lars
9277d79dff
- Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import numpy as np
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import pytest
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from giant.data.dataset import StepsDataset, train_val_split
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def _dummy(N=500, n_events=20):
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rng = np.random.default_rng(42)
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data = {"event_id": rng.integers(0, n_events, size=N)}
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cond_cont = rng.standard_normal((N, 9)).astype(np.float32)
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cond_cat = rng.integers(0, 3, size=(N, 2)).astype(np.int64)
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target = rng.standard_normal((N, 6)).astype(np.float32)
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return data, cond_cont, cond_cat, target
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def test_dataset_length():
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data, cond_cont, cond_cat, target = _dummy()
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assert len(StepsDataset(cond_cont, cond_cat, target)) == len(target)
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def test_dataset_item_shapes():
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data, cond_cont, cond_cat, target = _dummy()
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c, k, t = StepsDataset(cond_cont, cond_cat, target)[0]
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assert c.shape == (9,)
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assert k.shape == (2,)
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assert t.shape == (6,)
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def test_split_sizes_sum_to_total():
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data, cond_cont, cond_cat, target = _dummy(N=500)
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train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
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assert len(train_ds) + len(val_ds) == 500
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def test_split_no_empty_sets():
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data, cond_cont, cond_cat, target = _dummy(N=500, n_events=20)
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train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
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assert len(val_ds) > 0
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assert len(train_ds) > 0
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def test_split_event_leakage():
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"""Train and val must not share any event_id."""
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N = 1000
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n_events = 50
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rng = np.random.default_rng(7)
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event_ids = rng.integers(0, n_events, size=N)
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data = {"event_id": event_ids}
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cond_cont = rng.standard_normal((N, 9)).astype(np.float32)
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cond_cat = rng.integers(0, 3, size=(N, 2)).astype(np.int64)
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target = rng.standard_normal((N, 6)).astype(np.float32)
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train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
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# Recover which event_ids ended up in each split via the indices
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# (The dataset doesn't store event_ids, so we check via the original mask logic)
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unique_events = np.unique(event_ids)
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rng2 = np.random.default_rng(42)
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rng2.shuffle(unique_events)
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n_val = max(1, int(len(unique_events) * 0.2))
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val_events = set(unique_events[:n_val].tolist())
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train_events = set(unique_events[n_val:].tolist())
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assert val_events.isdisjoint(train_events)
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