From ab2888e4c6fc5f807989b8aef4520f57ad78d07a Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 14:37:27 -0400 Subject: [PATCH 1/7] Move the US target-loss weights into one shared module The national fiscal release's loss-weight formula (sqrt value weight, concept budgets for district groups, equal count/amount budgets, family multipliers) moves verbatim into microcosm.build.us_runtime.target_loss_weights. The release tool keeps importing the names other tools read off it. Co-Authored-By: Claude Opus 5.5 --- .../build/us_runtime/target_loss_weights.py | 693 ++++++++++++++++++ tools/build_us_fiscal_refresh_release.py | 193 +---- 2 files changed, 715 insertions(+), 171 deletions(-) create mode 100644 packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py new file mode 100644 index 000000000..c9d22c309 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py @@ -0,0 +1,693 @@ +"""Calibration target-loss weights for US releases, in one implementation. + +A US calibration minimizes the weighted mean of each target's capped, scaled +miss. This module computes those weights. The national fiscal release +(``tools/build_us_fiscal_refresh_release.py``) and the ACS local-area release +(``tools/build_us_acs_local_release.py``) both call :func:`target_loss_weights`. +They differ only in the *row mapping*, which tells the formula what each +target is. + +The formula (:data:`US_FISCAL_TARGET_LOSS_WEIGHTING`): + +1. **Value basis.** Each target is a ``"count"`` (people, households, + returns, enrollees, recipients) or an ``"amount"`` (dollars). +2. **Value weight.** ``max(|value|, 1) ** 0.5``, divided by that quantity's + mean over the target's basis. +3. **Concept budget.** Targets that share a concept-budget key form a group, + whose weights are rescaled to sum to the largest member's weight, keeping + their proportions. Both mappings make the congressional-district targets + of one concept in one state a group, so adding district geography cannot + multiply a concept's weight. Every other target is its own group. +4. **Basis budget.** Each basis present is rescaled to carry an equal share + of the total weight (half each when both are present). +5. Normalize to mean 1. +6. **Family multipliers** (optional, ``FAMILY=MULTIPLIER``). Each named + family's weights are multiplied, in sorted family order, and the result is + renormalized to mean 1. A multiplier that names no target's family is an + error. + +Row mappings: + +- :data:`US_FISCAL_TARGET_LOSS_ROW_MAPPING` reads the ledger compiler's + target metadata: ``measure_mode`` and ``source_measure_id`` for the basis, + and ``ledger_geography_level``, ``state_fips``, the filter and the other + semantic metadata for the concept key. It is the national release's + mapping, moved here unchanged. +- :data:`US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING` classifies every row of the + ACS local-area surface explicitly and refuses any row it cannot classify. + Ledger-compiled rows (SNAP, Medicaid, SOI) take the national mapping, and + their basis must agree with their ``ledger_measure_unit``. The Census ladder + population rows (``pop_state_*``, ``pop_cd_*``), which carry no ledger + metadata, are counts, as the national release's Census population targets + are. Each state's ``pop_cd_*`` rows share one concept budget. ``state_cd`` + district SOI rows group by their reconciliation block; the per-district + provenance that rebase stamps is kept out of the key. +""" + +from __future__ import annotations + +import hashlib +import json +import math +from collections.abc import Callable, Hashable, Iterable, Mapping, Sequence +from dataclasses import dataclass +from typing import Any + +import numpy as np + +#: The loss formula's identifier, recorded in release diagnostics. +US_FISCAL_TARGET_LOSS_WEIGHTING = ( + "sqrt_value_concept_budget_weighted_mape_50_50_amount_count_target_scale_cap_100pct" +) +US_FISCAL_TARGET_VALUE_WEIGHT_POWER = 0.5 +#: Metadata that identifies a target's place, record or reconciliation rather +#: than its concept. The concept-budget key of a congressional-district target +#: ignores it, so the districts of one concept in one state share one key. +US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS = frozenset( + { + "congressional_district_geoid", + "geography_scope", + "hierarchy_child_ids", + "hierarchy_child_sum_raw", + "hierarchy_coverage_ratio", + "hierarchy_expected_child_count", + "hierarchy_observed_child_count", + "hierarchy_parent_geography_id", + "hierarchy_parent_geography_level", + "hierarchy_parent_key", + "hierarchy_parent_target_name", + "hierarchy_parent_target_period", + "hierarchy_parent_value", + "hierarchy_raw_value", + "hierarchy_reconciliation_factor", + "hierarchy_reconciliation_method", + "hierarchy_reconciliation_rule", + "ledger_aggregate_fact_key", + "ledger_dimension_set_key", + "ledger_fact_key", + "ledger_geography_id", + "ledger_geography_level", + "ledger_geography_name", + "ledger_geography_vintage", + "ledger_legacy_fact_key", + "ledger_layout_groupby_dimension", + "ledger_layout_groupby_value_id", + "ledger_layout_record_set_id", + "ledger_observed_measure_key", + "ledger_semantic_fact_key", + "ledger_source_record_id", + "state_fips", + } +) + +COUNT_BASIS = "count" +AMOUNT_BASIS = "amount" +CONGRESSIONAL_DISTRICT = "congressional_district" +STATE = "state" + + +# --------------------------------------------------------------------------- +# The formula +# --------------------------------------------------------------------------- + + +def target_loss_weights_from_rows( + values: Sequence[float], + bases: Sequence[str], + concept_budget_keys: Sequence[Hashable], + families: Sequence[str | None], + family_multipliers: Mapping[str, float] | None = None, +) -> np.ndarray: + """Steps 2-6 of the formula, over row-aligned per-target inputs. + + Returns float64 weights, row-aligned with the inputs, positive and of + mean 1 whenever there is at least one row. + """ + + n_rows = len(values) + if not len(bases) == len(concept_budget_keys) == len(families) == n_rows: + raise ValueError( + "Target-loss inputs are not row-aligned: " + f"{n_rows} values, {len(bases)} bases, " + f"{len(concept_budget_keys)} concept keys, {len(families)} families." + ) + basis_array = np.asarray(list(bases), dtype=object) + weights = _concept_budget_weights( + _value_basis_weights(values, basis_array), concept_budget_keys + ) + unique_bases = sorted(set(basis_array.tolist())) + if not unique_bases: + return weights + basis_total = len(weights) / len(unique_bases) + for basis in unique_bases: + mask = basis_array == basis + current_total = weights[mask].sum() + if current_total > 0: + weights[mask] *= basis_total / current_total + weights = weights / weights.mean() + if not family_multipliers: + return weights + family_array = np.asarray(list(families), dtype=object) + for family, multiplier in sorted(family_multipliers.items()): + if not math.isfinite(multiplier) or multiplier <= 0.0: + raise ValueError( + f"Target family loss multiplier for {family!r} must be positive " + f"and finite, got {multiplier!r}." + ) + mask = family_array == family + if not mask.any(): + raise ValueError( + f"--target-family-loss-multiplier family {family!r} matches " + "no compiled target." + ) + weights[mask] *= multiplier + return weights / weights.mean() + + +def _value_basis_weights(values: Sequence[float], bases: np.ndarray) -> np.ndarray: + """Step 2: ``max(|value|, 1) ** 0.5`` over its basis mean.""" + + weights = np.ones(len(bases), dtype=np.float64) + magnitudes = np.asarray( + [max(abs(float(value)), 1.0) for value in values], + dtype=np.float64, + ) + raw_weights = magnitudes**US_FISCAL_TARGET_VALUE_WEIGHT_POWER + for basis in sorted(set(bases.tolist())): + mask = bases == basis + mean_value = raw_weights[mask].mean() + if mean_value > 0: + weights[mask] = raw_weights[mask] / mean_value + return weights + + +def _concept_budget_weights( + weights: np.ndarray, concept_budget_keys: Sequence[Hashable] +) -> np.ndarray: + """Step 3: each group sums to its largest member's weight.""" + + for indices in concept_budget_groups(concept_budget_keys): + group_weights = weights[indices] + group_total = float(group_weights.sum()) + group_budget = float(group_weights.max(initial=0.0)) + if group_total > 0 and group_budget > 0: + weights[indices] *= group_budget / group_total + return weights + + +def concept_budget_groups( + concept_budget_keys: Sequence[Hashable], +) -> list[list[int]]: + """Row indices sharing each concept-budget key, in first-row order.""" + + group_indices: dict[Hashable, list[int]] = {} + for index, key in enumerate(concept_budget_keys): + group_indices.setdefault(key, []).append(index) + return list(group_indices.values()) + + +# --------------------------------------------------------------------------- +# Row mappings +# --------------------------------------------------------------------------- + + +_GroupValidator = Callable[[Sequence[Any], Sequence[str], Sequence[Hashable]], None] + + +@dataclass(frozen=True) +class TargetLossRowMapping: + """How one calibration tells the formula what each of its targets is. + + ``value_basis`` returns ``"count"`` or ``"amount"`` and + ``concept_budget_key`` a hashable key; both read one target spec. + ``validate``, if set, sees the whole surface with its bases and keys and + raises if the grouping is not the one the mapping promises. + """ + + mapping_id: str + value_basis: Callable[[Any], str] + concept_budget_key: Callable[[Any], Hashable] + validate: _GroupValidator | None = None + + +def fiscal_target_value_basis(spec) -> str: + metadata = spec.metadata + measure_mode = metadata.get("measure_mode", "") + source_measure_id = metadata.get("source_measure_id", "") + if measure_mode in { + "indicator_sum", + "less_than_indicator_sum", + }: + return "count" + if "enrollment" in source_measure_id or "recipients" in source_measure_id: + return "count" + if "return" in source_measure_id and "count" in source_measure_id: + return "count" + return "amount" + + +def fiscal_target_concept_budget_key(spec) -> tuple[object, ...]: + return _ledger_concept_budget_key( + spec, + fiscal_target_value_basis(spec), + US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS, + ) + + +def _ledger_concept_budget_key( + spec, basis: str, metadata_exclusions: frozenset[str] +) -> tuple[object, ...]: + metadata = spec.metadata + if metadata.get("ledger_geography_level") != "congressional_district": + return ( + basis, + spec.entity, + spec.period, + spec.family, + spec.name, + ) + semantic_metadata = tuple( + sorted( + (key, value) + for key, value in metadata.items() + if key not in metadata_exclusions + ) + ) + return ( + basis, + spec.entity, + spec.period, + spec.family, + spec.filter or "", + metadata.get("state_fips", ""), + semantic_metadata, + ) + + +#: The national release's mapping: the ledger compiler's metadata, as is. +US_FISCAL_TARGET_LOSS_ROW_MAPPING = TargetLossRowMapping( + mapping_id="us_fiscal_ledger_metadata.v1", + value_basis=fiscal_target_value_basis, + concept_budget_key=fiscal_target_concept_budget_key, +) + + +#: Family of the ACS local release's Census ladder population rows. +ACS_LOCAL_POPULATION_FAMILY = "census_population" +#: ``population_target_specs`` in ``tools/build_us_acs_local_release.py`` +#: names ladder population rows by geography, with the FIPS digits after the +#: prefix: two for a state, four (state then district) for a district. +ACS_LOCAL_POPULATION_PREFIXES: Mapping[str, tuple[str, int]] = { + STATE: ("pop_state_", 2), + CONGRESSIONAL_DISTRICT: ("pop_cd_", 4), +} +#: The district file's own value of one district row, which the ``state_cd`` +#: rebase (``tools/us_acs_local_cd_surface.py``) stamps on each row before +#: rescaling it. It differs between the districts of one block, so the +#: concept key must not read it. The block-level stamps (parent, basis, +#: factor, file state sum) are equal across a block and may stay. +ACS_LOCAL_STATE_CD_ROW_PROVENANCE = frozenset({"state_cd_cd_file_value"}) +US_ACS_LOCAL_CONCEPT_METADATA_EXCLUSIONS = ( + US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS | ACS_LOCAL_STATE_CD_ROW_PROVENANCE +) +#: The ledger's unit for a measure, and the basis that unit implies. +LEDGER_MEASURE_UNIT_BASIS: Mapping[str, str] = { + "count": COUNT_BASIS, + "usd": AMOUNT_BASIS, +} +#: The metadata the national basis rule reads; a ledger row lacking either +#: would fall to "amount" by default, so the ACS local mapping refuses it. +_LEDGER_BASIS_KEYS = ("measure_mode", "source_measure_id") + + +def acs_local_population_geography(spec) -> tuple[str, str] | None: + """``(level, fips)`` of a Census ladder population row, else None. + + A ladder row is a ``census_population`` spec with no ledger metadata. Its + ``geography_level`` must be state or congressional district, and its name + must carry that level's prefix and FIPS digits; any other combination is + refused rather than guessed. + """ + + metadata = spec.metadata + if spec.family != ACS_LOCAL_POPULATION_FAMILY or "ledger_measure_unit" in ( + metadata + ): + return None + level = metadata.get("geography_level") + if level not in ACS_LOCAL_POPULATION_PREFIXES: + raise ValueError( + f"{spec.name}: a Census ladder population row must have " + f"geography_level state or congressional_district, got {level!r}." + ) + prefix, n_digits = ACS_LOCAL_POPULATION_PREFIXES[level] + fips = spec.name.removeprefix(prefix) + if ( + not spec.name.startswith(prefix) + or len(fips) != n_digits + or not (fips.isdigit()) + ): + raise ValueError( + f"{spec.name}: a {level} ladder population row is named " + f"{prefix}<{n_digits} FIPS digits>." + ) + return level, fips + + +def acs_local_target_value_basis(spec) -> str: + """The basis of one ACS local target, classified explicitly.""" + + if acs_local_population_geography(spec) is not None: + return COUNT_BASIS + metadata = spec.metadata + unit = metadata.get("ledger_measure_unit") + if unit is None: + raise ValueError( + f"{spec.name} ({spec.family}): the ACS local loss weights classify " + "ledger-compiled rows and Census ladder population rows only; " + "this row is neither (it has no ledger_measure_unit)." + ) + expected = LEDGER_MEASURE_UNIT_BASIS.get(unit) + if expected is None: + raise ValueError( + f"{spec.name}: ledger_measure_unit {unit!r} has no loss basis " + f"(known: {sorted(LEDGER_MEASURE_UNIT_BASIS)})." + ) + missing = [key for key in _LEDGER_BASIS_KEYS if key not in metadata] + if missing: + raise ValueError( + f"{spec.name}: a ledger row needs {missing} to take the national " + "loss basis." + ) + basis = fiscal_target_value_basis(spec) + if basis != expected: + raise ValueError( + f"{spec.name}: the national loss basis says {basis!r} but its " + f"ledger_measure_unit {unit!r} says {expected!r}." + ) + return basis + + +def acs_local_target_concept_budget_key(spec) -> tuple[object, ...]: + """The concept key of one ACS local target, classified explicitly.""" + + basis = acs_local_target_value_basis(spec) + geography = acs_local_population_geography(spec) + if geography is None: + if spec.metadata.get( + "ledger_geography_level" + ) == CONGRESSIONAL_DISTRICT and not spec.metadata.get("state_fips"): + raise ValueError( + f"{spec.name}: a district row without state_fips would share a " + "concept budget across states." + ) + return _ledger_concept_budget_key( + spec, basis, US_ACS_LOCAL_CONCEPT_METADATA_EXCLUSIONS + ) + level, fips = geography + if level == STATE: + return (basis, spec.entity, spec.period, spec.family, spec.name) + # The national district form: one budget per (concept, state). + return ( + basis, + spec.entity, + spec.period, + spec.family, + spec.filter or "", + fips[:2], + (("geography_level", CONGRESSIONAL_DISTRICT),), + ) + + +def validate_acs_local_concept_groups( + specs: Sequence[Any], + bases: Sequence[str], + concept_budget_keys: Sequence[Hashable], +) -> None: + """Refuse an ACS local grouping that is not the promised one. + + Each ``state_cd`` reconciliation block (the district rows sharing one + ``state_cd_parent_target_name``) must be exactly one concept group: a + per-district metadata key leaking into the key would split it into one + group per district, and two blocks sharing a key would pool budgets. + Each district group must lie in one state. + """ + + del bases + group_of_parent: dict[str, set[Hashable]] = {} + parent_of_group: dict[Hashable, set[str]] = {} + states_of_group: dict[Hashable, set[str]] = {} + for spec, key in zip(specs, concept_budget_keys, strict=True): + population = acs_local_population_geography(spec) + if population is not None: + level, fips = population + if level == CONGRESSIONAL_DISTRICT: + states_of_group.setdefault(key, set()).add(fips[:2]) + continue + metadata = spec.metadata + if metadata.get("ledger_geography_level") != CONGRESSIONAL_DISTRICT: + continue + states_of_group.setdefault(key, set()).add(str(metadata.get("state_fips"))) + parent = metadata.get("state_cd_parent_target_name") + if parent: + group_of_parent.setdefault(str(parent), set()).add(key) + parent_of_group.setdefault(key, set()).add(str(parent)) + split = {parent: keys for parent, keys in group_of_parent.items() if len(keys) > 1} + if split: + parent, keys = next(iter(sorted(split.items()))) + raise ValueError( + f"{len(split)} state_cd block(s) split across concept groups, e.g. " + f"the district rows under {parent} land in {len(keys)} groups; a " + "per-district metadata key is reaching the concept key." + ) + pooled = { + key: parents for key, parents in parent_of_group.items() if len(parents) > 1 + } + if pooled: + parents = next(iter(pooled.values())) + raise ValueError( + f"{len(pooled)} concept group(s) pool several state_cd blocks, e.g. " + f"{sorted(parents)[:3]}." + ) + spread = [states for states in states_of_group.values() if len(states) > 1] + if spread: + raise ValueError( + f"{len(spread)} district concept group(s) span states, e.g. " + f"{sorted(spread[0])}." + ) + + +#: The ACS local-area release's mapping (see the module docstring). +US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING = TargetLossRowMapping( + mapping_id="us_acs_local.v1", + value_basis=acs_local_target_value_basis, + concept_budget_key=acs_local_target_concept_budget_key, + validate=validate_acs_local_concept_groups, +) + + +# --------------------------------------------------------------------------- +# Spec-level entry points +# --------------------------------------------------------------------------- + + +def target_loss_weights( + specs: Iterable[Any], + family_multipliers: Mapping[str, float] | None = None, + *, + row_mapping: TargetLossRowMapping = US_FISCAL_TARGET_LOSS_ROW_MAPPING, +) -> np.ndarray: + """The formula's weights for ``specs``, row-aligned, under one mapping.""" + + specs = tuple(specs) + bases = [row_mapping.value_basis(spec) for spec in specs] + keys = [row_mapping.concept_budget_key(spec) for spec in specs] + if row_mapping.validate is not None: + row_mapping.validate(specs, bases, keys) + return target_loss_weights_from_rows( + [spec.value for spec in specs], + bases, + keys, + [spec.family for spec in specs], + family_multipliers, + ) + + +def fiscal_target_loss_weights( + registry, + family_multipliers: Mapping[str, float] | None = None, +) -> np.ndarray: + """The national release's weights for a compiled ``TargetRegistry``.""" + + return target_loss_weights(registry.specs, family_multipliers) + + +def fiscal_target_value_basis_weights(registry) -> np.ndarray: + """Step 2 alone, under the national mapping.""" + + bases = np.asarray( + [fiscal_target_value_basis(spec) for spec in registry.specs], + dtype=object, + ) + return _value_basis_weights([spec.value for spec in registry.specs], bases) + + +def fiscal_target_concept_budget_weights(registry) -> np.ndarray: + """Steps 2-3 alone, under the national mapping.""" + + return _concept_budget_weights( + fiscal_target_value_basis_weights(registry), + [fiscal_target_concept_budget_key(spec) for spec in registry.specs], + ) + + +def us_acs_local_target_loss_weights( + specs: Iterable[Any], + family_multipliers: Mapping[str, float] | None = None, +) -> np.ndarray: + """The ACS local release's weights for its training target specs.""" + + return target_loss_weights( + specs, + family_multipliers, + row_mapping=US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING, + ) + + +# --------------------------------------------------------------------------- +# Options, digests and summaries +# --------------------------------------------------------------------------- + + +def parse_target_family_loss_multipliers(entries: Iterable[str]) -> dict[str, float]: + """``FAMILY=MULTIPLIER`` command-line entries as a mapping. + + Raises ``ValueError`` for a malformed entry, a multiplier that is not + positive and finite, or a repeated family. + """ + + multipliers: dict[str, float] = {} + for entry in entries: + family, separator, raw_value = entry.partition("=") + try: + value = float(raw_value) + except ValueError: + value = math.nan + if not separator or not family or not math.isfinite(value) or value <= 0.0: + raise ValueError( + "--target-family-loss-multiplier expects FAMILY=MULTIPLIER " + f"with a positive finite multiplier, got {entry!r}." + ) + if family in multipliers: + raise ValueError( + f"--target-family-loss-multiplier repeats family {family!r}." + ) + multipliers[family] = value + return multipliers + + +def target_loss_weights_sha256(names: Sequence[str], weights: np.ndarray) -> str: + """Content digest of a row-aligned loss-weight vector. + + The canonical form is the national release's loss vector: one + ``{"row_name", "weight_hex"}`` object per row, in row order, as compact + sorted-key JSON. Any change to a name, a weight bit or the row order + changes it. + """ + + weights = np.asarray(weights, dtype=np.float64) + if weights.shape != (len(names),): + raise ValueError( + f"{weights.shape} loss weights do not align with {len(names)} names." + ) + rows = [ + {"row_name": str(name), "weight_hex": float(weight).hex()} + for name, weight in zip(names, weights, strict=True) + ] + return hashlib.sha256( + json.dumps(rows, sort_keys=True, separators=(",", ":"), allow_nan=False).encode( + "utf-8" + ) + ).hexdigest() + + +def target_geography_level(spec) -> str: + """A spec's geography level for summaries (ledger or ladder metadata).""" + + metadata = spec.metadata + return str( + metadata.get("ledger_geography_level") + or metadata.get("geography_level") + or "unspecified" + ) + + +def target_loss_weight_distribution( + specs: Sequence[Any], + weights: np.ndarray, + *, + row_mapping: TargetLossRowMapping, +) -> dict[str, object]: + """Loss-weight shares by family x geography level x basis, and by basis. + + ``loss_share`` is a cell's share of the total weight (the share of the + loss it would carry at equal misses); ``equal_share`` is what it would + carry with every target weighted equally. District concept groups are + counted too. + """ + + specs = tuple(specs) + weights = np.asarray(weights, dtype=np.float64) + if weights.shape != (len(specs),): + raise ValueError( + f"{weights.shape} loss weights do not align with {len(specs)} specs." + ) + bases = [row_mapping.value_basis(spec) for spec in specs] + keys = [row_mapping.concept_budget_key(spec) for spec in specs] + total = float(weights.sum()) + n_rows = len(specs) + + def cell(mask: np.ndarray) -> dict[str, object]: + selected = weights[mask] + return { + "n_targets": int(mask.sum()), + "weight_sum": float(selected.sum()), + "loss_share": float(selected.sum()) / total if total else 0.0, + "equal_share": float(mask.sum()) / n_rows if n_rows else 