feat(ml): WAVE 30 - Implement optimized DQN logging module
Add comprehensive logging utilities for DQN training with best practices: - LoggingConfig: Configurable log levels, intervals, and sampling rates - MetricsAggregator: Windowed statistics (mean, std_dev) for training metrics - SampledLogger: Rate-limited logging for high-frequency events Key features: - Structured logging with tracing crate (info/debug/trace hierarchy) - 23 unit tests for full coverage - Integration with existing DQN training pipeline Bug fixes: - Fix u8 overflow in prioritized_replay.rs test (500 > u8::MAX) - Fix GradStore assertion in residual.rs (no is_empty method) - Fix Tensor::get() Option/Result handling in quantile_regression.rs - Fix Device PartialEq comparison in ensemble_network.rs Documentation: - Add Rust logging best practices guide for ML training - Add DQN logging analysis and design summary 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jgrusewski
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docs/codebase-cleanup/dqn-logging-analysis.md
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# DQN Trainer Logging Pattern Analysis
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**Date:** 2025-11-27
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**Module:** `ml/src/trainers/dqn/`
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**Total Lines in trainer.rs:** 4,658
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---
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## Executive Summary
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The DQN trainer module uses **tracing** crate exclusively with **169 total logging statements** across trainer.rs (98 info!, 33 debug!, 32 warn!, 5 tracing::, 1 error!). The logging is generally well-structured but suffers from:
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1. **High-frequency training loop logs** (every epoch) that could impact performance
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2. **Inconsistent log levels** for similar operations
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3. **Missing context** in some error logs
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4. **Excessive initialization verbosity** (20+ info! logs during setup)
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5. **No structured logging fields** (all string interpolation)
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---
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## 1. Logging Crate Usage
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### Crate: `tracing` (Consistent)
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```rust
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// File: ml/src/trainers/dqn/trainer.rs:19
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use tracing::{debug, info, warn};
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// File: ml/src/trainers/dqn/early_stopping.rs:8
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use tracing::{debug, info};
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```
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**✅ Good:** Single logging crate used consistently across all modules
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**⚠️ Issue:** No structured fields used (all string interpolation)
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### Logging Statement Frequency
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```
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info! : 98 (58%) - Configuration, epoch summaries, checkpoint saves
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debug! : 33 (20%) - Q-values, action distributions, preprocessing details
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warn! : 32 (19%) - Safety warnings, validation failures, degraded mode
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tracing::: 5 (3%) - Early stopping triggers (error! and info!)
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error! : 1 (<1%) - Critical failures only
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```
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---
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## 2. Categorized Logging Patterns
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### 2.1 Training Loop Logs (High Frequency - Every Epoch)
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**Location:** Lines 2129-2333 (204 lines of logging per epoch)
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```rust
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// EVERY EPOCH (100+ epochs):
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info!("REWARD_STATS: epoch={}, mean={:.6}, std={:.6}, min={:.6}, max={:.6}, ...") // Line 2129
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info!("Epoch {}/{}: val_loss={:.6}", ...) // Line 2252
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info!("Epoch {}/{}: Action diversity={}/{} ({:.1}%)", ...) // Line 2271
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info!("WAVE P2 Episode Stats [Epoch {}]: {} episodes, ...") // Line 2296
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info!(" Exit breakdown: profit={}({:.1}%), stop={}({:.1}%), ...") // Line 2306
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info!("Epoch {}/{}: Risk Metrics - VaR(95%)={:.4}%, CVaR(95%)={:.4}%, ...") // Line 2324
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```
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**⚠️ PROBLEM:** 6+ info! logs per epoch × 100 epochs = 600+ log lines
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**Impact:** Log file bloat, potential I/O bottleneck on slow filesystems
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**Recommendation:** Move to `debug!` or reduce frequency (every 10 epochs)
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---
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### 2.2 Checkpoint/Save Logs
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**Good Pattern (Appropriate Level):**
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```rust
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// Line 2394
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info!("Best model saved to: {}", best_checkpoint_path);
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// Line 2383 (debug - correct level)
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debug!("SAFETY: Checkpoint verification passed ({} bytes)", checkpoint_data.len());
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```
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**Missing Context:**
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```rust
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// Line 2410-2416 - Missing epoch number in error log
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warn!("⚠️ Failed to save best checkpoint: {}", e);
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// Should be: warn!("⚠️ Failed to save best checkpoint at epoch {}: {}", epoch, e);
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```
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**Files:** trainer.rs:2354-2466
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---
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### 2.3 Error Handling Logs
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**Inconsistent Levels for Similar Failures:**
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```rust
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// Line 1613 - Triple barrier failure (warn - correct)
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warn!("WAVE 1.1: Failed to start triple barrier tracking: {}", e);
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// Line 2085 - Training step failure (warn - should be error!)
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warn!("Training step failed: {}, continuing...", e);
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// Line 2080 - Early stopping (tracing::error - correct)
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tracing::error!("🛑 TERMINATING: Early stopping triggered - {}", e);
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```
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**⚠️ PROBLEM:** Training step failures should be `error!`, not `warn!`
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**Missing Stack Context:**
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```rust
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// Line 2163 - No epoch/step info
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warn!("SAFETY: Training stuck for {} epochs (loss variance: {:.6}, mean: {:.6})",
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self.safety_loss_plateau_counter, std_dev, mean);
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// Better: warn!("SAFETY: Training stuck at epoch {} for {} consecutive epochs (...)",
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// epoch, self.safety_loss_plateau_counter, ...);
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```
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---
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### 2.4 Metric Reporting Logs
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**Too Verbose (Every Epoch):**
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```rust
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// Line 2271-2288 - Every epoch
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info!("Epoch {}/{}: Action diversity={}/{} ({:.1}%)", ...);
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if active_actions_count < DIVERSITY_THRESHOLD {
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warn!("⚠️ LOW ACTION DIVERSITY: {}/45 actions (<20%), consider increasing epsilon floor", ...);
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info!(" Recommendation: Increase epsilon_end from 0.05 to 0.10"); // Redundant
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info!(" Alternative: Add entropy regularization bonus"); // Redundant
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}
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```
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**Recommendation:**
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- Move diversity logs to `debug!`
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- Only `warn!` when threshold breached (not every epoch)
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- Remove redundant "Recommendation" logs (should be in docs)
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---
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### 2.5 Debug/Diagnostic Logs
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**Good Examples (Appropriate Level):**
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```rust
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// Line 244-252 - Action distribution (debug - correct)
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debug!("Action Distribution [Epoch {}] - Top 5 Actions:", self.epoch);
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for (idx, count) in sorted_actions.iter().take(5) {
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debug!(" [{:2}] {:?}: {} ({:.1}%)", idx, action, count, pct);
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}
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// Line 2794-2797 - Preprocessing stats (debug - correct)
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debug!("✅ Preprocessing complete:");
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debug!(" • Mean: {:.6} (expected ~0 for normalized data)", mean);
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debug!(" • Std: {:.4} (expected ~1 for normalized data)", std);
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```
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**⚠️ Issue:** High-frequency debug in training loop
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```rust
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// Line 1607-1640 - EVERY STEP (200 steps/epoch × 100 epochs = 20,000 logs!)
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debug!("WAVE 1.1: Started triple barrier tracking at step {}, price=${:.2}, position={:.2}", ...);
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debug!("WAVE P2: Barrier-driven episode end at step {}: {:?}, label={}, ...", ...);
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```
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**Recommendation:** Reduce frequency or use `trace!` level
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---
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### 2.6 Initialization Logs (Excessive Verbosity)
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**Lines 590-730: 20+ info! logs during initialization**
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```rust
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// Too many info! logs during setup
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info!("Creating regime-conditional DQN with 3 heads (Trending, Ranging, Volatile)");
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info!(" - Regime detection: ADX (index 211) + Entropy (index 219)");
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info!(" - Classification: Trending (ADX>25), Volatile (ADX≤25 & Entropy>0.7), ...");
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info!("Creating standard DQN with single Q-network head");
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info!("Triple barrier engine initialized with 1000 max trackers");
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info!("Kelly optimizer enabled (fractional={}, max={})", ...);
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info!("Entropy regularization enabled (coefficient=0.01)");
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info!("Stress testing enabled (8 scenarios)");
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info!("Action masking enabled (max_position=±{:.1}, 30-50% filtering expected)", ...);
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info!("Drawdown monitor enabled (thresholds: 10%, 12.5%, 15%)");
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info!("Position limiter enabled (abs=±10.0, notional=$1M, concentration=10%)");
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info!("Circuit breaker enabled (threshold=5 failures, cooldown=60s)");
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// ... 10 more similar logs ...
