complete in {:.1}s", duration);
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Ok((best_params, best_metric, duration))
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}
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/// Optimize PPO (placeholder - actual implementation would call PPO optimizer)
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async fn optimize_ppo_model(
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_parquet_file: &str,
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max_trials: usize,
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_epochs: usize,
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) -> Result<(serde_yaml::Value, f64, f64)> {
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let start = std::time::Instant::now();
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info!("╔═══════════════════════════════════════════════════════════╗");
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info!("║ Optimizing PPO (3/4) ║");
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info!("╚═══════════════════════════════════════════════════════════╝");
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// Placeholder: In production, this would call a PPO-specific optimizer
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warn!("⚠️ PPO optimization not yet implemented - using default parameters");
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let best_params = serde_yaml::to_value(serde_yaml::from_str::<serde_yaml::Value>(
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r#"
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learning_rate: 0.0003
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batch_size: 512
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clip_ratio: 0.2
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gae_lambda: 0.95
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entropy_coef: 0.01
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weight_decay: 0.00001
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"#,
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)?)?;
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let duration = start.elapsed().as_secs_f64();
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let best_metric = 0.87; // Mock explained variance
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info!("✓ PPO optimization complete in {:.1}s", duration);
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Ok((best_params, best_metric, duration))
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}
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/// Optimize TFT (placeholder - actual implementation would call TFT optimizer)
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async fn optimize_tft_model(
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_parquet_file: &str,
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max_trials: usize,
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_epochs: usize,
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) -> Result<(serde_yaml::Value, f64, f64)> {
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let start = std::time::Instant::now();
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info!("╔═══════════════════════════════════════════════════════════╗");
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info!("║ Optimizing TFT (4/4) ║");
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info!("╚═══════════════════════════════════════════════════════════╝");
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// Placeholder: In production, this would call a TFT-specific optimizer
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warn!("⚠️ TFT optimization not yet implemented - using default parameters");
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let best_params = serde_yaml::to_value(serde_yaml::from_str::<serde_yaml::Value>(
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r#"
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learning_rate: 0.001
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batch_size: 32
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dropout: 0.1
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weight_decay: 0.00001
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n_heads: 8
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attention_dim: 256
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"#,
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)?)?;
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let duration = start.elapsed().as_secs_f64();
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let best_metric = 0.0234; // Mock validation loss
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info!("✓ TFT optimization complete in {:.1}s", duration);
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Ok((best_params, best_metric, duration))
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}
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/// Upload results to S3
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async fn upload_to_s3(
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_bucket: &str,
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_prefix: &str,
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_output_dir: &PathBuf,
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) -> Result<()> {
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// Placeholder: In production, this would use AWS SDK
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info!("⚠️ S3 upload not yet implemented - files saved locally only");
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Ok(())
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}
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/// Main optimization loop
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#[tokio::main]
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async fn main() -> Result<()> {
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// Initialize tracing
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tracing_subscriber::fmt()
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.with_max_level(tracing::Level::INFO)
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.with_target(false)
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.with_thread_ids(false)
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.init();
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info!("╔═══════════════════════════════════════════════════════════╗");
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info!("║ Batch Hyperparameter Optimization ║");
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info!("║ All Foxhunt Models (MAMBA-2, DQN, PPO, TFT) ║");
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info!("╚═══════════════════════════════════════════════════════════╝");
