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>
95 lines
2.7 KiB
Rust
95 lines
2.7 KiB
Rust
//! DQN model integration test
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//!
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//! Verifies: create model -> forward pass -> valid output range
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//! Uses lightweight config for fast execution (<10s)
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use ml::dqn::dqn::DQNConfig;
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use ml::ensemble::adapters::DqnInferenceAdapter;
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use ml::ensemble::inference_adapter::{FeatureVector, ModelInferenceAdapter};
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fn small_dqn_config() -> DQNConfig {
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DQNConfig {
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state_dim: 51,
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num_actions: 45,
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hidden_dims: vec![32, 32],
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..Default::default()
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}
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}
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#[test]
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fn test_dqn_adapter_produces_valid_prediction() {
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let adapter = DqnInferenceAdapter::new(small_dqn_config())
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.expect("DqnInferenceAdapter::new should succeed");
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assert_eq!(adapter.model_name(), "DQN");
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assert!(adapter.is_ready());
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let fv = FeatureVector {
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values: vec![0.1; 51],
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timestamp: 1_700_000_000_000_000,
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};
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let pred = adapter.predict(&fv).expect("DQN predict should succeed");
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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!(pred.confidence.is_finite(), "confidence must not be NaN/Inf");
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assert!(
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pred.metadata.q_values.is_some(),
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"DQN should include Q-values in metadata"
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);
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}
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#[test]
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fn test_dqn_deterministic_inference() {
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let adapter = DqnInferenceAdapter::new(small_dqn_config())
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.expect("DqnInferenceAdapter::new should succeed");
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let fv = FeatureVector {
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values: vec![0.5; 51],
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timestamp: 1_700_000_000_000_000,
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};
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let pred1 = adapter.predict(&fv).expect("predict 1");
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let pred2 = adapter.predict(&fv).expect("predict 2");
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assert_eq!(
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pred1.direction, pred2.direction,
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"DQN inference should be deterministic"
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);
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assert_eq!(
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pred1.confidence, pred2.confidence,
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"DQN confidence should be deterministic"
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);
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}
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#[test]
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fn test_dqn_different_inputs_different_outputs() {
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let adapter = DqnInferenceAdapter::new(small_dqn_config())
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.expect("DqnInferenceAdapter::new should succeed");
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let fv_low = FeatureVector {
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values: vec![0.0; 51],
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timestamp: 1_700_000_000_000_000,
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};
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let fv_high = FeatureVector {
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values: vec![1.0; 51],
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timestamp: 1_700_000_000_000_000,
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};
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let pred_low = adapter.predict(&fv_low).expect("predict low");
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let pred_high = adapter.predict(&fv_high).expect("predict high");
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assert!(pred_low.direction.is_finite());
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assert!(pred_high.direction.is_finite());
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}
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