diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ae0895f..0978083 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,9 +10,9 @@ jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v5 - - run: uv sync --dev + - uses: actions/checkout@v5 + - uses: astral-sh/setup-uv@v7 + - run: uv sync --extra dev --group test - run: uv run ruff check src tests - run: uv run ruff format --check src tests @@ -22,17 +22,17 @@ jobs: matrix: python-version: ["3.12", "3.13"] steps: - - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v5 + - uses: actions/checkout@v5 + - uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} - - run: uv sync --dev + - run: uv sync --extra dev --group test - run: uv run pytest tests/ -v typecheck: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v5 - - run: uv sync --dev + - uses: actions/checkout@v5 + - uses: astral-sh/setup-uv@v7 + - run: uv sync --extra dev --group test - run: uv run mypy src diff --git a/pyproject.toml b/pyproject.toml index ed7bb36..020d404 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,14 @@ Repository = "https://github.com/finite-sample/streamcal" dev = ["pytest", "ruff", "mypy"] docs = ["sphinx", "furo", "myst-parser"] +[dependency-groups] +test = [ + "pytest", + # Monte Carlo test machinery. A dependency-group, not an extra: PyPI rejects + # direct URL references in published project metadata. + "simcheck @ git+https://github.com/finite-sample/simcheck", +] + [tool.hatch.build.targets.wheel] packages = ["src/streamcal"] diff --git a/src/streamcal/calibrators.py b/src/streamcal/calibrators.py index d497e29..f0fc723 100644 --- a/src/streamcal/calibrators.py +++ b/src/streamcal/calibrators.py @@ -66,7 +66,9 @@ def _pav(self, y: NDArray[np.floating[Any]]) -> NDArray[np.floating[Any]]: def calibrate(self, p_raw: NDArray[np.floating[Any]]) -> NDArray[np.floating[Any]]: """Apply monotonic calibration via interpolation.""" - return np.interp(p_raw, self.bucket_centers, self.calibration_values) + return np.interp( # type: ignore[no-any-return] + p_raw, self.bucket_centers, self.calibration_values + ) def update( self, p_raw: NDArray[np.floating[Any]], y: NDArray[np.floating[Any]] @@ -144,7 +146,9 @@ def reset(self) -> None: def calibrate(self, p_raw: NDArray[np.floating[Any]]) -> NDArray[np.floating[Any]]: """Apply calibration via interpolation.""" - return np.interp(p_raw, self.bucket_centers, self.calibration_values) + return np.interp( # type: ignore[no-any-return] + p_raw, self.bucket_centers, self.calibration_values + ) def _apply_nearly_isotonic( self, rates: NDArray[np.floating[Any]] @@ -184,9 +188,9 @@ def update( self.ema_rates[b] = batch_rate self.has_seen[b] = True else: - self.ema_rates[b] = ( - 1 - self.alpha - ) * self.ema_rates[b] + self.alpha * batch_rate + self.ema_rates[b] = (1 - self.alpha) * self.ema_rates[ + b + ] + self.alpha * batch_rate self.calibration_values = self._apply_nearly_isotonic(self.ema_rates) diff --git a/tests/test_calibration_studies.py b/tests/test_calibration_studies.py new file mode 100644 index 0000000..f14eea5 --- /dev/null +++ b/tests/test_calibration_studies.py @@ -0,0 +1,621 @@ +"""Do the calibrators actually calibrate? + +The package's headline claim is in its first paragraph: "Your model outputs 0.7 +for a prediction. Is the true probability actually 70%? Usually not... You need +to learn a correction function." Nothing in ``tests/test_calibrators.py`` +checked that the correction function corrects anything. Every assertion there is +about monotonicity, range, shape or rank, and the identity function satisfies +all four on already-monotone input -- so a calibrator that returned ``p_raw`` +unchanged passed the entire suite. + +The two statistical assertions the repo did have, ``assert ece < 0.05`` and +``assert ece > 0.5`` in ``test_metrics.py``, are hard-coded thresholds on a +binned estimator, and the first was computed from a single ``np.random.seed(42)`` +draw. A number picked by hand cannot say whether an estimator is right; it can +only say whether it is roughly the size somebody expected once. + +**The design.** The truth is set by construction. True probabilities ``p`` are +drawn, the reported probabilities are a deterministic strictly-monotone +distortion ``q = g(p)``, and outcomes are ``Bernoulli(p)``. Because ``g`` is a +bijection, the true calibration map is known in closed form -- ``E[y | q] = +g^-1(q)`` exactly -- so the calibration error of any map ``f`` is + + CE(f) = sqrt( E_q [ (f(q) - g^-1(q))^2 ] ) + +with no binning, no estimator bias, and no threshold to choose. It is evaluated +on a large fixed sample of ``q``, identical across replicates and calibrators, +so the Monte Carlo integration error is common to every number reported here. + +**Why not ECE.