0fa7aa41c0
- DQN: adapter creation, deterministic inference, varied inputs - PPO: adapter creation, deterministic inference, short input padding - TFT: sequence buffering, valid prediction after warmup - Mamba2: sequence buffering, valid prediction, deterministic SSM - Ensemble: all 4 models -> EnsembleCoordinator -> trading decision - Smoke: full pipeline with 10 sequential predictions, stability check This establishes the production baseline proving the ML pipeline works end-to-end with all 4 models contributing to ensemble decisions. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
102 lines
2.8 KiB
Rust
102 lines
2.8 KiB
Rust
//! TFT model integration test
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//!
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//! Verifies: sequence buffering -> forward pass -> quantile output
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//! TFT requires sequence_length observations before producing real predictions.
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//! Uses lightweight config for fast execution (<10s)
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use ml::ensemble::adapters::TftInferenceAdapter;
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use ml::ensemble::inference_adapter::{FeatureVector, ModelInferenceAdapter};
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use ml::tft::TFTConfig;
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fn small_tft_config() -> TFTConfig {
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TFTConfig {
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input_dim: 20,
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hidden_dim: 32,
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num_heads: 2,
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num_layers: 1,
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prediction_horizon: 5,
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sequence_length: 4,
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num_quantiles: 9,
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num_static_features: 6,
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num_known_features: 6,
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num_unknown_features: 8,
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dropout_rate: 0.0,
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..Default::default()
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}
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}
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const SEQ_LEN: usize = 4;
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fn make_fv(ts: i64) -> FeatureVector {
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FeatureVector {
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values: vec![0.1; 51],
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timestamp: ts,
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}
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}
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#[test]
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fn test_tft_adapter_buffers_before_predicting() {
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let adapter = TftInferenceAdapter::new(small_tft_config(), SEQ_LEN)
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.expect("TftInferenceAdapter::new should succeed");
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assert_eq!(adapter.model_name(), "TFT");
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assert!(
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!adapter.is_ready(),
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"TFT should not be ready with empty buffer"
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);
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// Feed SEQ_LEN - 1 feature vectors: should return neutral (0.0, 0.0)
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for i in 0..(SEQ_LEN - 1) {
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let pred = adapter
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.predict(&make_fv(1_700_000_000 + i as i64))
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.expect("predict should not error during buffering");
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assert_eq!(
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pred.direction, 0.0,
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"Should return neutral direction while buffering"
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);
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assert_eq!(
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pred.confidence, 0.0,
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"Should return zero confidence while buffering"
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);
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}
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assert!(!adapter.is_ready(), "TFT should still not be ready");
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}
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#[test]
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fn test_tft_produces_valid_prediction_after_warmup() {
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let adapter = TftInferenceAdapter::new(small_tft_config(), SEQ_LEN)
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.expect("TftInferenceAdapter::new should succeed");
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// Fill buffer
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for i in 0..SEQ_LEN {
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let _ = adapter.predict(&make_fv(1_700_000_000 + i as i64));
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}
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assert!(
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adapter.is_ready(),
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"TFT should be ready after filling buffer"
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);
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// Next prediction should produce real values
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let pred = adapter
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.predict(&make_fv(1_700_000_000 + SEQ_LEN as i64))
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.expect("TFT predict should succeed after warmup");
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assert!(
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pred.direction >= -1.0 && pred.direction <= 1.0,
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"direction {} out of [-1,1]",
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pred.direction
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);
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assert!(
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pred.confidence >= 0.0 && pred.confidence <= 1.0,
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"confidence {} out of [0,1]",
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pred.confidence
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);
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assert!(pred.direction.is_finite(), "direction must not be NaN/Inf");
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assert!(
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pred.confidence.is_finite(),
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"confidence must not be NaN/Inf"
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);
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}
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