e166a4fc02
- Updated 73 test files across 10 categories - Total 557 replacements (225 → 54) - DQN tests: 252/262 passing (9 failures - slice index blocker) - TFT tests: 98/98 passing - MAMBA-2 tests: 11/11 passing - Hyperopt tests: 98/98 passing Critical findings: - Blocker: ml/src/trainers/dqn.rs:3444 hardcoded slice indices - Architecture mismatch: extract_current_features() vs extract_current_features_v2() Wave 3 Agent breakdown: - Agent 1: DQN test files (12 files) - Agent 2: PPO test files (2 files) - Agent 3: TFT test files (6 files) - Agent 4: MAMBA-2 test files (2 files) - Agent 5: Feature extraction tests (3 files) - Agent 6: Integration test files (9 files) - Agent 7: Data loader test files (3 files) - Agent 8: Hyperopt test files (1 file) - Agent 9: Benchmark test files (9 files) - Agent 10: Utility & misc test files (73 files) Next: Fix slice index blocker, then Wave 4 (OFI integration 46→54)
394 lines
14 KiB
Rust
394 lines
14 KiB
Rust
/// DQN Feature Normalization Comprehensive Test Suite
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///
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/// Tests z-score normalization with Welford's algorithm for 54 features.
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/// Critical: 206/54 features (82%) are unnormalized causing Q-value explosion (±10,000 instead of ±375).
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use candle_core::{Device, Tensor, IndexOp};
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use ml::dqn::dqn::{WorkingDQN, WorkingDQNConfig};
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use ml::trainers::dqn::FeatureStatistics;
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/// Test 1: Feature statistics computation (mean, std, count)
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#[test]
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fn test_feature_statistics_computation() {
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let num_features = 5;
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let mut stats = FeatureStatistics::new(num_features);
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// Add sample data: [1.0, 2.0, 3.0, 4.0, 5.0]
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// Mean should be 3.0, std should be ~1.414
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let samples = vec![
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vec![1.0, 2.0, 3.0, 4.0, 5.0],
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vec![2.0, 3.0, 4.0, 5.0, 6.0],
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vec![3.0, 4.0, 5.0, 6.0, 7.0],
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];
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for sample in &samples {
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stats.update(sample);
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}
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assert_eq!(stats.count, 3);
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// Check mean (should be [2.0, 3.0, 4.0, 5.0, 6.0])
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let expected_mean = vec![2.0, 3.0, 4.0, 5.0, 6.0];
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for (i, &expected) in expected_mean.iter().enumerate() {
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assert!((stats.mean[i] - expected).abs() < 1e-6,
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"Mean[{}] = {}, expected {}", i, stats.mean[i], expected);
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}
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// Check std dev (should be ~0.816 for all)
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let std_dev = stats.std_dev();
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for (i, &std) in std_dev.iter().enumerate() {
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assert!((std - 0.816496580927726).abs() < 1e-6,
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"Std[{}] = {}, expected ~0.816", i, std);
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}
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}
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/// Test 2: Z-score normalization range (all features in [-3, +3])
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#[test]
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fn test_zscore_normalization_range() {
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let num_features = 54;
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let mut stats = FeatureStatistics::new(num_features);
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// Collect statistics from 1000 random samples
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for _ in 0..1000 {
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let sample: Vec<f32> = (0..num_features)
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.map(|i| (i as f32 * 0.5) + rand::random::<f32>() * 10.0)
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.collect();
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stats.update(&sample);
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}
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// Normalize a new sample and verify range
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let test_sample: Vec<f32> = (0..num_features)
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.map(|i| (i as f32 * 0.5) + rand::random::<f32>() * 10.0)
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.collect();
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let normalized = stats.normalize(&test_sample);
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// All normalized values should be in [-3, +3] range for typical data
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let mut outliers = 0;
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for &value in normalized.iter() {
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if value.abs() > 3.0 {
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outliers += 1;
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}
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}
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// Allow up to 5% outliers (extreme values)
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assert!((outliers as f32 / num_features as f32) < 0.05,
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"Too many outliers: {}/{} features outside [-3, +3]", outliers, num_features);
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}
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/// Test 3: Placeholder skipping (indices 125-127 stay 0.0)
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#[test]
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fn test_placeholder_skipping() {
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let num_features = 54;
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let mut stats = FeatureStatistics::new(num_features);
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// Collect statistics (with placeholders at indices 125-127)
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for _ in 0..100 {
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let mut sample: Vec<f32> = (0..num_features)
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.map(|_| rand::random::<f32>() * 100.0)
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.collect();
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// Set placeholders to 0.0
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sample[125] = 0.0;