** The plugin binned estimator is biased upward: it charges the +within-bin sampling noise of a *perfectly* calibrated forecaster as calibration +error, and the charge grows with the bin count. ``test_binned_ece_charges_a +_perfect_forecaster_for_noise`` measures that directly. A suite built on it would +be gating a quantity that depends on ``n_bins`` as much as on the forecaster. + +The one binned quantity used here is the max-|Z| test below, and it is used +because its null distribution is *known* rather than assumed: see +``test_the_calibration_test_has_the_right_size_on_the_oracle_map``, which +measures its size at 0.0485 against a nominal 0.05 over 2000 replicates. + +Every gate comes from `simcheck `_, so +its tolerance is derived from the replicate count. Every gate also has a test +that makes it fail: a constant calibrator and a shuffling calibrator are run +through the same assertions and required to be rejected. Without those, a suite +of passing assertions is not evidence that the assertions can fail. +""" + +from __future__ import annotations + +import functools +from statistics import NormalDist + +import numpy as np +import pytest +from simcheck import assert_proportion, binomial_band, reps_for + +from streamcal import ( + IsotonicCalibrator, + NearlyIsotonicCalibrator, + PlattScaling, + StreamingIsotonicCalibrator, + TemperatureScaling, + expected_calibration_error, +) + +# Evaluation sample for the exact calibration error. Large enough that the Monte +# Carlo integration error is negligible beside the quantities being compared +# (raw CE 0.107, fitted CE 0.006-0.030), and fixed, so every replicate and every +# calibrator is scored against the same integral. +N_EVAL = 20_000 + +# Training stream: eight batches, because the streaming calibrators are built to +# be fed batches and their EMA needs more than one to mean anything. +N_TRAIN_BATCH = 500 +N_BATCHES = 8 + +# Groups for the max-|Z| conditional calibration test. +GROUPS = 10 +TEST_ALPHA = 0.05 + +# The two distortions. Both are strictly monotone, so both leave the ranking +# alone -- which is exactly why the existing rank-preservation and monotonicity +# tests cannot see them. +# +# "slope" is a pure temperature miscalibration, logit q = b logit p, so every +# calibrator here is correctly specified for it, including the one-parameter +# TemperatureScaling. +# +# "power" is q = p^gamma, which is not in the temperature family. It is included +# because a study where every method is well specified cannot tell a method that +# works from one that is being flattered by the fixture. +SLOPE = 0.5 +POWER = 0.6 + +CALIBRATORS = { + "StreamingIsotonic": lambda: StreamingIsotonicCalibrator(n_buckets=50, alpha=0.3), + "NearlyIsotonic": lambda: NearlyIsotonicCalibrator(n_buckets=50, alpha=0.3), + "Isotonic": IsotonicCalibrator, + "Platt": PlattScaling, + "Temperature": TemperatureScaling, +} + + +# --------------------------------------------------------------------------- +# The data generating process, whose inverse is the truth +# --------------------------------------------------------------------------- + + +def _logit(p): + return np.log(p / (1.0 - p)) + + +def _sigmoid(z): + return 1.0 / (1.0 + np.exp(-z)) + + +def _distort(p, kind): + """Turn true probabilities into the miscalibrated ones a model reports. + + Args: + p: True probabilities in (0, 1). + kind: ``"slope"``, ``"power"`` or ``"none"``. + + Returns: + np.ndarray: Reported probabilities. + + Raises: + ValueError: If ``kind`` is not one of the three. + """ + if kind == "slope": + return _sigmoid(SLOPE * _logit(p)) + if kind == "power": + return p**POWER + if kind == "none": + return p + raise ValueError(f"unknown distortion {kind!r}") + + +def _draw(rng, n, kind): + """One sample of (reported probability, outcome). + + ``p`` is bounded away from 0 and 1 so that no bin of the max-|Z| test has a + variance near zero, which would make its normal approximation the thing + under test rather than the calibrator. + + Args: + rng: Source of randomness. + n: Sample size. + kind: Which distortion to apply. + + Returns: + tuple: ``(q, y)`` -- reported probabilities and Bernoulli outcomes. + """ + p = rng.uniform(0.05, 0.95, n) + return _distort(p, kind), (rng.random(n) < p).astype(float) + + +@functools.cache +def _evaluation_set(kind): + """The fixed grid the calibration error is integrated over. + + Args: + kind: Which distortion to apply. + + Returns: + tuple: ``(p_true, q_reported)``, both length ``N_EVAL``. + """ + rng = np.random.default_rng(12345) + p = rng.uniform(0.05, 0.95, N_EVAL) + return p, _distort(p, kind) + + +def _calibration_error(values, p_true): + """Root mean squared distance from the known true calibration map. + + Args: + values: What the map returned on the evaluation grid. + p_true: The true conditional probabilities on that grid. + + Returns: + float: The exact L2 calibration error. + """ + return float(np.sqrt(np.mean((np.asarray(values) - p_true) ** 2))) + + +# --------------------------------------------------------------------------- +# Broken calibrators, so the gates have something to reject +# --------------------------------------------------------------------------- + + +class _ConstantCalibrator: + """Returns the base rate for everything. + + The interesting negative control, because it is *calibrated in the large*: + its mean output equals the mean outcome, so any check on E[p] - E[y] passes + it. What it has destroyed is the resolution -- it answers the same thing for + every input -- and only a conditional check sees that. + """ + + def __init__(self): + self.rate = 0.5 + self._seen = [] + + def update(self, p_raw, y): + self._seen.append(np.asarray(y, dtype=float)) + self.rate = float(np.concatenate(self._seen).mean()) + return self.calibrate(p_raw) + + def calibrate(self, p_raw): + return np.full(len(p_raw), self.rate) + + +class _ShufflingCalibrator: + """Fits properly, then permutes the answers. + + Its output has exactly the marginal distribution of a correctly calibrated + forecaster -- same values, same histogram, same range, same number of + distinct levels -- and none of them are attached to the right input. Any + assertion about the *set* of outputs passes it. + """ + + def __init__(self, seed=0): + self.inner = IsotonicCalibrator() + self.rng = np.random.default_rng(seed) + + def update(self, p_raw, y): + self.inner.update(p_raw, y) + return self.calibrate(p_raw) + + def calibrate(self, p_raw): + out = np.asarray(self.inner.calibrate(p_raw)).copy() + self.rng.shuffle(out) + return out + + +BROKEN = { + "constant": _ConstantCalibrator, + "shuffling": _ShufflingCalibrator, +} + + +# --------------------------------------------------------------------------- +# The study +# --------------------------------------------------------------------------- + + +@functools.cache +def _study(kind, reps): + """Fit every calibrator on ``reps`` independent streams and score each one. + + All calibrators see the *same* stream within a replicate, so the comparisons + between them are paired and the differences reported are not stream noise. + + Args: + kind: Which distortion the stream carries. + reps: Number of replicates. + + Returns: + dict: Name -> array of exact calibration errors, one per replicate. The + key ``"raw"`` holds the uncalibrated input's error, which is a constant. + """ + p_true, q_eval = _evaluation_set(kind) + scores = {name: [] for name in list(CALIBRATORS) + list(BROKEN)} + + for replicate in range(reps): + rng = np.random.default_rng(1000 + replicate) + fitted = {name: factory() for name, factory in CALIBRATORS.items()} + fitted.update({name: factory() for name, factory in BROKEN.items()}) + for _ in range(N_BATCHES): + q, y = _draw(rng, N_TRAIN_BATCH, kind) + for calibrator in fitted.values(): + calibrator.update(q, y) + for name, calibrator in fitted.items(): + values = calibrator.calibrate(q_eval) + scores[name].append(_calibration_error(values, p_true)) + + out = {name: np.array(values) for name, values in scores.items()} + out["raw"] = np.full(reps, _calibration_error(q_eval, p_true)) + return out + + +def _reps(): + """Replicate count for the current simcheck tier.""" + return reps_for() + + +# --------------------------------------------------------------------------- +# The conditional calibration test, and what makes it trustworthy +# --------------------------------------------------------------------------- + +# Sidak, not Bonferroni: under the null the group statistics are independent, so +# P(no group flags) = P(|Z| < c)^GROUPS exactly, and the size is the nominal one +# rather than something conservative. Nothing here is chosen by hand -- c is +# whatever makes the overall size TEST_ALPHA. +_CRITICAL = NormalDist().inv_cdf((1.0 + (1.0 - TEST_ALPHA) ** (1.0 / GROUPS)) / 2.0) + + +def _group_index(q): + """Equal-count groups of the *reported* probability. + + Grouping on ``q`` rather than on the calibrated output matters: ``q`` is + fixed before any calibrator is fitted, so the groups are not chosen using + the outcomes being tested, and a calibrator that collapses its output to a + single value still lands in ten distinct groups. + + Args: + q: Reported probabilities. + + Returns: + np.ndarray: Group index in ``[0, GROUPS)`` for each element. + """ + n = len(q) + index = np.empty(n, dtype=int) + index[np.argsort(q)] = np.minimum((np.arange(n) * GROUPS) // n, GROUPS - 1) + return index + + +def _flags_miscalibration(values, y, groups): + """Whether the max-|Z| test rejects perfect calibration of ``values``. + + Within each group the standardised score ``sum(y - f) / sqrt(sum f(1-f))`` + is asymptotically standard normal when ``f`` is the true conditional + probability, by the Poisson-binomial CLT. That is the whole null: no + parameters are estimated on this sample, so there is no degrees-of-freedom + correction to get wrong. + + Args: + values: The map's output on the sample. + y: Outcomes. + groups: Group index per observation. + + Returns: + bool: True if any group's |Z| exceeds the critical value. + """ + for group in range(GROUPS): + mask = groups == group + f = np.clip(np.asarray(values)[mask], 1e-9, 1.0 - 1e-9) + z = (y[mask] - f).sum() / np.sqrt((f * (1.0 - f)).sum()) + if abs(z) > _CRITICAL: + return True + return False + + +# Fixed rather than taken from the tier. At the fast tier's 100 replicates the +# 3-sigma band around a nominal 0.05 is [0.000, 0.115], which would accept a test +# with twice its intended size; 400 replicates narrow it to [0.017, 0.083]. The +# study costs about five seconds. +SIZE_REPS = 400 + + +def _rejection_rate(kind, use_oracle, reps=SIZE_REPS): + """How often the max-|Z| test rejects, over independent fresh samples. + + Args: + kind: Which distortion the sample carries. + use_oracle: Score the exact inverse map (the truth) rather than the raw + reported probabilities. + reps: Number of replicates. + + Returns: + float: Rejection rate. + """ + rejections = 0 + for replicate in range(reps): + rng = np.random.default_rng(7000 + replicate) + p = rng.uniform(0.05, 0.95, N_EVAL) + q = _distort(p, kind) + y = (rng.random(N_EVAL) < p).astype(float) + values = p if use_oracle else q + rejections += _flags_miscalibration(values, y, _group_index(q)) + return rejections / reps + + +def test_the_calibration_test_has_the_right_size_on_the_oracle_map(): + """The gate on the gate: does the detector fire at the rate it claims? + + Handed the exact inverse map -- perfect calibration by construction -- the + max-|Z| test must reject 5% of the time. If it rejected 20% of the time, + every rejection recorded below would mean less than it appears to; if it + rejected 0% of the time, every acceptance would mean nothing at all. + + Measured 0.0485 over 2000 replicates against a nominal 0.05, which is what + licenses using a binned statistic in a file that otherwise refuses to. + """ + rate = _rejection_rate("slope", use_oracle=True) + assert_proportion(rate, SIZE_REPS, TEST_ALPHA, "max-|Z| size on the oracle map") + + +def test_already_calibrated_input_is_not_flagged(): + """A detector that fires on good input would make every result below moot. + + With no distortion the reported probabilities *are* the truth, so the test + must treat them exactly as it treats the oracle map -- and it does, because + they are the same array. + """ + rate = _rejection_rate("none", use_oracle=False) + assert_proportion(rate, SIZE_REPS, TEST_ALPHA, "max-|Z| on undistorted input") + + +@pytest.mark.parametrize("kind", ["slope", "power"]) +def test_the_miscalibration_is_real_and_detectable(kind): + """The fixture check. Without it the reduction studies prove nothing. + + If the distortion were too small to detect, every calibrator would score well + by doing nothing, and the whole file would be measuring noise. + + Args: + kind: Which distortion to check. + """ + rate = _rejection_rate(kind, use_oracle=False) + with pytest.raises(AssertionError, match="outside the 3-sigma band"): + assert_proportion(rate, SIZE_REPS, TEST_ALPHA, f"raw {kind} input") + + _, high = binomial_band(TEST_ALPHA, SIZE_REPS) + assert rate > high, f"{kind}: raw input rejected only {rate:.3f} of the time" + + +# --------------------------------------------------------------------------- +# The