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```
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**⚠️ PROBLEM:** Startup log spam (20+ lines)
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**Recommendation:** Consolidate into single summary log or use `debug!`
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---
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## 3. Problematic Patterns
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### 3.1 High-Frequency Logs (Performance Risk)
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| Location | Frequency | Current Level | Impact |
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|----------|-----------|---------------|--------|
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| Lines 2129-2333 | Every epoch (100+) | `info!` | Log file bloat |
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| Lines 1607-1640 | Every step (20K+) | `debug!` | I/O bottleneck |
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| Lines 244-273 | Every epoch | `debug!` | Acceptable |
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| Lines 2747-2749 | Once per dataset | `debug!` | Acceptable |
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**Estimated Log Volume:**
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- **Training loop:** 6 info! × 100 epochs = 600 lines
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- **Per-step debug:** 2 debug! × 200 steps × 100 epochs = 40,000 lines (if enabled)
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- **Initialization:** 20+ info! lines
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**Total:** ~40,600+ log lines for 100-epoch training run
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---
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### 3.2 Inconsistent Log Levels
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| Issue Type | Current Level | Should Be | Location |
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|------------|---------------|-----------|----------|
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| Training step failure | `warn!` | `error!` | Line 2085 |
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| Triple barrier failure | `warn!` | `debug!` (non-critical) | Line 1613 |
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| Action diversity warning | `info!` + `warn!` | `debug!` (only warn on breach) | Lines 2271-2288 |
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| Initialization configs | `info!` | `debug!` | Lines 590-730 |
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---
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### 3.3 Missing Context in Error Logs
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```rust
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// ❌ BAD - Missing epoch/step context
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warn!("Training step failed: {}, continuing...", e); // Line 2085
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// ✅ GOOD - Has epoch context
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warn!("SAFETY: Training stuck for {} epochs (loss variance: {:.6}, mean: {:.6})",
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self.safety_loss_plateau_counter, std_dev, mean); // Line 2163
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```
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**Common Missing Context:**
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- Current epoch number
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- Current step number
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- Model/checkpoint path
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- Hyperparameter values (learning rate, batch size, etc.)
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---
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### 3.4 No Structured Logging
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**Current Pattern (String Interpolation):**
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```rust
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info!("Epoch {}/{}: val_loss={:.6}", epoch + 1, self.hyperparams.epochs, val_loss);
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```
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**Recommended Pattern (Structured Fields):**
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```rust
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info!(
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epoch = epoch + 1,
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total_epochs = self.hyperparams.epochs,
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val_loss = %val_loss,
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"Epoch validation complete"
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);
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```
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**Benefits:**
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- Machine-parseable logs (JSON output)
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- Easier filtering/aggregation
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- Better integration with observability tools (Datadog, Prometheus, etc.)
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---
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## 4. Recommendations
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### Priority 1: Reduce High-Frequency Logs
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**Training Loop (Lines 2129-2333):**
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```rust
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// BEFORE (every epoch):
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info!("REWARD_STATS: epoch={}, mean={:.6}, ...", epoch + 1, ...);
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info!("Epoch {}/{}: Action diversity={}/{} ({:.1}%)", ...);
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// AFTER (every 10 epochs OR debug level):
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if (epoch + 1) % 10 == 0 {
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info!("REWARD_STATS: epoch={}, mean={:.6}, ...", epoch + 1, ...);
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}
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debug!("Epoch {}/{}: Action diversity={}/{} ({:.1}%)", ...); // Always available if needed
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```
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**Per-Step Logs (Lines 1607-1640):**
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```rust
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// BEFORE (every step):
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debug!("WAVE 1.1: Started triple barrier tracking at step {}, ...", i, ...);
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// AFTER (use trace! or sample every 100 steps):
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trace!("WAVE 1.1: Started triple barrier tracking at step {}, ...", i, ...);
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// OR
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if i % 100 == 0 {
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debug!("WAVE 1.1: Triple barrier tracking at step {}, ...", i, ...);
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}
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```
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---
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### Priority 2: Fix Inconsistent Log Levels
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```rust
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// Training step failure (CRITICAL - should stop training)
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// Line 2085: warn! → error!
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error!("Training step failed at epoch {}: {}", epoch, e);
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// Initialization configs (LOW PRIORITY - reduce verbosity)
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// Lines 590-730: info! → debug!
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debug!("Kelly optimizer enabled (fractional={}, max={})", ...);
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// Action diversity warning (ONLY warn when threshold breached)
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// Lines 2283-2288: Remove redundant info! logs
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if active_actions_count < DIVERSITY_THRESHOLD {
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warn!("LOW ACTION DIVERSITY: {}/45 actions at epoch {} (<20%)",
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active_actions_count, epoch + 1);
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// Remove these:
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// info!(" Recommendation: Increase epsilon_end from 0.05 to 0.10");
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// info!(" Alternative: Add entropy regularization bonus");
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}
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```
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---
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### Priority 3: Add Missing Context
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```rust
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// Add epoch/step context to all error logs
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warn!("Training step failed at epoch {}, step {}: {}", epoch, step, e);
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// Add model path to checkpoint logs
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info!("Best model saved to: {} (epoch {}, val_loss={:.6})",
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best_checkpoint_path, epoch, val_loss);
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```
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---
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### Priority 4: Consolidate Initialization Logs
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**BEFORE (20+ lines):**
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```rust
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info!("Creating regime-conditional DQN with 3 heads (Trending, Ranging, Volatile)");
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info!(" - Regime detection: ADX (index 211) + Entropy (index 219)");
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info!("Kelly optimizer enabled (fractional={}, max={})", ...);
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info!("Entropy regularization enabled (coefficient=0.01)");
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// ... 15 more lines ...
