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20c251e c9216b0 20c251e a25d8fa 313eafb 20c251e 313eafb 20c251e f6c1f86 20c251e f6c1f86 20c251e f6c1f86 20c251e c9216b0 20c251e a25d8fa ed7442b 313eafb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | from __future__ import annotations
from pathlib import Path
from types import SimpleNamespace
from dovla_cil.data.schema import RewardInfo
from dovla_cil.generation.pipeline import generate_cil_dataset
from dovla_cil.tasks.library import built_in_toy_tasks
from dovla_cil.training.losses import InterventionalLossWeights
from dovla_cil.training.trainer import (
DoVLATrainer,
TrainerConfig,
_best_records_by_group,
_coerce_policy_target_action,
_cross_state_pair_indices,
_load_policy_target_action_map,
_reward_utility_values,
)
from dovla_cil.utils.io import read_json
def test_trainer_runs_one_epoch_and_writes_checkpoints(tmp_path: Path) -> None:
dataset_dir = tmp_path / "cil"
run_dir = tmp_path / "run"
generate_cil_dataset(
backend="toy",
tasks=built_in_toy_tasks()[:3],
out_dir=dataset_dir,
num_states_per_task=2,
k=4,
seed=5,
shard_size=8,
inline_observations=True,
)
result = DoVLATrainer(
TrainerConfig(
dataset_dir=dataset_dir,
output_dir=run_dir,
epochs=1,
batch_groups=2,
records_per_group=4,
hidden_dim=64,
learning_rate=1e-3,
seed=5,
device="cpu",
)
).train()
assert (run_dir / "latest.pt").exists()
assert (run_dir / "best.pt").exists()
assert (run_dir / "best_policy.pt").exists()
assert "rank_acc" in result["history"][0]["val"]
assert "bc_loss" in result["best_policy"]
metrics = read_json(run_dir / "metrics.json")
assert "rank_acc" in metrics["history"][0]["val"]
assert "best_policy" in metrics
def test_trainer_can_supervise_typed_proposal_head(tmp_path: Path) -> None:
dataset_dir = tmp_path / "cil"
run_dir = tmp_path / "run"
generate_cil_dataset(
backend="toy",
tasks=built_in_toy_tasks()[:2],
out_dir=dataset_dir,
num_states_per_task=2,
k=4,
seed=9,
shard_size=8,
inline_observations=True,
)
result = DoVLATrainer(
TrainerConfig(
dataset_dir=dataset_dir,
output_dir=run_dir,
epochs=1,
batch_groups=2,
records_per_group=4,
hidden_dim=64,
learning_rate=1e-3,
seed=9,
device="cpu",
proposal_types=("expert", "near_miss"),
losses=InterventionalLossWeights(proposal=1.0),
)
).train()
assert "proposal_loss" in result["history"][0]["val"]
resolved = read_json(run_dir / "resolved_config.json")
assert resolved["proposal_types"] == ["expert", "near_miss"]
def test_field_utility_includes_terminal_success_bonus() -> None:
records = [
SimpleNamespace(
reward=RewardInfo(
progress=0.4,
success=False,
terminal_success=False,
)
),
SimpleNamespace(
reward=RewardInfo(
progress=0.4,
success=True,
terminal_success=True,
)
),
]
assert _reward_utility_values(records) == [0.4, 1.4]
def test_cross_state_pairs_preserve_task_and_reward_order() -> None:
records = [
SimpleNamespace(
task_id="pick",
group_id=f"g{group}",
reward=SimpleNamespace(score=reward),
)
for group, reward in ((0, 0.1), (0, 0.9), (1, 0.2), (1, 0.8), (2, 0.4))
]
pairs = _cross_state_pair_indices(records, pair_count=12, seed=7)
assert len(pairs) == 12
for better, worse in pairs:
assert records[better].task_id == records[worse].task_id
assert records[better].group_id != records[worse].group_id
assert records[better].reward.score > records[worse].reward.score
def test_cross_state_scope_rejects_lattice_field_objective(tmp_path: Path) -> None:
try:
TrainerConfig(
dataset_dir=tmp_path,
output_dir=tmp_path / "out",
objective="lattice_field",
pair_scope="cross_state",
)
except ValueError as exc:
assert "legacy objective" in str(exc)
else: # pragma: no cover - protects baseline semantics
raise AssertionError(
"cross-state pairs cannot silently leave same-state field edges active"
)
def test_policy_target_type_filter_selects_best_allowed_candidate() -> None:
records = [
SimpleNamespace(
group_id="g0",
candidate_type="expert",
reward=SimpleNamespace(score=2.0),
rank_within_group=0,
record_id="expert",
),
SimpleNamespace(
group_id="g0",
candidate_type="near_miss",
reward=SimpleNamespace(score=1.5),
rank_within_group=1,
record_id="near",
),
SimpleNamespace(
group_id="g1",
candidate_type="expert",
reward=SimpleNamespace(score=1.0),
rank_within_group=0,
record_id="fallback",
),
]
selected = _best_records_by_group(records, candidate_types=("near_miss",))
assert {record.group_id: record.record_id for record in selected} == {
"g0": "near",
"g1": "fallback",
}
def test_policy_target_map_overrides_group_target_with_fallback() -> None:
records = [
SimpleNamespace(
group_id="g0",
candidate_type="expert",
reward=SimpleNamespace(score=2.0),
rank_within_group=0,
record_id="expert",
),
SimpleNamespace(
group_id="g0",
candidate_type="near_miss",
reward=SimpleNamespace(score=1.5),
rank_within_group=1,
record_id="field_choice",
),
SimpleNamespace(
group_id="g1",
candidate_type="near_miss",
reward=SimpleNamespace(score=0.7),
rank_within_group=1,
record_id="fallback",
),
]
selected = _best_records_by_group(
records,
candidate_types=("near_miss",),
target_record_ids={"g0": "field_choice"},
)
assert {record.group_id: record.record_id for record in selected} == {
"g0": "field_choice",
"g1": "fallback",
}
def test_policy_target_action_map_loads_continuous_targets(tmp_path: Path) -> None:
path = tmp_path / "targets.json"
path.write_text(
"""
{
"targets": {
"g0": {"record_id": "r0", "action_values": [[0.1, 0.2], [0.3, 0.4]]},
"g1": "legacy_record_id"
}
}
"""
)
loaded = _load_policy_target_action_map(path)
assert loaded == {"g0": [[0.1, 0.2], [0.3, 0.4]]}
assert _coerce_policy_target_action(
loaded["g0"],
action_dim=3,
action_horizon=3,
) == [[0.1, 0.2, 0.0], [0.3, 0.4, 0.0], [0.0, 0.0, 0.0]]
|