foxhunt/ml/examples/model_registry_api.rs

//! Model Registry API Example
//!
//! This example demonstrates how to use the model registry system
//! for tracking ML model versions, metadata, and production deployments.
//!
//! # Usage
//!
//! ```bash
//! # Start PostgreSQL (via docker-compose)
//! docker-compose up -d postgres
//!
//! # Run the example
//! cargo run --example model_registry_api
//! ```

use ml::model_registry::{ModelRegistry, ModelVersionMetadata, RegistryStatistics};
use ml::ModelType;
use std::error::Error;

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
    // Initialize tracing
    tracing_subscriber::fmt::init();

    println!("🚀 Model Registry API Example");
    println!("===============================\n");

    // Initialize registry
    let database_url = std::env::var("DATABASE_URL")
        .unwrap_or_else(|_| "postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt".to_string());

    let s3_base_path = "s3://foxhunt-ml-models/";

    println!("📊 Connecting to database: {}", database_url);
    let registry = ModelRegistry::new(&database_url, s3_base_path).await?;
    println!("✅ Registry initialized\n");

    // Example 1: Register a DQN model
    println!("📝 Example 1: Registering DQN model v1.0.0");
    println!("-------------------------------------------");

    let mut dqn_metadata = ModelVersionMetadata::new(
        "dqn-v1.0.0".to_string(),
        ModelType::DQN,
        "1.0.0".to_string(),
        "databento_2024_Q4".to_string(),
        "s3://foxhunt-ml-models/dqn/1.0.0/".to_string(),
    );

    // Add hyperparameters
    dqn_metadata.add_hyperparameter("epochs", serde_json::json!(500));
    dqn_metadata.add_hyperparameter("batch_size", serde_json::json!(128));
    dqn_metadata.add_hyperparameter("learning_rate", serde_json::json!(0.0001));
    dqn_metadata.add_hyperparameter("gamma", serde_json::json!(0.99));

    // Add training metrics
    dqn_metadata.add_metric("final_loss", serde_json::json!(0.001));
    dqn_metadata.add_metric("best_epoch", serde_json::json!(487));
    dqn_metadata.add_metric("training_time_seconds", serde_json::json!(168));
    dqn_metadata.add_metric("sharpe_ratio", serde_json::json!(2.3));

    // Set checksum
    dqn_metadata.set_checksum("sha256:abc123def456...".to_string());

    // Add custom metadata
    dqn_metadata.add_metadata("trainer", "ml_training_service");
    dqn_metadata.add_metadata("gpu_type", "RTX 3050 Ti");
    dqn_metadata.add_metadata("dataset_size", "10M samples");

    // Register model
    registry.register_version(&dqn_metadata).await?;
    println!("✅ DQN v1.0.0 registered as experimental\n");

    // Example 2: Register a MAMBA model
    println!("📝 Example 2: Registering MAMBA model v1.0.0");
    println!("----------------------------------------------");

    let mut mamba_metadata = ModelVersionMetadata::new(
        "mamba-v1.0.0".to_string(),
        ModelType::MAMBA,
        "1.0.0".to_string(),
        "databento_2024_Q4".to_string(),
        "s3://foxhunt-ml-models/mamba/1.0.0/".to_string(),
    );

    mamba_metadata.add_hyperparameter("state_size", serde_json::json!(16));
    mamba_metadata.add_hyperparameter("seq_len", serde_json::json!(100));
    mamba_metadata.add_metric("final_loss", serde_json::json!(0.0008));
    mamba_metadata.add_metric("sharpe_ratio", serde_json::json!(2.5));
    mamba_metadata.set_checksum("sha256:mamba123...".to_string());

    registry.register_version(&mamba_metadata).await?;
    println!("✅ MAMBA v1.0.0 registered as experimental\n");

    // Example 3: Mark DQN as production
    println!("📝 Example 3: Promoting DQN to production");
    println!("------------------------------------------");

    registry.mark_production("dqn-v1.0.0").await?;
    println!("✅ DQN v1.0.0 promoted to production\n");

    // Example 4: Query models
    println!("📝 Example 4: Querying models");
    println!("-----------------------------");

    // Get production models
    let production_models = registry.get_production_models().await?;
    println!("🏭 Production models: {}", production_models.len());
    for model in &production_models {
        println!("  - {} ({})", model.model_id, format!("{:?}", model.model_type));
        println!("    Version: {}", model.version);
        println!("    Trained: {}", model.training_date.format("%Y-%m-%d %H:%M:%S"));
        println!("    S3: {}", model.s3_location);
    }
    println!();

    // Get experimental models
    let experimental_models = registry.get_experimental_models().await?;
    println!("🔬 Experimental models: {}", experimental_models.len());
    for model in &experimental_models {
        println!("  - {} ({})", model.model_id, format!("{:?}", model.model_type));
    }
    println!();