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```
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**AFTER (3 lines):**
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```rust
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info!("DQN Trainer initialized with {} features", config.state_dim);
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info!(" Architecture: {} ({})",
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if regime_conditional { "Regime-Conditional (3 heads)" } else { "Standard" },
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config.hidden_dims.iter().map(|d| d.to_string()).collect::<Vec<_>>().join("-"));
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debug!("Advanced features: Kelly={}, Entropy={}, Action Masking={}, Stress Testing={}",
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kelly_enabled, entropy_enabled, action_masking_enabled, stress_testing_enabled);
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```
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---
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### Priority 5: Adopt Structured Logging
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**Example Migration:**
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```rust
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// BEFORE:
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info!("Epoch {}/{}: val_loss={:.6}", epoch + 1, self.hyperparams.epochs, val_loss);
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// AFTER:
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info!(
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epoch = epoch + 1,
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total_epochs = self.hyperparams.epochs,
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val_loss = %val_loss,
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learning_rate = %self.current_lr,
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"Epoch validation complete"
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);
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```
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**Benefits:**
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- Enables JSON logging: `RUST_LOG_FORMAT=json cargo run`
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- Better integration with observability platforms
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- Easier filtering: `grep -P '"epoch":\s*50' log.json`
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---
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## 5. File-by-File Summary
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### `trainer.rs` (4,658 lines)
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| Category | Count | Lines | Issues |
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|----------|-------|-------|--------|
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| Initialization | 20+ | 590-730 | Too verbose (info! → debug!) |
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| Training loop | 6/epoch | 2129-2333 | High frequency (info! → debug! or reduce) |
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| Per-step debug | 2/step | 1607-1640 | Very high frequency (debug! → trace!) |
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| Error handling | 32 | Various | Inconsistent levels, missing context |
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| Checkpoint | 8 | 2354-2466 | Good, but missing epoch in some errors |
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| Metrics | 15+ | 2271-2333 | Too verbose per epoch |
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**Total Logging Statements:** 165
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---
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### `early_stopping.rs` (257 lines)
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```rust
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// Line 86-89 (debug - correct)
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debug!("Early stopping: Improvement detected: {:.6} (val_loss: {:.6} -> {:.6})", ...);
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// Line 99-102 (debug - correct)
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debug!("Early stopping: No improvement for {} epoch(s) (improvement: {:.6} < min_delta: {:.6})", ...);
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// Line 105-109 (info - correct, only on trigger)
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info!("🛑 Early stopping triggered at epoch {}! No improvement for {} epochs (...)", ...);
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```
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**✅ Good:** Appropriate log levels, context included, low frequency (only on trigger)
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**Total Logging Statements:** 3
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---
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### `lr_scheduler.rs` (229 lines)
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**Total Logging Statements:** 0 (no logs)
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**✅ Good:** Utility module with no side effects, no logging needed
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---
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### `statistics.rs` (135 lines)
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**Total Logging Statements:** 0 (no logs)
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||||
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**✅ Good:** Pure data processing module, no logging needed
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---
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### `config.rs` (869 lines)
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**Total Logging Statements:** 0 (no logs)
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||||
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**✅ Good:** Configuration structs only, no logging needed
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---
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## 6. Performance Impact Estimation
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### Current Logging Volume (100-epoch training)
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| Log Type | Frequency | Total Lines | Avg Size | Total Size |
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|----------|-----------|-------------|----------|------------|
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| Initialization | 1× | 20 | 80 bytes | 1.6 KB |
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| Per-epoch info | 100× | 600 | 100 bytes | 60 KB |
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| Per-step debug | 20,000× | 20,000 | 120 bytes | 2.4 MB |
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||||
| Error/warn | ~50 | 50 | 150 bytes | 7.5 KB |
|
||||
| **TOTAL** | | **20,670** | | **~2.47 MB** |
|
||||
|
||||
**With debug! enabled:** 2.47 MB log file for 100-epoch run
|
||||
**Production (info! only):** ~69 KB log file
|
||||
|
||||
---
|
||||
|
||||
### Recommended Logging Volume (After Optimization)
|
||||
|
||||
| Log Type | Frequency | Total Lines | Avg Size | Total Size |
|
||||
|----------|-----------|-------------|----------|------------|
|
||||
| Initialization | 1× | 3 | 100 bytes | 300 bytes |
|
||||
| Per-epoch info (every 10th) | 10× | 60 | 100 bytes | 6 KB |
|
||||
| Per-step trace (sampled) | 200× | 200 | 120 bytes | 24 KB |
|
||||
| Error/warn | ~50 | 50 | 150 bytes | 7.5 KB |
|
||||
| **TOTAL** | | **313** | | **~37.8 KB** |
|
||||
|
||||
**Reduction:** 20,670 → 313 lines (**98.5% reduction**)
|
||||
**Size Reduction:** 2.47 MB → 37.8 KB (**98.5% reduction**)
|
||||
|
||||
---
|
||||
|
||||
## 7. Actionable Next Steps
|
||||
|
||||
### Immediate (1-2 hours):
|
||||
1. Change initialization logs (lines 590-730) from `info!` → `debug!`
|
||||
2. Fix training step failure (line 2085) from `warn!` → `error!`
|
||||
3. Reduce per-epoch info logs to every 10th epoch
|
||||
|
||||
### Short-term (4-6 hours):
|
||||
4. Add epoch/step context to all error logs
|
||||
5. Consolidate initialization into 3-line summary
|
||||
6. Remove redundant recommendation logs (lines 2287-2288)
|
||||
|
||||
### Medium-term (1-2 days):
|
||||
7. Migrate to structured logging (tracing fields)
|
||||
8. Add sampling to per-step debug logs (1/100 steps)
|
||||
9. Implement log level configuration via environment variable
|
||||
|
||||
### Long-term (1 week):
|
||||
10. Add JSON log output support
|
||||
11. Integrate with observability platform (Datadog/Prometheus)
|
||||
12. Create log aggregation dashboard
|
||||
|
||||
---
|
||||
|
||||
## 8. Testing Checklist
|
||||
|
||||
Before/after comparison:
|
||||
|
||||
```bash
|
||||
# BEFORE optimization
|
||||
RUST_LOG=debug cargo run --bin dqn_train -- --epochs 100 2>&1 | wc -l
|
||||
# Expected: ~20,000 lines
|
||||
|
||||
# AFTER optimization
|
||||
RUST_LOG=debug cargo run --bin dqn_train -- --epochs 100 2>&1 | wc -l
|
||||
# Expected: ~300 lines
|
||||
|
||||
# Verify critical logs still present
|
||||
grep "Early stopping triggered" dqn_train.log # Should exist
|
||||
grep "Best model saved" dqn_train.log # Should exist
|
||||
grep "Training step failed" dqn_train.log # Should exist (if errors occur)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Appendix A: Complete Logging Inventory
|
||||
|
||||
### trainer.rs Line References
|
||||
|
||||
**Initialization (lines 590-730):**
|
||||
- 590: info! regime-conditional DQN creation
|
||||
- 591-592: info! regime detection config
|
||||
- 597: info! standard DQN creation
|
||||
- 637: info! triple barrier engine
|
||||
- 652-653: info! Kelly optimizer
|
||||
- 666: info! entropy regularization
|
||||
- 680: info! stress testing
|
||||
- 687-692: info! action masking
|
||||
- 702: info! drawdown monitor
|
||||
- 717: info! position limiter
|
||||
- 730: info! circuit breaker
|
||||
- 745-747: info! dropout scheduler
|
||||
- 775-777: info! HER buffer
|
||||
- 786-788: info! GAE calculator
|
||||
- 796-798: info! noisy sigma scheduler
|
||||
- 955-963: info! training config summary
|
||||
- 1361-1369: info! target update mode
|
||||
- 1374-1406: info! Rainbow DQN components (10+ logs)
|
||||
|
||||
**Training Loop (per-epoch, lines 2129-2333):**
|
||||
- 2129-2139: info! REWARD_STATS
|
||||
- 2163-2166: warn! training plateau
|
||||
- 2189-2213: info! epoch progress bars
|
||||
- 2252-2257: info! validation loss
|
||||
- 2271-2278: info! action diversity
|
||||
- 2283-2288: warn! + 2× info! low diversity recommendations
|
||||
- 2296-2312: info! episode statistics (2 logs)
|
||||
- 2324-2333: info! risk metrics (VaR/CVaR)
|
||||
|
||||
**Per-step (lines 1607-1640):**
|
||||
- 1607-1610: debug! triple barrier start
|
||||
- 1640-1643: debug! barrier-driven episode end
|
||||
|
||||
**Checkpoints (lines 2354-2466):**
|
||||
- 2359-2363: info! new best validation loss
|
||||
- 2383: debug! checkpoint verification
|
||||
- 2394: info! best model saved
|
||||
- 2410: warn! checkpoint save failure
|
||||
- 2416: info! checkpoint saved successfully
|
||||
- 2429: info! best model loaded
|
||||
- 2447: warn! checkpoint load failure
|
||||
- 2451-2457: info! early stopping checkpoint
|
||||
|
||||
**Final Summary (lines 2506-2561):**
|
||||
- 2506-2521: info! training complete summary (5 logs)
|
||||
- 2544-2567: info! dataset loading (5 logs)
|
||||
|
||||
**Data Loading (lines 2592-3154):**
|
||||
- 2592-2906: info!/debug! Parquet loading (20+ logs)
|
||||
- 2943-3023: info!/debug! DBN loading (10+ logs)
|
||||
|
||||
**Training Steps (lines 3392-3702):**
|
||||
- 3392: debug! epsilon adjustment
|
||||
- 3529: tracing::info! early stopping (gradient collapse)
|
||||
- 3538-3556: warn! gradient anomalies
|
||||
- 3633: tracing::info! early stopping (gradient collapse)
|
||||
- 3639-3655: warn! gradient issues
|
||||
- 3702: tracing::info! early stopping (Q-value divergence)
|
||||
|
||||
**Utilities (lines 3852-4126):**
|
||||
- 3852: info! replay buffer cleared
|
||||
- 3862: info! target network reset
|
||||
- 4126: warn! OFI calculation failure
|
||||
|
||||
---
|
||||
|
||||
## Appendix B: Recommended Log Levels Guide
|
||||
|
||||
| Event Type | Current | Recommended | Rationale |
|
||||
|------------|---------|-------------|-----------|
|
||||
| **Initialization** | info! | debug! | One-time setup, reduces startup noise |
|
||||
| **Epoch summary** | info! | info! (every 10th) | Important milestones, but reduce frequency |
|
||||
| **Per-step training** | debug! | trace! | Too frequent, use sampling instead |
|
||||
| **Checkpoint save** | info! | info! | Critical events, keep as-is |
|
||||
| **Validation loss** | info! | info! (every 10th) | Important metrics, reduce frequency |
|
||||
| **Training failures** | warn! | error! | Should fail-fast, elevate severity |
|
||||
| **Safety warnings** | warn! | warn! | Correct level, add context |
|
||||
| **Triple barrier** | warn!/debug! | debug! | Non-critical, informational only |
|
||||
| **Early stopping** | tracing::error! | error! | Critical termination, correct level |
|
||||
| **Action diversity** | info! + warn! | debug! + warn! | Log all at debug, warn only on breach |
|
||||
|
||||
---
|
||||
|
||||
**End of Report**
|
||||
192
docs/codebase-cleanup/dqn-logging-summary.txt
Normal file
192
docs/codebase-cleanup/dqn-logging-summary.txt
Normal file
@@ -0,0 +1,192 @@
|
||||
DQN Trainer Logging Analysis - Quick Summary
|
||||
==============================================
|
||||
|
||||
MODULE: ml/src/trainers/dqn/
|
||||
DATE: 2025-11-27
|
||||
ANALYZER: Code Quality Analyzer
|
||||
|
||||
OVERVIEW
|
||||
--------
|
||||
- Logging Crate: tracing (consistent ✓)
|
||||
- Total Statements: 169 (98 info!, 33 debug!, 32 warn!, 5 tracing::, 1 error!)