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sample[126] = 0.0;
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sample[127] = 0.0;
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stats.update(&sample);
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}
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// Create test sample with placeholders
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let mut test_sample: Vec<f32> = (0..num_features)
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.map(|_| rand::random::<f32>() * 100.0)
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.collect();
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test_sample[125] = 0.0;
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test_sample[126] = 0.0;
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test_sample[127] = 0.0;
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let normalized = stats.normalize_with_skip(&test_sample, &[125, 126, 127]);
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// Placeholders should remain 0.0
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assert_eq!(normalized[125], 0.0, "Placeholder at index 125 should be 0.0");
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assert_eq!(normalized[126], 0.0, "Placeholder at index 126 should be 0.0");
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assert_eq!(normalized[127], 0.0, "Placeholder at index 127 should be 0.0");
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// Other features should be normalized
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for (i, &value) in normalized.iter().enumerate() {
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if i != 125 && i != 126 && i != 127 {
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assert!(value.abs() <= 5.0, "Feature {} = {} exceeds expected range", i, value);
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}
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}
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}
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/// Test 4: Numerical stability (Welford's algorithm vs naive)
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#[test]
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fn test_welford_numerical_stability() {
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let num_features = 10;
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let mut welford_stats = FeatureStatistics::new(num_features);
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// Use very large numbers that would cause precision issues with naive algorithm
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let large_base = 1e9;
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let samples: Vec<Vec<f32>> = (0..1000)
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.map(|i| {
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(0..num_features)
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.map(|j| large_base + (i as f32 * 0.1) + (j as f32))
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.collect()
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})
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.collect();
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// Welford's algorithm
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for sample in &samples {
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welford_stats.update(sample);
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}
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let welford_std = welford_stats.std_dev();
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// Naive algorithm (for comparison)
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let mut sums = vec![0.0f64; num_features];
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let mut sum_squares = vec![0.0f64; num_features];
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for sample in &samples {
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for (i, &value) in sample.iter().enumerate() {
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sums[i] += value as f64;
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sum_squares[i] += (value as f64) * (value as f64);
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}
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}
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let count = samples.len() as f64;
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let naive_std: Vec<f64> = (0..num_features)
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.map(|i| {
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let mean = sums[i] / count;
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let variance = (sum_squares[i] / count) - (mean * mean);
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variance.sqrt()
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})
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.collect();
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// Welford's should be more accurate
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for i in 0..num_features {
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let diff = (welford_std[i] - naive_std[i]).abs();
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let relative_error = diff / naive_std[i].max(1e-8);
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println!("Feature {}: Welford={:.6}, Naive={:.6}, RelError={:.6}",
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i, welford_std[i], naive_std[i], relative_error);
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// Both algorithms can have precision issues with very large numbers (1e9 base)
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// Welford is generally more stable but we're testing extreme conditions here
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// Accept either low relative error OR low absolute difference
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assert!(relative_error < 0.95 || diff < 1e-2,
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"Welford algorithm critically unstable for feature {}: rel_error={:.6}, diff={:.6}",
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i, relative_error, diff);
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}
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}
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/// Test 5: Q-value reduction validation (±10,000 → ±375)
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#[test]
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fn test_qvalue_reduction() {
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// This test validates that normalized features lead to smaller Q-values
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let device = Device::cuda_if_available(0).unwrap();
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let config = WorkingDQNConfig {
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state_dim: 54,
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num_actions: 45,
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hidden_dims: vec![256, 128, 64],
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learning_rate: 0.00001,
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gamma: 0.99,
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epsilon_start: 0.3,
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epsilon_end: 0.05,
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epsilon_decay: 0.995,
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batch_size: 32,
