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import json
import math
import tempfile
import unittest
from pathlib import Path
from vons.calibration import (
ABSTAIN_ALL_THRESHOLD,
CALIBRATION_VERSION,
apply_calibration,
apply_vector_calibration,
calibrate_report,
fit_abstention_threshold,
fit_abstention_threshold_details,
fit_brier_abstention_threshold_details,
fit_temperature,
fit_temperature_details,
fit_vector_scaling_details,
)
from vons.data import Example, write_jsonl
from vons.evaluation import METRIC_VERSION, Prediction, nll, summarize, write_report
def _example(identifier: str, label: str | None, answerable: bool, split: str = "calibration") -> Example:
return Example(
id=identifier,
task_group="calibration",
state="state",
question="choose",
options=("a", "b"),
label=label,
answerable=answerable,
split=split,
provenance={},
metadata={"scenario_id": identifier},
)
def _reject_constant(value: str) -> float:
raise ValueError(f"non-strict JSON constant {value}")
class CalibrationTests(unittest.TestCase):
def test_temperature_fit_finds_interior_optimum_and_does_not_increase_nll(self) -> None:
examples = [_example(f"r{index}", "a" if index < 3 else "b", True) for index in range(4)]
predictions = [Prediction(f"r{index}", "a", {"a": 0.6, "b": 0.4}, 0.6, False) for index in range(4)]
details = fit_temperature_details(examples, predictions)
calibrated = apply_calibration(examples, predictions, temperature=details.temperature, abstention_threshold=0.0)
self.assertGreater(details.temperature, details.lower)
self.assertLess(details.temperature, details.upper)
self.assertEqual(details.rows, 4)
self.assertFalse(details.at_lower_bound)
self.assertFalse(details.at_upper_bound)
self.assertEqual(fit_temperature(examples, predictions), details.temperature)
self.assertLess(nll(examples, calibrated), nll(examples, predictions))
def test_temperature_regularization_prevents_boundary_locking(self) -> None:
examples = [_example(f"s{index}", "a", True) for index in range(4)]
predictions = [Prediction(f"s{index}", "a", {"a": 0.9, "b": 0.1}, 0.9, False) for index in range(4)]
details = fit_temperature_details(examples, predictions)
self.assertFalse(details.at_lower_bound)
self.assertFalse(details.at_upper_bound)
self.assertGreater(details.temperature, 0.1)
self.assertLess(details.temperature, 10.0)
unregularized = fit_temperature_details(
examples, predictions, log_temperature_l2=0.0
)
self.assertTrue(unregularized.at_lower_bound)
self.assertAlmostEqual(unregularized.temperature, 0.1)
def test_vector_scaling_fits_all_classes_inside_bounds(self) -> None:
options = ("a", "b", "c", "d")
examples = []
predictions = []
for index, label in enumerate(options * 5):
examples.append(
Example(
str(index),
"calibration",
"state",
"choose",
options,
label,
True,
"calibration",
{},
{"scenario_id": str(index)},
)
)
probabilities = {option: 0.1 for option in options}
probabilities[label] = 0.7
predictions.append(
Prediction(str(index), label, probabilities, 0.7, False)
)
details = fit_vector_scaling_details(examples, predictions)
calibrated = apply_vector_calibration(
examples,
predictions,
temperatures=details.temperatures,
biases=details.biases,
abstention_threshold=0.0,
)
self.assertEqual(set(details.temperatures), set(options))
self.assertTrue(all(0.1 < value < 10.0 for value in details.temperatures.values()))
self.assertFalse(details.at_lower_bound)
self.assertFalse(details.at_upper_bound)
self.assertLess(details.nll_after, details.nll_before)
self.assertLess(nll(examples, calibrated), nll(examples, predictions))
def test_brier_threshold_is_fitted_from_calibrated_losses(self) -> None:
examples = [
_example("strong", "a", True),
_example("weak", "b", True),
_example("wrong", "b", True),
]
predictions = [
Prediction("strong", "a", {"a": 0.95, "b": 0.05}, 0.95, False),
Prediction("weak", "b", {"a": 0.4, "b": 0.6}, 0.6, False),
Prediction("wrong", "a", {"a": 0.9, "b": 0.1}, 0.9, False),
]
parameters = {"a": 1.0, "b": 1.0}
details = fit_brier_abstention_threshold_details(
examples,
predictions,
temperatures=parameters,
biases={"a": 0.0, "b": 0.0},
target_brier=0.25,
)
self.assertFalse(details.abstain_all)
self.assertEqual(details.target_brier, 0.25)
self.assertLessEqual(details.covered_brier, details.target_brier)
