diff --git a/changelog.d/fix-assert-near-float32.fixed.md b/changelog.d/fix-assert-near-float32.fixed.md new file mode 100644 index 00000000..4ef21279 --- /dev/null +++ b/changelog.d/fix-assert-near-float32.fixed.md @@ -0,0 +1 @@ +Compare `assert_near` operands at float32 when one operand is already float32, otherwise at float64. Keeps the H6 fix that catches dollar-level differences on multi-million-dollar float64 values, without surfacing float32 storage rounding (e.g. `8.91` stored as float32 vs the Python literal `8.91`) as spurious test failures in YAML tests against float-typed Variables. diff --git a/changelog.d/fix-fast-cache-guards.fixed.md b/changelog.d/fix-fast-cache-guards.fixed.md new file mode 100644 index 00000000..f5503d83 --- /dev/null +++ b/changelog.d/fix-fast-cache-guards.fixed.md @@ -0,0 +1 @@ +Guard all `_fast_cache` mutation sites in `Simulation` against the attribute being missing. `Simulation.__init__` initialises `self._fast_cache = {}`, but country-package subclasses (e.g. `policyengine_uk.Simulation`) legitimately override `__init__` without calling `super().__init__`, so `set_input`, `delete_arrays`, and `purge_cache_of_invalid_values` were raising `AttributeError` on those subclasses. diff --git a/policyengine_core/simulations/simulation.py b/policyengine_core/simulations/simulation.py index 8815419d..17f60273 100644 --- a/policyengine_core/simulations/simulation.py +++ b/policyengine_core/simulations/simulation.py @@ -825,10 +825,12 @@ def purge_cache_of_invalid_values(self) -> None: # We wait for the end of calculate(), signalled by an empty stack, before purging the cache if self.tracer.stack: return + _fast_cache = getattr(self, "_fast_cache", None) for _name, _period in self.invalidated_caches: holder = self.get_holder(_name) holder.delete_arrays(_period) - self._fast_cache.pop((_name, _period), None) + if _fast_cache is not None: + _fast_cache.pop((_name, _period), None) self.invalidated_caches = set() def calculate_add( @@ -1189,14 +1191,17 @@ def delete_arrays(self, variable: str, period: Period = None) -> None: True """ self.get_holder(variable).delete_arrays(period) + _fast_cache = getattr(self, "_fast_cache", None) if period is None: - self._fast_cache = { - k: v for k, v in self._fast_cache.items() if k[0] != variable - } + if _fast_cache is not None: + self._fast_cache = { + k: v for k, v in _fast_cache.items() if k[0] != variable + } else: if not isinstance(period, Period): period = periods.period(period) - self._fast_cache.pop((variable, period), None) + if _fast_cache is not None: + _fast_cache.pop((variable, period), None) def get_known_periods(self, variable: str) -> List[Period]: """ @@ -1241,7 +1246,9 @@ def set_input(self, variable_name: str, period: Period, value: ArrayLike) -> Non if (variable.end is not None) and (period.start.date > variable.end): return self.get_holder(variable_name).set_input(period, value, self.branch_name) - self._fast_cache.pop((variable_name, period), None) + _fast_cache = getattr(self, "_fast_cache", None) + if _fast_cache is not None: + _fast_cache.pop((variable_name, period), None) def get_variable_population(self, variable_name: str) -> Population: variable = self.tax_benefit_system.get_variable( diff --git a/policyengine_core/tools/__init__.py b/policyengine_core/tools/__init__.py index df8e7cc2..f95fe5d9 100644 --- a/policyengine_core/tools/__init__.py +++ b/policyengine_core/tools/__init__.py @@ -43,13 +43,24 @@ def assert_near( if isinstance(target_value, str): target_value = eval_expression(target_value) - # Use float64 here so we don't silently lose precision on values - # above ~16M (float32 only carries ~7 decimal digits). Under float32, - # ``25_000_001`` and ``25_000_000`` round to the same number and a - # test expecting one would pass on the other (bug H6). - target_value = np.array(target_value).astype(np.float64) - - value = np.array(value).astype(np.float64) + # Choose comparison dtype: + # - Default to float64 so we don't silently lose precision on values + # above ~16M (float32 only carries ~7 decimal digits). Under + # float32, ``25_000_001`` and ``25_000_000`` round to the same + # number and a test expecting one would pass on the other (bug H6). + # - But if ``value`` is already float32 (because it came out of a + # float-typed Variable, which PolicyEngine stores as float32), + # promoting to float64 would surface the float32 rounding that's + # baked into storage — ``8.91`` stored as float32 compares unequal + # to the Python-literal ``8.91``. That rounding is not a regression + # surfaced by the H6 fix; it's a property of the storage dtype. + # Compare at float32 in that case to keep H6's coverage for real + # precision bugs (float64/int operands) without surfacing + # pre-existing float32 storage artefacts. + _value_array = np.asarray(value) + _compare_dtype = np.float32 if _value_array.dtype == np.float32 else np.float64 + target_value = np.array(target_value).astype(_compare_dtype) + value = _value_array.astype(_compare_dtype) diff = abs(target_value - value) if absolute_error_margin is not None: assert (diff <= absolute_error_margin).all(), ( diff --git a/policyengine_core/tools/test_runner.py b/policyengine_core/tools/test_runner.py index 57b9ab8a..03276236 100644 --- a/policyengine_core/tools/test_runner.py +++ b/policyengine_core/tools/test_runner.py @@ -488,12 +488,25 @@ def assert_near( target_value = eval_expression(target_value) try: - # Use float64 here so we don't silently lose precision on values - # above ~16M (float32 only carries ~7 decimal digits). Under float32, - # ``25_000_001`` and ``25_000_000`` round to the same number and a - # test expecting one would pass on the other (bug H6). - target_value = np.array(target_value).astype(np.float64) - value = np.array(value).astype(np.float64) + # Choose comparison dtype: + # - Default to float64 so we don't silently lose precision on + # values above ~16M (float32 only carries ~7 decimal digits). + # Under float32, ``25_000_001`` and ``25_000_000`` round to the + # same number and a test expecting one would pass on the other + # (bug H6). + # - But if ``value`` is already float32 (because it came out of a + # float-typed Variable, which PolicyEngine stores as float32), + # promoting to float64 would surface the float32 rounding that's + # baked into storage — e.g. ``8.91`` stored as float32 compares + # unequal to the Python-literal ``8.91``. That rounding is not a + # regression; it's a property of the storage dtype. Compare at + # float32 in that case so we keep the H6 fix for real precision + # bugs (float64/int operands) without surfacing pre-existing + # float32 storage artefacts. + _value_array = np.asarray(value) + _compare_dtype = np.float32 if _value_array.dtype == np.float32 else np.float64 + target_value = np.array(target_value).astype(_compare_dtype) + value = _value_array.astype(_compare_dtype) except ValueError: # Data type not translatable to floating point, assert complete equality assert np.array(value) == np.array(target_value), "{}{} differs from {}".format( diff --git a/tests/core/test_assert_near_precision.py b/tests/core/test_assert_near_precision.py index 69a42c66..362cd9dd 100644 --- a/tests/core/test_assert_near_precision.py +++ b/tests/core/test_assert_near_precision.py @@ -9,6 +9,7 @@ from __future__ import annotations +import numpy as np import pytest from policyengine_core.tools import assert_near @@ -25,3 +26,20 @@ def test_assert_near_detects_large_integer_difference(): def test_assert_near_still_passes_within_margin(): # Float64 precision must not break legitimate near-equality checks. assert_near(1.0, 1.0005, absolute_error_margin=1e-2) + + +def test_assert_near_accepts_float32_storage_rounding(): + # PolicyEngine stores float Variables