foxhunt/WAVE_8_9_TFT_STATIC_CONTEXT_CONTRIBUTION.md

Wave 8.9: TFT Static Context Contribution Validation

Objective: Validate that static context features have measurable impact on TFT predictions.

Status: ✅ TEST SUITE COMPLETE (7 comprehensive tests implemented)

Date: 2025-10-15

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Executive Summary

Implemented comprehensive test suite to validate that static context features in the Temporal Fusion Transformer (TFT) have measurable, bounded impact on predictions, following Wave 7.5 architectural analysis findings.

Key Results

• Test Coverage: 7 comprehensive tests covering basic contribution, ablation study, feature importance, context gating, architectural imbalance, horizon sensitivity, and extreme values
• Implementation: 700+ lines of production-grade test code in /home/jgrusewski/Work/foxhunt/ml/tests/tft_static_context_contribution_tests.rs
• Architectural Context: 5 static features vs 60 timesteps × 241 features = 14,460 temporal parameters (2,892:1 ratio)
• Expected Impact: Mean absolute difference 0.001-0.1 (small but measurable, not dominant)

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Test Suite Architecture

Test 1: Basic Static Context Contribution

Purpose: Validate that static context has measurable but bounded effect on predictions.

#[test]
fn test_tft_static_context_contribution_basic() -> Result<(), MLError>

Approach:

• Compare predictions with static context = all zeros vs static context = signal (2.0)
• Compute mean absolute difference across all predictions
• Validate difference is in range [0.001, 1.0]

Success Criteria:

• mean_diff > 0.001 → Static context affects predictions
• mean_diff < 1.0 → Effect is bounded (not dominant)

Expected Behavior (from Wave 7.5):

• Weak but non-zero effect due to architectural imbalance
• Context projection layer adds static features to temporal representations
• Xavier initialization ensures non-zero weights

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Test 2: Ablation Study - With/Without Static Context

Purpose: Compare model performance with informative vs null static context.

#[test]
fn test_tft_static_context_ablation_study() -> Result<(), MLError>

Approach:

• Forward pass with random static features (0.5 std dev)
• Forward pass with null static features (all zeros)
• Compute mean and max differences

Success Criteria:

• mean_diff > 0.0001 → Measurable contribution
• max_diff > mean_diff → Localized impact visible

Rationale:

• Ablation studies are gold standard for feature importance
• Compares informative signal vs null baseline
• Tests whether model can distinguish meaningful static context

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Test 3: Individual Feature Importance

Purpose: Measure impact of each static feature independently.

#[test]
fn test_tft_static_feature_individual_importance() -> Result<(), MLError>

Approach:

• Baseline: all static features = 0
• Test: set one feature to 1.0, others to 0
• Repeat for all 5 static features
• Measure individual impact

Success Criteria:

• At least one feature has impact > 0.0001
• All impacts < 0.5 (bounded)

Expected Results:

• Variable Selection Network learns differential importance
• Some features may have stronger influence than others
• Softmax gating produces normalized attention weights

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Test 4: Context Projection Layer Activity

Purpose: Verify context projection layer has non-zero, active weights.

#[test]
fn test_tft_static_context_projection_active() -> Result<(), MLError>

Approach:

• Test 4 distinct static context patterns (zeros, ones, mid-range, gradient)
• Verify each pattern produces different predictions
• Validates projection layer is active (not identity/zero)

Success Criteria:

• Different static contexts → different predictions
• mean_diff > 0.0001 between any two patterns

Mechanism:

• Context projection: static_encoder (GRNStack) transforms static features
• Application: apply_static_context() broadcasts and adds to temporal features
• Xavier initialization ensures non-degenerate weights

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Test 5: Architectural Imbalance Analysis

Purpose: Document and validate temporal/static feature ratio imbalance.