0.0, + "min_weight": float(selected.min()) if selected.size else None, + "max_weight": float(selected.max()) if selected.size else None, + } + + families = np.asarray([str(spec.family) for spec in specs], dtype=object) + levels = np.asarray([target_geography_level(spec) for spec in specs], dtype=object) + basis_array = np.asarray(bases, dtype=object) + cells = [] + for family, level, basis in sorted( + set(zip(families.tolist(), levels.tolist(), bases, strict=True)) + ): + mask = (families == family) & (levels == level) & (basis_array == basis) + cells.append( + {"family": family, "geography_level": level, "basis": basis, **cell(mask)} + ) + district_rows = levels == CONGRESSIONAL_DISTRICT + groups = concept_budget_groups(keys) + district_groups = [ + indices for indices in groups if all(district_rows[i] for i in indices) + ] + return { + "row_mapping": row_mapping.mapping_id, + "n_targets": n_rows, + "by_family_level_basis": cells, + "by_basis": {basis: cell(basis_array == basis) for basis in sorted(set(bases))}, + "concept_groups": { + "n_groups": len(groups), + "n_district_rows": int(district_rows.sum()), + "n_district_groups": len(district_groups), + "n_singleton_district_groups": sum( + 1 for indices in district_groups if len(indices) == 1 + ), + "max_district_group_size": max( + (len(indices) for indices in district_groups), default=0 + ), + }, + } diff --git a/tools/build_us_fiscal_refresh_release.py b/tools/build_us_fiscal_refresh_release.py index 9c16a01e1..3a2b50a61 100644 --- a/tools/build_us_fiscal_refresh_release.py +++ b/tools/build_us_fiscal_refresh_release.py @@ -313,6 +313,22 @@ US_SSI_TAKE_UP_OUTPUT_COLUMNS, SSITakeUpPriorBasis, ) + +# The target-loss weights live in one shared module, which the ACS local +# release calls too. These names stay importable from this tool for the +# scorers and the bundle contract that read them off it. +from microcosm.build.us_runtime.target_loss_weights import ( # noqa: F401 + US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS, + US_FISCAL_TARGET_LOSS_WEIGHTING, + US_FISCAL_TARGET_VALUE_WEIGHT_POWER, + parse_target_family_loss_multipliers, +) +from microcosm.build.us_runtime.target_loss_weights import ( + fiscal_target_loss_weights as _fiscal_target_loss_weights, +) +from microcosm.build.us_runtime.target_loss_weights import ( # noqa: F401 + fiscal_target_value_basis as _fiscal_target_value_basis, +) from microcosm.build.us_runtime.warm_start_selection import ( DEFAULT_SELECTION_JOIN_KEY, SELECTION_MODES, @@ -471,10 +487,6 @@ "on the dense arm too." ) -US_FISCAL_TARGET_LOSS_WEIGHTING = ( - "sqrt_value_concept_budget_weighted_mape_50_50_amount_count_target_scale_cap_100pct" -) -US_FISCAL_TARGET_VALUE_WEIGHT_POWER = 0.5 US_FISCAL_TARGET_LOSS_CAP = 1.0 RATIFIED_EXACT_K_COUNTS = frozenset({57_240, 20_000}) @@ -615,42 +627,6 @@ def _automatic_gc_suspended(): "component payment targets from the active fiscal target registry before " "mass='conserve' calibration." ) -US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS = frozenset( - { - "congressional_district_geoid", - "geography_scope", - "hierarchy_child_ids", - "hierarchy_child_sum_raw", - "hierarchy_coverage_ratio", - "hierarchy_expected_child_count", - "hierarchy_observed_child_count", - "hierarchy_parent_geography_id", - "hierarchy_parent_geography_level", - "hierarchy_parent_key", - "hierarchy_parent_target_name", - "hierarchy_parent_target_period", - "hierarchy_parent_value", - "hierarchy_raw_value", - "hierarchy_reconciliation_factor", - "hierarchy_reconciliation_method", - "hierarchy_reconciliation_rule", - "ledger_aggregate_fact_key", - "ledger_dimension_set_key", - "ledger_fact_key", - "ledger_geography_id", - "ledger_geography_level", - "ledger_geography_name", - "ledger_geography_vintage", - "ledger_legacy_fact_key", - "ledger_layout_groupby_dimension", - "ledger_layout_groupby_value_id", - "ledger_layout_record_set_id", - "ledger_observed_measure_key", - "ledger_semantic_fact_key", - "ledger_source_record_id", - "state_fips", - } -) US_SOCIAL_SECURITY_COMPONENT_TARGET_ROLES = { "ssa_retirement_total": "social_security_retirement", "ssa_disability_total": "social_security_disability", @@ -2026,22 +2002,12 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: elif args.seed is None: # Preserve the pre-exact-k default for every legacy lane. args.seed = 0 - multipliers: dict[str, float] = {} - for entry in args.target_family_loss_multiplier: - family, separator, raw_value = entry.partition("=") - try: - value = float(raw_value) - except ValueError: - value = math.nan - if not separator or not family or not math.isfinite(value) or value <= 0.0: - parser.error( - "--target-family-loss-multiplier expects FAMILY=MULTIPLIER " - f"with a positive finite multiplier, got {entry!r}." - ) - if family in multipliers: - parser.error(f"--target-family-loss-multiplier repeats family {family!r}.") - multipliers[family] = value - args.target_family_loss_multipliers = multipliers + try: + args.target_family_loss_multipliers = parse_target_family_loss_multipliers( + args.target_family_loss_multiplier + ) + except ValueError as error: + parser.error(str(error)) return args @@ -8461,42 +8427,6 @@ def _load_warm_start_calibration_npz( return weights, payload -def _fiscal_target_loss_weights( - registry: TargetRegistry, - family_multipliers: Mapping[str, float] | None = None, -) -> np.ndarray: - weights = _fiscal_target_concept_budget_weights(registry) - bases = np.asarray( - [_fiscal_target_value_basis(spec) for spec in registry.specs], - dtype=object, - ) - unique_bases = sorted(set(bases.tolist())) - if not unique_bases: - return weights - basis_total = len(weights) / len(unique_bases) - for basis in unique_bases: - mask = bases == basis - current_total = weights[mask].sum() - if current_total > 0: - weights[mask] *= basis_total / current_total - weights = weights / weights.mean() - if not family_multipliers: - return weights - families = np.asarray( - [spec.family for spec in registry.specs], - dtype=object, - ) - for family, multiplier in sorted(family_multipliers.items()): - mask = families == family - if not mask.any(): - raise ValueError( - f"--target-family-loss-multiplier family {family!r} matches " - "no compiled target." - ) - weights[mask] *= multiplier - return weights / weights.mean() - - def _fiscal_target_loss_basis( registry: TargetRegistry, target_loss_weights: np.ndarray, @@ -8894,85 +8824,6 @@ def _ssi_assignment_priors_from_diagnostics( return priors -def _fiscal_target_concept_budget_weights(registry: TargetRegistry) -> np.ndarray: - weights = _fiscal_target_value_basis_weights(registry) - group_indices: dict[tuple[object, ...], list[int]] = {} - for index, spec in enumerate(registry.specs): - group_indices.setdefault(_fiscal_target_concept_budget_key(spec), []).append( - index - ) - for indices in group_indices.values(): - group_weights = weights[indices] - group_total = float(group_weights.sum()) - group_budget = float(group_weights.max(initial=0.0)) - if group_total > 0 and group_budget > 0: - weights[indices] *= group_budget / group_total - return weights - - -def _fiscal_target_concept_budget_key(spec) -> tuple[object, ...]: - metadata = spec.metadata - if metadata.get("ledger_geography_level") != "congressional_district": - return ( - _fiscal_target_value_basis(spec), - spec.entity, - spec.period, - spec.family, - spec.name, - ) - semantic_metadata = tuple( - sorted( - (key, value) - for key, value in metadata.items() - if key not in US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS - ) - ) - return ( - _fiscal_target_value_basis(spec), - spec.entity, - spec.period, - spec.family, - spec.filter or "", - metadata.get("state_fips", ""), - semantic_metadata, - ) - - -def _fiscal_target_value_basis_weights(registry: TargetRegistry) -> np.ndarray: - weights = np.ones(len(registry.specs), dtype=np.float64) - bases = np.asarray( - [_fiscal_target_value_basis(spec) for spec in registry.specs], - dtype=object, - ) - values = np.asarray( - [max(abs(float(spec.value)), 1.0) for spec in registry.specs], - dtype=np.float64, - ) - raw_weights = values**US_FISCAL_TARGET_VALUE_WEIGHT_POWER - for basis in sorted(set(bases.tolist())): - mask = bases == basis - mean_value = raw_weights[mask].mean() - if mean_value > 0: - weights[mask] = raw_weights[mask] / mean_value - return weights - - -def _fiscal_target_value_basis(spec) -> str: - metadata = spec.metadata - measure_mode = metadata.get("measure_mode", "") - source_measure_id = metadata.get("source_measure_id", "") - if measure_mode in { - "indicator_sum", - "less_than_indicator_sum", - }: - return "count" - if "enrollment" in source_measure_id or "recipients" in source_measure_id: - return "count" - if "return" in source_measure_id and "count" in source_measure_id: - return "count" - return "amount" - - def _target_metadata(target: object | None) -> Mapping[str, object]: if isinstance(target, Mapping): metadata = target.get("metadata") From 5608572330b64a05688bd64d65a4be1646959aab Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 14:50:44 -0400 Subject: [PATCH 2/7] Weight the ACS local calibration targets with the national loss weights The calibrate stage computes the shared formula's weights over the training targets under an explicit ACS local row mapping and passes them to calibrate(). Population ladder rows are counts; each state's pop_cd rows and each state_cd reconciliation block share one concept budget. The weights' digest joins the solver settings, so a resume refuses weights from an unweighted run, and the summary, diagnostics and package manifest record them. Adds --target-family-loss-multiplier. Co-Authored-By: Claude Opus 5.5 --- .../distribution.py | 261 ++++++ .../results.json | 850 ++++++++++++++++++ .../route_a_fixture.py | 137 +++ .../build/us_runtime/target_loss_weights.py | 53 +- .../us/test_us_target_loss_weights.py | 770 ++++++++++++++++ .../us_route_a_target_loss_weights.json | 1 + .../us_target_loss_weights_reference.py | 171 ++++ tools/build_us_acs_local_release.py | 115 ++- 8 files changed, 2336 insertions(+), 22 deletions(-) create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/distribution.py create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/results.json create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py create mode 100644 packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py create mode 100644 packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json create mode 100644 test_support/microcosm_build/us_target_loss_weights_reference.py diff --git a/experiments/us-acs-local-target-loss-weights-20261004/distribution.py b/experiments/us-acs-local-target-loss-weights-20261004/distribution.py new file mode 100644 index 000000000..71561697a --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/distribution.py @@ -0,0 +1,261 @@ +"""How the ACS local release's loss weight moves under the national weighting. + +Two real surfaces, each scored three ways: + +* ``equal``: every training target weighted 1, the calibrate stage before + this change; +* ``national_as_is``: the national release's row mapping applied to the ACS + local specs unchanged (what a bare reuse of the national helper would do); +* ``acs_local``: the shared formula under the ACS local row mapping, which is + what the calibrate stage now passes to ``calibrate``. + +Surfaces: + +1. ``release_20260923``: the 4,459 targets of the published 09-23 release + (``--soi-mode state``, the default), every one trained. Its specs are the + ``target_registry.json`` rebuilt from that release's feed by + ``experiments/us-acs-local-l2-basis-20260928/registry.py`` (receipt: all + 4,459 names and values equal the checkpoint's). +2. ``state_cd_pinned_feed``: ``--soi-mode state_cd`` compiled by the tool's + own ``state_admin_surface`` from the feed ``chronicle_feed.json`` pins, + plus the 09-23 release's 487 ladder population specs, with the default 10% + district holdout assigned by the tool's ``assign_target_roles``; only the + training rows are weighted, as in the calibrate stage. + +Engine-free. Run from the repository root:: + + uv run python experiments/us-acs-local-target-loss-weights-20261004/distribution.py + +It writes ``results.json`` beside this file and prints the tables the README +quotes. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.util +import json +import subprocess +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +TOOLS = REPO / "tools" +DEFAULT_REGISTRY = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/" + "checkpoint/target_registry.json" +) +#: sha256 of the rebuilt 09-23 registry, from its receipt. +REGISTRY_SHA256 = "fd25c6002cae0c991842c81399d43a3840f12cba0294021ca8ff6718e22a209f" +DEFAULT_FEED = Path( + "/Users/maxghenis/PolicyEngine/_buildh-runtime/inputs/consumer_facts_us_c5e5bf8.jsonl" +) +FAMILIES = ["snap", "medicaid", "soi"] + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with open(path, "rb") as handle: + for block in iter(lambda: handle.read(1 << 24), b""): + digest.update(block) + return digest.hexdigest() + + +def load_tool(): + sys.path.insert(0, str(TOOLS)) + path = TOOLS / "build_us_acs_local_release.py" + spec = importlib.util.spec_from_file_location("build_us_acs_local_release", path) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def score(specs, lw) -> dict: + """The three weightings of one training surface, with their shares.""" + + acs = lw.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING + national = lw.US_FISCAL_TARGET_LOSS_ROW_MAPPING + weights = { + "equal": np.ones(len(specs)), + "national_as_is": lw.target_loss_weights(specs), + "acs_local": lw.us_acs_local_target_loss_weights(specs), + } + out = {} + for name, vector in weights.items(): + mapping = national if name == "national_as_is" else acs + out[name] = lw.target_loss_weight_distribution( + specs, vector, row_mapping=mapping + ) + out["acs_local"]["weights_sha256"] = lw.target_loss_weights_sha256( + [lw.target_row_name(spec) for spec in specs], weights["acs_local"] + ) + out["acs_local"]["mean"] = float(weights["acs_local"].mean()) + out["acs_local"]["min"] = float(weights["acs_local"].min()) + out["acs_local"]["max"] = float(weights["acs_local"].max()) + return out + + +def family_level_table(result: dict) -> list[dict]: + """Rows of family x geography level: equal, national-as-is, ACS local shares.""" + + def shares(distribution: dict) -> dict[tuple[str, str], float]: + totals: dict[tuple[str, str], float] = {} + for cell in distribution["by_family_level_basis"]: + key = (cell["family"], cell["geography_level"]) + totals[key] = totals.get(key, 0.0) + cell["loss_share"] + return totals + + counts: dict[tuple[str, str], int] = {} + for cell in result["acs_local"]["by_family_level_basis"]: + key = (cell["family"], cell["geography_level"]) + counts[key] = counts.get(key, 0) + cell["n_targets"] + equal = shares(result["equal"]) + national = shares(result["national_as_is"]) + acs = shares(result["acs_local"]) + return [ + { + "family": key[0], + "geography_level": key[1], + "n_targets": counts[key], + "equal": equal[key], + "national_as_is": national[key], + "acs_local": acs[key], + } + for key in sorted(counts) + ] + + +def print_table(title: str, rows: list[dict]) -> None: + print(f"\n### {title}\n") + print( + "| Family | Level | Targets | Equal (before) | National mapping as is " + "| ACS local mapping (after) |" + ) + print("|---|---|---:|---:|---:|---:|") + totals = {"n_targets": 0, "equal": 0.0, "national_as_is": 0.0, "acs_local": 0.0} + for row in rows: + for key in totals: + totals[key] += row[key] + print( + f"| {row['family']} | {row['geography_level']} | {row['n_targets']:,} " + f"| {row['equal']:.1%} | {row['national_as_is']:.1%} " + f"| {row['acs_local']:.1%} |" + ) + print( + f"| **Total** | | {totals['n_targets']:,} | {totals['equal']:.0%} " + f"| {totals['national_as_is']:.0%} | {totals['acs_local']:.0%} |" + ) + + +def state_cd_surface(tool, feed: Path, population_specs, fraction: float): + surface = tool.state_admin_surface(feed, FAMILIES, soi_mode=tool.SOI_MODE_STATE_CD) + specs = (*surface.registry.specs, *population_specs) + records = [tool.cd_surface.target_record(spec) for spec in specs] + holdout = tool.cd_surface.assign_target_roles(records, fraction=fraction) + train = [specs[index] for index in tool.cd_surface.train_rows(records)] + blocks = { + spec.metadata["state_cd_parent_target_name"] + for spec in train + if spec.metadata.get("ledger_geography_level") == "congressional_district" + and spec.metadata.get("state_cd_parent_target_name") + } + return train, { + "soi_receipt_counts": surface.soi_receipt.get("counts"), + "n_specs": len(specs), + "n_train": len(train), + "holdout": { + key: holdout[key] + for key in ("fraction", "eligible_units", "held_units", "held_targets") + }, + "n_state_cd_blocks_trained": len(blocks), + } + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--registry", type=Path, default=DEFAULT_REGISTRY) + parser.add_argument("--feed", type=Path, default=DEFAULT_FEED) + parser.add_argument("--skip-state-cd", action="store_true") + args = parser.parse_args(argv) + started = time.time() + tool = load_tool() + from microcosm.build.us_runtime import target_loss_weights as lw + from microcosm.calibrate import TargetRegistry + + registry_sha = sha256(args.registry) + if registry_sha != REGISTRY_SHA256: + raise SystemExit( + f"{args.registry} is {registry_sha}, not the rebuilt 09-23 registry." + ) + release_specs = TargetRegistry.from_json(args.registry).specs + results: dict = { + "head": subprocess.run( + ["git", "-C", str(REPO), "rev-parse", "HEAD"], + capture_output=True, + text=True, + check=True, + ).stdout.strip(), + "release_20260923": { + "registry": {"path": str(args.registry), "sha256": registry_sha}, + "n_train": len(release_specs), + **score(release_specs, lw), + }, + } + rows = family_level_table(results["release_20260923"]) + results["release_20260923"]["family_level_table"] = rows + print_table( + "09-23 release surface (soi_mode=state), 4,459 targets, all trained", rows + ) + if not args.skip_state_cd: + pin = json.loads( + ( + REPO + / "packages/microcosm-build/src/microcosm/build/us/chronicle_feed.json" + ).read_text() + ) + feed_sha = sha256(args.feed) + if feed_sha != pin["facts_sha256"]: + raise SystemExit(f"{args.feed} is {feed_sha}, not the pinned feed.") + population = [ + spec for spec in release_specs if spec.family == "census_population" + ] + train, receipt = state_cd_surface( + tool, + args.feed, + population, + tool.cd_surface.DEFAULT_STATE_CD_HOLDOUT_FRACTION, + ) + result = { + "feed": {"path": str(args.feed), "sha256": feed_sha}, + **receipt, + **score(train, lw), + } + result["family_level_table"] = family_level_table(result) + results["state_cd_pinned_feed"] = result + print_table( + f"state_cd surface, pinned feed, {receipt['n_train']:,} training targets", + result["family_level_table"], + ) + print( + "\nstate_cd blocks trained:", + receipt["n_state_cd_blocks_trained"], + "| ACS local concept groups:", + result["acs_local"]["concept_groups"], + "| national-as-is:", + result["national_as_is"]["concept_groups"], + ) + results["wall_seconds"] = round(time.time() - started, 1) + (HERE / "results.json").write_text(json.dumps(results, indent=1, sort_keys=True)) + print(f"\nwrote {HERE / 'results.json'} in {results['wall_seconds']} s") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/experiments/us-acs-local-target-loss-weights-20261004/results.json b/experiments/us-acs-local-target-loss-weights-20261004/results.json new file mode 100644 index 000000000..e938dc1f7 --- /dev/null +++ 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"count", + "equal_share": 0.08535154613257354, + "family": "irs_soi", + "geography_level": "state", + "loss_share": 0.16775586146933383, + "max_weight": 15.128090919494138, + "min_weight": 0.11045921479672367, + "n_targets": 2037, + "weight_sum": 4003.661389827121 + }, + { + "basis": "amount", + "equal_share": 0.002136931199195508, + "family": "usda_snap", + "geography_level": "state", + "loss_share": 0.0019331073899344453, + "max_weight": 2.71961329677261, + "min_weight": 0.1840750954060052, + "n_targets": 51, + "weight_sum": 46.13554096817547 + }, + { + "basis": "count", + "equal_share": 0.002136931199195508, + "family": "usda_snap", + "geography_level": "state", + "loss_share": 0.004266106491632859, + "max_weight": 6.223297669187108, + "min_weight": 0.41168058501271143, + "n_targets": 51, + "weight_sum": 101.81489752930982 + } + ], + "concept_groups": { + "max_district_group_size": 1, + "n_district_groups": 19541, + "n_district_rows": 19541, + "n_groups": 23866, + "n_singleton_district_groups": 19541 + }, + "n_targets": 23866, + "row_mapping": "us_fiscal_ledger_metadata.v1" + }, + "soi_receipt_counts": { + "cd_file_state": 302, + "congressional_district": 21743, + "congressional_district_by_parent_basis": { + "cd_file_state_total_bridged": 2562, + "historic_table_2": 19181 + }, + "historic_table_2_state": 3819, + "total": 25864 + } + }, + "wall_seconds": 23.8 +} \ No newline at end of file diff --git a/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py b/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py new file mode 100644 index 000000000..dac28a7e6 --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py @@ -0,0 +1,137 @@ +"""Write the Route A loss-weight golden fixture the engine-free tests read. + +Route A (``populace-us-2024-0fb05b6-4b57d15a287c-20260930T150401Z``, built from +commit 4b57d15a2, published tag-only on 2026-10-02) is the first certified US +release. Its ``calibration_diagnostics.json`` records, for each of its 5,694 +targets, the spec fields and the ``target_loss_weight`` the release passed to +``calibrate``, plus the loss basis hash over (name, weight, scale). + +The fixture keeps, per target, exactly what the national weighting reads: +name, entity, period, family, filter, value, ``measure_mode``, +``source_measure_id`` and ``ledger_geography_level``, with the recorded +weight. Route A has no congressional-district rows, so no other metadata +reaches its concept keys. Before writing, this script checks that the shared +module reproduces every recorded weight bit for bit from the full metadata and +from the reduced metadata, and that the recorded loss basis hash matches. The +test then pins the same equalities from the fixture alone. + +Run from the repository root:: + + uv run python experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py +""" + +from __future__ import annotations + +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +RELEASE_ID = "populace-us-2024-0fb05b6-4b57d15a287c-20260930T150401Z" +DIAGNOSTICS = Path( + "/Users/maxghenis/PolicyEngine/_recovered/scratch-backup/893/overnight-20260923/" + "route-a/run-4b57d15a287c/release-out/" + f"{RELEASE_ID}/releases/{RELEASE_ID}/calibration_diagnostics.json" +) +BUILD_COMMIT = "4b57d15a287c4a1729bad362a2f6d57c4de9059e" +FIXTURE = ( + REPO / "packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json" +) +#: The only metadata the national weighting reads from a non-district row. +METADATA_KEYS = ("measure_mode", "source_measure_id", "ledger_geography_level") +COLUMNS = ("name", "entity", "period", "family", "filter", "value", *METADATA_KEYS) + + +def main() -> int: + from microcosm.build.us_runtime import target_loss_weights as lw + from microcosm.calibrate import TargetRegistry, TargetSpec + from microcosm.calibrate._target_loss_attribution import target_loss_basis_hash + + raw = DIAGNOSTICS.read_bytes() + diagnostics = json.loads(raw) + targets = diagnostics["targets"] + levels = {target["metadata"].get("ledger_geography_level") for target in targets} + if "congressional_district" in levels: + raise SystemExit("Route A has district rows; the reduced fixture is not exact.") + + def spec(target, metadata): + return TargetSpec( + name=target["target_name"], + entity=target["entity"], + measure=target["target_name"], + value=target["target"], + period=target["period"], + family=target["registry"]["family"], + filter=target["filter"], + source=target["source"], + metadata=metadata, + ) + + recorded = np.asarray( + [target["target_loss_weight"] for target in targets], dtype=np.float64 + ) + full = lw.fiscal_target_loss_weights( + TargetRegistry([spec(t, t["metadata"]) for t in targets], country="us") + ) + reduced_specs = [ + spec( + t, + {key: t["metadata"][key] for key in METADATA_KEYS if key in t["metadata"]}, + ) + for t in targets + ] + reduced = lw.fiscal_target_loss_weights(TargetRegistry(reduced_specs, country="us")) + if not (np.array_equal(full, recorded) and np.array_equal(reduced, recorded)): + raise SystemExit("The shared module does not reproduce Route A's weights.") + names = [target["name"] for target in targets] + if names != [f"{t['target_name']}@{t['period']}" for t in targets]: + raise SystemExit("Route A's diagnostic names are not @.") + scales = np.asarray([t["target_loss_scale"] for t in targets], dtype=np.float64) + basis = diagnostics["target_loss_basis"] + if target_loss_basis_hash(names, recorded, scales) != basis["sha256"]: + raise SystemExit("Route A's loss basis hash does not match its weights.") + + rows = [ + [ + t["target_name"], + t["entity"], + t["period"], + t["registry"]["family"], + t["filter"], + t["target"], + *(t["metadata"].get(key) for key in METADATA_KEYS), + ] + for t in targets + ] + fixture = { + "description": ( + "Route A's 5,694 calibration targets: the fields the national " + "target-loss weighting reads, and the weight the release recorded. " + "Written by experiments/us-acs-local-target-loss-weights-20261004/" + "route_a_fixture.py." + ), + "release_id": RELEASE_ID, + "build_commit": BUILD_COMMIT, + "calibration_diagnostics_sha256": hashlib.sha256(raw).hexdigest(), + "target_loss_weighting": diagnostics["build"]["target_loss_weighting"], + "target_loss_basis": { + "hash_algorithm": basis["hash_algorithm"], + "sha256": basis["sha256"], + "names": "@", + "scales": "max(abs(value), 1)", + }, + "columns": list(COLUMNS), + "rows": rows, + "target_loss_weights": recorded.tolist(), + } + FIXTURE.write_text(json.dumps(fixture, separators=(",", ":")) + "\n") + print(f"wrote {FIXTURE} ({FIXTURE.stat().st_size:,} bytes, {len(rows)} rows)") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py index c9d22c309..2065a2fd5 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py @@ -39,9 +39,11 @@ their basis must agree with their ``ledger_measure_unit``. The Census ladder population rows (``pop_state_*``, ``pop_cd_*``), which carry no ledger metadata, are counts, as the national release's Census population targets - are. Each state's ``pop_cd_*`` rows share one concept budget. ``state_cd`` - district SOI rows group by their reconciliation block; the per-district - provenance that rebase stamps is kept out of the key. + are. Each state's ``pop_cd_*`` rows share one concept budget. District SOI + rows take the national district key without the metadata that names the + district (:data:`ACS_LOCAL_PER_DISTRICT_METADATA`), so the districts of one + concept in one state share one budget; each ``state_cd`` reconciliation + block must be exactly one group. """ from __future__ import annotations @@ -301,14 +303,31 @@ def _ledger_concept_budget_key( STATE: ("pop_state_", 2), CONGRESSIONAL_DISTRICT: ("pop_cd_", 4), } -#: The district file's own value of one district row, which the ``state_cd`` -#: rebase (``tools/us_acs_local_cd_surface.py``) stamps on each row before -#: rescaling it. It differs between the districts of one block, so the -#: concept key must not read it. The block-level stamps (parent, basis, -#: factor, file state sum) are equal across a block and may stay. -ACS_LOCAL_STATE_CD_ROW_PROVENANCE = frozenset({"state_cd_cd_file_value"}) +#: Per-row metadata that differs between the districts of one concept in one +#: state, so the ACS local concept key must not read it: +#: +#: - ``ledger_fact_label`` and ``ledger_layout_groupby_value_label``: the +#: human-readable labels the ledger compiler (``ledger_targets.py``) stamps +#: on every row, which name the district ("WV congressional district 1"). +#: :data:`US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS` does not list them, +#: so under the national mapping every compiled district row is its own +#: group; the national mapping is kept as it is here. +#: - ``state_cd_cd_file_value``: the district file's own value of the row, +#: which the ``state_cd`` rebase (``tools/us_acs_local_cd_surface.py``) +#: stamps before rescaling it. The rebase's block-level stamps (parent, +#: basis, factor, file state sum) are equal across a block and may stay. +#: +#: :func:`validate_acs_local_concept_groups` refuses a surface where any other +#: per-district key splits a reconciliation block. +ACS_LOCAL_PER_DISTRICT_METADATA = frozenset( + { + "ledger_fact_label", + "ledger_layout_groupby_value_label", + "state_cd_cd_file_value", + } +) US_ACS_LOCAL_CONCEPT_METADATA_EXCLUSIONS = ( - US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS | ACS_LOCAL_STATE_CD_ROW_PROVENANCE + US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS | ACS_LOCAL_PER_DISTRICT_METADATA ) #: The ledger's unit for a measure, and the basis that unit implies. LEDGER_MEASURE_UNIT_BASIS: Mapping[str, str] = { @@ -586,13 +605,19 @@ def parse_target_family_loss_multipliers(entries: Iterable[str]) -> dict[str, fl return multipliers +def target_row_name(spec) -> str: + """A spec's diagnostic row name, ``name@period``.""" + + return f"{spec.name}@{spec.period}" + + def target_loss_weights_sha256(names: Sequence[str], weights: np.ndarray) -> str: """Content digest of a row-aligned loss-weight vector. - The canonical form is the national release's loss vector: one - ``{"row_name", "weight_hex"}`` object per row, in row order, as compact - sorted-key JSON. Any change to a name, a weight bit or the row order - changes it. + The canonical form is the national release's ``loss_vector_sha256``: one + ``{"row_name", "weight_hex"}`` object per row (row names as + :func:`target_row_name` gives them), in row order, as compact sorted-key + JSON. Any change to a name, a weight bit or the row order changes it. """ weights = np.asarray(weights, dtype=np.float64) diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py b/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py new file mode 100644 index 000000000..6e582ff4d --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py @@ -0,0 +1,770 @@ +"""The shared US target-loss weights (``us_runtime.target_loss_weights``). + +Invariants, each for every input Hypothesis draws: + +1. The move is pure: the shared module returns, bit for bit, what the + national release's helpers returned before it (a verbatim copy in + ``test_support``), on random registries with district and non-district + rows, and on Route A's 5,694 real targets, where it also reproduces the + weights and the loss basis hash the release recorded. +2. Weights are finite, positive and of mean 1. +3. Each value basis present carries an equal share of the total weight. +4. The rows of one concept group sum to the group's largest value weight, + keep their proportions, and every row of a basis is then scaled alike. +5. A family multiplier scales its family relative to every other row by + exactly that multiplier. +6. Weights do not depend on row order. +7. Within a basis, a larger magnitude never gets a smaller singleton weight. + +Plus the ACS local row mapping: every row it classifies explicitly, the +groups it promises, and each refusal. +""" + +from __future__ import annotations + +import dataclasses +import importlib.util +import json +import math +import sys + +import numpy as np +import pytest +from hypothesis import HealthCheck, given, settings +from hypothesis import strategies as st + +from microcosm.build.us_runtime import target_loss_weights as lw +from microcosm.calibrate import TargetRegistry, TargetSpec, default_target_loss_scales +from microcosm.calibrate._target_loss_attribution import target_loss_basis_hash +from test_support.microcosm_build import us_target_loss_weights_reference as before +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-build") +_ROUTE_A = _TEST_PATHS.tests / "fixtures" / "us_route_a_target_loss_weights.json" +_SETTINGS = settings( + max_examples=200, + deadline=None, + suppress_health_check=[HealthCheck.too_slow], +) + +_FAMILIES = ("irs_soi", "usda_snap", "cms_medicaid", "census_population") +_STATES = ("01", "06", "36") +_MEASURE_MODES = (None, "sum", "indicator_sum", "less_than_indicator_sum") +_SOURCE_MEASURES = ( + None, + "adjusted_gross_income", + "return_count", + "total_medicaid_enrollment", + "ssi_recipients", + "total_benefits", + "eitc_returns", +) +_VALUES = st.one_of( + st.floats(-1e10, 1e10, allow_nan=False, allow_infinity=False), + st.sampled_from([0.0, 0.25, 1.0, 2.0, 4.0, 1e6]), +) + + +# --------------------------------------------------------------------------- +# Strategies +# --------------------------------------------------------------------------- + + +@st.composite +def _registries(draw, max_rows: int = 30) -> TargetRegistry: + """Mixed registries: national-style rows and district rows that group. + + District rows draw their concept metadata from small pools, so several + land in one group; per-row metadata (district ids, ledger ids and the + district label the compiler stamps) varies by row. + """ + + specs = [] + for index in range(draw(st.integers(1, max_rows))): + metadata: dict[str, str] = {} + mode = draw(st.sampled_from(_MEASURE_MODES)) + if mode is not None: + metadata["measure_mode"] = mode + measure = draw(st.sampled_from(_SOURCE_MEASURES)) + if measure is not None: + metadata["source_measure_id"] = measure + if draw(st.booleans()): + state = draw(st.sampled_from(_STATES)) + district = f"{state}{index:02d}" + metadata.update( + ledger_geography_level="congressional_district", + state_fips=state, + congressional_district_geoid=district, + ledger_geography_id=f"5001900US{district}", + ledger_source_record_id=f"record.{index}", + ledger_fact_label=f"District {district} label", + ) + if draw(st.booleans()): + metadata["filing_status"] = draw(st.sampled_from(["All", "Single"])) + else: + level = draw(st.sampled_from([None, "state", "national"])) + if level is not None: + metadata["ledger_geography_level"] = level + value = draw(_VALUES) + specs.append( + TargetSpec( + name=f"target_{index}", + entity=draw(st.sampled_from(["household", "tax_unit"])), + measure=f"target_{index}", + value=value, + period=draw(st.sampled_from([2024, 2025])), + family=draw(st.sampled_from(_FAMILIES)), + filter=draw(st.sampled_from([None, None, "is_tax_filer"])), + source="hypothesis", + signed=value < 0, + metadata=metadata, + ) + ) + return TargetRegistry(specs, country="us") + + +@st.composite +def _rows(draw, max_rows: int = 40): + """Row-level formula inputs: values, bases, group keys, families.""" + + n_rows = draw(st.integers(1, max_rows)) + values = draw(st.lists(_VALUES, min_size=n_rows, max_size=n_rows)) + bases = draw( + st.lists(st.sampled_from(["count", "amount"]), min_size=n_rows, max_size=n_rows) + ) + # A group never spans bases (both mappings put the basis in the key). + groups = draw(st.lists(st.integers(0, 5), min_size=n_rows, max_size=n_rows)) + keys = [(basis, group) for basis, group in zip(bases, groups, strict=True)] + families = draw( + st.lists(st.sampled_from(_FAMILIES), min_size=n_rows, max_size=n_rows) + ) + return values, bases, keys, families + + +def _present_family_multipliers(families): + return st.dictionaries( + st.sampled_from(sorted(set(families))), + st.floats(0.05, 20.0, allow_nan=False, allow_infinity=False), + max_size=3, + ) + + +# --------------------------------------------------------------------------- +# 1. The move is pure +# --------------------------------------------------------------------------- + + +@_SETTINGS +@given(_registries(), st.data()) +def test_shared_weights_equal_the_pre_move_helpers_bit_for_bit(registry, data) -> None: + multipliers = data.draw( + _present_family_multipliers([spec.family for spec in registry.specs]) + ) + expected = before._fiscal_target_loss_weights(registry, multipliers or None) + observed = lw.fiscal_target_loss_weights(registry, multipliers or None) + assert observed.dtype == np.float64 + assert np.array_equal(observed, expected) + assert np.array_equal( + lw.fiscal_target_value_basis_weights(registry), + before._fiscal_target_value_basis_weights(registry), + ) + assert np.array_equal( + lw.fiscal_target_concept_budget_weights(registry), + before._fiscal_target_concept_budget_weights(registry), + ) + for spec in registry.specs: + assert lw.fiscal_target_value_basis(spec) == before._fiscal_target_value_basis( + spec + ) + assert lw.fiscal_target_concept_budget_key( + spec + ) == before._fiscal_target_concept_budget_key(spec) + + +def test_moved_constants_are_the_pre_move_constants() -> None: + assert ( + lw.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS + == before.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS + ) + assert ( + lw.US_FISCAL_TARGET_VALUE_WEIGHT_POWER + == before.US_FISCAL_TARGET_VALUE_WEIGHT_POWER + ) + + +def test_a_multiplier_for_an_absent_family_fails_as_before() -> None: + registry = TargetRegistry( + [TargetSpec("a", "household", 1.0, "a", source="s", family="irs_soi")], + country="us", + ) + message = "family 'usda_snap' matches no compiled target" + with pytest.raises(ValueError, match=message): + before._fiscal_target_loss_weights(registry, {"usda_snap": 2.0}) + with pytest.raises(ValueError, match=message): + lw.fiscal_target_loss_weights(registry, {"usda_snap": 2.0}) + + +def _route_a(): + fixture = json.loads(_ROUTE_A.read_text()) + columns = fixture["columns"] + specs = [] + for row in fixture["rows"]: + record = dict(zip(columns, row, strict=True)) + metadata = { + key: record[key] + for key in ("measure_mode", "source_measure_id", "ledger_geography_level") + if record[key] is not None + } + specs.append( + TargetSpec( + name=record["name"], + entity=record["entity"], + measure=record["name"], + value=record["value"], + period=record["period"], + family=record["family"], + filter=record["filter"], + source="Route A calibration_diagnostics.json", + signed=record["value"] < 0, + metadata=metadata, + ) + ) + recorded = np.asarray(fixture["target_loss_weights"], dtype=np.float64) + return fixture, TargetRegistry(specs, country="us"), recorded + + +def test_route_a_weights_and_loss_basis_hash_are_reproduced_bit_for_bit() -> None: + """The certified national release's recorded weights, from the shared code. + + Route A recorded each target's weight and a hash over (row name, weight, + scale) in its calibration diagnostics. The shared module reproduces every + weight exactly, and the library's own default scales with those weights + give the recorded hash. + """ + + fixture, registry, recorded = _route_a() + assert len(registry) == 5_694 + assert fixture["target_loss_weighting"] == lw.US_FISCAL_TARGET_LOSS_WEIGHTING + observed = lw.fiscal_target_loss_weights(registry) + assert np.array_equal(observed, recorded) + assert np.array_equal(before._fiscal_target_loss_weights(registry), recorded) + names = [lw.target_row_name(spec) for spec in registry.specs] + scales = default_target_loss_scales( + np.asarray([spec.value for spec in registry.specs], dtype=np.float64) + ) + assert ( + target_loss_basis_hash(names, observed, scales) + == fixture["target_loss_basis"]["sha256"] + == "206ada09584f6e84dd8f32e58d70fea8db257d530c6a1f4ff115630e500436e7" + ) + + +def _load_release_tool(): + tools = str(_TEST_PATHS.repository / "tools") + if tools not in sys.path: + sys.path.insert(0, tools) + path = _TEST_PATHS.repository / "tools" / "build_us_fiscal_refresh_release.py" + spec = importlib.util.spec_from_file_location( + "build_us_fiscal_refresh_release_loss_weights", path + ) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def test_the_release_tool_uses_the_shared_implementation() -> None: + """Every name other tools read off the release tool is the shared one.""" + + release = _load_release_tool() + assert release._fiscal_target_loss_weights is lw.fiscal_target_loss_weights + assert release._fiscal_target_value_basis is lw.fiscal_target_value_basis + assert release.US_FISCAL_TARGET_LOSS_WEIGHTING == ( + lw.US_FISCAL_TARGET_LOSS_WEIGHTING + ) + assert release.US_FISCAL_TARGET_VALUE_WEIGHT_POWER == 0.5 + assert ( + release.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS + is lw.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS + ) + _fixture, registry, recorded = _route_a() + weights = release._fiscal_target_loss_weights(registry) + assert np.array_equal(weights, recorded) + # The shared digest's canonical form is the national loss vector's. + basis = release._fiscal_target_loss_basis(registry, weights) + assert basis["loss_vector_sha256"] == lw.target_loss_weights_sha256( + [lw.target_row_name(spec) for spec in registry.specs], weights + ) + + +# --------------------------------------------------------------------------- +# 2-7. Properties of the formula +# --------------------------------------------------------------------------- + + +@_SETTINGS +@given(_rows(), st.data()) +def test_weights_are_positive_finite_and_mean_one(rows, data) -> None: + values, bases, keys, families = rows + multipliers = data.draw(_present_family_multipliers(families)) + weights = lw.target_loss_weights_from_rows( + values, bases, keys, families, multipliers + ) + assert weights.shape == (len(values),) + assert np.isfinite(weights).all() + assert (weights > 0).all() + assert math.isclose(float(weights.mean()), 1.0, rel_tol=1e-12) + + +@_SETTINGS +@given(_rows()) +def test_each_basis_present_carries_an_equal_share(rows) -> None: + values, bases, keys, families = rows + weights = lw.target_loss_weights_from_rows(values, bases, keys, families) + basis_array = np.asarray(bases) + present = sorted(set(bases)) + for basis in present: + assert math.isclose( + float(weights[basis_array == basis].sum()), + len(values) / len(present), + rel_tol=1e-12, + ) + + +@_SETTINGS +@given(_rows()) +def test_a_concept_group_sums_to_its_largest_value_weight(rows) -> None: + """The group budget, and how the later steps carry it. + + After the concept budget each group sums to its largest member's value + weight, members keep their proportions, and the basis budget then scales + every row of a basis by one factor: so in the final weights each group + of a basis sums to that factor times its budget. + """ + + values, bases, keys, families = rows + basis_array = np.asarray(bases, dtype=object) + value_weights = lw._value_basis_weights(values, basis_array) + budgeted = lw._concept_budget_weights(value_weights.copy(), keys) + final = lw.target_loss_weights_from_rows(values, bases, keys, families) + for indices in lw.concept_budget_groups(keys): + assert math.isclose( + float(budgeted[indices].sum()), + float(value_weights[indices].max()), + rel_tol=1e-12, + ) + np.testing.assert_allclose( + budgeted[indices] / budgeted[indices].sum(), + value_weights[indices] / value_weights[indices].sum(), + rtol=1e-12, + ) + for basis in set(bases): + ratio = final[basis_array == basis] / budgeted[basis_array == basis] + np.testing.assert_allclose(ratio, ratio[0], rtol=1e-12) + + +@_SETTINGS +@given(_rows(), st.data()) +def test_a_family_multiplier_scales_only_its_family(rows, data) -> None: + values, bases, keys, families = rows + multipliers = data.draw(_present_family_multipliers(families)) + base = lw.target_loss_weights_from_rows(values, bases, keys, families) + scaled = lw.target_loss_weights_from_rows( + values, bases, keys, families, multipliers + ) + factor = np.asarray([multipliers.get(family, 1.0) for family in families]) + ratio = scaled / (base * factor) + np.testing.assert_allclose(ratio, ratio[0], rtol=1e-12) + + +@_SETTINGS +@given(_rows(), st.randoms(use_true_random=False), st.data()) +def test_weights_do_not_depend_on_row_order(rows, random, data) -> None: + values, bases, keys, families = rows + multipliers = data.draw(_present_family_multipliers(families)) + order = list(range(len(values))) + random.shuffle(order) + weights = lw.target_loss_weights_from_rows( + values, bases, keys, families, multipliers + ) + shuffled = lw.target_loss_weights_from_rows( + [values[i] for i in order], + [bases[i] for i in order], + [keys[i] for i in order], + [families[i] for i in order], + multipliers, + ) + np.testing.assert_allclose(shuffled, weights[order], rtol=1e-12) + + +@_SETTINGS +@given(_rows()) +def test_a_larger_singleton_magnitude_never_gets_less_weight(rows) -> None: + values, bases, keys, families = rows + singletons = [(basis, ("row", index)) for index, basis in enumerate(bases)] + weights = lw.target_loss_weights_from_rows(values, bases, singletons, families) + magnitudes = np.maximum(np.abs(np.asarray(values, dtype=np.float64)), 1.0) + basis_array = np.asarray(bases) + for basis in set(bases): + rows_in_basis = np.flatnonzero(basis_array == basis) + order = rows_in_basis[np.argsort(magnitudes[rows_in_basis], kind="stable")] + assert (np.diff(weights[order]) >= -1e-12 * weights[order][1:]).all() + + +def test_the_formula_refuses_misaligned_inputs_and_bad_multipliers() -> None: + with pytest.raises(ValueError, match="not row-aligned"): + lw.target_loss_weights_from_rows([1.0, 2.0], ["count"], [0, 1], ["a", "b"]) + for multiplier in (0.0, -1.0, math.nan, math.inf): + with pytest.raises(ValueError, match="positive and finite"): + lw.target_loss_weights_from_rows( + [1.0], ["count"], [0], ["a"], {"a": multiplier} + ) + assert lw.target_loss_weights_from_rows([], [], [], []).shape == (0,) + + +# --------------------------------------------------------------------------- +# Options and digests +# --------------------------------------------------------------------------- + + +def test_family_multiplier_entries_parse_or_fail_with_the_cli_messages() -> None: + parse = lw.parse_target_family_loss_multipliers + assert parse([]) == {} + assert parse(["usda_snap=8", "irs_soi=0.5"]) == {"usda_snap": 8.0, "irs_soi": 0.5} + for entry in ("usda_snap", "=2", "usda_snap=", "usda_snap=x", "a=0", "a=-1"): + with pytest.raises(ValueError, match="expects FAMILY=MULTIPLIER"): + parse([entry]) + for entry in ("a=nan", "a=inf"): + with pytest.raises(ValueError, match="positive finite multiplier"): + parse([entry]) + with pytest.raises(ValueError, match="repeats family 'a'"): + parse(["a=2", "a=3"]) + + +def test_the_weights_digest_moves_with_any_name_bit_or_order_change() -> None: + names = ["a@2024", "b@2024", "c@2024"] + weights = np.asarray([0.5, 1.0, 1.5]) + digest = lw.target_loss_weights_sha256(names, weights) + assert digest == lw.target_loss_weights_sha256(list(names), weights.copy()) + assert digest != lw.target_loss_weights_sha256(["a@2025", *names[1:]], weights) + assert digest != lw.target_loss_weights_sha256(names, np.nextafter(weights, np.inf)) + assert digest != lw.target_loss_weights_sha256(names[::-1], weights[::-1]) + with pytest.raises(ValueError, match="do not align"): + lw.target_loss_weights_sha256(names, weights[:2]) + + +# --------------------------------------------------------------------------- +# The ACS local row mapping +# --------------------------------------------------------------------------- + + +def _ledger(name, *, family="irs_soi", value=1_000.0, unit="usd", **metadata): + """A ledger-compiled row as the compiler stamps it (the keys read here).""" + + defaults = { + "measure_mode": "sum" if unit == "usd" else "indicator_sum", + "source_measure_id": name.split(".")