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20c251e | 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 | from __future__ import annotations
import pytest
from dovla_cil.training.losses import (
CompositeLoss,
behavior_cloning_loss,
causal_contrastive_loss,
effect_prediction_loss,
language_minimal_pair_loss,
lattice_cycle_residual,
lattice_field_loss,
pairwise_ranking_loss,
progress_loss,
regret_loss,
regret_targets,
same_state_pairwise_ranking_loss,
success_loss,
)
def test_ranking_loss_prefers_correct_order() -> None:
rewards = [1.0, 0.0, -1.0]
good_scores = [2.0, 1.0, 0.0]
bad_scores = [0.0, 1.0, 2.0]
good_loss = same_state_pairwise_ranking_loss(good_scores, rewards)
bad_loss = same_state_pairwise_ranking_loss(bad_scores, rewards)
assert good_loss < bad_loss
def test_ranking_loss_ignores_ties() -> None:
assert same_state_pairwise_ranking_loss([0.0, 1.0], [1.0, 1.0]) == 0.0
def test_ranking_loss_checks_shape() -> None:
with pytest.raises(ValueError):
same_state_pairwise_ranking_loss([0.0], [0.0, 1.0])
def test_regret_targets() -> None:
assert regret_targets([1.0, 0.25, -1.0]) == [0.0, 0.75, 2.0]
def test_pairwise_ranking_loss_lower_when_order_is_correct() -> None:
good = pairwise_ranking_loss([2.0, 1.0], [0.0, 0.0], [1.0, 1.0], [0.0, -1.0])
bad = pairwise_ranking_loss([0.0, 0.0], [2.0, 1.0], [1.0, 1.0], [0.0, -1.0])
assert good < bad
def test_regret_loss_zero_when_exact() -> None:
assert regret_loss([0.0, 0.5, 1.0], [0.0, 0.5, 1.0]) == 0.0
def test_behavior_cloning_loss_works() -> None:
assert behavior_cloning_loss([1.0, 2.0], [1.0, 4.0]) == pytest.approx(2.0)
def test_effect_prediction_loss_combines_continuous_and_binary_terms() -> None:
loss = effect_prediction_loss(
{"continuous": [0.0, 1.0], "binary_logits": [0.0]},
{"continuous": [0.0, 3.0], "binary": [1.0]},
)
assert loss > 0.0
def test_success_loss_prefers_correct_logits() -> None:
assert success_loss([3.0], [1.0]) < success_loss([-3.0], [1.0])
def test_progress_loss_works() -> None:
assert progress_loss([0.5], [0.5]) == pytest.approx(0.0)
def test_contrastive_loss_finite() -> None:
loss = causal_contrastive_loss(
[[1.0, 0.0]],
[[1.0, 0.0]],
[[0.0, 1.0]],
temperature=0.1,
)
assert float(loss) >= 0.0
def test_language_minimal_pair_loss_pushes_and_pulls() -> None:
close_different = language_minimal_pair_loss([[0.0, 0.0]], [[0.1, 0.0]], [True], margin=1.0)
far_different = language_minimal_pair_loss([[0.0, 0.0]], [[2.0, 0.0]], [True], margin=1.0)
same_identical = language_minimal_pair_loss([[0.0, 0.0]], [[0.0, 0.0]], [False], margin=1.0)
same_apart = language_minimal_pair_loss([[0.0, 0.0]], [[1.0, 0.0]], [False], margin=1.0)
assert far_different < close_different
assert same_identical < same_apart
def test_composite_returns_components_and_total() -> None:
output = CompositeLoss()(
predictions={
"pred_action": [1.0, 2.0],
"pred_regret": [0.0, 1.0],
"pred_scores_i": [2.0],
"pred_scores_j": [0.0],
},
targets={
"target_action": [1.0, 3.0],
"target_regret": [0.0, 1.0],
"rewards_i": [1.0],
"rewards_j": [0.0],
},
)
assert set(output) >= {"total", "bc", "rank", "regret"}
assert float(output["total"]) >= 0.0
def test_lattice_field_loss_is_invariant_to_state_reward_offsets() -> None:
torch = pytest.importorskip("torch")
potential = torch.tensor([0.2, -0.1, 0.7, 0.0])
utility = torch.tensor([0.8, 0.3, 0.6, 0.1])
effect = torch.tensor([[0.0], [0.2], [0.5], [0.1]])
target_effect = torch.tensor([[0.1], [0.4], [0.6], [0.0]])
group_ids = ["state-a", "state-a", "state-b", "state-b"]
base = lattice_field_loss(potential, utility, effect, target_effect, group_ids)
shifted = lattice_field_loss(
potential,
utility + torch.tensor([17.0, 17.0, -9.0, -9.0]),
effect,
target_effect,
group_ids,
)
assert torch.allclose(base["potential"], shifted["potential"])
assert base["edge_count"] == shifted["edge_count"] == 2
def test_lattice_field_is_zero_under_groupwise_gauge_shifts() -> None:
torch = pytest.importorskip("torch")
utility = torch.tensor([0.8, 0.3, 0.6, 0.1])
target_effect = torch.tensor([[0.1, 0.2], [0.4, 0.0], [0.6, -0.2], [0.0, 0.3]])
group_ids = ["state-a", "state-a", "state-b", "state-b"]
potential = utility + torch.tensor([5.0, 5.0, -2.0, -2.0])
predicted_effect = target_effect + torch.tensor(
[[1.0, -3.0], [1.0, -3.0], [-4.0, 2.0], [-4.0, 2.0]]
)
loss = lattice_field_loss(
potential,
utility,
predicted_effect,
target_effect,
group_ids,
)
assert float(loss["potential"]) == pytest.approx(0.0, abs=1e-7)
assert float(loss["effect"]) == pytest.approx(0.0, abs=1e-7)
def test_lattice_field_orientation_penalizes_reversed_edge_order() -> None:
torch = pytest.importorskip("torch")
utility = torch.tensor([1.0, 0.0])
effect = torch.zeros((2, 2))
correct = lattice_field_loss(
torch.tensor([1.0, 0.0]),
utility,
effect,
effect,
["state", "state"],
)
reversed_order = lattice_field_loss(
torch.tensor([0.0, 1.0]),
utility,
effect,
effect,
["state", "state"],
)
assert float(correct["potential"]) == pytest.approx(0.0, abs=1e-7)
assert float(reversed_order["orientation"]) > 0.0
assert float(reversed_order["potential"]) > float(correct["potential"])
assert float(reversed_order["preference"]) > float(correct["preference"])
def test_lattice_field_preference_is_group_offset_invariant() -> None:
torch = pytest.importorskip("torch")
potential = torch.tensor([0.4, -0.2, 2.0, 1.7])
utility = torch.tensor([0.9, 0.1, 0.8, 0.2])
effect = torch.zeros((4, 2))
group_ids = ["a", "a", "b", "b"]
base = lattice_field_loss(potential, utility, effect, effect, group_ids)
shifted = lattice_field_loss(
potential,
utility + torch.tensor([10.0, 10.0, -4.0, -4.0]),
effect,
effect,
group_ids,
)
assert torch.allclose(base["preference"], shifted["preference"])
def test_scalar_potential_has_zero_cycle_residual() -> None:
torch = pytest.importorskip("torch")
residual = lattice_cycle_residual(torch.tensor([0.3, -1.2, 2.4]), [[0, 1, 2]])
assert float(residual) == pytest.approx(0.0, abs=1e-7)
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