#[test]
fn test_tft_static_vs_temporal_feature_ratio() -> Result<(), MLError>

Architecture:

• Static parameters: 5 features
• Temporal parameters: 60 timesteps × 241 features = 14,460
• Ratio: 14,460 / 5 = 2,892:1 (temporal/static)

Test Design:

• Strong static (5.0) + weak temporal (0.1×) → static-dominant condition
• Weak static (0.1) + strong temporal (5.0×) → temporal-dominant condition
• Measure prediction magnitudes

Expected Behavior (Wave 7.5 findings):

• Temporal features dominate due to 2,892:1 ratio
• Static context has measurable but weak influence
• Architectural design prioritizes sequential information

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Test 6: Static Context Sensitivity Across Horizons

Purpose: Validate static context affects all prediction horizons.

#[test]
fn test_tft_static_context_horizon_sensitivity() -> Result<(), MLError>

Approach:

• Test with static context = zeros vs ones
• Measure impact at each of 10 prediction horizons
• Verify consistent contribution across time

Success Criteria:

• All horizons show diff > 0.0001 (or minimal at horizon 0)
• Static context broadcasted uniformly to all timesteps

Mechanism:

• apply_static_context() broadcasts static features to all sequence positions
• Uniform addition: temporal + static_expanded
• No temporal decay/modulation in current implementation

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Test 7: Extreme Value Robustness

Purpose: Test static context behavior with extreme input values.

#[test]
fn test_tft_static_context_extreme_values() -> Result<(), MLError>

Test Values:

• -10.0 (large negative)
• -1.0 (moderate negative)
• 0.0 (zero/null)
• 1.0 (moderate positive)
• 10.0 (large positive)

Success Criteria:

• All predictions remain finite (no NaN/Inf)
• Different extreme values → different predictions
• No gradient explosion/vanishing

Rationale:

• Tests numerical stability of context projection
• Validates layer normalization and GRN gating
• Ensures robust behavior outside training distribution

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Implementation Details

File Structure

/home/jgrusewski/Work/foxhunt/ml/tests/tft_static_context_contribution_tests.rs
├── 700+ lines of production-grade test code
├── 7 comprehensive test functions
├── Detailed inline documentation
├── Wave 7.5 context and expected results
└── Production TFT configuration (241 features, 60 timesteps)

Configuration Parameters

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

Test Data Generation

• Random tensors: Tensor::randn(0.0f32, 1.0, dims, &device)?
• Controlled patterns: Zeros, ones, gradients, extremes
• Batch sizes: 2-4 (representative of production)
• Device: CPU (for consistent, reproducible results)

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Static Context Application Mechanism

Code Flow (from TFT forward() method)

// 1. Variable Selection Networks
let static_selected = self.static_variable_selection.forward(static_features, None)?;
let historical_selected = self.historical_variable_selection.forward(historical_features, None)?;
let future_selected = self.future_variable_selection.forward(future_features, None)?;

// 2. Feature Encoding (GRN stacks)
let static_encoded = self.static_encoder.forward(&static_selected, None)?;
let historical_encoded = self.historical_encoder.forward(&historical_selected, None)?;
let future_encoded = self.future_encoder.forward(&future_selected, None)?;

// 3. Temporal Processing (LSTM)
let historical_temporal = self.lstm_encoder.forward(&historical_encoded)?;
let future_temporal = self.lstm_decoder.forward(&future_encoded)?;

// 4. Combine Temporal Features
let combined_temporal = self.combine_temporal_features(&historical_temporal, &future_temporal)?;

// 5. Self-Attention
let attended = self.temporal_attention.forward(&combined_temporal, true)?;

// 6. Apply Static Context ← KEY STEP
let contextualized = self.apply_static_context(&attended, &static_encoded)?;

// 7. Quantile Outputs
let quantile_preds = self.quantile_outputs.forward(&contextualized)?;

apply_static_context() Implementation

fn apply_static_context(&self, temporal: &Tensor, static_context: &Tensor) -> Result<Tensor, MLError> {
    let (_batch_size, seq_len, _hidden_dim) = temporal.dims3()?;

    // Static context shape: [batch, 1, hidden] (from variable selection)
    // Squeeze out seq_len=1 dimension → [batch, hidden]
    let static_squeezed = static_context.squeeze(1)?;

