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feat(hyperopt): enable ContinuousPPO hyperopt adapter

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Fix ParameterSpace trait mismatch by encoding integer/categorical params
(batch_size, num_epochs, learnable_std) in continuous space. Update
ContinuousPolicyConfig → FlowPolicyConfig, fix GAEConfig missing field,
and add explicit f64 type annotations.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-02-21 01:10:07 +01:00
parent 5935907cd7
commit 0fd7277455
2 changed files with 27 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files

108
ml/src/hyperopt/adapters/continuous_ppo.rs
View File

@@ -43,7 +43,8 @@ use crate::ppo::continuous_ppo::{
ContinuousPPO, ContinuousPPOConfig, ContinuousTrajectory, ContinuousTrajectoryBatch,
ContinuousTrajectoryStep,
};
use crate::ppo::continuous_policy::{ContinuousAction, ContinuousPolicyConfig};
use crate::ppo::continuous_policy::ContinuousAction;
use crate::ppo::flow_policy::FlowPolicyConfig;
use crate::ppo::gae::GAEConfig;
use crate::MLError;
@@ -121,26 +122,16 @@ impl ParameterSpace for ContinuousPPOParams {
(0.001_f64.ln(), 0.1_f64.ln()), // entropy_coeff (log scale)
(0.9, 0.99), // gae_lambda (linear)
(0.95, 0.999), // gamma (linear)
]
}
fn integer_bounds() -> Vec<(i64, i64)> {
vec![
(32, 256), // batch_size
(5, 15), // num_epochs
]
}
fn categorical_choices() -> Vec<Vec<String>> {
vec![
vec!["true".to_string(), "false".to_string()], // learnable_std
(32.0, 256.0), // batch_size (rounded to integer)
(5.0, 15.0), // num_epochs (rounded to integer)
(0.0, 1.0), // learnable_std (threshold at 0.5)
]
}
fn from_continuous(x: &[f64]) -> Result<Self, MLError> {
if x.len() != 9 {
if x.len() != 12 {
return Err(MLError::ConfigError {
reason: format!("Expected 9 continuous parameters, got {}", x.len()),
reason: format!("Expected 12 continuous parameters, got {}", x.len()),
});
}
@@ -154,10 +145,9 @@ impl ParameterSpace for ContinuousPPOParams {
entropy_coeff: x[6].exp(),
gae_lambda: x[7].clamp(0.9, 0.99),
gamma: x[8].clamp(0.95, 0.999),
// Default values for other fields (will be set by from_mixed)
learnable_std: true,
batch_size: 64,
num_epochs: 10,
batch_size: x[9].round().clamp(32.0, 256.0) as usize,
num_epochs: x[10].round().clamp(5.0, 15.0) as usize,
learnable_std: x[11] >= 0.5,
})
}
@@ -172,58 +162,12 @@ impl ParameterSpace for ContinuousPPOParams {
self.entropy_coeff.ln(),
self.gae_lambda,
self.gamma,
self.batch_size as f64,
self.num_epochs as f64,
if self.learnable_std { 1.0 } else { 0.0 },
]
}
fn from_integer(x: &[i64]) -> Result<Self, MLError> {
if x.len() != 2 {
return Err(MLError::ConfigError {
reason: format!("Expected 2 integer parameters, got {}", x.len()),
});
}
Ok(Self {
batch_size: x[0].clamp(32, 256) as usize,
num_epochs: x[1].clamp(5, 15) as usize,
// Default values for other fields
..Default::default()
})
}
fn to_integer(&self) -> Vec<i64> {
vec![self.batch_size as i64, self.num_epochs as i64]
}
fn from_categorical(x: &[usize]) -> Result<Self, MLError> {
if x.is_empty() {
return Err(MLError::ConfigError {
reason: "Expected 1 categorical parameter, got 0".to_string(),
});
}
Ok(Self {
learnable_std: x[0] == 0, // 0 = "true", 1 = "false"