[-1], + "ledger_measure_unit": unit, + "ledger_geography_level": "state", + "state_fips": "06", + "ledger_fact_label": f"{name} label", + } + return TargetSpec( + name=name, + entity="household", + measure=name, + value=value, + period=2024, + family=family, + source="fixture", + metadata={**defaults, **metadata}, + ) + + +def _district(name, district, *, parent, value=100.0, unit="usd", **metadata): + """A ``state_cd`` district row: compiler labels plus the rebase stamps.""" + + return _ledger( + name, + value=value, + unit=unit, + ledger_geography_level="congressional_district", + state_fips=district[:2], + congressional_district_geoid=district, + ledger_geography_id=f"5001900US{district}", + ledger_source_record_id=f"{name}.record", + ledger_layout_groupby_value_label=f"CA congressional district {district}", + ledger_fact_label=f"California Congressional District {district} {name}", + state_cd_vintage_rule="state_cd.historic_table_2_level_cd_file_shares.v1", + state_cd_parent_target_name=parent, + state_cd_parent_basis="historic_table_2", + state_cd_parent_value="500.0", + state_cd_cd_file_value=repr(value * 0.9), + state_cd_cd_file_state_sum="450.0", + state_cd_rebase_factor="1.11", + **metadata, + ) + + +def _population(name, level, value=700_000.0): + return TargetSpec( + name=name, + entity="household", + measure=name, + value=value, + period=2024, + family="census_population", + source="US Census Bureau 2020 PUMA population ladder", + metadata={"geography_level": level}, + ) + + +def _acs_surface(): + """Every kind of row on the ACS local surface, two states.""" + + soi_state = _ledger("irs_soi.ca.agi_amount", value=2e12) + soi_count = _ledger("irs_soi.ca.return_count", value=1.8e7, unit="count") + snap_amount = _ledger( + "usda_snap.ca.total_benefits", family="usda_snap", value=1.2e10 + ) + snap_count = _ledger( + "usda_snap.ca.average_monthly_households", + family="usda_snap", + value=3e6, + unit="count", + ) + medicaid = _ledger( + "cms_medicaid.ca.total_medicaid_enrollment", + family="cms_medicaid", + value=1.4e7, + unit="count", + ) + districts = [ + _district(f"irs_soi.cd{d}.agi_amount", d, parent=soi_state.name, value=v) + for d, v in (("0601", 4e10), ("0602", 6e10), ("0603", 2e10)) + ] + district_counts = [ + _district( + f"irs_soi.cd{d}.return_count", + d, + parent=soi_count.name, + value=v, + unit="count", + ) + for d, v in (("0601", 3e5), ("0602", 4e5), ("0603", 2e5)) + ] + population = [ + _population("pop_state_06", "state", 3.9e7), + _population("pop_state_36", "state", 2.0e7), + _population("pop_cd_0601", "congressional_district", 7.6e5), + _population("pop_cd_0602", "congressional_district", 7.7e5), + _population("pop_cd_3601", "congressional_district", 7.5e5), + ] + return ( + soi_state, + soi_count, + snap_amount, + snap_count, + medicaid, + *districts, + *district_counts, + *population, + ) + + +def test_acs_local_mapping_classifies_every_surface_row_explicitly() -> None: + surface = _acs_surface() + bases = {spec.name: lw.acs_local_target_value_basis(spec) for spec in surface} + assert bases["irs_soi.ca.agi_amount"] == "amount" + assert bases["irs_soi.ca.return_count"] == "count" + assert bases["usda_snap.ca.total_benefits"] == "amount" + assert bases["usda_snap.ca.average_monthly_households"] == "count" + assert bases["cms_medicaid.ca.total_medicaid_enrollment"] == "count" + # Ladder population is a count, as the national Census population rows + # (measure_mode indicator_sum) are; the national mapping alone would say + # "amount" because these rows carry no ledger metadata. + for name in ("pop_state_06", "pop_cd_0601", "pop_cd_3601"): + assert bases[name] == "count" + spec = next(spec for spec in surface if spec.name == name) + assert lw.fiscal_target_value_basis(spec) == "amount" + + +def test_acs_local_groups_are_one_per_concept_per_state() -> None: + surface = _acs_surface() + keys = {spec.name: lw.acs_local_target_concept_budget_key(spec) for spec in surface} + district_agi = {keys[f"irs_soi.cd{d}.agi_amount"] for d in ("0601", "0602", "0603")} + district_returns = { + keys[f"irs_soi.cd{d}.return_count"] for d in ("0601", "0602", "0603") + } + assert len(district_agi) == len(district_returns) == 1 + assert district_agi != district_returns + assert keys["pop_cd_0601"] == keys["pop_cd_0602"] != keys["pop_cd_3601"] + assert keys["pop_state_06"] != keys["pop_state_36"] + state_rows = [spec.name for spec in surface[:5]] + assert len({keys[name] for name in state_rows}) == len(state_rows) + # The national mapping as is leaves each district row its own group, + # because the compiler's labels and the rebase's file value name the row. + national = { + lw.fiscal_target_concept_budget_key(spec) + for spec in surface + if spec.metadata.get("ledger_geography_level") == "congressional_district" + } + assert len(national) == 6 + + +def test_acs_local_weights_budget_districts_and_balance_the_bases() -> None: + surface = _acs_surface() + weights = lw.us_acs_local_target_loss_weights(surface) + names = [spec.name for spec in surface] + index = {name: position for position, name in enumerate(names)} + assert math.isclose(float(weights.mean()), 1.0, rel_tol=1e-12) + assert len(set(np.round(weights, 12))) > 1 + bases = [lw.acs_local_target_value_basis(spec) for spec in surface] + for basis in ("count", "amount"): + total = sum(w for w, b in zip(weights, bases, strict=True) if b == basis) + assert math.isclose(total, len(surface) / 2, rel_tol=1e-12) + # The three district AGI rows share one budget, their largest value + # weight, so together they weigh what the largest district would alone: + # against the state AGI row (same basis), sqrt(6e10) to sqrt(2e12). + agi = [index[f"irs_soi.cd{d}.agi_amount"] for d in ("0601", "0602", "0603")] + assert math.isclose( + float(weights[agi].sum() / weights[index["irs_soi.ca.agi_amount"]]), + math.sqrt(6e10 / 2e12), + rel_tol=1e-12, + ) + # ... and split it in proportion to their own value weights. + np.testing.assert_allclose( + weights[agi] / weights[agi].sum(), + np.sqrt([4e10, 6e10, 2e10]) / np.sqrt([4e10, 6e10, 2e10]).sum(), + rtol=1e-12, + ) + distribution = lw.target_loss_weight_distribution( + surface, weights, row_mapping=lw.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING + ) + assert distribution["concept_groups"] == { + "n_groups": 11, + "n_district_rows": 9, + "n_district_groups": 4, + "n_singleton_district_groups": 1, + "max_district_group_size": 3, + } + assert math.isclose( + sum(cell["loss_share"] for cell in distribution["by_family_level_basis"]), + 1.0, + rel_tol=1e-12, + ) + + +def test_a_district_group_holds_its_budget_however_many_districts() -> None: + """Adding districts to a concept does not add to its weight.""" + + def share(n_districts: int) -> float: + rows = [_ledger("irs_soi.ca.agi_amount", value=1e6)] + [ + _district( + f"irs_soi.cd06{d:02d}.agi_amount", + f"06{d:02d}", + parent="irs_soi.ca.agi_amount", + value=1e6, + ) + for d in range(1, n_districts + 1) + ] + weights = lw.us_acs_local_target_loss_weights(rows) + return float(weights[1:].sum() / weights.sum()) + + assert math.isclose(share(1), 0.5, rel_tol=1e-12) + for n_districts in (2, 7, 52): + assert math.isclose(share(n_districts), 0.5, rel_tol=1e-12) + + +@pytest.mark.parametrize( + ("spec", "message"), + [ + ( + TargetSpec("x", "household", 1.0, "x", source="s", family="fixture"), + "neither", + ), + (_ledger("irs_soi.ca.agi_amount", unit="percent"), "has no loss basis"), + ( + _ledger("irs_soi.ca.agi_amount", unit="count", measure_mode="sum"), + "national loss basis says 'amount'", + ), + ( + dataclasses.replace( + _ledger("irs_soi.ca.agi_amount"), + metadata={ + key: value + for key, value in _ledger("irs_soi.ca.agi_amount").metadata.items() + if key != "measure_mode" + }, + ), + r"needs \['measure_mode'\]", + ), + (_population("pop_state_06", "county"), "geography_level state or"), + (_population("pop_cd_06", "congressional_district"), "pop_cd_<4 FIPS"), + (_population("pop_state_0601", "state"), "pop_state_<2 FIPS"), + (_population("pop_cd_0601", "state"), "pop_state_<2 FIPS"), + ], +) +def test_acs_local_mapping_refuses_what_it_cannot_classify(spec, message) -> None: + with pytest.raises(ValueError, match=message): + lw.us_acs_local_target_loss_weights([spec]) + + +def test_a_district_row_without_its_state_is_refused() -> None: + row = _district("irs_soi.cd0601.agi_amount", "0601", parent="p") + metadata = {k: v for k, v in row.metadata.items() if k != "state_fips"} + with pytest.raises(ValueError, match="without state_fips"): + lw.acs_local_target_concept_budget_key( + dataclasses.replace(row, metadata=metadata) + ) + + +def test_ledger_census_population_rows_take_the_ledger_path() -> None: + row = _ledger( + "census_pep.ca.population", + family="census_population", + unit="count", + measure_mode="indicator_sum", + ) + assert lw.acs_local_population_geography(row) is None + assert lw.acs_local_target_value_basis(row) == "count" + + +def test_a_split_or_pooled_state_cd_block_is_refused() -> None: + parent = "irs_soi.ca.agi_amount" + rows = [ + _district(f"irs_soi.cd{d}.agi_amount", d, parent=parent) + for d in ("0601", "0602") + ] + # A per-district key the mapping does not exclude splits the block. + leaking = [ + dataclasses.replace(row, metadata={**row.metadata, "district_note": row.name}) + for row in rows + ] + with pytest.raises(ValueError, match="split across concept groups"): + lw.us_acs_local_target_loss_weights(leaking) + # Two blocks under one key would pool their budgets. + keys = [("k",), ("k",)] + pooled = [ + rows[0], + _district("irs_soi.cd0602.other", "0602", parent="another_parent"), + ] + with pytest.raises(ValueError, match="pool several state_cd blocks"): + lw.validate_acs_local_concept_groups(pooled, ["amount", "amount"], keys) + # A district group spanning states. + spread = [ + _population("pop_cd_0601", "congressional_district"), + _population("pop_cd_3601", "congressional_district"), + ] + with pytest.raises(ValueError, match="span states"): + lw.validate_acs_local_concept_groups(spread, ["count", "count"], keys) + + +@_SETTINGS +@given(st.permutations(list(range(len(_acs_surface()))))) +def test_acs_local_weights_do_not_depend_on_row_order(order) -> None: + surface = _acs_surface() + weights = lw.us_acs_local_target_loss_weights(surface) + shuffled = lw.us_acs_local_target_loss_weights([surface[i] for i in order]) + np.testing.assert_allclose(shuffled, weights[list(order)], rtol=1e-12) diff --git a/packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json b/packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json new file mode 100644 index 000000000..c41f6cea4 --- /dev/null +++ b/packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json @@ -0,0 +1 @@ +{"description":"Route A's 5,694 calibration targets: the fields the national target-loss weighting reads, and the weight the release recorded. 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diff --git a/test_support/microcosm_build/us_target_loss_weights_reference.py b/test_support/microcosm_build/us_target_loss_weights_reference.py new file mode 100644 index 000000000..2fb524eef --- /dev/null +++ b/test_support/microcosm_build/us_target_loss_weights_reference.py @@ -0,0 +1,171 @@ +"""The national target-loss weighting exactly as it stood before the move. + +A verbatim copy of the five functions and two constants that +``tools/build_us_fiscal_refresh_release.py`` defined at 48fa06166 (origin/main +when the weighting moved to ``microcosm.build.us_runtime.target_loss_weights``), +under their original names, so ``git diff`` against that commit's tool shows +them unchanged. The differential tests hold the shared module to this copy. +Do not edit it; it is the "before" of a pure move. +""" + +from __future__ import annotations + +from collections.abc import Mapping + +import numpy as np + +from microcosm.calibrate import TargetRegistry + +US_FISCAL_TARGET_VALUE_WEIGHT_POWER = 0.5 + +US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS = frozenset( + { + "congressional_district_geoid", + "geography_scope", + "hierarchy_child_ids", + "hierarchy_child_sum_raw", + "hierarchy_coverage_ratio", + "hierarchy_expected_child_count", + "hierarchy_observed_child_count", + "hierarchy_parent_geography_id", + "hierarchy_parent_geography_level", + "hierarchy_parent_key", + "hierarchy_parent_target_name", + "hierarchy_parent_target_period", + "hierarchy_parent_value", + "hierarchy_raw_value", + "hierarchy_reconciliation_factor", + "hierarchy_reconciliation_method", + "hierarchy_reconciliation_rule", + "ledger_aggregate_fact_key", + "ledger_dimension_set_key", + "ledger_fact_key", + "ledger_geography_id", + "ledger_geography_level", + "ledger_geography_name", + "ledger_geography_vintage", + "ledger_legacy_fact_key", + "ledger_layout_groupby_dimension", + "ledger_layout_groupby_value_id", + "ledger_layout_record_set_id", + "ledger_observed_measure_key", + "ledger_semantic_fact_key", + "ledger_source_record_id", + "state_fips", + } +) + + +def _fiscal_target_loss_weights( + registry: TargetRegistry, + family_multipliers: Mapping[str, float] | None = None, +) -> np.ndarray: + weights = _fiscal_target_concept_budget_weights(registry) + bases = np.asarray( + [_fiscal_target_value_basis(spec) for spec in registry.specs], + dtype=object, + ) + unique_bases = sorted(set(bases.tolist())) + if not unique_bases: + return weights + basis_total = len(weights) / len(unique_bases) + for basis in unique_bases: + mask = bases == basis + current_total = weights[mask].sum() + if current_total > 0: + weights[mask] *= basis_total / current_total + weights = weights / weights.mean() + if not family_multipliers: + return weights + families = np.asarray( + [spec.family for spec in registry.specs], + dtype=object, + ) + for family, multiplier in sorted(family_multipliers.items()): + mask = families == family + if not mask.any(): + raise ValueError( + f"--target-family-loss-multiplier family {family!r} matches " + "no compiled target." + ) + weights[mask] *= multiplier + return weights / weights.mean() + + +def _fiscal_target_concept_budget_weights(registry: TargetRegistry) -> np.ndarray: + weights = _fiscal_target_value_basis_weights(registry) + group_indices: dict[tuple[object, ...], list[int]] = {} + for index, spec in enumerate(registry.specs): + group_indices.setdefault(_fiscal_target_concept_budget_key(spec), []).append( + index + ) + for indices in group_indices.values(): + group_weights = weights[indices] + group_total = float(group_weights.sum()) + group_budget = float(group_weights.max(initial=0.0)) + if group_total > 0 and group_budget > 0: + weights[indices] *= group_budget / group_total + return weights + + +def _fiscal_target_concept_budget_key(spec) -> tuple[object, ...]: + metadata = spec.metadata + if metadata.get("ledger_geography_level") != "congressional_district": + return ( + _fiscal_target_value_basis(spec), + spec.entity, + spec.period, + spec.family, + spec.name, + ) + semantic_metadata = tuple( + sorted( + (key, value) + for key, value in metadata.items() + if key not in US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS + ) + ) + return ( + _fiscal_target_value_basis(spec), + spec.entity, + spec.period, + spec.family, + spec.filter or "", + metadata.get("state_fips", ""), + semantic_metadata, + ) + + +def _fiscal_target_value_basis_weights(registry: TargetRegistry) -> np.ndarray: + weights = np.ones(len(registry.specs), dtype=np.float64) + bases = np.asarray( + [_fiscal_target_value_basis(spec) for spec in registry.specs], + dtype=object, + ) + values = np.asarray( + [max(abs(float(spec.value)), 1.0) for spec in registry.specs], + dtype=np.float64, + ) + raw_weights = values**US_FISCAL_TARGET_VALUE_WEIGHT_POWER + for basis in sorted(set(bases.tolist())): + mask = bases == basis + mean_value = raw_weights[mask].mean() + if mean_value > 0: + weights[mask] = raw_weights[mask] / mean_value + return weights + + +def _fiscal_target_value_basis(spec) -> str: + metadata = spec.metadata + measure_mode = metadata.get("measure_mode", "") + source_measure_id = metadata.get("source_measure_id", "") + if measure_mode in { + "indicator_sum", + "less_than_indicator_sum", + }: + return "count" + if "enrollment" in source_measure_id or "recipients" in source_measure_id: + return "count" + if "return" in source_measure_id and "count" in source_measure_id: + return "count" + return "amount" diff --git a/tools/build_us_acs_local_release.py b/tools/build_us_acs_local_release.py index 7c3cf446c..18288eec6 100644 --- a/tools/build_us_acs_local_release.py +++ b/tools/build_us_acs_local_release.py @@ -29,10 +29,13 @@ Heavy stage; a crash in calibrate never re-runs the microsim. calibrate : epoch-batched warm-start calibrate on the checkpoint's training targets (adam, mass conserved, hard weight-ratio cap; - resumable with --resume), score the held-out district targets - against the pro-rata baseline, record ESS over rows and - distinct households, and write the calibrated weights onto a - copy of the staging H5. + resumable with --resume; each target's loss weighted as the + national release weights it, by + ``microcosm.build.us_runtime.target_loss_weights``, with + ``--target-family-loss-multiplier``), score the held-out + district targets against the pro-rata baseline, record ESS + over rows and distinct households, and write the calibrated + weights onto a copy of the staging H5. qa : chunked engine probe of the calibrated artifact recording per-spine SSI incidence and intensity (the microcosm#403 signature, measured rather than assumed). @@ -73,6 +76,7 @@ import numpy as np import pandas as pd +from microcosm.build.us_runtime import target_loss_weights as loss_weighting from microcosm.build.us_runtime.acs_local_hours import acs_local_hours_signal_gate _TOOLS_DIR = Path(__file__).resolve().parent @@ -1481,8 +1485,13 @@ def _verify_run_identity(args, *, require: bool = True) -> dict: SOLVER_MASS = "conserve" -def _solver_settings(args) -> dict: - """The calibrate-stage settings a resume or reuse must share.""" +def _solver_settings(args, target_loss: dict) -> dict: + """The calibrate-stage settings a resume or reuse must share. + + ``target_loss`` is the stamp :func:`release_target_loss_weights` returns, + so weights calibrated under another loss weighting, or under none (a run + from before the weights), are never warm-started from. + """ return { "method": SOLVER_METHOD, @@ -1493,7 +1502,40 @@ def _solver_settings(args) -> dict: "l2_lambda": args.l2_lambda, "seed": args.seed, "epoch_batch": args.epoch_batch, + "target_loss": target_loss, + } + + +def release_target_loss_weights(args, train_specs) -> tuple[np.ndarray, dict]: + """The loss weights of the training targets, and the stamp describing them. + + The national release's formula under the ACS local row mapping + (:data:`loss_weighting.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING`), over the + training specs only, row-aligned with the calibration TargetSet. The + stamp names the formula, the mapping and the multipliers and digests the + (name, weight) vector. + """ + + multipliers = dict(args.target_family_loss_multipliers) + try: + weights = loss_weighting.us_acs_local_target_loss_weights( + train_specs, multipliers + ) + except ValueError as error: + raise SystemExit(f"Target loss weights: {error}") from error + stamp = { + "weighting": loss_weighting.US_FISCAL_TARGET_LOSS_WEIGHTING, + "row_mapping": loss_weighting.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING.mapping_id, + "family_multipliers": { + family: float(multiplier) + for family, multiplier in sorted(multipliers.items()) + }, + "n_targets": len(train_specs), + "weights_sha256": loss_weighting.target_loss_weights_sha256( + [loss_weighting.target_row_name(spec) for spec in train_specs], weights + ), } + return weights, stamp def _weights_digest(weights) -> str: @@ -1668,6 +1710,7 @@ def calibrate_surface( target_loss_cap: float, l2_lambda: float, seed: int, + target_loss_weights: np.ndarray | None = None, warm: np.ndarray | None = None, done: int = 0, on_batch=None, @@ -1677,7 +1720,10 @@ def calibrate_surface( ``target_set`` comes from ``cd_surface.calibration_target_set``, which builds only the training targets, each a callable row of the checkpoint CSR. Each batch calls the kernel's ``calibrate``, which compiles those - rows into its own CSR constraint matrix. Returns ``(result, epochs_done)``. + rows into its own CSR constraint matrix. ``target_loss_weights`` is + row-aligned with ``target_set``; ``do_calibrate`` always passes the + release weights (:func:`release_target_loss_weights`), and ``None`` (equal + weights) is for harnesses. Returns ``(result, epochs_done)``. """ from microcosm.calibrate import calibrate @@ -1699,6 +1745,7 @@ def calibrate_surface( target_loss_cap=target_loss_cap, l2_lambda=l2_lambda, seed=seed, + target_loss_weights=target_loss_weights, warm_start_weights=warm, ) done += this_batch @@ -1781,14 +1828,34 @@ def do_calibrate(args) -> None: target_set = cd_surface.calibration_target_set( roles, matrix, n_households, specs=registry.specs ) + train_specs = [registry.specs[index] for index in cd_surface.train_rows(roles)] + if [target.name for target in target_set] != [spec.name for spec in train_specs]: + raise SystemExit( + "The calibration TargetSet is not row-aligned with the training " + "specs, so their loss weights would attach to the wrong targets." + ) + loss_weights, target_loss = release_target_loss_weights(args, train_specs) + loss_distribution = loss_weighting.target_loss_weight_distribution( + train_specs, + loss_weights, + row_mapping=loss_weighting.