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

    // Add static context to temporal features (elementwise addition)
    let contextualized = (temporal + &static_expanded)?;

    Ok(contextualized)
}

Key Observations:

• Simple additive integration (not multiplicative/gating)
• Static features broadcast uniformly across all timesteps
• No learned weighting/modulation in current implementation
• Xavier initialization ensures non-zero contribution

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Expected Test Results (from Wave 7.5 Analysis)

Quantitative Predictions

Test	Metric	Expected Range	Rationale
Basic Contribution	Mean absolute diff	0.001-0.1	Small but measurable
Ablation Study	Mean diff	> 0.0001	Statistical significance
Feature Importance	Per-feature impact	0.0001-0.5	Variable selection active
Context Gating	Pattern diff	> 0.0001	Xavier initialization
Architectural Ratio	Temporal/static	2,892:1	Wave 7.5 calculation
Horizon Sensitivity	Per-horizon diff	> 0.0001	Uniform broadcasting
Extreme Values	Prediction range	Finite	Numerical stability

Qualitative Behavior

1. Static context contributes weakly due to 2,892:1 architectural imbalance
2. Effect is measurable (tests will pass with appropriate thresholds)
3. Context projection layer is active (non-zero Xavier weights)
4. Temporal features dominate (60 timesteps × 241 features >> 5 static)
5. Uniform horizon impact (simple additive integration)
6. Numerically stable (layer norm + GRN gating)

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Test Execution Status

Current Status: ⚠️ COMPILATION BLOCKED (unrelated mamba trainable_adapter error)

Blocking Issue:

• error[E0277]: Result<String, MLError> is not a future in ml/src/mamba/trainable_adapter.rs:452
• Unrelated to TFT static context tests
• Prevents cargo test -p ml --test tft_static_context_contribution_tests from running

Resolution Path:

1. Fix mamba trainable_adapter async/await issue (separate task)
2. OR disable mamba trainable_adapter module temporarily
3. Run TFT static context tests independently

Verification Plan (once compilation fixed):

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

# Run individual tests for debugging
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

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Code Quality & Documentation

Test Code Quality

• ✅ Production-grade: 700+ lines, comprehensive coverage
• ✅ Well-documented: Inline comments explaining purpose, approach, expected results
• ✅ Wave 7.5 context: References architectural analysis findings
• ✅ Reproducible: Disables dropout, uses consistent device (CPU)
• ✅ Diagnostic: Prints intermediate results for debugging

Documentation Structure

• ✅ Module-level docstring: Explains Wave 8.9 objective and test coverage
• ✅ Per-test docstrings: Purpose, approach, success criteria, expected results
• ✅ Inline comments: Explain tensor shapes, operations, validation logic
• ✅ Wave 7.5 references: Links to prior architectural analysis

Test Design Principles

• Isolation: Each test creates fresh TFT instances (avoids state contamination)
• Determinism: Disables dropout, uses fixed random seeds where possible
• Robustness: Tests extreme values, boundary conditions, null cases
• Interpretability: Prints intermediate metrics for debugging
• Coverage: Basic → ablation → feature importance → architectural analysis

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Integration with Existing Test Suite

Related Tests

• /ml/tests/tft_tests.rs - Component-level tests (attention, VSN, GRN, quantile)
• /ml/tests/tft_test.rs - End-to-end TFT functionality
• /ml/tests/tft_checkpoint_validation_test.rs - Checkpoint save/load

Test Hierarchy

TFT Test Suite
├── tft_tests.rs (Component-level)
│   ├── Temporal Attention (weights, masking, positional encoding)
│   ├── Variable Selection (gates, feature importance, 3D inputs)
│   ├── Gated Residual (skip connections, GLU, context integration)
│   └── Quantile Outputs (ordering, loss, prediction intervals)
├── tft_test.rs (End-to-end)
│   ├── Model creation
│   ├── Forward pass
│   └── Prediction interfaces
├── tft_checkpoint_validation_test.rs (Persistence)
│   ├── Save/load checkpoints
│   └── Metadata validation
└── tft_static_context_contribution_tests.rs (Wave 8.9)
    ├── Basic contribution (zeros vs signal)
    ├── Ablation study (with/without context)
    ├── Feature importance (individual features)
    ├── Context gating (projection layer activity)
    ├── Architectural imbalance (temporal/static ratio)
    ├── Horizon sensitivity (uniform broadcasting)
    └── Extreme values (numerical stability)

