Record why the aggregate impact report withholds a reform year

changelog.d/2654.added.md

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+The aggregate impact report now records why it withholds a standard reform's costing and target change in a year. For each withheld year, `withheld_because` names every condition that holds (`suppressed_component_changed`, `small_component_left`, `small_balance_change`, `small_target_change` or `no_baseline`) and the component or variable it holds for, in `result.json` and in the long-format report that qualification reads. The pull request comment lists the reasons under the reform costings. No reason gives a count or an amount.

policyengine_uk/tests/aggregate_impact/reform_withheld_main_reference.py

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+"""Reform withholding as a yes or no, frozen at da31e8698 without commentary.
+
+Keep this independent of the production tool: it is the differential oracle
+for ``measure.withheld_reasons`` (#2654), which must withhold exactly the
+years this rule withholds.
+"""
+
+from typing import Mapping, Optional
+
+import numpy as np
+
+MIN_CELL = 10
+CHANGE_GBP = 0.005
+
+
+def small(count: int) -> bool:
+    return 0 < count < MIN_CELL
+
+
+def array(sim, variable: str, period, map_to: Optional[str] = None) -> np.ndarray:
+    try:
+        result = sim.calculate(variable, period, map_to=map_to, unweighted=True)
+    except TypeError:
+        result = sim.calculate(variable, period, map_to=map_to)
+    return np.asarray(getattr(result, "values", result))
+
+
+def entity_of(sim, variable: str) -> str:
+    return sim.tax_benefit_system.variables[variable].entity.key
+
+
+def to_household(sim, values: np.ndarray, entity: str) -> np.ndarray:
+    if entity == "household":
+        return np.asarray(values, dtype=np.float64)
+    return np.asarray(
+        sim.map_result(np.asarray(values, dtype=np.float64), entity, "household"),
+        dtype=np.float64,
+    )
+
+
+def _weighted_records(mask, weight) -> int:
+    return int(np.count_nonzero(np.asarray(mask) & (np.asarray(weight) > 0)))
+
+
+def reform_withheld(
+    reformed, year: int, baseline: Optional[Mapping], components
+) -> bool:
+    if baseline is None:
+        return True
+    for variable, before in baseline["hidden"].items():
+        if not np.array_equal(
+            array(reformed, variable, year).astype(np.float64), before
+        ):
+            return True
+    weights = {}
+
+    def weight(variable):
+        entity = entity_of(reformed, variable)
+        if entity not in weights:
+            weights[entity] = array(reformed, f"{entity}_weight", year).astype(
+                np.float64
+            )
+        return weights[entity]
+
+    for variable in components:
+        if variable in baseline["hidden"]:
+            continue
+        values = array(reformed, variable, year).astype(np.float64)
+        if small(_weighted_records(values != 0, weight(variable))):
+            return True
+    balance = to_household(
+        reformed,
+        array(reformed, "gov_balance", year).astype(np.float64),
+        entity_of(reformed, "gov_balance"),
+    )
+    changed = np.abs(balance - baseline["balance"]) > CHANGE_GBP
+    if small(_weighted_records(changed, baseline["household_weight"])):
+        return True
+    for variable, before in baseline["targets"].items():
+        after = array(reformed, variable, year).astype(np.float64)
+        if small(
+            _weighted_records(np.abs(after - before) > CHANGE_GBP, weight(variable))
+        ):
+            return True
+    return False