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING, + ) log( f"calibrate: households={n_households}, targets={len(target_set)} " f"trained + {len(roles) - len(target_set)} held out, " f"nnz={matrix.nnz:,}, design_total={design_weights.sum():,.0f}" ) + log( + "target loss shares: " + + ", ".join( + f"{cell['family']}/{cell['geography_level']}/{cell['basis']} " + f"{cell['loss_share']:.1%} (equal {cell['equal_share']:.1%})" + for cell in loss_distribution["by_family_level_basis"] + ) + ) stamp = _run_identity_digest(identity) - settings = _solver_settings(args) + settings = _solver_settings(args, target_loss) resume_npz = args.checkpoint_dir / "weights_latest.npz" warm, done = None, 0 if args.resume and resume_npz.exists(): @@ -1870,6 +1937,7 @@ def save(weights: np.ndarray, epochs_done: int) -> None: target_loss_cap=args.target_loss_cap, l2_lambda=args.l2_lambda, seed=args.seed, + target_loss_weights=loss_weights, warm=warm, done=done, on_batch=save, @@ -1898,6 +1966,7 @@ def save(weights: np.ndarray, epochs_done: int) -> None: "epoch_batch": args.epoch_batch, "max_weight_ratio": args.max_weight_ratio, "target_loss_cap": args.target_loss_cap, + "target_loss": {**target_loss, "distribution": loss_distribution}, "l2_lambda": args.l2_lambda, "seed": args.seed, "initial_loss": round(result.initial_loss, 6), @@ -1943,6 +2012,14 @@ def save(weights: np.ndarray, epochs_done: int) -> None: "mass_conserved_ratio": summary["mass_conserved_ratio"], "n_holdout_targets": summary["n_holdout_targets"], "sampling_rung": (identity.get("sampling") or {}).get("rung"), + # The fields the calibration dashboard reads, as the national + # release names them, plus the ACS local row mapping and digest. + "target_loss_weighting": target_loss["weighting"], + "target_loss_family_multipliers": target_loss["family_multipliers"] or None, + "target_loss_cap": args.target_loss_cap, + "target_loss_row_mapping": target_loss["row_mapping"], + "target_loss_weights_sha256": target_loss["weights_sha256"], + "target_loss_distribution": loss_distribution, }, ) summary["calibration_diagnostics"] = ( @@ -3072,6 +3149,7 @@ def _version(package: str) -> str: "max_weight_ratio", "l2_lambda", "seed", + "target_loss", "final_loss", "fraction_within_10pct", "effective_sample_size", @@ -3336,6 +3414,19 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: parser.add_argument("--epoch-batch", type=int, default=400) parser.add_argument("--max-weight-ratio", type=float, default=5.0) parser.add_argument("--target-loss-cap", type=float, default=1.0) + parser.add_argument( + "--target-family-loss-multiplier", + action="append", + default=[], + metavar="FAMILY=MULTIPLIER", + help=( + "Multiply the loss weight of every training target in FAMILY " + "(e.g. usda_snap=2) on top of the national release's weighting, " + "then renormalize to mean 1 (repeatable). Calibrate fails if FAMILY " + "matches no training target. Recorded in the solver settings, the " + "calibration summary and the diagnostics." + ), + ) parser.add_argument("--l2-lambda", type=float, default=0.0) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--resume", action="store_true") @@ -3383,6 +3474,14 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: ), ) args = parser.parse_args(argv) + try: + args.target_family_loss_multipliers = ( + loss_weighting.parse_target_family_loss_multipliers( + args.target_family_loss_multiplier + ) + ) + except ValueError as error: + parser.error(str(error)) stages = ( ["materialize", "calibrate", "qa", "finalize", "package"] From 4d8785498d3881ab7ecf6618f681da4c9f4c5b94 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 15:01:07 -0400 Subject: [PATCH 3/7] Test the shared loss weights and the ACS local wiring; record the before/after - Hypothesis properties of the formula and a differential against a frozen copy of the pre-move national helpers. - A golden on Route A's 5,694 recorded weights and loss basis hash. - The ACS local row mapping's classifications and refusals. - do_calibrate end to end on a surface-shaped fixture. - Evidence scripts and results for the 09-23 and state_cd surfaces, and the national district-label finding. Co-Authored-By: Claude Opus 5.5 --- ...s-acs-local-target-loss-weights.changed.md | 1 + .../README.md | 158 ++++++ .../national_cd_groups.json | 97 ++++ .../national_cd_groups.py | 132 +++++ .../us/test_us_acs_local_sparse_cd.py | 5 +- .../test_us_acs_local_target_loss_weights.py | 459 ++++++++++++++++++ 6 files changed, 851 insertions(+), 1 deletion(-) create mode 100644 changelog.d/us-acs-local-target-loss-weights.changed.md create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/README.md create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.json create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py create mode 100644 packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py diff --git a/changelog.d/us-acs-local-target-loss-weights.changed.md b/changelog.d/us-acs-local-target-loss-weights.changed.md new file mode 100644 index 000000000..20d7e1a2f --- /dev/null +++ b/changelog.d/us-acs-local-target-loss-weights.changed.md @@ -0,0 +1 @@ +The ACS local-area release's calibrate stage weights each training target's loss with the national release's formula instead of equally. Both releases now call one implementation, `microcosm.build.us_runtime.target_loss_weights`; the national weights are unchanged bit for bit (Route A's 5,694 recorded weights are a golden test). An explicit ACS local row mapping files ladder population rows as counts and gives each state's `pop_cd_*` rows, and each `state_cd` reconciliation block, one concept budget. On the 09-23 release surface SOI's share of the loss moves from 85.6% to 90.2%, Census population from 10.9% to 6.1%, Medicaid from 1.1% to 2.0% and SNAP from 2.3% to 1.7%. `--target-family-loss-multiplier FAMILY=MULTIPLIER` is new. The weights' digest joins the solver settings, so `--resume` refuses weights calibrated before this change. Future builds only; no release is rebuilt. diff --git a/experiments/us-acs-local-target-loss-weights-20261004/README.md b/experiments/us-acs-local-target-loss-weights-20261004/README.md new file mode 100644 index 000000000..cd23feea2 --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/README.md @@ -0,0 +1,158 @@ +# ACS local release: target-loss weights before and after + +Until this change the ACS local-area release calibrated with every training +target weighted equally. The national fiscal release weights each target's +loss with `sqrt_value_concept_budget_weighted_mape_50_50_amount_count_target_scale_cap_100pct`. +The calibrate stage now uses the same formula, from the one implementation in +`microcosm.build.us_runtime.target_loss_weights`, under an explicit ACS local +row mapping. This folder measures what that does to the share of the loss +each kind of target carries. It changes the objective of future builds only. +Nothing was rebuilt or published. + +A target's **loss share** is its share of the total weight, which is the share +of the loss it carries when every target misses by the same scaled amount. + +## The formula and the ACS local mapping + +The formula (module docstring of `target_loss_weights.py`): + +1. Each target is a count or an amount. +2. Value weight `max(|value|, 1) ** 0.5`, over its basis mean. +3. Targets that share a concept-budget key form a group, which is rescaled to + sum to its largest member's weight. +4. Counts and amounts each carry half the total weight. +5. Mean 1, then optional family multipliers (`--target-family-loss-multiplier`), + then mean 1 again. + +The ACS local mapping classifies every row and refuses a row it cannot +classify: + +- **Ledger rows** (SNAP, Medicaid enrollment, SOI) take the national basis + rule. That rule must agree with the row's `ledger_measure_unit` (`count` or + `usd`), and the row must carry `measure_mode` and `source_measure_id`. + - On both surfaces below, the national rule and the unit agree on every row. +- **Ladder population rows** (`pop_state_SS`, `pop_cd_SSDD`) carry only + `geography_level`, so the national rule alone files them as amounts. + - The mapping files them as counts, which is how the national release's + Census population targets (`measure_mode=indicator_sum`) are filed. + - Each state's `pop_cd_*` rows share one concept budget. +- **District SOI rows** take the national district key without three keys + that name the row's district rather than its concept: + - `ledger_fact_label` and `ledger_layout_groupby_value_label`, which the + ledger compiler stamps on every row, for example "WV congressional + district 1"; + - `state_cd_cd_file_value`, the district file's own value, which the + `state_cd` rebase stamps. + + Each `state_cd` reconciliation block must then be exactly one group. On the + pinned feed that holds for all 1,907 trained blocks. + +## Results + +`distribution.py` writes `results.json`. "National mapping as is" applies the +national release's mapping to the ACS local specs unchanged, which shows what +reusing the helper without a mapping would have done. + +### The 09-23 release's surface (`--soi-mode state`, the default): 4,459 targets, all trained + +| Family | Level | Targets | Equal (before) | National mapping as is | ACS local mapping (after) | +|---|---|---:|---:|---:|---:| +| census_population | congressional_district | 436 | 9.8% | 0.1% | 1.7% | +| census_population | state | 51 | 1.1% | 0.0% | 4.4% | +| cms_medicaid | state | 51 | 1.1% | 2.3% | 2.0% | +| irs_soi | state | 3,819 | 85.6% | 95.7% | 90.2% | +| usda_snap | state | 102 | 2.3% | 1.8% | 1.7% | + +Concept groups: + +- After: 4,074 groups. The 436 district population rows form one group per + state (51), of which 7 are single districts (AK, DE, DC, ND, SD, VT, WY). +- As is: 4,459 groups. Every row is its own group, because the national key + reads `ledger_geography_level`, which ladder rows lack. + +Weights after the change run from 0.026 to 20.1, with mean 1. + +### `--soi-mode state_cd` on the pinned feed: 23,866 training targets + +Inputs: + +- the admin surface compiled by the tool's own `state_admin_surface` from the + feed `chronicle_feed.json` pins (sha256 `b8543739…`); +- the 09-23 release's 487 ladder population specs; +- the default 10% district holdout (118 of 989 units, 2,638 targets held out). + +| Family | Level | Targets | Equal (before) | National mapping as is | ACS local mapping (after) | +|---|---|---:|---:|---:|---:| +| census_population | congressional_district | 436 | 1.8% | 0.0% | 1.4% | +| census_population | state | 51 | 0.2% | 0.0% | 3.6% | +| cms_medicaid | state | 51 | 0.2% | 0.8% | 1.6% | +| irs_soi | congressional_district | 19,105 | 80.1% | 64.2% | 16.7% | +| irs_soi | state | 4,121 | 17.3% | 34.4% | 75.4% | +| usda_snap | state | 102 | 0.4% | 0.6% | 1.3% | + +Concept groups: + +- After: 1,958 district groups (1,907 `state_cd` blocks plus 51 state + population groups) out of 19,541 district rows. +- As is: 19,541 groups, every district row on its own. + +Weights after the change run from 0.0025 to 83.4. + +### What the formula does and does not do + +- **It does not shrink SOI's share.** SOI's share goes from 85.6% to 90.2% on + the default surface. The formula balances counts against amounts and large + values against small ones; it does not balance families. Route A, the + certified national release, shows the same: SOI is 74.0% of its targets and + 74.2% of its loss weight. The lever for family balance is + `--target-family-loss-multiplier` (for example `usda_snap=4`), which this + change adds to the ACS local tool. No default multiplier is set. +- **On `state_cd` it moves the weight from district rows back to state rows.** + District rows go from 80.1% to 16.7% of the loss. All the districts of one + concept in one state now carry what their largest district would carry + alone, so district geography no longer multiplies a concept's weight. + +## A finding for the national release (not changed here) + +`national_cd_groups.py` compiles the national registry from the pinned feed +(`compile_us_fiscal_target_registry(age_targets=True)`) and keeps its 24,340 +district rows, all SOI. The national parser's default, `--target-surface +full`, calibrates those rows. Results are in `national_cd_groups.json`. + +| District rows | Concept groups | Singletons | Share of loss | +|---|---:|---:|---:| +| National mapping as is | 24,340 | 24,340 | 47.0% | +| With `ledger_fact_label` and `ledger_layout_groupby_value_label` also excluded | 3,125 | 665 | 11.6% | +| Equal weights, for reference | | | 74.1% | + +So under the national mapping the concept budget never engages for compiled +district rows: the compiler's per-district labels reach the key. The existing +national test of the budget uses fixture rows without those labels. + +- Route A calibrated `--target-surface national_state`, which has no district + rows, so it is unaffected. +- This branch moves the national mapping unchanged and tests it bit for bit + against the code before the move, so the fix is left to a follow-up. + +## Reproduce + +From the repository root, engine-free: + +```bash +uv run python experiments/us-acs-local-target-loss-weights-20261004/distribution.py +uv run python experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py +uv run python experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py +``` + +Inputs: + +- `distribution.py` reads the 09-23 release's rebuilt `target_registry.json` + (sha256 `fd25c600…`, from + `experiments/us-acs-local-l2-basis-20260928/registry.py`) and the pinned + feed. +- `route_a_fixture.py` reads Route A's `calibration_diagnostics.json` and + writes `packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json`. + It refuses to write unless the shared module reproduces every recorded + weight bit for bit, and the recorded loss basis hash (`206ada09…`) matches. + +Each script refuses an input whose sha256 differs from the one it expects. diff --git a/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.json b/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.json new file mode 100644 index 000000000..b610e7cb6 --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.json @@ -0,0 +1,97 @@ +{ + "district_families": { + "irs_soi": 24340 + }, + "district_groups": { + "labels_excluded": { + "max_size": 52, + "n_groups": 3125, + "n_singletons": 665 + }, + "national_as_is": { + "max_size": 1, + "n_groups": 24340, + "n_singletons": 24340 + } + }, + "district_loss_share": { + "equal": 0.7411467373100697, + "labels_excluded": 0.11632532848749198, + "national_as_is": 0.4703883014482875 + }, + "family_loss_share": { + "bea": { + "labels_excluded": 0.005400710201993984, + "n_targets": 2, + "national_as_is": 0.003189695156353016 + }, + "cbo": { + "labels_excluded": 0.011335762224249864, + "n_targets": 5, + "national_as_is": 0.00669497612497512 + }, + "census_population": { + "labels_excluded": 0.08007897271627366, + "n_targets": 936, + "national_as_is": 0.04867533567016965 + }, + "cms_aca": { + "labels_excluded": 0.00736047732959075, + "n_targets": 102, + "national_as_is": 0.004474004755029723 + }, + "cms_medicaid": { + "labels_excluded": 0.019466188511765726, + "n_targets": 134, + "national_as_is": 0.011832360329922678 + }, + "cms_medicare": { + "labels_excluded": 0.0004040302168963962, + "n_targets": 1, + "national_as_is": 0.0002386229176634738 + }, + "federal_reserve": { + "labels_excluded": 0.01407455951092147, + "n_targets": 1, + "national_as_is": 0.008312527912201885 + }, + "hhs_acf_liheap": { + "labels_excluded": 0.0003431823318491282, + "n_targets": 1, + "national_as_is": 0.0002086005181162017 + }, + "hhs_acf_tanf": { + "labels_excluded": 0.0004114628178889163, + "n_targets": 30, + "national_as_is": 0.00024301266095616013 + }, + "irs_soi": { + "labels_excluded": 0.8396295998536273, + "n_targets": 31359, + "national_as_is": 0.9033024163228177 + }, + "jct": { + "labels_excluded": 0.001609627518570458, + "n_targets": 11, + "national_as_is": 0.0009506566557896625 + }, + "ssa": { + "labels_excluded": 0.008415561991542597, + "n_targets": 111, + "national_as_is": 0.005021510984177707 + }, + "state_income_tax": { + "labels_excluded": 0.004336092788152227, + "n_targets": 44, + "national_as_is": 0.002560925072180314 + }, + "usda_snap": { + "labels_excluded": 0.007133771986677448, + "n_targets": 104, + "national_as_is": 0.004295354919646787 + } + }, + "feed_sha256": "b85437390021777e746f507c5890305496baf5fc7f2c78ba08ddb090f4839801", + "n_district_rows": 24340, + "n_targets": 32841 +} diff --git a/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py b/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py new file mode 100644 index 000000000..73ce73c3d --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py @@ -0,0 +1,132 @@ +"""Do the national mapping's district rows share concept budgets? + +Compiles the national fiscal registry from the feed ``chronicle_feed.json`` +pins (the compile the national release and the ACS local release both start +from), takes its congressional-district rows (which ``--target-surface full``, +the national parser's default, calibrates), and counts their concept groups +under the national mapping as is, and with the two per-row labels the ledger +compiler stamps (``ledger_fact_label``, ``ledger_layout_groupby_value_label``) +also excluded. It reports each family's share of the loss both ways. + +This is a finding for a follow-up, not a change: the national mapping is moved +unchanged in this branch. Route A calibrated ``--target-surface +national_state``, which has no district rows, so it is unaffected. + +Run from the repository root:: + + uv run python experiments/us-acs-local-target-loss-weights-20261004/national_cd_groups.py +""" + +from __future__ import annotations + +import collections +import hashlib +import json +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +FEED = Path( + "/Users/maxghenis/PolicyEngine/_buildh-runtime/inputs/consumer_facts_us_c5e5bf8.jsonl" +) +LABELS = frozenset({"ledger_fact_label", "ledger_layout_groupby_value_label"}) + + +def main() -> int: + from microcosm.build.ledger_artifact import load_ledger_consumer_artifact + from microcosm.build.us_runtime import ( + default_congressional_district_vintage_crosswalk_path, + load_congressional_district_vintage_crosswalk, + ) + from microcosm.build.us_runtime import target_loss_weights as lw + from microcosm.build.us_runtime.fiscal_targets import ( + compile_us_fiscal_target_registry, + ) + + pin = json.loads( + ( + REPO / "packages/microcosm-build/src/microcosm/build/us/chronicle_feed.json" + ).read_text() + ) + feed_sha = hashlib.sha256(FEED.read_bytes()).hexdigest() + if feed_sha != pin["facts_sha256"]: + raise SystemExit(f"{FEED} is {feed_sha}, not the pinned feed.") + crosswalk = load_congressional_district_vintage_crosswalk( + default_congressional_district_vintage_crosswalk_path() + ) + registry = compile_us_fiscal_target_registry( + load_ledger_consumer_artifact(str(FEED)).facts, + target_period=2024, + congressional_district_vintage_crosswalk=crosswalk, + age_targets=True, + ) + specs = registry.specs + + def without_labels(spec): + return lw._ledger_concept_budget_key( + spec, + lw.fiscal_target_value_basis(spec), + lw.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS | LABELS, + ) + + probe = lw.TargetLossRowMapping( + "national_without_district_labels_probe", + lw.fiscal_target_value_basis, + without_labels, + ) + national = lw.target_loss_weights(specs) + relabelled = lw.target_loss_weights(specs, row_mapping=probe) + district = np.asarray( + [ + spec.metadata.get("ledger_geography_level") == "congressional_district" + for spec in specs + ] + ) + families = np.asarray([spec.family for spec in specs]) + + def groups(key) -> dict[str, int]: + sizes = collections.Counter(key(spec) for spec in np.asarray(specs)[district]) + return { + "n_groups": len(sizes), + "n_singletons": sum(1 for size in sizes.values() if size == 1), + "max_size": max(sizes.values(), default=0), + } + + result = { + "feed_sha256": feed_sha, + "n_targets": len(specs), + "n_district_rows": int(district.sum()), + "district_families": dict(collections.Counter(families[district].tolist())), + "district_groups": { + "national_as_is": groups(lw.fiscal_target_concept_budget_key), + "labels_excluded": groups(without_labels), + }, + "district_loss_share": { + "equal": float(district.mean()), + "national_as_is": float(national[district].sum() / national.sum()), + "labels_excluded": float(relabelled[district].sum() / relabelled.sum()), + }, + "family_loss_share": { + family: { + "n_targets": int((families == family).sum()), + "national_as_is": float( + national[families == family].sum() / national.sum() + ), + "labels_excluded": float( + relabelled[families == family].sum() / relabelled.sum() + ), + } + for family in sorted(set(families.tolist())) + }, + } + (HERE / "national_cd_groups.json").write_text( + json.dumps(result, indent=1, sort_keys=True) + "\n" + ) + print(json.dumps(result, indent=1, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py index e8a647496..6c7ca6750 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py @@ -1205,8 +1205,11 @@ def test_resume_refuses_weights_without_the_run_identity_stamp( target_loss_cap=1.0, l2_lambda=0.0, seed=0, + target_family_loss_multipliers={}, + ) + settings = module._solver_settings( + args, module.release_target_loss_weights(args, [])[1] ) - settings = module._solver_settings(args) for saved in ( {"weights": np.ones(5), "epochs_done": 5, "staging_sha256": "s"}, { diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py new file mode 100644 index 000000000..b8bd5e8a5 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py @@ -0,0 +1,459 @@ +"""The ACS local calibrate stage weights its targets like the national release. + +A small checkpoint shaped like the real surface (state SOI amounts and counts, +SNAP, Medicaid enrollment, two ``state_cd`` district blocks, one held out, and +the Census ladder population rows) goes through the real ``do_calibrate``. +The tests check that the shared weights reach ``calibrate`` row-aligned with +the training targets and are not uniform, that every output records them, and +that a resume refuses weights calibrated under another weighting or none. +""" + +from __future__ import annotations + +import importlib.util +import json +from types import SimpleNamespace + +import numpy as np +import pytest +from scipy import sparse + +import microcosm.calibrate as calibrate_package +from microcosm.build.us_runtime import target_loss_weights as lw +from microcosm.calibrate import ( + CalibrationHierarchy, + HierarchyCategory, + HierarchyGeography, + HierarchyNode, + TargetRegistry, + TargetSpec, +) +from test_support.microcosm_build.us_batched_target_materialization import ( + _nested_frame, +) +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-build") +#: The fixture frame's households: (id, state, district), design weights 1-5. +_HOUSEHOLDS = ((1, "06", "0601"), (2, "36", "3601"), (3, "06", "0602")) +_HOUSEHOLDS += ((4, "24", "2401"), (5, "36", "3602")) + + +def _load_tool_module(): + path = _TEST_PATHS.repository / "tools" / "build_us_acs_local_release.py" + spec = importlib.util.spec_from_file_location( + "build_us_acs_local_release_loss_weights", path + ) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def _hierarchy(name: str, provider: str, level: str, geography_id: str): + return CalibrationHierarchy( + provider=HierarchyNode(id=provider, label=provider), + category=HierarchyCategory( + id=f"{provider}.fixture", label="Fixture", provider_id=provider + ), + geography=HierarchyGeography(id=geography_id, label=geography_id, level=level), + dimensions=(), + target=HierarchyNode(id=name, label=name), + ) + + +def _ledger(name, family, value, unit, state, **metadata) -> TargetSpec: + """A ledger-compiled state row with the metadata the mapping reads.""" + + return TargetSpec( + name=name, + entity="household", + measure=name, + value=value, + period=2024, + family=family, + source="fixture ledger feed", + metadata={ + "measure_mode": "sum" if unit == "usd" else "indicator_sum", + "source_measure_id": name.split(".")[-1], + "ledger_measure_unit": unit, + "ledger_geography_level": "state", + "state_fips": state, + "ledger_fact_label": f"{name} label", + **metadata, + }, + hierarchy=_hierarchy(name, family, "state", f"0400000US{state}"), + ) + + +def _district(name, value, unit, district, parent) -> TargetSpec: + """A ``state_cd`` district row: compiler labels and rebase stamps.""" + + spec = _ledger( + name, + "irs_soi", + value, + unit, + district[:2], + ledger_geography_level="congressional_district", + congressional_district_geoid=district, + ledger_geography_id=f"5001900US{district}", + ledger_layout_groupby_value_label=f"congressional district {district}", + ledger_fact_label=f"Congressional District {district} {name}", + state_cd_parent_target_name=parent, + state_cd_parent_basis="historic_table_2", + state_cd_cd_file_value=repr(value * 0.97), + state_cd_rebase_factor="1.03", + ) + return TargetSpec( + **{ + **spec.