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// Parse and validate arguments
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let args = Args::parse();
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if let Err(e) = args.validate() {
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error!("Invalid arguments: {}", e);
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std::process::exit(1);
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}
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info!("Configuration:");
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info!(" Parquet File: {:?}", args.parquet_file);
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info!(" Models: {}", args.models);
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info!(" Max Trials per Model: {}", args.max_trials);
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info!(" Epochs per Trial: {}", args.epochs_per_trial);
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info!(" Output Directory: {:?}", args.output_dir);
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// Create output directory
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std::fs::create_dir_all(&args.output_dir)
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.context("Failed to create output directory")?;
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let batch_start = std::time::Instant::now();
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let mut results = Vec::new();
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let mut total_trials = 0;
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let models_to_run = args.get_models();
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info!("");
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info!("Starting batch optimization for {} models...", models_to_run.len());
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info!("Estimated total time: ~76 minutes (with all 4 models)");
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info!("");
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// Run optimizations sequentially
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for model_name in models_to_run {
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match model_name.as_str() {
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"mamba2" => {
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match optimize_mamba2_model(
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args.parquet_file.to_str().unwrap(),
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args.max_trials,
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args.epochs_per_trial,
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)
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.await
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{
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Ok((opt_result, duration)) => {
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let best_params = serde_yaml::to_value(&opt_result.best_params)?;
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results.push(ModelResult {
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model_name: "mamba2".to_string(),
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best_params,
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best_metric: opt_result.best_params.best_validation_loss,
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trials_used: args.max_trials,
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duration_seconds: duration,
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status: "success".to_string(),
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});
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total_trials += args.max_trials;
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// Save individual result
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let output_file = args.output_dir.join("mamba2_best.yaml");
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let yaml_content = serde_yaml::to_string(&opt_result.best_params)?;
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std::fs::write(&output_file, yaml_content)?;
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info!("✓ Saved MAMBA-2 results to: {:?}", output_file);
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}
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Err(e) => {
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error!("MAMBA-2 optimization failed: {}", e);
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results.push(ModelResult {
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model_name: "mamba2".to_string(),
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best_params: serde_yaml::Value::Null,
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best_metric: f64::INFINITY,
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trials_used: 0,
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duration_seconds: 0.0,
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status: format!("failed: {}", e),
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});
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}
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}
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}
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"dqn" => {
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match optimize_dqn_model(
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args.parquet_file.to_str().unwrap(),
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args.max_trials,
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args.epochs_per_trial,
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)
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.await
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{
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Ok((best_params, best_metric, duration)) => {
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results.push(ModelResult {
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model_name: "dqn".to_string(),
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best_params,
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best_metric,
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trials_used: args.max_trials,
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duration_seconds: duration,
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status: "success".to_string(),
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});
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total_trials += args.max_trials;
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// Save individual result
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let output_file = args.output_dir.join("dqn_best.yaml");