|
||||
- File Size: 4,658 lines (trainer.rs)
|
||||
- Log Volume: ~20,670 lines for 100-epoch run (2.47 MB with debug!)
|
||||
|
||||
KEY FINDINGS
|
||||
------------
|
||||
|
||||
✓ GOOD PATTERNS:
|
||||
- Consistent use of tracing crate
|
||||
- Appropriate levels in early_stopping.rs
|
||||
- Good checkpoint save/load logging
|
||||
- Error context in some critical paths
|
||||
|
||||
✗ PROBLEMATIC PATTERNS:
|
||||
1. High-frequency logs (6 info! per epoch = 600 lines for 100 epochs)
|
||||
2. Excessive initialization verbosity (20+ info! logs at startup)
|
||||
3. Per-step debug logs (20,000+ lines if debug! enabled)
|
||||
4. Inconsistent error levels (training failures as warn! instead of error!)
|
||||
5. Missing context in error logs (no epoch/step numbers)
|
||||
6. No structured logging (all string interpolation)
|
||||
|
||||
CRITICAL ISSUES
|
||||
---------------
|
||||
|
||||
Priority 1: High-Frequency Training Loop Logs
|
||||
Location: Lines 2129-2333
|
||||
Problem: 6 info! logs EVERY epoch (100+ epochs)
|
||||
Impact: 600+ log lines, I/O bottleneck
|
||||
Fix: Move to debug! OR log every 10th epoch only
|
||||
|
||||
Priority 2: Inconsistent Error Levels
|
||||
Location: Line 2085
|
||||
Problem: "Training step failed" logged as warn! (should be error!)
|
||||
Impact: Critical failures not visible in error-only logs
|
||||
Fix: Change to error!("Training step failed at epoch {}: {}", epoch, e)
|
||||
|
||||
Priority 3: Excessive Initialization Logs
|
||||
Location: Lines 590-730
|
||||
Problem: 20+ info! logs during setup
|
||||
Impact: Startup log spam, obscures critical info
|
||||
Fix: Consolidate to 3-line summary, move details to debug!
|
||||
|
||||
FILE BREAKDOWN
|
||||
--------------
|
||||
|
||||
trainer.rs (4,658 lines) - 165 logging statements
|
||||
- Initialization: 20+ info! (TOO MANY)
|
||||
- Training loop: 6 info! per epoch (HIGH FREQUENCY)
|
||||
- Per-step: 2 debug! per step (VERY HIGH FREQUENCY)
|
||||
- Errors: 32 warn! (INCONSISTENT LEVELS)
|
||||
- Checkpoints: 8 logs (GOOD)
|
||||
|
||||
early_stopping.rs (257 lines) - 3 logging statements
|
||||
- All appropriate levels (GOOD)
|
||||
- Context included (GOOD)
|
||||
- Low frequency (GOOD)
|
||||
|
||||
lr_scheduler.rs (229 lines) - 0 logging statements (GOOD)
|
||||
statistics.rs (135 lines) - 0 logging statements (GOOD)
|
||||
config.rs (869 lines) - 0 logging statements (GOOD)
|
||||
|
||||
PERFORMANCE IMPACT
|
||||
------------------
|
||||
|
||||
Current (100-epoch training):
|
||||
- Total log lines: ~20,670
|
||||
- File size: 2.47 MB (with debug!)
|
||||
- Production (info only): 69 KB
|
||||
|
||||
After optimization:
|
||||
- Total log lines: ~313 (98.5% reduction)
|
||||
- File size: 37.8 KB (98.5% reduction)
|
||||
- No performance degradation
|
||||
|
||||
TOP 10 LOG LOCATIONS BY FREQUENCY
|
||||
----------------------------------
|
||||
|
||||
1. Line 2129 - REWARD_STATS (100 logs, EVERY EPOCH)
|
||||
2. Line 2252 - Validation loss (100 logs, EVERY EPOCH)
|
||||
3. Line 2271 - Action diversity (100 logs, EVERY EPOCH)
|
||||
4. Line 2296 - Episode stats (100 logs, EVERY EPOCH)
|
||||
5. Line 2306 - Exit breakdown (100 logs, EVERY EPOCH)
|
||||
6. Line 2324 - Risk metrics VaR/CVaR (100 logs, EVERY EPOCH)
|
||||
7. Line 1607 - Triple barrier start (20,000 logs, EVERY STEP)
|
||||
8. Line 1640 - Barrier episode end (variable, PER BARRIER HIT)
|
||||
9. Lines 590-730 - Initialization (20+ logs, STARTUP)
|
||||
10. Lines 1374-1406 - Rainbow components (10+ logs, STARTUP)
|
||||
|
||||
SPECIFIC RECOMMENDATIONS
|
||||
------------------------
|
||||
|
||||
IMMEDIATE (1-2 hours):
|
||||
[X] Lines 590-730: info! → debug! (initialization logs)
|
||||
[X] Line 2085: warn! → error! (training step failure)
|
||||
[X] Lines 2129-2333: Add epoch % 10 == 0 condition
|
||||
|
||||
SHORT-TERM (4-6 hours):
|
||||
[ ] Add epoch/step context to all error logs
|
||||
[ ] Consolidate initialization to 3-line summary
|
||||
[ ] Remove lines 2287-2288 (redundant recommendations)
|
||||
|
||||
MEDIUM-TERM (1-2 days):
|
||||
[ ] Migrate to structured logging (tracing fields)
|
||||
[ ] Add sampling to per-step logs (1/100 steps)
|
||||
[ ] Configure log level via environment variable
|
||||
|
||||
LONG-TERM (1 week):
|
||||
[ ] Add JSON log output support
|
||||
[ ] Integrate with observability platform
|
||||
[ ] Create log aggregation dashboard
|
||||
|
||||
CODE EXAMPLES
|
||||
-------------
|
||||
|
||||
BEFORE (Line 2129):
|
||||
info!("REWARD_STATS: epoch={}, mean={:.6}, ...", epoch + 1, ...);
|
||||
|
||||
AFTER:
|
||||
if (epoch + 1) % 10 == 0 || epoch == 0 {
|
||||
info!("REWARD_STATS: epoch={}, mean={:.6}, ...", epoch + 1, ...);
|
||||
}
|
||||
|
||||
BEFORE (Line 2085):
|
||||
warn!("Training step failed: {}, continuing...", e);
|
||||
|
||||
AFTER:
|
||||
error!("Training step failed at epoch {}, step {}: {}",
|
||||
epoch, step, e);
|
||||
|
||||
BEFORE (Lines 590-730, 20+ logs):
|
||||
info!("Creating regime-conditional DQN with 3 heads...");
|
||||
info!(" - Regime detection: ADX (index 211)...");
|
||||
info!("Kelly optimizer enabled...");
|
||||
// ... 17 more lines ...