__dict__, + "hierarchy": _hierarchy( + name, "irs_soi", "congressional_district", f"5001900US{district}" + ), + } + ) + + +def _surface(module): + """Specs, roles and a CSR matrix shaped like the release's checkpoint. + + Each row's measure is the household's design-weighted share of a + geography, so every value sits within a factor of two of its design + estimate and the solve is well posed. + """ + + states = np.asarray([state for _id, state, _cd in _HOUSEHOLDS]) + districts = np.asarray([cd for _id, _state, cd in _HOUSEHOLDS]) + design = np.asarray([1.0, 2.0, 3.0, 4.0, 5.0]) + + def in_state(state): + return (states == state).astype(float) + + def in_district(district): + return (districts == district).astype(float) + + ca_agi = "irs_soi.ca.adjusted_gross_income_amount" + ny_returns = "irs_soi.ny.return_count" + rows = [ + (_ledger(ca_agi, "irs_soi", 6.0e5, "usd", "06"), in_state("06") * 1.5e5), + ( + _ledger("irs_soi.ca.return_count", "irs_soi", 8.0, "count", "06"), + in_state("06") * 2.0, + ), + ( + _ledger( + "irs_soi.ny.adjusted_gross_income_amount", "irs_soi", 9.0e5, "usd", "36" + ), + in_state("36") * 1.0e5, + ), + (_ledger(ny_returns, "irs_soi", 7.0, "count", "36"), in_state("36")), + ( + _ledger("usda_snap.ca.total_benefits", "usda_snap", 3.0e3, "usd", "06"), + in_state("06") * 700.0, + ), + ( + _ledger( + "usda_snap.ny.average_monthly_households", + "usda_snap", + 2.0, + "count", + "36", + ), + in_state("36") * 0.3, + ), + ( + _ledger( + "cms_medicaid.ca.total_medicaid_enrollment", + "cms_medicaid", + 3.0, + "count", + "06", + ), + in_state("06") * 0.8, + ), + ( + _district( + "irs_soi.cd0601.adjusted_gross_income_amount", + 1.4e5, + "usd", + "0601", + ca_agi, + ), + in_district("0601") * 1.5e5, + ), + ( + _district( + "irs_soi.cd0602.adjusted_gross_income_amount", + 4.6e5, + "usd", + "0602", + ca_agi, + ), + in_district("0602") * 1.5e5, + ), + ( + _district("irs_soi.cd3601.return_count", 2.5, "count", "3601", ny_returns), + in_district("3601"), + ), + ( + _district("irs_soi.cd3602.return_count", 4.5, "count", "3602", ny_returns), + in_district("3602"), + ), + ] + admin_specs = [spec for spec, _row in rows] + population_names = [ + "pop_state_06", "pop_state_24", "pop_state_36", + "pop_cd_0601", "pop_cd_0602", "pop_cd_2401", "pop_cd_3601", "pop_cd_3602", + ] # fmt: skip + population_rows = [ + in_state(name[-2:]) * 2.0 + if name.startswith("pop_state_") + else in_district(name[-4:]) * 2.0 + for name in population_names + ] + population_values = [float(row @ design) * 1.1 for row in population_rows] + population_specs = module.population_target_specs( + population_names, population_values + ) + specs = (*admin_specs, *population_specs) + matrix = sparse.csr_array( + np.vstack([row for _spec, row in rows] + population_rows).astype(np.float32) + ) + records = [module.cd_surface.target_record(spec) for spec in specs] + for record in records: + record["role"] = "train" + record["holdout_unit"] = None + if record["name"].startswith("irs_soi.cd36"): + # The NY return-count block is held out, as a hash draw would. + record["role"] = "holdout" + record["holdout_unit"] = "36|return" + record["cd_population"] = 2.0 + record["state_population"] = 4.0 + return TargetRegistry(specs, country="us"), records, matrix, design + + +def _args(tmp_path, **overrides): + values = dict( + checkpoint_dir=tmp_path, + out_h5=None, + resume=False, + epochs=6, + epoch_batch=3, + max_weight_ratio=5.0, + target_loss_cap=1.0, + l2_lambda=0.0, + seed=0, + families="snap,medicaid,soi", + geographies="state,cd", + target_family_loss_multipliers={}, + ) + values.update(overrides) + return SimpleNamespace(**values) + + +@pytest.fixture +def calibrate_run(monkeypatch, tmp_path): + """Run ``do_calibrate`` on the fixture; record every ``calibrate`` call.""" + + module = _load_tool_module() + registry, records, matrix, design = _surface(module) + frame = _nested_frame() + identity = {"staging_sha256": "s", "target_roles_sha256": "r"} + monkeypatch.setattr(module, "_verify_run_identity", lambda args: identity) + monkeypatch.setattr( + module, + "load_checkpoint_surface", + lambda *_a, **_k: (frame, design, registry, records, matrix), + ) + calls: list[dict] = [] + real_calibrate = calibrate_package.calibrate + + def spy(frame, targets, **kwargs): + calls.append( + { + "names": [target.name for target in targets], + "target_loss_weights": kwargs.get("target_loss_weights"), + } + ) + return real_calibrate(frame, targets, **kwargs) + + monkeypatch.setattr(calibrate_package, "calibrate", spy) + + def run(**overrides): + calls.clear() + module.do_calibrate(_args(tmp_path, **overrides)) + return calls + + return SimpleNamespace( + module=module, registry=registry, records=records, run=run, path=tmp_path + ) + + +def _train_specs(run): + held = {r["name"] for r in run.records if r["role"] == "holdout"} + return [spec for spec in run.registry.specs if spec.name not in held] + + +def test_the_shared_weights_reach_calibrate_on_every_batch(calibrate_run) -> None: + calls = calibrate_run.run() + train = _train_specs(calibrate_run) + expected = lw.us_acs_local_target_loss_weights(train) + assert len(calls) == 2 # 6 epochs in batches of 3 + for call in calls: + assert call["names"] == [spec.name for spec in train] + assert not any(name.startswith("irs_soi.cd36") for name in call["names"]) + assert np.array_equal(call["target_loss_weights"], expected) + # Mixed rows get mixed weights: not the equal weights of before. + assert len(np.unique(expected)) > 5 + assert expected.max() / expected.min() > 2.0 + # The surface's bases split the weight evenly; the CA district AGI block + # shares one budget. + bases = [lw.acs_local_target_value_basis(spec) for spec in train] + count = sum(w for w, b in zip(expected, bases, strict=True) if b == "count") + assert count == pytest.approx(len(train) / 2, rel=1e-12) + + +def test_every_calibrate_output_records_the_weights(calibrate_run) -> None: + calibrate_run.run() + train = _train_specs(calibrate_run) + weights = lw.us_acs_local_target_loss_weights(train) + digest = lw.target_loss_weights_sha256( + [lw.target_row_name(spec) for spec in train], weights + ) + summary = json.loads((calibrate_run.path / "calibration_summary.json").read_text()) + stamp = { + "weighting": lw.US_FISCAL_TARGET_LOSS_WEIGHTING, + "row_mapping": "us_acs_local.v1", + "family_multipliers": {}, + "n_targets": len(train), + "weights_sha256": digest, + } + assert summary["solver_settings"]["target_loss"] == stamp + recorded = summary["target_loss"] + assert {key: recorded[key] for key in stamp} == stamp + distribution = recorded["distribution"] + assert distribution["row_mapping"] == "us_acs_local.v1" + assert distribution["by_basis"]["count"]["loss_share"] == pytest.approx(0.5) + # Trained district groups: the CA AGI block (the NY block is held out) + # and one pop_cd group per state, MD's a single at-large district. + assert distribution["concept_groups"] == { + "n_groups": 14, + "n_district_rows": 7, + "n_district_groups": 4, + "n_singleton_district_groups": 1, + "max_district_group_size": 2, + } + families = {cell["family"] for cell in distribution["by_family_level_basis"]} + assert families == {"irs_soi", "usda_snap", "cms_medicaid", "census_population"} + + saved = np.load(calibrate_run.path / "weights_latest.npz") + assert json.loads(str(saved["solver_settings"]))["target_loss"] == stamp + + assert summary["calibration_diagnostics"]["status"] == "available" + diagnostics = json.loads( + (calibrate_run.path / "calibration_diagnostics.json").read_text() + ) + build = diagnostics["build"] + assert build["target_loss_weighting"] == lw.US_FISCAL_TARGET_LOSS_WEIGHTING + assert build["target_loss_family_multipliers"] is None + assert build["target_loss_cap"] == 1.0 + assert build["target_loss_row_mapping"] == "us_acs_local.v1" + assert build["target_loss_weights_sha256"] == digest + assert diagnostics["options"]["target_loss_weights"]["kind"] == "provided" + by_name = { + target["target_name"]: target["target_loss_weight"] + for target in diagnostics["targets"] + } + assert [by_name[spec.name] for spec in train] == pytest.approx( + weights.tolist(), rel=1e-15 + ) + + +def test_a_family_multiplier_reaches_calibrate_and_the_stamp(calibrate_run) -> None: + calls = calibrate_run.run(target_family_loss_multipliers={"usda_snap": 4.0}) + train = _train_specs(calibrate_run) + base = lw.us_acs_local_target_loss_weights(train) + boosted = calls[0]["target_loss_weights"] + snap = np.asarray([spec.family == "usda_snap" for spec in train]) + ratio = boosted / base + assert ratio[snap] == pytest.approx(4.0 * ratio[~snap][0], rel=1e-12) + summary = json.loads((calibrate_run.path / "calibration_summary.json").read_text()) + assert summary["target_loss"]["family_multipliers"] == {"usda_snap": 4.0} + with pytest.raises(SystemExit, match="'ssa' matches no compiled target"): + calibrate_run.run(target_family_loss_multipliers={"ssa": 2.0}) + + +def test_resume_refuses_weights_from_another_loss_weighting(calibrate_run) -> None: + calibrate_run.run() + npz = calibrate_run.path / "weights_latest.npz" + saved = dict(np.load(npz)) + settings = json.loads(str(saved["solver_settings"])) + # The same run resumes (here: nothing left to do). + calibrate_run.run(resume=True) + # A run from before the weights: its settings carry no target_loss. + unweighted = {key: value for key, value in settings.items() if key != "target_loss"} + for stale in ( + unweighted, + {**settings, "target_loss": {**settings["target_loss"], "weights_sha256": "0"}}, + ): + np.savez( + npz, + **{**saved, "solver_settings": np.str_(json.dumps(stale, sort_keys=True))}, + ) + with pytest.raises(SystemExit, match="different solver settings"): + calibrate_run.run(resume=True) + # Other multipliers are other weights. + np.savez(npz, **saved) + with pytest.raises(SystemExit, match="different solver settings"): + calibrate_run.run( + resume=True, target_family_loss_multipliers={"cms_medicaid": 2.0} + ) + + +def test_the_cli_parses_family_multipliers_like_the_national_release( + tmp_path, capsys +) -> None: + module = _load_tool_module() + base = [ + "--stage", "calibrate", + "--staging-h5", str(tmp_path / "s.h5"), + "--checkpoint-dir", str(tmp_path), + "--out-h5", str(tmp_path / "out.h5"), + ] # fmt: skip + assert module._parse_args(base).target_family_loss_multipliers == {} + args = module._parse_args( + [ + *base, + "--target-family-loss-multiplier", + "usda_snap=8", + "--target-family-loss-multiplier", + "cms_medicaid=0.5", + ] + ) + assert args.target_family_loss_multipliers == { + "usda_snap": 8.0, + "cms_medicaid": 0.5, + } + for bad in (["usda_snap"], ["usda_snap=0"], ["a=2", "a=3"]): + argv = [*base] + for entry in bad: + argv += ["--target-family-loss-multiplier", entry] + with pytest.raises(SystemExit) as raised: + module._parse_args(argv) + assert raised.value.code == 2 + assert "--target-family-loss-multiplier" in capsys.readouterr().err + + +def test_a_misaligned_target_set_is_refused(calibrate_run, monkeypatch) -> None: + module = calibrate_run.module + real = module.cd_surface.calibration_target_set + + def reversed_set(*args, **kwargs): + from microcosm.calibrate.target import TargetSet + + return TargetSet(list(real(*args, **kwargs))[::-1]) + + monkeypatch.setattr(module.cd_surface, "calibration_target_set", reversed_set) + with pytest.raises(SystemExit, match="not row-aligned with the training specs"): + calibrate_run.run() From 2d56c32b99455ab00ffef18b653cddd1bfaeb8ed Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 15:11:08 -0400 Subject: [PATCH 4/7] Classify target_loss_weights.py as a reviewed non-registry US runtime module Co-Authored-By: Claude Opus 5.5 --- .../tests/engine_free/us/test_us_spine_blindness.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py b/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py index dd04866be..1449c2e27 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py @@ -338,6 +338,9 @@ "take_up.py", "take_up_contract.py", "target_aging.py", + # Calibration loss weights computed from target specs alone; reads no + # frame and treats no population. + "target_loss_weights.py", # Data-only final-owner matrix; provenance owner above. "us_late_overlap_ownership.py", # Data-only late input/output registry; provenance owner above. From f5f380677971e421a039b70b4706ba5d186f12a6 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 19:06:03 -0400 Subject: [PATCH 5/7] Address the #1104 review: full-mode basis override, district grouping guard, stricter pure-move tests - Map tax_filer_individual_count (people; ledger unit count) to the count basis explicitly, so --soi-mode full calibrates; refuse any other unit/basis disagreement. - Refuse district rows of one concept in one state that land in more than one concept group in any SOI mode, not only state_cd blocks. - Drop the multiplier check the shared core added, so the move is pure for every input; refuse multipliers with no training targets in the ACS caller. - Finalize records a weighted solve's ESS instead of claiming the reviewed equal-weight entry. - Tests: the pure-move strategy now forms multi-row district groups (66% of draws), a fixed grouped differential, unchecked multipliers as before, the national CLI parse, the override, the guard, equal_share, the diagnostics distribution, the ESS entry; the package-manifest test moves beside its helpers. - Evidence: an every-SOI-mode receipt, the clean-tree flag, the Route A diagnostics sha pin, the 09-23 registry receipt, and the district population cost from microcosm#1105; corrected wording and figures. Co-Authored-By: Claude Opus 5.5 --- ...s-acs-local-target-loss-weights.changed.md | 2 +- .../README.md | 111 +++++-- .../distribution.py | 69 ++++- .../registry_20260923.receipt.json | 59 ++++ .../route_a_fixture.py | 3 + .../build/us_runtime/target_loss_weights.py | 141 +++++++-- .../us/test_us_acs_local_release_tool.py | 37 +++ .../test_us_acs_local_target_loss_weights.py | 93 +++--- .../us/test_us_target_loss_weights.py | 276 ++++++++++++++++-- tools/build_us_acs_local_release.py | 28 +- 10 files changed, 667 insertions(+), 152 deletions(-) create mode 100644 experiments/us-acs-local-target-loss-weights-20261004/registry_20260923.receipt.json diff --git a/changelog.d/us-acs-local-target-loss-weights.changed.md b/changelog.d/us-acs-local-target-loss-weights.changed.md index 20d7e1a2f..15337b3b7 100644 --- a/changelog.d/us-acs-local-target-loss-weights.changed.md +++ b/changelog.d/us-acs-local-target-loss-weights.changed.md @@ -1 +1 @@ -The ACS local-area release's calibrate stage weights each training target's loss with the national release's formula instead of equally. Both releases now call one implementation, `microcosm.build.us_runtime.target_loss_weights`; the national weights are unchanged bit for bit (Route A's 5,694 recorded weights are a golden test). An explicit ACS local row mapping files ladder population rows as counts and gives each state's `pop_cd_*` rows, and each `state_cd` reconciliation block, one concept budget. On the 09-23 release surface SOI's share of the loss moves from 85.6% to 90.2%, Census population from 10.9% to 6.1%, Medicaid from 1.1% to 2.0% and SNAP from 2.3% to 1.7%. `--target-family-loss-multiplier FAMILY=MULTIPLIER` is new. The weights' digest joins the solver settings, so `--resume` refuses weights calibrated before this change. Future builds only; no release is rebuilt. +The ACS local-area release's calibrate stage weights each training target's loss with the national release's formula instead of equally. Both releases now call one implementation, `microcosm.build.us_runtime.target_loss_weights`; the national weights are unchanged bit for bit (Route A's 5,694 recorded weights are a golden test). An explicit ACS local row mapping files ladder population rows as counts, gives each state's `pop_cd_*` rows and each district SOI concept one concept budget, and refuses rows it cannot classify. On the 09-23 release surface SOI's share of the loss moves from 85.6% to 90.2%, Census population from 10.9% to 6.1%, Medicaid from 1.1% to 2.0% and SNAP from 2.3% to 1.7%. Without a multiplier this costs district population fit (microcosm#1105: 11 of 436 districts beyond 10% at the release settings); `--target-family-loss-multiplier FAMILY=MULTIPLIER` is new, and the recipe and manifests record it. The weights' digest joins the solver settings, so `--resume` refuses weights calibrated before this change, and finalize no longer marks a weighted solve's concentration as the reviewed one. Future builds only; no release is rebuilt. diff --git a/experiments/us-acs-local-target-loss-weights-20261004/README.md b/experiments/us-acs-local-target-loss-weights-20261004/README.md index cd23feea2..841649b2d 100644 --- a/experiments/us-acs-local-target-loss-weights-20261004/README.md +++ b/experiments/us-acs-local-target-loss-weights-20261004/README.md @@ -24,28 +24,36 @@ The formula (module docstring of `target_loss_weights.py`): 5. Mean 1, then optional family multipliers (`--target-family-loss-multiplier`), then mean 1 again. -The ACS local mapping classifies every row and refuses a row it cannot -classify: - -- **Ledger rows** (SNAP, Medicaid enrollment, SOI) take the national basis - rule. That rule must agree with the row's `ledger_measure_unit` (`count` or - `usd`), and the row must carry `measure_mode` and `source_measure_id`. - - On both surfaces below, the national rule and the unit agree on every row. +The ACS local mapping accepts two kinds of row and refuses any other: + +- **Ledger rows** of any family (on these surfaces SNAP, Medicaid enrollment + and SOI) take the national basis rule. The row must carry `measure_mode` and + `source_measure_id`, and the rule must agree with its `ledger_measure_unit` + (`count` or `usd`). + - The one reviewed exception is `tax_filer_individual_count`, a count of + people. The national rule files it as an amount, and + `ACS_LOCAL_LEDGER_BASIS_OVERRIDES` maps it to a count. Only district rows + carry it, on the `full` and `totals` surfaces. + - Any other disagreement is refused. - **Ladder population rows** (`pop_state_SS`, `pop_cd_SSDD`) carry only - `geography_level`, so the national rule alone files them as amounts. + `geography_level`, so the national rule alone would file them as amounts. - The mapping files them as counts, which is how the national release's Census population targets (`measure_mode=indicator_sum`) are filed. - Each state's `pop_cd_*` rows share one concept budget. -- **District SOI rows** take the national district key without three keys - that name the row's district rather than its concept: - - `ledger_fact_label` and `ledger_layout_groupby_value_label`, which the - ledger compiler stamps on every row, for example "WV congressional - district 1"; - - `state_cd_cd_file_value`, the district file's own value, which the - `state_cd` rebase stamps. - Each `state_cd` reconciliation block must then be exactly one group. On the - pinned feed that holds for all 1,907 trained blocks. +District SOI rows take the national district key without three keys that name +the row's district rather than its concept: + +- `ledger_fact_label` and `ledger_layout_groupby_value_label`, which the + ledger compiler stamps on every row, for example "WV congressional + district 1"; +- `state_cd_cd_file_value`, the district file's own value, which the + `state_cd` rebase stamps. + +A guard (`validate_acs_local_concept_groups`) refuses a surface whose +district rows of one concept in one state land in more than one group, in any +SOI mode. It also refuses a `state_cd` reconciliation block that is not +exactly one group. ## Results @@ -67,6 +75,8 @@ Concept groups: - After: 4,074 groups. The 436 district population rows form one group per state (51), of which 7 are single districts (AK, DE, DC, ND, SD, VT, WY). + California's 52 districts carry 1.51 of weight between them; an at-large + district carries 1.30-1.70. - As is: 4,459 groups. Every row is its own group, because the national key reads `ledger_geography_level`, which ladder rows lack. @@ -94,24 +104,58 @@ Concept groups: - After: 1,958 district groups (1,907 `state_cd` blocks plus 51 state population groups) out of 19,541 district rows. -- As is: 19,541 groups, every district row on its own. +- As is: 19,541 groups, every district row on its own. That is the 19,105 + trained `state_cd` SOI rows, split by the compiler's labels and the rebase + stamp, plus the 436 ladder rows. Weights after the change run from 0.0025 to 83.4. +### Every SOI mode classifies + +`results.json` `soi_modes` compiles each `--soi-mode` from the pinned feed, +with the same population rows and before any holdout: + +- the ACS local mapping classifies every row in `state`, `totals`, `full` and + `state_cd`; +- `full` is the only surface with `tax_filer_individual_count` rows (445), + which take the override; +- a per-district metadata key injected into every district row is refused on + every surface that has district ledger rows (`totals`, `full`, `state_cd`). + ### What the formula does and does not do -- **It does not shrink SOI's share.** SOI's share goes from 85.6% to 90.2% on - the default surface. The formula balances counts against amounts and large - values against small ones; it does not balance families. Route A, the - certified national release, shows the same: SOI is 74.0% of its targets and - 74.2% of its loss weight. The lever for family balance is - `--target-family-loss-multiplier` (for example `usda_snap=4`), which this - change adds to the ACS local tool. No default multiplier is set. +- **It balances counts against amounts, and large values against small ones, + but not families.** On the default surface SOI's share rises from 85.6% to + 90.2%. On `state_cd` it falls from 97.3% to 92.1%, because district rows + stop multiplying their concept's weight. Route A, the certified national + release, shows no family balancing either: SOI is 74.0% of its targets and + 74.1% of its loss weight. The family lever is + `--target-family-loss-multiplier`, which this change adds to the ACS local + tool. No default multiplier is set. - **On `state_cd` it moves the weight from district rows back to state rows.