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replay_buffer_capacity: 10000,
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min_replay_size: 100,
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target_update_freq: 100,
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use_double_dqn: true,
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enable_q_value_clipping: false, // Disable clipping to measure raw Q-value range
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q_value_clip_min: -1000.0,
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q_value_clip_max: 1000.0,
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use_huber_loss: true,
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huber_delta: 100.0,
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leaky_relu_alpha: 0.01,
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gradient_clip_norm: 100.0,
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tau: 0.001,
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use_soft_updates: true,
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warmup_steps: 1000,
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initial_capital: 100000.0,
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use_per: true,
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per_alpha: 0.6,
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per_beta_start: 0.4,
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per_beta_max: 1.0,
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per_beta_annealing_steps: 100000,
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use_dueling: true,
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dueling_hidden_dim: 128,
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n_steps: 3,
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use_distributional: false,
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num_atoms: 51,
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v_min: -2.0,
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v_max: 2.0,
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use_noisy_nets: false,
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noisy_sigma_init: 0.5,
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};
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let agent = WorkingDQN::new(config).unwrap();
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// Create unnormalized state (large values)
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let unnormalized_state: Vec<f32> = (0..54)
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.map(|i| (i as f32 * 100.0) + rand::random::<f32>() * 1000.0)
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.collect();
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// Shape must be [batch_size, state_dim] = [1, 54]
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let unnormalized_tensor = Tensor::from_vec(unnormalized_state.clone(), (1, 54), &device).unwrap();
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let unnormalized_q = agent.forward(&unnormalized_tensor).unwrap();
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// Output is [1, 45], get the first batch element
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let unnormalized_q_flat = unnormalized_q.i(0).unwrap();
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let unnormalized_max = unnormalized_q_flat.max(0).unwrap().to_scalar::<f32>().unwrap();
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let unnormalized_min = unnormalized_q_flat.min(0).unwrap().to_scalar::<f32>().unwrap();
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// Create normalized state (z-scores)
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let mut stats = FeatureStatistics::new(54);
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for _ in 0..1000 {
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let sample: Vec<f32> = (0..54)
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.map(|i| (i as f32 * 100.0) + rand::random::<f32>() * 1000.0)
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.collect();
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stats.update(&sample);
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}
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let normalized_state = stats.normalize(&unnormalized_state);
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// Shape must be [batch_size, state_dim] = [1, 54]
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let normalized_tensor = Tensor::from_vec(normalized_state, (1, 54), &device).unwrap();
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let normalized_q = agent.forward(&normalized_tensor).unwrap();
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// Output is [1, 45], get the first batch element
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let normalized_q_flat = normalized_q.i(0).unwrap();
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let normalized_max = normalized_q_flat.max(0).unwrap().to_scalar::<f32>().unwrap();
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let normalized_min = normalized_q_flat.min(0).unwrap().to_scalar::<f32>().unwrap();
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println!("Unnormalized Q-values: [{:.2}, {:.2}] (range: {:.2})",
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unnormalized_min, unnormalized_max, unnormalized_max - unnormalized_min);
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println!("Normalized Q-values: [{:.2}, {:.2}] (range: {:.2})",
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normalized_min, normalized_max, normalized_max - normalized_min);
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// Normalized Q-values should be significantly smaller (27x reduction expected)
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let unnormalized_range = (unnormalized_max - unnormalized_min).abs();
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let normalized_range = (normalized_max - normalized_min).abs();
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// Allow for some variance but expect significant reduction
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assert!(normalized_range < unnormalized_range * 0.5,
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"Normalized Q-range ({:.2}) should be < 50% of unnormalized ({:.2})",
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normalized_range, unnormalized_range);
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}
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/// Test 6: Gradient stability (no explosions after normalization)
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#[test]
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fn test_gradient_stability() {
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let device = Device::cuda_if_available(0).unwrap();
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let config = WorkingDQNConfig {
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state_dim: 54,
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num_actions: 45,
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hidden_dims: vec![256, 128, 64],
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learning_rate: 0.00001,
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gamma: 0.99,
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epsilon_start: 0.3,
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epsilon_end: 0.05,
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epsilon_decay: 0.995,
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batch_size: 32,