self.assertGreater(details.threshold, 0.0)
def test_temperature_scaling_preserves_true_zero_support(self) -> None:
examples = [_example("zero", "a", True)]
predictions = [Prediction("zero", "a", {"a": 1.0, "b": 0.0}, 1.0, False)]
calibrated = apply_calibration(examples, predictions, temperature=20.0, abstention_threshold=0.0)
self.assertEqual(calibrated[0].probabilities, {"a": 1.0, "b": 0.0})
def test_invalid_probability_vectors_fail_closed(self) -> None:
examples = [_example("x", "a", True)]
for probabilities in ({"a": 0.5, "b": 0.2}, {"a": math.nan, "b": 1.0}, {"a": -0.5, "b": 1.5}):
with self.subTest(probabilities=probabilities), self.assertRaises(ValueError):
apply_calibration(
examples,
[Prediction("x", "a", probabilities, 0.5, False)],
temperature=1.0,
abstention_threshold=0.0,
)
def test_calibration_recomputes_argmax_and_never_covers_without_choice(self) -> None:
examples = [_example("was_abstained", "a", True), _example("stale_choice", "a", True)]
predictions = [
Prediction("was_abstained", None, {"a": 0.6, "b": 0.4}, 0.42, True),
Prediction("stale_choice", "b", {"a": 0.6, "b": 0.4}, 0.6, False),
]
calibrated = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=0.2)
self.assertEqual([(item.choice, item.abstained) for item in calibrated], [("a", False), ("a", False)])
def test_answerability_gate_is_preserved_and_labels_do_not_gate(self) -> None:
examples = [_example("low_answerability", "a", True), _example("unanswerable", None, False)]
predictions = [
Prediction("low_answerability", "a", {"a": 0.6, "b": 0.4}, 0.2, False),
Prediction("unanswerable", "a", {"a": 0.6, "b": 0.4}, 0.6, False),
]
default = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=0.0)
relaxed = apply_calibration(
examples,
predictions,
temperature=1.0,
abstention_threshold=0.0,
answerability_threshold=0.3,
)
self.assertEqual((default[0].choice, default[0].abstained), (None, True))
self.assertEqual((default[1].choice, default[1].abstained), ("a", False))
self.assertEqual((relaxed[0].choice, relaxed[0].abstained), ("a", False))
def test_failed_and_absent_distributions_remain_abstained(self) -> None:
examples = [_example("failed", "a", True), _example("absent", "a", True)]
predictions = [
Prediction("failed", None, {}, 0.0, False, error="timeout"),
Prediction("absent", "a", {}, 0.9, False),
]
calibrated = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=0.0)
self.assertEqual((calibrated[0].choice, calibrated[0].abstained, calibrated[0].error), (None, True, "timeout"))
self.assertEqual((calibrated[1].choice, calibrated[1].abstained, calibrated[1].probabilities), (None, True, {}))
def test_confidence_above_max_probability_fails_closed(self) -> None:
examples = [_example("x", "a", True)]
with self.assertRaisesRegex(ValueError, "max_probability"):
apply_calibration(
examples,
[Prediction("x", "a", {"a": 0.5, "b": 0.5}, 0.9, False)],
temperature=1.0,
abstention_threshold=0.0,
)
def test_threshold_fit_uses_argmax_and_counts_unanswerable_as_errors(self) -> None:
examples = [_example("correct", "a", True), _example("stale_choice", "a", True), _example("unknown", None, False)]
predictions = [
Prediction("correct", "a", {"a": 0.9, "b": 0.1}, 0.9, False),
Prediction("stale_choice", "b", {"a": 0.6, "b": 0.4}, 0.58, False),
Prediction("unknown", "a", {"a": 0.55, "b": 0.45}, 0.5, False),
]
details = fit_abstention_threshold_details(examples, predictions, target_risk=0.0)
calibrated = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=details.threshold)
self.assertAlmostEqual(details.threshold, 0.58)
self.assertEqual((details.rows, details.covered_rows, details.risk, details.abstain_all), (3, 2, 0.0, False))
self.assertEqual(fit_abstention_threshold(examples, predictions, target_risk=0.0), details.threshold)
self.assertEqual([item.abstained for item in calibrated], [False, False, True])
def test_all_abstain_sentinel_covers_nothing_even_at_confidence_one(self) -> None:
examples = [_example("wrong", "a", True)]
predictions = [Prediction("wrong", "b", {"a": 0.0, "b": 1.0}, 1.0, False)]
threshold = fit_abstention_threshold(examples, predictions, target_risk=0.0)
at_one = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=1.0)[0]
sentinel = apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=threshold)[0]