    // Get models by type
    let dqn_models = registry.get_models_by_type(ModelType::DQN).await?;
    println!("🎯 DQN models: {}", dqn_models.len());
    for model in &dqn_models {
        println!("  - {} (status: {})",
            model.model_id,
            if model.is_production { "production" }
            else if model.is_experimental { "experimental" }
            else { "unknown" }
        );
    }
    println!();

    // Example 5: Retrieve specific model
    println!("📝 Example 5: Retrieving specific model");
    println!("---------------------------------------");

    let retrieved = registry.get_model_by_version("dqn-v1.0.0").await?;
    println!("📦 Model: {}", retrieved.model_id);
    println!("   Type: {:?}", retrieved.model_type);
    println!("   Version: {}", retrieved.version);
    println!("   Training Date: {}", retrieved.training_date.format("%Y-%m-%d %H:%M:%S"));
    println!("   Data Source: {}", retrieved.data_source);
    println!("   S3 Location: {}", retrieved.s3_location);
    println!("   Checksum: {}", retrieved.checksum);
    println!("   Production: {}", retrieved.is_production);
    println!("   Experimental: {}", retrieved.is_experimental);
    println!("\n   Hyperparameters:");
    if let Some(obj) = retrieved.hyperparameters.as_object() {
        for (key, value) in obj {
            println!("     - {}: {}", key, value);
        }
    }
    println!("\n   Metrics:");
    if let Some(obj) = retrieved.metrics.as_object() {
        for (key, value) in obj {
            println!("     - {}: {}", key, value);
        }
    }
    println!("\n   Metadata:");
    for (key, value) in &retrieved.metadata {
        println!("     - {}: {}", key, value);
    }
    println!();

    // Example 6: Get registry statistics
    println!("📝 Example 6: Registry statistics");
    println!("---------------------------------");

    let stats: RegistryStatistics = registry.get_statistics().await?;
    println!("📊 Registry Statistics:");
    println!("   Total models: {}", stats.total_count);
    println!("   Production models: {}", stats.production_count);
    println!("   Experimental models: {}", stats.experimental_count);
    println!("   Archived models: {}", stats.archived_count);
    println!("   Model types: {}", stats.model_types_count);
    if let Some(latest) = stats.latest_training_date {
        println!("   Latest training: {}", latest.format("%Y-%m-%d %H:%M:%S"));
    }
    if let Some(earliest) = stats.earliest_training_date {
        println!("   Earliest training: {}", earliest.format("%Y-%m-%d %H:%M:%S"));
    }
    println!();

    // Example 7: Query by date range
    println!("📝 Example 7: Querying by date range");
    println!("------------------------------------");

    let now = chrono::Utc::now();
    let one_day_ago = now - chrono::Duration::days(1);

    let recent_models = registry.get_models_by_date_range(one_day_ago, now).await?;
    println!("📅 Models trained in last 24 hours: {}", recent_models.len());
    for model in &recent_models {
        println!("  - {} (trained {})",
            model.model_id,
            model.training_date.format("%Y-%m-%d %H:%M:%S")
        );
    }
    println!();

    // Example 8: Archive old model
    println!("📝 Example 8: Archiving model");
    println!("-----------------------------");

    // Register a model to archive
    let mut old_metadata = ModelVersionMetadata::new(
        "dqn-v0.9.0".to_string(),
        ModelType::DQN,
        "0.9.0".to_string(),
        "databento_2024_Q3".to_string(),
        "s3://foxhunt-ml-models/dqn/0.9.0/".to_string(),
    );
    old_metadata.set_checksum("sha256:old123...".to_string());
    registry.register_version(&old_metadata).await?;

    // Archive it
    registry.archive_model("dqn-v0.9.0").await?;
    println!("✅ DQN v0.9.0 archived\n");

    // Example 9: Error handling
    println!("📝 Example 9: Error handling");
    println!("----------------------------");

    match registry.get_model_by_version("nonexistent-model").await {
        Ok(_) => println!("❌ Should have failed!"),
        Err(e) => println!("✅ Correctly handled missing model: {}", e),
    }
    println!();

    println!("🎉 All examples completed successfully!");
    println!("\n💡 Key Features Demonstrated:");
    println!("   ✓ Model registration with metadata");
    println!("   ✓ Hyperparameter and metric tracking");
    println!("   ✓ Production/experimental tagging");
    println!("   ✓ Version queries (by ID, type, date)");
    println!("   ✓ Model archival and lifecycle management");
    println!("   ✓ Registry statistics and monitoring");
    println!("   ✓ Error handling and validation");

    Ok(())
}