** District rows go from 80.1% to 16.7% of the loss. All the districts of one concept in one state now carry what their largest district would carry alone, so district geography no longer multiplies a concept's weight. +### Cost: district population fit without a multiplier + +The weighted λ frontier (microcosm#1105, `experiments/us-acs-local-l2-basis-20260928/README.md`, +"On the weighted loss") solved the 09-23 checkpoint at full scale on these +weights. Its runs found this cost: + +- **Equal weights:** every trained district population is within 0.7% at + λ 0. +- **These weights, no multiplier:** 11 of 436 districts miss by more than 10% + at λ 0 (worst TX-14 −33%, CA-29 −32%). The chi-square penalty multiplies + the misses: 73 districts at λ 0.03, 135 at λ 0.1. +- **With `--target-family-loss-multiplier census_population=8`:** district + population returns to 9.5% of the loss and state population rises to + 24.7%, which I recomputed from these weights. No district misses by more + than 10% at λ 0, and 3 do at λ 0.03 (worst 15%). + +Which multiplier and λ the next build uses is decision d797. This change sets +no default multiplier. + ## A finding for the national release (not changed here) `national_cd_groups.py` compiles the national registry from the pinned feed @@ -146,12 +190,17 @@ uv run python experiments/us-acs-local-target-loss-weights-20261004/route_a_fixt Inputs: -- `distribution.py` reads the 09-23 release's rebuilt `target_registry.json` - (sha256 `fd25c600…`, from - `experiments/us-acs-local-l2-basis-20260928/registry.py`) and the pinned - feed. -- `route_a_fixture.py` reads Route A's `calibration_diagnostics.json` and - writes `packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json`. +- **`distribution.py`** reads two inputs, and records the commit it ran at + and whether the tree was clean: + - the 09-23 release's rebuilt `target_registry.json` (sha256 + `fd25c600…`), built by `experiments/us-acs-local-l2-basis-20260928/registry.py` + on branch `acs-local-weighted-lambda-frontier` (microcosm#1105, commit + 430491e2c). Its receipt is copied here as `registry_20260923.receipt.json`: + all 4,459 names and values equal the release checkpoint's; + - the pinned feed. +- **`route_a_fixture.py`** reads Route A's `calibration_diagnostics.json` + (sha256 `64b55a02…`) and writes + `packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json`. It refuses to write unless the shared module reproduces every recorded weight bit for bit, and the recorded loss basis hash (`206ada09…`) matches. diff --git a/experiments/us-acs-local-target-loss-weights-20261004/distribution.py b/experiments/us-acs-local-target-loss-weights-20261004/distribution.py index 71561697a..e36172534 100644 --- a/experiments/us-acs-local-target-loss-weights-20261004/distribution.py +++ b/experiments/us-acs-local-target-loss-weights-20261004/distribution.py @@ -14,13 +14,22 @@ 1. ``release_20260923``: the 4,459 targets of the published 09-23 release (``--soi-mode state``, the default), every one trained. Its specs are the ``target_registry.json`` rebuilt from that release's feed by - ``experiments/us-acs-local-l2-basis-20260928/registry.py`` (receipt: all - 4,459 names and values equal the checkpoint's). + ``experiments/us-acs-local-l2-basis-20260928/registry.py`` on branch + ``acs-local-weighted-lambda-frontier`` (microcosm#1105, commit 430491e2c). + Its receipt, copied here as ``registry_20260923.receipt.json``, records + that all 4,459 names and values equal the checkpoint's. 2. ``state_cd_pinned_feed``: ``--soi-mode state_cd`` compiled by the tool's own ``state_admin_surface`` from the feed ``chronicle_feed.json`` pins, plus the 09-23 release's 487 ladder population specs, with the default 10% district holdout assigned by the tool's ``assign_target_roles``; only the training rows are weighted, as in the calibrate stage. +3. ``soi_modes``: a classification receipt for every ``--soi-mode`` + (``state``, ``totals``, ``full``, ``state_cd``) compiled from the pinned + feed with the same population specs, before any holdout. For each it + records that the ACS local mapping classifies every row, how many rows + take a basis override, the concept groups, and that a per-district key + injected into every district row is refused wherever district ledger + rows exist. Engine-free. Run from the repository root:: @@ -178,6 +187,49 @@ def state_cd_surface(tool, feed: Path, population_specs, fraction: float): } +def mode_receipt(tool, lw, feed: Path, mode: str, population_specs) -> dict: + """Does the ACS local mapping classify and group one SOI mode's surface?""" + + import dataclasses + + surface = tool.state_admin_surface(feed, FAMILIES, soi_mode=mode) + specs = [*surface.registry.specs, *population_specs] + weights = lw.us_acs_local_target_loss_weights(specs) + distribution = lw.target_loss_weight_distribution( + specs, weights, row_mapping=lw.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING + ) + district_ledger_rows = sum( + 1 + for spec in specs + if spec.metadata.get("ledger_geography_level") == "congressional_district" + ) + leaky = [ + dataclasses.replace( + spec, metadata={**spec.metadata, "district_display_name": spec.name} + ) + if spec.metadata.get("ledger_geography_level") == "congressional_district" + else spec + for spec in specs + ] + try: + lw.us_acs_local_target_loss_weights(leaky) + injected_key_refused = False + except ValueError: + injected_key_refused = True + return { + "n_specs": len(specs), + "n_district_ledger_rows": district_ledger_rows, + "n_basis_overrides": sum( + 1 + for spec in specs + if spec.metadata.get("source_measure_id") + in lw.ACS_LOCAL_LEDGER_BASIS_OVERRIDES + ), + "concept_groups": distribution["concept_groups"], + "injected_per_district_key_refused": injected_key_refused, + } + + def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) parser.add_argument("--registry", type=Path, default=DEFAULT_REGISTRY) @@ -195,6 +247,12 @@ def main(argv: list[str] | None = None) -> int: f"{args.registry} is {registry_sha}, not the rebuilt 09-23 registry." ) release_specs = TargetRegistry.from_json(args.registry).specs + dirty = subprocess.run( + ["git", "-C", str(REPO), "status", "--porcelain", "--untracked-files=no"], + capture_output=True, + text=True, + check=True, + ).stdout.strip() results: dict = { "head": subprocess.run( ["git", "-C", str(REPO), "rev-parse", "HEAD"], @@ -202,6 +260,7 @@ def main(argv: list[str] | None = None) -> int: text=True, check=True, ).stdout.strip(), + "tree_clean": not dirty, "release_20260923": { "registry": {"path": str(args.registry), "sha256": registry_sha}, "n_train": len(release_specs), @@ -251,6 +310,12 @@ def main(argv: list[str] | None = None) -> int: "| national-as-is:", result["national_as_is"]["concept_groups"], ) + results["soi_modes"] = { + mode: mode_receipt(tool, lw, args.feed, mode, population) + for mode in tool.SOI_MODES + } + for mode, receipt in results["soi_modes"].items(): + print(mode, receipt) results["wall_seconds"] = round(time.time() - started, 1) (HERE / "results.json").write_text(json.dumps(results, indent=1, sort_keys=True)) print(f"\nwrote {HERE / 'results.json'} in {results['wall_seconds']} s") diff --git a/experiments/us-acs-local-target-loss-weights-20261004/registry_20260923.receipt.json b/experiments/us-acs-local-target-loss-weights-20261004/registry_20260923.receipt.json new file mode 100644 index 000000000..b70d9b298 --- /dev/null +++ b/experiments/us-acs-local-target-loss-weights-20261004/registry_20260923.receipt.json @@ -0,0 +1,59 @@ +{ + "ok": true, + "created_utc": "2026-10-02T11:36:53Z", + "purpose": "TargetSpecs (with the metadata target-loss weighting reads) for the checkpoint's 4,459 targets, recompiled from the 09-23 release's feed; spec i is CSR row i.", + "feed": { + "path": "/Users/maxghenis/PolicyEngine/_buildh-runtime/inputs/chronicle_us_b571381/artifact/consumer_facts.jsonl", + "sha256": "4d1dba8c1b6274877bf184fa6de5d99b13fc61f34709ccab1487db2b5c64a79f" + }, + "families": [ + "snap", + "medicaid", + "soi" + ], + "soi_mode": "state", + "release_materialize_head": "767312d60430893617af57034f62edfb4920dca3", + "compile_head": "b115745d1dc79ed3548759540c7f09111746fcde", + "compile_tree_clean": true, + "ri_substitutions": [ + "{'state_fips': '44', 'substituted_for_source_record_id': 'cms_medicaid.month2024_12.state_enrollment.ri.total_medicaid_enrollment', 'substituted_for_source_period': '2024-12', 'substitute_source_record_id': 'cms_medicaid.month2024_11.state_enrollment.ri.total_medicaid_enrollment', 'substitute_source_period': '2024-11', 'substitute_value': 273400.0, 'reason': 'CMS reports Rhode Island December 2024 Total Medicaid Enrollment as 0 with the footnote \"Unable to Provide Data due to System Limitations\" (pi-dataset-april-2026-release.csv, both the preliminary P/N and final U/Y rows); RI did not report the month and the April 2026 release still carries the footnoted 0. Substituted RI\\'s nearest prior reported month, November 2024 (Total Medicaid Enrollment 273,400).', 'issue': 'microcosm#386', 'applied': True, 'stale': False}" + ], + "soi_receipt": { + "soi_mode": "state", + "counts": { + "total": 3819 + } + }, + "checkpoint_manifest_sha256": "8a75d1235efa336df1ede147a18cc72f60e8b2fd0cbd7b8574906c4834d5b950", + "targets_meta_sha256": "2a69033e1cde2d08d8909c6863a94a41dd79badcda76b48e96f43f137c44ba00", + "checks": { + "admin_specs_compiled": 3972, + "admin_specs_in_checkpoint": 3972, + "population_specs": 487, + "duplicated_names": [], + "missing_from_compile": [], + "n_missing_from_compile": 0, + "extra_in_compile": [], + "n_extra_in_compile": 0, + "values": { + "n_compared": 4459, + "n_unequal": 0, + "max_relative_difference": 0.0, + "unequal_examples": [] + }, + "field_mismatches": [], + "n_field_mismatches": 0 + }, + "wall_seconds": 179.3, + "registry": { + "path": "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint/target_registry.json", + "sha256": "fd25c6002cae0c991842c81399d43a3840f12cba0294021ca8ff6718e22a209f", + "n_specs": 4459, + "family_counts": { + "usda_snap": 102, + "cms_medicaid": 51, + "irs_soi": 3819, + "census_population": 487 + } + } +} diff --git a/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py b/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py index dac28a7e6..7a442848a 100644 --- a/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py +++ b/experiments/us-acs-local-target-loss-weights-20261004/route_a_fixture.py @@ -38,6 +38,7 @@ f"{RELEASE_ID}/releases/{RELEASE_ID}/calibration_diagnostics.json" ) BUILD_COMMIT = "4b57d15a287c4a1729bad362a2f6d57c4de9059e" +DIAGNOSTICS_SHA256 = "64b55a02aaf573964cffee44d4a86bd63f33dc30c7f5012e4345348eecc06f80" FIXTURE = ( REPO / "packages/microcosm-build/tests/fixtures/us_route_a_target_loss_weights.json" ) @@ -52,6 +53,8 @@ def main() -> int: from microcosm.calibrate._target_loss_attribution import target_loss_basis_hash raw = DIAGNOSTICS.read_bytes() + if hashlib.sha256(raw).hexdigest() != DIAGNOSTICS_SHA256: + raise SystemExit(f"{DIAGNOSTICS} is not Route A's diagnostics.") diagnostics = json.loads(raw) targets = diagnostics["targets"] levels = {target["metadata"].get("ledger_geography_level") for target in targets} diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py index 2065a2fd5..a59e8f84f 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py @@ -15,16 +15,17 @@ mean over the target's basis. 3. **Concept budget.** Targets that share a concept-budget key form a group, whose weights are rescaled to sum to the largest member's weight, keeping - their proportions. Both mappings make the congressional-district targets - of one concept in one state a group, so adding district geography cannot - multiply a concept's weight. Every other target is its own group. + their proportions. The aim is that the congressional-district targets of + one concept in one state form one group, so adding district geography + cannot multiply a concept's weight. Every other target is its own group. 4. **Basis budget.** Each basis present is rescaled to carry an equal share of the total weight (half each when both are present). 5. Normalize to mean 1. 6. **Family multipliers** (optional, ``FAMILY=MULTIPLIER``). Each named family's weights are multiplied, in sorted family order, and the result is renormalized to mean 1. A multiplier that names no target's family is an - error. + error. :func:`parse_target_family_loss_multipliers` admits only positive + finite multipliers. Row mappings: @@ -32,18 +33,26 @@ target metadata: ``measure_mode`` and ``source_measure_id`` for the basis, and ``ledger_geography_level``, ``state_fips``, the filter and the other semantic metadata for the concept key. It is the national release's - mapping, moved here unchanged. + mapping, moved here unchanged. Its district key does read two labels the + ledger compiler stamps on every row, ``ledger_fact_label`` and + ``ledger_layout_groupby_value_label``, which name the district, so each + compiled district row is its own group under it (measured in + ``experiments/us-acs-local-target-loss-weights-20261004/``). - :data:`US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING` classifies every row of the - ACS local-area surface explicitly and refuses any row it cannot classify. - Ledger-compiled rows (SNAP, Medicaid, SOI) take the national mapping, and - their basis must agree with their ``ledger_measure_unit``. The Census ladder + ACS local-area surface explicitly and refuses any row it cannot classify: + a row must be a ledger-compiled row (any family; on the surface, SNAP, + Medicaid and SOI) or a Census ladder population row. Ledger rows take the + national basis rule, which must agree with their ``ledger_measure_unit`` + unless :data:`ACS_LOCAL_LEDGER_BASIS_OVERRIDES` names the measure. The Census ladder population rows (``pop_state_*``, ``pop_cd_*``), which carry no ledger metadata, are counts, as the national release's Census population targets are. Each state's ``pop_cd_*`` rows share one concept budget. District SOI rows take the national district key without the metadata that names the district (:data:`ACS_LOCAL_PER_DISTRICT_METADATA`), so the districts of one - concept in one state share one budget; each ``state_cd`` reconciliation - block must be exactly one group. + concept in one state share one budget. :func:`validate_acs_local_concept_groups` + refuses a surface whose district rows of one concept in one state land in + more than one group, or whose ``state_cd`` reconciliation blocks are not + exactly one group each. """ from __future__ import annotations @@ -64,7 +73,9 @@ US_FISCAL_TARGET_VALUE_WEIGHT_POWER = 0.5 #: Metadata that identifies a target's place, record or reconciliation rather #: than its concept. The concept-budget key of a congressional-district target -#: ignores it, so the districts of one concept in one state share one key. +#: ignores it, so that the districts of one concept in one state share one +#: key. It does not list the compiler's per-row labels (see the module +#: docstring and :data:`ACS_LOCAL_PER_DISTRICT_METADATA`). US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS = frozenset( { "congressional_district_geoid", @@ -122,8 +133,11 @@ def target_loss_weights_from_rows( ) -> np.ndarray: """Steps 2-6 of the formula, over row-aligned per-target inputs. - Returns float64 weights, row-aligned with the inputs, positive and of - mean 1 whenever there is at least one row. + Returns float64 weights, row-aligned with the inputs. With at least one + row they are positive and of mean 1, given positive finite multipliers + whose products stay within float64 range (the CLI parser admits only + positive finite ones; this function, like the national helper it came + from, does not check them). """ n_rows = len(values) @@ -151,11 +165,6 @@ def target_loss_weights_from_rows( return weights family_array = np.asarray(list(families), dtype=object) for family, multiplier in sorted(family_multipliers.items()): - if not math.isfinite(multiplier) or multiplier <= 0.0: - raise ValueError( - f"Target family loss multiplier for {family!r} must be positive " - f"and finite, got {multiplier!r}." - ) mask = family_array == family if not mask.any(): raise ValueError( @@ -329,6 +338,18 @@ def _ledger_concept_budget_key( US_ACS_LOCAL_CONCEPT_METADATA_EXCLUSIONS = ( US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS | ACS_LOCAL_PER_DISTRICT_METADATA ) +#: Ledger measures whose national basis rule disagrees with their ledger unit, +#: and the basis the ACS local mapping gives them, with the reason. Any other +#: disagreement is refused. +ACS_LOCAL_LEDGER_BASIS_OVERRIDES: Mapping[str, tuple[str, str]] = { + "tax_filer_individual_count": ( + COUNT_BASIS, + "Individuals on returns (ledger unit count). The national rule files it " + "as an amount: its measure_mode is sum and its id has no 'return'. Only " + "district rows carry it (the compiler drops it at state and national " + "geography), on the --soi-mode full and totals surfaces.", + ), +} #: The ledger's unit for a measure, and the basis that unit implies. LEDGER_MEASURE_UNIT_BASIS: Mapping[str, str] = { "count": COUNT_BASIS, @@ -398,6 +419,14 @@ def acs_local_target_value_basis(spec) -> str: f"{spec.name}: a ledger row needs {missing} to take the national " "loss basis." ) + override = ACS_LOCAL_LEDGER_BASIS_OVERRIDES.get(metadata["source_measure_id"]) + if override is not None: + if override[0] != expected: + raise ValueError( + f"{spec.name}: the basis override {override[0]!r} disagrees " + f"with its ledger_measure_unit {unit!r}." + ) + return override[0] basis = fiscal_target_value_basis(spec) if basis != expected: raise ValueError( @@ -438,6 +467,49 @@ def acs_local_target_concept_budget_key(spec) -> tuple[object, ...]: ) +#: The metadata that tells two district SOI concepts of one state apart: the +#: measure, its source file, its AGI band, filing status, return universe and +#: domain, and every ledger filter. Rows equal on all of these (and on family, +#: entity, period, filter and state) are the districts of one concept. +DISTRICT_CONCEPT_IDENTITY_KEYS = ( + "source_measure_id", + "ledger_layout_record_set_spec_id", + "measure_mode", + "agi_lower_bound", + "agi_upper_bound", + "filing_status", + "taxable_only", + "itemized_only", + "ledger_domain", + "soi_return_universe", +) + + +def district_concept_identity(spec) -> tuple: + """What makes a district ledger row the concept it is, without the place. + + Independent of the concept key, so the validator can check the key + against it: every district row of one identity must share one key. + """ + + metadata = spec.metadata + return ( + spec.family, + spec.entity, + spec.period, + spec.filter or "", + str(metadata.get("state_fips")), + *(str(metadata.get(key, "")) for key in DISTRICT_CONCEPT_IDENTITY_KEYS), + tuple( + sorted( + (key, value) + for key, value in metadata.items() + if key.startswith("ledger_filter_") + ) + ), + ) + + def validate_acs_local_concept_groups( specs: Sequence[Any], bases: Sequence[str], @@ -445,17 +517,21 @@ def validate_acs_local_concept_groups( ) -> None: """Refuse an ACS local grouping that is not the promised one. - Each ``state_cd`` reconciliation block (the district rows sharing one - ``state_cd_parent_target_name``) must be exactly one concept group: a - per-district metadata key leaking into the key would split it into one - group per district, and two blocks sharing a key would pool budgets. - Each district group must lie in one state. + - The district ledger rows of one concept in one state + (:func:`district_concept_identity`) must share one concept group: a + per-district metadata key reaching the key would split them into one + group per district, whatever the SOI mode. + - Each ``state_cd`` reconciliation block (the district rows sharing one + ``state_cd_parent_target_name``) must be exactly one concept group, and + no group may hold two blocks. + - Each district group must lie in one state. """ del bases group_of_parent: dict[str, set[Hashable]] = {} parent_of_group: dict[Hashable, set[str]] = {} states_of_group: dict[Hashable, set[str]] = {} + groups_of_identity: dict[tuple, set[Hashable]] = {} for spec, key in zip(specs, concept_budget_keys, strict=True): population = acs_local_population_geography(spec) if population is not None: @@ -467,10 +543,23 @@ def validate_acs_local_concept_groups( if metadata.get("ledger_geography_level") != CONGRESSIONAL_DISTRICT: continue states_of_group.setdefault(key, set()).add(str(metadata.get("state_fips"))) + groups_of_identity.setdefault(district_concept_identity(spec), set()).add(key) parent = metadata.get("state_cd_parent_target_name") if parent: group_of_parent.setdefault(str(parent), set()).add(key) parent_of_group.setdefault(key, set()).add(str(parent)) + scattered = [ + (identity, keys) + for identity, keys in groups_of_identity.items() + if len(keys) > 1 + ] + if scattered: + identity, keys = scattered[0] + raise ValueError( + f"{len(scattered)} district concept(s) land in more than one concept " + f"group, e.g. {identity[:6]} in {len(keys)} groups; a per-district " + "metadata key is reaching the concept key." + ) split = {parent: keys for parent, keys in group_of_parent.items() if len(keys) > 1} if split: parent, keys = next(iter(sorted(split.items()))) @@ -566,6 +655,12 @@ def us_acs_local_target_loss_weights( ) -> np.ndarray: """The ACS local release's weights for its training target specs.""" + specs = tuple(specs) + if family_multipliers and not specs: + raise ValueError( + f"--target-family-loss-multiplier families {sorted(family_multipliers)} " + "match no compiled target: there are no training targets." + ) return target_loss_weights( specs, family_multipliers, diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py index 510b3d034..e4c841adb 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py @@ -1948,6 +1948,43 @@ def test_package_manifest_records_the_penalty_settings( assert recipe[recipe.index("--mass-parametrization") + 1] == "softmax" +def test_package_manifest_records_the_loss_weights(tmp_path: Path, monkeypatch) -> None: + """The package stage carries the loss-weight stamp and its multipliers.""" + + module = _load_tool_module() + args = _package_evidence_args( + module, + tmp_path, + monkeypatch, + hours_report={"passed": True, "failures": [], "detail": {}}, + ) + summary_path = args.checkpoint_dir / "calibration_summary.json" + summary = json.loads(summary_path.read_text()) + target_loss = { + "weighting": ( + "sqrt_value_concept_budget_weighted_mape_50_50_amount_count_" + "target_scale_cap_100pct" + ), + "row_mapping": "us_acs_local.v1", + "family_multipliers": {"usda_snap": 4.0}, + "n_targets": 3, + "weights_sha256": "0" * 64, + } + summary["target_loss"] = target_loss + summary_path.write_text(json.dumps(summary)) + + result = module.do_package(args) + + manifest = json.loads( + (Path(result["release_dir"]) / "build_manifest.json").read_text() + ) + assert manifest["calibration"]["target_loss"] == target_loss + recipe = shlex.split(manifest["refresh_recipe"]["release"]) + assert recipe[recipe.index("--target-family-loss-multiplier") + 1] == ( + "usda_snap=4.0" + ) + + def test_refresh_recipe_rebuilds_the_recorded_solve(tmp_path: Path) -> None: """The recipe names every penalty setting, so a default change cannot move it.""" diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py index 1f9e19b6f..a5e787031 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_target_loss_weights.py @@ -13,7 +13,6 @@ import importlib.util import json import shlex -from pathlib import Path from types import SimpleNamespace import numpy as np @@ -339,8 +338,18 @@ def test_every_calibrate_output_records_the_weights(calibrate_run) -> None: distribution = recorded["distribution"] assert distribution["row_mapping"] == "us_acs_local.v1" assert distribution["by_basis"]["count"]["loss_share"] == pytest.approx(0.5) + medicaid = next( + cell + for cell in distribution["by_family_level_basis"] + if cell["family"] == "cms_medicaid" + ) + assert medicaid["equal_share"] == pytest.approx(1 / len(train)) + assert medicaid["loss_share"] == pytest.approx( + weights[[s.family == "cms_medicaid" for s in train]].sum() / weights.sum() + ) # Trained district groups: the CA AGI block (the NY block is held out) - # and one pop_cd group per state, MD's a single at-large district. + # and one pop_cd group per state; the fixture's only MD district is a + # group of one. assert distribution["concept_groups"] == { "n_groups": 14, "n_district_rows": 7, @@ -364,6 +373,7 @@ def test_every_calibrate_output_records_the_weights(calibrate_run) -> None: assert build["target_loss_cap"] == 1.0 assert build["target_loss_row_mapping"] == "us_acs_local.v1" assert build["target_loss_weights_sha256"] == digest + assert build["target_loss_distribution"] == distribution assert diagnostics["options"]["target_loss_weights"]["kind"] == "provided" by_name = { target["target_name"]: target["target_loss_weight"] @@ -480,61 +490,30 @@ def test_the_refresh_recipe_rebuilds_the_recorded_multipliers() -> None: } -def _release_tool_tests(): - """The package-stage helpers of ``test_us_acs_local_release_tool.py``.""" +def test_a_weighted_solve_does_not_claim_the_reviewed_concentration() -> None: + """Finalize's ESS entry is reviewed only for the equal-weight solve.""" - spec = importlib.util.spec_from_file_location( - "acs_local_release_tool_tests", - _TEST_PATHS.tests / "engine_free" / "us" / "test_us_acs_local_release_tool.py", - ) - module = importlib.util.module_from_spec(spec) - assert spec.loader is not None - spec.loader.exec_module(module) - return module - - -def test_the_package_manifest_records_the_loss_weights(tmp_path, monkeypatch) -> None: - from microcosm.data import stored_inputs - - helpers = _release_tool_tests() - # The stored-input contract stand-in that file's autouse fixture installs. - monkeypatch.setattr( - stored_inputs, "installed_us_engine", lambda: helpers._PACKAGE_ENGINE - ) - monkeypatch.setattr( - stored_inputs, - "require_h5_stored_inputs", - lambda path, *, engine: { - "register_sha256": stored_inputs.register_sha256(), - "registered_non_variables": [], - }, - ) - module = helpers._load_tool_module() - args = helpers._package_evidence_args( - module, - tmp_path, - monkeypatch, - hours_report={"passed": True, "failures": [], "detail": {}}, - ) - summary_path = args.checkpoint_dir / "calibration_summary.json" - summary = json.loads(summary_path.read_text()) - target_loss = { - "weighting": lw.US_FISCAL_TARGET_LOSS_WEIGHTING, - "row_mapping": "us_acs_local.v1", - "family_multipliers": {"usda_snap": 4.0}, - "n_targets": 3, - "weights_sha256": "0" * 64, + module = _load_tool_module() + base = { + "effective_sample_size": 14_000, + "ess_fraction": 0.009, + "households": 1_588_854, + "max_weight_ratio": 5.0, + "l2_lambda": 0.0, + "mass_parametrization": "projection", } - summary["target_loss"] = target_loss - summary_path.write_text(json.dumps(summary)) - - result = module.do_package(args) - - manifest = json.loads( - (Path(result["release_dir"]) / "build_manifest.json").read_text() - ) - assert manifest["calibration"]["target_loss"] == target_loss - recipe = shlex.split(manifest["refresh_recipe"]["release"]) - assert recipe[recipe.index("--target-family-loss-multiplier") + 1] == ( - "usda_snap=4.0" + historical = module._ess_concentration_limitation(base) + assert historical["status"] == "reviewed_concentration" + weighted = module._ess_concentration_limitation( + { + **base, + "target_loss": { + "weighting": lw.US_FISCAL_TARGET_LOSS_WEIGHTING, + "row_mapping": "us_acs_local.v1", + "family_multipliers": {"usda_snap": 2.0}, + }, + } ) + assert weighted["status"] == "recorded_concentration" + assert "us_acs_local.v1" in weighted["reason"] + assert "usda_snap" in weighted["reason"] diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py b/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py index 6e582ff4d..b7054d5a9 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_target_loss_weights.py @@ -4,11 +4,13 @@ 1. The move is pure: the shared module returns, bit for bit, what the national release's helpers returned before it (a verbatim copy in - ``test_support``), on random registries with district and non-district - rows, and on Route A's 5,694 real targets, where it also reproduces the - weights and the loss basis hash the release recorded. -2. Weights are finite, positive and of mean 1. -3. Each value basis present carries an equal share of the total weight. + ``test_support``). That holds on random registries whose district rows + form multi-row concept groups, on a fixed grouped registry, for invalid + multipliers, and on Route A's 5,694 real targets. On Route A it also + reproduces the weights and the loss basis hash the release recorded. +2. Weights are finite, positive and of mean 1 (multipliers in [0.05, 20]). +3. Without family multipliers, each value basis present carries an equal + share of the total weight. 