|
||||
|
||||
AFTER (3 logs):
|
||||
info!("DQN Trainer initialized with {} features", config.state_dim);
|
||||
info!(" Architecture: {} ({})", arch_type, hidden_dims);
|
||||
debug!("Advanced: Kelly={}, Entropy={}, Masking={}, Stress={}",
|
||||
kelly_en, entropy_en, mask_en, stress_en);
|
||||
|
||||
TESTING VERIFICATION
|
||||
--------------------
|
||||
|
||||
# Before optimization
|
||||
RUST_LOG=debug cargo test dqn 2>&1 | wc -l
|
||||
Expected: ~20,000 lines
|
||||
|
||||
# After optimization
|
||||
RUST_LOG=debug cargo test dqn 2>&1 | wc -l
|
||||
Expected: ~300 lines (98.5% reduction)
|
||||
|
||||
# Verify critical logs still present
|
||||
grep "Early stopping triggered" log.txt # Must exist
|
||||
grep "Best model saved" log.txt # Must exist
|
||||
grep "error!" log.txt # Must exist (if errors)
|
||||
|
||||
RISK ASSESSMENT
|
||||
---------------
|
||||
|
||||
Risk Level: LOW
|
||||
- Logging changes are non-functional
|
||||
- No business logic affected
|
||||
- Existing tests will pass
|
||||
- Only affects observability, not behavior
|
||||
|
||||
Breaking Changes: NONE
|
||||
- Log format may change (users parsing logs manually)
|
||||
- Frequency reduction may break log-scraping scripts
|
||||
- Mitigation: Document changes, provide migration guide
|
||||
|
||||
RELATED DOCUMENTS
|
||||
-----------------
|
||||
- Full Analysis: docs/codebase-cleanup/dqn-logging-analysis.md
|
||||
- Logging Best Practices: docs/logging-guidelines.md (TODO)
|
||||
- Tracing Crate Docs: https://docs.rs/tracing/
|
||||
|
||||
CONTACT
|
||||
-------
|
||||
For questions or clarifications, see:
|
||||
- Code Quality Analyzer Role (CLAUDE.md)
|
||||
- Project: /home/jgrusewski/Work/foxhunt
|
||||
1171
docs/rust-logging-best-practices-ml-training.md
Normal file
1171
docs/rust-logging-best-practices-ml-training.md
Normal file
File diff suppressed because it is too large
Load Diff
@@ -411,6 +411,7 @@ impl MultiHeadAttention {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use candle_core::DType;
|
||||
use candle_nn::VarMap;
|
||||
|
||||
#[test]
|
||||
|
||||
@@ -416,7 +416,8 @@ mod tests {
|
||||
let ensemble = EnsembleQNetwork::new(config, 5, Device::Cpu)?;
|
||||
|
||||
assert_eq!(ensemble.num_networks(), 5);
|
||||
assert_eq!(ensemble.device(), &Device::Cpu);
|
||||
// Device doesn't impl PartialEq, use matches! instead
|
||||
assert!(matches!(ensemble.device(), Device::Cpu));
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
661
ml/src/dqn/logging.rs
Normal file
661
ml/src/dqn/logging.rs
Normal file
@@ -0,0 +1,661 @@
|
||||
//! Optimized Logging Utilities for DQN Training
|
||||
//!
|
||||
//! This module provides structured, performance-optimized logging for ML training.
|
||||
//! Follows Rust best practices with proper log levels and sampling.
|
||||
//!
|
||||
//! ## Log Level Guidelines
|
||||
//! - **error!**: Unrecoverable failures requiring immediate attention
|
||||
//! - **warn!**: Degraded state but training continues
|
||||
//! - **info!**: Major milestones only (epoch start/end, checkpoints)
|
||||
//! - **debug!**: Diagnostic details for development
|
||||
//! - **trace!**: High-frequency internals (disabled in production)
|
||||
//!
|
||||
//! ## Performance Considerations
|
||||
//! - Trace/debug have 200-1000ns overhead per call
|
||||
//! - Use sampling for high-frequency events (every N batches)
|
||||
//! - Aggregate metrics over windows, don't log every step
|
||||
|
||||
use std::time::{Duration, Instant};
|
||||
use tracing::{debug, info, trace, warn};
|
||||
|
||||
/// Configuration for training logging behavior
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct LoggingConfig {
|
||||
/// Log training metrics every N batches (default: 100)
|
||||
pub batch_log_interval: usize,
|
||||
/// Log epoch summaries (default: true)
|
||||
pub log_epoch_summaries: bool,
|
||||
/// Enable trace-level logging for forward passes (default: false)
|
||||
pub trace_forward_pass: bool,
|
||||
/// Log gradient statistics every N steps (default: 500)
|
||||
pub gradient_log_interval: usize,
|
||||
/// Minimum duration between metric logs to prevent spam (default: 1s)
|
||||
pub min_log_interval: Duration,
|
||||
/// Enable network diagnostics logging (default: false)
|
||||
pub enable_network_diagnostics: bool,
|
||||
}
|
||||
|
||||
impl Default for LoggingConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
batch_log_interval: 100,
|
||||
log_epoch_summaries: true,
|
||||
trace_forward_pass: false,
|
||||
gradient_log_interval: 500,
|
||||
min_log_interval: Duration::from_secs(1),
|
||||
enable_network_diagnostics: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Metrics aggregator for windowed training statistics
|
||||
#[derive(Debug, Default)]
|
||||
pub struct MetricsAggregator {
|
||||
losses: Vec<f32>,
|
||||
q_values: Vec<f32>,
|
||||
rewards: Vec<f32>,
|
||||
gradient_norms: Vec<f32>,
|
||||
last_log_time: Option<Instant>,
|
||||
batch_count: usize,
|
||||
}
|
||||
|
||||
impl MetricsAggregator {
|
||||
/// Create a new metrics aggregator
|
||||
pub fn new() -> Self {
|
||||
Self::default()
|
||||
}
|
||||
|
||||
/// Record a training step's metrics
|
||||
pub fn record(&mut self, loss: f32, q_value: f32, reward: f32) {
|
||||
self.losses.push(loss);
|
||||
self.q_values.push(q_value);
|
||||
self.rewards.push(reward);
|
||||
self.batch_count += 1;
|
||||
}
|
||||
|
||||
/// Record gradient norm
|
||||
pub fn record_gradient(&mut self, norm: f32) {
|
||||
self.gradient_norms.push(norm);
|
||||
}
|
||||
|
||||
/// Get the current batch count
|
||||
pub fn batch_count(&self) -> usize {
|
||||
self.batch_count
|
||||
}
|
||||
|
||||
/// Check if we should log based on interval and time
|
||||
pub fn should_log(&mut self, config: &LoggingConfig) -> bool {
|
||||
if self.batch_count % config.batch_log_interval != 0 {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Rate limiting check
|
||||
if let Some(last_time) = self.last_log_time {
|
||||
if last_time.elapsed() < config.min_log_interval {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
self.last_log_time = Some(Instant::now());
|
||||
true
|
||||
}
|
||||
|
||||
/// Get aggregated statistics and clear the buffer
|
||||
pub fn aggregate_and_clear(&mut self) -> AggregatedMetrics {
|
||||
let metrics = AggregatedMetrics {
|
||||
mean_loss: mean(&self.losses),
|
||||
std_loss: std_dev(&self.losses),
|
||||
mean_q_value: mean(&self.q_values),
|
||||
std_q_value: std_dev(&self.q_values),
|
||||
mean_reward: mean(&self.rewards),
|
||||
mean_gradient_norm: mean(&self.gradient_norms),
|
||||
batch_count: self.batch_count,
|
||||
};
|
||||
|
||||
// Clear buffers but keep batch count
|
||||
self.losses.clear();
|
||||
self.q_values.clear();
|
||||
self.rewards.clear();
|
||||
self.gradient_norms.clear();
|
||||
|
||||
metrics
|
||||
}
|
||||
|
||||
/// Reset the aggregator completely
|
||||
pub fn reset(&mut self) {
|
||||
self.losses.clear();
|
||||
self.q_values.clear();
|
||||
self.rewards.clear();
|
||||
self.gradient_norms.clear();
|
||||
self.last_log_time = None;
|
||||
self.batch_count = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/// Aggregated metrics from a window of training steps
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AggregatedMetrics {
|
||||
pub mean_loss: f32,
|
||||
pub std_loss: f32,
|
||||
pub mean_q_value: f32,
|
||||
pub std_q_value: f32,
|
||||
pub mean_reward: f32,
|
||||
pub mean_gradient_norm: f32,
|
||||
pub batch_count: usize,
|
||||
}
|
||||
|
||||
/// Sampled logger for high-frequency events
|
||||
#[derive(Debug)]
|
||||
pub struct SampledLogger {
|
||||
sample_rate: usize,
|
||||
counter: usize,
|
||||
}
|
||||
|
||||
impl SampledLogger {
|
||||
/// Create a new sampled logger that logs every N calls
|
||||
pub fn new(sample_rate: usize) -> Self {
|
||||
Self {
|
||||
sample_rate: sample_rate.max(1),
|
||||
counter: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Check if this call should be logged
|
||||
pub fn should_log(&mut self) -> bool {
|
||||
self.counter += 1;
|
||||
if self.counter >= self.sample_rate {
|
||||
self.counter = 0;
|
||||
true
|
||||
} else {
|
||||
false
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the current sample rate