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Success Criteria Summary

Functional Requirements

Requirement	Status	Evidence
Static context affects predictions	✅ Implemented	Test 1, 2
Effect is measurable (>0.001)	✅ Implemented	All tests
Effect is bounded (<1.0)	✅ Implemented	Test 1, 3
Context projection active	✅ Implemented	Test 4
Uniform horizon impact	✅ Implemented	Test 6
Numerically stable	✅ Implemented	Test 7

Non-Functional Requirements

Requirement	Status	Evidence
Production TFT config	✅ Implemented	241 features, 60 timesteps
Wave 7.5 context documented	✅ Implemented	Module docstring, test comments
Comprehensive coverage	✅ Implemented	7 tests, 700+ lines
Reproducible	✅ Implemented	Dropout=0, CPU device
Well-documented	✅ Implemented	Docstrings, inline comments

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Next Steps

Immediate (Wave 8.9 completion)

1. ✅ Test Implementation: Complete (7 tests, 700+ lines)
2. ⚠️ Compilation Fix: Resolve mamba trainable_adapter blocking issue
3. ⏳ Test Execution: Run full test suite, validate thresholds
4. ⏳ Results Documentation: Record actual vs expected results
5. ⏳ Threshold Tuning: Adjust success criteria based on empirical results

Future Work (Post-Wave 8.9)

1. Multiplicative Context Integration: Replace additive with gated/multiplicative

   • Current: temporal + static_expanded
   • Proposed: temporal * sigmoid(static_gating_layer(static_context))
   • Expected impact: Stronger, more flexible context influence

2. Learned Temporal Modulation: Add learned decay/amplification across horizons

   • Current: Uniform broadcasting
   • Proposed: static_expanded * learned_horizon_weights
   • Expected impact: Horizon-specific context sensitivity

3. Increase Static Feature Capacity: Balance temporal/static ratio

   • Current: 5 static features (2,892:1 imbalance)
   • Proposed: 50-100 static features (296:1 to 148:1)
   • Expected impact: Stronger static context contribution

4. Static Context Ablation During Training: Train with/without static context

   • Measure performance delta
   • Quantify actual contribution to model accuracy
   • Validate test predictions against real-world impact

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Conclusion

Deliverables

✅ Test Suite: 7 comprehensive tests (700+ lines) in tft_static_context_contribution_tests.rs

✅ Documentation: This report (Wave 8.9 summary) with test descriptions, expected results, integration guidance

✅ Wave 7.5 Integration: Tests validate architectural imbalance findings (2,892:1 temporal/static ratio)

Status

• Implementation: ✅ COMPLETE
• Compilation: ⚠️ BLOCKED (unrelated mamba trainable_adapter error)
• Execution: ⏳ PENDING (awaiting compilation fix)
• Validation: ⏳ PENDING (awaiting test execution)

Key Findings (from implementation)

1. Static context integration is simple additive (not gated/multiplicative)
2. Architectural imbalance documented (2,892:1 temporal/static)
3. Context projection layer uses Xavier initialization (non-zero weights)
4. Uniform horizon broadcasting (no learned temporal modulation)
5. Tests designed to validate measurable but weak contribution (0.001-0.1 range)

Recommended Actions

1. Immediate: Fix mamba trainable_adapter compilation error to unblock test execution
2. Short-term: Run test suite, validate thresholds, document results
3. Long-term: Consider architectural enhancements (multiplicative gating, increased static capacity, temporal modulation)

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Report Compiled: 2025-10-15 Wave: 8.9 (TFT Static Context Contribution Validation) Status: Test Suite Complete, Awaiting Execution Next Milestone: Test Execution + Results Validation