| import numpy as np | |
| from src.experiments.deep import SequenceStandardizer, TF_MODEL_IDS, TORCH_MODEL_IDS | |
| def test_sequence_standardizer_is_train_fitted_and_finite(): | |
| X = np.array([[[1.0, np.nan], [3.0, 5.0]], [[5.0, 7.0], [7.0, 9.0]]]) | |
| scaler = SequenceStandardizer().fit(X[:1]) | |
| transformed = scaler.transform(X) | |
| assert transformed.dtype == np.float32 | |
| assert np.isfinite(transformed).all() | |
| assert np.allclose(transformed[:1].mean(axis=(0, 1)), [0.0, 0.0]) | |
| def test_sequence_registry_covers_ten_models(): | |
| assert len(TORCH_MODEL_IDS) + len(TF_MODEL_IDS) == 10 | |
| def test_physics_branch_uses_current_c_rate_channel(): | |
| import tensorflow as tf | |
| from src.models.deep.itransformer import AbsCumCurrentLayer, CURRENT_CHANNEL_INDEX | |
| from src.utils.config import SEQUENCE_FEATURE_COLS_V3 | |
| assert CURRENT_CHANNEL_INDEX == SEQUENCE_FEATURE_COLS_V3.index("current_c_rate") | |
| values = np.zeros((1, 3, len(SEQUENCE_FEATURE_COLS_V3)), dtype=np.float32) | |
| values[0, :, CURRENT_CHANNEL_INDEX] = [-1.0, -2.0, -3.0] | |
| values[0, :, SEQUENCE_FEATURE_COLS_V3.index("voltage_v")] = [4.0, 3.8, 3.6] | |
| observed = AbsCumCurrentLayer()(tf.constant(values)).numpy().reshape(-1) | |
| np.testing.assert_allclose(observed, [1.0, 3.0, 6.0]) | |
| def test_physics_itransformer_carries_configured_loss_weight(): | |
| from src.models.deep.itransformer import build_physics_itransformer | |
| model = build_physics_itransformer(8, 5, d_model=8, n_heads=2, n_blocks=1, lambda_phy=0.25) | |
| assert model.physics_loss_weight == 0.25 | |