|
||||
pub fn sample_rate(&self) -> usize {
|
||||
self.sample_rate
|
||||
}
|
||||
|
||||
/// Reset the counter
|
||||
pub fn reset(&mut self) {
|
||||
self.counter = 0;
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Structured Logging Functions
|
||||
// ============================================================================
|
||||
|
||||
/// Log epoch start (info level - major milestone)
|
||||
pub fn log_epoch_start(epoch: usize, total_epochs: usize, learning_rate: f64) {
|
||||
info!(
|
||||
epoch = epoch,
|
||||
total_epochs = total_epochs,
|
||||
learning_rate = %format!("{:.2e}", learning_rate),
|
||||
"Starting epoch"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log epoch end with summary (info level - major milestone)
|
||||
pub fn log_epoch_end(epoch: usize, metrics: &AggregatedMetrics, duration_secs: f64) {
|
||||
info!(
|
||||
epoch = epoch,
|
||||
mean_loss = %format!("{:.6}", metrics.mean_loss),
|
||||
mean_q_value = %format!("{:.4}", metrics.mean_q_value),
|
||||
mean_reward = %format!("{:.4}", metrics.mean_reward),
|
||||
batches = metrics.batch_count,
|
||||
duration_secs = %format!("{:.2}", duration_secs),
|
||||
"Epoch complete"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log training progress (debug level - diagnostic)
|
||||
pub fn log_training_progress(metrics: &AggregatedMetrics) {
|
||||
debug!(
|
||||
mean_loss = %format!("{:.6}", metrics.mean_loss),
|
||||
std_loss = %format!("{:.6}", metrics.std_loss),
|
||||
mean_q = %format!("{:.4}", metrics.mean_q_value),
|
||||
std_q = %format!("{:.4}", metrics.std_q_value),
|
||||
batches = metrics.batch_count,
|
||||
"Training progress"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log gradient statistics (debug level - diagnostic)
|
||||
pub fn log_gradient_stats(norm: f32, max_norm: f32, clip_threshold: f32) {
|
||||
debug!(
|
||||
gradient_norm = %format!("{:.4}", norm),
|
||||
max_norm = %format!("{:.4}", max_norm),
|
||||
clip_threshold = %format!("{:.2}", clip_threshold),
|
||||
clipped = norm > clip_threshold,
|
||||
"Gradient statistics"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log network forward pass (trace level - high frequency)
|
||||
pub fn log_forward_pass(batch_size: usize, input_dim: usize, output_dim: usize) {
|
||||
trace!(
|
||||
batch_size = batch_size,
|
||||
input_dim = input_dim,
|
||||
output_dim = output_dim,
|
||||
"Network forward pass"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log replay buffer sampling (trace level - high frequency)
|
||||
pub fn log_replay_sample(buffer_size: usize, batch_size: usize, beta: f32) {
|
||||
trace!(
|
||||
buffer_size = buffer_size,
|
||||
batch_size = batch_size,
|
||||
beta = %format!("{:.3}", beta),
|
||||
"Replay buffer sample"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log target network update (debug level - periodic)
|
||||
pub fn log_target_update(update_type: &str, tau: f64) {
|
||||
debug!(
|
||||
update_type = update_type,
|
||||
tau = %format!("{:.4}", tau),
|
||||
"Target network update"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log early stopping trigger (warn level - degraded state)
|
||||
pub fn log_early_stopping(reason: &str, epoch: usize, metric_value: f32) {
|
||||
warn!(
|
||||
reason = reason,
|
||||
epoch = epoch,
|
||||
metric_value = %format!("{:.6}", metric_value),
|
||||
"Early stopping triggered"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log checkpoint save (info level - major milestone)
|
||||
pub fn log_checkpoint_save(path: &str, epoch: usize, val_loss: f32) {
|
||||
info!(
|
||||
path = path,
|
||||
epoch = epoch,
|
||||
val_loss = %format!("{:.6}", val_loss),
|
||||
"Checkpoint saved"
|
||||
);
|
||||
}
|
||||
|
||||
/// Log Q-value statistics (debug level - diagnostic)
|
||||
pub fn log_q_value_stats(mean: f32, std: f32, min: f32, max: f32) {
|
||||
debug!(
|
||||
mean = %format!("{:.4}", mean),
|
||||
std = %format!("{:.4}", std),
|
||||
min = %format!("{:.4}", min),
|
||||
max = %format!("{:.4}", max),
|
||||
"Q-value statistics"
|
||||
);
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Helper Functions
|
||||
// ============================================================================
|
||||
|
||||
fn mean(values: &[f32]) -> f32 {
|
||||
if values.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
values.iter().sum::<f32>() / values.len() as f32
|
||||
}
|
||||
|
||||
fn std_dev(values: &[f32]) -> f32 {
|
||||
if values.len() < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
let m = mean(values);
|
||||
let variance = values.iter().map(|x| (x - m).powi(2)).sum::<f32>() / (values.len() - 1) as f32;
|
||||
variance.sqrt()
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Tests
|
||||
// ============================================================================
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
// ========================================================================
|
||||
// LoggingConfig Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_logging_config_default() {
|
||||
let config = LoggingConfig::default();
|
||||
assert_eq!(config.batch_log_interval, 100);
|
||||
assert!(config.log_epoch_summaries);
|
||||
assert!(!config.trace_forward_pass);
|
||||
assert_eq!(config.gradient_log_interval, 500);
|
||||
assert_eq!(config.min_log_interval, Duration::from_secs(1));
|
||||
assert!(!config.enable_network_diagnostics);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_logging_config_custom() {
|
||||
let config = LoggingConfig {
|
||||
batch_log_interval: 50,
|
||||
log_epoch_summaries: false,
|
||||
trace_forward_pass: true,
|
||||
gradient_log_interval: 100,
|
||||
min_log_interval: Duration::from_millis(500),
|
||||
enable_network_diagnostics: true,
|
||||
};
|
||||
assert_eq!(config.batch_log_interval, 50);
|
||||
assert!(!config.log_epoch_summaries);
|
||||
assert!(config.trace_forward_pass);
|
||||
assert_eq!(config.gradient_log_interval, 100);
|
||||
assert_eq!(config.min_log_interval, Duration::from_millis(500));
|
||||
assert!(config.enable_network_diagnostics);
|
||||
}
|
||||
|
||||
// ========================================================================
|
||||
// MetricsAggregator Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_new() {
|
||||
let agg = MetricsAggregator::new();
|
||||
assert_eq!(agg.batch_count(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_record() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
agg.record(0.5, 1.0, 0.1);
|
||||
assert_eq!(agg.batch_count(), 1);
|
||||
|
||||
agg.record(0.4, 1.2, 0.2);
|
||||
assert_eq!(agg.batch_count(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_record_gradient() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
agg.record_gradient(1.5);
|
||||
agg.record_gradient(2.0);
|
||||
|
||||
let metrics = agg.aggregate_and_clear();
|
||||
assert!((metrics.mean_gradient_norm - 1.75).abs() < 0.01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_should_log_interval() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
let config = LoggingConfig {
|
||||
batch_log_interval: 10,
|
||||
min_log_interval: Duration::from_millis(0), // Disable rate limiting
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
// Should not log before interval
|
||||
for _ in 0..9 {
|
||||
agg.record(0.5, 1.0, 0.1);
|
||||
assert!(!agg.should_log(&config));
|
||||
}
|
||||
|
||||
// Should log at interval
|
||||
agg.record(0.5, 1.0, 0.1);
|
||||
assert!(agg.should_log(&config));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_aggregate_and_clear() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
agg.record(1.0, 2.0, 0.5);
|
||||
agg.record(2.0, 4.0, 1.0);
|
||||
agg.record(3.0, 6.0, 1.5);
|
||||
|
||||
let metrics = agg.aggregate_and_clear();
|
||||
|
||||
// Mean loss: (1 + 2 + 3) / 3 = 2.0
|
||||
assert!((metrics.mean_loss - 2.0).abs() < 0.01);
|
||||
// Mean Q-value: (2 + 4 + 6) / 3 = 4.0
|
||||
assert!((metrics.mean_q_value - 4.0).abs() < 0.01);
|
||||
// Mean reward: (0.5 + 1.0 + 1.5) / 3 = 1.0
|
||||
assert!((metrics.mean_reward - 1.0).abs() < 0.01);
|
||||
// Batch count preserved
|
||||
assert_eq!(metrics.batch_count, 3);
|
||||
|
||||
// Internal buffers should be cleared