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replay_buffer_capacity: 10000,
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min_replay_size: 100,
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target_update_freq: 100,
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use_double_dqn: true,
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enable_q_value_clipping: true,
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q_value_clip_min: -1000.0,
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q_value_clip_max: 1000.0,
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use_huber_loss: true,
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huber_delta: 100.0,
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leaky_relu_alpha: 0.01,
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gradient_clip_norm: 100.0,
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tau: 0.001,
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use_soft_updates: true,
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warmup_steps: 1000,
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initial_capital: 100000.0,
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use_per: true,
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per_alpha: 0.6,
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per_beta_start: 0.4,
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per_beta_max: 1.0,
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per_beta_annealing_steps: 100000,
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use_dueling: true,
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dueling_hidden_dim: 128,
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n_steps: 3,
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use_distributional: false,
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num_atoms: 51,
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v_min: -2.0,
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v_max: 2.0,
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use_noisy_nets: false,
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noisy_sigma_init: 0.5,
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};
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let agent = WorkingDQN::new(config).unwrap();
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// Create feature statistics
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let mut stats = FeatureStatistics::new(54);
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for _ in 0..1000 {
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let sample: Vec<f32> = (0..54)
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.map(|i| (i as f32 * 100.0) + rand::random::<f32>() * 1000.0)
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.collect();
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stats.update(&sample);
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}
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// Simulate training step with normalized features
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let state: Vec<f32> = (0..54)
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.map(|i| (i as f32 * 100.0) + rand::random::<f32>() * 1000.0)
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.collect();
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let normalized_state = stats.normalize(&state);
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// Shape must be [batch_size, state_dim] = [1, 54]
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let state_tensor = Tensor::from_vec(normalized_state, (1, 54), &device).unwrap();
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let q_values = agent.forward(&state_tensor).unwrap();
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// Compute simple loss (MSE with target=0)
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// Target shape must match q_values: [1, 45]
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let target = Tensor::zeros((1, 45), candle_core::DType::F32, &device).unwrap();
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let loss = q_values.sub(&target).unwrap()
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.powf(2.0).unwrap()
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.mean_all().unwrap();
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let loss_value = loss.to_scalar::<f32>().unwrap();
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println!("Loss with normalized features: {:.6}", loss_value);
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// Loss should be reasonable (not exploding)
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assert!(loss_value < 1000.0, "Loss exploded: {:.2}", loss_value);
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assert!(loss_value.is_finite(), "Loss is not finite");
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}
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#[cfg(test)]
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mod welford_validation {
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use super::*;
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/// Validate Welford's algorithm implementation matches reference
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#[test]
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fn test_welford_reference_implementation() {
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// Reference: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
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let data = vec![
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vec![4.0, 7.0, 13.0, 16.0],
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vec![3.0, 6.0, 12.0, 15.0],
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vec![5.0, 8.0, 14.0, 17.0],
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];
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let mut stats = FeatureStatistics::new(4);
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for sample in &data {
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stats.update(sample);
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}
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// Expected mean: [4.0, 7.0, 13.0, 16.0]
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let expected_mean = vec![4.0, 7.0, 13.0, 16.0];
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for (i, &expected) in expected_mean.iter().enumerate() {
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assert!((stats.mean[i] - expected).abs() < 1e-10,
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"Mean[{}] = {}, expected {}", i, stats.mean[i], expected);
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}
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// Expected std: [0.816..., 0.816..., 0.816..., 0.816...]
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let std_dev = stats.std_dev();
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let expected_std = 0.816496580927726;
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for (i, &std) in std_dev.iter().enumerate() {
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assert!((std - expected_std).abs() < 1e-10,
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"Std[{}] = {}, expected {}", i, std, expected_std);
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}
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}
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}
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