self.assertEqual(threshold, ABSTAIN_ALL_THRESHOLD)
self.assertFalse(at_one.abstained)
self.assertEqual((sentinel.choice, sentinel.abstained), (None, True))
for invalid in (1.5, -0.1, math.nan):
with self.subTest(threshold=invalid), self.assertRaisesRegex(ValueError, "abstention_threshold"):
apply_calibration(examples, predictions, temperature=1.0, abstention_threshold=invalid)
def test_unknown_or_duplicate_prediction_ids_fail_closed(self) -> None:
examples = [_example("a", "a", True)]
with self.assertRaisesRegex(ValueError, "unknown example id"):
apply_calibration(
examples,
[Prediction("ghost", "a", {"a": 1.0}, 1.0, False)],
temperature=1.0,
abstention_threshold=0.0,
)
with self.assertRaisesRegex(ValueError, "duplicate prediction"):
fit_abstention_threshold(examples, [Prediction("a", "a", {"a": 1.0}, 1.0, False)] * 2)
def test_calibrate_report_records_provenance_and_rejects_identity_mismatch(self) -> None:
config = {
"model_id": "vons-test",
"backend": "direct",
"seed": 7,
"encoder": {"name": "encoder", "revision": "r1"},
"answerability_threshold": 0.8,
}
calibration_rows = [_example("c1", "a", True), _example("c2", "b", True), _example("c3", None, False)]
calibration_predictions = [
Prediction("c1", "a", {"a": 0.9, "b": 0.1}, 0.9, False),
Prediction("c2", "b", {"a": 0.3, "b": 0.7}, 0.63, False),
Prediction("c3", None, {"a": 0.6, "b": 0.4}, 0.3, True),
]
test_rows = [_example("t1", "a", True, "test"), _example("t2", "b", True, "test"), _example("t3", None, False, "test")]
test_predictions = [
Prediction("t1", "a", {"a": 0.8, "b": 0.2}, 0.8, False),
Prediction("t2", "b", {"a": 0.4, "b": 0.6}, 0.42, False),
Prediction("t3", None, {}, 0.0, False, error="timeout"),
]
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
calibration_input, calibration_report = root / "calibration.jsonl", root / "calibration.json"
test_input, test_report, output = root / "test.jsonl", root / "test.json", root / "calibrated.json"
write_jsonl(calibration_input, calibration_rows)
write_jsonl(test_input, test_rows)
write_report(calibration_report, config=config, summary=summarize(calibration_rows, calibration_predictions), predictions=calibration_predictions)
write_report(test_report, config=config, summary=summarize(test_rows, test_predictions), predictions=test_predictions)
arguments = {
"calibration_input": calibration_input,
"calibration_report": calibration_report,
"test_input": test_input,
"test_report": test_report,
}
calibrate_report(**arguments, output=output, target_brier=0.2)
payload = json.loads(output.read_text(encoding="utf-8"), parse_constant=_reject_constant)
fit = payload["calibration"]
self.assertEqual(fit["version"], CALIBRATION_VERSION)
self.assertEqual(fit["answerability_threshold"], 0.8)
self.assertEqual((fit["temperature_fit_rows"], fit["threshold_fit_rows"], fit["rows"]), (2, 3, 3))
self.assertEqual(fit["calibration_method"], "vector_scaling")
self.assertEqual(set(fit["temperatures"]), {"a", "b"})
self.assertEqual(set(fit["biases"]), {"a", "b"})
self.assertFalse(fit["temperature_at_lower_bound"])
self.assertEqual(fit["target_brier"], 0.2)
self.assertLessEqual(fit["nll_after"], fit["nll_before"])
self.assertLessEqual(fit["brier_after"], fit["brier_before"])
self.assertEqual(payload["inputs"]["test_report"]["sha256"], hashlib.sha256(test_report.read_bytes()).hexdigest())
self.assertEqual(payload["model_identity"]["model_id"], "vons-test")
self.assertTrue(any("checkpoint_sha256 unavailable" in item for item in payload["limitations"]))
self.assertEqual(payload["summary"]["metric_version"], METRIC_VERSION)
decisions = {item["example_id"]: (item["choice"], item["abstained"]) for item in payload["predictions"]}
self.assertEqual(decisions, {"t1": ("a", False), "t2": (None, True), "t3": (None, True)})
mismatched = root / "mismatched.json"
write_report(mismatched, config={**config, "model_id": "other"}, summary={}, predictions=test_predictions)
with self.assertRaisesRegex(ValueError, "model_id"):
calibrate_report(**{**arguments, "test_report": mismatched}, output=root / "rejected.json")
with self.assertRaisesRegex(ValueError, "overwrite"):
calibrate_report(**arguments, output=test_report)
self.assertFalse((root / "rejected.json").exists())
if __name__ == "__main__":
unittest.main()
|