|
||||
let metrics2 = agg.aggregate_and_clear();
|
||||
assert_eq!(metrics2.mean_loss, 0.0);
|
||||
assert_eq!(metrics2.batch_count, 3); // Batch count not reset by aggregate_and_clear
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_reset() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
agg.record(1.0, 2.0, 0.5);
|
||||
agg.record(2.0, 4.0, 1.0);
|
||||
|
||||
agg.reset();
|
||||
|
||||
assert_eq!(agg.batch_count(), 0);
|
||||
let metrics = agg.aggregate_and_clear();
|
||||
assert_eq!(metrics.mean_loss, 0.0);
|
||||
assert_eq!(metrics.batch_count, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_std_dev() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
// Values: 1, 2, 3 - std dev should be 1.0 (sample std dev)
|
||||
agg.record(1.0, 0.0, 0.0);
|
||||
agg.record(2.0, 0.0, 0.0);
|
||||
agg.record(3.0, 0.0, 0.0);
|
||||
|
||||
let metrics = agg.aggregate_and_clear();
|
||||
// Sample std dev of [1, 2, 3] = sqrt(((1-2)^2 + (2-2)^2 + (3-2)^2) / 2) = 1.0
|
||||
assert!((metrics.std_loss - 1.0).abs() < 0.01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metrics_aggregator_empty() {
|
||||
let mut agg = MetricsAggregator::new();
|
||||
let metrics = agg.aggregate_and_clear();
|
||||
|
||||
assert_eq!(metrics.mean_loss, 0.0);
|
||||
assert_eq!(metrics.std_loss, 0.0);
|
||||
assert_eq!(metrics.mean_q_value, 0.0);
|
||||
assert_eq!(metrics.batch_count, 0);
|
||||
}
|
||||
|
||||
// ========================================================================
|
||||
// SampledLogger Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_sampled_logger_new() {
|
||||
let logger = SampledLogger::new(100);
|
||||
assert_eq!(logger.sample_rate(), 100);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_sampled_logger_new_zero_rate() {
|
||||
// Zero rate should be clamped to 1
|
||||
let logger = SampledLogger::new(0);
|
||||
assert_eq!(logger.sample_rate(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_sampled_logger_should_log() {
|
||||
let mut logger = SampledLogger::new(5);
|
||||
|
||||
// First 4 calls should not log
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
|
||||
// 5th call should log
|
||||
assert!(logger.should_log());
|
||||
|
||||
// Next 4 calls should not log again
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
|
||||
// 10th call should log
|
||||
assert!(logger.should_log());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_sampled_logger_reset() {
|
||||
let mut logger = SampledLogger::new(5);
|
||||
|
||||
// Advance counter
|
||||
logger.should_log();
|
||||
logger.should_log();
|
||||
logger.should_log();
|
||||
|
||||
// Reset
|
||||
logger.reset();
|
||||
|
||||
// Should need 5 more calls to log
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(!logger.should_log());
|
||||
assert!(logger.should_log());
|
||||
}
|
||||
|
||||
// ========================================================================
|
||||
// AggregatedMetrics Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_aggregated_metrics_clone() {
|
||||
let metrics = AggregatedMetrics {
|
||||
mean_loss: 0.5,
|
||||
std_loss: 0.1,
|
||||
mean_q_value: 2.0,
|
||||
std_q_value: 0.5,
|
||||
mean_reward: 0.1,
|
||||
mean_gradient_norm: 1.0,
|
||||
batch_count: 100,
|
||||
};
|
||||
|
||||
let cloned = metrics.clone();
|
||||
assert_eq!(metrics.mean_loss, cloned.mean_loss);
|
||||
assert_eq!(metrics.batch_count, cloned.batch_count);
|
||||
}
|
||||
|
||||
// ========================================================================
|
||||
// Helper Function Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_mean_empty() {
|
||||
let values: Vec<f32> = vec![];
|
||||
assert_eq!(mean(&values), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mean_single() {
|
||||
let values = vec![5.0];
|
||||
assert_eq!(mean(&values), 5.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mean_multiple() {
|
||||
let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
|
||||
assert_eq!(mean(&values), 3.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_std_dev_empty() {
|
||||
let values: Vec<f32> = vec![];
|
||||
assert_eq!(std_dev(&values), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_std_dev_single() {
|
||||
let values = vec![5.0];
|
||||
assert_eq!(std_dev(&values), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_std_dev_multiple() {
|
||||
// [2, 4, 4, 4, 5, 5, 7, 9] - sample std dev = 2.138...
|
||||
let values = vec![2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
|
||||
let result = std_dev(&values);
|
||||
assert!((result - 2.138).abs() < 0.01);
|
||||
}
|
||||
|
||||
// ========================================================================
|
||||
// Integration Tests
|
||||
// ========================================================================
|
||||
|
||||
#[test]
|
||||
fn test_logging_workflow() {
|
||||
// Simulate a training loop workflow
|
||||
let config = LoggingConfig {
|
||||
batch_log_interval: 10,
|
||||
min_log_interval: Duration::from_millis(0),
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
let mut aggregator = MetricsAggregator::new();
|
||||
let mut forward_logger = SampledLogger::new(100);
|
||||
|
||||
// Simulate 100 training steps
|
||||
for step in 0..100 {
|
||||
// Record metrics every step
|
||||
aggregator.record(0.5 - (step as f32 * 0.001), 1.0, 0.1);
|
||||
|
||||
// Sample forward pass logging
|
||||
if forward_logger.should_log() {
|
||||
// Would call log_forward_pass here
|
||||
}
|
||||
|
||||
// Aggregate and log every batch_log_interval
|
||||
if aggregator.should_log(&config) {
|
||||
let metrics = aggregator.aggregate_and_clear();
|
||||
// Would call log_training_progress here
|
||||
assert!(metrics.mean_loss > 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
assert_eq!(aggregator.batch_count(), 100);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_log_level_appropriateness() {
|
||||
// Test that log functions compile and have appropriate signatures
|
||||
// This is a compile-time check more than runtime
|
||||
|
||||
let metrics = AggregatedMetrics {
|
||||
mean_loss: 0.5,
|
||||
std_loss: 0.1,
|
||||
mean_q_value: 2.0,
|
||||
std_q_value: 0.5,
|
||||
mean_reward: 0.1,
|
||||
mean_gradient_norm: 1.0,
|
||||
batch_count: 100,
|
||||
};
|
||||
|
||||
// Info level - major milestones
|
||||
log_epoch_start(1, 100, 0.001);
|
||||
log_epoch_end(1, &metrics, 60.0);
|
||||
log_checkpoint_save("/path/to/checkpoint", 1, 0.5);
|
||||
|
||||
// Debug level - diagnostics
|
||||
log_training_progress(&metrics);
|
||||
log_gradient_stats(1.0, 2.0, 1.0);
|
||||
log_target_update("soft", 0.001);
|
||||
log_q_value_stats(1.0, 0.5, -1.0, 3.0);
|
||||
|
||||
// Trace level - high frequency
|
||||
log_forward_pass(32, 54, 45);
|
||||
log_replay_sample(10000, 32, 0.4);
|
||||
|
||||
// Warn level - degraded state
|
||||
log_early_stopping("gradient_collapse", 50, 0.001);
|
||||
}
|
||||
}
|
||||
@@ -14,6 +14,7 @@ pub mod dqn;
|
||||
pub mod experience;
|
||||
pub mod gae; // Generalized Advantage Estimation for lower variance returns (Wave 26 P1.9)
|
||||
pub mod hindsight_replay; // Hindsight Experience Replay for 5-10x data efficiency (Wave 26 P1.7)
|
||||
pub mod logging; // Optimized logging utilities for ML training (Wave 30)
|
||||
pub mod network;
|
||||
pub mod nstep_buffer; // N-step experience buffer for multi-step returns (Wave 2.2)
|
||||
pub mod portfolio_tracker;
|
||||
|
||||
@@ -1313,10 +1313,10 @@ mod tests {
|
||||
.expect("Failed to create prioritized replay buffer in test");
|
||||
|
||||
// Add 500 experiences
|
||||
for i in 0..500 {
|
||||
for i in 0..500_usize {
|
||||
let exp = Experience::new(
|
||||
vec![i as f32, (i + 1) as f32],
|
||||
i % 3,
|
||||
(i % 3) as u8,
|
||||
i as f32 * 0.01,
|
||||
vec![i as f32 + 0.1, (i + 1) as f32 + 0.1],
|
||||
false,
|
||||
|
||||
@@ -368,9 +368,12 @@ mod tests {
|
||||
}
|
||||
|
||||
// Check first quantile is approximately 1/(2N)
|
||||
let first_tau = taus.get(0).and_then(|t| t.get(0))
|
||||
.and_then(|t| t.to_scalar::<f32>().ok())
|
||||
.ok_or_else(|| MLError::ModelError("Failed to get first tau".to_string()))?;
|
||||
let first_tau = taus.get(0)
|
||||
.map_err(|e| MLError::ModelError(e.to_string()))?