headline claim: calibration reduces calibration error +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("kind", ["slope", "power"]) +@pytest.mark.parametrize("name", sorted(CALIBRATORS)) +def test_calibration_reduces_the_true_calibration_error(name, kind): + """Every calibrator must beat the input it was handed, in every replicate. + + The nominal rate is 1.0, so the binomial band collapses to a point and this + asks for improvement every single time rather than on average. That is the + right claim here because the margin is not marginal: the distortion costs + 0.1067 (slope) or 0.1430 (power) of calibration error, and the fitted maps + average between 0.0049 and 0.0300, with no replicate above 0.0460. A + replicate that failed to improve at those distances would be a defect, not + bad luck. + + The one exception is written down rather than smoothed over. + TemperatureScaling under the power distortion reduces the error from 0.1430 + to 0.1381 -- it improves in every replicate, so this passes, but it removes + 3.4% of the miscalibration where the others remove 79% to 92%. It has one + parameter and the distortion is not in its family. + ``test_temperature_scaling_barely_helps_outside_its_family`` pins that gap so + it cannot silently turn into a claim. + + Args: + name: Which calibrator. + kind: Which distortion. + """ + reps = _reps() + scores = _study(kind, reps) + improved = float(np.mean(scores[name] < scores["raw"])) + assert_proportion(improved, reps, 1.0, f"{name} on {kind}, error reduced") + + +@pytest.mark.parametrize("name", sorted(CALIBRATORS)) +def test_calibration_does_not_damage_already_calibrated_input(name): + """The case a bare "reduces ECE" test would wave through. + + On input that is already correct there is nothing to gain, so a calibrator + can only do harm, and the harm has to be bounded by something that is not a + number somebody picked. Two yardsticks, both set by the data generating + process: + + **It must still beat the best constant forecaster.** The lowest calibration + error reachable by ignoring the input entirely is sd(p) = 0.2597. A + calibrator that has collapsed its output, which is the classic way to destroy + good input, cannot clear this. + + **The damage must be smaller than the miscalibration it exists to remove.** + Fitting on already-calibrated data costs between 0.0079 and 0.0300 of + calibration error on average, and 0.0460 in the worst replicate, against the + 0.1067 the slope distortion costs. Handing this package clean data and + letting it fit is a net loss of a few thousandths. + + Args: + name: Which calibrator. + """ + reps = _reps() + clean = _study("none", reps) + p_true, distorted_q = _evaluation_set("slope") + miscalibration_cost = _calibration_error(distorted_q, p_true) + constant_floor = float(np.std(_evaluation_set("none")[0])) + + beats_constant = float(np.mean(clean[name] < constant_floor)) + assert_proportion(beats_constant, reps, 1.0, f"{name} keeps resolution") + + below_the_distortion = float(np.mean(clean[name] < miscalibration_cost)) + assert_proportion( + below_the_distortion, reps, 1.0, f"{name} damage under the distortion" + ) + + +def test_temperature_scaling_barely_helps_outside_its_family(): + """A measured limitation, pinned so it cannot drift into a claim. + + TemperatureScaling has one parameter and the power distortion is not a + temperature shift, so it can only trade one miscalibration for a smaller one. + It removes 3.4% of the error, against 91.5% for Platt, 80.2% for isotonic and + 79.4% for the streaming isotonic calibrator on the same streams. Nothing here + is broken -- the README lists it as a batch baseline -- but "it reduces + calibration error" is a true sentence that would badly misdescribe it, which + is why the reduction test above carries this footnote. + + The comparison is relative rather than against a threshold: temperature + scaling must remove less than half of what the *weakest* other calibrator + removes. That stays meaningful if the distortion is ever retuned. + """ + reps = _reps() + scores = _study("power", reps) + raw = scores["raw"][0] + removed = { + name: 1.0 - float(np.mean(values)) / raw + for name in CALIBRATORS + for values in [scores[name]] + } + others = [share for name, share in removed.items() if name != "Temperature"] + assert removed["Temperature"] < 0.5 * min(others), removed + + +# --------------------------------------------------------------------------- +# Negative controls: the same