foxhunt/WAVE_8_9_QUICK_REFERENCE.md

Wave 8.9 Quick Reference: TFT Static Context Contribution Tests

Status: ✅ TEST SUITE COMPLETE (7 tests, 700+ lines)

Compilation: ⚠️ BLOCKED by unrelated mamba trainable_adapter error

File: /home/jgrusewski/Work/foxhunt/ml/tests/tft_static_context_contribution_tests.rs

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Test Summary

#	Test Name	Purpose	Success Criteria
1	test_tft_static_context_contribution_basic	Zeros vs signal	0.001 < diff < 1.0
2	test_tft_static_context_ablation_study	With vs without	diff > 0.0001
3	test_tft_static_feature_individual_importance	Per-feature impact	At least 1 feature > 0.0001
4	test_tft_static_context_projection_active	Projection layer active	Different patterns → different preds
5	test_tft_static_vs_temporal_feature_ratio	Architectural imbalance	Ratio = 2,892:1 (temporal/static)
6	test_tft_static_context_horizon_sensitivity	Uniform broadcasting	All horizons > 0.0001
7	test_tft_static_context_extreme_values	Numerical stability	Finite predictions

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Architectural Context (Wave 7.5)

Static Parameters: 5 features

Temporal Parameters: 60 timesteps × 241 features = 14,460

Ratio: 14,460 / 5 = 2,892:1 (temporal dominates)

Expected Impact: Mean absolute difference 0.001-0.1 (small but measurable)

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Static Context Application

// Step 1: Variable Selection Network (learnable feature importance)
let static_selected = self.static_variable_selection.forward(static_features, None)?;

// Step 2: GRN Encoding (gated residual network)
let static_encoded = self.static_encoder.forward(&static_selected, None)?;

// Step 3: Temporal Processing (LSTM + Attention)
let attended = self.temporal_attention.forward(&combined_temporal, true)?;

// Step 4: Apply Static Context (additive integration)
let contextualized = self.apply_static_context(&attended, &static_encoded)?;
//                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
//                   KEY STEP: temporal + static_expanded

// Step 5: Quantile Predictions
let quantile_preds = self.quantile_outputs.forward(&contextualized)?;

apply_static_context() Mechanism

fn apply_static_context(temporal: &Tensor, static_context: &Tensor) -> Result<Tensor, MLError> {
    // Static context: [batch, 1, hidden] → squeeze → [batch, hidden]
    let static_squeezed = static_context.squeeze(1)?;

    // Broadcast to match temporal sequence: [batch, seq_len, hidden]
    let static_expanded = static_squeezed.unsqueeze(1)?.repeat(&[1, seq_len, 1])?;

    // Additive integration (elementwise addition)
    let contextualized = (temporal + &static_expanded)?;  // ← Simple addition

    Ok(contextualized)
}

Key Observations:

• ✅ Additive (not multiplicative/gating)
• ✅ Uniform broadcasting (no temporal decay)
• ✅ Xavier initialization (non-zero weights)

---

How to Run Tests (once compilation fixed)

# Run all static context tests
cargo test -p ml --test tft_static_context_contribution_tests -- --nocapture

# Run individual tests
cargo test -p ml --test tft_static_context_contribution_tests test_tft_static_context_contribution_basic -- --nocapture
cargo test -p ml --test tft_static_context_contribution_tests test_tft_static_context_ablation_study -- --nocapture
cargo test -p ml --test tft_static_context_contribution_tests test_tft_static_feature_individual_importance -- --nocapture

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Compilation Blocker

Error: error[E0277]: Result<String, MLError> is not a future in ml/src/mamba/trainable_adapter.rs:452

Fix Options:

1. Remove .await? from line 452 (change save_checkpoint().await? to save_checkpoint()?)
2. OR disable mamba trainable_adapter module temporarily
3. OR fix mamba async API mismatch

Not a TFT issue - mamba and TFT modules are independent

---

Expected Results (from Wave 7.5 Analysis)

Quantitative Thresholds

• Basic contribution: 0.001 < mean_diff < 1.0
• Ablation study: mean_diff > 0.0001, max_diff > mean_diff
• Feature importance: At least 1 feature with impact > 0.0001
• Projection activity: All pattern pairs differ by > 0.0001
• Architectural ratio: temporal/static > 1000
• Horizon sensitivity: All horizons differ by > 0.0001
• Extreme values: All predictions finite (no NaN/Inf)

Qualitative Behavior

1. Static context contributes weakly (2,892:1 imbalance)
2. Effect is measurable (tests will pass)
3. Context projection active (Xavier init)
4. Temporal features dominate (60×241 >> 5)
5. Uniform horizon impact (simple broadcast)
6. Numerically stable (layer norm + GRN)

---

Test Configuration

let config = TFTConfig {
    input_dim: 241,
    hidden_dim: 64,
    num_heads: 4,
    num_layers: 3,
    prediction_horizon: 5-10,
    sequence_length: 60,
    num_quantiles: 9,
    num_static_features: 5,      // ← Static context dimension
    num_known_features: 10,
    num_unknown_features: 241,   // ← Temporal feature dimension
    dropout_rate: 0.0-0.1,       // Disabled for reproducibility
    ..Default::default()
};

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Next Actions

Immediate (Wave 8.9 completion)

1. ✅ Test implementation (7 tests, 700+ lines)
2. ⚠️ Fix mamba trainable_adapter compilation error
3. ⏳ Run test suite
4. ⏳ Validate thresholds
5. ⏳ Document results

Future Enhancements

1. Multiplicative gating: temporal * sigmoid(static_gating(static))
2. Temporal modulation: static * learned_horizon_weights
3. Increase static capacity: 5 → 50-100 features (reduce imbalance)
4. Training ablation: Measure performance delta with/without static context

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Key Files

• Test Suite: /home/jgrusewski/Work/foxhunt/ml/tests/tft_static_context_contribution_tests.rs (700+ lines)
• TFT Implementation: /home/jgrusewski/Work/foxhunt/ml/src/tft/mod.rs (forward, apply_static_context)
• Wave 8.9 Report: /home/jgrusewski/Work/foxhunt/WAVE_8_9_TFT_STATIC_CONTEXT_CONTRIBUTION.md (comprehensive)
• Quick Reference: /home/jgrusewski/Work/foxhunt/WAVE_8_9_QUICK_REFERENCE.md (this file)

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Updated: 2025-10-15 Status: Test suite complete, awaiting execution Next: Fix compilation, run tests, validate results