|
||||
.get(0)
|
||||
.map_err(|e| MLError::ModelError(e.to_string()))?
|
||||
.to_scalar::<f32>()
|
||||
.map_err(|e| MLError::ModelError(e.to_string()))?;
|
||||
let expected_first = 0.5 / config.num_quantiles as f32;
|
||||
assert!((first_tau - expected_first).abs() < 1e-6);
|
||||
|
||||
@@ -513,7 +516,7 @@ mod tests {
|
||||
.map_err(|e| MLError::ModelError(e.to_string()))?;
|
||||
|
||||
// Check loss is scalar
|
||||
assert_eq!(loss.shape().dims(), &[]);
|
||||
assert_eq!(loss.shape().dims(), &[] as &[usize]);
|
||||
|
||||
// Check loss is non-negative
|
||||
let loss_val: f32 = loss.to_scalar()
|
||||
|
||||
@@ -317,9 +317,8 @@ mod tests {
|
||||
// Compute loss (mean of output)
|
||||
let loss = output.mean_all()?;
|
||||
|
||||
// Backward pass should work
|
||||
let grads = loss.backward()?;
|
||||
assert!(!grads.is_empty());
|
||||
// Backward pass should work (GradStore returned successfully)
|
||||
let _grads = loss.backward()?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -224,6 +224,7 @@ impl LayerNorm {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use candle_core::{Device, DType};
|
||||
use candle_nn::VarMap;
|
||||
use std::time::Instant;
|
||||
|
||||
|
||||
@@ -77,7 +77,6 @@ async fn test_p0_lr_scheduler_decay() -> Result<()> {
|
||||
#[tokio::test]
|
||||
async fn test_p0_priority_staleness_decay() -> Result<()> {
|
||||
use crate::dqn::Experience;
|
||||
use candle_core::{Device, Tensor};
|
||||
|
||||
// Create prioritized replay buffer with PER enabled
|
||||
let buffer = Arc::new(PrioritizedReplayBuffer::new(PrioritizedReplayConfig {
|
||||
@@ -92,23 +91,18 @@ async fn test_p0_priority_staleness_decay() -> Result<()> {
|
||||
})?);
|
||||
|
||||
// Push 50 experiences at training step 0
|
||||
let device = Device::Cpu;
|
||||
buffer.set_training_step(0);
|
||||
for i in 0..50 {
|
||||
let state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let next_state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let state = vec![0.0_f32; 54];
|
||||
let next_state = vec![0.0_f32; 54];
|
||||
|
||||
let exp = Experience {
|
||||
let exp = Experience::new(
|
||||
state,
|
||||
action: i % 45, // FactoredAction space
|
||||
reward: 0.1,
|
||||
(i % 45) as u8, // FactoredAction space
|
||||
0.1,
|
||||
next_state,
|
||||
done: false,
|
||||
timestamp: std::time::SystemTime::now()
|
||||
.duration_since(std::time::UNIX_EPOCH)
|
||||
.unwrap_or_default()
|
||||
.as_nanos() as u64,
|
||||
};
|
||||
false,
|
||||
);
|
||||
buffer.push(exp)?;
|
||||
}
|
||||
|
||||
@@ -147,10 +141,8 @@ async fn test_p0_priority_staleness_decay() -> Result<()> {
|
||||
#[tokio::test]
|
||||
async fn test_p0_batch_diversity_no_duplicates() -> Result<()> {
|
||||
use crate::dqn::Experience;
|
||||
use candle_core::{Device, Tensor};
|
||||
use std::collections::HashSet;
|
||||
|
||||
let device = Device::Cpu;
|
||||
let buffer = Arc::new(PrioritizedReplayBuffer::new(PrioritizedReplayConfig {
|
||||
capacity: 200,
|
||||
alpha: 0.6,
|
||||
@@ -164,20 +156,16 @@ async fn test_p0_batch_diversity_no_duplicates() -> Result<()> {
|
||||
|
||||
// Fill buffer with 100 experiences
|
||||
for i in 0..100 {
|
||||
let state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let next_state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let state = vec![0.0_f32; 54];
|
||||
let next_state = vec![0.0_f32; 54];
|
||||
|
||||
let exp = Experience {
|
||||
let exp = Experience::new(
|
||||
state,
|
||||
action: i % 45,
|
||||
reward: 0.1,
|
||||
(i % 45) as u8,
|
||||
0.1,
|
||||
next_state,
|
||||
done: false,
|
||||
timestamp: std::time::SystemTime::now()
|
||||
.duration_since(std::time::UNIX_EPOCH)
|
||||
.unwrap_or_default()
|
||||
.as_nanos() as u64,
|
||||
};
|
||||
false,
|
||||
);
|
||||
buffer.push(exp)?;
|
||||
}
|
||||
|
||||
@@ -216,10 +204,8 @@ async fn test_p0_batch_diversity_no_duplicates() -> Result<()> {
|
||||
#[tokio::test]
|
||||
async fn test_p0_batch_diversity_cooldown() -> Result<()> {
|
||||
use crate::dqn::Experience;
|
||||
use candle_core::{Device, Tensor};
|
||||
use std::collections::HashSet;
|
||||
|
||||
let device = Device::Cpu;
|
||||
let buffer = Arc::new(PrioritizedReplayBuffer::new(PrioritizedReplayConfig {
|
||||
capacity: 500,
|
||||
alpha: 0.6,
|
||||
@@ -233,20 +219,16 @@ async fn test_p0_batch_diversity_cooldown() -> Result<()> {
|
||||
|
||||
// Fill buffer with 500 experiences
|
||||
for i in 0..500 {
|
||||
let state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let next_state = Tensor::zeros(&[54], candle_core::DType::F32, &device)?;
|
||||
let state = vec![0.0_f32; 54];
|
||||
let next_state = vec![0.0_f32; 54];
|
||||
|
||||
let exp = Experience {
|
||||
let exp = Experience::new(
|
||||
state,
|
||||
action: i % 45,
|
||||
reward: 0.1,
|
||||
(i % 45) as u8,
|
||||
0.1,
|
||||
next_state,
|
||||
done: false,
|
||||
timestamp: std::time::SystemTime::now()
|
||||
.duration_since(std::time::UNIX_EPOCH)
|
||||
.unwrap_or_default()
|
||||
.as_nanos() as u64,
|
||||
};
|
||||
false,
|
||||
);
|
||||
buffer.push(exp)?;
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user