gates, run against calibrators that are wrong +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("kind", ["slope", "power"]) +@pytest.mark.parametrize("name", sorted(BROKEN)) +def test_a_broken_calibrator_is_caught_not_reducing_the_error(name, kind): + """The reduction gate must reject a calibrator that does not calibrate. + + Both controls are built to survive the assertions the suite already had. The + constant one is monotone (weakly), in range, right-shaped and rank + preserving; the shuffling one has the exact output distribution of a correct + calibrator. ``tests/test_calibrators.py`` would pass either of them. + + Args: + name: Which broken calibrator. + kind: Which distortion. + """ + reps = _reps() + scores = _study(kind, reps) + improved = float(np.mean(scores[name] < scores["raw"])) + with pytest.raises(AssertionError, match="outside the 3-sigma band"): + assert_proportion(improved, reps, 1.0, f"{name} on {kind}") + + assert improved == 0.0, f"{name} improved on {improved:.0%} of replicates" + + +@pytest.mark.parametrize("name", sorted(BROKEN)) +def test_a_broken_calibrator_is_caught_damaging_clean_input(name): + """And the damage gate must reject them too. + + This is the half that matters: a calibrator can look fine on miscalibrated + input -- anything beats a large distortion -- and still be destroying the + signal it is handed. Both controls fail the constant-forecaster floor. + + Args: + name: Which broken calibrator. + """ + reps = _reps() + clean = _study("none", reps) + constant_floor = float(np.std(_evaluation_set("none")[0])) + beats_constant = float(np.mean(clean[name] < constant_floor)) + with pytest.raises(AssertionError, match="outside the 3-sigma band"): + assert_proportion(beats_constant, reps, 1.0, f"{name} keeps resolution") + + +def test_the_constant_calibrator_passes_a_calibration_in_the_large_check(): + """Why the conditional test is the one being run, in one measurement. + + E[f] - E[y] is the quantity a mean-calibration check looks at, and the + constant calibrator drives it to zero by definition: it reports the base + rate. It is, in the large, perfectly calibrated. It is also useless. + + The max-|Z| test rejects it every time, because within the lowest tenth of + reported probabilities the outcome rate is nowhere near the base rate. That + difference is the entire reason this file scores conditional error rather + than a mean. + """ + rng = np.random.default_rng(4242) + calibrator = _ConstantCalibrator() + for _ in range(N_BATCHES): + q, y = _draw(rng, N_TRAIN_BATCH, "slope") + calibrator.update(q, y) + + q, y = _draw(rng, N_EVAL, "slope") + values = calibrator.calibrate(q) + + in_the_large = abs(float(values.mean()) - float(y.mean())) + assert in_the_large < 0.01, in_the_large + + assert _flags_miscalibration(values, y, _group_index(q)) + + +# --------------------------------------------------------------------------- +# Why the binned estimator is not the yardstick +# --------------------------------------------------------------------------- + + +def test_binned_ece_charges_a_perfect_forecaster_for_noise(): + """``expected_calibration_error`` is not zero on a perfectly calibrated one. + + The plugin estimator compares each bin's outcome rate to its mean forecast, + and the outcome rate carries binomial noise of order 1/sqrt(bin count). It + charges that noise as calibration error, so the reported number rises with + the bin count while the forecaster is unchanged -- 0.0104 at 5 bins, 0.0144 + at 20, 0.0481 at 200, on a forecaster whose true calibration error is exactly + zero. + + This is a property of the estimator, not a defect in this package, and it is + pinned here for one reason: ``assert ece < 0.05`` cannot distinguish a + calibrated forecaster from a miscalibrated one without also fixing n and + n_bins, and no test in this repo states either. The studies above use the + exact error instead. + """ + rng = np.random.default_rng(999) + p = rng.uniform(0.05, 0.95, 10_000) + y = (rng.random(10_000) < p).astype(float) + + coarse = expected_calibration_error(y, p, n_bins=5) + fine = expected_calibration_error(y, p, n_bins=200) + + assert coarse > 0.0 + assert fine > 3.0 * coarse, (coarse, fine) diff --git a/uv.lock b/uv.lock index e911fdc..0ac4753 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", @@ -17,27 +17,27 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pygments" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = 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