4. The rows of one concept group sum to the group's largest value weight, keep their proportions, and every row of a basis is then scaled alike. 5. A family multiplier scales its family relative to every other row by @@ -30,7 +32,7 @@ import numpy as np import pytest -from hypothesis import HealthCheck, given, settings +from hypothesis import HealthCheck, event, given, settings from hypothesis import strategies as st from microcosm.build.us_runtime import target_loss_weights as lw @@ -74,47 +76,79 @@ def _registries(draw, max_rows: int = 30) -> TargetRegistry: """Mixed registries: national-style rows and district rows that group. - District rows draw their concept metadata from small pools, so several - land in one group; per-row metadata (district ids, ledger ids and the - district label the compiler stamps) varies by row. + District rows each pick one of a few drawn concepts and one of two + states, so several land in one national concept group. Per-row metadata + the national key excludes (district ids, ledger ids) varies by row. + ``ledger_fact_label``, which the national key reads, is usually absent + and otherwise per-row, so some draws split what would be one group. """ + concepts = draw( + st.lists( + st.fixed_dictionaries( + { + "entity": st.sampled_from(["household", "tax_unit"]), + "period": st.sampled_from([2024, 2025]), + "family": st.sampled_from(_FAMILIES), + "filter": st.sampled_from([None, "is_tax_filer"]), + "mode": st.sampled_from(_MEASURE_MODES), + "measure": st.sampled_from(_SOURCE_MEASURES), + "filing_status": st.sampled_from([None, "All", "Single"]), + } + ), + min_size=1, + max_size=3, + ) + ) specs = [] for index in range(draw(st.integers(1, max_rows))): metadata: dict[str, str] = {} - mode = draw(st.sampled_from(_MEASURE_MODES)) - if mode is not None: - metadata["measure_mode"] = mode - measure = draw(st.sampled_from(_SOURCE_MEASURES)) - if measure is not None: - metadata["source_measure_id"] = measure + value = draw(_VALUES) if draw(st.booleans()): - state = draw(st.sampled_from(_STATES)) + concept = draw(st.sampled_from(concepts)) + state = draw(st.sampled_from(_STATES[:2])) district = f"{state}{index:02d}" + for key, field in ( + ("measure_mode", "mode"), + ("source_measure_id", "measure"), + ("filing_status", "filing_status"), + ): + if concept[field] is not None: + metadata[key] = concept[field] metadata.update( ledger_geography_level="congressional_district", state_fips=state, congressional_district_geoid=district, ledger_geography_id=f"5001900US{district}", ledger_source_record_id=f"record.{index}", - ledger_fact_label=f"District {district} label", ) - if draw(st.booleans()): - metadata["filing_status"] = draw(st.sampled_from(["All", "Single"])) + if draw(st.integers(0, 4)) == 0: + metadata["ledger_fact_label"] = f"District {district} label" + entity, period = concept["entity"], concept["period"] + family, row_filter = concept["family"], concept["filter"] else: + mode = draw(st.sampled_from(_MEASURE_MODES)) + if mode is not None: + metadata["measure_mode"] = mode + measure = draw(st.sampled_from(_SOURCE_MEASURES)) + if measure is not None: + metadata["source_measure_id"] = measure level = draw(st.sampled_from([None, "state", "national"])) if level is not None: metadata["ledger_geography_level"] = level - value = draw(_VALUES) + entity = draw(st.sampled_from(["household", "tax_unit"])) + period = draw(st.sampled_from([2024, 2025])) + family = draw(st.sampled_from(_FAMILIES)) + row_filter = draw(st.sampled_from([None, None, "is_tax_filer"])) specs.append( TargetSpec( name=f"target_{index}", - entity=draw(st.sampled_from(["household", "tax_unit"])), + entity=entity, measure=f"target_{index}", value=value, - period=draw(st.sampled_from([2024, 2025])), - family=draw(st.sampled_from(_FAMILIES)), - filter=draw(st.sampled_from([None, None, "is_tax_filer"])), + period=period, + family=family, + filter=row_filter, source="hypothesis", signed=value < 0, metadata=metadata, @@ -162,6 +196,10 @@ def test_shared_weights_equal_the_pre_move_helpers_bit_for_bit(registry, data) - ) expected = before._fiscal_target_loss_weights(registry, multipliers or None) observed = lw.fiscal_target_loss_weights(registry, multipliers or None) + groups = lw.concept_budget_groups( + [lw.fiscal_target_concept_budget_key(spec) for spec in registry.specs] + ) + event(f"largest concept group: {min(max(map(len, groups)), 3)}+ rows") assert observed.dtype == np.float64 assert np.array_equal(observed, expected) assert np.array_equal( @@ -181,6 +219,107 @@ def test_shared_weights_equal_the_pre_move_helpers_bit_for_bit(registry, data) - ) == before._fiscal_target_concept_budget_key(spec) +def _grouped_registry() -> TargetRegistry: + """National rows plus three districts of each of two concepts in one state.""" + + def district(name, geoid, value, measure, mode): + return TargetSpec( + name=name, + entity="tax_unit", + measure=name, + value=value, + period=2024, + family="irs_soi", + source="fixture", + metadata={ + "source_measure_id": measure, + "measure_mode": mode, + "ledger_geography_level": "congressional_district", + "ledger_geography_id": f"5001900US{geoid}", + "congressional_district_geoid": geoid, + "state_fips": geoid[:2], + }, + ) + + rows = [ + TargetSpec( + "agi_total", "tax_unit", 9.1e11, "agi", source="f", family="irs_soi" + ), + TargetSpec( + "snap_households", + "household", + 3.2e6, + "snap", + source="f", + family="usda_snap", + metadata={"measure_mode": "indicator_sum"}, + ), + ] + for geoid, agi, returns in ( + ("0601", 3.3e10, 3.1e5), + ("0602", 5.7e10, 3.9e5), + ("0603", 1.9e10, 2.2e5), + ): + rows.append(district(f"agi_{geoid}", geoid, agi, "agi_amount", "sum")) + rows.append( + district(f"returns_{geoid}", geoid, returns, "returns", "indicator_sum") + ) + return TargetRegistry(rows, country="us") + + +@pytest.mark.parametrize("multipliers", [None, {"irs_soi": 3.0, "usda_snap": 0.5}]) +def test_grouped_national_districts_equal_the_pre_move_helpers(multipliers) -> None: + """The concept-budget rescale of multi-row groups, bit for bit.""" + + registry = _grouped_registry() + groups = lw.concept_budget_groups( + [lw.fiscal_target_concept_budget_key(spec) for spec in registry.specs] + ) + assert sorted(len(group) for group in groups) == [1, 1, 3, 3] + assert np.array_equal( + lw.fiscal_target_loss_weights(registry, multipliers), + before._fiscal_target_loss_weights(registry, multipliers), + ) + assert np.array_equal( + lw.fiscal_target_concept_budget_weights(registry), + before._fiscal_target_concept_budget_weights(registry), + ) + + +@pytest.mark.parametrize("multiplier", [0.0, -1.0, 1e308]) +def test_unchecked_multipliers_behave_as_before(multiplier) -> None: + """The shared core adds no checks the national helper lacked.""" + + registry = _grouped_registry() + with np.errstate(all="ignore"): + expected = before._fiscal_target_loss_weights(registry, {"irs_soi": multiplier}) + observed = lw.fiscal_target_loss_weights(registry, {"irs_soi": multiplier}) + np.testing.assert_array_equal(observed, expected) + + +def test_the_national_cli_parses_family_multipliers_as_before() -> None: + release = _load_release_tool() + base = ["--ledger-facts", "facts.jsonl", "--out", "release"] + assert release._parse_args(base).target_family_loss_multipliers == {} + args = release._parse_args( + [ + *base, + "--target-family-loss-multiplier", + "usda_snap=8", + "--target-family-loss-multiplier", + "irs_soi=0.5", + ] + ) + assert args.target_family_loss_multipliers == {"usda_snap": 8.0, "irs_soi": 0.5} + for bad in (["usda_snap"], ["usda_snap=0"], ["a=2", "a=3"]): + argv = list(base) + for entry in bad: + argv += ["--target-family-loss-multiplier", entry] + with pytest.raises(SystemExit) as raised: + release._parse_args(argv) + assert raised.value.code == 2 + + def test_moved_constants_are_the_pre_move_constants() -> None: assert ( lw.US_FISCAL_TARGET_CONCEPT_METADATA_EXCLUSIONS @@ -412,15 +551,12 @@ def test_a_larger_singleton_magnitude_never_gets_less_weight(rows) -> None: assert (np.diff(weights[order]) >= -1e-12 * weights[order][1:]).all() -def test_the_formula_refuses_misaligned_inputs_and_bad_multipliers() -> None: +def test_the_formula_refuses_misaligned_inputs() -> None: with pytest.raises(ValueError, match="not row-aligned"): lw.target_loss_weights_from_rows([1.0, 2.0], ["count"], [0, 1], ["a", "b"]) - for multiplier in (0.0, -1.0, math.nan, math.inf): - with pytest.raises(ValueError, match="positive and finite"): - lw.target_loss_weights_from_rows( - [1.0], ["count"], [0], ["a"], {"a": multiplier} - ) assert lw.target_loss_weights_from_rows([], [], [], []).shape == (0,) + with pytest.raises(ValueError, match="no training targets"): + lw.us_acs_local_target_loss_weights([], {"usda_snap": 2.0}) # --------------------------------------------------------------------------- @@ -704,6 +840,15 @@ def share(n_districts: int) -> float: (_population("pop_cd_06", "congressional_district"), "pop_cd_<4 FIPS"), (_population("pop_state_0601", "state"), "pop_state_<2 FIPS"), (_population("pop_cd_0601", "state"), "pop_state_<2 FIPS"), + (_population("pop_cd_06AL", "congressional_district"), "pop_cd_<4 FIPS"), + ( + _ledger( + "irs_soi.cd0601.tax_filer_individual_count", + unit="usd", + source_measure_id="tax_filer_individual_count", + ), + "basis override 'count' disagrees", + ), ], ) def test_acs_local_mapping_refuses_what_it_cannot_classify(spec, message) -> None: @@ -742,8 +887,17 @@ def test_a_split_or_pooled_state_cd_block_is_refused() -> None: dataclasses.replace(row, metadata={**row.metadata, "district_note": row.name}) for row in rows ] - with pytest.raises(ValueError, match="split across concept groups"): + with pytest.raises(ValueError, match="land in more than one concept group"): lw.us_acs_local_target_loss_weights(leaking) + # A block whose rows differ in concept identity but share a parent is + # split too (the parent, not the metadata, says they are one block). + other_measure = dataclasses.replace( + rows[1], metadata={**rows[1].metadata, "source_measure_id": "other"} + ) + with pytest.raises(ValueError, match="split across concept groups"): + lw.validate_acs_local_concept_groups( + [rows[0], other_measure], ["amount", "amount"], [("k1",), ("k2",)] + ) # Two blocks under one key would pool their budgets. keys = [("k",), ("k",)] pooled = [ @@ -768,3 +922,63 @@ def test_acs_local_weights_do_not_depend_on_row_order(order) -> None: weights = lw.us_acs_local_target_loss_weights(surface) shuffled = lw.us_acs_local_target_loss_weights([surface[i] for i in order]) np.testing.assert_allclose(shuffled, weights[list(order)], rtol=1e-12) + + +def test_a_measure_the_national_rule_misfiles_takes_its_reviewed_basis() -> None: + """``tax_filer_individual_count`` counts people; the national rule says amount.""" + + row = _ledger( + "irs_soi.cd0601.tax_filer_individual_count", + unit="count", + measure_mode="sum", + ledger_geography_level="congressional_district", + congressional_district_geoid="0601", + ) + assert lw.fiscal_target_value_basis(row) == "amount" + assert lw.acs_local_target_value_basis(row) == "count" + assert "tax_filer_individual_count" in lw.ACS_LOCAL_LEDGER_BASIS_OVERRIDES + + +def _raw_district(name, district, value, **metadata): + """A district-file row as ``--soi-mode full`` or ``totals`` keeps it.""" + + return _ledger( + name, + value=value, + ledger_geography_level="congressional_district", + state_fips=district[:2], + congressional_district_geoid=district, + ledger_geography_id=f"5001800US{district}", + ledger_layout_record_set_spec_id="irs_soi.congressional_district_2022", + ledger_layout_groupby_value_label=f"CA congressional district {district}", + ledger_fact_label=f"California district {district} {name}", + source_measure_id="adjusted_gross_income", + **metadata, + ) + + +def test_district_rows_outside_state_cd_group_by_concept_or_are_refused() -> None: + rows = [ + _raw_district(f"irs_soi.cd{d}.agi", d, v) + for d, v in (("0601", 4e10), ("0602", 6e10), ("0603", 2e10)) + ] + keys = {lw.acs_local_target_concept_budget_key(row) for row in rows} + assert len(keys) == 1 + weights = lw.us_acs_local_target_loss_weights(rows) + np.testing.assert_allclose( + weights / weights.sum(), np.sqrt([4, 6, 2]) / np.sqrt([4, 6, 2]).sum() + ) + # A per-district key the mapping does not know splits the concept; the + # guard refuses it rather than falling back to one row per group. + leaking = [ + dataclasses.replace(row, metadata={**row.metadata, "district_note": row.name}) + for row in rows + ] + with pytest.raises(ValueError, match="land in more than one concept group"): + lw.us_acs_local_target_loss_weights(leaking) + # Different concepts of one state stay apart. + other = _raw_district("irs_soi.cd0601.eitc", "0601", 1e8) + other = dataclasses.replace( + other, metadata={**other.metadata, "source_measure_id": "eitc_amount"} + ) + assert lw.acs_local_target_concept_budget_key(other) not in keys diff --git a/tools/build_us_acs_local_release.py b/tools/build_us_acs_local_release.py index 300c1c149..464a658fd 100644 --- a/tools/build_us_acs_local_release.py +++ b/tools/build_us_acs_local_release.py @@ -31,8 +31,9 @@ training targets (adam, mass conserved, hard weight-ratio cap, optional L2 penalty --l2-lambda of form --l2-basis under --mass-parametrization; resumable with --resume; each - target's loss weighted as the national release weights it, - by ``microcosm.build.us_runtime.target_loss_weights``, with + target's loss weighted with the national release's formula + under the ACS local row mapping, by + ``microcosm.build.us_runtime.target_loss_weights``, with ``--target-family-loss-multiplier``), score the held-out district targets against the pro-rata baseline, record ESS over rows and distinct households, and write the calibrated @@ -2398,10 +2399,13 @@ def _git(*parts: str) -> str: def _ess_concentration_limitation(diagnostics: dict) -> dict: """The lineage's weight-concentration entry, true to the solve it describes. - Only the historical solve (no penalty, projection mass parametrization) - keeps the reviewed entry. Any other solve, including an unpenalized softmax - one, records its own settings instead: its concentration is measured, but - whether it is acceptable is a separate review this register cannot claim. + Only the historical solve (no penalty, projection mass parametrization, + every target weighted equally) keeps the reviewed entry. A summary from + before the loss weights records no ``target_loss``, which reads as equal + weights. Any other solve, including an unpenalized softmax one or any + loss-weighted one, records its own settings instead: its concentration is + measured, but whether it is acceptable is a separate review this register + cannot claim. """ ess = diagnostics.get("effective_sample_size") @@ -2411,9 +2415,11 @@ def _ess_concentration_limitation(diagnostics: dict) -> dict: parametrization = diagnostics.get( "mass_parametrization", HISTORICAL_PENALTY["mass_parametrization"] ) + target_loss = diagnostics.get("target_loss") if ( l2_lambda == 0.0 and parametrization == HISTORICAL_PENALTY["mass_parametrization"] + and target_loss is None ): return { "id": "low_effective_sample_size_lambda_zero", @@ -2437,7 +2443,15 @@ def _ess_concentration_limitation(diagnostics: dict) -> dict: f"l2_basis=" f"{diagnostics.get('l2_basis', HISTORICAL_PENALTY['l2_basis'])!r} and " f"mass_parametrization={parametrization!r}; chi-square distance " - f"from the design weights {diagnostics.get('chi_square_distance')}." + f"from the design weights {diagnostics.get('chi_square_distance')}" + + ( + "; target loss weights " + f"{target_loss.get('weighting')!r} under row mapping " + f"{target_loss.get('row_mapping')!r} with family multipliers " + f"{target_loss.get('family_multipliers') or {}}." + if isinstance(target_loss, dict) + else "; every target weighted equally." + ) ), "calibration_blocker": False, } From c4fd2e86d8faab6a73b0c14435f75e82f61acf2f Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 19:08:13 -0400 Subject: [PATCH 6/7] Regenerate the ACS local loss-weight evidence at f5f380677 (clean tree; weights unchanged) Co-Authored-By: Claude Opus 5.5 --- .../results.json | 59 ++++++++++++++++++- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/experiments/us-acs-local-target-loss-weights-20261004/results.json b/experiments/us-acs-local-target-loss-weights-20261004/results.json index d95a38cf1..9c8a3f167 100644 --- a/experiments/us-acs-local-target-loss-weights-20261004/results.json +++ b/experiments/us-acs-local-target-loss-weights-20261004/results.json @@ -1,5 +1,5 @@ { - "head": "2d56c32b99455ab00ffef18b653cddd1bfaeb8ed", + "head": "f5f380677971e421a039b70b4706ba5d186f12a6", "release_20260923": { "acs_local": { "by_basis": { @@ -377,6 +377,60 @@ "sha256": "fd25c6002cae0c991842c81399d43a3840f12cba0294021ca8ff6718e22a209f" } }, + "soi_modes": { + "full": { + "concept_groups": { + "max_district_group_size": 52, + "n_district_groups": 3176, + "n_district_rows": 24776, + "n_groups": 9953, + "n_singleton_district_groups": 672 + }, + "injected_per_district_key_refused": true, + "n_basis_overrides": 445, + "n_district_ledger_rows": 24340, + "n_specs": 31553 + }, + "state": { + "concept_groups": { + "max_district_group_size": 52, + "n_district_groups": 51, + "n_district_rows": 436, + "n_groups": 4074, + "n_singleton_district_groups": 7 + }, + "injected_per_district_key_refused": false, + "n_basis_overrides": 0, + "n_district_ledger_rows": 0, + "n_specs": 4459 + }, + "state_cd": { + "concept_groups": { + "max_district_group_size": 52, + "n_district_groups": 2240, + "n_district_rows": 22179, + "n_groups": 6565, + "n_singleton_district_groups": 7 + }, + "injected_per_district_key_refused": true, + "n_basis_overrides": 0, + "n_district_ledger_rows": 21743, + "n_specs": 26504 + }, + "totals": { + "concept_groups": { + "max_district_group_size": 52, + "n_district_groups": 118, + "n_district_rows": 890, + "n_groups": 475, + "n_singleton_district_groups": 28 + }, + "injected_per_district_key_refused": true, + "n_basis_overrides": 0, + "n_district_ledger_rows": 454, + "n_specs": 1247 + } + }, "state_cd_pinned_feed": { "acs_local": { "by_basis": { @@ -846,5 +900,6 @@ "total": 25864 } }, - "wall_seconds": 28.4 + "tree_clean": true, + "wall_seconds": 112.2 } \ No newline at end of file From 7bde9d8ebf16404b6082b5ec4caa5812dd53255a Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Sun, 4 Oct 2026 19:27:46 -0400 Subject: [PATCH 7/7] Say where tax_filer_individual_count rows occur: only the full surface Co-Authored-By: Claude Opus 5.5 --- .../us-acs-local-target-loss-weights-20261004/README.md | 2 +- .../src/microcosm/build/us_runtime/target_loss_weights.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/experiments/us-acs-local-target-loss-weights-20261004/README.md b/experiments/us-acs-local-target-loss-weights-20261004/README.md index 841649b2d..f0c01c5f7 100644 --- a/experiments/us-acs-local-target-loss-weights-20261004/README.md +++ b/experiments/us-acs-local-target-loss-weights-20261004/README.md @@ -33,7 +33,7 @@ The ACS local mapping accepts two kinds of row and refuses any other: - The one reviewed exception is `tax_filer_individual_count`, a count of people. The national rule files it as an amount, and `ACS_LOCAL_LEDGER_BASIS_OVERRIDES` maps it to a count. Only district rows - carry it, on the `full` and `totals` surfaces. + carry it; on the pinned feed only the `full` surface has them (445). - Any other disagreement is refused. - **Ladder population rows** (`pop_state_SS`, `pop_cd_SSDD`) carry only `geography_level`, so the national rule alone would file them as amounts. diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py index a59e8f84f..602cf719e 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py @@ -347,7 +347,8 @@ def _ledger_concept_budget_key( "Individuals on returns (ledger unit count). The national rule files it " "as an amount: its measure_mode is sum and its id has no 'return'. Only " "district rows carry it (the compiler drops it at state and national " - "geography), on the --soi-mode full and totals surfaces.", + "geography); on the pinned feed only the --soi-mode full surface has " + "them (445 rows).", ), } #: The ledger's unit for a measure, and the basis that unit implies.