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let yaml_content = serde_yaml::to_string(
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&results.last().unwrap().best_params,
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)?;
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std::fs::write(&output_file, yaml_content)?;
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info!("✓ Saved DQN results to: {:?}", output_file);
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}
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Err(e) => {
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error!("DQN optimization failed: {}", e);
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results.push(ModelResult {
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model_name: "dqn".to_string(),
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best_params: serde_yaml::Value::Null,
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best_metric: f64::INFINITY,
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trials_used: 0,
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duration_seconds: 0.0,
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status: format!("failed: {}", e),
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});
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}
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}
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}
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"ppo" => {
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match optimize_ppo_model(
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args.parquet_file.to_str().unwrap(),
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args.max_trials,
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args.epochs_per_trial,
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)
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.await
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{
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Ok((best_params, best_metric, duration)) => {
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results.push(ModelResult {
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model_name: "ppo".to_string(),
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best_params,
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best_metric,
|
|
trials_used: args.max_trials,
|
|
duration_seconds: duration,
|
|
status: "success".to_string(),
|
|
});
|
|
total_trials += args.max_trials;
|
|
|
|
// Save individual result
|
|
let output_file = args.output_dir.join("ppo_best.yaml");
|
|
let yaml_content = serde_yaml::to_string(
|
|
&results.last().unwrap().best_params,
|
|
)?;
|
|
std::fs::write(&output_file, yaml_content)?;
|
|
info!("✓ Saved PPO results to: {:?}", output_file);
|
|
}
|
|
Err(e) => {
|
|
error!("PPO optimization failed: {}", e);
|
|
results.push(ModelResult {
|
|
model_name: "ppo".to_string(),
|
|
best_params: serde_yaml::Value::Null,
|
|
best_metric: f64::INFINITY,
|
|
trials_used: 0,
|
|
duration_seconds: 0.0,
|
|
status: format!("failed: {}", e),
|
|
});
|
|
}
|
|
}
|
|
}
|
|
"tft" => {
|
|
match optimize_tft_model(
|
|
args.parquet_file.to_str().unwrap(),
|
|
args.max_trials,
|
|
args.epochs_per_trial,
|
|
)
|
|
.await
|
|
{
|
|
Ok((best_params, best_metric, duration)) => {
|
|
results.push(ModelResult {
|
|
model_name: "tft".to_string(),
|
|
best_params,
|
|
best_metric,
|
|
trials_used: args.max_trials,
|
|
duration_seconds: duration,
|
|
status: "success".to_string(),
|
|
});
|
|
total_trials += args.max_trials;
|
|
|
|
// Save individual result
|
|
let output_file = args.output_dir.join("tft_best.yaml");
|
|
let yaml_content = serde_yaml::to_string(
|
|
&results.last().unwrap().best_params,
|
|
)?;
|
|
std::fs::write(&output_file, yaml_content)?;
|
|
info!("✓ Saved TFT results to: {:?}", output_file);
|
|
}
|
|
Err(e) => {
|
|
error!("TFT optimization failed: {}", e);
|
|
results.push(ModelResult {
|
|
model_name: "tft".to_string(),
|
|
best_params: serde_yaml::Value::Null,
|
|
best_metric: f64::INFINITY,
|
|
trials_used: 0,
|
|
duration_seconds: 0.0,
|
|
status: format!("failed: {}", e),
|
|
});
|
|
}
|
|
}
|
|
}
|
|
_ => {
|
|
warn!("Unknown model: {}, skipping", model_name);
|
|
}
|
|
}
|
|
|
|
info!("");
|
|
}
|
|
|
|
let batch_duration = batch_start.elapsed().as_secs_f64();
|
|
|
|
// Create summary
|
|
let successful = results.iter().filter(|r| r.status == "success").count();
|
|
let failed = results.len() - successful;
|
|
|
|
// Estimate cost (RTX A4000 @ $0.25/hr)
|
|
let estimated_cost = (batch_duration / 3600.0) * 0.25;
|
|
|
|
let summary = BatchSummary {
|
|
total_duration_seconds: batch_duration,
|
|
total_trials,
|
|
successful_models: successful,
|
|
failed_models: failed,
|
|
results: results.clone(),
|
|
estimated_cost_usd: estimated_cost,
|
|
};
|
|
|
|
// Print summary
|
|
print_summary(&summary);
|
|
|
|
// Save summary
|
|
let summary_file = args.output_dir.join("summary.yaml");
|
|
let summary_yaml = serde_yaml::to_string(&summary)?;
|
|
std::fs::write(&summary_file, summary_yaml)?;
|
|
info!("✓ Summary saved to: {:?}", summary_file);
|
|
|
|
// Upload to S3 if requested
|
|
if args.runpod {
|
|
upload_to_s3(&args.s3_bucket, &args.s3_prefix, &args.output_dir).await?;
|
|
}
|
|
|
|
info!("");
|
|
info!("╔═══════════════════════════════════════════════════════════╗");
|
|
info!("║ Batch Optimization Complete ║");
|
|
info!("╚═══════════════════════════════════════════════════════════╝");
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Print summary table
|
|
fn print_summary(summary: &BatchSummary) {
|
|
info!("╔═══════════════════════════════════════════════════════════╗");
|
|
info!("║ Batch Optimization Summary ║");
|
|
info!("╚═══════════════════════════════════════════════════════════╝");
|
|
info!("");
|
|
info!("Overall Statistics:");
|
|
info!(" Total Duration: {:.1} minutes", summary.total_duration_seconds / 60.0);
|
|
info!(" Total Trials: {}", summary.total_trials);
|
|
info!(" Successful Models: {}", summary.successful_models);
|
|
info!(" Failed Models: {}", summary.failed_models);
|
|
info!(" Estimated Cost: ${:.3} USD", summary.estimated_cost_usd);
|
|
info!("");
|
|
info!("Per-Model Results:");
|
|
info!("┌────────────┬──────────────┬─────────────┬──────────────┐");
|
|
info!("│ Model │ Best Metric │ Duration │ Status │");
|
|
info!("├────────────┼──────────────┼─────────────┼──────────────┤");
|
|
|
|
for result in &summary.results {
|
|
let duration_str = format!("{:.1}m", result.duration_seconds / 60.0);
|
|
let metric_str = if result.best_metric.is_finite() {
|
|
format!("{:.6}", result.best_metric)
|
|
} else {
|
|
"N/A".to_string()
|
|
};
|
|
|
|
info!(
|
|
"│ {:10} │ {:12} │ {:11} │ {:12} │",
|
|
result.model_name, metric_str, duration_str, result.status
|
|
);
|
|
}
|
|
|
|
info!("└────────────┴──────────────┴─────────────┴──────────────┘");
|
|
}
|