as float32. Literals like 8.91 + # can't be represented exactly in float32 — they round to about + # 8.90999985. If a simulation returns the float32-rounded value and the + # YAML test expects the Python literal 8.91 with + # ``absolute_error_margin=0``, comparing at float64 would surface the + # storage rounding as a false test failure even though nothing about + # the calculation is wrong. Compare at float32 when one operand is + # already float32 so those tests keep passing, while the int/float64 + # H6 case above still fails. + value = np.float32(8.91) + assert_near(value, 8.91, absolute_error_margin=0) + + value_array = np.array([8.91, 12.21, 7.3], dtype=np.float32) + assert_near(value_array, [8.91, 12.21, 7.3], absolute_error_margin=0) diff --git a/tests/core/test_fast_cache_guards.py b/tests/core/test_fast_cache_guards.py new file mode 100644 index 00000000..455aa2bf --- /dev/null +++ b/tests/core/test_fast_cache_guards.py @@ -0,0 +1,76 @@ +"""Regression tests: cache-manipulation methods on ``Simulation`` must not +crash when ``_fast_cache`` is missing. + +``Simulation.__init__`` sets ``self._fast_cache = {}`` as the first step of +initialisation. Country-package subclasses (e.g. ``policyengine_uk.Simulation``) +can legitimately override ``__init__`` without calling ``super().__init__`` +— instead they set the handful of attributes they need directly. In that +case ``_fast_cache`` never gets initialised, so any cache-mutation path +that assumes the attribute exists raised ``AttributeError`` during +``build_from_single_year_dataset`` / ``set_input`` / ``delete_arrays``. + +The defensive fix is to guard the bare ``.pop`` / ``.items`` / re-assign +sites the same way the read-side fast path in ``calculate()`` already does +— ``getattr(self, "_fast_cache", None)`` and skip the cache write when +it's ``None``. Core owns this protection so every downstream subclass +doesn't have to mirror the attribute. +""" + +from __future__ import annotations + +import types + +import numpy as np +import pytest + +from policyengine_core.simulations import Simulation + + +def _bare_simulation(): + """Create a ``Simulation`` instance that bypasses ``__init__`` entirely. + + Mirrors the pattern a country subclass would hit when overriding + ``__init__`` and forgetting to initialise ``_fast_cache``. + """ + return Simulation.__new__(Simulation) + + +def test_set_input_without_fast_cache_attribute(): + sim = _bare_simulation() + + # Stand-ins for the parts ``set_input`` touches — we're not exercising + # them here; we just need the cache-pop step not to crash. + sim.start_instant = None + sim.branch_name = "default" + sim.tax_benefit_system = types.SimpleNamespace( + get_variable=lambda name, check_existence=True: types.SimpleNamespace(end=None) + ) + sim.get_holder = lambda name: types.SimpleNamespace( + set_input=lambda period, value, branch: None + ) + + # The actual assertion: this line previously raised + # ``AttributeError: 'Simulation' object has no attribute '_fast_cache'``. + sim.set_input("variable_name", "2024", [1, 2, 3]) + + +def test_delete_arrays_without_fast_cache_attribute(): + sim = _bare_simulation() + sim.get_holder = lambda name: types.SimpleNamespace( + delete_arrays=lambda period: None + ) + # No _fast_cache attribute — must not crash + sim.delete_arrays("variable", period=None) + sim.delete_arrays("variable", period="2024") + + +def test_purge_cache_of_invalid_values_without_fast_cache_attribute(): + sim = _bare_simulation() + sim.tracer = types.SimpleNamespace(stack=[]) + sim.invalidated_caches = {("variable_name", "2024")} + sim.get_holder = lambda name: types.SimpleNamespace( + delete_arrays=lambda period: None + ) + + sim.purge_cache_of_invalid_values() + assert sim.invalidated_caches == set() diff --git a/uv.lock b/uv.lock index 38f5ae76..92032733 100644 --- 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