// Default values for other fields
..Default::default()
})
}
fn to_categorical(&self) -> Vec<usize> {
vec![if self.learnable_std { 0 } else { 1 }]
}
fn from_mixed(continuous: &[f64], integer: &[i64], categorical: &[usize]) -> Result<Self, MLError> {
let mut params = Self::from_continuous(continuous)?;
let int_params = Self::from_integer(integer)?;
let cat_params = Self::from_categorical(categorical)?;
params.batch_size = int_params.batch_size;
params.num_epochs = int_params.num_epochs;
params.learnable_std = cat_params.learnable_std;
Ok(params)
}
fn param_names() -> Vec<&'static str> {
vec![
"policy_lr",
@@ -443,14 +387,13 @@ impl HyperparameterOptimizable for ContinuousPPOTrainer {
MLError::ModelError(format!("Failed to create training directories: {}", e))
})?;
// Create continuous PPO config
let policy_config = ContinuousPolicyConfig {
// Create continuous PPO config using FlowPolicyConfig
let policy_config = FlowPolicyConfig {
state_dim: 54,
hidden_dims: vec![128, 64],
action_min: params.action_min as f32,
action_max: params.action_max as f32,
init_log_std: params.init_log_std as f32,
learnable_std: params.learnable_std,
action_dim: 1,
context_dim: 128,
num_layers: 4,
scale_clamp: 5.0,
};
let ppo_config = ContinuousPPOConfig {
@@ -465,6 +408,7 @@ impl HyperparameterOptimizable for ContinuousPPOTrainer {
gae_config: GAEConfig {
gamma: params.gamma as f32,
lambda: params.gae_lambda as f32,
normalize_advantages: true,
},
batch_size: params.batch_size,
mini_batch_size: 64,
@@ -652,9 +596,9 @@ impl ContinuousPPOTrainer {
/// Compute maximum drawdown from episode returns
fn compute_max_drawdown(&self, returns: &[f64]) -> f64 {
let mut cumulative = 0.0;
let mut peak = 0.0;
let mut max_drawdown = 0.0;
let mut cumulative: f64 = 0.0;
let mut peak: f64 = 0.0;
let mut max_drawdown: f64 = 0.0;
for &ret in returns {
cumulative += ret;
@@ -689,14 +633,12 @@ mod tests {
};
let continuous = params.to_continuous();
let integer = params.to_integer();
let categorical = params.to_categorical();
let recovered = ContinuousPPOParams::from_mixed(&continuous, &integer, &categorical).unwrap();
let recovered = ContinuousPPOParams::from_continuous(&continuous).unwrap();
assert!((recovered.policy_lr - params.policy_lr).abs() < 1e-10);
assert!((recovered.value_lr - params.value_lr).abs() < 1e-10);
assert_eq!(recovered.batch_size, params.batch_size);
assert_eq!(recovered.num_epochs, params.num_epochs);
assert_eq!(recovered.learnable_std, params.learnable_std);
}

4
ml/src/hyperopt/adapters/mod.rs
View File

@@ -49,7 +49,7 @@
// Active adapters (production-ready)
pub mod async_data_loader;
// pub mod continuous_ppo; // TODO: Fix ParameterSpace trait implementation
pub mod continuous_ppo;
pub mod dqn;
pub mod mamba2;
pub mod ppo;
@@ -57,7 +57,7 @@ pub mod tft;
// Re-export adapters for convenience
pub use async_data_loader::AsyncDataLoader;
// pub use continuous_ppo::{ContinuousPPOMetrics, ContinuousPPOParams, ContinuousPPOTrainer};
pub use continuous_ppo::{ContinuousPPOMetrics, ContinuousPPOParams, ContinuousPPOTrainer};
pub use dqn::{DQNMetrics, DQNParams, DQNTrainer};
pub use mamba2::{Mamba2Metrics, Mamba2Params, Mamba2Trainer};
pub use ppo::{PPOMetrics, PPOParams, PPOTrainer};